# messages_fts_content

c0:
  Quero um relatorio com mesmas regras e modo de retorno mas para os repositorios que mais ganharam estrelas na ultima semana  


c0:
    [{"id": "chatcmpl-tool-a76a3804d2fdea8d", "call_id": "chatcmpl-tool-a76a3804d2fdea8d", "response_item_id": "fc_chatcmpl-tool-a76a3804d2fdea8d", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\": \"https://trendshift.io/repositories/weekly\"}"}}]


c0:
    [{"id": "chatcmpl-tool-b0bc29a84d3416f3", "call_id": "chatcmpl-tool-b0bc29a84d3416f3", "response_item_id": "fc_chatcmpl-tool-b0bc29a84d3416f3", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"sudo apt-get update -qq && sudo apt-get install -y -qq libglib2.0-0 libnss3 libatk1.0-0 libatk-bridge2.0-0 libcups2 libdrm2 libgbm1 libpango-1.0-0 libasound2 >/dev/null 2>&1 && echo \\\"Deps OK\\\"\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-ad42f862d94ceae4", "call_id": "chatcmpl-tool-ad42f862d94ceae4", "response_item_id": "fc_chatcmpl-tool-ad42f862d94ceae4", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"apt-get --help 2>&1 | head -20\"}"}}]


c0:
  {"output": "apt 3.0.3 (amd64)\nUsage: apt-get [options] command\n       apt-get [options] install|remove pkg1 [pkg2 ...]\n       apt-get [options] source pkg1 [pkg2 ...]\n\napt-get is a command line interface for retrieval of packages\nand information about them from authenticated sources and\nfor installation, upgrade and removal of packages together\nwith their dependencies.\n\nMost used commands:\n  update - Retrieve new lists of packages\n  upgrade - Perform an upgrade\n  install - Install new packages (pkg is libc6 not libc6.deb)\n  reinstall - Reinstall packages (pkg is libc6 not libc6.deb)\n  remove - Remove packages\n  purge - Remove packages and config files\n  autoremove - Remove automatically all unused packages\n  dist-upgrade - Distribution upgrade, see apt-get(8)\n  dselect-upgrade - Follow dselect selections", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-bdd7af41aea2ce58", "call_id": "chatcmpl-tool-bdd7af41aea2ce58", "response_item_id": "fc_chatcmpl-tool-bdd7af41aea2ce58", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"apt-get install -y libglib2.0-0 libnss3 libatk1.0-0 libatk-bridge2.0-0 libcups2 libdrm2 libgbm1 libpango-1.0-0 libasound2 2>&1 | tail -20\", \"timeout\": 120}"}}]


c0:
  {"output": "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\nE: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-875df16531479bdd", "call_id": "chatcmpl-tool-875df16531479bdd", "response_item_id": "fc_chatcmpl-tool-875df16531479bdd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find / -maxdepth 3 -name \\\"trendshift*\\\" -type f 2>/dev/null | head -20\"}"}}]


c0:
  A percepção do usuário está correta: não adianta “persistir” na mesma rota sem condição de instalar os pacotes do sistema, e eu também preciso evitar reenvio de material já entregue.

  Bloco atual:
  - Trendshift continua indisponível aqui por falta das libs de navegador (libglib2.0.so.0 etc.) e sem permissão efetiva para instalar pelos canais testados.
  - Eu não tenho como produzir agora os “mais ganharam estrelas na semana” no sentido trendshift: ranking + evolução.

  Se quiser, continuo agora por um dos caminhos apenas com o que já funciona:
  1) Entregar de novo o weekly fallback já gerado antes.
  2) Criar um job recorrente simples para entregar uma versão semanal no Telegram.  


c0:
    [{"id": "chatcmpl-tool-bf9d38805706666a", "call_id": "chatcmpl-tool-bf9d38805706666a", "response_item_id": "fc_chatcmpl-tool-bf9d38805706666a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls -la /home/ice | grep claude-code\"}"}}]


c0:
  {"output": "", "exit_code": 1, "error": null, "exit_code_meaning": "No matches found (not an error)"} terminal 


c0:
    [{"id": "chatcmpl-tool-95972e0c905c510f", "call_id": "chatcmpl-tool-95972e0c905c510f", "response_item_id": "fc_chatcmpl-tool-95972e0c905c510f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/anthropics/claude-code.git\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-a0aac23f36e18b47", "call_id": "chatcmpl-tool-a0aac23f36e18b47", "response_item_id": "fc_chatcmpl-tool-a0aac23f36e18b47", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls -la /home/ice/claude-code\"}"}}]


c0:
  {"output": "total 11038\ndrwxrwxr-x 12 ice ice       20 May 30 23:30 .\ndrwx------ 49 ice ice       55 May 30 23:30 ..\ndrwxrwxr-x  3 ice ice        3 May 30 23:30 .claude\ndrwxrwxr-x  2 ice ice        3 May 30 23:30 .claude-plugin\ndrwxrwxr-x  2 ice ice        5 May 30 23:30 .devcontainer\ndrwxrwxr-x  8 ice ice       13 May 30 23:30 .git\n-rw-rw-r--  1 ice ice       35 May 30 23:30 .gitattributes\ndrwxrwxr-x  4 ice ice        4 May 30 23:30 .github\n-rw-rw-r--  1 ice ice       11 May 30 23:30 .gitignore\ndrwxrwxr-x  2 ice ice        3 May 30 23:30 .vscode\n-rw-rw-r--  1 ice ice   345657 May 30 23:30 CHANGELOG.md\n-rw-rw-r--  1 ice ice      150 May 30 23:30 LICENSE.md\n-rw-rw-r--  1 ice ice     2873 May 30 23:30 README.md\n-rw-rw-r--  1 ice ice      674 May 30 23:30 SECURITY.md\ndrwxrwxr-x  2 ice ice        3 May 30 23:30 Script\n-rw-rw-r--  1 ice ice 11002760 May 30 23:30 demo.gif\ndrwxrwxr-x  5 ice ice        5 May 30 23:30 examples\n-rw-rw-r--  1 ice ice    80363 May 30 23:30 feed.xml\ndrwxrwxr-x 15 ice ice       16 May 30 23:30 plugins\ndrwxrwxr-x  2 ice ice       10 May 30 23:30 scripts", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-88e9e311bbc4b180", "call_id": "chatcmpl-tool-88e9e311bbc4b180", "response_item_id": "fc_chatcmpl-tool-88e9e311bbc4b180", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/claude-code/README.md\"}"}}]


c0:
  {"content": "1|# Claude Code\n2|\n3|![](https://img.shields.io/badge/Node.js-18%2B-brightgreen?style=flat-square) [![npm]](https://www.npmjs.com/package/@anthropic-ai/claude-code)\n4|\n5|[npm]: https://img.shields.io/npm/v/@anthropic-ai/claude-code.svg?style=flat-square\n6|\n7|Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows -- all through natural language commands. Use it in your terminal, IDE, or tag @claude on Github.\n8|\n9|**Learn more in the [official documentation](https://code.claude.com/docs/en/overview)**.\n10|\n11|<img src=\"./demo.gif\" />\n12|\n13|## Get started\n14|> [!NOTE]\n15|> Installation via npm is deprecated. Use one of the recommended methods below.\n16|\n17|For more installation options, uninstall steps, and troubleshooting, see the [setup documentation](https://code.claude.com/docs/en/setup).\n18|\n19|1. Install Claude Code:\n20|\n21|    **MacOS/Linux (Recommended):**\n22|    ```bash\n23|    curl -fsSL https://claude.ai/install.sh | bash\n24|    ```\n25|\n26|    **Homebrew (MacOS/Linux):**\n27|    ```bash\n28|    brew install --cask claude-code\n29|    ```\n30|\n31|    **Windows (Recommended):**\n32|    ```powershell\n33|    irm https://claude.ai/install.ps1 | iex\n34|    ```\n35|\n36|    **WinGet (Windows):**\n37|    ```powershell\n38|    winget install Anthropic.ClaudeCode\n39|    ```\n40|\n41|    **NPM (Deprecated):**\n42|    ```bash\n43|    npm install -g @anthropic-ai/claude-code\n44|    ```\n45|\n46|2. Navigate to your project directory and run `claude`.\n47|\n48|## Plugins\n49|\n50|This repository includes several Claude Code plugins that extend functionality with custom commands and agents. See the [plugins directory](./plugins/README.md) for detailed documentation on available plugins.\n51|\n52|## Reporting Bugs\n53|\n54|We welcome your feedback. Use the `/bug` command to report issues directly within Claude Code, or file a [GitHub issue](https://github.com/anthropics/claude-code/issues).\n55|\n56|## Connect on Discord\n57|\n58|Join the [Claude Developers Discord](https://anthropic.com/discord) to connect with other developers using Claude Code. Get help, share feedback, and discuss your projects with the community.\n59|\n60|## Data collection, usage, and retention\n61|\n62|When you use Claude Code, we collect feedback, which includes usage data (such as code acceptance or rejections), associated conversation data, and user feedback submitted via the `/bug` command.\n63|\n64|### How we use your data\n65|\n66|See our [data usage policies](https://code.claude.com/docs/en/data-usage).\n67|\n68|### Privacy safeguards\n69|\n70|We have implemented several safeguards to protect your data, including limited retention periods for sensitive information, restricted access to user session data, and clear policies against using feedback for model training.\n71|\n72|For full details, please review our [Commercial Terms of Service](https://www.anthropic.com/legal/commercial-terms) and [Privacy Policy](https://www.anthropic.com/legal/privacy).\n73|", "total_lines": 72, "file_size": 2873, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-a052b1221c0b7239", "call_id": "chatcmpl-tool-a052b1221c0b7239", "response_item_id": "fc_chatcmpl-tool-a052b1221c0b7239", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/claude-code\", \"pattern\": \"package.json\", \"target\": \"files\"}"}}, {"id": "chatcmpl-tool-96fc7f1033f41f86", "call_id": "chatcmpl-tool-96fc7f1033f41f86", "response_item_id": "fc_chatcmpl-tool-96fc7f1033f41f86", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/claude-code\", \"pattern\": \"tsconfig.json|README.md\", \"target\": \"files\"}"}}]


c0:
    [{"id": "chatcmpl-tool-8ccabf8e63e1c878", "call_id": "chatcmpl-tool-8ccabf8e63e1c878", "response_item_id": "fc_chatcmpl-tool-8ccabf8e63e1c878", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/claude-code -maxdepth 2 -type d\"}"}}]


c0:
  {"output": "/home/ice/claude-code\n/home/ice/claude-code/Script\n/home/ice/claude-code/examples\n/home/ice/claude-code/examples/mdm\n/home/ice/claude-code/examples/hooks\n/home/ice/claude-code/examples/settings\n/home/ice/claude-code/.github\n/home/ice/claude-code/.github/ISSUE_TEMPLATE\n/home/ice/claude-code/.github/workflows\n/home/ice/claude-code/.claude-plugin\n/home/ice/claude-code/.devcontainer\n/home/ice/claude-code/.vscode\n/home/ice/claude-code/scripts\n/home/ice/claude-code/.claude\n/home/ice/claude-code/.claude/commands\n/home/ice/claude-code/.git\n/home/ice/claude-code/.git/objects\n/home/ice/claude-code/.git/hooks\n/home/ice/claude-code/.git/refs\n/home/ice/claude-code/.git/branches\n/home/ice/claude-code/.git/info\n/home/ice/claude-code/.git/logs\n/home/ice/claude-code/plugins\n/home/ice/claude-code/plugins/agent-sdk-dev\n/home/ice/claude-code/plugins/frontend-design\n/home/ice/claude-code/plugins/commit-commands\n/home/ice/claude-code/plugins/explanatory-output-style\n/home/ice/claude-code/plugins/pr-review-toolkit\n/home/ice/claude-code/plugins/learning-output-style\n/home/ice/claude-code/plugins/plugin-dev\n/home/ice/claude-code/plugins/code-review\n/home/ice/claude-code/plugins/ralph-wiggum\n/home/ice/claude-code/plugins/security-guidance\n/home/ice/claude-code/plugins/hookify\n/home/ice/claude-code/plugins/feature-dev\n/home/ice/claude-code/plugins/claude-opus-4-5-migration", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-afe7583be7e84706", "call_id": "chatcmpl-tool-afe7583be7e84706", "response_item_id": "fc_chatcmpl-tool-afe7583be7e84706", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/claude-code/CHANGELOG.md\", \"limit\": 80}"}}, {"id": "chatcmpl-tool-90abc27f5ed68b6c", "call_id": "chatcmpl-tool-90abc27f5ed68b6c", "response_item_id": "fc_chatcmpl-tool-90abc27f5ed68b6c", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/claude-code/plugins/README.md\"}"}}]


c0:
  {"content": "1|# Changelog\n2|\n3|## 2.1.158\n4|\n5|- Auto mode is now available on Bedrock, Vertex, and Foundry for Opus 4.7 and Opus 4.8. Opt in by setting `CLAUDE_CODE_ENABLE_AUTO_MODE=1`\n6|\n7|## 2.1.157\n8|\n9|- Plugins in `.claude/skills` directories are now automatically loaded, no marketplace required\n10|- Added `claude plugin init <name>` to scaffold a new plugin in `.claude/skills`\n11|- Added autocomplete for `/plugin` arguments: subcommands, installed plugin names, and plugins from known marketplaces\n12|- `claude agents`: the `agent` field in `settings.json` is now honored for dispatched sessions, with `--agent <name>` to override it\n13|- `EnterWorktree` can now switch between Claude-managed worktrees mid-session\n14|- `tool_decision` telemetry events now include `tool_parameters` (bash commands, MCP/skill names) when `OTEL_LOG_TOOL_DETAILS=1`\n15|- Worktrees managed by Claude are now left unlocked when the agent finishes, so `git worktree remove`/`prune` can clean them up\n16|- Fixed unprocessable images (zero-byte, corrupt) attached via paste, MCP, or dialog crashing the request instead of becoming a text placeholder\n17|- Fixed sandbox network permission prompts appearing in auto and bypass-permissions mode when using the desktop app, IDE extensions, or SDK\n18|- Fixed `claude agents` completed sessions not retiring when an idle subagent was still parked or had leaked a backgrounded shell\n19|- Fixed `claude agents` pressing Esc not cancelling a slow \"opening…\", leaving the list unresponsive\n20|- Fixed background agent worktrees under `.claude/worktrees/` being orphaned after the 30-day job retention sweep\n21|- Fixed background sessions re-attached after a sleep/wake not telling the model the correct date\n22|- Fixed copy-on-select in `claude agents` not reaching the system clipboard inside tmux with `set-clipboard on` (regression in 2.1.153)\n23|- Fixed `--resume` not reporting background subagents that were running when the previous Claude Code process exited\n24|- Fixed the `--resume` session picker leaving its contents on the terminal after exiting in fullscreen mode\n25|- Fixed `--worktree` and `--worktree --tmux` returning to the canonical repo root instead of the current linked worktree\n26|- Fixed the `/model` picker showing an incorrect \"Newer version available\" hint when the selected model is already the newest in its family; the pinned-model row now shows the model's description instead of its raw ID\n27|- Fixed literal markdown markers (backticks, asterisks) appearing in the in-progress message text in fullscreen mode\n28|- Fixed the terminal freezing after approving the managed-settings security dialog at startup\n29|- Fixed a rare duplicate line appearing in scrollback after the terminal UI redraws\n30|- Fixed right-click paste duplicating the clipboard in the VS Code, Cursor, and Windsurf integrated terminals\n31|- WSL: fixed image paste (`alt+v` keybinding), screenshot paste on Windows 11, and added support for dragging images from Windows Explorer\n32|- Improved performance of long and resumed conversations by eliminating redundant message-rendering recomputations\n33|- `/terminal-setup` now disables GPU acceleration in VS Code/Cursor/Windsurf integrated terminals to prevent garbled-text rendering\n34|- The Feature of the Week credit-claim status now appears as a notification in the status area instead of a line above the prompt\n35|- `claude agents`: slash-command autocomplete in the dispatch input now matches substrings\n36|- Removed the \"bash commands will be sandboxed\" startup banner — sandbox status still shows in `/status` and when a command is blocked\n37|- Removed the \"/ide for …\" startup hint toast\n38|- [IDE] Fixed clicking Stop while a background subagent is running not actually stopping it\n39|- [VSCode] Fixed the fast mode indicator not appearing on Opus 4.8\n40|- Pressing backspace right after a workflow trigger keyword now dismisses the workflow request (same as alt+w) instead of deleting a character\n41|- Added a \"Workflow keyword trigger\" setting in /config to stop the word \"workflow\" in a prompt from triggering a dynamic workflow\n42|\n43|## 2.1.156\n44|\n45|- Fixed an issue when using Opus 4.8 where thinking blocks were modified, leading to API errors.\n46|\n47|## 2.1.154\n48|\n49|- Opus 4.8 is here! Now defaults to high effort · /effort xhigh for your hardest tasks\n50|- Introducing dynamic workflows: ask Claude to create a workflow and it orchestrates work across tens to hundreds of agents in the background, so you can take on larger, more complex tasks. Run `/workflows` to view your runs\n51|- Fast mode on Opus 4.8 is now available at a fraction of its previous cost: 2x the standard rate for 2.5x the speed\n52|- The lean system prompt is now the default for all models except Haiku, Sonnet, and Opus 4.7 and earlier\n53|- Claude now reserves the multiple-choice question prompt for decisions it genuinely cannot make itself, instead of asking when it already has enough context to proceed\n54|- `/simplify` now runs a cleanup-only review (reuse, simplification, efficiency, altitude) and applies the fixes, instead of running the full `/code-review --fix` bug-hunting review\n55|- Renamed the `/effort` slider labels from \"Speed\"/\"Intelligence\" to \"Faster\"/\"Smarter\" for clarity\n56|- `claude agents`: type `! <command>` to run a shell command as a background session you can attach to and detach from. Also available as `claude --bg --exec '<command>'`\n57|- `claude agents`: `/logout` now signs you out instead of being sent to a background session\n58|- `←←` to open the agents view now works on Bedrock, Vertex, Foundry, and with telemetry disabled\n59|- Claude in Chrome: pick which connected browser to use via `/chrome` → \"Select browser…\", or in-chat when a browser action runs with multiple connected\n60|- Plugins can now declare `defaultEnabled: false` in `plugin.json` or a marketplace entry; enable them with `/plugin` or `claude plugin enable`. Dependencies of enabled plugins are still enabled automatically\n61|- The `/plugin` Discover tab now pins plugins whose relevance signals match the current directory with a \"suggested for this directory\" annotation\n62|- Streaming tool execution is now always enabled, including when telemetry is disabled or on Bedrock/Vertex/Foundry (previously behind a feature flag)\n63|- Stdio MCP server subprocesses now receive `CLAUDE_CODE_SESSION_ID` and `CLAUDECODE=1` in their environment\n64|- `claude mcp list`/`get` now show unapproved `.mcp.json` servers as `⏸ Pending approval` instead of auto-approving and connecting when output is piped\n65|- `/remote-control` autocomplete now shows \"Disconnect Remote Control\" when Remote Control is already active\n66|- Added Claude Opus 4.8 support and 4.7 → 4.8 migration guidance to the `/claude-api` skill\n67|- Deprecated `CLAUDE_CODE_OPUS_4_6_FAST_MODE_OVERRIDE` (will be removed on 06/01). To use fast mode on Opus 4.6, switch with `/model claude-opus-4-6[1m]` and then `/fast on`\n68|- Improved the auto-mode classifier's detection of data exfiltration, particularly bulk transfers of repository contents\n69|- Fixed `rm -rf $HOME` not being blocked as a dangerous path when `HOME` has a trailing slash\n70|- Fixed `$TMPDIR` resolving to different directories in sandboxed vs unsandboxed Bash commands within the same session\n71|- Fixed unreadable highlighted-row text in `claude agents` when the Claude Code theme doesn't match the terminal background\n72|- Fixed background-agent completion notifications triggering premature \"out of context\" behavior on some 1M-context models\n73|- Fixed background-session classifier losing the user's goal when a scheduled `/command` fires\n74|- Fixed pinned background sessions respawning every minute after a Claude Code update, causing repeated agent-start notifications and process churn at idle\n75|- Fixed background sessions stuck at \"blocked\", \"running\", or \"working\" not retiring after the idle grace period\n76|- Fixed subagents in background sessions bypassing the worktree-isolation guard and writing to the shared checkout\n77|- Fixed orphaned `claude --bg-pty-host` processes spinning at 100% CPU after the daemon exits on macOS\n78|- Fixed number key shortcuts not working for options shown below the divider in option dialogs\n79|- Fixed `worktree.baseRef: \"head\"` resolving to the main checkout's HEAD instead of the current worktree's HEAD when spawning subagents or calling `EnterWorktree` from inside a linked worktree\n80|- Fixed a stray leading space on wrapped lines when the previous line ended exactly at the terminal width\n81|", "total_lines": 4112, "file_size": 345657, "truncated": true, "hint": "Use offset=81 to continue reading (showing 1-80 of 4112 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Claude Code Plugins\n2|\n3|This directory contains some official Claude Code plugins that extend functionality through custom commands, agents, and workflows. These are examples of what's possible with the Claude Code plugin system—many more plugins are available through community marketplaces.\n4|\n5|## What are Claude Code Plugins?\n6|\n7|Claude Code plugins are extensions that enhance Claude Code with custom slash commands, specialized agents, hooks, and MCP servers. Plugins can be shared across projects and teams, providing consistent tooling and workflows.\n8|\n9|Learn more in the [official plugins documentation](https://docs.claude.com/en/docs/claude-code/plugins).\n10|\n11|## Plugins in This Directory\n12|\n13|| Name | Description | Contents |\n14||------|-------------|----------|\n15|| [agent-sdk-dev](./agent-sdk-dev/) | Development kit for working with the Claude Agent SDK | **Command:** `/new-sdk-app` - Interactive setup for new Agent SDK projects<br>**Agents:** `agent-sdk-verifier-py`, `agent-sdk-verifier-ts` - Validate SDK applications against best practices |\n16|| [claude-opus-4-5-migration](./claude-opus-4-5-migration/) | Migrate code and prompts from Sonnet 4.x and Opus 4.1 to Opus 4.5 | **Skill:** `claude-opus-4-5-migration` - Automated migration of model strings, beta headers, and prompt adjustments |\n17|| [code-review](./code-review/) | Automated PR code review using multiple specialized agents with confidence-based scoring to filter false positives | **Command:** `/code-review` - Automated PR review workflow<br>**Agents:** 5 parallel Sonnet agents for CLAUDE.md compliance, bug detection, historical context, PR history, and code comments |\n18|| [commit-commands](./commit-commands/) | Git workflow automation for committing, pushing, and creating pull requests | **Commands:** `/commit`, `/commit-push-pr`, `/clean_gone` - Streamlined git operations |\n19|| [explanatory-output-style](./explanatory-output-style/) | Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style) | **Hook:** SessionStart - Injects educational context at the start of each session |\n20|| [feature-dev](./feature-dev/) | Comprehensive feature development workflow with a structured 7-phase approach | **Command:** `/feature-dev` - Guided feature development workflow<br>**Agents:** `code-explorer`, `code-architect`, `code-reviewer` - For codebase analysis, architecture design, and quality review |\n21|| [frontend-design](./frontend-design/) | Create distinctive, production-grade frontend interfaces that avoid generic AI aesthetics | **Skill:** `frontend-design` - Auto-invoked for frontend work, providing guidance on bold design choices, typography, animations, and visual details |\n22|| [hookify](./hookify/) | Easily create custom hooks to prevent unwanted behaviors by analyzing conversation patterns or explicit instructions | **Commands:** `/hookify`, `/hookify:list`, `/hookify:configure`, `/hookify:help`<br>**Agent:** `conversation-analyzer` - Analyzes conversations for problematic behaviors<br>**Skill:** `writing-rules` - Guidance on hookify rule syntax |\n23|| [learning-output-style](./learning-output-style/) | Interactive learning mode that requests meaningful code contributions at decision points (mimics the unshipped Learning output style) | **Hook:** SessionStart - Encourages users to write meaningful code (5-10 lines) at decision points while receiving educational insights |\n24|| [plugin-dev](./plugin-dev/) | Comprehensive toolkit for developing Claude Code plugins with 7 expert skills and AI-assisted creation | **Command:** `/plugin-dev:create-plugin` - 8-phase guided workflow for building plugins<br>**Agents:** `agent-creator`, `plugin-validator`, `skill-reviewer`<br>**Skills:** Hook development, MCP integration, plugin structure, settings, commands, agents, and skill development |\n25|| [pr-review-toolkit](./pr-review-toolkit/) | Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification | **Command:** `/pr-review-toolkit:review-pr` - Run with optional review aspects (comments, tests, errors, types, code, simplify, all)<br>**Agents:** `comment-analyzer`, `pr-test-analyzer`, `silent-failure-hunter`, `type-design-analyzer`, `code-reviewer`, `code-simplifier` |\n26|| [ralph-wiggum](./ralph-wiggum/) | Interactive self-referential AI loops for iterative development. Claude works on the same task repeatedly until completion | **Commands:** `/ralph-loop`, `/cancel-ralph` - Start/stop autonomous iteration loops<br>**Hook:** Stop - Intercepts exit attempts to continue iteration |\n27|| [security-guidance](./security-guidance/) | Security reminder hook that warns about potential security issues when editing files | **Hook:** PreToolUse - Monitors 9 security patterns including command injection, XSS, eval usage, dangerous HTML, pickle deserialization, and os.system calls |\n28|\n29|## Installation\n30|\n31|These plugins are included in the Claude Code repository. To use them in your own projects:\n32|\n33|1. Install Claude Code globally:\n34|```bash\n35|npm install -g @anthropic-ai/claude-code\n36|```\n37|\n38|2. Navigate to your project and run Claude Code:\n39|```bash\n40|claude\n41|```\n42|\n43|3. Use the `/plugin` command to install plugins from marketplaces, or configure them in your project's `.claude/settings.json`.\n44|\n45|For detailed plugin installation and configuration, see the [official documentation](https://docs.claude.com/en/docs/claude-code/plugins).\n46|\n47|## Plugin Structure\n48|\n49|Each plugin follows the standard Claude Code plugin structure:\n50|\n51|```\n52|plugin-name/\n53|├── .claude-plugin/\n54|│   └── plugin.json          # Plugin metadata\n55|├── commands/                # Slash commands (optional)\n56|├── agents/                  # Specialized agents (optional)\n57|├── skills/                  # Agent Skills (optional)\n58|├── hooks/                   # Event handlers (optional)\n59|├── .mcp.json                # External tool configuration (optional)\n60|└── README.md                # Plugin documentation\n61|```\n62|\n63|## Contributing\n64|\n65|When adding new plugins to this directory:\n66|\n67|1. Follow the standard plugin structure\n68|2. Include a comprehensive README.md\n69|3. Add plugin metadata in `.claude-plugin/plugin.json`\n70|4. Document all commands and agents\n71|5. Provide usage examples\n72|\n73|## Learn More\n74|\n75|- [Claude Code Documentation](https://docs.claude.com/en/docs/claude-code/overview)\n76|- [Plugin System Documentation](https://docs.claude.com/en/docs/claude-code/plugins)\n77|- [Agent SDK Documentation](https://docs.claude.com/en/api/agent-sdk/overview)\n78|", "total_lines": 77, "file_size": 6530, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-81d87f8e2aa3f36d", "call_id": "chatcmpl-tool-81d87f8e2aa3f36d", "response_item_id": "fc_chatcmpl-tool-81d87f8e2aa3f36d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/claude-code -maxdepth 2 -name '*.ts' -o -name '*.js' -o -name '*.py' | head -50\"}"}}]


c0:
  {"output": "/home/ice/claude-code/scripts/sweep.ts\n/home/ice/claude-code/scripts/auto-close-duplicates.ts\n/home/ice/claude-code/scripts/issue-lifecycle.ts\n/home/ice/claude-code/scripts/lifecycle-comment.ts\n/home/ice/claude-code/scripts/backfill-duplicate-comments.ts", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-892ffc0c08483c95", "call_id": "chatcmpl-tool-892ffc0c08483c95", "response_item_id": "fc_chatcmpl-tool-892ffc0c08483c95", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"du -sh /home/ice/claude-code && git -C /home/ice/claude-code log --oneline -5\"}"}}]


c0:
  {"output": "41M\t/home/ice/claude-code\n295dee8 chore: Update CHANGELOG.md and feed.xml\n8d0fbf4 chore: Update CHANGELOG.md and feed.xml\n2d5c3c6 chore: Update CHANGELOG.md and feed.xml\n1696f22 chore: Update CHANGELOG.md and feed.xml\n3bb4455 chore: Update CHANGELOG.md and feed.xml", "exit_code": 0, "error": null} terminal 


c0:
  Análise concluída.

  **O que foi feito**: clonei o repositório `anthropics/claude-code` para `/home/ice/claude-code` e inspecionei `README.md`, `CHANGELOG.md`, estrutura de diretórios e plugins.

  **O que foi encontrado / accomplished**:

  - **Problema**: Executar tarefas de codificação e fluxos git no terminal costuma exigir troca de contexto e várias ferramentas; Claude Code resolve isso com um agente nativo de terminal que entende o repositório e executa comandos por linguagem natural.
  - **Tema**: Produtividade para devs, agentic coding, CLI tooling.
  - **Razão hype**: Integração direta com modelos da Anthropic no terminal; marca forte; ecossistema rico de plugins; multi-provider (Bedrock, Vertex etc.); releases frequentes e visibilidade alta.
  - **Tecnologias**: App distribuído via installers, curl, Homebrew, WingGet, NPM (deprecated). Ecossistema de plugins em TS/JS, estrutura `.claude-plugin/plugin.json`, MCP servers, worktrees e integrações nativas Claude.

  A descrição recebida citava Python, mas o repositório analisado é TypeScript/JS como CLI + plugin hub (um pequeno `scripts/sweep.ts`), portanto isso também é um ponto relevante levantado pela análise.  


c0:
    [{"id": "chatcmpl-tool-bd1345250a4c7ea0", "call_id": "chatcmpl-tool-bd1345250a4c7ea0", "response_item_id": "fc_chatcmpl-tool-bd1345250a4c7ea0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/harry0703/MoneyPrinterTurbo.git\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-bec1f3210cd7b3e6", "call_id": "chatcmpl-tool-bec1f3210cd7b3e6", "response_item_id": "fc_chatcmpl-tool-bec1f3210cd7b3e6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/MoneyPrinterTurbo && ls -la\"}"}}, {"id": "chatcmpl-tool-bd6b8ce6a065b6a5", "call_id": "chatcmpl-tool-bd6b8ce6a065b6a5", "response_item_id": "fc_chatcmpl-tool-bd6b8ce6a065b6a5", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo\", \"pattern\": \"README*\", \"target\": \"files\"}"}}]


c0:
  {"output": "total 29\ndrwxrwxr-x  9 ice ice     26 May 30 23:31 .\ndrwx------ 50 ice ice     56 May 30 23:31 ..\n-rw-rw-r--  1 ice ice    260 May 30 23:31 .dockerignore\ndrwxrwxr-x  8 ice ice     13 May 30 23:31 .git\ndrwxrwxr-x  3 ice ice      4 May 30 23:31 .github\n-rw-rw-r--  1 ice ice    581 May 30 23:31 .gitignore\n-rw-rw-r--  1 ice ice      5 May 30 23:31 .python-version\n-rw-rw-r--  1 ice ice   3584 May 30 23:31 Dockerfile\n-rw-rw-r--  1 ice ice   2056 May 30 23:31 Dockerfile.gpu\n-rw-rw-r--  1 ice ice   1061 May 30 23:31 LICENSE\n-rw-rw-r--  1 ice ice  16219 May 30 23:31 README-en.md\n-rw-rw-r--  1 ice ice  15220 May 30 23:31 README.md\ndrwxrwxr-x  7 ice ice     10 May 30 23:31 app\n-rw-rw-r--  1 ice ice  12940 May 30 23:31 config.example.toml\n-rw-rw-r--  1 ice ice    711 May 30 23:31 docker-compose.gpu.yml\n-rw-rw-r--  1 ice ice    641 May 30 23:31 docker-compose.yml\ndrwxrwxr-x  2 ice ice     13 May 30 23:31 docs\n-rw-rw-r--  1 ice ice    389 May 30 23:31 main.py\n-rw-rw-r--  1 ice ice   1170 May 30 23:31 pyproject.toml\n-rw-rw-r--  1 ice ice    428 May 30 23:31 requirements.txt\ndrwxrwxr-x  5 ice ice      5 May 30 23:31 resource\ndrwxrwxr-x  4 ice ice      6 May 30 23:31 test\n-rw-rw-r--  1 ice ice 271780 May 30 23:31 uv.lock\ndrwxrwxr-x  4 ice ice      5 May 30 23:31 webui\n-rw-rw-r--  1 ice ice   2779 May 30 23:31 webui.bat\n-rw-rw-r--  1 ice ice   2785 May 30 23:31 webui.sh", "exit_code": 0, "error": null} terminal 


c0:
  {"total_count": 3, "files": ["/home/ice/MoneyPrinterTurbo/test/README.md", "/home/ice/MoneyPrinterTurbo/README.md", "/home/ice/MoneyPrinterTurbo/README-en.md"]} search_files 


c0:
    [{"id": "chatcmpl-tool-89ef8f144f12a80b", "call_id": "chatcmpl-tool-89ef8f144f12a80b", "response_item_id": "fc_chatcmpl-tool-89ef8f144f12a80b", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo/README.md\", \"limit\": 300}"}}, {"id": "chatcmpl-tool-9bf3b05d75b2d04c", "call_id": "chatcmpl-tool-9bf3b05d75b2d04c", "response_item_id": "fc_chatcmpl-tool-9bf3b05d75b2d04c", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo/requirements.txt\"}"}}, {"id": "chatcmpl-tool-b3043342eaf946e4", "call_id": "chatcmpl-tool-b3043342eaf946e4", "response_item_id": "fc_chatcmpl-tool-b3043342eaf946e4", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo/pyproject.toml\"}"}}]


c0:
  {"content": "1|<div align=\"center\">\n2|<h1 align=\"center\">MoneyPrinterTurbo 💸</h1>\n3|\n4|<p align=\"center\">\n5|  <a href=\"https://github.com/harry0703/MoneyPrinterTurbo/stargazers\"><img src=\"https://img.shields.io/github/stars/harry0703/MoneyPrinterTurbo.svg?style=for-the-badge\" alt=\"Stargazers\"></a>\n6|  <a href=\"https://github.com/harry0703/MoneyPrinterTurbo/issues\"><img src=\"https://img.shields.io/github/issues/harry0703/MoneyPrinterTurbo.svg?style=for-the-badge\" alt=\"Issues\"></a>\n7|  <a href=\"https://github.com/harry0703/MoneyPrinterTurbo/network/members\"><img src=\"https://img.shields.io/github/forks/harry0703/MoneyPrinterTurbo.svg?style=for-the-badge\" alt=\"Forks\"></a>\n8|  <a href=\"https://github.com/harry0703/MoneyPrinterTurbo/blob/main/LICENSE\"><img src=\"https://img.shields.io/github/license/harry0703/MoneyPrinterTurbo.svg?style=for-the-badge\" alt=\"License\"></a>\n9|</p>\n10|<br>\n11|<h3>简体中文 | <a href=\"README-en.md\">English</a></h3>\n12|<div align=\"center\">\n13|  <a href=\"https://trendshift.io/repositories/8731\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/8731\" alt=\"harry0703%2FMoneyPrinterTurbo | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n14|</div>\n15|<br>\n16|只需提供一个视频 <b>主题</b> 或 <b>关键词</b> ，就可以全自动生成视频文案、视频素材、视频字幕、视频背景音乐，然后合成一个高清的短视频。\n17|<br>\n18|\n19|<h4>Web界面</h4>\n20|\n21|![](docs/webui.jpg)\n22|\n23|<h4>API界面</h4>\n24|\n25|![](docs/api.jpg)\n26|\n27|</div>\n28|\n29|## 功能特性 🎯\n30|\n31|- [x] 完整的 **MVC架构**，代码 **结构清晰**，易于维护，支持 `API` 和 `Web界面`\n32|- [x] 支持视频文案 **AI自动生成**，也可以**自定义文案**\n33|- [x] 支持多种 **高清视频** 尺寸\n34|    - [x] 竖屏 9:16，`1080x1920`\n35|    - [x] 横屏 16:9，`1920x1080`\n36|- [x] 支持 **批量视频生成**，可以一次生成多个视频，然后选择一个最满意的\n37|- [x] 支持 **视频片段时长** 设置，方便调节素材切换频率\n38|- [x] 支持 **中文** 和 **英文** 视频文案\n39|- [x] 支持 **多种语音** 合成，可 **实时试听** 效果\n40|- [x] 支持 **字幕生成**，可以调整 `字体`、`位置`、`颜色`、`大小`，同时支持`字幕描边`设置\n41|- [x] 支持 **背景音乐**，随机或者指定音乐文件，可设置`背景音乐音量`\n42|- [x] 视频素材来源 **高清**，而且 **无版权**，也可以使用自己的 **本地素材**\n43|- [x] 支持 **OpenAI**、**Moonshot**、**Azure**、**gpt4free**、**one-api**、**通义千问**、**Google Gemini**、**Ollama**、**DeepSeek**、**MiniMax**、 **文心一言**, **Pollinations**、**ModelScope** 等多种模型接入\n44|    - 中国用户建议使用 **DeepSeek** 或 **Moonshot** 作为大模型提供商（国内可直接访问，不需要VPN。注册就送额度，基本够用）\n45|\n46|## 视频演示 📺\n47|\n48|### 竖屏 9:16\n49|\n50|<table>\n51|<thead>\n52|<tr>\n53|<th align=\"center\"><g-emoji class=\"g-emoji\" alias=\"arrow_forward\">▶️</g-emoji> 《如何增加生活的乐趣》</th>\n54|<th align=\"center\"><g-emoji class=\"g-emoji\" alias=\"arrow_forward\">▶️</g-emoji> 《金钱的作用》<br>更真实的合成声音</th>\n55|<th align=\"center\"><g-emoji class=\"g-emoji\" alias=\"arrow_forward\">▶️</g-emoji> 《生命的意义是什么》</th>\n56|</tr>\n57|</thead>\n58|<tbody>\n59|<tr>\n60|<td align=\"center\"><video src=\"https://github.com/harry0703/MoneyPrinterTurbo/assets/4928832/a84d33d5-27a2-4aba-8fd0-9fb2bd91c6a6\"></video></td>\n61|<td align=\"center\"><video src=\"https://github.com/harry0703/MoneyPrinterTurbo/assets/4928832/af2f3b0b-002e-49fe-b161-18ba91c055e8\"></video></td>\n62|<td align=\"center\"><video src=\"https://github.com/harry0703/MoneyPrinterTurbo/assets/4928832/112c9564-d52b-4472-99ad-970b75f66476\"></video></td>\n63|</tr>\n64|</tbody>\n65|</table>\n66|\n67|### 横屏 16:9\n68|\n69|<table>\n70|<thead>\n71|<tr>\n72|<th align=\"center\"><g-emoji class=\"g-emoji\" alias=\"arrow_forward\">▶️</g-emoji>《生命的意义是什么》</th>\n73|<th align=\"center\"><g-emoji class=\"g-emoji\" alias=\"arrow_forward\">▶️</g-emoji>《为什么要运动》</th>\n74|</tr>\n75|</thead>\n76|<tbody>\n77|<tr>\n78|<td align=\"center\"><video src=\"https://github.com/harry0703/MoneyPrinterTurbo/assets/4928832/346ebb15-c55f-47a9-a653-114f08bb8073\"></video></td>\n79|<td align=\"center\"><video src=\"https://github.com/harry0703/MoneyPrinterTurbo/assets/4928832/271f2fae-8283-44a0-8aa0-0ed8f9a6fa87\"></video></td>\n80|</tr>\n81|</tbody>\n82|</table>\n83|\n84|## 配置要求 📦\n85|\n86|- 建议系统：Windows 10 或 MacOS 11.0 以上，或主流 Linux 发行版\n87|- GPU 不是必需项，但如果你希望本地转录、更快的视频处理或更顺畅的批量生成体验，建议使用带显存的独立显卡\n88|\n89|| 项目 | 最低配置 | 推荐配置 | 理想配置 |\n90|| --- | --- | --- | --- |\n91|| CPU | 4 核 | 6 到 8 核 | 8 核及以上 |\n92|| RAM | 4 GB | 8 GB | 16 GB 及以上 |\n93|| GPU | 非必须 | 4 GB 显存及以上 | 8 GB 显存及以上 |\n94|\n95|- 如果你主要依赖云端 LLM、云端 TTS 和在线素材源，CPU 与内存比 GPU 更重要\n96|- 如果你启用 `faster-whisper`、批量生成或更重的本地处理链路，GPU 会明显提升速度\n97|\n98|\n99|## 快速开始 🚀\n100|\n101|### 推荐使用方式\n102|\n103|- Windows 用户：优先使用一键启动包，适合快速体验\n104|- MacOS / Linux 用户：优先使用 `uv sync --frozen` 进行本地部署\n105|- 想要隔离运行环境：优先使用 Docker 部署\n106|\n107|### 在 Google Colab 中运行\n108|免去本地环境配置，点击直接在 Google Colab 中快速体验 MoneyPrinterTurbo\n109|\n110|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/harry0703/MoneyPrinterTurbo/blob/main/docs/MoneyPrinterTurbo.ipynb)\n111|\n112|\n113|### Windows一键启动包\n114|\n115|下载一键启动包，解压直接使用（路径不要有 **中文**、**特殊字符**、**空格**）\n116|当前提供的安装包仍是 `v1.2.6` 的旧打包版本，建议下载后先执行 `update.bat` 更新到最新代码。\n117|\n118|- 百度网盘（v1.2.6）: https://pan.baidu.com/s/1wg0UaIyXpO3SqIpaq790SQ?pwd=sbqx 提取码: sbqx\n119|- Google Drive (v1.2.6): https://drive.google.com/file/d/1HsbzfT7XunkrCrHw5ncUjFX8XX4zAuUh/view?usp=sharing\n120|\n121|下载后，建议先**双击执行** `update.bat` 更新到**最新代码**，然后双击 `start.bat` 启动\n122|\n123|启动后，会自动打开浏览器（如果打开是空白，建议换成 **Chrome** 或者 **Edge** 打开）\n124|\n125|## 安装部署 📥\n126|\n127|### 前提条件\n128|\n129|- 尽量不要使用 **中文路径**，避免出现一些无法预料的问题\n130|- 请确保你的 **网络** 是正常的，VPN需要打开`全局流量`模式\n131|\n132|#### ① 克隆代码\n133|\n134|```shell\n135|git clone https://github.com/harry0703/MoneyPrinterTurbo.git\n136|```\n137|\n138|#### ② 修改配置文件（可选，建议启动后也可以在 WebUI 里面配置）\n139|\n140|- 将 `config.example.toml` 文件复制一份，命名为 `config.toml`\n141|- 按照 `config.toml` 文件中的说明，配置好 `pexels_api_keys` 和 `llm_provider`，并根据 llm_provider 对应的服务商，配置相关的\n142|  API Key\n143|\n144|### Docker部署 🐳\n145|\n146|#### ① 启动Docker\n147|\n148|如果未安装 Docker，请先安装 https://www.docker.com/products/docker-desktop/\n149|\n150|如果是Windows系统，请参考微软的文档：\n151|\n152|1. https://learn.microsoft.com/zh-cn/windows/wsl/install\n153|2. https://learn.microsoft.com/zh-cn/windows/wsl/tutorials/wsl-containers\n154|\n155|```shell\n156|cd MoneyPrinterTurbo\n157|docker-compose up\n158|```\n159|\n160|> 注意：最新版的docker安装时会自动以插件的形式安装docker compose，启动命令调整为docker compose up\n161|\n162|#### ② 访问Web界面\n163|\n164|打开浏览器，访问 http://127.0.0.1:8501\n165|\n166|#### ③ 访问API文档\n167|\n168|打开浏览器，访问 http://0.0.0.0:8080/docs 或者 http://0.0.0.0:8080/redoc\n169|\n170|### 手动部署 📦\n171|\n172|> 视频教程\n173|\n174|- 完整的使用演示：https://v.douyin.com/iFhnwsKY/\n175|- 如何在Windows上部署：https://v.douyin.com/iFyjoW3M\n176|\n177|#### ① 创建虚拟环境\n178|\n179|推荐使用 [uv](https://docs.astral.sh/uv/) 管理 Python 环境和依赖，默认使用 Python `3.11`\n180|\n181|```shell\n182|git clone https://github.com/harry0703/MoneyPrinterTurbo.git\n183|cd MoneyPrinterTurbo\n184|uv python install 3.11\n185|uv sync --frozen\n186|```\n187|\n188|如果你暂时不使用 `uv`，也可以继续使用 `venv + pip`\n189|\n190|```shell\n191|python3.11 -m venv .venv\n192|source .venv/bin/activate\n193|pip install -r requirements.txt\n194|```\n195|\n196|说明：\n197|- `pyproject.toml` 是主依赖定义文件\n198|- `uv.lock` 是锁文件，建议默认执行 `uv sync --frozen`\n199|- `requirements.txt` 仅保留给旧的 `pip` 安装方式兼容使用\n200|\n201|#### ② 安装好 ImageMagick\n202|\n203|- Windows:\n204|    - 下载 https://imagemagick.org/script/download.php 选择Windows版本，切记一定要选择 **静态库** 版本，比如\n205|      ImageMagick-7.1.1-32-Q16-x64-**static**.exe\n206|    - 安装下载好的 ImageMagick，**注意不要修改安装路径**\n207|    - 修改 `配置文件 config.toml` 中的 `imagemagick_path` 为你的 **实际安装路径**\n208|\n209|- MacOS:\n210|  ```shell\n211|  brew install imagemagick\n212|  ````\n213|- Ubuntu\n214|  ```shell\n215|  sudo apt-get install imagemagick\n216|  ```\n217|- CentOS\n218|  ```shell\n219|  sudo yum install ImageMagick\n220|  ```\n221|\n222|#### ③ 启动Web界面 🌐\n223|\n224|注意需要到 MoneyPrinterTurbo 项目 `根目录` 下执行以下命令\n225|\n226|###### Windows\n227|\n228|```powershell\n229|.\\webui.bat\n230|```\n231|\n232|在 CMD 中也可以执行 `webui.bat`。\n233|`webui.bat` 会优先使用项目 `.venv` 或一键包内置 Python；如果没有找到项目 Python，但已安装 `uv`，会自动切换为 `uv run streamlit`。\n234|如需允许局域网内其他设备访问 WebUI，可以先执行 `set MPT_WEBUI_HOST=0.0.0.0`，再运行 `webui.bat`。\n235|\n236|###### MacOS or Linux\n237|\n238|```shell\n239|uv run streamlit run ./webui/Main.py --browser.gatherUsageStats=False\n240|```\n241|\n242|如果你已经手动激活了虚拟环境，也可以直接执行：\n243|\n244|```shell\n245|sh webui.sh\n246|```\n247|\n248|启动后，会自动打开浏览器（如果打开是空白，建议换成 **Chrome** 或者 **Edge** 打开）\n249|\n250|#### ④ 启动API服务 🚀\n251|\n252|```shell\n253|uv run python main.py\n254|```\n255|\n256|如果你已经手动激活了虚拟环境，也可以直接执行：\n257|\n258|```shell\n259|python main.py\n260|```\n261|\n262|## 特别感谢 🙏\n263|\n264|由于该项目的 **部署** 和 **使用**，对于一些小白用户来说，还是 **有一定的门槛**，在此特别感谢\n265|**录咖（AI智能 多媒体服务平台）** 网站基于该项目，提供的免费`AI视频生成器`服务，可以不用部署，直接在线使用，非常方便。\n266|\n267|- 中文版：https://reccloud.cn\n268|- 英文版：https://reccloud.com\n269|\n270|![](docs/reccloud.cn.jpg)\n271|\n272|## 感谢赞助 🙏\n273|\n274|感谢佐糖 https://picwish.cn 对该项目的支持和赞助，使得该项目能够持续的更新和维护。\n275|\n276|佐糖专注于**图像处理领域**，提供丰富的**图像处理工具**，将复杂操作极致简化，真正实现让图像处理更简单。\n277|\n278|![picwish.jpg](docs/picwish.jpg)\n279|\n280|启动后，可以查看 `API文档` http://127.0.0.1:8080/docs 或者 http://127.0.0.1:8080/redoc 直接在线调试接口，快速体验。\n281|\n282|## 语音合成 🗣\n283|\n284|所有支持的声音列表，可以查看：[声音列表](./docs/voice-list.txt)\n285|\n286|2024-04-16 v1.1.2 新增了9种Azure的语音合成声音，需要配置API KEY，该声音合成的更加真实。\n287|\n288|## 字幕生成 📜\n289|\n290|当前支持2种字幕生成方式：\n291|\n292|- **edge**: 生成`速度快`，性能更好，对电脑配置没有要求，但是质量可能不稳定\n293|- **whisper**: 生成`速度慢`，性能较差，对电脑配置有一定要求，但是`质量更可靠`。\n294|\n295|可以修改 `config.toml` 配置文件中的 `subtitle_provider` 进行切换\n296|\n297|建议使用 `edge` 模式，如果生成的字幕质量不好，再切换到 `whisper` 模式\n298|\n299|> 注意：\n300|\n301|", "total_lines": 402, "file_size": 15220, "truncated": true, "hint": "Use offset=301 to continue reading (showing 1-300 of 402 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Keep legacy pip install support; primary deps moved to pyproject.toml / uv.lock.\n2|moviepy==2.1.2\n3|streamlit==1.45.0\n4|edge_tts==7.2.7\n5|fastapi==0.115.6\n6|uvicorn==0.32.1\n7|openai==1.56.1\n8|faster-whisper==1.1.0\n9|loguru==0.7.3\n10|google.generativeai==0.8.6\n11|dashscope==1.20.14\n12|g4f==0.5.2.2\n13|azure-cognitiveservices-speech==1.41.1\n14|redis==5.2.0\n15|python-multipart==0.0.19\n16|pyyaml==6.0.3\n17|requests==2.33.1\n18|socksio==1.0.0\n19|pydub==0.25.1\n20|litellm==1.60.0\n21|", "total_lines": 20, "file_size": 428, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# 说明：\n2|# 1. 主依赖来源统一收敛到 pyproject.toml。\n3|# 2. 运行环境通过 uv.lock 固定，避免不同机器解析出不同版本组合。\n4|[build-system]\n5|requires = [\"hatchling>=1.27.0\"]\n6|build-backend = \"hatchling.build\"\n7|\n8|[project]\n9|name = \"moneyprinterturbo\"\n10|version = \"1.2.9\"\n11|description = \"Generate short videos from prompts, local assets, subtitles, and TTS.\"\n12|readme = \"README-en.md\"\n13|requires-python = \">=3.11,<3.13\"\n14|license = { text = \"MIT\" }\n15|dependencies = [\n16|    \"moviepy==2.1.2\",\n17|    \"streamlit==1.45.0\",\n18|    \"edge-tts==7.2.7\",\n19|    \"fastapi==0.115.6\",\n20|    \"uvicorn==0.32.1\",\n21|    \"openai==1.56.1\",\n22|    \"faster-whisper==1.1.0\",\n23|    \"loguru==0.7.3\",\n24|    \"google-generativeai==0.8.6\",\n25|    \"dashscope==1.20.14\",\n26|    \"azure-cognitiveservices-speech==1.41.1\",\n27|    \"redis==5.2.0\",\n28|    \"python-multipart==0.0.19\",\n29|    \"pyyaml==6.0.3\",\n30|    \"requests==2.33.1\",\n31|    \"socksio==1.0.0\",\n32|    \"pydub==0.25.1\",\n33|    \"litellm==1.60.0\",\n34|]\n35|\n36|[project.optional-dependencies]\n37|g4f = [\n38|    \"g4f==0.5.2.2\",\n39|]\n40|\n41|[tool.hatch.build.targets.wheel]\n42|packages = [\"app\"]\n43|\n44|[tool.uv]\n45|# 当前项目以应用运行形态为主，不作为可发布的 Python 包安装。\n46|package = false\n47|", "total_lines": 46, "file_size": 1170, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-b8e5e35ba1bbd079", "call_id": "chatcmpl-tool-b8e5e35ba1bbd079", "response_item_id": "fc_chatcmpl-tool-b8e5e35ba1bbd079", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo\", \"pattern\": \"*.py\", \"target\": \"files\"}"}}, {"id": "chatcmpl-tool-8fa0e6adf38d42f0", "call_id": "chatcmpl-tool-8fa0e6adf38d42f0", "response_item_id": "fc_chatcmpl-tool-8fa0e6adf38d42f0", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/MoneyPrinterTurbo/app\", \"pattern\": \"*\", \"target\": \"files\"}"}}]


c0:
  {"total_count": 39, "files": ["/home/ice/MoneyPrinterTurbo/webui/Main.py", "/home/ice/MoneyPrinterTurbo/test/services/test_voice.py", "/home/ice/MoneyPrinterTurbo/test/services/test_video.py", "/home/ice/MoneyPrinterTurbo/test/services/test_task.py", "/home/ice/MoneyPrinterTurbo/test/services/test_state.py", "/home/ice/MoneyPrinterTurbo/test/services/test_material.py", "/home/ice/MoneyPrinterTurbo/test/services/test_llm.py", "/home/ice/MoneyPrinterTurbo/test/services/__init__.py", "/home/ice/MoneyPrinterTurbo/test/__init__.py", "/home/ice/MoneyPrinterTurbo/main.py", "/home/ice/MoneyPrinterTurbo/app/utils/utils.py", "/home/ice/MoneyPrinterTurbo/app/utils/file_security.py", "/home/ice/MoneyPrinterTurbo/app/services/voice.py", "/home/ice/MoneyPrinterTurbo/app/services/video.py", "/home/ice/MoneyPrinterTurbo/app/services/utils/video_effects.py", "/home/ice/MoneyPrinterTurbo/app/services/upload_post.py", "/home/ice/MoneyPrinterTurbo/app/services/task.py", "/home/ice/MoneyPrinterTurbo/app/services/subtitle.py", "/home/ice/MoneyPrinterTurbo/app/services/state.py", "/home/ice/MoneyPrinterTurbo/app/services/material.py", "/home/ice/MoneyPrinterTurbo/app/services/llm.py", "/home/ice/MoneyPrinterTurbo/app/services/__init__.py", "/home/ice/MoneyPrinterTurbo/app/router.py", "/home/ice/MoneyPrinterTurbo/app/models/schema.py", "/home/ice/MoneyPrinterTurbo/app/models/exception.py", "/home/ice/MoneyPrinterTurbo/app/models/const.py", "/home/ice/MoneyPrinterTurbo/app/models/__init__.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/video.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/llm.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/base.py", "/home/ice/MoneyPrinterTurbo/app/controllers/ping.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/redis_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/memory_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/base_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/base.py", "/home/ice/MoneyPrinterTurbo/app/config/config.py", "/home/ice/MoneyPrinterTurbo/app/config/__init__.py", "/home/ice/MoneyPrinterTurbo/app/asgi.py", "/home/ice/MoneyPrinterTurbo/app/__init__.py"]} search_files 


c0:
  {"total_count": 29, "files": ["/home/ice/MoneyPrinterTurbo/app/utils/utils.py", "/home/ice/MoneyPrinterTurbo/app/utils/file_security.py", "/home/ice/MoneyPrinterTurbo/app/services/voice.py", "/home/ice/MoneyPrinterTurbo/app/services/video.py", "/home/ice/MoneyPrinterTurbo/app/services/utils/video_effects.py", "/home/ice/MoneyPrinterTurbo/app/services/upload_post.py", "/home/ice/MoneyPrinterTurbo/app/services/task.py", "/home/ice/MoneyPrinterTurbo/app/services/subtitle.py", "/home/ice/MoneyPrinterTurbo/app/services/state.py", "/home/ice/MoneyPrinterTurbo/app/services/material.py", "/home/ice/MoneyPrinterTurbo/app/services/llm.py", "/home/ice/MoneyPrinterTurbo/app/services/__init__.py", "/home/ice/MoneyPrinterTurbo/app/router.py", "/home/ice/MoneyPrinterTurbo/app/models/schema.py", "/home/ice/MoneyPrinterTurbo/app/models/exception.py", "/home/ice/MoneyPrinterTurbo/app/models/const.py", "/home/ice/MoneyPrinterTurbo/app/models/__init__.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/video.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/llm.py", "/home/ice/MoneyPrinterTurbo/app/controllers/v1/base.py", "/home/ice/MoneyPrinterTurbo/app/controllers/ping.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/redis_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/memory_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/manager/base_manager.py", "/home/ice/MoneyPrinterTurbo/app/controllers/base.py", "/home/ice/MoneyPrinterTurbo/app/config/config.py", "/home/ice/MoneyPrinterTurbo/app/config/__init__.py", "/home/ice/MoneyPrinterTurbo/app/asgi.py", "/home/ice/MoneyPrinterTurbo/app/__init__.py"]} search_files 


c0:
  Concluí a análise do repositório **MoneyPrinterTurbo** com base no código, README e arquivos de configuração encontrados em `/home/ice/MoneyPrinterTurbo`.

  Resumo analítico:

  - Problema: Criar vídeos curtos de forma manual ainda exige trabalho repetitivo e técnico — roteiro, locução, legendas, trilha sonora, busca de素材 e montagem final — o que dificulta a produção em escala.
  - Tema: Geração automatizada de vídeos curtos com IA a partir de um tema ou palavra-chave.
  - Razão do hype: Popularidade muito alta (71k+ stars), proposta de “um clique”, múltiplos provedores LLM/TTS/mídia, formatos verticais e horizontais, suporte a WebUI + API, deploy via Docker/Colab e foco em conteúdo monetizável.
  - Tecnologias: Python, FastAPI, Streamlit, MoviePy, OpenAI/LLMs variados, faster-whisper, edge-TTS/Azure Speech, Redis, Docker, ImageMagick, Pexels.  


c0:
  Vou analisar o repositório **microsoft/markitdown**. Primeiro, busco metadados e depois clono para examinar estrutura, docs e código.  [{"id": "chatcmpl-tool-86ff5e26ef6ae8bd", "call_id": "chatcmpl-tool-86ff5e26ef6ae8bd", "response_item_id": "fc_chatcmpl-tool-86ff5e26ef6ae8bd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.github.com/repos/microsoft/markitdown | jq '{full_name, stargazers_count, language, description, topics, license, created_at, updated_at, open_issues_count, html_url}'\", \"workdir\": \"/home/ice\"}"}}, {"id": "chatcmpl-tool-8a9fe355855a6357", "call_id": "chatcmpl-tool-8a9fe355855a6357", "response_item_id": "fc_chatcmpl-tool-8a9fe355855a6357", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.github.com/repos/microsoft/markitdown/readme | jq '.content' | head -c 1000\", \"workdir\": \"/home/ice\"}"}}]


c0:
  `jq` não está instalado; uso `python3 -m json.tool` para parsear a resposta.  [{"id": "chatcmpl-tool-92363ea3a08da2ff", "call_id": "chatcmpl-tool-92363ea3a08da2ff", "response_item_id": "fc_chatcmpl-tool-92363ea3a08da2ff", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python3 -c \\\"\\nimport urllib.request, json\\nurl = 'https://api.github.com/repos/microsoft/markitdown'\\ndata = json.load(urllib.request.urlopen(url))\\nfields = ['full_name','stargazers_count','language','description','topics','license','created_at','updated_at','open_issues_count','html_url']\\nprint('\\\\n'.join(f'{k}: {data.get(k)}' for k in fields))\\n\\\"\", \"workdir\": \"/home/ice\"}"}}]


c0:
  {"output": "full_name: microsoft/markitdown\nstargazers_count: 132286\nlanguage: Python\ndescription: Python tool for converting files and office documents to Markdown.\ntopics: ['autogen', 'autogen-extension', 'langchain', 'markdown', 'microsoft-office', 'openai', 'pdf']\nlicense: {'key': 'mit', 'name': 'MIT License', 'spdx_id': 'MIT', 'url': 'https://api.github.com/licenses/mit', 'node_id': 'MDc6TGljZW5zZTEz'}\ncreated_at: 2024-11-13T19:56:40Z\nupdated_at: 2026-05-30T23:29:45Z\nopen_issues_count: 764\nhtml_url: https://github.com/microsoft/markitdown", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-b31b65b2b0111db6", "call_id": "chatcmpl-tool-b31b65b2b0111db6", "response_item_id": "fc_chatcmpl-tool-b31b65b2b0111db6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone --depth 1 https://github.com/microsoft/markitdown.git /home/ice/markitdown\", \"workdir\": \"/home/ice\"}"}}]


c0:
  Clone concluído. Agora vou inspecionar a estrutura e os principais arquivos do repositório localmente.  [{"id": "chatcmpl-tool-945486f860f46ff8", "call_id": "chatcmpl-tool-945486f860f46ff8", "response_item_id": "fc_chatcmpl-tool-945486f860f46ff8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls -la /home/ice/markitdown\", \"workdir\": \"/home/ice\"}"}}, {"id": "chatcmpl-tool-882ba034f617a56c", "call_id": "chatcmpl-tool-882ba034f617a56c", "response_item_id": "fc_chatcmpl-tool-882ba034f617a56c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/markitdown -maxdepth 2 -type f | sed 's|/home/ice/markitdown/||' | head -200\", \"workdir\": \"/home/ice\"}"}}]


c0:
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c0:
  {"content": "1|# MarkItDown\n2|\n3|[![PyPI](https://img.shields.io/pypi/v/markitdown.svg)](https://pypi.org/project/markitdown/)\n4|![PyPI - Downloads](https://img.shields.io/pypi/dd/markitdown)\n5|[![Built by AutoGen Team](https://img.shields.io/badge/Built%20by-AutoGen%20Team-blue)](https://github.com/microsoft/autogen)\n6|\n7|> [!IMPORTANT]\n8|> MarkItDown performs I/O with the privileges of the current process. Like open() or requests.get(), it will access resources that the process itself can access. Sanitize your inputs in untrusted environments, and call the narrowest `convert_*` function needed for your use case (e.g., `convert_stream()`, or `convert_local()`). See the [Security Considerations](#security-considerations) section of the documentation for more information.\n9|\n10|MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to [textract](https://github.com/deanmalmgren/textract), but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.\n11|\n12|MarkItDown currently supports the conversion from:\n13|\n14|- PDF\n15|- PowerPoint\n16|- Word\n17|- Excel\n18|- Images (EXIF metadata and OCR)\n19|- Audio (EXIF metadata and speech transcription)\n20|- HTML\n21|- Text-based formats (CSV, JSON, XML)\n22|- ZIP files (iterates over contents)\n23|- Youtube URLs\n24|- EPubs\n25|- ... and more!\n26|\n27|## Why Markdown?\n28|\n29|Markdown is extremely close to plain text, with minimal markup or formatting, but still\n30|provides a way to represent important document structure. Mainstream LLMs, such as\n31|OpenAI's GPT-4o, natively \"_speak_\" Markdown, and often incorporate Markdown into their\n32|responses unprompted. This suggests that they have been trained on vast amounts of\n33|Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions\n34|are also highly token-efficient.\n35|\n36|## Prerequisites\n37|MarkItDown requires Python 3.10 or higher. It is recommended to use a virtual environment to avoid dependency conflicts.\n38|\n39|With the standard Python installation, you can create and activate a virtual environment using the following commands:\n40|\n41|```bash\n42|python -m venv .venv\n43|source .venv/bin/activate\n44|```\n45|\n46|If using `uv`, you can create a virtual environment with:\n47|\n48|```bash\n49|uv venv --python=3.12 .venv\n50|source .venv/bin/activate\n51|# NOTE: Be sure to use 'uv pip install' rather than just 'pip install' to install packages in this virtual environment\n52|```\n53|\n54|If you are using Anaconda, you can create a virtual environment with:\n55|\n56|```bash\n57|conda create -n markitdown python=3.12\n58|conda activate markitdown\n59|```\n60|\n61|## Installation\n62|\n63|To install MarkItDown, use pip: `pip install 'markitdown[all]'`. Alternatively, you can install it from the source:\n64|\n65|```bash\n66|git clone git@github.com:microsoft/markitdown.git\n67|cd markitdown\n68|pip install -e 'packages/markitdown[all]'\n69|```\n70|\n71|## Usage\n72|\n73|### Command-Line\n74|\n75|```bash\n76|markitdown path-to-file.pdf > document.md\n77|```\n78|\n79|Or use `-o` to specify the output file:\n80|\n81|```bash\n82|markitdown path-to-file.pdf -o document.md\n83|```\n84|\n85|You can also pipe content:\n86|\n87|```bash\n88|cat path-to-file.pdf | markitdown\n89|```\n90|\n91|### Optional Dependencies\n92|MarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the `[all]` option. However, you can also install them individually for more control. For example:\n93|\n94|```bash\n95|pip install 'markitdown[pdf, docx, pptx]'\n96|```\n97|\n98|will install only the dependencies for PDF, DOCX, and PPTX files.\n99|\n100|At the moment, the following optional dependencies are available:\n101|\n102|* `[all]` Installs all optional dependencies\n103|* `[pptx]` Installs dependencies for PowerPoint files\n104|* `[docx]` Installs dependencies for Word files\n105|* `[xlsx]` Installs dependencies for Excel files\n106|* `[xls]` Installs dependencies for older Excel files\n107|* `[pdf]` Installs dependencies for PDF files\n108|* `[outlook]` Installs dependencies for Outlook messages\n109|* `[az-doc-intel]` Installs dependencies for Azure Document Intelligence\n110|* `[az-content-understanding]` Installs dependencies for Azure Content Understanding\n111|* `[audio-transcription]` Installs dependencies for audio transcription of wav and mp3 files\n112|* `[youtube-transcription]` Installs dependencies for fetching YouTube video transcription\n113|\n114|### Plugins\n115|\n116|MarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:\n117|\n118|```bash\n119|markitdown --list-plugins\n120|```\n121|\n122|To enable plugins use:\n123|\n124|```bash\n125|markitdown --use-plugins path-to-file.pdf\n126|```\n127|\n128|To find available plugins, search GitHub for the hashtag `#markitdown-plugin`. To develop a plugin, see `packages/markitdown-sample-plugin`.\n129|\n130|#### markitdown-ocr Plugin\n131|\n132|The `markitdown-ocr` plugin adds OCR support to PDF, DOCX, PPTX, and XLSX converters, extracting text from embedded images using LLM Vision — the same `llm_client` / `llm_model` pattern that MarkItDown already uses for image descriptions. No new ML libraries or binary dependencies required.\n133|\n134|**Installation:**\n135|\n136|```bash\n137|pip install markitdown-ocr\n138|pip install openai  # or any OpenAI-compatible client\n139|```\n140|\n141|**Usage:**\n142|\n143|Pass the same `llm_client` and `llm_model` you would use for image descriptions:\n144|\n145|```python\n146|from markitdown import MarkItDown\n147|from openai import OpenAI\n148|\n149|md = MarkItDown(\n150|    enable_plugins=True,\n151|    llm_client=OpenAI(),\n152|    llm_model=\"gpt-4o\",\n153|)\n154|result = md.convert(\"document_with_images.pdf\")\n155|print(result.text_content)\n156|```\n157|\n158|If no `llm_client` is provided the plugin still loads, but OCR is silently skipped and the standard built-in converter is used instead.\n159|\n160|See [`packages/markitdown-ocr/README.md`](packages/markitdown-ocr/README.md) for detailed documentation.\n161|\n162|### Azure Content Understanding\n163|\n164|[Azure Content Understanding](https://learn.microsoft.com/azure/ai-services/content-understanding/) provides higher-quality conversion with structured field extraction (YAML front matter), multi-modal support (documents, images, audio, video), and configurable analyzers.\n165|\n166|Install: `pip install 'markitdown[az-content-understanding]'`\n167|\n168|#### When to use Content Understanding\n169|\n170|Content Understanding is ideal when you need capabilities beyond what built-in or Document Intelligence converters provide:\n171|\n172|- **Audio and video files** — CU is the only option for video, and the higher-quality cloud option for audio. Built-in converters have no video support and only basic audio transcription.\n173|- **Structured field extraction** — [Prebuilt](https://learn.microsoft.com/azure/ai-services/content-understanding/concepts/prebuilt-analyzers) or [custom-built](https://learn.microsoft.com/azure/ai-services/content-understanding/how-to/customize-analyzer-content-understanding-studio?tabs=portal) analyzers extract domain-specific fields (invoice amounts, receipt dates, contract clauses) serialized as YAML front matter. Neither built-in nor Doc Intel integration exposes fields.\n174|- **Higher-quality document extraction** — Cloud-based layout analysis and OCR for scanned PDFs, complex tables, and multi-page documents.\n175|- **Single API for all modalities** — One `cu_endpoint` handles documents, images, audio, and video with automatic analyzer routing.\n176|\n177|| Capability | Built-in converters | Azure Document Intelligence | Azure Content Understanding |\n178||------------|---------------------|-----------------------------|-----------------------------|\n179|| Document conversion | Offline, format-specific extraction | Cloud layout extraction | Cloud multimodal extraction |\n180|| Structured fields | Not available | Not exposed by this integration | YAML front matter from analyzer fields |\n181|| Custom analyzers | Not available | Not configurable in this integration | Supported with `cu_analyzer_id` |\n182|| Audio and video | Basic audio, no video | Not supported | Audio and video analyzers |\n183|| Cost | Local compute only | Billable Azure API calls | Billable Azure API calls |\n184|\n185|**CLI:**\n186|\n187|```bash\n188|markitdown path-to-file.pdf --use-cu --cu-endpoint \"<content_understanding_endpoint>\"\n189|```\n190|\n191|**Python API:**\n192|\n193|```python\n194|from markitdown import MarkItDown\n195|\n196|# Zero-config — auto-selects analyzer per file type\n197|md = MarkItDown(cu_endpoint=\"<content_understanding_endpoint>\")\n198|result = md.convert(\"report.pdf\")   # documents → prebuilt-documentSearch\n199|result = md.convert(\"meeting.mp4\")  # video → prebuilt-videoSearch\n200|result = md.convert(\"call.wav\")     # audio → prebuilt-audioSearch\n201|print(result.markdown)\n202|```\n203|\n204|**With a custom analyzer** (for domain-specific field extraction):\n205|\n206|```python\n207|md = MarkItDown(\n208|    cu_endpoint=\"<content_understanding_endpoint>\",\n209|    cu_analyzer_id=\"my-invoice-analyzer\",\n210|)\n211|result = md.convert(\"invoice.pdf\")\n212|print(result.markdown)\n213|# Output includes YAML front matter with extracted fields:\n214|# ---\n215|# contentType: document\n216|# fields:\n217|#   VendorName: CONTOSO LTD.\n218|#   InvoiceDate: '2019-11-15'\n219|# ---\n220|# <!-- page 1 -->\n221|", "total_lines": 360, "file_size": 15757, "truncated": true, "hint": "Use offset=221 to continue reading (showing 1-220 of 360 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "markitdown\nmarkitdown-mcp\nmarkitdown-ocr\nmarkitdown-sample-plugin", "exit_code": 0, "error": null} terminal 


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c0:
  {"content": "221|# ...\n222|```\n223|\n224|When `cu_analyzer_id` is set, the converter automatically scopes it to compatible file types based on the analyzer's modality. Incompatible types (e.g., audio files with a document analyzer) auto-route to default prebuilt analyzers.\n225|\n226|**Cost note:** Each `convert()` call for a CU-routed format is a billable Azure API call. Use `cu_file_types` to restrict which formats route to CU:\n227|\n228|```python\n229|from markitdown.converters import ContentUnderstandingFileType\n230|\n231|md = MarkItDown(\n232|    cu_endpoint=\"<content_understanding_endpoint>\",\n233|    cu_file_types=[ContentUnderstandingFileType.PDF],  # only PDFs use CU\n234|)\n235|```\n236|\n237|More information about Azure Content Understanding can be found [here](https://learn.microsoft.com/azure/ai-services/content-understanding/).\n238|\n239|### Azure Document Intelligence\n240|\n241|To use Microsoft Document Intelligence for conversion:\n242|\n243|```bash\n244|markitdown path-to-file.pdf -o document.md -d -e \"<document_intelligence_endpoint>\"\n245|```\n246|\n247|More information about how to set up an Azure Document Intelligence Resource can be found [here](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/how-to-guides/create-document-intelligence-resource?view=doc-intel-4.0.0)\n248|\n249|### Python API\n250|\n251|Basic usage in Python:\n252|\n253|```python\n254|from markitdown import MarkItDown\n255|\n256|md = MarkItDown(enable_plugins=False) # Set to True to enable plugins\n257|result = md.convert(\"test.xlsx\")\n258|print(result.text_content)\n259|```\n260|\n261|Document Intelligence conversion in Python:\n262|\n263|```python\n264|from markitdown import MarkItDown\n265|\n266|md = MarkItDown(docintel_endpoint=\"<document_intelligence_endpoint>\")\n267|result = md.convert(\"test.pdf\")\n268|print(result.text_content)\n269|```\n270|\n271|To use Large Language Models for image descriptions (currently only for pptx and image files), provide `llm_client` and `llm_model`:\n272|\n273|```python\n274|from markitdown import MarkItDown\n275|from openai import OpenAI\n276|\n277|client = OpenAI()\n278|md = MarkItDown(llm_client=client, llm_model=\"gpt-4o\", llm_prompt=\"optional custom prompt\")\n279|result = md.convert(\"example.jpg\")\n280|print(result.text_content)\n281|```\n282|\n283|### Docker\n284|\n285|```sh\n286|docker build -t markitdown:latest .\n287|docker run --rm -i markitdown:latest < ~/your-file.pdf > output.md\n288|```\n289|\n290|## Contributing\n291|\n292|This project welcomes contributions and suggestions. Most contributions require you to agree to a\n293|Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us\n294|the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.\n295|\n296|When you submit a pull request, a CLA bot will automatically determine whether you need to provide\n297|a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions\n298|provided by the bot. You will only need to do this once across all repos using our CLA.\n299|\n300|This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).\n301|For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or\n302|contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n303|\n304|### How to Contribute\n305|\n306|You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are of course just suggestions and you are welcome to contribute in any way you like.\n307|\n308|<div align=\"center\">\n309|\n310||            | All                                                          | Especially Needs Help from Community                                                                                                      |\n311|| ---------- | ------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------- |\n312|| **Issues** | [All Issues](https://github.com/microsoft/markitdown/issues) | [Issues open for contribution](https://github.com/microsoft/markitdown/issues?q=is%3Aissue+is%3Aopen+label%3A%22open+for+contribution%22) |\n313|| **PRs**    | [All PRs](https://github.com/microsoft/markitdown/pulls)     | [PRs open for reviewing](https://github.com/microsoft/markitdown/pulls?q=is%3Apr+is%3Aopen+label%3A%22open+for+reviewing%22)              |\n314|\n315|</div>\n316|\n317|### Running Tests and Checks\n318|\n319|- Navigate to the MarkItDown package:\n320|\n321|  ```sh\n322|  cd packages/markitdown\n323|  ```\n324|\n325|- Install `hatch` in your environment and run tests:\n326|\n327|  ```sh\n328|  pip install hatch  # Other ways of installing hatch: https://hatch.pypa.io/dev/install/\n329|  hatch shell\n330|  hatch test\n331|  ```\n332|\n333|  (Alternative) Use the Devcontainer which has all the dependencies installed:\n334|\n335|  ```sh\n336|  # Reopen the project in Devcontainer and run:\n337|  hatch test\n338|  ```\n339|\n340|- Run pre-commit checks before submitting a PR: `pre-commit run --all-files`\n341|\n342|### Security Considerations\n343|\n344|MarkItDown performs I/O with the privileges of the current process. Like `open()` or `requests.get()`, it will access resources that the process itself can access. \n345|\n346|**Sanitize your inputs:** Do not pass untrusted input directly to MarkItDown. If any part of the input may be controlled by an untrusted user or system, such as in hosted or server-side applications, it must be validated and restricted before calling MarkItDown. Depending on your environment, this may include restricting file paths, limiting URI schemes and network destinations, and blocking access to private, loopback, link-local, or metadata-service addresses. \n347|\n348|**Call only the conversion method you need:** Prefer the narrowest conversion API that fits your use case. MarkItDown's `convert()` method is intentionally permissive and can handle local files, remote URIs, and byte streams. If your application only needs to read local files, call `convert_local()` instead. If you need more control over URI fetching, call `requests.get()` yourself and pass the response object to `convert_response()`. For maximum control, open a stream to the input you want converted and call `convert_stream()`.\n349|\n350|### Contributing 3rd-party Plugins\n351|\n352|You can also contribute by creating and sharing 3rd party plugins. See `packages/markitdown-sample-plugin` for more details.\n353|\n354|## Trademarks\n355|\n356|This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft\n357|trademarks or logos is subject to and must follow\n358|[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).\n359|Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.\n360|Any use of third-party trademarks or logos are subject to those third-party's policies.\n361|", "total_lines": 360, "file_size": 15757, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|import mimetypes\n2|import os\n3|import re\n4|import sys\n5|import shutil\n6|import traceback\n7|import io\n8|from dataclasses import dataclass\n9|from importlib.metadata import entry_points\n10|from typing import Any, List, Dict, Optional, Union, BinaryIO\n11|from pathlib import Path\n12|from urllib.parse import urlparse\n13|from warnings import warn\n14|import requests\n15|import magika\n16|import charset_normalizer\n17|import codecs\n18|\n19|from ._stream_info import StreamInfo\n20|from ._uri_utils import parse_data_uri, file_uri_to_path\n21|\n22|from .converters import (\n23|    PlainTextConverter,\n24|    HtmlConverter,\n25|    RssConverter,\n26|    WikipediaConverter,\n27|    YouTubeConverter,\n28|    IpynbConverter,\n29|    BingSerpConverter,\n30|    PdfConverter,\n31|    DocxConverter,\n32|    XlsxConverter,\n33|    XlsConverter,\n34|    PptxConverter,\n35|    ImageConverter,\n36|    AudioConverter,\n37|    OutlookMsgConverter,\n38|    ZipConverter,\n39|    EpubConverter,\n40|    DocumentIntelligenceConverter,\n41|    ContentUnderstandingConverter,\n42|    CsvConverter,\n43|)\n44|\n45|from ._base_converter import DocumentConverter, DocumentConverterResult\n46|\n47|from ._exceptions import (\n48|    FileConversionException,\n49|    UnsupportedFormatException,\n50|    FailedConversionAttempt,\n51|)\n52|\n53|\n54|# Lower priority values are tried first.\n55|PRIORITY_SPECIFIC_FILE_FORMAT = (\n56|    0.0  # e.g., .docx, .pdf, .xlsx, Or specific pages, e.g., wikipedia\n57|)\n58|PRIORITY_GENERIC_FILE_FORMAT = (\n59|    10.0  # Near catch-all converters for mimetypes like text/*, etc.\n60|)\n61|\n62|\n63|_plugins: Union[None, List[Any]] = None  # If None, plugins have not been loaded yet.\n64|\n65|\n66|def _load_plugins() -> Union[None, List[Any]]:\n67|    \"\"\"Lazy load plugins, exiting early if already loaded.\"\"\"\n68|    global _plugins\n69|\n70|    # Skip if we've already loaded plugins\n71|    if _plugins is not None:\n72|        return _plugins\n73|\n74|    # Load plugins\n75|    _plugins = []\n76|    for entry_point in entry_points(group=\"markitdown.plugin\"):\n77|        try:\n78|            _plugins.append(entry_point.load())\n79|        except Exception:\n80|            tb = traceback.format_exc()\n81|            warn(f\"Plugin '{entry_point.name}' failed to load ... skipping:\\n{tb}\")\n82|\n83|    return _plugins\n84|\n85|\n86|@dataclass(kw_only=True, frozen=True)\n87|class ConverterRegistration:\n88|    \"\"\"A registration of a converter with its priority and other metadata.\"\"\"\n89|\n90|    converter: DocumentConverter\n91|    priority: float\n92|\n93|\n94|class MarkItDown:\n95|    \"\"\"(In preview) An extremely simple text-based document reader, suitable for LLM use.\n96|    This reader will convert common file-types or webpages to Markdown.\"\"\"\n97|\n98|    def __init__(\n99|        self,\n100|        *,\n101|        enable_builtins: Union[None, bool] = None,\n102|        enable_plugins: Union[None, bool] = None,\n103|        **kwargs,\n104|    ):\n105|        self._builtins_enabled = False\n106|        self._plugins_enabled = False\n107|\n108|        requests_session = kwargs.get(\"requests_session\")\n109|        if requests_session is None:\n110|            self._requests_session = requests.Session()\n111|            # Signal that we prefer markdown over HTML, etc. if the server supports it.\n112|            # e.g., https://blog.cloudflare.com/markdown-for-agents/\n113|            self._requests_session.headers.update(\n114|                {\n115|                    \"Accept\": \"text/markdown, text/html;q=0.9, text/plain;q=0.8, */*;q=0.1\"\n116|                }\n117|            )\n118|        else:\n119|            self._requests_session = requests_session\n120|\n121|        self._magika = magika.Magika()\n122|\n123|        # TODO - remove these (see enable_builtins)\n124|        self._llm_client: Any = None\n125|        self._llm_model: Union[str | None] = None\n126|        self._llm_prompt: Union[str | None] = None\n127|        self._exiftool_path: Union[str | None] = None\n128|        self._style_map: Union[str | None] = None\n129|\n130|        # Register the converters\n131|        self._converters: List[ConverterRegistration] = []\n132|\n133|        if (\n134|            enable_builtins is None or enable_builtins\n135|        ):  # Default to True when not specified\n136|            self.enable_builtins(**kwargs)\n137|\n138|        if enable_plugins:\n139|            self.enable_plugins(**kwargs)\n140|\n141|    def enable_builtins(self, **kwargs) -> None:\n142|        \"\"\"\n143|        Enable and register built-in converters.\n144|        Built-in converters are enabled by default.\n145|        This method should only be called once, if built-ins were initially disabled.\n146|        \"\"\"\n147|        if not self._builtins_enabled:\n148|            # TODO: Move these into converter constructors\n149|            self._llm_client = kwargs.get(\"llm_client\")\n150|            self._llm_model = kwargs.get(\"llm_model\")\n151|            self._llm_prompt = kwargs.get(\"llm_prompt\")\n152|            self._exiftool_path = kwargs.get(\"exiftool_path\")\n153|            self._style_map = kwargs.get(\"style_map\")\n154|\n155|            if self._exiftool_path is None:\n156|                self._exiftool_path = os.getenv(\"EXIFTOOL_PATH\")\n157|\n158|            # Still none? Check well-known paths\n159|            if self._exiftool_path is None:\n160|                candidate = shutil.which(\"exiftool\")\n161|                if candidate:\n162|                    candidate = os.path.abspath(candidate)\n163|                    if any(\n164|                        d == os.path.dirname(candidate)\n165|                        for d in [\n166|                            \"/usr/bin\",\n167|                            \"/usr/local/bin\",\n168|                            \"/opt\",\n169|                            \"/opt/bin\",\n170|                            \"/opt/local/bin\",\n171|                            \"/opt/homebrew/bin\",\n172|                            \"C:\\\\Windows\\\\System32\",\n173|                            \"C:\\\\Program Files\",\n174|                            \"C:\\\\Program Files (x86)\",\n175|                        ]\n176|                    ):\n177|                        self._exiftool_path = candidate\n178|\n179|            # Register converters for successful browsing operations\n180|            # Later registrations are tried first / take higher priority than earlier registrations\n181|            # To this end, the most specific converters should appear below the most generic converters\n182|            self.register_converter(\n183|                PlainTextConverter(), priority=PRIORITY_GENERIC_FILE_FORMAT\n184|            )\n185|            self.register_converter(\n186|                ZipConverter(markitdown=self), priority=PRIORITY_GENERIC_FILE_FORMAT\n187|            )\n188|            self.register_converter(\n189|                HtmlConverter(), priority=PRIORITY_GENERIC_FILE_FORMAT\n190|            )\n191|            self.register_converter(RssConverter())\n192|            self.register_converter(WikipediaConverter())\n193|            self.register_converter(YouTubeConverter())\n194|            self.register_converter(BingSerpConverter())\n195|            self.register_converter(DocxConverter())\n196|            self.register_converter(XlsxConverter())\n197|            self.register_converter(XlsConverter())\n198|            self.register_converter(PptxConverter())\n199|            self.register_converter(AudioConverter())\n200|            self.register_converter(ImageConverter())\n201|            self.register_converter(IpynbConverter())\n202|            self.register_converter(PdfConverter())\n203|            self.register_converter(OutlookMsgConverter())\n204|            self.register_converter(EpubConverter())\n205|            self.register_converter(CsvConverter())\n206|\n207|            # Register Document Intelligence converter at the top of the stack if endpoint is provided\n208|            docintel_endpoint = kwargs.get(\"docintel_endpoint\")\n209|            if docintel_endpoint is not None:\n210|                docintel_args: Dict[str, Any] = {}\n211|                docintel_args[\"endpoint\"] = docintel_endpoint\n212|\n213|                docintel_credential = kwargs.get(\"docintel_credential\")\n214|                if docintel_credential is not None:\n215|                    docintel_args[\"credential\"] = docintel_credential\n216|\n217|                docintel_types = kwargs.get(\"docintel_file_types\")\n218|                if docintel_types is not None:\n219|                    docintel_args[\"file_types\"] = docintel_types\n220|\n221|                docintel_version = kwargs.get(\"docintel_api_version\")\n222|                if docintel_version is not None:\n223|                    docintel_args[\"api_version\"] = docintel_version\n224|\n225|                self.register_converter(\n226|                    DocumentIntelligenceConverter(**docintel_args),\n227|                )\n228|\n229|            # Register Content Understanding converter at the top of the stack if endpoint is provided\n230|            cu_endpoint = kwargs.get(\"cu_endpoint\")\n231|            if cu_endpoint is not None:\n232|                cu_args: Dict[str, Any] = {}\n233|                cu_args[\"endpoint\"] = cu_endpoint\n234|\n235|                cu_credential = kwargs.get(\"cu_credential\")\n236|                if cu_credential is not None:\n237|                    cu_args[\"credential\"] = cu_credential\n238|\n239|                cu_analyzer_id = kwargs.get(\"cu_analyzer_id\")\n240|                if cu_analyzer_id is not None:\n241|                    cu_args[\"analyzer_id\"] = cu_analyzer_id\n242|\n243|                cu_file_types = kwargs.get(\"cu_file_types\")\n244|                if cu_file_types is not None:\n245|                    cu_args[\"file_types\"] = cu_file_types\n246|\n247|                self.register_converter(\n248|                    ContentUnderstandingConverter(**cu_args),\n249|                )\n250|\n251|            self._builtins_enabled = True\n252|        else:\n253|            warn(\"Built-in converters are already enabled.\", RuntimeWarning)\n254|\n255|    def enable_plugins(self, **kwargs) -> None:\n256|        \"\"\"\n257|        Enable and register converters provided by plugins.\n258|        Plugins are disabled by default.\n259|        This method should only be called once, if plugins were initially disabled.\n260|        \"\"\"\n261|        if not self._plugins_enabled:\n262|            # Load plugins\n263|            plugins = _load_plugins()\n264|            assert plugins is not None\n265|            for plugin in plugins:\n266|                try:\n267|                    plugin.register_converters(self, **kwargs)\n268|                except Exception:\n269|                    tb = traceback.format_exc()\n270|                    warn(f\"Plugin '{plugin}' failed to register converters:\\n{tb}\")\n271|            self._plugins_enabled = True\n272|        else:\n273|            warn(\"Plugins converters are already enabled.\", RuntimeWarning)\n274|\n275|    def convert(\n276|        self,\n277|        source: Union[str, requests.Response, Path, BinaryIO],\n278|        *,\n279|        stream_info: Optional[StreamInfo] = None,\n280|        **kwargs: Any,\n281|    ) -> DocumentConverterResult:  # TODO: deal with kwargs\n282|        \"\"\"\n283|        Args:\n284|            - source: can be a path (str or Path), url, or a requests.response object\n285|            - stream_info: optional stream info to use for the conversion. If None, infer from source\n286|            - kwargs: additional arguments to pass to the converter\n287|        \"\"\"\n288|\n289|        # Local path or url\n290|        if isinstance(source, str):\n291|            if (\n292|                source.startswith(\"http:\")\n293|                or source.startswith(\"https:\")\n294|                or source.startswith(\"file:\")\n295|                or source.startswith(\"data:\")\n296|            ):\n297|                # Rename the url argument to mock_url\n298|                # (Deprecated -- use stream_info)\n299|                _kwargs = {k: v for k, v in kwargs.items()}\n300|                if \"url\" in _kwargs:\n301|                    _kwargs[\"mock_url\"] = _kwargs[\"url\"]\n302|                    del _kwargs[\"url\"]\n303|\n304|                return self.convert_uri(source, stream_info=stream_info, **_kwargs)\n305|            else:\n306|                return self.convert_local(source, stream_info=stream_info, **kwargs)\n307|        # Path object\n308|        elif isinstance(source, Path):\n309|            return self.convert_local(source, stream_info=stream_info, **kwargs)\n310|        # Request response\n311|        elif isinstance(source, requests.Response):\n312|            return self.convert_response(source, stream_info=stream_info, **kwargs)\n313|        # Binary stream\n314|        elif (\n315|            hasattr(source, \"read\")\n316|            and callable(source.read)\n317|            and not isinstance(source, io.TextIOBase)\n318|        ):\n319|            return self.convert_stream(source, stream_info=stream_info, **kwargs)\n320|        else:\n321|            raise TypeError(\n322|                f\"Invalid source type: {type(source)}. Expected str, requests.Response, BinaryIO.\"\n323|            )\n324|\n325|    def convert_local(\n326|        self,\n327|        path: Union[str, Path],\n328|        *,\n329|        stream_info: Optional[StreamInfo] = None,\n330|        file_extension: Optional[str] = None,  # Deprecated -- use stream_info\n331|        url: Optional[str] = None,  # Deprecated -- use stream_info\n332|        **kwargs: Any,\n333|    ) -> DocumentConverterResult:\n334|        if isinstance(path, Path):\n335|            path = str(path)\n336|\n337|        # Build a base StreamInfo object from which to start guesses\n338|        base_guess = StreamInfo(\n339|            local_path=path,\n340|            extension=os.path.splitext(path)[1],\n341|            filename=os.path.basename(path),\n342|        )\n343|\n344|        # Extend the base_guess with any additional info from the arguments\n345|        if stream_info is not None:\n346|            base_guess = base_guess.copy_and_update(stream_info)\n347|\n348|        if file_extension is not None:\n349|            # Deprecated -- use stream_info\n350|            base_guess = base_guess.copy_and_update(extension=file_extension)\n351|\n352|        if url is not None:\n353|            # Deprecated -- use stream_info\n354|            base_guess = base_guess.copy_and_update(url=url)\n355|\n356|        with open(path, \"rb\") as fh:\n357|            guesses = self._get_stream_info_guesses(\n358|                file_stream=fh, base_guess=base_guess\n359|            )\n360|            return self._convert(file_stream=fh, stream_info_guesses=guesses, **kwargs)\n361|\n362|    def convert_stream(\n363|        self,\n364|        stream: BinaryIO,\n365|        *,\n366|        stream_info: Optional[StreamInfo] = None,\n367|        file_extension: Optional[str] = None,  # Deprecated -- use stream_info\n368|        url: Optional[str] = None,  # Deprecated -- use stream_info\n369|        **kwargs: Any,\n370|    ) -> DocumentConverterResult:\n371|        guesses: List[StreamInfo] = []\n372|\n373|        # Do we have anything on which to base a guess?\n374|        base_guess = None\n375|        if stream_info is not None or file_extension is not None or url is not None:\n376|            # Start with a non-Null base guess\n377|            if stream_info is None:\n378|                base_guess = StreamInfo()\n379|            else:\n380|                base_guess = stream_info\n381|\n382|            if file_extension is not None:\n383|                # Deprecated -- use stream_info\n384|                assert base_guess is not None  # for mypy\n385|                base_guess = base_guess.copy_and_update(extension=file_extension)\n386|\n387|            if url is not None:\n388|                # Deprecated -- use stream_info\n389|                assert base_guess is not None  # for mypy\n390|                base_guess = base_guess.copy_and_update(url=url)\n391|\n392|        # Check if we have a seekable stream. If not, load the entire stream into memory.\n393|        if not stream.seekable():\n394|            buffer = io.BytesIO()\n395|            while True:\n396|                chunk = stream.read(4096)\n397|                if not chunk:\n398|                    break\n399|                buffer.write(chunk)\n400|            buffer.seek(0)\n401|            stream = buffer\n402|\n403|        # Add guesses based on stream content\n404|        guesses = self._get_stream_info_guesses(\n405|            file_stream=stream, base_guess=base_guess or StreamInfo()\n406|        )\n407|        return self._convert(file_stream=stream, stream_info_guesses=guesses, **kwargs)\n408|\n409|    def convert_url(\n410|        self,\n411|        url: str,\n412|        *,\n413|        stream_info: Optional[StreamInfo] = None,\n414|        file_extension: Optional[str] = None,\n415|        mock_url: Optional[str] = None,\n416|        **kwargs: Any,\n417|    ) -> DocumentConverterResult:\n418|        \"\"\"Alias for convert_uri()\"\"\"\n419|        # convert_url will likely be deprecated in the future in favor of convert_uri\n420|        return self.convert_uri(\n421|            url,\n422|            stream_info=stream_info,\n423|            file_extension=file_extension,\n424|            mock_url=mock_url,\n425|            **kwargs,\n426|        )\n427|\n428|    def convert_uri(\n429|        self,\n430|        uri: str,\n431|        *,\n432|        stream_info: Optional[StreamInfo] = None,\n433|        file_extension: Optional[str] = None,  # Deprecated -- use stream_info\n434|        mock_url: Optional[\n435|            str\n436|        ] = None,  # Mock the request as if it came from a different URL\n437|        **kwargs: Any,\n438|    ) -> DocumentConverterResult:\n439|        uri = uri.strip()\n440|\n441|        # File URIs\n442|        if uri.startswith(\"file:\"):\n443|            netloc, path = file_uri_to_path(uri)\n444|            if netloc and netloc != \"localhost\":\n445|                raise ValueError(\n446|                    f\"Unsupported file URI: {uri}. Netloc must be empty or localhost.\"\n447|                )\n448|            return self.convert_local(\n449|                path,\n450|                stream_info=stream_info,\n451|                file_extension=file_extension,\n452|                url=mock_url,\n453|                **kwargs,\n454|            )\n455|        # Data URIs\n456|        elif uri.startswith(\"data:\"):\n457|            mimetype, attributes, data = parse_data_uri(uri)\n458|\n459|            base_guess = StreamInfo(\n460|                mimetype=mimetype,\n461|                charset=attributes.get(\"charset\"),\n462|            )\n463|            if stream_info is not None:\n464|                base_guess = base_guess.copy_and_update(stream_info)\n465|\n466|            return self.convert_stream(\n467|                io.BytesIO(data),\n468|                stream_info=base_guess,\n469|                file_extension=file_extension,\n470|                url=mock_url,\n471|                **kwargs,\n472|            )\n473|        # HTTP/HTTPS URIs\n474|        elif uri.startswith(\"http:\") or uri.startswith(\"https:\"):\n475|            response = self._requests_session.get(uri, stream=True)\n476|            response.raise_for_status()\n477|            return self.convert_response(\n478|                response,\n479|                stream_info=stream_info,\n480|                file_extension=file_extension,\n481|                url=mock_url,\n482|                **kwargs,\n483|            )\n484|        else:\n485|            raise ValueError(\n486|                f\"Unsupported URI scheme: {uri.split(':')[0]}. Supported schemes are: file:, data:, http:, https:\"\n487|            )\n488|\n489|    def convert_response(\n490|        self,\n491|        response: requests.Response,\n492|        *,\n493|        stream_info: Optional[StreamInfo] = None,\n494|        file_extension: Optional[str] = None,  # Deprecated -- use stream_info\n495|        url: Optional[str] = None,  # Deprecated -- use stream_info\n496|        **kwargs: Any,\n497|    ) -> DocumentConverterResult:\n498|        # If there is a content-type header, get the mimetype and charset (if present)\n499|        mimetype: Optional[str] = None\n500|        charset: Optional[str] = None\n501|", "total_lines": 806, "file_size": 31560, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 806 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Análise concluída. O que foi feito:
  - Consultados metadados públicos do repositório via GitHub API
  - Clonado o repositório localmente em `/home/ice/markitdown`
  - Inspecionados estrutura de pastas, README e principais arquivos Python

  ## Resultado

  - Problema: Transformar arquivos diversificados em Markdown estruturado para uso em LLMs e pipelines de análise textual
  - Tema: Conversão universal de documentos para Markdown
  - Razão do hype: adoção massiva por equipes de IA, integração com Azure e fácil uso via CLI/Python, com suporte a PDF, Office, mídia, OCR e plugins
  - Tecnologias: Python, pypandoc, python-pptx, python-docx, openpyxl, xlrd, markdownify, requests, magika, charset-normalizer, integração Azure, Docker, Devcontainer e sistema de plugins  


c0:
  Vou buscar informações sobre o repositório `revfactory/harness` no GitHub e, se possível, cloná-lo localmente para analisar como os dados correspondem ao que foi informado.  [{"id": "chatcmpl-tool-9189070cfa9af41d", "call_id": "chatcmpl-tool-9189070cfa9af41d", "response_item_id": "fc_chatcmpl-tool-9189070cfa9af41d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.github.com/repos/revfactory/harness | jq '{full_name, description, language, stargazers_count, html_url}'\"}"}}]


c0:
    [{"id": "chatcmpl-tool-8605cd35f14e607c", "call_id": "chatcmpl-tool-8605cd35f14e607c", "response_item_id": "fc_chatcmpl-tool-8605cd35f14e607c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s https://api.github.com/repos/revfactory/harness | python3 -c \\\"import sys, json; d=json.load(sys.stdin); print('Nome:', d.get('full_name')); print('Descri\u00e7\u00e3o:', d.get('description')); print('Linguagem:', d.get('language')); print('Estrelas:', d.get('stargazers_count')); print('URL:', d.get('html_url'))\\\"\"}"}}]


c0:
  {"output": "Nome: revfactory/harness\nDescrição: A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.\nLinguagem: HTML\nEstrelas: 4244\nURL: https://github.com/revfactory/harness", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-869e7af948174889", "call_id": "chatcmpl-tool-869e7af948174889", "response_item_id": "fc_chatcmpl-tool-869e7af948174889", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone --depth 1 https://github.com/revfactory/harness.git /tmp/harness-repo\", \"timeout\": 60}"}}]


c0:
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c0:
  Análise rápida do repositório `revfactory/harness`:

  - O que eu fiz: validei diretamente pela API do GitHub o dado reportado (repo existe, descrição igual à informada e 4.244 estrelas), clonei o repositório superficialmente e examinei sua estrutura e arquivos centrais.

  - Problema: Dificuldade de estruturar manualmente equipes de agentes especializados, definir seus papéis e gerar as skills que eles devem usar.
  - Tema: Meta-skill / orquestração de agentes.
  - Razão hype: Reduz o custo cognitivo na definição de agentes; já traz papéis e fluxos prontos; combina com a tendência de agentes como força de trabalho modular.
  - Tecnologias: reportado como HTML, mas na prática é um framework declarativo em Markdown com páginas estáticas (index.html, privacy.html) e assets visuais.  


c0:
    [{"id": "chatcmpl-tool-840e2b1779a71de4", "call_id": "chatcmpl-tool-840e2b1779a71de4", "response_item_id": "fc_chatcmpl-tool-840e2b1779a71de4", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone --depth=1 https://github.com/EveryInc/compound-engineering-plugin.git\", \"timeout\": 120}"}}]


c0:
  {"output": "Cloning into 'compound-engineering-plugin'...", "exit_code": 0, "error": null} terminal 


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  [Subdirectory context discovered: compound-engineering-plugin/AGENTS.md]
  # Agent Instructions

  This repository primarily houses the `compound-engineering` coding-agent plugin and the Claude Code marketplace/catalog metadata used to distribute it.

  It also contains:
  - the Bun/TypeScript CLI that converts Claude Code plugins into other agent platform formats
  - additional plugins under `plugins/`, such as `coding-tutor`
  - shared release and metadata infrastructure for the CLI, marketplace, and plugins

  `AGENTS.md` is the canonical repo instruction file. Root `CLAUDE.md` exists only as a compatibility shim for tools and conversions that still look for it.

  ## Quick Start

  ```bash
  bun install
  bun test                  # full test suite
  bun run release:validate  # check plugin/marketplace consistency
  ```

  ## Working Agreement

  - **Branching:** Create a feature branch for any non-trivial change. If already on the correct branch for the task, keep using it; do not create additional branches or worktrees unless explicitly requested.
  - **Merge policy:** All changes to `main` go through pull requests. Direct pushes and direct merges are not allowed; branch protection on `main` enforces this by requiring the `test` status check to pass. The direct path bypasses `release:validate`, the test suite, and PR title validation — past direct merges have caused version drift requiring multi-PR recovery (see `docs/solutions/workflow/release-please-version-drift-recovery.md`).
  - **Safety:** Do not delete or overwrite user data. Avoid destructive commands.
  - **Testing:** Run `bun test` after changes that affect parsing, conversion, or output.
  - **Release versioning:** Releases are prepared by release automation, not normal feature PRs. The repo now has multiple release components (`cli`, `compound-engineering`, `coding-tutor`, `marketplace`). GitHub release PRs and GitHub Releases are the canonical release-notes surface for new releases; root `CHANGELOG.md` is only a pointer to that history. Use conventional titles such as `feat:` and `fix:` so release automation can classify change intent, but do not hand-bump release-owned versions or hand-author release notes in routine PRs.
  - **Linked versions (cli + compound-engineering):** The `linked-versions` release-please plugin keeps `cli` and `compound-engineering` at the same version. This is intentional -- it simplifies version tracking across the CLI and the plugin it ships. A consequence is that a release with only plugin changes will still bump the CLI version (and vice versa). The CLI changelog may also include commits that `exclude-paths` would normally filter, because `linked-versions` overrides exclusion logic when forcing a synced bump. This is a known upstream release-please limitation, not a misconfiguration. Do not flag linked-version bumps as unnecessary.
  - **Output Paths:** Keep OpenCode output at `opencode.json` and `.opencode/{agents,skills,plugins}`. For OpenCode, command go to `~/.config/opencode/commands/<name>.md`; `opencode.json` is deep-merged (never overwritten wholesale).
  - **Scratch Space:** Default to OS temp. Use `.context/` only when explicitly justified by the rules below.
    - **Default: OS temp** — covers most scratch, including per-run throwaway AND cross-invocation reusable, regardless of whether a repo is present or whether other skills may read the files. A stable OS-temp prefix handles cross-skill and cross-invocation coordination equally well as an in-repo path; repo-adjacency is rarely the relevant property.
      - **Per-run throwaway**: `mktemp -d -t <prefix>-XXXXXX` (OS handles cleanup). Use for files consumed once and discarded — captured screenshots, stitched GIFs, intermediate build outputs, recordings, delegation prompts/results, single-run checkpoints. The resulting path is opaque (on macOS it resolves under `$TMPDIR`/`/var/folders/...`) — that is appropriate for throwaway files users are not meant to access.
      - **Cross-invocation reusable**: stable path `/tmp/compound-engineering/<skill-name>/<run-id>/` — **not** `mktemp -d` — so later invocations of the same skill can discover sibling run-ids. Use `/tmp` directly rather than `$TMPDIR` so paths stay accessible: `$TMPDIR` on macOS resolves to `/var/folders/64/.../T/`, which is hostile for users who want to inspect checkpoints, grep them, or copy them out. The per-user isolation `$TMPDIR` provides is not valuable for cross-invocation reusable scratch where users are the intended audience. Use for caches keyed by session, checkpoints meant to survive context compaction within a loose session, or any state where later runs of the same skill need to locate prior outputs.
    - **Exception: `.context/`** — use only when the artifact is genuinely bound to the CWD repo AND meets at least one of:
      - (a) **User-curated**: the user is expected to inspect, manipulate, or manually curate the artifact outside the skill (e.g., a per-repo TODO database, a per-spec optimization log that survives across sessions on the same checkout).
      - (b) **Repo+branch-inseparable**: the artifact's meaning is inseparable from this specific repo or branch (e.g., branch-specific resume state that a user expects to pick up again in the same checkout).
      - (c) **Path is core UX**: surfacing the artifact path back to the user is a core part of the skill's output and that path is easier to communicate as a repo-relative location than an OS-temp one.
      Namespace under `.context/compound-engineering/<workflow-or-skill-name>/`, add a per-run subdirectory when concurrent runs are plausible, and decide cleanup behavior per the artifact's lifecycle (per-run scratch clears on success; user-curated state persists). "Shared between skills" is not by itself sufficient — OS temp handles that equally well.
    - **Durable outputs** (plans, specs, learnings, docs, final deliverables) belong in `docs/` or another repo-tracked location, not in either scratch tier.
    - **Cross-platform note:** `/tmp` is writable on macOS (symlink to `/private/tmp`), Linux, and WSL. `mktemp -d -t <prefix>-XXXXXX` also works on all three. Skills authored here assume Unix-like shells; native Windows is not a current target.
  - **Character encoding:**
    - **Identifiers** (file names, agent names, command names): ASCII only -- converters and regex patterns depend on it.
    - **Markdown tables:** Use pipe-delimited (`| col | col |`), never box-drawing characters.
    - **Prose and skill content:** Unicode is fine (emoji, punctuation, etc.). Prefer ASCII arrows (`->`, `<-`) over Unicode arrows in code blocks and terminal examples.

  ## Directory Layout

  ```
  src/              CLI entry point, parsers, converters, target writers
  plugins/          Plugin workspaces (compound-engineering, coding-tutor)
  .claude-plugin/   Claude marketplace catalog metadata
  tests/            Converter, writer, and CLI tests + fixtures
  docs/             Requirements, plans, solutions, and target specs
  ```

  ## Repo Surfaces

  Changes in this repo may affect one or more of these surfaces:

  - `compound-engineering` under `plugins/compound-engineering/`
  - the Claude marketplace catalog under `.claude-plugin/`
  - the converter/install CLI in `src/` and `package.json`
  - secondary plugins such as `plugins/coding-tutor/`

  Do not assume a repo change is "just CLI" or "just plugin" without checking which surface owns the affected files.

  ## Plugin Maintenance

  When changing `plugins/compound-engineering/` content:

  - Update substantive docs like `plugins/compound-engineering/README.md` when the plugin behavior, inventory, or usage changes.
  - Do not hand-bump release-owned versions in plugin or marketplace manifests.
  - Do not hand-add release entries to `CHANGELOG.md` or treat it as the canonical source for new releases.
  - Run `bun run release:validate` if agents, commands, skills, MCP servers, or release-owned descriptions/counts may have changed.
  - When removing a skill, agent, or command, add its name to both cleanup registries so stale flat-install artifacts are swept on upgrade:
    - `STALE_SKILL_DIRS` / `STA

  [...truncated AGENTS.md: 19,439 chars total] search_files 


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c0:
  {"content": "1|# Compound Engineering\n2|\n3|[![Build Status](https://github.com/EveryInc/compound-engineering-plugin/actions/workflows/ci.yml/badge.svg)](https://github.com/EveryInc/compound-engineering-plugin/actions/workflows/ci.yml)\n4|[![npm](https://img.shields.io/npm/v/@every-env/compound-plugin)](https://www.npmjs.com/package/@every-env/compound-plugin)\n5|\n6|AI skills and agents that make each unit of engineering work easier than the last.\n7|\n8|## Philosophy\n9|\n10|**Each unit of engineering work should make subsequent units easier -- not harder.**\n11|\n12|Traditional development accumulates technical debt. Every feature adds complexity. Every bug fix leaves behind a little more local knowledge that someone has to rediscover later. The codebase gets larger, the context gets harder to hold, and the next change becomes slower.\n13|\n14|Compound engineering inverts this. 80% is in planning and review, 20% is in execution:\n15|\n16|- Plan thoroughly before writing code with `/ce-brainstorm` and `/ce-plan`\n17|- Review to catch issues and calibrate judgment with `/ce-code-review` and `/ce-doc-review`\n18|- Codify knowledge so it is reusable with `/ce-compound`\n19|- Keep quality high so future changes are easy\n20|\n21|The point is not ceremony. The point is leverage. A good brainstorm makes the plan sharper. A good plan makes execution smaller. A good review catches the pattern, not just the bug. A good compound note means the next agent does not have to learn the same lesson from scratch.\n22|\n23|**Learn more**\n24|\n25|- [Full component reference](plugins/compound-engineering/README.md) - all agents and skills\n26|- [Compound engineering: how Every codes with agents](https://every.to/chain-of-thought/compound-engineering-how-every-codes-with-agents)\n27|- [The story behind compounding engineering](https://every.to/source-code/my-ai-had-already-fixed-the-code-before-i-saw-it)\n28|\n29|## Workflow\n30|\n31|`/ce-strategy` is upstream of the loop -- it captures the product's target problem, approach, persona, metrics, and tracks as a short durable anchor at `STRATEGY.md`. Ideate, brainstorm, and plan read it as grounding when present, so strategy choices flow into feature conception, prioritization, and spec.\n32|\n33|The core loop is: brainstorm the requirements, plan the implementation, work through the plan, review the result, compound the learning, then repeat with better context.\n34|\n35|Use `/ce-ideate` before the loop when you want the agent to generate and critique bigger ideas before choosing one to brainstorm. It produces a ranked ideation artifact, not requirements, plans, or code.\n36|\n37|| Skill | Purpose |\n38||-------|---------|\n39|| `/ce-strategy` | Create or maintain `STRATEGY.md` -- the product's target problem, approach, persona, key metrics, and tracks. Read as grounding by ideate, brainstorm, and plan |\n40|| `/ce-ideate` | Optional big-picture ideation: generate and critically evaluate grounded ideas, then route the strongest one into brainstorming |\n41|| `/ce-brainstorm` | Interactive Q&A to think through a feature or problem and write a right-sized requirements doc before planning |\n42|| `/ce-plan` | Turn feature ideas into detailed implementation plans |\n43|| `/ce-work` | Execute plans with worktrees and task tracking |\n44|| `/ce-debug` | Systematically reproduce failures, trace root cause, and implement fixes |\n45|| `/ce-code-review` | Multi-agent code review before merging |\n46|| `/ce-compound` | Document learnings to make future work easier |\n47|| `/ce-product-pulse` | Generate a single-page, time-windowed pulse report on usage, performance, errors, and followups. Saves to `docs/pulse-reports/` |\n48|\n49|`/ce-product-pulse` is the read-side companion -- a time-windowed report on what users actually experienced and how the product performed over a given window (24h, 7d, etc.), saved to `docs/pulse-reports/` so past pulses form a browseable timeline of user outcomes. The next strategy update and the next brainstorm get real signal to anchor to.\n50|\n51|Each cycle compounds: brainstorms sharpen plans, plans inform future plans, reviews catch more issues, patterns get documented.\n52|\n53|## Quick Example\n54|\n55|A typical cycle starts by turning a rough idea into a requirements doc, then planning from that doc before handing execution to `/ce-work`:\n56|\n57|```text\n58|/ce-brainstorm \"make background job retries safer\"\n59|/ce-plan docs/brainstorms/background-job-retry-safety-requirements.md\n60|/ce-work\n61|/ce-code-review\n62|/ce-compound\n63|```\n64|\n65|For a focused bug investigation:\n66|\n67|```text\n68|/ce-debug \"the checkout webhook sometimes creates duplicate invoices\"\n69|/ce-code-review\n70|/ce-compound\n71|```\n72|\n73|## Getting Started\n74|\n75|After installing, run `/ce-setup` in any project. It checks your environment, installs missing tools, and bootstraps project config.\n76|\n77|The `compound-engineering` plugin currently ships 37 skills and 51 agents. See the [full component reference](plugins/compound-engineering/README.md) for the complete inventory.\n78|\n79|---\n80|\n81|## Install\n82|\n83|### Claude Code\n84|\n85|```text\n86|/plugin marketplace add EveryInc/compound-engineering-plugin\n87|/plugin install compound-engineering\n88|```\n89|\n90|### Cursor\n91|\n92|In Cursor Agent chat, install from the plugin marketplace:\n93|\n94|```text\n95|/add-plugin compound-engineering\n96|```\n97|\n98|Or search for \"compound engineering\" in the plugin marketplace.\n99|\n100|### Codex\n101|\n102|Three steps: register the marketplace, install the agent set, then install the plugin through Codex's TUI.\n103|\n104|1. **Register the marketplace with Codex:**\n105|\n106|   ```bash\n107|   codex plugin marketplace add EveryInc/compound-engineering-plugin\n108|   ```\n109|\n110|2. **Install the Compound Engineering agents** (Codex's plugin spec does not register custom agents yet):\n111|\n112|   ```bash\n113|   bunx @every-env/compound-plugin install compound-engineering --to codex\n114|   ```\n115|\n116|3. **Install the plugin through Codex's TUI:** launch `codex`, run `/plugins`, find the **Compound Engineering** marketplace, select the **compound-engineering** plugin, and choose **Install**. Restart Codex after install completes. Codex's CLI can register marketplaces, but it does not currently expose a plugin-install subcommand for plugins from an added marketplace -- the `/plugins` TUI install is required for CE skills.\n117|\n118|All three steps are needed. The marketplace registration plus TUI install handles skills; the Bun step adds the review, research, and workflow agents that skills like `$ce-code-review`, `$ce-plan`, and `$ce-work` spawn in Codex. Without the agent step, delegating skills will report missing agents.\n119|\n120|For a non-default Codex profile, run every Codex-related step against the same `CODEX_HOME`. This example installs CE into a `work` profile:\n121|\n122|```bash\n123|CODEX_HOME=\"$HOME/.codex/profiles/work\" codex plugin marketplace add EveryInc/compound-engineering-plugin\n124|CODEX_HOME=\"$HOME/.codex/profiles/work\" bunx @every-env/compound-plugin install compound-engineering --to codex\n125|CODEX_HOME=\"$HOME/.codex/profiles/work\" codex\n126|```\n127|\n128|Inside Codex, run `/plugins`, select **Compound Engineering**, then install **compound-engineering**. The marketplace step only makes the plugin available; the TUI install is what activates the native CE skills for that profile.\n129|\n130|For local development from this checkout, register the current worktree and use the local CLI:\n131|\n132|```bash\n133|CODEX_HOME=\"$HOME/.codex/profiles/work\" codex plugin marketplace add \"$PWD\"\n134|CODEX_HOME=\"$HOME/.codex/profiles/work\" bun run src/index.ts install ./plugins/compound-engineering --to codex\n135|CODEX_HOME=\"$HOME/.codex/profiles/work\" codex\n136|```\n137|\n138|> **Heads up:** once Codex's native plugin spec supports custom agents, the Bun agent step goes away. The TUI install alone will be sufficient.\n139|\n140|If you previously used the Bun-only Codex install, back up stale CE artifacts before switching:\n141|\n142|```bash\n143|bunx @every-env/compound-plugin cleanup --target codex\n144|```\n145|\n146|### GitHub Copilot\n147|\n148|For **VS Code Copilot Agent Plugins**:\n149|\n150|1. Run `Chat: Install Plugin from Source` from the VS Code command palette\n151|2. Use `EveryInc/compound-engineering-plugin` for the repo\n152|3. Select `compound-engineering` when VS Code shows the plugins in this repository\n153|\n154|For **Copilot CLI**, use:\n155|\n156|Inside Copilot CLI:\n157|\n158|```text\n159|/plugin marketplace add EveryInc/compound-engineering-plugin\n160|/plugin install compound-engineering@compound-engineering-plugin\n161|```\n162|\n163|From a shell with the `copilot` binary:\n164|\n165|```bash\n166|copilot plugin marketplace add EveryInc/compound-engineering-plugin\n167|copilot plugin install compound-engineering@compound-engineering-plugin\n168|```\n169|\n170|Copilot CLI reads the existing Claude-compatible plugin manifests, so no separate Bun install step is needed.\n171|\n172|If you previously used the old Bun Copilot install, back up stale CE artifacts before switching to the native plugin:\n173|\n174|```bash\n175|bunx @every-env/compound-plugin cleanup --target copilot\n176|```\n177|\n178|### Factory Droid\n179|\n180|From a shell with the `droid` binary:\n181|\n182|```bash\n183|droid plugin marketplace add https://github.com/EveryInc/compound-engineering-plugin\n184|droid plugin install compound-engineering@compound-engineering-plugin\n185|```\n186|\n187|Droid uses `plugin@marketplace` plugin IDs; here `compound-engineering` is the plugin and `compound-engineering-plugin` is the marketplace name. Droid installs the existing Claude Code-compatible plugin and translates the format automatically, so no Bun install step is needed.\n188|\n189|If you previously used the old Bun Droid install, back up stale CE artifacts before switching to the native plugin:\n190|\n191|```bash\n192|bunx @every-env/compound-plugin cleanup --target droid\n193|```\n194|\n195|### Qwen Code\n196|\n197|```bash\n198|qwen extensions install EveryInc/compound-engineering-plugin:compound-engineering\n199|```\n200|\n201|", "total_lines": 413, "file_size": 18624, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 413 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "201|Qwen Code installs Claude Code-compatible plugins directly from GitHub and converts the plugin format during install, so no Bun install step is needed.\n202|\n203|If you previously used the old Bun Qwen install, back up stale CE artifacts before switching to the native extension:\n204|\n205|```bash\n206|bunx @every-env/compound-plugin cleanup --target qwen\n207|```\n208|\n209|### OpenCode, Pi, Gemini, and Kiro\n210|\n211|This repo includes a Bun/TypeScript installer that converts the Compound Engineering plugin to OpenCode, Pi, Gemini CLI, and Kiro CLI.\n212|\n213|```bash\n214|bunx @every-env/compound-plugin install compound-engineering --to opencode\n215|bunx @every-env/compound-plugin install compound-engineering --to pi\n216|bunx @every-env/compound-plugin install compound-engineering --to gemini\n217|bunx @every-env/compound-plugin install compound-engineering --to kiro\n218|```\n219|\n220|**Pi prerequisites.** Pi does not ship a native subagent primitive, so the Pi install depends on [nicobailon/pi-subagents](https://github.com/nicobailon/pi-subagents) (required) and recommends [edlsh/pi-ask-user](https://github.com/edlsh/pi-ask-user) for richer blocking user questions:\n221|\n222|```bash\n223|pi install npm:pi-subagents    # required — provides the `subagent` tool used by skills that dispatch parallel agents\n224|pi install npm:pi-ask-user     # recommended — provides the `ask_user` tool; skills fall back to numbered options in chat when it is missing\n225|```\n226|\n227|To auto-detect custom-install targets and install to all:\n228|\n229|```bash\n230|bunx @every-env/compound-plugin install compound-engineering --to all\n231|```\n232|\n233|The custom install targets run CE legacy cleanup during install. To run cleanup manually for a specific target:\n234|\n235|```bash\n236|bunx @every-env/compound-plugin cleanup --target codex\n237|bunx @every-env/compound-plugin cleanup --target opencode\n238|bunx @every-env/compound-plugin cleanup --target pi\n239|bunx @every-env/compound-plugin cleanup --target gemini\n240|bunx @every-env/compound-plugin cleanup --target kiro\n241|bunx @every-env/compound-plugin cleanup --target copilot   # old Bun installs only\n242|bunx @every-env/compound-plugin cleanup --target droid     # old Bun installs only\n243|bunx @every-env/compound-plugin cleanup --target qwen      # old Bun installs only\n244|bunx @every-env/compound-plugin cleanup --target windsurf  # deprecated legacy installs only\n245|```\n246|\n247|Cleanup moves known CE artifacts into a `compound-engineering/legacy-backup/` directory under the target root.\n248|\n249|---\n250|\n251|## Local Development\n252|\n253|```bash\n254|bun install\n255|bun test\n256|bun run release:validate\n257|```\n258|\n259|### From your local checkout\n260|\n261|For active development -- edits to the plugin source are reflected immediately.\n262|\n263|**Claude Code** -- add a shell alias so your local copy loads alongside your normal plugins:\n264|\n265|```bash\n266|alias cce='claude --plugin-dir ~/Code/compound-engineering-plugin/plugins/compound-engineering'\n267|```\n268|\n269|Run `cce` instead of `claude` to test your changes. Your production install stays untouched.\n270|\n271|**Codex and other targets** -- run the local CLI against your checkout:\n272|\n273|```bash\n274|# from the repo root\n275|bun run src/index.ts install ./plugins/compound-engineering --to codex\n276|\n277|# same pattern for other targets\n278|bun run src/index.ts install ./plugins/compound-engineering --to opencode\n279|```\n280|\n281|### From a pushed branch\n282|\n283|For testing someone else's branch or your own branch from a worktree, without switching checkouts. Uses `--branch` to clone the branch to a deterministic cache directory.\n284|\n285|> **Unpushed local branches**: If the branch exists only in a local worktree and has not been pushed, point `--plugin-dir` directly at the worktree path instead (e.g. `claude --plugin-dir /path/to/worktree/plugins/compound-engineering`).\n286|\n287|**Claude Code** -- use `plugin-path` to get the cached clone path:\n288|\n289|```bash\n290|# from the repo root\n291|bun run src/index.ts plugin-path compound-engineering --branch feat/new-agents\n292|# Output:\n293|#   claude --plugin-dir ~/.cache/compound-engineering/branches/compound-engineering-feat~new-agents/plugins/compound-engineering\n294|```\n295|\n296|The cache path is deterministic. Re-running updates the checkout to the latest commit on that branch.\n297|\n298|**Codex, OpenCode, and other targets** -- pass `--branch` to `install`:\n299|\n300|```bash\n301|# from the repo root\n302|bun run src/index.ts install compound-engineering --to codex --branch feat/new-agents\n303|\n304|# works with any target\n305|bun run src/index.ts install compound-engineering --to opencode --branch feat/new-agents\n306|\n307|# combine with --also for multiple targets\n308|bun run src/index.ts install compound-engineering --to codex --also opencode --branch feat/new-agents\n309|```\n310|\n311|Both features use the `COMPOUND_PLUGIN_GITHUB_SOURCE` env var to resolve the repository, defaulting to `https://github.com/EveryInc/compound-engineering-plugin`.\n312|\n313|### Shell aliases\n314|\n315|Add to `~/.zshrc` or `~/.bashrc`. All aliases use the local CLI so there is no dependency on npm publishing. `plugin-path` prints just the path to stdout, so it composes with `$()`.\n316|\n317|```bash\n318|CE_REPO=~/Code/compound-engineering-plugin\n319|\n320|ce-cli() { bun run \"$CE_REPO/src/index.ts\" \"$@\"; }\n321|\n322|# --- Local checkout (active development) ---\n323|alias cce='claude --plugin-dir $CE_REPO/plugins/compound-engineering'\n324|\n325|codex-ce() {\n326|  ce-cli install \"$CE_REPO/plugins/compound-engineering\" --to codex \"$@\"\n327|}\n328|\n329|# --- Pushed branch (testing PRs, worktree workflows) ---\n330|ccb() {\n331|  claude --plugin-dir \"$(ce-cli plugin-path compound-engineering --branch \"$1\")\" \"${@:2}\"\n332|}\n333|\n334|codex-ceb() {\n335|  ce-cli install compound-engineering --to codex --branch \"$1\" \"${@:2}\"\n336|}\n337|```\n338|\n339|Usage:\n340|\n341|```bash\n342|cce                              # local checkout with Claude Code\n343|codex-ce                         # install local checkout to Codex\n344|ccb feat/new-agents              # test a pushed branch with Claude Code\n345|ccb feat/new-agents --verbose    # extra flags forwarded to claude\n346|codex-ceb feat/new-agents        # install a pushed branch to Codex\n347|```\n348|\n349|Codex installs keep generated plugin skills isolated under `~/.codex/skills/compound-engineering/` and do not write new files into `~/.agents`. The installer removes old CE-managed `.agents/skills` symlinks when it can prove they point back to CE's Codex-managed store, which prevents stale Codex installs from shadowing Copilot's native plugin install.\n350|\n351|## Troubleshooting\n352|\n353|### Codex skills work but review or research delegation fails\n354|\n355|Run the agent install step:\n356|\n357|```bash\n358|bunx @every-env/compound-plugin install compound-engineering --to codex\n359|```\n360|\n361|Native Codex plugin install handles skills. The Bun step installs the custom agents those skills delegate to.\n362|\n363|### Codex shows stale or duplicate CE skills\n364|\n365|Back up old Bun-installed artifacts before switching to the native Codex plugin flow:\n366|\n367|```bash\n368|bunx @every-env/compound-plugin cleanup --target codex\n369|```\n370|\n371|### Copilot, Droid, or Qwen loads stale CE skills\n372|\n373|Back up old Bun-installed artifacts before using the native plugin path:\n374|\n375|```bash\n376|bunx @every-env/compound-plugin cleanup --target copilot\n377|bunx @every-env/compound-plugin cleanup --target droid\n378|bunx @every-env/compound-plugin cleanup --target qwen\n379|```\n380|\n381|## Limitations\n382|\n383|Codex native plugin install currently handles skills, not custom agents. The documented Bun followup is required until Codex supports agents in its native plugin spec.\n384|\n385|OpenCode, Pi, Gemini, and Kiro installs are converter-backed and may change as those target formats evolve.\n386|\n387|Release versions are owned by release automation. Routine feature PRs should not hand-bump plugin or marketplace manifest versions.\n388|\n389|## FAQ\n390|\n391|### Do I need Bun for Claude Code?\n392|\n393|No. Claude Code installs directly from the plugin marketplace. Bun is only needed for converter-backed targets, Codex's current agent followup, local development, and cleanup of old converted installs.\n394|\n395|### Why does Codex need a separate Bun step?\n396|\n397|Codex's native plugin flow installs skills from the Codex plugin manifest. It does not currently install the custom reviewer, researcher, and workflow agents that Compound Engineering skills delegate to. The Bun step fills that gap.\n398|\n399|### Where do I see all available skills and agents?\n400|\n401|", "total_lines": 413, "file_size": 18624, "truncated": true, "hint": "Use offset=401 to continue reading (showing 201-400 of 413 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|# Compounding Engineering Plugin\n2|\n3|AI-powered development tools that get smarter with every use. Make each unit of engineering work easier than the last.\n4|\n5|## Getting Started\n6|\n7|After installing, run `/ce-setup` in any project. It diagnoses your environment, installs missing tools, and bootstraps project config in one interactive flow.\n8|\n9|## Components\n10|\n11|| Component | Count |\n12||-----------|-------|\n13|| Agents | 50+ |\n14|| Skills | 38+ |\n15|\n16|## Skills\n17|\n18|The primary entry points for engineering work, invoked as slash commands. Detailed user-facing documentation for many skills lives in [`docs/skills/`](../../docs/skills/) — each linked skill name below points to its page (purpose, novel mechanics, use cases, chain position). Skills without dedicated docs are still listed; their `SKILL.md` in the source tree is authoritative.\n19|\n20|### Core Workflow\n21|\n22|`ce-strategy` anchors the loop upstream; `ce-product-pulse` closes it with a read on user outcomes.\n23|\n24|| Skill | Description |\n25||-------|-------------|\n26|| [`/ce-strategy`](../../docs/skills/ce-strategy.md) | Create or maintain `STRATEGY.md` — the product's target problem, approach, persona, key metrics, and tracks. Re-runnable to update. Read as grounding by `/ce-ideate`, `/ce-brainstorm`, and `/ce-plan` when present |\n27|| [`/ce-ideate`](../../docs/skills/ce-ideate.md) | Optional big-picture ideation: generate and critically evaluate grounded ideas, then route the strongest one into brainstorming |\n28|| [`/ce-brainstorm`](../../docs/skills/ce-brainstorm.md) | Interactive Q&A to think through a feature or problem and write a right-sized requirements doc before planning. Pass `output:html` to write the doc as a single self-contained HTML file instead of markdown (exclusive — md OR html, never both) |\n29|| [`/ce-plan`](../../docs/skills/ce-plan.md) | Create structured plans for any multi-step task -- software features, research workflows, events, study plans -- with automatic confidence checking. Pass `output:html` to write the plan as a single self-contained HTML file instead of markdown (exclusive — md OR html, never both) |\n30|| [`/ce-code-review`](../../docs/skills/ce-code-review.md) | Structured code review with tiered persona agents, confidence gating, and dedup pipeline |\n31|| [`/ce-work`](../../docs/skills/ce-work.md) | Execute work items systematically |\n32|| [`/ce-debug`](../../docs/skills/ce-debug.md) | Systematically find root causes and fix bugs -- traces causal chains, forms testable hypotheses, and implements test-first fixes |\n33|| [`/ce-compound`](../../docs/skills/ce-compound.md) | Document solved problems to compound team knowledge |\n34|| [`/ce-compound-refresh`](../../docs/skills/ce-compound-refresh.md) | Refresh stale or drifting learnings and decide whether to keep, update, replace, or archive them |\n35|| [`/ce-optimize`](../../docs/skills/ce-optimize.md) | Run iterative optimization loops with parallel experiments, measurement gates, and LLM-as-judge quality scoring |\n36|| [`/ce-product-pulse`](../../docs/skills/ce-product-pulse.md) | Generate a single-page, time-windowed report on usage, performance, errors, and followups. Saves reports to `docs/pulse-reports/` as a browseable timeline of what users experienced |\n37|\n38|### Research & Context\n39|\n40|| Skill | Description |\n41||-------|-------------|\n42|| [`/ce-sessions`](../../docs/skills/ce-sessions.md) | Ask questions about session history across Claude Code, Codex, and Cursor |\n43|| [`/ce-slack-research`](../../docs/skills/ce-slack-research.md) | Search Slack for interpreted organizational context -- decisions, constraints, and discussion arcs |\n44|| [`ce-riffrec-feedback-analysis`](../../docs/skills/ce-riffrec-feedback-analysis.md) | Convert [Riffrec](https://github.com/kieranklaassen/riffrec) recordings, videos, audio, or notes into structured feedback. Routes between setup, quick bug report, and extensive analysis that hands off to `ce-brainstorm` |\n45|\n46|### Git Workflow\n47|\n48|| Skill | Description |\n49||-------|-------------|\n50|| [`ce-clean-gone-branches`](../../docs/skills/ce-clean-gone-branches.md) | Clean up local branches whose remote tracking branch is gone |\n51|| [`ce-commit`](../../docs/skills/ce-commit.md) | Create a git commit with a value-communicating message |\n52|| [`ce-commit-push-pr`](../../docs/skills/ce-commit-push-pr.md) | Commit, push, and open a PR with an adaptive description; also update an existing PR description, or generate a description on its own without committing |\n53|| [`ce-worktree`](../../docs/skills/ce-worktree.md) | Manage Git worktrees for parallel development |\n54|\n55|### Workflow Utilities\n56|\n57|| Skill | Description |\n58||-------|-------------|\n59|| [`/ce-demo-reel`](../../docs/skills/ce-demo-reel.md) | Capture a visual demo reel (GIF demos, terminal recordings, screenshots) for PRs with project-type-aware tier selection |\n60|| [`/ce-report-bug`](../../docs/skills/ce-report-bug.md) | Report a bug in the compound-engineering plugin |\n61|| [`/ce-resolve-pr-feedback`](../../docs/skills/ce-resolve-pr-feedback.md) | Resolve PR review feedback in parallel |\n62|| [`/ce-test-browser`](../../docs/skills/ce-test-browser.md) | Run browser tests on PR-affected pages |\n63|| [`/ce-test-xcode`](../../docs/skills/ce-test-xcode.md) | Build and test iOS apps on simulator using XcodeBuildMCP |\n64|| [`/ce-setup`](../../docs/skills/ce-setup.md) | Diagnose environment, install missing tools, and bootstrap project config |\n65|| [`/ce-update`](../../docs/skills/ce-update.md) | Check compound-engineering plugin version and fix stale cache (Claude Code only) |\n66|| [`/ce-release-notes`](../../docs/skills/ce-release-notes.md) | Summarize recent compound-engineering plugin releases, or answer a question about a past release with a version citation |\n67|\n68|### Development Frameworks\n69|\n70|| Skill | Description |\n71||-------|-------------|\n72|| `ce-agent-native-architecture` | Build AI agents using prompt-native architecture |\n73|| `ce-dhh-rails-style` | Write Ruby/Rails code in DHH's 37signals style |\n74|| [`ce-frontend-design`](../../docs/skills/ce-frontend-design.md) | Create production-grade frontend interfaces |\n75|\n76|### Review & Quality\n77|\n78|| Skill | Description |\n79||-------|-------------|\n80|| [`ce-doc-review`](../../docs/skills/ce-doc-review.md) | Review documents using parallel persona agents for role-specific feedback |\n81|| [`/ce-simplify-code`](../../docs/skills/ce-simplify-code.md) | Simplify recent code changes for reuse, quality, and efficiency — parallel reviewers find issues, fixes applied, behavior verified by tests |\n82|\n83|### Content & Collaboration\n84|\n85|| Skill | Description |\n86||-------|-------------|\n87|| [`ce-proof`](../../docs/skills/ce-proof.md) | Create, edit, and share documents via Proof collaborative editor |\n88|\n89|### Automation & Tools\n90|\n91|| Skill | Description |\n92||-------|-------------|\n93|| `ce-gemini-imagegen` | Generate and edit images using Google's Gemini API |\n94|\n95|### Beta / Experimental\n96|\n97|| Skill | Description |\n98||-------|-------------|\n99|| [`ce-polish-beta`](../../docs/skills/ce-polish-beta.md) | Human-in-the-loop polish phase after /ce-code-review — verifies review + CI, starts a dev server from `.claude/launch.json`, generates a testable checklist, and dispatches polish sub-agents for fixes. Emits stacked-PR seeds for oversized work |\n100|| `ce-dogfood-beta` | Diff-scoped browser QA of the active branch: builds an exhaustive test matrix of every change, drives the app with agent-browser, then auto-fixes issues, adds regression tests, and commits each fix until green |\n101|| `/lfg` | Full autonomous engineering workflow |\n102|\n103|## Agents\n104|\n105|Agents are specialized subagents invoked by skills — you typically don't call these directly.\n106|\n107|### Review\n108|\n109|| Agent | Description |\n110||-------|-------------|\n111|| `ce-agent-native-reviewer` | Verify features are agent-native (action + context parity) |\n112|| `ce-api-contract-reviewer` | Detect breaking API contract changes |\n113|| `ce-architecture-strategist` | Analyze architectural decisions and compliance |\n114|| `ce-code-simplicity-reviewer` | Final pass for simplicity and minimalism |\n115|| `ce-correctness-reviewer` | Logic errors, edge cases, state bugs |\n116|| `ce-data-integrity-guardian` | Database migrations and data integrity |\n117|| `ce-data-migration-reviewer` | Schema drift, migration safety, mapping verification, deploy-window checks |\n118|| `ce-deployment-verification-agent` | Create Go/No-Go deployment checklists for risky data changes |\n119|| `ce-julik-frontend-races-reviewer` | Review JavaScript/Stimulus code for race conditions |\n120|| `ce-maintainability-reviewer` | Coupling, complexity, naming, dead code |\n121|| `ce-pattern-recognition-specialist` | Analyze code for patterns and anti-patterns |\n122|| `ce-performance-oracle` | Performance analysis and optimization |\n123|| `ce-performance-reviewer` | Runtime performance with confidence calibration |\n124|| `ce-reliability-reviewer` | Production reliability and failure modes |\n125|| `ce-security-reviewer` | Exploitable vulnerabilities with confidence calibration |\n126|| `ce-security-sentinel` | Security audits and vulnerability assessments |\n127|| `ce-swift-ios-reviewer` | Swift and iOS code review -- SwiftUI state, retain cycles, concurrency, Core Data threading, accessibility |\n128|| `ce-testing-reviewer` | Test coverage gaps, weak assertions |\n129|| `ce-project-standards-reviewer` | CLAUDE.md and AGENTS.md compliance |\n130|| `ce-adversarial-reviewer` | Construct failure scenarios to break implementations across component boundaries |\n131|\n132|### Document Review\n133|\n134|| Agent | Description |\n135||-------|-------------|\n136|| `ce-coherence-reviewer` | Review documents for internal consistency, contradictions, and terminology drift |\n137|| `ce-design-lens-reviewer` | Review plans for missing design decisions, interaction states, and AI slop risk |\n138|| `ce-feasibility-reviewer` | Evaluate whether proposed technical approaches will survive contact with reality |\n139|| `ce-product-lens-reviewer` | Challenge problem framing, evaluate scope decisions, surface goal misalignment |\n140|| `ce-scope-guardian-reviewer` | Challenge unjustified complexity, scope creep, and premature abstractions |\n141|| `ce-security-lens-reviewer` | Evaluate plans for security gaps at the plan level (auth, data, APIs) |\n142|| `ce-adversarial-document-reviewer` | Challenge premises, surface unstated assumptions, and stress-test decisions |\n143|\n144|### Research\n145|\n146|| Agent | Description |\n147||-------|-------------|\n148|| `ce-best-practices-researcher` | Gather external best practices and examples |\n149|| `ce-framework-docs-researcher` | Research framework documentation and best practices |\n150|| `ce-git-history-analyzer` | Analyze git history and code evolution |\n151|| `ce-issue-intelligence-analyst` | Analyze GitHub issues to surface recurring themes and pain patterns |\n152|| `ce-learnings-researcher` | Search institutional learnings for relevant past solutions |\n153|| `ce-repo-research-analyst` | Research repository structure and conventions |\n154|| `ce-session-historian` | Search prior Claude Code, Codex, and Cursor sessions for related investigation context |\n155|| `ce-slack-researcher` | Search Slack for organizational context relevant to the current task |\n156|| `ce-web-researcher` | Perform iterative web research and return structured external grounding (prior art, adjacent solutions, market signals, cross-domain analogies) |\n157|\n158|### Design\n159|\n160|| Agent | Description |\n161||-------|-------------|\n162|| `ce-design-implementation-reviewer` | Verify UI implementations match Figma designs |\n163|| `ce-design-iterator` | Iteratively refine UI through systematic design iterations |\n164|| `ce-figma-design-sync` | Synchronize web implementations with Figma designs |\n165|\n166|### Workflow\n167|\n168|| Agent | Description |\n169||-------|-------------|\n170|| `ce-pr-comment-resolver` | Address PR comments and implement fixes |\n171|| `ce-spec-flow-analyzer` | Analyze user flows and identify gaps in specifications |\n172|\n173|### Docs\n174|\n175|| Agent | Description |\n176||-------|-------------|\n177|| `ce-ankane-readme-writer` | Create READMEs following Ankane-style template for Ruby gems |\n178|\n179|## Installation\n180|\n181|See the repo root [Install section](../../README.md#install) for current installation instructions across Claude Code, Codex, Cursor, Copilot, Droid, Qwen, and converter-backed targets.\n182|\n183|Then run `/ce-setup` to check your environment and install recommended tools.\n184|\n185|## Version History\n186|\n187|See the repo root [CHANGELOG.md](../../CHANGELOG.md) for canonical release history.\n188|\n189|## License\n190|\n191|MIT\n192|", "total_lines": 191, "file_size": 12200, "truncated": false, "is_binary": false, "is_image": false}

  [Subdirectory context discovered: compound-engineering-plugin/plugins/compound-engineering/AGENTS.md]
  # Plugin Instructions

  These instructions apply when working under `plugins/compound-engineering/`.
  They supplement the repo-root `AGENTS.md`.

  # Compounding Engineering Plugin Development

  ## Runtime vs Authoring Context

  **This plugin's `AGENTS.md` and `CLAUDE.md` files are authoring context — they do not ship with the installed plugin.** Skills are packaged and installed into end-user environments (their own repos, or folders that may not even be git repos), where they run against *the user's* `AGENTS.md`/`CLAUDE.md`, not this repo's.

  Consequences:

  - Behavioral rules that govern skill *runtime* behavior must live inside the skill itself — in `SKILL.md` or files under its `references/`. Guidance placed in this file is invisible at runtime.
  - When two or more skills share a behavioral principle, duplicate the guidance into each skill (inline for short rules, `references/` for longer ones). There is no cross-skill shared-file mechanism (see "File References in Skills" below).
  - Do not propose that runtime guidance for ce-ideate, ce-brainstorm, ce-plan, or any other skill live in this AGENTS.md or in the repo-root AGENTS.md. Those files only shape how contributors edit the plugin.

  This is easy to miss because authoring feels like using: you edit the plugin while running inside this repo, and the repo's AGENTS.md is loaded — but that load does not follow the installed skill into a user's environment.

  ## Versioning Requirements

  **IMPORTANT**: Routine PRs should not cut releases for this plugin.

  The repo uses an automated release process to prepare plugin releases, including version selection and changelog generation. Because multiple PRs may merge before the next release, contributors cannot know the final released version from within an individual PR.

  **If `bun run release:validate` reports drift, see `docs/solutions/workflow/release-please-version-drift-recovery.md`** for the file-relationship map, the recovery decision tree (forward-sync vs. backward-revert vs. `release-as` pin), and worked examples. That doc answers questions the rules below don't: *why these files are release-managed, how they sync via `extra-files` and `linked-versions`, and what to do when the rules below were violated.*

  ### Contributor Rules

  - Do **not** manually bump `.claude-plugin/plugin.json` version in a normal feature PR.
  - Do **not** manually bump `.cursor-plugin/plugin.json` version in a normal feature PR.
  - Do **not** manually bump `.codex-plugin/plugin.json` version in a normal feature PR — release-please owns this via `extra-files` in `.github/release-please-config.json`, parallel to the Claude and Cursor entries.
  - Do **not** manually bump `.claude-plugin/marketplace.json` plugin version in a normal feature PR.
  - Do **not** hand-edit `.agents/plugins/marketplace.json` except to add or remove a plugin. Plugin-list, name, and description drift between the Claude, Cursor, and Codex marketplaces is caught by `bun run release:validate`.
  - Do **not** cut a release section in the canonical root `CHANGELOG.md` for a normal feature PR.
  - Do update substantive docs that are part of the actual change, such as `README.md`, component tables, usage instructions, or counts when they would otherwise become inaccurate.

  ### Pre-Commit Checklist

  Before committing ANY changes:

  - [ ] No manual release-version bump in `.claude-plugin/plugin.json`
  - [ ] No manual release-version bump in `.cursor-plugin/plugin.json`
  - [ ] No manual release-version bump in `.codex-plugin/plugin.json`
  - [ ] No manual release-version bump in `.claude-plugin/marketplace.json`
  - [ ] No manual release entry added to the root `CHANGELOG.md`
  - [ ] `bun run release:validate` passes (enforces Claude/Cursor/Codex manifest parity)
  - [ ] README.md component counts verified
  - [ ] README.md tables accurate (agents, commands, skills)
  - [ ] plugin.json description matches current counts

  ### Directory Structure

  ```
  agents/
  └── ce-*.md  # All agents live flat under agents/, prefixed with ce-

  skills/
  ├── ce-*/          # Core workflow skills (ce-plan, ce-code-review, etc.)
  └── */             # All other skills
  ```

  Agents are grouped topically in `README.md` (Review, Document Review, Research, Design, Workflow, Docs) for reader navigation — those groupings are conceptual, not filesystem subdirectories.

  > **Note:** Commands were migrated to skills in v2.39.0. All former
  > `/command-name` slash commands now live under `skills/command-name/SKILL.md`
  > and work identically in Claude Code. Other targets may convert or map these references differently.

  ## Debugging Plugin Bugs

  Developers of this plugin also use it via their marketplace install (`~/.claude/plugins/`). When a developer reports a bug they experienced while using a skill or agent, the installed version may be older than the repo. Glob for the component name under `~/.claude/plugins/` and diff the installed content against the repo version.

  - **Repo already has the fix**: The developer's install is stale. Tell them to reinstall the plugin or use `--plugin-dir` to load skills from the repo checkout. No code change needed.
  - **Both versions have the bug**: Proceed with the fix normally.

  Important: Just because the developer's installed plugin may be out of date, it's possible both old and current repo versions have the bug. The proper fix is to still fix the repo version.

  ## Naming Convention

  **All skills and agents** use the `ce-` prefix to unambiguously identify them as compound-engineering components:
  - `/ce-brainstorm` - Explore requirements and approaches before planning
  - `/ce-plan` - Create implementation plans
  - `/ce-code-review` - Run comprehensive code reviews
  - `/ce-work` - Execute work items systematically
  - `/ce-compound` - Document solved problems

  **Why `ce-`?** Claude Code has built-in `/plan` and `/review` commands. The `ce-` prefix (short for compound-engineering) makes it immediately clear these components belong to this plugin. The hyphen is used instead of a colon to avoid filesystem issues on Windows and to align directory names with frontmatter names.

  **Agents** follow the same convention: `ce-adversarial-reviewer`, `ce-learnings-researcher`, etc. When referencing agents from skills, use the bare `ce-<agent-name>` form (e.g., `ce-adversarial-reviewer`) — the `ce-` prefix is sufficient for uniqueness across plugins.

  **The `ce-` prefix is required for every new skill and agent — no exceptions.** Three legacy skills (`every-style-editor`, `file-todos`, `lfg`) predate the rule and remain unprefixed; they are pinned in `tests/frontmatter.test.ts` as the only allowed exceptions. Do not add to that allowlist. When adding a new skill, the directory name, the SKILL.md `name:` frontmatter, and any README references must all start with `ce-`. The frontmatter test enforces this and will fail on a missing prefix.

  ## Known External Limitations

  **Proof HITL surfaces a ghost "AI collaborator" agent** (noted 2026-04-16, may change): The Proof API auto-joins any header-less `/state` read under a synthetic `ai:auto-<hash>` identity, so docs created by the `skills/proof/` HITL workflow show a phantom participant alongside `Compound Engineering`. The only way to suppress it is to set `ownerId: "agent:ai:compound-engineering"` on create — but that transfers document ownership to the agent and prevents the user from claiming it into their Proof library, so we don't use it. Treat as cosmetic noise; don't reintroduce the `ownerId` workaround. Tracked upstream: https://github.com/EveryInc/proof/issues/951.

  ## Skill Design Principles

  Skills are guardrails for an intelligent agent, not a step-by-step controller for a non-intelligent one. The principles below were learned from real-world testing and should guide future skill edits.

  **Calibrate prescription level to the failure mode.** Three rough levels:

  - **Hard rules** for deterministic safety (e.g., "don't silently `cd` to another repo and write outputs there"). The agent's judgment must not va

  [...truncated AGENTS.md: 38,806 chars total] read_file 


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c0:
  Resumo da análise do repositório **EveryInc/compound-engineering-plugin**.

  - **Problema**
    No desenvolvimento com IA, cada tarefa repetitiva força o agente (ou a equipe) a reaprender padrões, decisões e lições anteriores, gerando retrabalho e dívida técnica crescente. Ferramentas isoladas não compartilham contexto entre si, então o conhecimento se perde a cada sessão.

  - **Tema**
    *Compound engineering* — um workflow estruturado para IA (brainstorm, plano, execução, revisão, compounding) que transforma conhecimento em ativo reutilizável. Não é só sobre escrever código, mas garantir que cada unidade de trabalho torne a próxima mais fácil, com componentes especializados e rastreáveis.

  - **Razão do hype**
    Promessa de **compounding**, não automação cega: cada sessão documenta aprendizados, padrões e riscos, reduzindo custo cognitivo ao longo do tempo. A adoção é impulsionada por:
    - suporte a múltiplas plataformas de IA (Claude Code, Codex, Cursor, Copilot, Droid, Qwen, OpenCode, Pi, Gemini, Kiro)
    - ecossistema maduro (50+ agentes e 37 skills)
    - filosofia ligada a *leverage* do time, alinhamento cultural ao invés de substituição por IA

  - **Tecnologias**
    - TypeScript (runtime moderno, rigor de tipos)
    - Bun (CLI, estrutura do plugin, utilitários)
    - Citty (framework de CLI)
    - js-yaml (parse de frontmatter/metadados)
    - semantic-release + release-please (automação de versões e changelog)
    - tsconfig com ES2022 + strict + Bun types  


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  {"content": "1|[build-system]\n2|requires = [\"setuptools>=64\", \"wheel\", \"setuptools_scm>=8\"]\n3|build-backend = \"setuptools.build_meta\"\n4|\n5|[project]\n6|name = \"voxcpm\"\n7|dynamic = [\"version\"]\n8|description = \"VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning\"\n9|readme = \"README.md\"\n10|license = \"Apache-2.0\"\n11|authors = [\n12|    {name = \"OpenBMB\", email = \"openbmb@gmail.com\"}\n13|]\n14|maintainers = [\n15|    {name = \"OpenBMB\", email = \"openbmb@gmail.com\"}\n16|]\n17|keywords = [\"voxcpm\", \"text-to-speech\", \"tts\", \"speech-synthesis\", \"voice-cloning\", \"ai\", \"deep-learning\", \"pytorch\"]\n18|classifiers = [\n19|    \"Development Status :: 3 - Alpha\",\n20|    \"Intended Audience :: Developers\",\n21|    \"Operating System :: OS Independent\",\n22|    \"Programming Language :: Python :: 3\",\n23|    \"Programming Language :: Python :: 3.10\",\n24|    \"Programming Language :: Python :: 3.11\",\n25|]\n26|requires-python = \">=3.10\"\n27|dependencies = [\n28|    \"torch>=2.5.0\",\n29|    \"torchaudio>=2.5.0\",\n30|    \"torchcodec\",\n31|    \"transformers>=4.36.2\",\n32|    \"einops\",\n33|    \"gradio>=6,<7\",\n34|    \"inflect\",\n35|    \"addict\",\n36|    \"wetext\",\n37|    \"modelscope>=1.22.0\",\n38|    \"datasets>=3,<4\",\n39|    \"huggingface-hub\",\n40|    \"pydantic\",\n41|    \"tqdm\",\n42|    \"simplejson\",\n43|    \"sortedcontainers\",\n44|    \"soundfile\",\n45|    \"librosa\",\n46|    \"matplotlib\",\n47|    \"funasr\",\n48|    \"spaces\",\n49|    \"argbind\",\n50|    \"safetensors\"\n51|\n52|]\n53|\n54|[project.optional-dependencies]\n55|dev = [\n56|    \"pytest>=6.0\",\n57|    \"pytest-cov>=2.0\",\n58|    \"black>=21.0\",\n59|    \"flake8>=3.8\",\n60|    \"pre-commit>=2.0\",\n61|]\n62|\n63|[project.scripts]\n64|voxcpm = \"voxcpm.cli:main\"\n65|\n66|[project.urls]\n67|Homepage = \"https://github.com/OpenBMB/VoxCPM\"\n68|Repository = \"https://github.com/OpenBMB/VoxCPM.git\"\n69|Documentation = \"https://github.com/OpenBMB/VoxCPM#readme\"\n70|\"Bug Tracker\" = \"https://github.com/OpenBMB/VoxCPM/issues\"\n71|\n72|[tool.setuptools.packages.find]\n73|where = [\"src\"]\n74|include = [\"voxcpm*\"]\n75|\n76|[tool.setuptools.package-dir]\n77|\"\" = \"src\"\n78|\n79|[tool.setuptools_scm]\n80|version_scheme = \"post-release\"\n81|\n82|[tool.black]\n83|line-length = 120\n84|target-version = ['py310']\n85|include = '\\.pyi?$'\n86|extend-exclude = '''\n87|/(\n88|  # directories\n89|  \\.eggs\n90|  | \\.git\n91|  | \\.hg\n92|  | \\.tox\n93|  | \\.venv\n94|  | build\n95|  | dist\n96|)/\n97|'''\n98|", "total_lines": 97, "file_size": 2100, "truncated": false, "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|<h2 align=\"center\">VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning</h2>\n2|\n3|<p align=\"center\">\n4|  <b>English</b> | <a href=\"./README_zh.md\">中文</a>\n5|</p>\n6|\n7|<p align=\"center\">\n8|  <a href=\"https://github.com/OpenBMB/VoxCPM/\"><img src=\"https://img.shields.io/badge/Project%20Page-GitHub-blue\" alt=\"Project Page\"></a>\n9|  <a href=\"https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo\"><img src=\"https://img.shields.io/badge/Live%20Playground-Demo-orange\" alt=\"Live Playground\"></a>\n10|  <a href=\"https://voxcpm.readthedocs.io/en/latest/\"><img src=\"https://img.shields.io/badge/Docs-ReadTheDocs-8CA1AF\" alt=\"Documentation\"></a>\n11|  <a href=\"https://huggingface.co/openbmb/VoxCPM2\"><img src=\"https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-VoxCPM2-yellow\" alt=\"Hugging Face\"></a>\n12|  <a href=\"https://modelscope.cn/models/OpenBMB/VoxCPM2\"><img src=\"https://img.shields.io/badge/ModelScope-VoxCPM2-purple\" alt=\"ModelScope\"></a>\n13|  <a href=\"https://openbmb.github.io/voxcpm2-demopage/\"><img src=\"https://img.shields.io/badge/DemoPage-Audio Samples-red\"></a>\n14|  \n15|</p>\n16|\n17|<div align=\"center\">\n18|  <img src=\"assets/voxcpm_logo.png\" alt=\"VoxCPM Logo\" width=\"35%\">\n19|  <br><br>\n20|  <a href=\"https://trendshift.io/repositories/17704\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/17704\" alt=\"OpenBMB%2FVoxCPM | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n21|</div>\n22|\n23|<br>\n24|\n25|<p align=\"center\">\n26|  👋 Join our community for discussion and support!\n27|  <br>\n28|  <a href=\"./assets/feishu-group.png\" style=\"display:inline-block;vertical-align:middle; margin-left: 10px;\">\n29|    <img src=\"./assets/feishu-logo.png\" width=\"16\" height=\"16\" style=\"vertical-align:middle;\"> Feishu\n30|  </a>\n31|  &nbsp;|&nbsp;\n32|  <a href=\"https://discord.gg/KZUx7tVNwz\" style=\"display:inline-block;vertical-align:middle;\">\n33|    <img src=\"./assets/discord-logo.png\" width=\"16\" height=\"16\" style=\"vertical-align:middle;\"> Discord\n34|  </a>\n35|</p>\n36|\n37|VoxCPM is a **tokenizer-free** Text-to-Speech system that directly generates continuous speech representations via an end-to-end **diffusion autoregressive architecture**, bypassing discrete tokenization to achieve highly natural and expressive synthesis.\n38|\n39|**VoxCPM2** is the latest major release — a **2B** parameter model trained on **over 2 million hours** of multilingual speech data, now supporting **30 languages**, **Voice Design**, **Controllable Voice Cloning**, and **48kHz** studio-quality audio output. Built on a [MiniCPM-4](https://github.com/OpenBMB/MiniCPM) backbone.\n40|\n41|### ✨ Highlights\n42|\n43|- 🌍 **30-Language Multilingual** — Input text in any of the 30 supported languages and synthesize directly, no language tag needed\n44|- 🎨 **Voice Design** — Create a brand-new voice from a natural-language description alone (gender, age, tone, emotion, pace …), no reference audio required\n45|- 🎛️ **Controllable Cloning** — Clone any voice from a short reference clip, with optional style guidance to steer emotion, pace, and expression while preserving the original timbre\n46|- 🎙️ **Ultimate Cloning** — Reproduce every vocal nuance: provide both reference audio and its transcript, and the model continues seamlessly from the reference, faithfully preserving every vocal detail — timbre, rhythm, emotion, and style (same as VoxCPM1.5)\n47|- 🔊 **48kHz High-Quality Audio** — Accepts 16kHz reference audio and directly outputs 48kHz studio-quality audio via AudioVAE V2's asymmetric encode/decode design, with built-in super-resolution — no external upsampler needed\n48|- 🧠 **Context-Aware Synthesis** — Automatically infers appropriate prosody and expressiveness from text content\n49|- ⚡ **Real-Time Streaming** — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13 accelerated by [Nano-vLLM](https://github.com/a710128/nanovllm-voxcpm) or [vLLM-Omni](https://github.com/vllm-project/vllm-omni) — official vLLM omni-modal serving for VoxCPM2 with PagedAttention and an OpenAI-compatible API\n50|- 📜 **Fully Open-Source & Commercial-Ready** — Weights and code released under the [Apache-2.0](LICENSE) license, free for commercial use\n51|\n52|\n53|<summary><b>🌍 Supported Languages (30)</b></summary>\n54|<br>\n55|Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese\n56|\n57|Chinese Dialect: 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话\n58|\n59|\n60|### News\n61|\n62|* **[2026.04]** 🔥 We release **VoxCPM2** — 2B, 30 languages, Voice Design & Controllable Voice Cloning, 48kHz audio output! [Weights](https://huggingface.co/openbmb/VoxCPM2) | [Docs](https://voxcpm.readthedocs.io/en/latest/) | [Playground](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo)\n63|* **[2025.12]** 🎉 Open-source **VoxCPM1.5** [weights](https://huggingface.co/openbmb/VoxCPM1.5) with SFT & LoRA fine-tuning. (**🏆 #1 GitHub Trending**)\n64|* **[2025.09]** 🔥 Release VoxCPM [Technical Report](https://arxiv.org/abs/2509.24650).\n65|* **[2025.09]** 🎉 Open-source **VoxCPM-0.5B** [weights](https://huggingface.co/openbmb/VoxCPM-0.5B) (**🏆 #1 HuggingFace Trending**)\n66|\n67|---\n68|\n69|## Contents\n70|\n71|- [Quick Start](#-quick-start)\n72|  - [Installation](#installation)\n73|  - [Python API](#python-api)\n74|  - [CLI Usage](#cli-usage)\n75|  - [Web Demo](#web-demo)\n76|  - [Production Deployment](#-production-deployment-nano-vllm)\n77|- [Models & Versions](#-models--versions)\n78|- [Performance](#-performance)\n79|- [Fine-tuning](#%EF%B8%8F-fine-tuning)\n80|- [Documentation](#-documentation)\n81|- [Ecosystem & Community](#-ecosystem--community)\n82|- [Risks and Limitations](#%EF%B8%8F-risks-and-limitations)\n83|- [Citation](#-citation)\n84|\n85|---\n86|\n87|## 🚀 Quick Start\n88|\n89|### Installation\n90|\n91|```sh\n92|pip install voxcpm\n93|```\n94|\n95|> **Requirements:** Python ≥ 3.10 (<3.13), PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See [Quick Start Docs](https://voxcpm.readthedocs.io/en/latest/quickstart.html) for details.\n96|\n97|### Python API\n98|\n99|#### 🗣️ Text-to-Speech\n100|\n101|```python\n102|from voxcpm import VoxCPM\n103|import soundfile as sf\n104|\n105|model = VoxCPM.from_pretrained(\n106|  \"openbmb/VoxCPM2\",\n107|  load_denoiser=False,\n108|)\n109|\n110|wav = model.generate(\n111|    text=\"VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.\",\n112|    cfg_value=2.0,\n113|    inference_timesteps=10,\n114|)\n115|sf.write(\"demo.wav\", wav, model.tts_model.sample_rate)\n116|print(\"saved: demo.wav\")\n117|```\n118|\n119|If you prefer downloading from ModelScope first, you can use:\n120|\n121|```bash\n122|pip install modelscope\n123|```\n124|\n125|```python\n126|from modelscope import snapshot_download\n127|snapshot_download(\"OpenBMB/VoxCPM2\", local_dir='./pretrained_models/VoxCPM2') # specify the local directory to save the model\n128|\n129|from voxcpm import VoxCPM\n130|import soundfile as sf\n131|model = VoxCPM.from_pretrained(\"./pretrained_models/VoxCPM2\", load_denoiser=False)\n132|\n133|wav = model.generate(\n134|    text=\"VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.\",\n135|    cfg_value=2.0,\n136|    inference_timesteps=10,\n137|)\n138|sf.write(\"demo.wav\", wav, model.tts_model.sample_rate)\n139|```\n140|\n141|#### 🎨 Voice Design\n142|\n143|Create a voice from a natural-language description — no reference audio needed. **Format:** put the description in parentheses at the start of `text`(e.g. `\"(your voice description)The text to synthesize.\"`):\n144|\n145|```python\n146|wav = model.generate(\n147|    text=\"(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!\",\n148|    cfg_value=2.0,\n149|    inference_timesteps=10,\n150|)\n151|sf.write(\"voice_design.wav\", wav, model.tts_model.sample_rate)\n152|```\n153|\n154|#### 🎛️ Controllable Voice Cloning\n155|\n156|Upload a reference audio. The model clones the timbre, and you can still use control instructions to adjust speed, emotion, or style.\n157|\n158|```python\n159|wav = model.generate(\n160|    text=\"This is a cloned voice generated by VoxCPM2.\",\n161|    reference_wav_path=\"path/to/voice.wav\",\n162|)\n163|sf.write(\"clone.wav\", wav, model.tts_model.sample_rate)\n164|\n165|wav = model.generate(\n166|    text=\"(slightly faster, cheerful tone)This is a cloned voice with style control.\",\n167|    reference_wav_path=\"path/to/voice.wav\",\n168|    cfg_value=2.0,\n169|    inference_timesteps=10,\n170|)\n171|sf.write(\"controllable_clone.wav\", wav, model.tts_model.sample_rate)\n172|```\n173|\n174|#### 🎙️ Ultimate Cloning\n175|\n176|Provide both the reference audio and its exact transcript for audio-continuation-based cloning with every vocal nuance reproduced. For maximum cloning similarity, pass the same reference clip to both `reference_wav_path` and `prompt_wav_path` as shown below:\n177|\n178|```python\n179|wav = model.generate(\n180|    text=\"This is an ultimate cloning demonstration using VoxCPM2.\",\n181|    prompt_wav_path=\"path/to/voice.wav\",\n182|    prompt_text=\"The transcript of the reference audio.\",\n183|    reference_wav_path=\"path/to/voice.wav\", # optional, for better simliarity \n184|)\n185|sf.write(\"hifi_clone.wav\", wav, model.tts_model.sample_rate)\n186|```\n187|\n188|<details>\n189|<summary><b>🔄 Streaming API</b></summary>\n190|\n191|```python\n192|import numpy as np\n193|\n194|chunks = []\n195|for chunk in model.generate_streaming(\n196|    text=\"Streaming text to speech is easy with VoxCPM!\",\n197|):\n198|    chunks.append(chunk)\n199|wav = np.concatenate(chunks)\n200|sf.write(\"streaming.wav\", wav, model.tts_model.sample_rate)\n201|", "total_lines": 633, "file_size": 29023, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 633 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|\"\"\"\n2|VoxCPM: A Tokenizer-free speech generation model\n3|\n4|This module contains the main VoxCPM model implementation, including configuration classes\n5|and the core VoxCPMModel for text-to-speech generation.\n6|\n7|Copyright 2026 OpenBMB\n8|Licensed under the Apache License, Version 2.0 (the \"License\");\n9|you may not use this file except in compliance with the License.\n10|You may obtain a copy of the License at\n11|\n12|    http://www.apache.org/licenses/LICENSE-2.0\n13|\n14|Unless required by applicable law or agreed to in writing, software\n15|distributed under the License is distributed on an \"AS IS\" BASIS,\n16|WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n17|See the License for the specific language governing permissions and\n18|limitations under the License.\n19|\"\"\"\n20|\n21|import os\n22|import sys\n23|from typing import Tuple, Union, Generator, List, Optional\n24|\n25|import torch\n26|import torch.nn as nn\n27|import warnings\n28|import librosa\n29|import numpy as np\n30|from einops import rearrange\n31|from pydantic import BaseModel\n32|\n33|try:\n34|    from safetensors.torch import load_file\n35|\n36|    SAFETENSORS_AVAILABLE = True\n37|except ImportError:\n38|    SAFETENSORS_AVAILABLE = False\n39|from tqdm import tqdm\n40|from transformers import LlamaTokenizerFast\n41|\n42|from ..modules.audiovae import AudioVAEV2, AudioVAEConfigV2\n43|from ..modules.layers import ScalarQuantizationLayer\n44|from ..modules.layers.lora import apply_lora_to_named_linear_modules\n45|from ..modules.locdit import CfmConfig, UnifiedCFM, VoxCPMLocDiTV2\n46|from ..modules.locenc import VoxCPMLocEnc\n47|from ..modules.minicpm4 import MiniCPM4Config, MiniCPMModel\n48|from .utils import (\n49|    get_dtype,\n50|    mask_multichar_chinese_tokens,\n51|    next_and_close,\n52|    pick_runtime_dtype,\n53|    resolve_runtime_device,\n54|)\n55|\n56|\n57|# A simple function to trim audio silence using VAD, not used default\n58|def _trim_audio_silence_vad(audio: torch.Tensor, sample_rate: int, max_silence_ms: float = 200.0, top_db: float = 35.0) -> torch.Tensor:\n59|    if audio.numel() == 0:\n60|        return audio\n61|    y = audio.squeeze(0).numpy()\n62|    n = len(y)\n63|    frame_length = 2048\n64|    hop_length = 512\n65|    ref = np.max(np.abs(y))\n66|    if ref <= 0:\n67|        return audio\n68|    threshold = ref * (10.0 ** (-top_db / 20.0))\n69|\n70|    try:\n71|        _, (start, end) = librosa.effects.trim(\n72|            y, top_db=top_db, ref=np.max, frame_length=frame_length, hop_length=hop_length\n73|        )\n74|    except Exception:\n75|        start, end = 0, n\n76|\n77|    # Find the last frame with continuous energy, trim the long pseudo-silence at the end (low energy background noise, etc.)\n78|    n_frames = max(0, (n - frame_length) // hop_length + 1)\n79|    last_voice_frame = -1\n80|    for j in range(n_frames):\n81|        idx = j * hop_length\n82|        if idx + frame_length > n:\n83|            break\n84|        rms = np.sqrt(np.mean(y[idx : idx + frame_length] ** 2))\n85|        if rms >= threshold:\n86|            last_voice_frame = j\n87|    if last_voice_frame >= 0:\n88|        end_by_vad = min(n, (last_voice_frame + 1) * hop_length + (frame_length - hop_length))\n89|        end = min(end, end_by_vad)\n90|\n91|    max_silence_samples = int(max_silence_ms * sample_rate / 1000.0)\n92|    new_start = max(0, start - max_silence_samples)\n93|    new_end = min(n, end + max_silence_samples)\n94|    return audio[:, new_start:new_end]\n95|\n96|\n97|class VoxCPMEncoderConfig(BaseModel):\n98|    hidden_dim: int = 1024\n99|    ffn_dim: int = 4096\n100|    num_heads: int = 16\n101|    num_layers: int = 4\n102|    kv_channels: int = None\n103|\n104|\n105|class VoxCPMDitConfig(BaseModel):\n106|    hidden_dim: int = 1024\n107|    ffn_dim: int = 4096\n108|    num_heads: int = 16\n109|    num_layers: int = 4\n110|    kv_channels: int = None\n111|    dit_mean_mode: bool = False\n112|\n113|    cfm_config: CfmConfig\n114|\n115|\n116|class VoxCPMConfig(BaseModel):\n117|    lm_config: MiniCPM4Config\n118|    patch_size: int = 4\n119|    feat_dim: int = 64\n120|    residual_lm_num_layers: int = 8\n121|    residual_lm_no_rope: bool = False\n122|    scalar_quantization_latent_dim: int = 512\n123|    scalar_quantization_scale: int = 9\n124|\n125|    encoder_config: VoxCPMEncoderConfig\n126|    dit_config: VoxCPMDitConfig\n127|    audio_vae_config: Optional[AudioVAEConfigV2] = None\n128|\n129|    max_length: int = 8192\n130|    device: str = \"cuda\"\n131|    dtype: str = \"bfloat16\"\n132|\n133|\n134|class LoRAConfig(BaseModel):\n135|    enable_lm: bool = False  # Apply LoRA to base_lm + residual_lm\n136|    enable_dit: bool = False  # Apply LoRA to VoxCPMLocDiT\n137|    enable_proj: bool = False  # Apply LoRA to projection Linear layers\n138|\n139|    r: int = 8\n140|    alpha: int = 16\n141|    dropout: float = 0.0\n142|\n143|    # Target linear layer names for LM & DiT (matched by attribute name)\n144|    target_modules_lm: list[str] = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n145|    target_modules_dit: list[str] = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n146|    # Projection layer attribute names to find on VoxCPM2Model\n147|    target_proj_modules: list[str] = [\"enc_to_lm_proj\", \"lm_to_dit_proj\", \"res_to_dit_proj\", \"fusion_concat_proj\"]\n148|\n149|\n150|VoxCPMConfig.model_rebuild()\n151|\n152|\n153|class VoxCPM2Model(nn.Module):\n154|    def __init__(\n155|        self,\n156|        config: VoxCPMConfig,\n157|        tokenizer: LlamaTokenizerFast,\n158|        audio_vae: AudioVAEV2,\n159|        lora_config: LoRAConfig = None,\n160|        device: str | None = None,\n161|    ):\n162|        super().__init__()\n163|        self.config = config\n164|        self.lora_config = lora_config\n165|        self.feat_dim = config.feat_dim\n166|        self.patch_size = config.patch_size\n167|        self.device = resolve_runtime_device(device, config.device)\n168|        self.config.device = self.device\n169|        resolved_dtype = pick_runtime_dtype(self.device, self.config.dtype)\n170|        if resolved_dtype != self.config.dtype:\n171|            print(\n172|                f\"[voxcpm2] adjusted dtype {self.config.dtype} -> {resolved_dtype} for device {self.device}\",\n173|                file=sys.stderr,\n174|            )\n175|            self.config.dtype = resolved_dtype\n176|        print(f\"Running on device: {self.device}, dtype: {self.config.dtype}\", file=sys.stderr)\n177|\n178|        # Text-Semantic LM\n179|        self.base_lm = MiniCPMModel(config.lm_config)\n180|        self.base_lm.setup_cache(1, config.max_length, self.device, get_dtype(self.config.dtype))\n181|\n182|        self.text_tokenizer = mask_multichar_chinese_tokens(tokenizer)\n183|        self.audio_start_token = 101\n184|        self.audio_end_token = 102\n185|        self.ref_audio_start_token = 103\n186|        self.ref_audio_end_token = 104\n187|\n188|        # Residual Acoustic LM\n189|        residual_lm_config = config.lm_config.model_copy(deep=True)\n190|        residual_lm_config.num_hidden_layers = config.residual_lm_num_layers\n191|        residual_lm_config.vocab_size = 0\n192|        residual_lm_config.no_rope = config.residual_lm_no_rope\n193|        self.residual_lm = MiniCPMModel(residual_lm_config)\n194|        self.residual_lm.setup_cache(1, config.max_length, self.device, get_dtype(self.config.dtype))\n195|\n196|        # Local Encoder\n197|        encoder_config = config.lm_config.model_copy(deep=True)\n198|        encoder_config.hidden_size = config.encoder_config.hidden_dim\n199|        encoder_config.intermediate_size = config.encoder_config.ffn_dim\n200|        encoder_config.num_attention_heads = config.encoder_config.num_heads\n201|        encoder_config.num_hidden_layers = config.encoder_config.num_layers\n202|        encoder_config.kv_channels = config.encoder_config.kv_channels\n203|        encoder_config.vocab_size = 0\n204|        self.feat_encoder = VoxCPMLocEnc(encoder_config, input_dim=config.feat_dim)\n205|\n206|        # Local DiT\n207|        decoder_config = config.lm_config.model_copy(deep=True)\n208|        decoder_config.hidden_size = config.dit_config.hidden_dim\n209|        decoder_config.intermediate_size = config.dit_config.ffn_dim\n210|        decoder_config.num_attention_heads = config.dit_config.num_heads\n211|        decoder_config.num_hidden_layers = config.dit_config.num_layers\n212|        decoder_config.kv_channels = config.dit_config.kv_channels\n213|        decoder_config.vocab_size = 0\n214|        self.feat_decoder = UnifiedCFM(\n215|            in_channels=config.feat_dim,\n216|            cfm_params=config.dit_config.cfm_config,\n217|            estimator=VoxCPMLocDiTV2(decoder_config, in_channels=config.feat_dim),\n218|            mean_mode=config.dit_config.dit_mean_mode,\n219|        )\n220|\n221|        # Projection layers\n222|        self.fsq_layer = ScalarQuantizationLayer(\n223|            config.lm_config.hidden_size,\n224|            config.lm_config.hidden_size,\n225|            config.scalar_quantization_latent_dim,\n226|            config.scalar_quantization_scale,\n227|        )\n228|        self.enc_to_lm_proj = nn.Linear(config.encoder_config.hidden_dim, config.lm_config.hidden_size)\n229|        self.lm_to_dit_proj = nn.Linear(config.lm_config.hidden_size, config.dit_config.hidden_dim)\n230|        self.res_to_dit_proj = nn.Linear(config.lm_config.hidden_size, config.dit_config.hidden_dim)\n231|        self.fusion_concat_proj = nn.Linear(config.lm_config.hidden_size * 2, config.lm_config.hidden_size)\n232|\n233|        # Stop Predictor\n234|        self.stop_proj = nn.Linear(config.lm_config.hidden_size, config.lm_config.hidden_size)\n235|        self.stop_actn = nn.SiLU()\n236|        self.stop_head = nn.Linear(config.lm_config.hidden_size, 2, bias=False)\n237|        self.stop_loss = nn.CrossEntropyLoss(reduction=\"none\")\n238|\n239|        # Audio VAE\n240|        self.audio_vae = audio_vae\n241|        self.chunk_size = audio_vae.chunk_size\n242|        self._decode_chunk_size = getattr(audio_vae, \"decode_chunk_size\", audio_vae.chunk_size)\n243|        self._encode_sample_rate = audio_vae.sample_rate\n244|        self.sample_rate = getattr(audio_vae, \"out_sample_rate\", audio_vae.sample_rate)\n245|\n246|        if self.lora_config is not None:\n247|            self._apply_lora()\n248|\n249|    def _apply_lora(self):\n250|        \"\"\"注入 LoRA 到 LM / DiT / 投影层\"\"\"\n251|        cfg = self.lora_config\n252|        lora_kwargs = dict(r=cfg.r, alpha=cfg.alpha, dropout=cfg.dropout)\n253|\n254|        # LM: base_lm + residual_lm\n255|        if cfg.enable_lm:\n256|            for lm in [self.base_lm, self.residual_lm]:\n257|                apply_lora_to_named_linear_modules(lm, target_submodule_names=cfg.target_modules_lm, **lora_kwargs)\n258|\n259|        # DiT: feat_decoder.estimator\n260|        if cfg.enable_dit:\n261|            apply_lora_to_named_linear_modules(\n262|                self.feat_decoder.estimator, target_submodule_names=cfg.target_modules_dit, **lora_kwargs\n263|            )\n264|\n265|        # 投影层\n266|        if cfg.enable_proj:\n267|            from ..modules.layers.lora import LoRALinear\n268|\n269|            for attr_name in cfg.target_proj_modules:\n270|                module = getattr(self, attr_name, None)\n271|                if isinstance(module, nn.Linear):\n272|                    setattr(self, attr_name, LoRALinear(base=module, **lora_kwargs))\n273|\n274|    def optimize(self, disable: bool = False):\n275|        if disable:\n276|            return self\n277|        try:\n278|            if self.device != \"cuda\":\n279|                raise ValueError(\"VoxCPMModel can only be optimized on CUDA device\")\n280|            try:\n281|                import triton  # noqa: F401\n282|            except ImportError:\n283|                raise ValueError(\"triton is not installed\")\n284|            self.base_lm.forward_step = torch.compile(self.base_lm.forward_step, mode=\"reduce-overhead\", fullgraph=True)\n285|            self.residual_lm.forward_step = torch.compile(\n286|                self.residual_lm.forward_step, mode=\"reduce-overhead\", fullgraph=True\n287|            )\n288|            self._feat_encoder_raw = self.feat_encoder\n289|            self.feat_encoder = torch.compile(self.feat_encoder, mode=\"reduce-overhead\", fullgraph=True)\n290|            self.feat_decoder.estimator = torch.compile(\n291|                self.feat_decoder.estimator, mode=\"reduce-overhead\", fullgraph=True\n292|            )\n293|        except Exception as e:\n294|            print(f\"Warning: torch.compile disabled - {e}\", file=sys.stderr)\n295|        return self\n296|\n297|    def forward(\n298|        self,\n299|        text_tokens: torch.Tensor,\n300|        text_mask: torch.Tensor,\n301|        audio_feats: torch.Tensor,\n302|        audio_mask: torch.Tensor,\n303|        loss_mask: torch.Tensor,\n304|        position_ids: torch.Tensor,\n305|        labels: torch.Tensor,\n306|        *,\n307|        progress: float = 0.0,\n308|        sample_generate: bool = False,\n309|    ):\n310|        del position_ids  # not used yet\n311|\n312|        text_tokens = text_tokens.to(self.device, dtype=torch.long)\n313|        text_mask = text_mask.to(self.device, dtype=self._dtype())\n314|        audio_feats = audio_feats.to(self.device, dtype=self._dtype())\n315|        audio_mask = audio_mask.to(self.device, dtype=self._dtype())\n316|        loss_mask = loss_mask.to(self.device, dtype=self._dtype())\n317|        labels = labels.to(self.device, dtype=torch.long)\n318|\n319|        B, T, P, D = audio_feats.shape\n320|        feat_embed = self.feat_encoder(audio_feats)\n321|        feat_embed = self.enc_to_lm_proj(feat_embed)\n322|\n323|        scale_emb = getattr(self.config.lm_config, \"scale_emb\", 1.0)\n324|        if not getattr(self.config.lm_config, \"use_mup\", False):\n325|            scale_emb = 1.0\n326|        text_embed = self.base_lm.embed_tokens(text_tokens) * scale_emb\n327|        combined_embed = text_mask.unsqueeze(-1) * text_embed + audio_mask.unsqueeze(-1) * feat_embed\n328|\n329|        enc_outputs, _ = self.base_lm(inputs_embeds=combined_embed, is_causal=True)\n330|        enc_outputs = enc_outputs.to(self._dtype())\n331|        enc_outputs = self.fsq_layer(enc_outputs) * audio_mask.unsqueeze(-1) + enc_outputs * text_mask.unsqueeze(-1)\n332|        lm_hidden = torch.cat((torch.zeros_like(enc_outputs[:, 0:1, :]), enc_outputs[:, :-1, :]), dim=1)\n333|\n334|        residual_inputs = self.fusion_concat_proj(\n335|            torch.cat((enc_outputs, audio_mask.unsqueeze(-1) * feat_embed), dim=-1)\n336|        )\n337|        residual_outputs, _ = self.residual_lm(inputs_embeds=residual_inputs, is_causal=True)\n338|        residual_outputs = residual_outputs.to(self._dtype())\n339|        residual_hidden = torch.cat(\n340|            (torch.zeros_like(residual_outputs[:, 0:1, :]), residual_outputs[:, :-1, :]),\n341|            dim=1,\n342|        )\n343|\n344|        dit_hidden = torch.cat((self.lm_to_dit_proj(lm_hidden), self.res_to_dit_proj(residual_hidden)), dim=-1)\n345|        dit_hidden = rearrange(dit_hidden, \"b t c -> (b t) c\")\n346|\n347|        # Keep diffusion inputs in the same dtype as the model (e.g., bfloat16)\n348|        target_dtype = self._dtype()\n349|\n350|        feat_gt = rearrange(audio_feats.to(target_dtype), \"b t p d -> (b t) p d\")\n351|        feat_cond = torch.cat(\n352|            (torch.zeros_like(audio_feats[:, 0:1, ...]), audio_feats[:, :-1, ...]),\n353|            dim=1,\n354|        )\n355|        feat_cond = rearrange(feat_cond.to(target_dtype), \"b t p d -> (b t) p d\")\n356|\n357|        loss_seq_mask = loss_mask.unsqueeze(-1).repeat(1, 1, self.patch_size)\n358|        loss_seq_mask = rearrange(loss_seq_mask, \"b t p -> (b t) p 1\").to(target_dtype)\n359|\n360|        diff_loss = self.feat_decoder.compute_loss(\n361|            feat_gt.transpose(1, 2).contiguous(),\n362|            dit_hidden,\n363|            cond=feat_cond.transpose(1, 2).contiguous(),\n364|            tgt_mask=loss_seq_mask.transpose(1, 2).contiguous(),\n365|            progress=progress,\n366|        )\n367|\n368|        stop_logits = self.stop_head(self.stop_actn(self.stop_proj(lm_hidden)))\n369|        stop_losses = self.stop_loss(stop_logits.transpose(1, 2), labels)\n370|        denom = torch.clamp(loss_mask.sum(), min=1.0)\n371|        stop_loss = (stop_losses * loss_mask).sum() / denom\n372|\n373|        feat_pred = None\n374|        if sample_generate:\n375|            feat_cond_for_sample = feat_cond.transpose(1, 2).contiguous()\n376|            feat_pred_seq = self.feat_decoder(\n377|                mu=dit_hidden,\n378|                patch_size=self.patch_size,\n379|                cond=feat_cond_for_sample,\n380|                n_timesteps=10,\n381|            )\n382|            feat_pred = rearrange(feat_pred_seq.transpose(1, 2), \"(b t) d p -> b d (t p)\", b=B, p=self.patch_size)\n383|\n384|        feat_gt_tensor = rearrange(feat_gt, \"(b t) p d -> b d (t p)\", b=B, p=self.patch_size)\n385|\n386|        return {\n387|            \"loss/diff\": diff_loss,\n388|            \"loss/stop\": stop_loss,\n389|            \"feat_gt\": feat_gt_tensor,\n390|            \"feat_pred\": feat_pred,\n391|        }\n392|\n393|    def _dtype(self):\n394|        return get_dtype(self.config.dtype)\n395|\n396|    def _encode_wav(\n397|        self,\n398|        wav_path: str,\n399|        padding_mode: str = \"right\",\n400|        trim_silence_vad: bool = False,\n401|    ) -> torch.Tensor:\n402|        \"\"\"Load, trim, pad and VAE-encode an audio file.\n403|\n404|        Args:\n405|            wav_path: path to the audio file.\n406|            padding_mode: \"right\" (default) or \"left\" padding for alignment.\n407|            trim_silence_vad: whether to apply VAD-based silence trimming.\n408|\n409|        Returns:\n410|            audio_feat: (T, P, D) tensor of latent patches.\n411|        \"\"\"\n412|        audio, _ = librosa.load(wav_path, sr=self._encode_sample_rate, mono=True)\n413|        audio = torch.from_numpy(audio).unsqueeze(0)\n414|        if trim_silence_vad:\n415|            audio = _trim_audio_silence_vad(audio, self._encode_sample_rate, max_silence_ms=200.0)\n416|        patch_len = self.patch_size * self.chunk_size\n417|        if audio.size(1) % patch_len != 0:\n418|            padding_size = patch_len - audio.size(1) % patch_len\n419|            pad = (padding_size, 0) if padding_mode == \"left\" else (0, padding_size)\n420|            audio = torch.nn.functional.pad(audio, pad)\n421|        feat = self.audio_vae.encode(audio.to(self.device), self._encode_sample_rate).cpu()\n422|        return feat.view(self.audio_vae.latent_dim, -1, self.patch_size).permute(1, 2, 0)\n423|\n424|    def _make_ref_prefix(self, ref_feat: torch.Tensor, device: torch.device):\n425|        \"\"\"Build the [ref_start ref_audio ref_end] prefix segments.\n426|\n427|        Returns:\n428|            tokens, feats, text_mask, audio_mask  (all 1-D / 2-D tensors)\n429|        \"\"\"\n430|        ref_len = ref_feat.size(0)\n431|        z1 = torch.zeros((1, self.patch_size, self.audio_vae.latent_dim), dtype=torch.float32, device=device)\n432|        tokens = torch.cat(\n433|            [\n434|                torch.tensor([self.ref_audio_start_token], dtype=torch.int32, device=device),\n435|                torch.zeros(ref_len, dtype=torch.int32, device=device),\n436|                torch.tensor([self.ref_audio_end_token], dtype=torch.int32, device=device),\n437|            ]\n438|        )\n439|        feats = torch.cat([z1, ref_feat, z1], dim=0)\n440|        t_mask = torch.cat(\n441|            [\n442|                torch.tensor([1], dtype=torch.int32),\n443|                torch.zeros(ref_len, dtype=torch.int32),\n444|                torch.tensor([1], dtype=torch.int32),\n445|            ]\n446|        ).to(device)\n447|        a_mask = torch.cat(\n448|            [\n449|                torch.tensor([0], dtype=torch.int32),\n450|                torch.ones(ref_len, dtype=torch.int32),\n451|                torch.tensor([0], dtype=torch.int32),\n452|            ]\n453|        ).to(device)\n454|        return tokens, feats, t_mask, a_mask\n455|\n456|    def generate(self, *args, **kwargs) -> torch.Tensor:\n457|        return next_and_close(self._generate(*args, streaming=False, **kwargs))\n458|\n459|    def generate_streaming(self, *args, **kwargs) -> Generator[torch.Tensor, None, None]:\n460|        return self._generate(*args, streaming=True, **kwargs)\n461|\n462|    @torch.inference_mode()\n463|    def _generate(\n464|        self,\n465|        target_text: str,\n466|        prompt_text: str = \"\",\n467|        prompt_wav_path: str = \"\",\n468|        reference_wav_path: str = \"\",\n469|        min_len: int = 2,\n470|        max_len: int = 2000,\n471|        inference_timesteps: int = 10,\n472|        cfg_value: float = 2.0,\n473|        retry_badcase: bool = False,\n474|        retry_badcase_max_times: int = 3,\n475|        retry_badcase_ratio_threshold: float = 6.0,\n476|        trim_silence_vad: bool = False,\n477|        streaming: bool = False,\n478|        streaming_prefix_len: int = 4,\n479|    ) -> Generator[torch.Tensor, None, None]:\n480|        if retry_badcase and streaming:\n481|            warnings.warn(\"Retry on bad cases is not supported in streaming mode, setting retry_badcase=False.\")\n482|            retry_badcase = False\n483|\n484|        if reference_wav_path and prompt_wav_path:\n485|            # Combined mode: reference isolation prefix + continuation suffix\n486|            text = prompt_text + target_text\n487|            text_token = torch.LongTensor(self.text_tokenizer(text))\n488|            text_token = torch.cat(\n489|                [\n490|                    text_token,\n491|                    torch.tensor([self.audio_start_token], dtype=torch.int32, device=text_token.device),\n492|                ],\n493|                dim=-1,\n494|            )\n495|            text_length = text_token.shape[0]\n496|\n497|            ref_feat = self._encode_wav(\n498|                reference_wav_path,\n499|                padding_mode=\"right\",\n500|                trim_silence_vad=trim_silence_vad,\n501|", "total_lines": 1256, "file_size": 53800, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 1256 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|from .config import MiniCPM4Config\n2|import torch\n3|import torch.nn as nn\n4|from typing import List, Tuple\n5|import math\n6|from .cache import StaticKVCache\n7|\n8|\n9|def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):\n10|    old_dtype = hidden.dtype\n11|    variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)\n12|    hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)\n13|    return hidden * weight\n14|\n15|\n16|class MiniCPMRMSNorm(nn.Module):\n17|    def __init__(self, hidden_size, eps=1e-6):\n18|        \"\"\"\n19|        MiniCPMRMSNorm is equivalent to T5LayerNorm\n20|        \"\"\"\n21|        super().__init__()\n22|        self.weight = nn.Parameter(torch.ones(hidden_size))\n23|        self.variance_epsilon = eps\n24|\n25|    def forward(self, hidden_states):\n26|        return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)\n27|\n28|\n29|def rotate_half(x):\n30|    \"\"\"Rotates half the hidden dims of the input.\"\"\"\n31|    x1, x2 = x.chunk(2, dim=-1)\n32|    return torch.cat((-x2, x1), dim=-1)\n33|\n34|\n35|def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):\n36|    \"\"\"\n37|    Args:\n38|        q: Tensor(batch_size, num_heads, seq_len, head_dim)\n39|        k: Tensor(batch_size, num_key_value_heads, seq_len, head_dim)\n40|        cos: Tensor(seq_len, head_dim)\n41|        sin: Tensor(seq_len, head_dim)\n42|    Returns:\n43|        Tensor(batch_size, num_heads, seq_len, head_dim), Tensor(batch_size, num_key_value_heads, seq_len, head_dim)\n44|    \"\"\"\n45|    orig_dtype = q.dtype\n46|    q = q.to(torch.float32)\n47|    k = k.to(torch.float32)\n48|    q_embed = (q * cos) + (rotate_half(q) * sin)\n49|    k_embed = (k * cos) + (rotate_half(k) * sin)\n50|    return q_embed.to(orig_dtype), k_embed.to(orig_dtype)\n51|\n52|\n53|class MiniCPMLongRoPE(nn.Module):\n54|    \"\"\"MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla\"\"\"\n55|\n56|    def __init__(self, config: MiniCPM4Config):\n57|        super().__init__()\n58|        self.config = config\n59|        self.dim = config.kv_channels if config.kv_channels else config.hidden_size // config.num_attention_heads\n60|        self.base = config.rope_theta\n61|        self.max_position_embeddings = config.max_position_embeddings\n62|\n63|        self.short_factor = config.rope_scaling.short_factor\n64|        self.long_factor = config.rope_scaling.long_factor\n65|        self.original_max_position_embeddings = config.rope_scaling.original_max_position_embeddings\n66|\n67|        scale = self.max_position_embeddings / self.original_max_position_embeddings\n68|        self.scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))\n69|        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))\n70|        self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n71|\n72|        self.max_seq_len_cached = 0\n73|\n74|        self.register_buffer(\"cos_cached\", torch.empty(0), persistent=False)\n75|        self.register_buffer(\"sin_cached\", torch.empty(0), persistent=False)\n76|\n77|        self._set_cos_sin_cache(seq_len=self.max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32)\n78|\n79|    def _set_cos_sin_cache(self, seq_len, device, dtype):\n80|        \"\"\"设置cos和sin缓存\"\"\"\n81|        self.max_seq_len_cached = seq_len\n82|        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n83|\n84|        if seq_len > self.original_max_position_embeddings:\n85|            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=device)\n86|        else:\n87|            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=device)\n88|\n89|        freqs = torch.mul(\n90|            torch.outer(t, 1.0 / ext_factors).to(device=device), self.inv_freq.to(device=device).to(dtype)\n91|        )\n92|\n93|        # 创建embeddings\n94|        emb = torch.cat((freqs, freqs), dim=-1)\n95|\n96|        self.cos_cached = emb.cos().to(dtype) * self.scaling_factor\n97|        self.sin_cached = emb.sin().to(dtype) * self.scaling_factor\n98|\n99|    def forward(self, position_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:\n100|        \"\"\"\n101|        Args:\n102|            position_ids: Tensor(seq_len) 或 Tensor(batch_size, seq_len)\n103|        Returns:\n104|            Tensor(seq_len, head_dim), Tensor(seq_len, head_dim)\n105|        \"\"\"\n106|        cos = self.cos_cached[position_ids]\n107|        sin = self.sin_cached[position_ids]\n108|\n109|        return cos, sin\n110|\n111|\n112|class MiniCPMAttention(nn.Module):\n113|    def __init__(self, config: MiniCPM4Config, layer_idx: int):\n114|        super().__init__()\n115|        self.config = config\n116|        self.layer_idx = layer_idx\n117|        self.hidden_size = config.hidden_size\n118|        self.num_heads = config.num_attention_heads\n119|        self.head_dim = (\n120|            config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels\n121|        )\n122|        self.num_key_value_heads = config.num_key_value_heads\n123|        self.num_key_value_groups = self.num_heads // self.num_key_value_heads\n124|        self.max_position_embeddings = config.max_position_embeddings\n125|        self.rope_theta = 10000.0\n126|\n127|        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)\n128|        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)\n129|        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)\n130|        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)\n131|\n132|    def forward(\n133|        self,\n134|        hidden_states: torch.Tensor,\n135|        position_emb: Tuple[torch.Tensor, torch.Tensor],\n136|        is_causal: bool,\n137|    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n138|        bsz, q_len, _ = hidden_states.size()\n139|\n140|        query_states = self.q_proj(hidden_states)\n141|        key_states = self.k_proj(hidden_states)\n142|        value_states = self.v_proj(hidden_states)\n143|\n144|        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n145|        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n146|        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n147|\n148|        if position_emb is not None:\n149|            cos, sin = position_emb\n150|            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)\n151|\n152|        # ref: https://github.com/pytorch/pytorch/issues/163597\n153|        # there is a bug in MPS for non-contiguous tensors, so we need to make them contiguous\n154|        query_states = query_states.contiguous()\n155|        key_states = key_states.contiguous()\n156|        value_states = value_states.contiguous()\n157|        attn_output = torch.nn.functional.scaled_dot_product_attention(\n158|            query_states,\n159|            key_states,\n160|            value_states,\n161|            is_causal=is_causal,\n162|            enable_gqa=True,\n163|        )\n164|\n165|        attn_output = attn_output.transpose(1, 2).contiguous()\n166|        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)\n167|\n168|        attn_output = self.o_proj(attn_output)\n169|\n170|        past_key_value = (key_states, value_states)\n171|        return attn_output, past_key_value\n172|\n173|    def forward_step(\n174|        self,\n175|        hidden_states: torch.Tensor,\n176|        position_emb: Tuple[torch.Tensor, torch.Tensor],\n177|        position_id: int,\n178|        kv_cache: Tuple[torch.Tensor, torch.Tensor],\n179|    ) -> torch.Tensor:\n180|        bsz, _ = hidden_states.size()\n181|\n182|        query_states = self.q_proj(hidden_states)\n183|        key_states = self.k_proj(hidden_states)\n184|        value_states = self.v_proj(hidden_states)\n185|\n186|        query_states = query_states.view(bsz, 1, self.num_heads, self.head_dim).transpose(1, 2)\n187|        key_states = key_states.view(bsz, 1, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n188|        value_states = value_states.view(bsz, 1, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n189|\n190|        if position_emb is not None:\n191|            cos, sin = position_emb\n192|            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)\n193|\n194|        key_cache, value_cache = kv_cache\n195|\n196|        key_cache[:, :, position_id, :] = key_states\n197|        value_cache[:, :, position_id, :] = value_states\n198|\n199|        # Use an explicit broadcastable mask shape for SDPA. A 1D mask can\n200|        # trigger a CPU-side dimension bug in some PyTorch versions.\n201|        attn_mask = (torch.arange(key_cache.size(2), device=key_cache.device) <= position_id).view(1, 1, 1, -1)\n202|\n203|        # ref: https://github.com/pytorch/pytorch/issues/163597\n204|        # there is a bug in MPS for non-contiguous tensors, so we need to make them contiguous\n205|        query_states = query_states.contiguous()\n206|        key_cache = key_cache.contiguous()\n207|        value_cache = value_cache.contiguous()\n208|        attn_output = torch.nn.functional.scaled_dot_product_attention(\n209|            query_states,\n210|            key_cache,\n211|            value_cache,\n212|            attn_mask=attn_mask,\n213|            enable_gqa=True,\n214|        )\n215|\n216|        attn_output = attn_output.transpose(1, 2).contiguous()\n217|        attn_output = attn_output.reshape(bsz, self.num_heads * self.head_dim)\n218|        attn_output = self.o_proj(attn_output)\n219|\n220|        return attn_output\n221|\n222|\n223|class MiniCPMMLP(nn.Module):\n224|    def __init__(self, config):\n225|        super().__init__()\n226|        self.config = config\n227|        self.hidden_size = config.hidden_size\n228|        self.intermediate_size = config.intermediate_size\n229|        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n230|        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n231|        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)\n232|        self.act_fn = nn.SiLU()\n233|\n234|    def forward(self, x):\n235|        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n236|\n237|\n238|class MiniCPMDecoderLayer(nn.Module):\n239|    def __init__(self, config: MiniCPM4Config, layer_idx: int):\n240|        super().__init__()\n241|        self.hidden_size = config.hidden_size\n242|        self.self_attn = MiniCPMAttention(config=config, layer_idx=layer_idx)\n243|\n244|        self.mlp = MiniCPMMLP(config)\n245|        self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n246|        self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n247|\n248|        self.scale_depth = config.scale_depth\n249|        self.num_hidden_layers = config.num_hidden_layers\n250|        self.use_mup = config.use_mup\n251|\n252|    def forward(\n253|        self,\n254|        hidden_states: torch.Tensor,\n255|        position_emb: Tuple[torch.Tensor, torch.Tensor],\n256|        is_causal: bool,\n257|    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n258|        \"\"\"\n259|        Args:\n260|            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n261|            position_ids (`torch.LongTensor`): position ids of shape `(batch_size, seq_len)`\n262|            is_causal (`bool`): whether the attention mask is causal\n263|        \"\"\"\n264|        residual = hidden_states\n265|        hidden_states = self.input_layernorm(hidden_states)\n266|        # Self Attention\n267|        hidden_states, present_key_value = self.self_attn(\n268|            hidden_states=hidden_states,\n269|            position_emb=position_emb,\n270|            is_causal=is_causal,\n271|        )\n272|\n273|        if self.use_mup:\n274|            hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n275|        else:\n276|            hidden_states = residual + hidden_states\n277|\n278|        # Fully Connected\n279|        residual = hidden_states\n280|        hidden_states = self.post_attention_layernorm(hidden_states)\n281|\n282|        hidden_states = self.mlp(hidden_states)\n283|        if self.use_mup:\n284|            hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n285|        else:\n286|            hidden_states = residual + hidden_states\n287|\n288|        return hidden_states, present_key_value\n289|\n290|    def forward_step(\n291|        self,\n292|        hidden_states: torch.Tensor,\n293|        position_emb: Tuple[torch.Tensor, torch.Tensor],\n294|        position_id: torch.Tensor,\n295|        kv_cache: Tuple[torch.Tensor, torch.Tensor],\n296|    ) -> torch.Tensor:\n297|        residual = hidden_states\n298|        hidden_states = self.input_layernorm(hidden_states)\n299|        # Self Attention\n300|        hidden_states = self.self_attn.forward_step(\n301|            hidden_states=hidden_states,\n302|            position_emb=position_emb,\n303|            position_id=position_id,\n304|            kv_cache=kv_cache,\n305|        )\n306|\n307|        if self.use_mup:\n308|            hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n309|        else:\n310|            hidden_states = residual + hidden_states\n311|\n312|        # Fully Connected\n313|        residual = hidden_states\n314|        hidden_states = self.post_attention_layernorm(hidden_states)\n315|\n316|        hidden_states = self.mlp(hidden_states)\n317|        if self.use_mup:\n318|            hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n319|        else:\n320|            hidden_states = residual + hidden_states\n321|\n322|        return hidden_states\n323|\n324|\n325|class MiniCPMModel(nn.Module):\n326|    \"\"\"\n327|    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]\n328|\n329|    Args:\n330|        config: MiniCPMConfig\n331|    \"\"\"\n332|\n333|    def __init__(self, config: MiniCPM4Config):\n334|        super().__init__()\n335|        self.vocab_size = config.vocab_size\n336|        self.config = config\n337|\n338|        if config.vocab_size > 0:\n339|            self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)\n340|        else:\n341|            self.embed_tokens = nn.Identity()\n342|\n343|        self.layers = nn.ModuleList(\n344|            [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]\n345|        )\n346|\n347|        self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n348|        if config.no_rope:\n349|            self.rope_emb = None\n350|        else:\n351|            self.rope_emb = MiniCPMLongRoPE(config)\n352|\n353|        self.kv_cache = None\n354|\n355|    def forward(\n356|        self,\n357|        inputs_embeds: torch.Tensor,\n358|        is_causal: bool = True,\n359|    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:\n360|        \"\"\"\n361|        Args:\n362|            inputs_embeds: Tensor(batch_size, seq_length, hidden_size)\n363|            is_causal: bool, whether the attention mask is causal\n364|        Returns:\n365|            hidden_states: Tensor(batch_size, seq_length, hidden_size)\n366|            next_decoder_cache: List[(batch_size, num_heads, seq_length, head_dim), (batch_size, num_heads, seq_length, head_dim)]\n367|        \"\"\"\n368|        if self.rope_emb is not None:\n369|            position_ids = torch.arange(0, inputs_embeds.size(1), dtype=torch.long, device=inputs_embeds.device)\n370|            position_emb = self.rope_emb(position_ids)\n371|        else:\n372|            position_emb = None\n373|        hidden_states = inputs_embeds\n374|\n375|        next_decoder_cache = []\n376|\n377|        for decoder_layer in self.layers:\n378|\n379|            hidden_states, this_cache = decoder_layer(\n380|                hidden_states,\n381|                position_emb,\n382|                is_causal,\n383|            )\n384|            next_decoder_cache.append(this_cache)\n385|        hidden_states = self.norm(hidden_states)\n386|        return hidden_states, next_decoder_cache\n387|\n388|    def forward_step(\n389|        self,\n390|        inputs_embeds: torch.Tensor,\n391|        position_id: torch.Tensor,\n392|    ) -> torch.Tensor:\n393|        \"\"\"\n394|        Args:\n395|            inputs_embeds: Tensor(batch_size, hidden_size)\n396|        Returns:\n397|            hidden_states: Tensor(batch_size, hidden_size)\n398|        \"\"\"\n399|        assert self.kv_cache is not None, \"KV cache is not setup\"\n400|\n401|        if self.rope_emb is not None:\n402|            position_emb = self.rope_emb(position_id)\n403|        else:\n404|            position_emb = None\n405|        hidden_states = inputs_embeds\n406|\n407|        for i, decoder_layer in enumerate(self.layers):\n408|            hidden_states = decoder_layer.forward_step(\n409|                hidden_states,\n410|                position_emb,\n411|                position_id,\n412|                self.kv_cache.get_layer_cache(i),\n413|            )\n414|\n415|        hidden_states = self.norm(hidden_states)\n416|        return hidden_states\n417|\n418|    def setup_cache(self, batch_size: int, max_length: int, device, dtype: torch.dtype):\n419|        self.kv_cache = StaticKVCache(\n420|            num_layers=self.config.num_hidden_layers,\n421|            num_kv_heads=self.config.num_key_value_heads,\n422|            dim_kv_head=(\n423|                self.config.hidden_size // self.config.num_attention_heads\n424|                if self.config.kv_channels is None\n425|                else self.config.kv_channels\n426|            ),\n427|            batch_size=batch_size,\n428|            device=device,\n429|            dtype=dtype,\n430|            max_length=max_length,\n431|        )\n432|", "total_lines": 431, "file_size": 16655, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
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c0:
  {"content": "1|<h2 align=\"center\">VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning</h2>\n2|\n3|<p align=\"center\">\n4|  <b>English</b> | <a href=\"./README_zh.md\">中文</a>\n5|</p>\n6|\n7|<p align=\"center\">\n8|  <a href=\"https://github.com/OpenBMB/VoxCPM/\"><img src=\"https://img.shields.io/badge/Project%20Page-GitHub-blue\" alt=\"Project Page\"></a>\n9|  <a href=\"https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo\"><img src=\"https://img.shields.io/badge/Live%20Playground-Demo-orange\" alt=\"Live Playground\"></a>\n10|  <a href=\"https://voxcpm.readthedocs.io/en/latest/\"><img src=\"https://img.shields.io/badge/Docs-ReadTheDocs-8CA1AF\" alt=\"Documentation\"></a>\n11|  <a href=\"https://huggingface.co/openbmb/VoxCPM2\"><img src=\"https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-VoxCPM2-yellow\" alt=\"Hugging Face\"></a>\n12|  <a href=\"https://modelscope.cn/models/OpenBMB/VoxCPM2\"><img src=\"https://img.shields.io/badge/ModelScope-VoxCPM2-purple\" alt=\"ModelScope\"></a>\n13|  <a href=\"https://openbmb.github.io/voxcpm2-demopage/\"><img src=\"https://img.shields.io/badge/DemoPage-Audio Samples-red\"></a>\n14|  \n15|</p>\n16|\n17|<div align=\"center\">\n18|  <img src=\"assets/voxcpm_logo.png\" alt=\"VoxCPM Logo\" width=\"35%\">\n19|  <br><br>\n20|  <a href=\"https://trendshift.io/repositories/17704\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/17704\" alt=\"OpenBMB%2FVoxCPM | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n21|</div>\n22|\n23|<br>\n24|\n25|<p align=\"center\">\n26|  👋 Join our community for discussion and support!\n27|  <br>\n28|  <a href=\"./assets/feishu-group.png\" style=\"display:inline-block;vertical-align:middle; margin-left: 10px;\">\n29|    <img src=\"./assets/feishu-logo.png\" width=\"16\" height=\"16\" style=\"vertical-align:middle;\"> Feishu\n30|  </a>\n31|  &nbsp;|&nbsp;\n32|  <a href=\"https://discord.gg/KZUx7tVNwz\" style=\"display:inline-block;vertical-align:middle;\">\n33|    <img src=\"./assets/discord-logo.png\" width=\"16\" height=\"16\" style=\"vertical-align:middle;\"> Discord\n34|  </a>\n35|</p>\n36|\n37|VoxCPM is a **tokenizer-free** Text-to-Speech system that directly generates continuous speech representations via an end-to-end **diffusion autoregressive architecture**, bypassing discrete tokenization to achieve highly natural and expressive synthesis.\n38|\n39|**VoxCPM2** is the latest major release — a **2B** parameter model trained on **over 2 million hours** of multilingual speech data, now supporting **30 languages**, **Voice Design**, **Controllable Voice Cloning**, and **48kHz** studio-quality audio output. Built on a [MiniCPM-4](https://github.com/OpenBMB/MiniCPM) backbone.\n40|\n41|### ✨ Highlights\n42|\n43|- 🌍 **30-Language Multilingual** — Input text in any of the 30 supported languages and synthesize directly, no language tag needed\n44|- 🎨 **Voice Design** — Create a brand-new voice from a natural-language description alone (gender, age, tone, emotion, pace …), no reference audio required\n45|- 🎛️ **Controllable Cloning** — Clone any voice from a short reference clip, with optional style guidance to steer emotion, pace, and expression while preserving the original timbre\n46|- 🎙️ **Ultimate Cloning** — Reproduce every vocal nuance: provide both reference audio and its transcript, and the model continues seamlessly from the reference, faithfully preserving every vocal detail — timbre, rhythm, emotion, and style (same as VoxCPM1.5)\n47|- 🔊 **48kHz High-Quality Audio** — Accepts 16kHz reference audio and directly outputs 48kHz studio-quality audio via AudioVAE V2's asymmetric encode/decode design, with built-in super-resolution — no external upsampler needed\n48|- 🧠 **Context-Aware Synthesis** — Automatically infers appropriate prosody and expressiveness from text content\n49|- ⚡ **Real-Time Streaming** — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13 accelerated by [Nano-vLLM](https://github.com/a710128/nanovllm-voxcpm) or [vLLM-Omni](https://github.com/vllm-project/vllm-omni) — official vLLM omni-modal serving for VoxCPM2 with PagedAttention and an OpenAI-compatible API\n50|- 📜 **Fully Open-Source & Commercial-Ready** — Weights and code released under the [Apache-2.0](LICENSE) license, free for commercial use\n51|\n52|\n53|<summary><b>🌍 Supported Languages (30)</b></summary>\n54|<br>\n55|Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese\n56|\n57|Chinese Dialect: 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话\n58|\n59|\n60|### News\n61|\n62|* **[2026.04]** 🔥 We release **VoxCPM2** — 2B, 30 languages, Voice Design & Controllable Voice Cloning, 48kHz audio output! [Weights](https://huggingface.co/openbmb/VoxCPM2) | [Docs](https://voxcpm.readthedocs.io/en/latest/) | [Playground](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo)\n63|* **[2025.12]** 🎉 Open-source **VoxCPM1.5** [weights](https://huggingface.co/openbmb/VoxCPM1.5) with SFT & LoRA fine-tuning. (**🏆 #1 GitHub Trending**)\n64|* **[2025.09]** 🔥 Release VoxCPM [Technical Report](https://arxiv.org/abs/2509.24650).\n65|* **[2025.09]** 🎉 Open-source **VoxCPM-0.5B** [weights](https://huggingface.co/openbmb/VoxCPM-0.5B) (**🏆 #1 HuggingFace Trending**)\n66|\n67|---\n68|\n69|## Contents\n70|\n71|- [Quick Start](#-quick-start)\n72|  - [Installation](#installation)\n73|  - [Python API](#python-api)\n74|  - [CLI Usage](#cli-usage)\n75|  - [Web Demo](#web-demo)\n76|  - [Production Deployment](#-production-deployment-nano-vllm)\n77|- [Models & Versions](#-models--versions)\n78|- [Performance](#-performance)\n79|- [Fine-tuning](#%EF%B8%8F-fine-tuning)\n80|- [Documentation](#-documentation)\n81|- [Ecosystem & Community](#-ecosystem--community)\n82|- [Risks and Limitations](#%EF%B8%8F-risks-and-limitations)\n83|- [Citation](#-citation)\n84|\n85|---\n86|\n87|## 🚀 Quick Start\n88|\n89|### Installation\n90|\n91|```sh\n92|pip install voxcpm\n93|```\n94|\n95|> **Requirements:** Python ≥ 3.10 (<3.13), PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See [Quick Start Docs](https://voxcpm.readthedocs.io/en/latest/quickstart.html) for details.\n96|\n97|### Python API\n98|\n99|#### 🗣️ Text-to-Speech\n100|\n101|```python\n102|from voxcpm import VoxCPM\n103|import soundfile as sf\n104|\n105|model = VoxCPM.from_pretrained(\n106|  \"openbmb/VoxCPM2\",\n107|  load_denoiser=False,\n108|)\n109|\n110|wav = model.generate(\n111|    text=\"VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.\",\n112|    cfg_value=2.0,\n113|    inference_timesteps=10,\n114|)\n115|sf.write(\"demo.wav\", wav, model.tts_model.sample_rate)\n116|print(\"saved: demo.wav\")\n117|```\n118|\n119|If you prefer downloading from ModelScope first, you can use:\n120|\n121|```bash\n122|pip install modelscope\n123|```\n124|\n125|```python\n126|from modelscope import snapshot_download\n127|snapshot_download(\"OpenBMB/VoxCPM2\", local_dir='./pretrained_models/VoxCPM2') # specify the local directory to save the model\n128|\n129|from voxcpm import VoxCPM\n130|import soundfile as sf\n131|model = VoxCPM.from_pretrained(\"./pretrained_models/VoxCPM2\", load_denoiser=False)\n132|\n133|wav = model.generate(\n134|    text=\"VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.\",\n135|    cfg_value=2.0,\n136|    inference_timesteps=10,\n137|)\n138|sf.write(\"demo.wav\", wav, model.tts_model.sample_rate)\n139|```\n140|\n141|#### 🎨 Voice Design\n142|\n143|Create a voice from a natural-language description — no reference audio needed. **Format:** put the description in parentheses at the start of `text`(e.g. `\"(your voice description)The text to synthesize.\"`):\n144|\n145|```python\n146|wav = model.generate(\n147|    text=\"(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!\",\n148|    cfg_value=2.0,\n149|    inference_timesteps=10,\n150|)\n151|sf.write(\"voice_design.wav\", wav, model.tts_model.sample_rate)\n152|```\n153|\n154|#### 🎛️ Controllable Voice Cloning\n155|\n156|Upload a reference audio. The model clones the timbre, and you can still use control instructions to adjust speed, emotion, or style.\n157|\n158|```python\n159|wav = model.generate(\n160|    text=\"This is a cloned voice generated by VoxCPM2.\",\n161|    reference_wav_path=\"path/to/voice.wav\",\n162|)\n163|sf.write(\"clone.wav\", wav, model.tts_model.sample_rate)\n164|\n165|wav = model.generate(\n166|    text=\"(slightly faster, cheerful tone)This is a cloned voice with style control.\",\n167|    reference_wav_path=\"path/to/voice.wav\",\n168|    cfg_value=2.0,\n169|    inference_timesteps=10,\n170|)\n171|sf.write(\"controllable_clone.wav\", wav, model.tts_model.sample_rate)\n172|```\n173|\n174|#### 🎙️ Ultimate Cloning\n175|\n176|Provide both the reference audio and its exact transcript for audio-continuation-based cloning with every vocal nuance reproduced. For maximum cloning similarity, pass the same reference clip to both `reference_wav_path` and `prompt_wav_path` as shown below:\n177|\n178|```python\n179|wav = model.generate(\n180|    text=\"This is an ultimate cloning demonstration using VoxCPM2.\",\n181|    prompt_wav_path=\"path/to/voice.wav\",\n182|    prompt_text=\"The transcript of the reference audio.\",\n183|    reference_wav_path=\"path/to/voice.wav\", # optional, for better simliarity \n184|)\n185|sf.write(\"hifi_clone.wav\", wav, model.tts_model.sample_rate)\n186|```\n187|\n188|<details>\n189|<summary><b>🔄 Streaming API</b></summary>\n190|\n191|```python\n192|import numpy as np\n193|\n194|chunks = []\n195|for chunk in model.generate_streaming(\n196|    text=\"Streaming text to speech is easy with VoxCPM!\",\n197|):\n198|    chunks.append(chunk)\n199|wav = np.concatenate(chunks)\n200|sf.write(\"streaming.wav\", wav, model.tts_model.sample_rate)\n201|```\n202|</details>\n203|\n204|### CLI Usage\n205|\n206|```bash\n207|# Voice design (no reference audio needed)\n208|voxcpm design \\\n209|  --text \"VoxCPM2 brings studio-quality multilingual speech synthesis.\" \\\n210|  --output out.wav\n211|\n212|# Controllable voice cloning with style control\n213|voxcpm design \\\n214|  --text \"VoxCPM2 brings studio-quality multilingual speech synthesis.\" \\\n215|  --control \"Young female voice, warm and gentle, slightly smiling\" \\\n216|  --output out.wav\n217|\n218|# Voice cloning (reference audio)\n219|voxcpm clone \\\n220|  --text \"This is a voice cloning demo.\" \\\n221|  --reference-audio path/to/voice.wav \\\n222|  --output out.wav\n223|\n224|# Ultimate cloning (prompt audio + transcript)\n225|voxcpm clone \\\n226|  --text \"This is a voice cloning demo.\" \\\n227|  --prompt-audio path/to/voice.wav \\\n228|  --prompt-text \"reference transcript\" \\\n229|  --reference-audio path/to/voice.wav \\ # optional, for better simliarity\n230|  --output out.wav\n231|\n232|# Batch processing\n233|voxcpm batch --input examples/input.txt --output-dir outs\n234|\n235|# Help\n236|voxcpm --help\n237|```\n238|\n239|### Web Demo\n240|\n241|```bash\n242|python app.py --port 8808  # then open in browser: http://localhost:8808\n243|```\n244|\n245|Use `--device` to choose the runtime device:\n246|\n247|```bash\n248|python app.py --device auto\n249|```\n250|\n251|Supported values are `auto`, `cpu`, `mps`, `cuda`, and `cuda:N`. On Apple Silicon Macs, `auto` uses MPS when available.\n252|\n253|### 🚢 Production Deployment (Nano-vLLM)\n254|\n255|For high-throughput serving, use [**Nano-vLLM-VoxCPM**](https://github.com/a710128/nanovllm-voxcpm) — a dedicated inference engine built on Nano-vLLM with concurrent request support and an async API.\n256|\n257|```bash\n258|pip install nano-vllm-voxcpm\n259|```\n260|\n261|```python\n262|from nanovllm_voxcpm import VoxCPM\n263|import numpy as np, soundfile as sf\n264|\n265|server = VoxCPM.from_pretrained(model=\"/path/to/VoxCPM\", devices=[0])\n266|chunks = list(server.generate(target_text=\"Hello from VoxCPM!\"))\n267|sf.write(\"out.wav\", np.concatenate(chunks), 48000)\n268|server.stop()\n269|```\n270|\n271|> **RTF as low as ~0.13 on NVIDIA RTX 4090** (vs ~0.3 with the standard PyTorch implementation), with support for batched concurrent requests and a FastAPI HTTP server. See the [Nano-vLLM-VoxCPM repo](https://github.com/a710128/nanovllm-voxcpm) for deployment details.\n272|\n273|### 🏭 Production Serving (vLLM-Omni)\n274|\n275|For production multi-tenant deployments, use [**vLLM-Omni**](https://github.com/vllm-project/vllm-omni) — the official vLLM project's omni-modal extension with native **VoxCPM2** support. PagedAttention KV cache, continuous batching, and a drop-in **OpenAI-compatible** `/v1/audio/speech` endpoint.\n276|\n277|```bash\n278|# Install from source (latest main — vllm-omni is rapidly evolving)\n279|uv pip install vllm==0.19.0 --torch-backend=auto\n280|git clone https://github.com/vllm-project/vllm-omni.git && cd vllm-omni\n281|uv pip install -e .\n282|```\n283|\n284|See the [vLLM-Omni installation guide](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/) for other platforms (ROCm, XPU, MUSA, NPU) and Docker images.\n285|\n286|```bash\n287|# Launch an OpenAI-compatible TTS server (--omni enables omni-modal serving)\n288|vllm serve openbmb/VoxCPM2 --omni --port 8000\n289|\n290|# Call it from any OpenAI client\n291|curl http://localhost:8000/v1/audio/speech \\\n292|  -H \"Content-Type: application/json\" \\\n293|  -d '{\"model\":\"openbmb/VoxCPM2\",\"input\":\"Hello from VoxCPM2 on vLLM-Omni!\",\"voice\":\"default\"}' \\\n294|  --output out.wav\n295|```\n296|\n297|> Built on the upstream vLLM scheduler, with batched concurrent requests, streaming chunk delivery, and multi-GPU deployment out of the box. See the [VoxCPM2 example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/voxcpm2) for full deployment recipes.\n298|\n299|> **Full parameter reference, multi-scenario examples, and voice cloning tips →** [Quick Start Guide](https://voxcpm.readthedocs.io/en/latest/quickstart.html) | [Usage Guide](https://voxcpm.readthedocs.io/en/latest/usage_guide.html) | [Cookbook](https://voxcpm.readthedocs.io/en/latest/cookbook.html)\n300|\n301|", "total_lines": 633, "file_size": 29023, "truncated": true, "hint": "Use offset=301 to continue reading (showing 1-300 of 633 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Analisei o repositório `OpenBMB/VoxCPM`. Abaixo está o resumo estruturado:

  ## Problema
  - **Compatibility limitada**: exige Python `>=3.10` e `<3.13`. Se o ambiente do usuário for Python 3.13, não há suporte oficial.
  - **Dependência forte de GPU**: melhores resultados exigem CUDA ≥ 12.0 e hardware considerável (ex.: RTF baixo cite RTX 4090).
  - **Riscos de uso inadequado**: README dedica seção a riscos, alertando para deepfake, spoofing de voz e questões éticas/de consentimento.
  - **Inexistência de docs locais completas**: README remete documentação extensa para site externo (ReadTheDocs), com alguns tópicos ainda marcados como “Coming soon”.

  ## Tema
  - Síntese de voz (Text-to-Speech) neural de última geração
  - Clonagem de voz e *voice design* criativo
  - Geração multilíngue contínua de fala

  ## Razão do Hype
  - **Arquitetura tokenizer-free**: evita tokenização discreta, gerando representações contínuas e prosódia mais natural.
  - **Multilíngue robusto**: suporta 30 idiomas sem tag de idioma explícita.
  - **Voice Design**: permite criar vozes novas apenas por descrição textual (gênero, idade, tom, emoção), sem áudio de referência.
  - **Clonagem realista + controle**: combina clonagem “ultimate” (com referência + transcripto) com controle de estilo preservando timbre.
  - **Alta qualidade de áudio**: saída nativa em 48 kHz com *super-resolution* embutida.
  - **Open-source e comercial**: Apache-2.0, pesos liberados; já foi #1 trending no GitHub e HuggingFace.
  - **Velocidade de inferência**: RTF ~0.3 em RTX 4090 e ~0.13 via Nano-vLLM/vLLM-Omni, habilitando uso produtivo com *streaming*.

  ## Tecnologias
  - **Python 3.10+**, **PyTorch 2.5+**, **CUDA 12+**
  - **MiniCPM-4** como backbone LM
  - **AudioVAE V2** para codificação/decodificação assimétrica de áudio
  - **Diffusion Autoregressivo + CFM / Local DiT** para geração contínua
  - Stack de áudio: **librosa**, **soundfile**, **torchaudio**, **torchcodec**
  - **Transformers** (Hugging Face) e **ModelScope** para distribuição de pesos
  - **Gradio** para demonstração web e **vLLM-Omni / Nano-vLLM** para servindo em produção
  - **LoRA** para fine-tuning eficiente

  Nenhum arquivo foi criado ou modificado durante a análise.  


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c0:
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c0:
  {"output": "total 229\ndrwxrwxr-x 18 ice ice    34 May 30 23:32 .\ndrwx------ 54 ice ice    60 May 30 23:32 ..\ndrwxrwxr-x  3 ice ice     3 May 30 23:32 .claude\ndrwxrwxr-x  8 ice ice    14 May 30 23:32 .git\ndrwxrwxr-x  4 ice ice     5 May 30 23:32 .github\n-rw-rw-r--  1 ice ice  1267 May 30 23:32 .gitignore\n-rw-rw-r--  1 ice ice   401 May 30 23:32 .gitmodules\n-rw-rw-r--  1 ice ice   968 May 30 23:32 .metadata\ndrwxrwxr-x  2 ice ice     3 May 30 23:32 .run\n-rw-rw-r--  1 ice ice 10636 May 30 23:32 AGENTS.md\n-rw-rw-r--  1 ice ice 12853 May 30 23:32 CHANGELOG.md\n-rw-rw-r--  1 ice ice   117 May 30 23:32 CLAUDE.md\n-rw-rw-r--  1 ice ice 35149 May 30 23:32 LICENSE\n-rw-rw-r--  1 ice ice  3358 May 30 23:32 README.md\n-rw-rw-r--  1 ice ice  3288 May 30 23:32 README_zh_CN.md\n-rw-rw-r--  1 ice ice  1079 May 30 23:32 analysis_options.yaml\ndrwxrwxr-x  7 ice ice    11 May 30 23:32 android\ndrwxrwxr-x  2 ice ice     6 May 30 23:32 arb\ndrwxrwxr-x  5 ice ice     5 May 30 23:32 assets\n-rw-rw-r--  1 ice ice   479 May 30 23:32 build.yaml\ndrwxrwxr-x  5 ice ice    20 May 30 23:32 core\n-rw-rw-r--  1 ice ice    36 May 30 23:32 distribute_options.yaml\ndrwxrwxr-x 15 ice ice    18 May 30 23:32 lib\ndrwxrwxr-x  5 ice ice     7 May 30 23:32 linux\ndrwxrwxr-x  8 ice ice    11 May 30 23:32 macos\ndrwxrwxr-x  9 ice ice     9 May 30 23:32 plugins\n-rwxrwxr-x  1 ice ice 54134 May 30 23:32 pubspec.lock\n-rwxrwxr-x  1 ice ice  2826 May 30 23:32 pubspec.yaml\n-rw-rw-r--  1 ice ice  1551 May 30 23:32 release_telegram.py\ndrwxrwxr-x  3 ice ice     3 May 30 23:32 services\n-rwxrwxr-x  1 ice ice 10542 May 30 23:32 setup.dart\ndrwxrwxr-x  2 ice ice     6 May 30 23:32 snapshots\ndrwxrwxr-x  8 ice ice     9 May 30 23:32 test\ndrwxrwxr-x  5 ice ice     8 May 30 23:32 windows", "exit_code": 0, "error": null}

  [Subdirectory context discovered: FlClash/AGENTS.md]
  # AGENTS.md

  This file provides guidance for AI coding agents working with code in this repository.

  ## Project Overview

  FlClash is a multi-platform proxy client based on ClashMeta (mihomo), built with Flutter. Supports Android, Windows,
  macOS, and Linux. Material You design with Surfboard-like UI.

  ## Common Development Commands

  ### Building

  ```bash
  # Update submodules first (ClashMeta Go core lives in core/Clash.Meta/)
  git submodule update --init --recursive

  # Full package build (Go core + Flutter + packaging) via setup.dart
  dart setup.dart macos
  dart setup.dart linux
  dart setup.dart windows
  dart setup.dart android

  # Build only the Go core (skip Flutter packaging)
  bash plugins/setup/buildkit/run_build_tool.sh macos
  bash plugins/setup/buildkit/run_build_tool.sh linux
  bash plugins/setup/buildkit/run_build_tool.sh windows
  bash plugins/setup/buildkit/run_build_tool.sh android
  ```

  ### Flutter Development

  ```bash
  # Project is pinned with FVM (.fvmrc currently uses Flutter 3.35.7)
  fvm flutter pub get
  fvm flutter run
  fvm flutter test

  # Plain Flutter also works when your global SDK matches the project constraints
  flutter pub get
  flutter run        # Run on connected device/desktop
  flutter test        # Run all tests (use flutter test, not dart test — models pull in Flutter types)
  ```

  ### Code Generation

  Required after modifying models, providers, or database schema:

  ```bash
  dart run build_runner build --delete-conflicting-outputs
  dart run build_runner watch  # Continuous regeneration
  ```

  Code generation covers: Riverpod providers (`riverpod_generator`), models (`freezed`, `json_serializable`), and database
  tables (`drift_dev`).

  ### Testing

  Tests use `package:test/test.dart` for pure Dart logic (common utils, models) and `flutter_test` for provider/widget tests.
  `mocktail` is the mocking framework.

  ```bash
  flutter test test/models/      # Model serialization & extension round-trip tests
  flutter test test/core/        # CoreController tests (mocked CoreHandlerInterface)
  flutter test test/providers/   # Riverpod provider tests (config & app state notifiers)
  flutter test test/common/      # Utility function tests (utils, string, iterable, fixed, etc.)
  flutter test test/database/    # Database type converter tests
  flutter test test/widgets/     # Widget-level rendering/interaction tests
  flutter test test/setup_test.dart
  flutter test plugins/proxy/test/proxy_test.dart  # Dart tests for bundled plugin packages
  ```

  Root `flutter test` only discovers the root package's `test/` directory by default. Include bundled plugin Dart tests by
  passing their paths explicitly, or run `flutter test` from that plugin package directory. Native plugin tests under
  platform folders (for example Windows C++ tests) are not run by `flutter test`.

  **Mocking `CoreHandlerInterface`:** Use `CoreController.test(mock)` to inject a mock interface. Call
  `CoreController.resetInstance()` in `tearDown` to clean up the singleton between tests. Remember to
  `registerFallbackValue()` for freezed params used with `any()` matchers.

  **Provider tests:** Use `ProviderContainer` directly (no widget tree needed for simple notifiers). The Riverpod
  generated `update()` method takes a callback: `notifier.update((state) => newValue)`.

  **Model round-trip tests:** Always go through `jsonEncode`/`jsonDecode` when testing freezed models with
  nested objects — `toJson()` stores child objects directly (not as maps), so direct `fromJson(toJson())`
  fails for nested freezed types.

  ### Build Dependencies

  **Linux:** `sudo apt-get install libayatana-appindicator3-dev libkeybinder-3.0-dev`

  **Windows:** GCC and Inno Setup. `ANDROID_NDK` env var for Android builds.

  **macOS:** `npm install -g appdmg` for DMG creation.

  ## Architecture

  ### Core Integration (Go ClashMeta <-> Flutter)

  This is the most important architectural concept. The Go proxy core (`core/`) operates in two modes:

  - **Android (lib mode):** Go core compiled as C shared library (`libclash.so`) via `go build -buildmode=c-shared` with
    CGO. Flutter calls it via FFI through the `service` plugin. Dart-side: `lib/core/lib.dart` (`CoreLib` class).

  - **Desktop (core mode):** Go core runs as a separate process with `CGO_ENABLED=0`. Flutter communicates via
    JSON-over-socket (Unix socket on macOS/Linux, TCP on Windows). Dart-side: `lib/core/service.dart` (`CoreService`
    class).

  `lib/core/controller.dart` (`CoreController`) selects the implementation based on platform. `lib/core/interface.dart`
  defines the shared `CoreHandlerInterface`.

  Go core key files: `core/hub.go` (handler functions), `core/action.go` (dispatch), `core/lib.go` (CGO exports),
  `core/server.go` (socket server).

  ### State Management (Riverpod)

  Provider files in `lib/providers/`:

  - `app.dart` - Runtime/UI state (logs, traffic, delays, loading, navigation)
  - `config.dart` - Persistent config providers (app settings, theme, VPN, proxy style)
  - `state.dart` - Derived/computed providers (navigation, proxy, tray, color scheme)
  - `action.dart` - Business logic notifiers (setup, backup, core lifecycle, proxy selection)
  - `database.dart` - Drift database provider wrappers

  `globalState` (`lib/state.dart`) is a singleton holding app lifecycle, timers, theme, and the start/stop state.
  Providers are generated into `lib/providers/generated/`.

  ### Database (Drift/SQLite)

  Type-safe SQLite via Drift in `lib/database/`. Current schema version is 2. Tables are `Profiles`, `Scripts`, `Rules`,
  `ProfileRuleLinks` (`profile_rule_mapping`), `ProxyGroups`, and `IconRecords` (`icon_records`). Rule scenes distinguish
  global added rules, profile added rules, profile custom rules, and disabled links. Uses fractional indexing for rule and
  proxy-group ordering.

  Generated Drift output lives in `lib/database/generated/database.g.dart`. After schema changes, run code generation and
  add/update focused database tests under `test/database/` when converter or migration behavior changes.

  ### Manager Stack (Widget Tree)

  Managers are nested InheritedWidgets/StatefulWidgets in `lib/application.dart`:

  ```
  AppEnvManager > StatusManager > ThemeManager
    > [Desktop: WindowManager > TrayManager > HotKeyManager > ProxyManager]
    > ConnectivityManager > CoreManager > AppStateManager
    > [Mobile: AndroidManager > VpnManager | Desktop: WindowHeaderContainer]
  ```

  Each manager in `lib/manager/` handles a specific platform concern. Desktop-only managers are conditionally inserted.

  ### Core Controller + Actions

  `lib/core/controller.dart` (`CoreController`) is a singleton facade over `CoreHandlerInterface`. All 25+ public methods
  delegate to the platform-specific interface (Android FFI or desktop socket). Has `@visibleForTesting` constructor and
  `resetInstance()` for test injection.

  Business logic lives in Riverpod notifier classes in `lib/providers/action.dart` (~960 lines, should be split):

  - `CommonAction` — update check, common UI operations
  - `SetupAction` — config setup, TUN management
  - `BackupAction` — backup/restore with WebDAV sync
  - `CoreAction` — core lifecycle (init, connect, restart, shutdown)
  - `SystemAction` — system integration (tray, exit, brightness)
  - `StoreAction` — profile storage operations
  - `ThemeAction` — theme state updates
  - `ProxiesAction` — group management, proxy selection
  - `ProfilesAction` — profile CRUD, auto-update, import

  ### Platform Managers (`lib/manager/`)

  Desktop: `WindowManager`, `TrayManager`, `HotKeyManager`, `ProxyManager`
  Mobile: `AndroidManager`, `TileManager`, `VpnManager`
  Shared: `ConnectivityManager`, `CoreManager`, `AppStateManager`, `StatusManager`, `ThemeManager`

  ### Build System

  `setup.dart` (project root) is the release build orchestrator:

  1. On Windows, pre-builds Go core via `dart run build_tool windows` and reads `core_sha256.json`
  2. Writes `env.json` (APP_ENV)
  3. Passes SHA256 as `--dart-define=CORE_SHA256=$val` (compile-time embedded, secure; Windows only)
  4. Activates `flutter_distributor` for packaging

  Go core building is handled by `build_tool

  [...truncated AGENTS.md: 10,595 chars total] terminal 


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c0:
  {"content": "1|<div>\n2|\n3|[**简体中文**](README_zh_CN.md)\n4|\n5|</div>\n6|\n7|## FlClash\n8|\n9|[![Downloads](https://img.shields.io/github/downloads/chen08209/FlClash/total?style=flat-square&logo=github)](https://github.com/chen08209/FlClash/releases/)[![Last Version](https://img.shields.io/github/release/chen08209/FlClash/all.svg?style=flat-square)](https://github.com/chen08209/FlClash/releases/)[![License](https://img.shields.io/github/license/chen08209/FlClash?style=flat-square)](LICENSE)\n10|\n11|[![Channel](https://img.shields.io/badge/Telegram-Channel-blue?style=flat-square&logo=telegram)](https://t.me/FlClash)\n12|\n13|A multi-platform proxy client based on ClashMeta, simple and easy to use, open-source and ad-free.\n14|\n15|on Desktop:\n16|<p style=\"text-align: center;\">\n17|    <img alt=\"desktop\" src=\"snapshots/desktop.gif\">\n18|</p>\n19|\n20|on Mobile:\n21|<p style=\"text-align: center;\">\n22|    <img alt=\"mobile\" src=\"snapshots/mobile.gif\">\n23|</p>\n24|\n25|## Features\n26|\n27|✈️ Multi-platform: Android, Windows, macOS and Linux\n28|\n29|💻 Adaptive multiple screen sizes, Multiple color themes available\n30|\n31|💡 Based on Material You Design, [Surfboard](https://github.com/getsurfboard/surfboard)-like UI\n32|\n33|☁️ Supports data sync via WebDAV\n34|\n35|✨ Support subscription link, Dark mode\n36|\n37|## Use\n38|\n39|### Linux\n40|\n41|⚠️ Make sure to install the following dependencies before using them\n42|\n43|   ```bash\n44|    sudo apt-get install libayatana-appindicator3-dev\n45|    sudo apt-get install libkeybinder-3.0-dev\n46|   ```\n47|\n48|### Android\n49|\n50|Support the following actions\n51|\n52|   ```bash\n53|    com.follow.clash.action.START\n54|    \n55|    com.follow.clash.action.STOP\n56|    \n57|    com.follow.clash.action.TOGGLE\n58|   ```\n59|\n60|## Download\n61|\n62|<a href=\"https://chen08209.github.io/FlClash-fdroid-repo/repo?fingerprint=789D6D32668712EF7672F9E58DEEB15FBD6DCEEC5AE7A4371EA72F2AAE8A12FD\"><img alt=\"Get it on F-Droid\" src=\"snapshots/get-it-on-fdroid.svg\" width=\"200px\"/></a> <a href=\"https://github.com/chen08209/FlClash/releases\"><img alt=\"Get it on GitHub\" src=\"snapshots/get-it-on-github.svg\" width=\"200px\"/></a>\n63|\n64|## Build\n65|\n66|1. Update submodules\n67|   ```bash\n68|   git submodule update --init --recursive\n69|   ```\n70|\n71|2. Install `Flutter` and `Golang` environment\n72|\n73|3. Build Application\n74|\n75|    - android\n76|\n77|        1. Install `Android SDK`, `Android NDK`\n78|\n79|        2. Set `ANDROID_NDK` environment variable\n80|\n81|        3. Run build script\n82|\n83|           ```bash\n84|           dart setup.dart android\n85|           ```\n86|\n87|    - windows\n88|\n89|        1. Requires a Windows client\n90|\n91|        2. Install `GCC`, `Inno Setup`\n92|\n93|        3. Run build script\n94|\n95|           ```bash\n96|           dart setup.dart windows\n97|           ```\n98|\n99|    - linux\n100|\n101|", "total_lines": 131, "file_size": 3358, "truncated": true, "hint": "Use offset=101 to continue reading (showing 1-100 of 131 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|name: fl_clash\n2|description: A multi-platform proxy client based on ClashMeta, simple and easy to use, open-source and ad-free.\n3|publish_to: 'none'\n4|version: 0.8.93+202****2901\n5|environment:\n6|  sdk: '>=3.8.0 <4.0.0'\n7|\n8|dependencies:\n9|  flutter:\n10|    sdk: flutter\n11|  flutter_localizations:\n12|    sdk: flutter\n13|  intl: any\n14|  path_provider: ^2.1.5\n15|  path: ^1.9.0\n16|  shared_preferences: ^2.5.3\n17|  window_manager:\n18|    git:\n19|      url: https://github.com/chen08209/window_manager\n20|      ref: main\n21|      path: packages/window_manager\n22|  dynamic_color: ^1.8.1\n23|  proxy:\n24|    path: plugins/proxy\n25|  window_ext:\n26|    path: plugins/window_ext\n27|  launch_at_startup: ^0.5.1\n28|  json_annotation: ^4.11.0\n29|  file_picker: ^11.0.2\n30|  mobile_scanner: ^7.0.1\n31|  app_links: ^7.0.0\n32|  win32_registry: ^2.0.0\n33|  tray_manager:\n34|    path: plugins/tray_manager/packages/tray_manager\n35|  wifi_ssid:\n36|    path: plugins/wifi_ssid\n37|  collection: ^1.19.1\n38|  animations: ^2.1.1\n39|  package_info_plus: ^9.0.0\n40|  url_launcher: ^6.3.2\n41|  freezed_annotation: ^3.1.0\n42|  image_picker: ^1.2.0\n43|  ffi: ^2.1.4\n44|\n45|  webdav_client: ^1.2.2\n46|  dio: ^5.8.0+1\n47|  win32: ^5.5.1\n48|  re_editor:\n49|    git:\n50|      url: https://github.com/chen08209/re-editor\n51|      ref: main\n52|  re_highlight: ^0.0.3\n53|  archive: ^4.0.7\n54|  emoji_regex: ^0.0.5\n55|  hotkey_manager: ^0.2.3\n56|  uni_platform: ^0.1.3\n57|  device_info_plus: ^12.2.0\n58|  connectivity_plus: ^7.0.0\n59|  screen_retriever: ^0.2.0\n60|  defer_pointer: ^0.0.2\n61|  flutter_riverpod: ^3.3.1\n62|  riverpod_annotation: ^4.0.2\n63|  riverpod: ^3.2.1\n64|  material_color_utilities: ^0.13.0\n65|  flutter_js:\n66|    git:\n67|      url: https://github.com/chen08209/flutter_js\n68|      ref: master\n69|  flutter_svg: ^2.1.0\n70|  flutter_cache_manager: ^3.4.1\n71|  crypto: ^3.0.3\n72|  yaml_writer:\n73|    git:\n74|      url: https://github.com/chen08209/yaml_writer\n75|      ref: master\n76|  super_sliver_list: ^0.4.1\n77|  drift: ^2.31.0\n78|  drift_flutter: ^0.2.8\n79|  fractional_indexing: ^1.0.0\n80|  reorderable_grid: ^1.0.13\n81|  smooth_sheets: ^1.0.2\n82|  rust_api:\n83|    path: plugins/rust_api\n84|  setup:\n85|    path: plugins/setup\n86|dev_dependencies:\n87|  flutter_test:\n88|    sdk: flutter\n89|  flutter_lints: ^6.0.0\n90|  json_serializable: ^6.8.0\n91|  build_runner: ^2.14.1\n92|  args: ^2.4.2\n93|  freezed: ^3.2.5\n94|  riverpod_generator: ^4.0.3\n95|  riverpod_lint: ^3.1.3\n96|\n97|  drift_dev: ^2.31.0\n98|  intl_utils: ^2.8.14\n99|  test: ^1.30.0\n100|  mocktail: ^1.0.4\n101|\n102|flutter:\n103|  uses-material-design: true\n104|  assets:\n105|    - assets/data/\n106|    - assets/fonts/\n107|    - assets/images/\n108|    - assets/images/avatar/\n109|    - assets/images/empty/\n110|    - assets/images/icon/\n111|  fonts:\n112|    - family: JetBrainsMono\n113|      fonts:\n114|        - asset: assets/fonts/JetBrainsMono-Regular.ttf\n115|    - family: Twemoji\n116|      fonts:\n117|        - asset: assets/fonts/Twemoji.Mozilla.ttf\n118|    - family: Icons\n119|      fonts:\n120|        - asset: assets/fonts/Icons.ttf\n121|flutter_intl:\n122|  enabled: true\n123|  class_name: AppLocalizations\n124|  arb_dir: arb\n125|  output_dir: lib/l10n", "total_lines": 124, "file_size": 2826, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# AGENTS.md\n2|\n3|This file provides guidance for AI coding agents working with code in this repository.\n4|\n5|## Project Overview\n6|\n7|FlClash is a multi-platform proxy client based on ClashMeta (mihomo), built with Flutter. Supports Android, Windows,\n8|macOS, and Linux. Material You design with Surfboard-like UI.\n9|\n10|## Common Development Commands\n11|\n12|### Building\n13|\n14|```bash\n15|# Update submodules first (ClashMeta Go core lives in core/Clash.Meta/)\n16|git submodule update --init --recursive\n17|\n18|# Full package build (Go core + Flutter + packaging) via setup.dart\n19|dart setup.dart macos\n20|dart setup.dart linux\n21|dart setup.dart windows\n22|dart setup.dart android\n23|\n24|# Build only the Go core (skip Flutter packaging)\n25|bash plugins/setup/buildkit/run_build_tool.sh macos\n26|bash plugins/setup/buildkit/run_build_tool.sh linux\n27|bash plugins/setup/buildkit/run_build_tool.sh windows\n28|bash plugins/setup/buildkit/run_build_tool.sh android\n29|```\n30|\n31|### Flutter Development\n32|\n33|```bash\n34|# Project is pinned with FVM (.fvmrc currently uses Flutter 3.35.7)\n35|fvm flutter pub get\n36|fvm flutter run\n37|fvm flutter test\n38|\n39|# Plain Flutter also works when your global SDK matches the project constraints\n40|flutter pub get\n41|flutter run        # Run on connected device/desktop\n42|flutter test        # Run all tests (use flutter test, not dart test — models pull in Flutter types)\n43|```\n44|\n45|### Code Generation\n46|\n47|Required after modifying models, providers, or database schema:\n48|\n49|```bash\n50|dart run build_runner build --delete-conflicting-outputs\n51|dart run build_runner watch  # Continuous regeneration\n52|```\n53|\n54|Code generation covers: Riverpod providers (`riverpod_generator`), models (`freezed`, `json_serializable`), and database\n55|tables (`drift_dev`).\n56|\n57|### Testing\n58|\n59|Tests use `package:test/test.dart` for pure Dart logic (common utils, models) and `flutter_test` for provider/widget tests.\n60|`mocktail` is the mocking framework.\n61|\n62|```bash\n63|flutter test test/models/      # Model serialization & extension round-trip tests\n64|flutter test test/core/        # CoreController tests (mocked CoreHandlerInterface)\n65|flutter test test/providers/   # Riverpod provider tests (config & app state notifiers)\n66|flutter test test/common/      # Utility function tests (utils, string, iterable, fixed, etc.)\n67|flutter test test/database/    # Database type converter tests\n68|flutter test test/widgets/     # Widget-level rendering/interaction tests\n69|flutter test test/setup_test.dart\n70|flutter test plugins/proxy/test/proxy_test.dart  # Dart tests for bundled plugin packages\n71|```\n72|\n73|Root `flutter test` only discovers the root package's `test/` directory by default. Include bundled plugin Dart tests by\n74|passing their paths explicitly, or run `flutter test` from that plugin package directory. Native plugin tests under\n75|platform folders (for example Windows C++ tests) are not run by `flutter test`.\n76|\n77|**Mocking `CoreHandlerInterface`:** Use `CoreController.test(mock)` to inject a mock interface. Call\n78|`CoreController.resetInstance()` in `tearDown` to clean up the singleton between tests. Remember to\n79|`registerFallbackValue()` for freezed params used with `any()` matchers.\n80|\n81|**Provider tests:** Use `ProviderContainer` directly (no widget tree needed for simple notifiers). The Riverpod\n82|generated `update()` method takes a callback: `notifier.update((state) => newValue)`.\n83|\n84|**Model round-trip tests:** Always go through `jsonEncode`/`jsonDecode` when testing freezed models with\n85|nested objects — `toJson()` stores child objects directly (not as maps), so direct `fromJson(toJson())`\n86|fails for nested freezed types.\n87|\n88|### Build Dependencies\n89|\n90|**Linux:** `sudo apt-get install libayatana-appindicator3-dev libkeybinder-3.0-dev`\n91|\n92|**Windows:** GCC and Inno Setup. `ANDROID_NDK` env var for Android builds.\n93|\n94|**macOS:** `npm install -g appdmg` for DMG creation.\n95|\n96|## Architecture\n97|\n98|### Core Integration (Go ClashMeta <-> Flutter)\n99|\n100|This is the most important architectural concept. The Go proxy core (`core/`) operates in two modes:\n101|\n102|- **Android (lib mode):** Go core compiled as C shared library (`libclash.so`) via `go build -buildmode=c-shared` with\n103|  CGO. Flutter calls it via FFI through the `service` plugin. Dart-side: `lib/core/lib.dart` (`CoreLib` class).\n104|\n105|- **Desktop (core mode):** Go core runs as a separate process with `CGO_ENABLED=0`. Flutter communicates via\n106|  JSON-over-socket (Unix socket on macOS/Linux, TCP on Windows). Dart-side: `lib/core/service.dart` (`CoreService`\n107|  class).\n108|\n109|`lib/core/controller.dart` (`CoreController`) selects the implementation based on platform. `lib/core/interface.dart`\n110|defines the shared `CoreHandlerInterface`.\n111|\n112|Go core key files: `core/hub.go` (handler functions), `core/action.go` (dispatch), `core/lib.go` (CGO exports),\n113|`core/server.go` (socket server).\n114|\n115|### State Management (Riverpod)\n116|\n117|Provider files in `lib/providers/`:\n118|\n119|- `app.dart` - Runtime/UI state (logs, traffic, delays, loading, navigation)\n120|- `config.dart` - Persistent config providers (app settings, theme, VPN, proxy style)\n121|- `state.dart` - Derived/computed providers (navigation, proxy, tray, color scheme)\n122|- `action.dart` - Business logic notifiers (setup, backup, core lifecycle, proxy selection)\n123|- `database.dart` - Drift database provider wrappers\n124|\n125|`globalState` (`lib/state.dart`) is a singleton holding app lifecycle, timers, theme, and the start/stop state.\n126|Providers are generated into `lib/providers/generated/`.\n127|\n128|### Database (Drift/SQLite)\n129|\n130|Type-safe SQLite via Drift in `lib/database/`. Current schema version is 2. Tables are `Profiles`, `Scripts`, `Rules`,\n131|`ProfileRuleLinks` (`profile_rule_mapping`), `ProxyGroups`, and `IconRecords` (`icon_records`). Rule scenes distinguish\n132|global added rules, profile added rules, profile custom rules, and disabled links. Uses fractional indexing for rule and\n133|proxy-group ordering.\n134|\n135|Generated Drift output lives in `lib/database/generated/database.g.dart`. After schema changes, run code generation and\n136|add/update focused database tests under `test/database/` when converter or migration behavior changes.\n137|\n138|### Manager Stack (Widget Tree)\n139|\n140|Managers are nested InheritedWidgets/StatefulWidgets in `lib/application.dart`:\n141|\n142|```\n143|AppEnvManager > StatusManager > ThemeManager\n144|  > [Desktop: WindowManager > TrayManager > HotKeyManager > ProxyManager]\n145|  > ConnectivityManager > CoreManager > AppStateManager\n146|  > [Mobile: AndroidManager > VpnManager | Desktop: WindowHeaderContainer]\n147|```\n148|\n149|Each manager in `lib/manager/` handles a specific platform concern. Desktop-only managers are conditionally inserted.\n150|\n151|### Core Controller + Actions\n152|\n153|`lib/core/controller.dart` (`CoreController`) is a singleton facade over `CoreHandlerInterface`. All 25+ public methods\n154|delegate to the platform-specific interface (Android FFI or desktop socket). Has `@visibleForTesting` constructor and\n155|`resetInstance()` for test injection.\n156|\n157|Business logic lives in Riverpod notifier classes in `lib/providers/action.dart` (~960 lines, should be split):\n158|\n159|- `CommonAction` — update check, common UI operations\n160|- `SetupAction` — config setup, TUN management\n161|- `BackupAction` — backup/restore with WebDAV sync\n162|- `CoreAction` — core lifecycle (init, connect, restart, shutdown)\n163|- `SystemAction` — system integration (tray, exit, brightness)\n164|- `StoreAction` — profile storage operations\n165|- `ThemeAction` — theme state updates\n166|- `ProxiesAction` — group management, proxy selection\n167|- `ProfilesAction` — profile CRUD, auto-update, import\n168|\n169|### Platform Managers (`lib/manager/`)\n170|\n171|Desktop: `WindowManager`, `TrayManager`, `HotKeyManager`, `ProxyManager`\n172|Mobile: `AndroidManager`, `TileManager`, `VpnManager`\n173|Shared: `ConnectivityManager`, `CoreManager`, `AppStateManager`, `StatusManager`, `ThemeManager`\n174|\n175|### Build System\n176|\n177|`setup.dart` (project root) is the release build orchestrator:\n178|\n179|1. On Windows, pre-builds Go core via `dart run build_tool windows` and reads `core_sha256.json`\n180|2. Writes `env.json` (APP_ENV)\n181|3. Passes SHA256 as `--dart-define=CORE_SHA256=$val` (compile-time embedded, secure; Windows only)\n182|4. Activates `flutter_distributor` for packaging\n183|\n184|Go core building is handled by `build_tool`, a standalone Dart CLI in `plugins/setup/buildkit/build_tool/`.\n185|Platform build hooks inside `flutter build` trigger `build_tool` automatically:\n186|\n187|- **macOS:** podspec script phase → `build_pod.sh` → `build_tool macos`\n188|- **Linux:** CMake include → `buildkit/cmake/buildkit.cmake` → `build_tool linux`\n189|- **Windows:** CMake include → `buildkit/cmake/buildkit.cmake` → `build_tool windows` (debug: `--dev` via `CMAKE_BUILD_TYPE`)\n190|- **Android:** Gradle include → `buildkit/gradle/plugin.gradle` → `build_tool android`\n191|\n192|**Windows helper auth (release):** Core SHA256 is embedded in both the Flutter app (`--dart-define`) and the Rust\n193|helper (`TOKEN` env var during cargo build). The Dart app pings the helper and verifies the token matches.\n194|\n195|**Windows helper auth (debug):** The Rust helper skips token verification when built in debug mode\n196|(`cfg!(debug_assertions)`), so `flutter run` works without any SHA256 dance.\n197|\n198|`plugins/setup/` is an FFI plugin that exists solely as a build harness — it carries no Dart API, only platform build hooks\n199|(podspec, CMake, Gradle) that trigger Go compilation. Windows builds also compile a Rust helper (`services/helper/`) via\n200|`RustBuilder`.\n201|\n202|Build configuration defaults live in `build_tool/lib/src/options.dart` and can be overridden via `build_config.yaml`\n203|in the project root.\n204|\n205|Architecture detection is automatic (host arch via `uname -m` on Unix, `PROCESSOR_ARCHITECTURE` on Windows). The\n206|`--description` flag passed to flutter_distributor adds arch suffix to artifact names (e.g.,\n207|`FlClash-0.8.93-macos-arm64.dmg`).\n208|\n209|### Local Plugins (`plugins/`)\n210|\n211|- `setup` - Build harness FFI plugin (triggers Go/Rust compilation per platform)\n212|- `proxy` - System proxy configuration\n213|- `rust_api` - Flutter Rust Bridge FFI plugin (named pipe / local socket communication)\n214|- `tray_manager` - System tray (forked/custom)\n215|- `wifi_ssid` - Wi-Fi SSID detection\n216|- `window_ext` - Window extensions\n217|- `flutter_distributor` - App packaging/distribution\n218|\n219|### Rust Helper Service (`services/helper/`)\n220|\n221|Windows-only privileged helper for starting the core as admin and managing TUN. Built with\n222|`cargo build --release --features windows-service`. Token-based auth with Flutter app.\n223|\n224|### Localization\n225|\n226|ARB files in `arb/`. Generated via `flutter_intl` into `lib/l10n/`. Use `AppLocalizations.of(context)!` for strings.\n227|\n228|**Supported locales:** `en`, `zh_CN`, `ja`, `ru`\n229|\n230|**Access patterns:**\n231|\n232|- In widgets with BuildContext: `context.appLocalizations.key` (import `common.dart`)\n233|- In controllers/providers/non-widget code: `currentAppLocalizations.key` (import `app_localizations.dart`)\n234|", "total_lines": 233, "file_size": 10636, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  **Problema**
  Usuários precisam de um gerenciamento de proxy/VPN unificado, multiplataforma e fácil de usar, sem anúncios e sem fragmentação entre sistemas. Hoje o ecossistema mantém várias apps separadas por plataforma, com configurações inconsistentes e UX pouco ou muito poluída.

  **Tema**
  Cliente proxy open-source multiplataforma (Android, Windows, macOS, Linux) baseado no ClashMeta, com foco em simplicidade e usabilidade. A seção de usuário é interface Flutter com Material You.

  **Razão do hype**
  - Estrelas altas para um projeto de cliente proxy (~40k)
  - Combina robustez do ClashMeta + UI acessível estilo Surfboard
  - Multiplataforma real com uma base de código única
  - Ad-free e open source, com suporte a WebDAV para sincronia de perfis
  - Empacotamento automatizado por plataforma e setup bem documentado

  **Tecnologias**
  - Flutter + Dart (aplicação principal)
  - Go (ClashMeta / mihomo) como core nativo
  - Riverpod + Freezed + Drift/Type-safe SQLite
  - FFI/CGO no Android e JSON-over-socket no desktop
  - Rust (helper de privilégios no Windows)
  - Build system próprio: `setup.dart`, `build_tool`, plugins locais: `proxy`, `rust_api`, `tray_manager`, `window_manager`  


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c0:
  {"content": "1|# π RuView\n2|\n3|<p align=\"center\">\n4|  <a href=\"https://cognitum.one/seed\">\n5|    <img src=\"assets/ruview-seed.png\" alt=\"RuView - WiFi DensePose\" width=\"100%\">\n6|  </a>\n7|</p>\n8|<p align=\"center\">\n9|  <a href=\"https://cognitum.one/seed\">\n10|    <img src=\"assets/seed.png\" alt=\"Cognitum Seed\" width=\"100%\">\n11|  </a>\n12|</p>\n13|\n14|## **See through walls with WiFi** ##\n15|\n16|**Turn ordinary WiFi into a spatial intelligence / sensing system.** Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.\n17|\n18|Works natively with the four major smart-home ecosystems: **[Home Assistant](docs/integrations/home-assistant.md)** via the HA-DISCO MQTT publisher, **[Apple Home & HomePod](docs/user-guide-apple-homepod.md)** as a discoverable HAP-1.1 bridge, **[Google Home](docs/integrations/home-assistant.md)** + **[Amazon Alexa](docs/integrations/home-assistant.md)** via the same HA bridge or a [Matter](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.\n19|\n20|[![Works with Home Assistant](https://img.shields.io/badge/Works%20with-Home%20Assistant-blue?logo=home-assistant&logoColor=white&labelColor=41BDF5)](docs/integrations/home-assistant.md) [![Works with Matter](https://img.shields.io/badge/Works%20with-Matter-blue?labelColor=4285F4)](docs/adr/ADR-122-bfld-ruview-ha-matter-exposure.md) [![Works with Apple Home](https://img.shields.io/badge/Works%20with-Apple%20Home-black?logo=apple)](docs/user-guide-apple-homepod.md) [![Works with Google Home](https://img.shields.io/badge/Works%20with-Google%20Home-blue?logo=googlehome)](docs/integrations/home-assistant.md) [![Works with Alexa](https://img.shields.io/badge/Works%20with-Alexa-blue?logo=amazon&logoColor=white&labelColor=00CAFF)](docs/integrations/home-assistant.md)\n21|\n22|> Drop into any **Home Assistant** install with one `--mqtt` flag. Or pair into **Apple Home / Google Home / Alexa / SmartThings** as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See [`docs/integrations/home-assistant.md`](docs/integrations/home-assistant.md) · [ADR-115](docs/adr/ADR-115-home-assistant-integration.md).\n23|\n24|### π RuView is a WiFi sensing platform that turns radio signals into spatial intelligence.\n25|\n26|Every WiFi router already fills your space with radio waves. When people move, breathe, or even sit still, they disturb those waves in measurable ways. RuView captures these disturbances using Channel State Information (CSI) from low-cost ESP32 sensors and turns them into actionable data: who's there, what they're doing, and whether they're okay.\n27|\n28|**What it senses:**\n29|- **Presence and occupancy** — detect people through walls, count them, track entries and exits\n30|- **Vital signs** — breathing rate and heart rate, contactless, while sleeping or sitting\n31|- **Activity recognition** — walking, sitting, gestures, falls — from temporal CSI patterns\n32|- **Environment mapping** — RF fingerprinting identifies rooms, detects moved furniture, spots new objects\n33|- **Sleep quality** — overnight monitoring with sleep stage classification and apnea screening\n34|\n35|Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](https://cognitum.one), RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.\n36|\n37|The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.\n38|\n39|RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized), runs in microseconds on a Raspberry Pi, and reports 100% presence accuracy on the validation set. No cameras, no wearables, no app on the user's phone.\n40|\n41|### Built for low-power edge applications\n42|\n43|[Edge modules](#edge-intelligence-adr-041) are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.\n44|\n45|[![Rust 1.85+](https://img.shields.io/badge/rust-1.85+-orange.svg)](https://www.rust-lang.org/)\n46|[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n47|[![Tests: 1463](https://img.shields.io/badge/tests-1463%20passed-brightgreen.svg)](https://github.com/ruvnet/RuView)\n48|[![Docker: multi-arch](https://img.shields.io/badge/docker-amd64%20%2B%20arm64-blue.svg)](https://hub.docker.com/r/ruvnet/wifi-densepose)\n49|[![Vital Signs](https://img.shields.io/badge/vital%20signs-breathing%20%2B%20heartbeat-red.svg)](#vital-sign-detection)\n50|[![ESP32 Ready](https://img.shields.io/badge/ESP32--S3-CSI%20streaming-purple.svg)](#esp32-s3-hardware-pipeline)\n51|[![crates.io](https://img.shields.io/crates/v/wifi-densepose-ruvector.svg)](https://crates.io/crates/wifi-densepose-ruvector)\n52|[![Downloads](https://img.shields.io/badge/downloads-10M%2B-brightgreen.svg)](#-edge-module-catalog)\n53|\n54| \n55|> | What | How | Speed / scale |\n56|> |------|-----|---------------|\n57|> | 🫁 **Breathing rate** | Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6–30 BPM, real-time |\n58|> | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |\n59|> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained), 100% validation accuracy) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |\n60|> | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |\n61|> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)) | 8.4 ms cold-start on a Pi 5 |\n62|> | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |\n63|> | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |\n64|> | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |\n65|> | 🌍 **World model prediction** | OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with `occworld_retrain.py` ([ADR-147](docs/adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)) | 15 frames × 200×200×16 vox |\n66|> | 🧱 **Through-wall sensing** | Fresnel-zone geometry + multipath modeling | Up to ~5 m, signal-dependent |\n67|> | 🧠 **Edge intelligence** | **105-cog catalog** ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) live from `app-registry.json` — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain | $140 total BOM |\n68|> | 🎯 **Camera-free pre-training** | Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face | 84 s/epoch retrain on M4 Pro |\n69|> | 📷 **Camera-supervised fine-tune** | MediaPipe + ESP32 CSI paired training, end-to-end Candle pipeline on RTX 5080 ([ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md)) | 2.1 s for 400 epochs (~5 ms/epoch) |\n70|> | 📡 **Multi-frequency mesh** | Channel hopping across 6 bands, TDM slot scheduling ([ADR-029](docs/adr/ADR-029-multifrequency-mesh.md)) | 3× sensing bandwidth |\n71|> | 🌐 **3D point cloud fusion** | Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model | 22 ms pipeline · 19K+ points/frame |\n72|>\n73|> Browse the full 105-module catalog (with practical descriptions, sizes, and difficulty) below in [🧩 Edge Module Catalog](#-edge-module-catalog), or visit [seed.cognitum.one/store](https://seed.cognitum.one/store).\n74|>\n75|> 🤗 **Pretrained weights**: download from [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — see [Loading the pretrained model](#loading-the-pretrained-model) below for one-command setup.\n76|\n77|```bash\n78|# Option 1: Docker (simulated data, no hardware needed)\n79|docker pull ruvnet/wifi-densepose:latest\n80|docker run -p 3000:3000 ruvnet/wifi-densepose:latest\n81|# Open http://localhost:3000\n82|\n83|# Option 2a: Live sensing with ESP32-S3 hardware ($9)\n84|# Flash firmware, provision WiFi, and start sensing:\n85|python -m esptool --chip esp32s3 --port COM9 --baud 460800 \\\n86|  write_flash 0x0 bootloader.bin 0x8000 partition-table.bin \\\n87|  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin\n88|python firmware/esp32-csi-node/provision.py --port COM9 \\\n89|  --ssid \"YourWiFi\" --password \"secret\" --target-ip 192.168.1.20\n90|\n91|# Option 2b: WiFi 6 + 802.15.4 research sensing with ESP32-C6 ($6-10, ADR-110)\n92|# Same csi-node firmware compiled for the C6 target — picks up the C6\n93|# overlay (sdkconfig.defaults.esp32c6) automatically.\n94|cd firmware/esp32-csi-node\n95|idf.py set-target esp32c6 && idf.py build\n96|idf.py -p COM6 flash\n97|# C6 boot extras (vs S3): HE-LTF subcarrier tagging in ADR-018 bytes 18-19,\n98|#   802.15.4 mesh time-sync on channel 15, TWT setup when the AP supports it,\n99|#   opt-in LP-core wake-on-motion for ~5 µA battery seed nodes.\n100|# v0.6.7 adds: real LP-core RISC-V motion-gate program (debounce + motion\n101|#   counter) and a Wi-Fi 6 soft-AP with TWT Responder so two C6 boards can\n102|#   benchmark real iTWT without buying an 11ax router. Both default off,\n103|#   flip CONFIG_C6_{LP_CORE,SOFTAP_HE}_ENABLE to turn them on.\n104|\n105|# Option 3: Full system with Cognitum Seed ($140)\n106|# ESP32 streams CSI → bridge forwards to Seed for persistent storage + kNN + witness chain\n107|node scripts/rf-scan.js --port 5006           # Live RF room scan\n108|node scripts/snn-csi-processor.js --port 5006  # SNN real-time learning\n109|node scripts/mincut-person-counter.js --port 5006  # Correct person counting\n110|\n111|# Option 4: Python — live on PyPI (ADR-117)\n112|pip install ruview                        # or: pip install wifi-densepose\n113|# Both ship the same compiled PyO3 wheel (~250 KB, abi3-py310, Linux/macOS/Windows).\n114|# Add [client] for the asyncio WebSocket + paho-mqtt clients:\n115|pip install \"ruview[client]\"              # or: pip install \"wifi-densepose[client]\"\n116|\n117|# from ruview import BreathingExtractor, HeartRateExtractor   # equivalent to:\n118|# from wifi_densepose import BreathingExtractor, HeartRateExtractor\n119|# from ruview.client import SensingClient, RuViewMqttClient\n120|```\n121|\n122|[![PyPI ruview](https://img.shields.io/pypi/v/ruview?label=ruview)](https://pypi.org/project/ruview/) [![PyPI wifi-densepose](https://img.shields.io/pypi/v/wifi-densepose?label=wifi-densepose)](https://pypi.org/project/wifi-densepose/)\n123|\n124|> [!NOTE]\n125|> **CSI-capable hardware recommended.** Presence, vital signs, through-wall sensing, and all advanced capabilities require Channel State Information (CSI) from an ESP32-S3 ($9) or research NIC. The Docker image runs with simulated data for evaluation. Consumer WiFi laptops provide RSSI-only presence detection.\n126|\n127|> **Hardware options** for live CSI capture:\n128|>\n129|> | Option | Hardware | Cost | Full CSI | Capabilities |\n130|> |--------|----------|------|----------|-------------|\n131|> | **ESP32 + Cognitum Seed** (recommended) | ESP32-S3 + [Cognitum Seed](https://cognitum.one) | ~$140 | Yes | Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy |\n132|> | **ESP32 Mesh** | 3-6× ESP32-S3 + WiFi router | ~$54 | Yes | Same capabilities as above without the persistent-memory features |\n133|> | **ESP32-C6 research node** ([ADR-110](docs/adr/ADR-110-esp32-c6-firmware-extension.md), [witness](docs/WITNESS-LOG-110.md), [reviewer guide](docs/ADR-110-REVIEW-GUIDE.md), [firmware v0.7.0](https://github.com/ruvnet/RuView/releases/tag/v0.7.0-esp32)) | ESP32-C6-DevKit ($6–10) | ~$10 | Yes (Wi-Fi 6 capable) | Same CSI pipeline as S3 with the dual-target firmware. **Firmware-side ADR-110 substrate now closed** (v0.7.0): ESP-NOW cross-board mesh quantified at **99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression** over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). **Hardware-gated for measurement**: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split. |\n134|> | **Research NIC** | Intel 5300 / Atheros AR9580 | ~$50-100 | Yes | Full CSI with 3x3 MIMO |\n135|> | **Any WiFi** | Windows, macOS, or Linux laptop | $0 | No | RSSI-only: coarse presence and motion (see [tutorial #36](https://github.com/ruvnet/RuView/issues/36)) |\n136|>\n137|> No hardware? Verify the signal processing pipeline with the deterministic reference signal: `python archive/v1/data/proof/verify.py`\n138|>\n139|---\n140|\n141|\n142|  <a href=\"https://ruvnet.github.io/RuView/\">\n143|    <img src=\"assets/v2-screen.png\" alt=\"WiFi DensePose — Live pose detection with setup guide\" width=\"800\">\n144|  </a>\n145|  <br>\n146|  <em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>\n147|  <br><br>\n148|  <a href=\"https://ruvnet.github.io/RuView/\"><strong>▶ Live Observatory Demo</strong></a>\n149|  &nbsp;|&nbsp;\n150|  <a href=\"https://ruvnet.github.io/RuView/pose-fusion.html\"><strong>▶ Dual-Modal Pose Fusion Demo</strong></a>\n151|  &nbsp;|&nbsp;\n152|  <a href=\"https://ruvnet.github.io/RuView/pointcloud/\"><strong>▶ Live 3D Point Cloud</strong></a>\n153|  &nbsp;|&nbsp;\n154|  <a href=\"https://ruvnet.github.io/RuView/three.js/\"><strong>▶ three.js Demos (5)</strong></a>\n155|\n156|> The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.\n157|>\n158|> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).\n159|>\n160|> **three.js scene gallery** at [`/three.js/`](https://ruvnet.github.io/RuView/three.js/) — five progressively richer ADR-097 demos: helpers, cinematic, GLTF skinned, FBX skinned, and a live MediaPipe→Mixamo retargeting feed driven by ESP32 CSI. Demos 04 and 05 require a local Mixamo `X Bot.fbx` (license boundary — not redistributed).\n161|\n162|\n163|## 🤗 Pretrained model on Hugging Face\n164|\n165|Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **100% presence accuracy** on the validation set, 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.\n166|\n167|```bash\n168|# Download the model bundle\n169|pip install huggingface_hub\n170|huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wifi-densepose-pretrained\n171|```\n172|\n173|**What works today vs. what's pending wiring:**\n174|\n175|| Consumer | Format used | Status |\n176||----------|-------------|--------|\n177|| Python training / evaluation / embedding extraction | `model.safetensors` | ✅ Works — load with `safetensors.torch.load_file` |\n178|| Inspect / re-export the bundle | `model.rvf.jsonl` (line-by-line JSON) | ✅ Works — plain JSONL |\n179|| Sensing-server `--model <PATH>` flag | binary RVF (`RVFS` magic) | ⚠️ Loader does not yet accept the JSONL container |\n180|\n181|**Known gap:** the HF model ships in JSONL RVF format, but `v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs` only parses the binary RVF segment format. Pointing `--model` at `model.rvf.jsonl` currently errors with `invalid magic at offset 0: expected 0x52564653, got 0x7974227B` and the live pipeline degrades to null output rather than falling back to heuristic mode — so for the live sensing-server, run **without** `--model` until a JSONL adapter lands (or the model is re-published as binary RVF). Use the weights from Python / training in the meantime.\n182|\n183|**Quantization choices** (all in the HF repo): `model-q2.bin` (4 KB) · `model-q4.bin` ⭐ recommended (8 KB) · `model-q8.bin` (16 KB) · `model.safetensors` full (48 KB)\n184|\n185|The separate **17-keypoint pose-estimation model** is not in this release — pipeline is implemented but keypoint weights are still pending. Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9.\n186|\n187|\n188|## 🧩 Edge Module Catalog\n189|\n190|<details>\n191|<summary><b>🧩 105 edge modules ready to install on a Cognitum appliance</b> &mdash; live catalog from <code>app-registry.json</code> v2.1.0 (updated 2026-05-13). Browse + install at <a href=\"https://seed.cognitum.one/store\">seed.cognitum.one/store</a> or your local appliance <code>http://&lt;appliance&gt;:9000/cogs</code>.</summary>\n192|\n193|Each module is a small signed binary (~400 KB) that runs alongside the WiFi-DensePose sensing stack on a Cognitum-V0 appliance. The catalog updates over the air &mdash; your appliance fetches it via <code>GET /api/v1/edge/registry</code> ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) and verifies each binary against an Ed25519 signature ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)) before install.\n194|\n195|### 🫀 Health &mdash; <sub>14 modules</sub>\n196|\n197|| ID | What it does | Size | Difficulty |\n198||----|--------------|-----:|:----------:|\n199|| `air-quality-index` | Track indoor air quality with CO2 and particle sensors | 8 KB | Easy |\n200|| `baby-cry` | Sustained mid-band energy detector for nursery / infant monitoring. Audio-only, no camera. | 451 KB | Easy |\n201|| `breathing-sync` | Detects when two people breathe in sync | 10 KB | Hard |\n202|| `cardiac-arrhythmia` | Spots irregular heartbeats and abnormal heart rhythms | 8 KB | Hard |\n203|| `cough-detect` | Acoustic transient + spectral cough detector with 30s cluster aggregation. Early-warning signal for respiratory illness. | 451 KB | Easy |\n204|| `dream-stage` | Tracks your sleep stages — light, deep, and dreaming | 14 KB | Hard |\n205|| `fall-detect` | Two-stage impact + stillness fall detector over ambient feature stream (ESP32 motion / mic). Optional ruview-mode for CSI-based pose reinforcement. | 402 KB | Easy |\n206|| `gait-analysis` | Detects walking problems and scores fall risk | 12 KB | Hard |\n207|| `health-monitor` | Contactless heart rate, breathing, sleep, and fall alerts | 30 KB | Med |\n208|| `respiratory-distress` | Alerts when breathing becomes labored or dangerously fast | 10 KB | Hard |\n209|| `seizure-detect` | Recognizes seizures and sends immediate alerts | 10 KB | Hard |\n210|| `sleep-apnea` | Detects when someone stops breathing during sleep | 4 KB | Easy |\n211|| `snore-monitor` | Periodic low-band energy tracker for sleep-quality / apnea-risk trending. Companion to sleep-apnea cog. | 451 KB | Easy |\n212|| `vital-trend` | Tracks breathing and heart rate trends over weeks | 6 KB | Med |\n213|\n214|### 🔒 Security &mdash; <sub>14 modules</sub>\n215|\n216|| ID | What it does | Size | Difficulty |\n217||----|--------------|-----:|:----------:|\n218|| `audit-logger` | Record every action for compliance — tamper-proof log | 8 KB | Easy |\n219|| `behavioral-profiler` | Learns normal behavior and flags anything unusual | 12 KB | Hard |\n220|| `fleet-auth` | Manage device certificates and access across all seeds | 12 KB | Med |\n221|| `glass-break` | Two-phase bang + shatter acoustic detector. Distinguishes glass break from ordinary impulse noise. | 451 KB | Easy |\n222|| `gunshot-detect` | Saturating peak + exponential decay acoustic detector with optional ruview CSI motion-drop reinforcement. | 451 KB | Easy |\n223|| `intrusion` | Alerts when an unauthorized person enters a room | 6 KB | Med |\n224|| `intrusion-detect-ml` | Detect network attacks using machine learning | 14 KB | Hard |\n225|| `loitering` | Alerts when someone lingers too long in one spot | 3 KB | Easy |\n226|| `network-firewall` | Block unauthorized network access per cog | 6 KB | Easy |\n227|| `panic-motion` | Detects sudden panicked or erratic movement | 6 KB | Med |\n228|| `perimeter-breach` | Guards multiple zones and shows entry direction | 10 KB | Med |\n229|| `prompt-shield` | Blocks signal replay and injection attacks on the seed | 10 KB | Med |\n230|| `tailgating` | Catches when someone sneaks in behind a badge holder | 6 KB | Med |\n231|| `weapon-detect` | Detects concealed metal objects on a person | 8 KB | Hard |\n232|\n233|### 🏢 Building &mdash; <sub>11 modules</sub>\n234|\n235|| ID | What it does | Size | Difficulty |\n236||----|--------------|-----:|:----------:|\n237|| `beehive-monitor` | Acoustic hive state classifier. Detects healthy / chaotic / queenless / swarming / robbing via hum-band energy + chaos + piping autocorr. | 451 KB | Easy |\n238|| `elevator-count` | Counts how many people are in an elevator | 8 KB | Med |\n239|| `energy-audit` | Learns your schedule and cuts wasted energy | 6 KB | Med |\n240|| `frost-warning` | Predicts frost 6 hours ahead via temperature trend + dewpoint-depression gate. Field/orchard agriculture. | 451 KB | Easy |\n241|| `hvac-presence` | Turns heating and cooling on when you arrive | 3 KB | Easy |\n242|| `lighting-zones` | Turns lights on and off as people move between rooms | 4 KB | Easy |\n243|| `meeting-room` | Shows if a meeting room is free or occupied | 5 KB | Easy |\n244|| `occupancy-zones` | Counts people in each room through walls | 8 KB | Med |\n245|| `predictive-maintenance` | Vibration harmonic analyzer for rotating equipment. Tracks F1 / 2×F1 / high-order / sideband energy to score degradation severity. | 451 KB | Easy |\n246|| `smoke-fire` | Multi-signal smoke and fire detector. Fuses acoustic crackle, thermal drift proxy, and optional ruview CSI plume signature. Not a UL-listed replacement for code-required smoke alarms. | 451 KB | Easy |\n247|| `water-leak` | Persistent low-amplitude hiss + periodic drip acoustic detector with multi-minute persistence gate. Two-stage likely → confirmed. | 451 KB | Easy |\n248|\n249|### 🛍️ Retail &mdash; <sub>7 modules</sub>\n250|\n251|| ID | What it does | Size | Difficulty |\n252||----|--------------|-----:|:----------:|\n253|| `customer-flow` | Counts foot traffic in and out of each entrance | 8 KB | Med |\n254|| `dwell-heatmap` | Shows where customers spend the most time | 6 KB | Med |\n255|| `package-detect` | Sustained CSI-shift detector for porch / loading bay package arrivals and departures. Requires ESP32 CSI ruview input. | 451 KB | Easy |\n256|| `parking-occupancy` | Per-zone parking occupancy via ESP32 CSI subcarrier-amplitude shift. Tracks utilization and churn-per-hour. Requires ruview. | 451 KB | Easy |\n257|| `queue-length` | Estimates line length and wait time | 6 KB | Med |\n258|| `shelf-engagement` | Detects when customers interact with products | 6 KB | Med |\n259|| `table-turnover` | Tracks which restaurant tables are free or occupied | 4 KB | Easy |\n260|\n261|### 🏭 Industrial &mdash; <sub>7 modules</sub>\n262|\n263|| ID | What it does | Size | Difficulty |\n264||----|--------------|-----:|:----------:|\n265|| `clean-room` | Enforces max headcount in controlled environments | 4 KB | Easy |\n266|| `confined-space` | Monitors workers in tight spaces for safety | 5 KB | Med |\n267|| `forklift-proximity` | Warns if a forklift gets too close to workers | 10 KB | Hard |\n268|| `livestock-monitor` | Monitors animals for distress, escape, or illness | 6 KB | Med |\n269|| `ppe-compliance` | Cog-composition layer: alerts when ruview-densepose detects presence in a restricted zone without an accompanying PPE-camera-cog confirmation vector. | 387 KB | Easy |\n270|| `slip-fall-zone` | Pre-fall risk detector. Fires when motion-variance drop, splash audio, and optional cautious-gait CSI all signal elevated slip risk. | 451 KB | Easy |\n271|| `structural-vibration` | Detects dangerous vibrations in buildings or machines | 8 KB | Hard |\n272|\n273|### 🔬 Research &mdash; <sub>12 modules</sub>\n274|\n275|| ID | What it does | Size | Difficulty |\n276||----|--------------|-----:|:----------:|\n277|| `emotion-detect` | Reads stress and calm from body language and breathing | 10 KB | Hard |\n278|| `energy-harvester` | Optimize solar and battery for off-grid seed deployment | 6 KB | Med |\n279|| `gesture-language` | Recognizes sign language gestures in real time | 12 KB | Hard |\n280|| `ghost-hunter` | Finds unexplained environmental anomalies — for fun | 10 KB | Hard |\n281|| `happiness-score` | Estimates well-being from movement and mood signals | 8 KB | Med |\n282|| `hyperbolic-space` | Maps data into curved space for tree-like structures | 12 KB | Hard |\n283|| `music-conductor` | Reads a conductor's gestures for tempo and dynamics | 12 KB | Hard |\n284|| `plant-growth` | Tracks plant growth rate and day/night cycles | 8 KB | Med |\n285|| `rain-detect` | Detects when rain starts, stops, and how heavy it is | 6 KB | Med |\n286|| `ruview-densepose` | Full body pose tracking from WiFi — no cameras needed | 50 KB | Hard |\n287|| `sound-classifier` | Identify sounds like glass break, alarm, or baby cry | 16 KB | Hard |\n288|| `time-crystal` | Experiments with repeating time-pattern symmetry | 12 KB | Hard |\n289|\n290|### 🤖 Ai &mdash; <sub>15 modules</sub>\n291|\n292|| ID | What it does | Size | Difficulty |\n293||----|--------------|-----:|:----------:|\n294|| `anomaly-attractor` | Learns what's normal and catches anything weird | 10 KB | Hard |\n295|| `cognitive-pipeline` | FastGRNN anomaly gate + SmolLM2 sparse-LLM inference for on-device Pi Zero 2W cognitive events | 320 KB | Hard |\n296|| `dtw-gesture-learn` | Teach custom hand gestures by showing examples | 14 KB | Med |\n297|| `ewc-lifelong` | Learns new things without forgetting old lessons | 8 KB | Hard |\n298|| `federated-learning` | Train AI across seeds without sharing raw data | 18 KB | Hard |\n299|| `goap-autonomy` | Plans and executes goals on its own | 14 KB | Hard |\n300|| `meta-adapt` | Automatically tunes itself for best performance | 10 KB | Hard |\n301|", "total_lines": 632, "file_size": 58046, "truncated": true, "hint": "Use offset=301 to continue reading (showing 1-300 of 632 lines)", "is_binary": false, "is_image": false}

  [Subdirectory context discovered: RuView/CLAUDE.md]
  # Claude Code Configuration — WiFi-DensePose + Claude Flow V3

  ## Project: wifi-densepose

  WiFi-based human pose estimation using Channel State Information (CSI).
  Dual codebase: Python v1 (`v1/`) and Rust port (`v2/`).
  ### Key Rust Crates
  | Crate | Description |
  |-------|-------------|
  | `wifi-densepose-core` | Core types, traits, error types, CSI frame primitives |
  | `wifi-densepose-signal` | SOTA signal processing + RuvSense multistatic sensing (16 modules) |
  | `wifi-densepose-nn` | Neural network inference (ONNX, PyTorch, Candle backends) |
  | `wifi-densepose-train` | Training pipeline with ruvector integration + ruview_metrics |
  | `wifi-densepose-mat` | Mass Casualty Assessment Tool — disaster survivor detection |
  | `wifi-densepose-hardware` | ESP32 aggregator, TDM protocol, channel hopping firmware |
  | `wifi-densepose-ruvector` | RuVector v2.0.4 integration + cross-viewpoint fusion (5 modules) |
  | `wifi-densepose-wasm` | WebAssembly bindings for browser deployment |
  | `wifi-densepose-cli` | CLI tool (`wifi-densepose` binary) |
  | `wifi-densepose-sensing-server` | Lightweight Axum server for WiFi sensing UI |
  | `wifi-densepose-wifiscan` | Multi-BSSID WiFi scanning (ADR-022) |
  | `wifi-densepose-vitals` | ESP32 CSI-grade vital sign extraction (ADR-021) |
  | `nvsim` | Deterministic NV-diamond magnetometer pipeline simulator (ADR-089) — standalone leaf, WASM-ready |
  | `vendor/rvcsi` (submodule) | **rvCSI** — edge RF sensing runtime (ADR-095/096): 9 crates (`rvcsi-core`/`-dsp`/`-events`/`-adapter-file`/`-adapter-nexmon`/`-ruvector`/`-runtime`/`-node`/`-cli`). Lives in its own repo ([github.com/ruvnet/rvcsi](https://github.com/ruvnet/rvcsi)), vendored here under `vendor/rvcsi`, published to crates.io as `rvcsi-* 0.3.x` and to npm as `@ruv/rvcsi`. Not a `v2/` workspace member — depend on the published crates (or the submodule's `crates/rvcsi-*` paths). Normalized `CsiFrame`/`CsiWindow`/`CsiEvent` schema, validate-before-FFI, reusable DSP, typed confidence-scored events, the napi-c Nexmon shim (real nexmon_csi `.pcap` from a Raspberry Pi 5 / 4 / 3B+ — BCM43455c0), the napi-rs SDK, the `rvcsi` CLI, a Claude Code plugin. |
  | `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4 compat, Ruflo AI-agent integration |

  ### RuvSense Modules (`signal/src/ruvsense/`)
  | Module | Purpose |
  |--------|---------|
  | `multiband.rs` | Multi-band CSI frame fusion, cross-channel coherence |
  | `phase_align.rs` | Iterative LO phase offset estimation, circular mean |
  | `multistatic.rs` | Attention-weighted fusion, geometric diversity |
  | `coherence.rs` | Z-score coherence scoring, DriftProfile |
  | `coherence_gate.rs` | Accept/PredictOnly/Reject/Recalibrate gate decisions |
  | `pose_tracker.rs` | 17-keypoint Kalman tracker with AETHER re-ID embeddings |
  | `field_model.rs` | SVD room eigenstructure, perturbation extraction |
  | `tomography.rs` | RF tomography, ISTA L1 solver, voxel grid |
  | `longitudinal.rs` | Welford stats, biomechanics drift detection |
  | `intention.rs` | Pre-movement lead signals (200-500ms) |
  | `cross_room.rs` | Environment fingerprinting, transition graph |
  | `gesture.rs` | DTW template matching gesture classifier |
  | `adversarial.rs` | Physically impossible signal detection, multi-link consistency |
  | `cir.rs` | ADR-134 CSI→CIR via ISTA L1 sparse recovery (NeumannSolver warm-start) |
  | `calibration.rs` | ADR-135 empty-room baseline (Welford amplitude + von Mises phase, drift trigger) |

  ### Cross-Viewpoint Fusion (`ruvector/src/viewpoint/`)
  | Module | Purpose |
  |--------|---------|
  | `attention.rs` | CrossViewpointAttention, GeometricBias, softmax with G_bias |
  | `geometry.rs` | GeometricDiversityIndex, Cramer-Rao bounds, Fisher Information |
  | `coherence.rs` | Phase phasor coherence, hysteresis gate |
  | `fusion.rs` | MultistaticArray aggregate root, domain events |

  ### RuVector v2.0.4 Integration (ADR-016 complete, ADR-017 proposed)
  All 5 ruvector crates integrated in workspace:
  - `ruvector-mincut` → `metrics.rs` (DynamicPersonMatcher) + `subcarrier_selection.rs`
  - `ruvector-attn-mincut` → `model.rs` (apply_antenna_attention) + `spectrogram.rs`
  - `ruvector-temporal-tensor` → `dataset.rs` (CompressedCsiBuffer) + `breathing.rs`
  - `ruvector-solver` → `subcarrier.rs` (sparse interpolation 114→56) + `triangulation.rs`
  - `ruvector-attention` → `model.rs` (apply_spatial_attention) + `bvp.rs`

  ### Architecture Decisions
  43 ADRs in `docs/adr/` (ADR-001 through ADR-043). Key ones:
  - ADR-014: SOTA signal processing (Accepted)
  - ADR-015: MM-Fi + Wi-Pose training datasets (Accepted)
  - ADR-016: RuVector training pipeline integration (Accepted — complete)
  - ADR-017: RuVector signal + MAT integration (Proposed — next target)
  - ADR-024: Contrastive CSI embedding / AETHER (Accepted)
  - ADR-027: Cross-environment domain generalization / MERIDIAN (Accepted)
  - ADR-028: ESP32 capability audit + witness verification (Accepted)
  - ADR-029: RuvSense multistatic sensing mode (Proposed)
  - ADR-030: RuvSense persistent field model (Proposed)
  - ADR-031: RuView sensing-first RF mode (Proposed)
  - ADR-032: Multistatic mesh security hardening (Proposed)
  - ADR-148: Drone swarm control system / `ruview-swarm` (In Progress)

  ### Supported Hardware

  | Device | Port | Chip | Role | Cost |
  |--------|------|------|------|------|
  | ESP32-S3 (8MB flash) | COM9 (ruvzen, was COM7) | Xtensa dual-core | WiFi CSI sensing node | ~$9 |
  | ESP32-S3 SuperMini (4MB) | — | Xtensa dual-core | WiFi CSI (compact) | ~$6 |
  | ESP32-C6 + Seeed MR60BHA2 | COM12 (ruvzen, was COM4) | RISC-V + 60 GHz FMCW | mmWave HR/BR/presence + WiFi CSI | ~$15 |
  | HLK-LD2410 | — | 24 GHz FMCW | Presence + distance | ~$3 |

  **Not supported:** ESP32 (original), ESP32-C3 — single-core, can't run CSI DSP pipeline.

  ### Build & Test Commands (this repo)
  ```bash
  # Rust — full workspace tests (1,031+ tests, ~2 min)
  cd v2
  cargo test --workspace --no-default-features

  # Rust — single crate check (no GPU needed)
  cargo check -p wifi-densepose-train --no-default-features

  # Python — deterministic proof verification (SHA-256)
  python archive/v1/data/proof/verify.py

  # Python — test suite
  cd archive/v1 && python -m pytest tests/ -x -q
  ```

  ### ESP32 Firmware Build (Windows — Python subprocess required)
  ```bash
  # Build 8MB firmware (real WiFi CSI mode, no mocks)
  # See CLAUDE.local.md for the full Python subprocess command
  # Key: must strip MSYSTEM env vars for ESP-IDF v5.4 on Git Bash

  # Build 4MB firmware
  cp sdkconfig.defaults.4mb sdkconfig.defaults
  # then same build process

  # Flash to COM7
  # [python, idf_py, '-p', 'COM7', 'flash']

  # Provision WiFi
  python firmware/esp32-csi-node/provision.py --port COM7 \
    --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20

  # Monitor serial
  python -m serial.tools.miniterm COM7 115200
  ```

  ### Firmware Release Process
  1. Build 8MB from `sdkconfig.defaults.template` (no mock)
  2. Build 4MB from `sdkconfig.defaults.4mb` (no mock)
  3. Save 6 binaries: `esp32-csi-node.bin`, `bootloader.bin`, `partition-table.bin`, `ota_data_initial.bin`, `esp32-csi-node-4mb.bin`, `partition-table-4mb.bin`
  4. Tag: `git tag v0.X.Y-esp32 && git push origin v0.X.Y-esp32`
  5. Release: `gh release create v0.X.Y-esp32 <binaries> --title "..." --notes-file ...`
  6. Verify on real hardware (COM7) before publishing
  7. **CRITICAL:** Always test with real WiFi CSI, not mock mode — mock missed the Kconfig threshold bug

  ### Crate Publishing Order
  Crates must be published in dependency order:
  1. `wifi-densepose-core` (no internal deps)
  2. `wifi-densepose-vitals` (no internal deps)
  3. `wifi-densepose-wifiscan` (no internal deps)
  4. `wifi-densepose-hardware` (no internal deps)
  5. `wifi-densepose-signal` (depends on core)
  6. `wifi-densepose-nn` (no internal deps, workspace only)
  7. `wifi-densepose-ruvector` (no internal deps, workspace only)
  8. `wifi-densepose-train` (depends on signal, nn)
  9. `wifi-densepose-mat` (depends on core, signal, nn)
  10. `wifi-densepos

  [...truncated CLAUDE.md: 18,246 chars total] read_file 


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  {"content": "1|[workspace]\n2|resolver = \"2\"\n3|members = [\n4|    \"crates/wifi-densepose-core\",\n5|    \"crates/wifi-densepose-signal\",\n6|    \"crates/wifi-densepose-nn\",\n7|    # wifi-densepose-api / -db / -config: removed in #578.\n8|    # The crate names were reserved early for an envisioned REST/database/config\n9|    # split, but no implementation followed and no code referenced them. The\n10|    # functionality they would provide is covered today by:\n11|    #   - REST/WS: `wifi-densepose-sensing-server` (Axum)\n12|    #   - Config:  per-crate config + CLI args in `wifi-densepose-sensing-server`\n13|    #             and `wifi-densepose-desktop`\n14|    #   - DB:      no persistent state; system is real-time\n15|    # If we ever need any of these as a published surface, they can be\n16|    # reintroduced with a real implementation.\n17|    \"crates/wifi-densepose-hardware\",\n18|    \"crates/wifi-densepose-wasm\",\n19|    \"crates/wifi-densepose-cli\",\n20|    \"crates/wifi-densepose-mat\",\n21|    \"crates/wifi-densepose-train\",\n22|    \"crates/wifi-densepose-sensing-server\",\n23|    \"crates/wifi-densepose-wifiscan\",\n24|    \"crates/wifi-densepose-vitals\",\n25|    \"crates/wifi-densepose-ruvector\",\n26|    \"crates/wifi-densepose-desktop\",\n27|    \"crates/wifi-densepose-pointcloud\",\n28|    \"crates/wifi-densepose-geo\",\n29|    \"crates/wifi-densepose-worldgraph\",  # ADR-139 — WorldGraph environmental digital twin\n30|    \"crates/wifi-densepose-engine\",      # ADR-135..146 integration/composition layer\n31|    \"crates/nvsim\",\n32|    \"crates/nvsim-server\",\n33|    \"crates/homecore\",                 # ADR-127 — HOMECORE state machine\n34|    \"crates/homecore-plugins\",         # ADR-128 — HOMECORE-PLUGINS WASM runtime (P1 scaffold)\n35|    \"crates/homecore-api\",             # ADR-130 — HOMECORE REST + WS API\n36|    \"crates/homecore-automation\",         # ADR-129 — HOMECORE automation engine\n37|    \"crates/homecore-recorder\",        # ADR-132 — HOMECORE state recorder\n38|    \"crates/homecore-migrate\",         # ADR-134 — HOMECORE migration from Python HA\n39|    # ADR-100/ADR-101: Cognitum Cog packaging — first Cog from this repo.\n40|    # Ships the wifi-densepose pose-estimation model as a signed binary +\n41|    # JSONL manifest installable by the Cognitum V0 appliance (cognitum-v0,\n42|    # cognitum-cluster-*, ruvultra). The companion appliance-side crate\n43|    # lives in cognitum-one/v0-appliance as `cognitum-pose-estimation`.\n44|    \"crates/cog-pose-estimation\",\n45|    # ADR-103: Learned multi-person counter (SOTA path) — replaces the\n46|    # PR #491 slot heuristic with a Candle network + Stoer-Wagner fusion.\n47|    # Motivated by #499 ghost-skeleton reports.\n48|    \"crates/cog-person-count\",\n49|    # ADR-116: Home Assistant + Matter Cognitum Seed cog. Wraps the\n50|    # ADR-115 MQTT publisher as a Seed-installable artifact with\n51|    # mDNS, embedded broker, RuVector thresholds, Ed25519 witness.\n52|    \"crates/cog-ha-matter\",\n53|    # ADR-118: BFLD — Beamforming Feedback Layer for Detection. The\n54|    # privacy/safety layer that measures and gates identity leakage from\n55|    # WiFi BFI captures. Sub-ADRs: 119 (frame), 120 (privacy class),\n56|    # 121 (identity risk), 122 (HA/Matter), 123 (capture path).\n57|    \"crates/wifi-densepose-bfld\",\n58|    # ADR-147: OccWorld thin-client bridge — WorldGraph PersonTrack history →\n59|    # OccWorld Python subprocess → TrajectoryPrior injection into pose tracker.\n60|    \"crates/wifi-densepose-worldmodel\",\n61|    # ADR-147 (Phase 5): OccWorld TransVQVAE ported to Candle — native Rust\n62|    # inference without Python/IPC overhead. Loaded alongside the Python bridge\n63|    # as a faster alternative once Phase-5 weights are available.\n64|    \"crates/wifi-densepose-occworld-candle\",\n65|    # rvCSI — edge RF sensing runtime (ADR-095 platform, ADR-096 FFI/crate layout):\n66|    # lives in its own repo (https://github.com/ruvnet/rvcsi), vendored here as\n67|    # `vendor/rvcsi` and published to crates.io as `rvcsi-*` 0.3.x. Depend on the\n68|    # published crates (or the submodule's `crates/rvcsi-*` paths) — not as v2\n69|    # workspace members, since `vendor/rvcsi/Cargo.toml` is its own workspace.\n70|    \"crates/homecore-hap\",         # ADR-125 — Apple Home HomeKit Accessory Protocol bridge\n71|    \"crates/homecore-assist\",      # ADR-133 — HOMECORE voice assistant + ruflo bridge\n72|    \"crates/homecore-server\",      # iter-9 — HOMECORE integration binary (all 8 crates wired together)\n73|    \"crates/ruview-swarm\",                         # ADR-148 — drone swarm control system\n74|]\n75|# ADR-040: WASM edge crate targets wasm32-unknown-unknown (no_std),\n76|# excluded from workspace to avoid breaking `cargo test --workspace`.\n77|# Build separately: cargo build -p wifi-densepose-wasm-edge --target wasm32-unknown-unknown --release\n78|#\n79|# ADR-128 P2: example WASM plugin — also wasm32-only (no_std, cdylib),\n80|# excluded for the same reason. Build separately:\n81|#   cargo build --target wasm32-unknown-unknown --release -p homecore-plugin-example\n82|exclude = [\n83|    \"crates/wifi-densepose-wasm-edge\",\n84|    \"crates/homecore-plugin-example\",\n85|]\n86|\n87|[workspace.package]\n88|version = \"0.3.0\"\n89|edition = \"2021\"\n90|authors = [\"rUv <ruv@ruv.net>\", \"WiFi-DensePose Contributors\"]\n91|license = \"MIT OR Apache-2.0\"\n92|repository = \"https://github.com/ruvnet/wifi-densepose\"\n93|documentation = \"https://docs.rs/wifi-densepose\"\n94|keywords = [\"wifi\", \"densepose\", \"csi\", \"pose-estimation\", \"rust\"]\n95|categories = [\"science\", \"computer-vision\", \"wasm\"]\n96|\n97|[workspace.dependencies]\n98|# Core utilities\n99|thiserror = \"2.0\"\n100|anyhow = \"1.0\"\n101|serde = { version = \"1.0\", features = [\"derive\"] }\n102|serde_json = \"1.0\"\n103|serde_yaml = \"0.9\"\n104|tokio = { version = \"1.35\", features = [\"full\"] }\n105|tracing = \"0.1\"\n106|tracing-subscriber = { version = \"0.3\", features = [\"env-filter\", \"json\"] }\n107|\n108|# Signal processing\n109|ndarray = { version = \"0.17\", features = [\"serde\"] }\n110|ndarray-linalg = { version = \"0.18\", features = [\"openblas-static\"] }\n111|rustfft = \"6.1\"\n112|num-complex = \"0.4\"\n113|num-traits = \"0.2\"\n114|\n115|# Neural network\n116|tch = \"0.24\"\n117|ort = { version = \"2.0.0-rc.11\" }\n118|candle-core = \"0.4\"\n119|candle-nn = \"0.4\"\n120|\n121|# Web framework\n122|axum = { version = \"0.7\", features = [\"ws\", \"macros\"] }\n123|tower = { version = \"0.4\", features = [\"full\"] }\n124|tower-http = { version = \"0.6\", features = [\"cors\", \"trace\", \"compression-gzip\"] }\n125|hyper = { version = \"1.1\", features = [\"full\"] }\n126|\n127|# Database\n128|sqlx = { version = \"0.7\", features = [\"runtime-tokio\", \"postgres\", \"sqlite\", \"uuid\", \"chrono\", \"json\"] }\n129|redis = { version = \"0.24\", features = [\"tokio-comp\", \"connection-manager\"] }\n130|\n131|# Configuration\n132|config = \"0.14\"\n133|dotenvy = \"0.15\"\n134|envy = \"0.4\"\n135|\n136|# WASM\n137|wasm-bindgen = \"0.2\"\n138|wasm-bindgen-futures = \"0.4\"\n139|js-sys = \"0.3\"\n140|web-sys = { version = \"0.3\", features = [\"console\", \"Window\", \"WebSocket\"] }\n141|getrandom = { version = \"0.2\", features = [\"js\"] }\n142|\n143|# Hardware\n144|serialport = \"4.3\"\n145|pcap = \"1.1\"\n146|\n147|# Graph algorithms (for min-cut assignment in metrics)\n148|petgraph = \"0.6\"\n149|\n150|# Data loading\n151|ndarray-npy = \"0.10\"\n152|walkdir = \"2.4\"\n153|\n154|# Hashing (for proof)\n155|sha2 = \"0.10\"\n156|\n157|# CSV logging\n158|csv = \"1.3\"\n159|\n160|# Progress bars\n161|indicatif = \"0.17\"\n162|\n163|# CLI\n164|clap = { version = \"4.4\", features = [\"derive\", \"env\"] }\n165|\n166|# rvCSI: napi-rs (Rust -> Node bindings) + napi-c (C-shim build glue)\n167|napi = { version = \"2.16\", default-features = false, features = [\"napi8\"] }\n168|napi-derive = \"2.16\"\n169|napi-build = \"2.1\"\n170|cc = \"1.0\"\n171|libc = \"0.2\"\n172|\n173|# Testing\n174|criterion = { version = \"0.5\", features = [\"html_reports\"] }\n175|proptest = \"1.4\"\n176|mockall = \"0.12\"\n177|wiremock = \"0.5\"\n178|\n179|# midstreamer integration (published on crates.io)\n180|# 0.1.0 was yanked; upgrade to latest 0.3/0.2 releases which pull in\n181|# quinn-proto >=0.11.14 (fixes RUSTSEC-2026-0037) and\n182|# rustls-webpki >=0.103.13 (fixes RUSTSEC-2026-0049/0098/0099/0104).\n183|midstreamer-quic = \"0.3\"\n184|midstreamer-scheduler = \"0.2\"\n185|midstreamer-temporal-compare = \"0.2\"\n186|midstreamer-attractor = \"0.2\"\n187|\n188|# ruvector integration (published on crates.io)\n189|# Vendored at v2.1.0 in vendor/ruvector; using crates.io versions until published.\n190|ruvector-core = \"2.2.0\"\n191|ruvector-mincut = \"2.0.4\"\n192|ruvector-attn-mincut = \"2.0.4\"\n193|ruvector-temporal-tensor = \"2.0.6\"\n194|ruvector-solver = \"2.0.4\"\n195|ruvector-attention = \"2.0.4\"\n196|ruvector-crv = \"0.1.1\"\n197|ruvector-gnn = { version = \"2.0.5\", default-features = false }\n198|\n199|\n200|# Internal crates\n201|", "total_lines": 227, "file_size": 9190, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 227 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  # 📡 Análise do Repositório RuView

  ## 🎯 Problema
  O RuView resolve o problema de **detectar pessoas e medir sinais vitais sem câmeras nem wearables**. Usuários precisam monitorar presença, movimentos, frequência respiratória e cardíaca em ambientes — tradicionalmente isso exigia câmeras (questões de privacidade) ou dispositivos vestíveis (desconforto/custo).

  ## 🏷️ Tema
  **WiFi Sensing / Spatial Intelligence** — O projeto transforma sinais WiFi comuns em dados espaciais acionáveis através da análise de **Channel State Information (CSI)** capturada por hardware de baixo custo.

  ## 🔥 Razões do Hype

  - **Ver através de paredes** com sinais WiFi normais — sem câmeras
  - **Hardware barato**: ESP32-S3 por ~$9, ou ESP32-C6 por ~$6-10
  - **100% de precisão na detecção de presença** no dataset de validação (segundo README)
  - **Sem nuvem, sem internet**: processamento 100% edge/local
  - **Integração nativa** com Home Assistant, Apple Home, Google Home, Alexa/Matter
  - **Modelo pré-treinado no Hugging Face** (`ruvnet/wifi-densepose-pretrained`) — 8 KB quantizado em 4-bit
  - **105 módulos edge** catalogados para diferentes casos de uso (saúde, segurança, varejo, indústria)
  - Demonstrações live: pose 3D, point cloud, dual-modal fusion

  ## ⚙️ Tecnologias

  ### Rust (core)
  - **Workspace com ~30 crates**: `wifi-densepose-core`, `-signal`, `-nn`, `-hardware`, `-sensing-server`, `-vitals`, `-ruvector`, `-wasm`, `-pointcloud`, `-worldmodel`
  - **Neural nets**: Candle, ONNX Runtime, PyTorch (tch)
  - **Signal processing**: `ndarray`, `rustfft`, `ndarray-linalg`
  - **Server**: Axum + Tokio + Tower
  - **Serialização**: serde, serde_json, serde_yaml
  - **Testes**: criterion, proptest, mockall, wiremock

  ### Hardware/Firmware
  - **ESP32-S3** e **ESP32-C6** (Xtensa/RISC-V)
  - ESP-IDF v5.4
  - Nexmon CSI capture (pcap)
  - TDM slot scheduling, channel hopping (6 bandas WiFi)

  ### Integrações
  - **MQTT**, **HomeKit (HAP)**, **Matter**
  - Docker multi-arch (amd64 + arm64)
  - WASM edge modules
  - **Ed25519** witness chain, criptografia

  ### Bibliotecas relacionadas
  - **RuVector v2.0.4** — vetores, min-cut, attention, temporal tensors
  - **rvCSI** (submodulo/vendored em `vendor/rvcsi`) — edge RF sensing runtime, 9 crates com bindings Node/napi
  - **Cognitum Seed** — memória persistente, kNN, attestation

  ### Python
  - PyO3 wheels abi3 (~250 KB) publicadas no PyPI como `ruview` / `wifi-densepose`
  - Hugging Face Hub para download dos pesos
  - Candle + MediaPipe para fine-tuning de pose

  ## 📐 Arquitetura
  O sistema é dual-codebase: **Python v1** (`archive/v1/`) e **Rust port v2** (`v2/`). O fluxo é: ESP32 captura CSI → processamento DSP no edge → inferência de modelo pequeno → dados publicados via MQTT/WebSocket → integração em smart-home ecosystems.

  ---

  *Análise baseada em README.md, CLAUDE.md, v2/Cargo.toml e estrutura do workspace.*  


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c0:
  {"content": "1|![main image](https://cdn-images-1.medium.com/max/5200/1*r99Hq3YBd5FTTWLNYKKvPw.png)\n2|\n3|<div align=\"center\">\n4|\n5|<!-- omit in toc -->\n6|# Train LLM From Scratch\n7|  \n8|![Python](https://img.shields.io/badge/Python-3.8%2B-blue) ![License](https://img.shields.io/badge/License-MIT-green) ![Contributions](https://img.shields.io/badge/Contributions-Welcome-blue) [![Docs](https://img.shields.io/badge/Docs-Available-success)](#step-by-step-code-explanation)\n9|\n10|**I am Looking for a PhD position in AI**. [GitHub](https://github.com/FareedKhan-dev)\n11|\n12|</div>\n13|\n14|I implemented a transformer model from scratch using PyTorch, based on the paper [Attention is All You Need](https://arxiv.org/abs/1706.03762). You can use my scripts to train your own **billion** or **million** parameter LLM using a single GPU.\n15|\n16|Below is the output of the trained 13 million parameter LLM:\n17|\n18|```\n19|In ***1978, The park was returned to the factory-plate that \n20|the public share to the lower of the electronic fence that \n21|follow from the Station's cities. The Canal of ancient Western \n22|nations were confined to the city spot. The villages were directly \n23|linked to cities in China that revolt that the US budget and in\n24|Odambinais is uncertain and fortune established in rural areas.\n25|```\n26|<!-- omit in toc -->\n27|## Table of Contents\n28|- [Training Data Info](#training-data-info)\n29|- [Prerequisites and Training Time](#prerequisites-and-training-time)\n30|- [Code Structure](#code-structure)\n31|- [Usage](#usage)\n32|- [Step by Step Code Explanation](#step-by-step-code-explanation)\n33|  - [Importing Libraries](#importing-libraries)\n34|  - [Preparing the Training Data](#preparing-the-training-data)\n35|  - [Transformer Overview](#transformer-overview)\n36|  - [Multi Layer Perceptron (MLP)](#multi-layer-perceptron-mlp)\n37|  - [Single Head Attention](#single-head-attention)\n38|  - [Multi Head Attention](#multi-head-attention)\n39|  - [Transformer Block](#transformer-block)\n40|  - [The Final Model](#the-final-model)\n41|  - [Batch Processing](#batch-processing)\n42|  - [Training Parameters](#training-parameters)\n43|  - [Training the Model](#training-the-model)\n44|  - [Saving the Trained Model](#saving-the-trained-model)\n45|  - [Training Loss](#training-loss)\n46|  - [Generating Text](#generating-text)\n47|- [What’s Next](#whats-next)\n48|\n49|## Training Data Info\n50|\n51|Training data is from the Pile dataset, which is a diverse, open-source, and large-scale dataset for training language models. The Pile dataset is a collection of 22 diverse datasets, including text from books, articles, websites, and more. The total size of the Pile dataset is 825GB, Below is the sample of the training data:\n52|\n53|```python\n54|Line: 0 \n55|{\n56|  \"text\": \"Effect of sleep quality ... epilepsy.\",\n57|  \"meta\": {\n58|    \"pile_set_name\": \"PubMed Abstracts\"\n59|  }\n60|}\n61|\n62|Line: 1\n63|{\n64|  \"text\": \"LLMops a new GitHub Repository ...\",\n65|  \"meta\": {\n66|    \"pile_set_name\": \"Github\"\n67|  }\n68|}\n69|```\n70|\n71|## Prerequisites and Training Time\n72|\n73|Make sure you have a basic understanding of object-oriented programming (OOP), neural networks (NN) and PyTorch to understand the code. Below are some resources to help you get started:\n74|\n75|| Topic               | Video Link                                                |\n76||---------------------|-----------------------------------------------------------|\n77|| OOP                 | [OOP Video](https://www.youtube.com/watch?v=Ej_02ICOIgs&pp=ygUKb29wIHB5dGhvbg%3D%3D) |\n78|| Neural Network      | [Neural Network Video](https://www.youtube.com/watch?v=Jy4wM2X21u0&pp=ygUbbmV1cmFsIG5ldHdvcmsgcHl0aG9uIHRvcmNo) |\n79|| Pytorch             | [Pytorch Video](https://www.youtube.com/watch?v=V_xro1bcAuA&pp=ygUbbmV1cmFsIG5ldHdvcmsgcHl0aG9uIHRvcmNo) |\n80|\n81|You will need a GPU to train your model. Colab or Kaggle T4 will work for training a 13+ million-parameter model, but they will fail for billion-parameter training. Take a look at the comparison:\n82|\n83|| GPU Name                 | Memory | Data Size | 2B LLM Training | 13M LLM Training | Max Practical LLM Size (Training) |\n84||--------------------------|--------|-----------|-----------------|------------------|-----------------------------------|\n85|| NVIDIA A100              | 40 GB  | Large     | ✔               | ✔                | ~6B–8B                             |\n86|| NVIDIA V100              | 16 GB  | Medium    | ✘               | ✔                | ~2B                               |\n87|| AMD Radeon VII           | 16 GB  | Medium    | ✘               | ✔                | ~1.5B–2B                          |\n88|| NVIDIA RTX 3090          | 24 GB  | Large     | ✔               | ✔                | ~3.5B–4B                          |\n89|| Tesla P100               | 16 GB  | Medium    | ✘               | ✔                | ~1.5B–2B                          |\n90|| NVIDIA RTX 3080          | 10 GB  | Medium    | ✘               | ✔                | ~1.2B                             |\n91|| AMD RX 6900 XT           | 16 GB  | Large     | ✘               | ✔                | ~2B                               |\n92|| NVIDIA GTX 1080 Ti       | 11 GB  | Medium    | ✘               | ✔                | ~1.2B                             |\n93|| Tesla T4                 | 16 GB  | Small     | ✘               | ✔                | ~1.5B–2B                          |\n94|| NVIDIA Quadro RTX 8000   | 48 GB  | Large     | ✔               | ✔                | ~8B–10B                           |\n95|| NVIDIA RTX 4070          | 12 GB  | Medium    | ✘               | ✔                | ~1.5B                             |\n96|| NVIDIA RTX 4070 Ti       | 12 GB  | Medium    | ✘               | ✔                | ~1.5B                             |\n97|| NVIDIA RTX 4080          | 16 GB  | Medium    | ✘               | ✔                | ~2B                               |\n98|| NVIDIA RTX 4090          | 24 GB  | Large     | ✔               | ✔                | ~4B                               |\n99|| NVIDIA RTX 4060 Ti       | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n100|| NVIDIA RTX 4060          | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n101|| NVIDIA RTX 4050          | 6 GB   | Small     | ✘               | ✔                | ~0.75B                            |\n102|| NVIDIA RTX 3070          | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n103|| NVIDIA RTX 3060 Ti       | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n104|| NVIDIA RTX 3060          | 12 GB  | Medium    | ✘               | ✔                | ~1.5B                             |\n105|| NVIDIA RTX 3050          | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n106|| NVIDIA GTX 1660 Ti       | 6 GB   | Small     | ✘               | ✔                | ~0.75B                            |\n107|| AMD RX 7900 XTX          | 24 GB  | Large     | ✔               | ✔                | ~3.5B–4B                          |\n108|| AMD RX 7900 XT           | 20 GB  | Large     | ✔               | ✔                | ~3B                               |\n109|| AMD RX 7800 XT           | 16 GB  | Medium    | ✘               | ✔                | ~2B                               |\n110|| AMD RX 7700 XT           | 12 GB  | Medium    | ✘               | ✔                | ~1.5B                             |\n111|| AMD RX 7600              | 8 GB   | Small     | ✘               | ✔                | ~1B                               |\n112|\n113|The 13M LLM training is the training of a 13+ million-parameter model, and the 2B LLM training is the training of a 2+ billion-parameter model. The data size is categorized as small, medium, and large. The small data size is around 1 GB, the medium data size is around 5 GB, and the large data size is around 10 GB.\n114|\n115|## Code Structure\n116|\n117|The codebase is organized as follows:\n118|```bash\n119|train-llm-from-scratch/\n120|├── src/          \n121|│   ├── models/   \n122|│   │   ├── mlp.py       # Definition of the Multi-Layer Perceptron (MLP) module\n123|│   │   ├── attention.py # Definitions for attention mechanisms (single-head, multi-head)\n124|│   │   ├── transformer_block.py # Definition of a single Transformer block\n125|│   │   ├── transformer.py     # Definition of the main Transformer model\n126|├── config/       \n127|│   └── config.py    # Contains default configurations (model parameters, file paths, etc.)\n128|├── data_loader/  \n129|│   └── data_loader.py # Contains functions for creating data loaders/iterators\n130|├── scripts/      \n131|│   ├── train_transformer.py # Script for training the Transformer model\n132|│   ├── data_download.py   # Script for downloading the dataset\n133|│   ├── data_preprocess.py # Script for preprocessing the downloaded data\n134|│   ├── generate_text.py   # Script for generating text using a trained model\n135|├── data/         # Directory to store the dataset\n136|│   ├── train/     # Contains training data\n137|│   └── val/       # Contains validation data\n138|├── models/       # Directory where trained models are saved\n139|```\n140|\n141|`scripts/` directory contains scripts for downloading the dataset, preprocessing the data, training the model, and generating text using the trained model. `src/models/` directory contains the implementation of the transformer model, multi-layer perceptron (MLP), attention mechanisms, and transformer blocks.`config/` directory contains the configuration file with default parameters. `data_loader/` directory contains functions for creating data loaders/iterators.\n142|\n143|## Usage\n144|\n145|Clone the repository and navigate to the directory:\n146|```bash\n147|git clone https://github.com/FareedKhan-dev/train-llm-from-scratch.git\n148|cd train-llm-from-scratch\n149|```\n150|\n151|if you encounter any issues regarding the imports, make sure to change pythonpath to the root directory of the project:\n152|```bash\n153|export PYTHONPATH=\"${PYTHONPATH}:/path/to/train-llm-from-scratch\"\n154|\n155|# or if you are already in the train-llm-from-scratch directory\n156|export PYTHONPATH=\"$PYTHONPATH:.\"\n157|```\n158|\n159|Install the required dependencies:\n160|```bash\n161|pip install -r requirements.txt\n162|```\n163|\n164|You can modify the transformer architecture under `src/models/transformer.py` and the training configurations under `config/config.py`.\n165|\n166|\n167|To download the training data, run:\n168|```bash\n169|python scripts/data_download.py\n170|```\n171|\n172|The script supports the following arguments:\n173|* `--train_max`: Maximum number of training files to download. Default is 1 (Max equal to 30) Each file is around 11 GB.\n174|* `--train_dir`: Directory for storing training data. Default is `data/train`.\n175|* `--val_dir`: Directory for storing validation data. Default is `data/val`.\n176|\n177|To preprocess the downloaded data, run:\n178|```bash\n179|python scripts/data_preprocess.py\n180|```\n181|\n182|The script supports the following arguments:\n183|- `--train_dir`: Directory where the training data files are stored (default is `data/train`).\n184|- `--val_dir`: Directory where the validation data files are stored (default is `data/val`).\n185|- `--out_train_file`: Path to store the processed training data in HDF5 format (default is `data/train/pile_train.h5`).\n186|- `--out_val_file`: Path to store the processed validation data in HDF5 format (default is `data/val/pile_dev.h5`).\n187|- `--tokenizer_name`: Name of the tokenizer to use for processing the data (default is `r50k_base`).\n188|- `--max_data`: Maximum number of JSON objects ([lines](#training-data-info)) to process from each dataset (both train and validation). The default is 1000.\n189|\n190|Now that the data is preprocessed, you can train the 13 million parameter llm by changing the configuration in `config/config.py` to this:\n191|\n192|```python\n193|# Define vocabulary size and transformer configuration (3 Billion)\n194|VOCAB_SIZE = 50304          # Number of unique tokens in the vocabulary\n195|CONTEXT_LENGTH = 128        # Maximum sequence length for the model\n196|N_EMBED = 128               # Dimension of the embedding space\n197|N_HEAD = 8                  # Number of attention heads in each transformer block\n198|N_BLOCKS = 1               # Number of transformer blocks in the model\n199|```\n200|\n201|To train the model, run:\n202|```bash\n203|python scripts/train_transformer.py\n204|```\n205|\n206|It will start training the model and save the trained model in the `models/` default directory or the directory specified in the configuration file.\n207|\n208|To generate text using the trained model, run:\n209|```bash\n210|python scripts/generate_text.py --model_path models/your_model.pth --input_text hi\n211|```\n212|\n213|The script supports the following arguments:\n214|- `--model_path`: Path to the trained model.\n215|- `--input_text`: Initial text prompt for generating new text.\n216|- `--max_new_tokens`: Maximum number of tokens to generate (default is 100).\n217|\n218|It will generate text based on the input prompt using the trained model.\n219|\n220|## Step by Step Code Explanation\n221|\n222|This section is for those who want to understand the code in detail. I will explain the code step by step, starting from importing the libraries to training the model and generating text.\n223|\n224|Previously, I wrote an article on Medium about creating a [2.3+ million-parameter](https://levelup.gitconnected.com/building-a-million-parameter-llm-from-scratch-using-python-f612398f06c2) LLM using the Tiny Shakespeare dataset, but the output didn’t make sense. Here is a sample output:\n225|\n226|```bash\n227|# 2.3 Million Parameter LLM Output\n228|ZELBETH:\n229|Sey solmenter! tis tonguerered if\n230|Vurint as steolated have loven OID the queend refore\n231|Are been, good plmp:\n232|\n233|Proforne, wiftes swleen, was no blunderesd a a quain beath!\n234|Tybell is my gateer stalk smend as be matious dazest\n235|```\n236|\n237|I had a thought, what if I make the transformer architecture smaller and less complex, and the training data more diverse? Then, how big of a model could a single person, using their nearly dead GPU, create in terms of parameters that can speak proper grammar and generate text that makes some sense?\n238|\n239|I found that **13+ million-parameter** models are enough to start making sense in terms of proper grammar and punctuation, which is a positive point. This means we can use a very specific dataset to further fine-tune our previously trained model for a narrowed task. We might end up with a model under 1 billion parameters or even around 500 million parameters that is perfect for our specific use case, especially for running it on private data securely.\n240|\n241|I recommend you **first train a 13+ million-parameter** model using the script available in my GitHub repository. You will get results within one day, instead of waiting for a longer time, or if your local GPU might not be strong enough to train a billion-parameter model.\n242|\n243|### Importing Libraries\n244|\n245|Let’s import the required libraries that will be used throughout this blog:\n246|\n247|```python\n248|# PyTorch for deep learning functions and tensors\n249|import torch\n250|import torch.nn as nn\n251|import torch.nn.functional as F\n252|\n253|# Numerical operations and arrays handling\n254|import numpy as np\n255|\n256|# Handling HDF5 files\n257|import h5py\n258|\n259|# Operating system and file management\n260|import os\n261|\n262|# Command-line argument parsing\n263|import argparse\n264|\n265|# HTTP requests and interactions\n266|import requests\n267|\n268|# Progress bar for loops\n269|from tqdm import tqdm\n270|\n271|# JSON handling\n272|import json\n273|\n274|# Zstandard compression library\n275|import zstandard as zstd\n276|\n277|# Tokenization library for large language models\n278|import tiktoken\n279|\n280|# Math operations (used for advanced math functions)\n281|import math\n282|```\n283|\n284|### Preparing the Training Data\n285|\n286|Our training dataset needs to be diverse, containing information from different domains, and The Pile is the right choice for it. Although it is 825 GB in size, we will stick to only a small portion of it, i.e., 5%–10%. Let’s first download the dataset and see how it works. I will be downloading the version available on [HuggingFace](https://huggingface.co/datasets/monology/pile-uncopyrighted).\n287|\n288|```python\n289|# Download validation dataset\n290|!wget https://huggingface.co/datasets/monology/pile-uncopyrighted/resolve/main/val.jsonl.zst\n291|\n292|# Download the first part of the training dataset\n293|!wget https://huggingface.co/datasets/monology/pile-uncopyrighted/resolve/main/train/00.jsonl.zst\n294|\n295|# Download the second part of the training dataset\n296|!wget https://huggingface.co/datasets/monology/pile-uncopyrighted/resolve/main/train/01.jsonl.zst\n297|\n298|# Download the third part of the training dataset\n299|!wget https://huggingface.co/datasets/monology/pile-uncopyrighted/resolve/main/train/02.jsonl.zst\n300|```\n301|\n302|It will take some time to download, but you can also limit the training dataset to just one file, `00.jsonl.zst`, instead of three. It is already split into train/val/test. Once it's done, make sure to place the files correctly in their respective directories.\n303|\n304|```python\n305|import os\n306|import shutil\n307|import glob\n308|\n309|# Define directory structure\n310|train_dir = \"data/train\"\n311|val_dir = \"data/val\"\n312|\n313|# Create directories if they don't exist\n314|os.makedirs(train_dir, exist_ok=True)\n315|os.makedirs(val_dir, exist_ok=True)\n316|\n317|# Move all train files (e.g., 00.jsonl.zst, 01.jsonl.zst, ...)\n318|train_files = glob.glob(\"*.jsonl.zst\")\n319|for file in train_files:\n320|    if file.startswith(\"val\"):\n321|        # Move validation file\n322|        dest = os.path.join(val_dir, file)\n323|    else:\n324|        # Move training file\n325|        dest = os.path.join(train_dir, file)\n326|    shutil.move(file, dest)\n327|\n328|Our dataset is in the .jsonl.zst format, which is a compressed file format commonly used for storing large datasets. It combines JSON Lines (.jsonl), where each line represents a valid JSON object, with Zstandard (.zst) compression. Let's read a sample of one of the downloaded files and see how it looks.\n329|\n330|in_file = \"data/val/val.jsonl.zst\"  # Path to our validation file\n331|\n332|with zstd.open(in_file, 'r') as in_f:\n333|    for i, line in tqdm(enumerate(in_f)):  # Read first 5 lines\n334|        data = json.loads(line)\n335|        print(f\"Line {i}: {data}\")  # Print the raw data for inspection\n336|        if i == 2:\n337|            break\n338|```\n339|\n340|The output of the above code is this:\n341|\n342|```python\n343|#### OUTPUT ####\n344|Line: 0 \n345|{\n346|  \"text\": \"Effect of sleep quality ... epilepsy.\",\n347|  \"meta\": {\n348|    \"pile_set_name\": \"PubMed Abstracts\"\n349|  }\n350|}\n351|\n352|Line: 1\n353|{\n354|  \"text\": \"LLMops a new GitHub Repository ...\",\n355|  \"meta\": {\n356|    \"pile_set_name\": \"Github\"\n357|  }\n358|}\n359|```\n360|\n361|Now we need to encode (tokenize) our dataset. Our goal is to have an LLM that can at least output proper words. For that, we need to use an already available tokenizer. We will use the tiktoken open-source tokenizer by OpenAI. We will use the r50k_base tokenizer, which is used for the ChatGPT (GPT-3) model, to tokenize our dataset.\n362|\n363|We need to create a function for this to avoid duplication, as we will be tokenizing both the train and validation datasets.\n364|\n365|```python\n366|def process_files(input_dir, output_file):\n367|    \"\"\"\n368|    Process all .zst files in the specified input directory and save encoded tokens to an HDF5 file.\n369|\n370|    Args:\n371|        input_dir (str): Directory containing input .zst files.\n372|        output_file (str): Path to the output HDF5 file.\n373|    \"\"\"\n374|    with h5py.File(output_file, 'w') as out_f:\n375|        # Create an expandable dataset named 'tokens' in the HDF5 file\n376|        dataset = out_f.create_dataset('tokens', (0,), maxshape=(None,), dtype='i')\n377|        start_index = 0\n378|\n379|        # Iterate through all .zst files in the input directory\n380|        for filename in sorted(os.listdir(input_dir)):\n381|            if filename.endswith(\".jsonl.zst\"):\n382|                in_file = os.path.join(input_dir, filename)\n383|                print(f\"Processing: {in_file}\")\n384|\n385|                # Open the .zst file for reading\n386|                with zstd.open(in_file, 'r') as in_f:\n387|                    # Iterate through each line in the compressed file\n388|                    for line in tqdm(in_f, desc=f\"Processing {filename}\"):\n389|                        # Load the line as JSON\n390|                        data = json.loads(line)\n391|\n392|                        # Append the end-of-text token to the text and encode it\n393|                        text = data['text'] + \"<|endoftext|>\"\n394|                        encoded = enc.encode(text, allowed_special={'<|endoftext|>'})\n395|                        encoded_len = len(encoded)\n396|\n397|                        # Calculate the end index for the new tokens\n398|                        end_index = start_index + encoded_len\n399|\n400|                        # Expand the dataset size and store the encoded tokens\n401|                        dataset.resize(dataset.shape[0] + encoded_len, axis=0)\n402|                        dataset[start_index:end_index] = encoded\n403|\n404|                        # Update the start index for the next batch of tokens\n405|                        start_index = end_index\n406|```\n407|\n408|There are two important points regarding this function:\n409|\n410| 1. We are storing the tokenized data in an HDF5 file, which allows us flexibility for quicker data access while training the model.\n411|\n412| 2. Appending the `<|endoftext|>` token marks the end of each text sequence, signaling to the model that it has reached the end of a meaningful context, which helps in generating coherent outputs.\n413|\n414|Now we can simply encode our train and validation datasets using:\n415|\n416|```python\n417|# Define tokenized data output directories\n418|out_train_file = \"data/train/pile_train.h5\"\n419|out_val_file = \"data/val/pile_dev.h5\"\n420|\n421|# Loading tokenizer of (GPT-3/GPT-2 Model)\n422|enc = tiktoken.get_encoding('r50k_base')\n423|\n424|# Process training data\n425|process_files(train_dir, out_train_file)\n426|\n427|# Process validation data\n428|process_files(val_dir, out_val_file)\n429|```\n430|\n431|Let’s take a look at the sample of our tokenized data:\n432|\n433|```python\n434| with h5py.File(out_val_file, 'r') as file:\n435|     # Access the 'tokens' dataset\n436|     tokens_dataset = file['tokens']\n437|     \n438|     # Print the dtype of the dataset\n439|     print(f\"Dtype of 'tokens' dataset: {tokens_dataset.dtype}\")\n440|     \n441|     # load and print the first few elements of the dataset\n442|     print(\"First few elements of the 'tokens' dataset:\")\n443|     print(tokens_dataset[:10])  # First 10 token\n444|```\n445|\n446|The output of the above code is this:\n447|\n448|```python\n449|#### OUTPUT ####\n450|Dtype of 'tokens' dataset: int32\n451|\n452|First few elements of the 'tokens' dataset:\n453|[ 2725  6557    83 23105   157   119   229    77  5846  2429]\n454|```\n455|We have prepared our dataset for training. Now we will code the transformer architecture and look into its theory correspondingly.\n456|\n457|### Transformer Overview\n458|\n459|Let’s have a quick look at how a transformer architecture is used to process and understand text. It works by breaking text into smaller pieces called tokens and predicting the next token in the sequence. A transformer has many layers, called transformer blocks, stacked on top of each other, with a final layer at the end to make the prediction.\n460|\n461|Each transformer block has two main components:\n462|\n463|* **Self-Attention Heads**: These figure out which parts of the input are most important for the model to focus on. For example, when processing a sentence, the attention heads can highlight relationships between words, such as how a pronoun relates to the noun it refers to.\n464|\n465|* **MLP (Multi-Layer Perceptron)**: This is a simple feed-forward neural network. It takes the information emphasized by the attention heads and processes it further. The MLP has an input layer that receives data from the attention heads, a hidden layer that adds complexity to the processing, and an output layer that passes the results to the next transformer block.\n466|\n467|Together, the attention heads act as the “what to think about” part, while the MLP is the “how to think about it” part. Stacking many transformer blocks allows the model to understand complex patterns and relationships in the text, but this is not always guaranteed.\n468|\n469|Instead of looking at the original paper diagram, let’s visualize a simpler and easier architecture diagram that we will be coding.\n470|\n471|![Transformer Architecture by [Fareed Khan](undefined)](https://cdn-images-1.medium.com/max/11808/1*QXmeA-H52C-p82AwawslbQ.png)\n472|\n473|Let’s read through the flow of our architecture that we will be coding:\n474|\n475| 1. Input tokens are converted to embeddings and combined with position information.\n476|\n477| 2. The model has 64 identical transformer blocks that process data sequentially.\n478|\n479| 3. Each block first runs multi-head attention to look at relationships between tokens.\n480|\n481| 4. Each block then processes data through an MLP that expands and then compresses the data.\n482|\n483| 5. Each step uses residual connections (shortcuts) to help information flow.\n484|\n485| 6. Layer normalization is used throughout to stabilize training.\n486|\n487| 7. The attention mechanism calculates which tokens should pay attention to each other.\n488|\n489| 8. The MLP expands the data to 4x size, applies ReLU, and then compresses it back down.\n490|\n491| 9. The model uses 16 attention heads to capture different types of relationships.\n492|\n493| 10. The final layer converts the processed data into vocabulary-sized predictions.\n494|\n495| 11. The model generates text by repeatedly predicting the next most likely token.\n496|\n497|### Multi Layer Perceptron (MLP)\n498|\n499|MLP is a fundamental building block within the transformer’s feed-forward network. Its role is to introduce non-linearity and learn complex relationships within the embedded representations. When defining an MLP module, an important parameter is n_embed, which defines the dimensionality of the input embedding.\n500|\n501|", "total_lines": 1240, "file_size": 60650, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 1240 lines)", "is_binary": false, "is_image": false} read_file 


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  {"content": "1|# --- Configuration ---\n2|\n3|# Define vocabulary size and transformer configuration (3 Billion)\n4|VOCAB_SIZE = 50304          # Number of unique tokens in the vocabulary\n5|CONTEXT_LENGTH = 512        # Maximum sequence length for the model\n6|N_EMBED = 2048              # Dimension of the embedding space\n7|N_HEAD = 16                 # Number of attention heads in each transformer block\n8|N_BLOCKS = 64               # Number of transformer blocks in the model\n9|\n10|# Paths to training and development datasets\n11|TRAIN_PATH = \"data/train/pile_train.h5\"  # File path for the training dataset\n12|DEV_PATH = \"data/val/pile_dev.h5\"      # File path for the validation dataset\n13|\n14|# Transformer training parameters\n15|T_BATCH_SIZE = 32          # Number of samples per training batch\n16|T_CONTEXT_LENGTH = 16      # Context length for training batches\n17|T_TRAIN_STEPS = 200000     # Total number of training steps\n18|T_EVAL_STEPS = 1000        # Frequency (in steps) to perform evaluation\n19|T_EVAL_ITERS = 250         # Number of iterations to evaluate the model\n20|T_LR_DECAY_STEP = 50000    # Step at which to decay the learning rate\n21|T_LR = 5e-4                # Initial learning rate for training\n22|T_LR_DECAYED = 5e-5        # Learning rate after decay\n23|T_OUT_PATH = \"models/transformer_B.pt\"  # Path to save the trained model\n24|\n25|# Device configuration\n26|DEVICE = 'cuda'\n27|\n28|# Store all configurations in a dictionary for easy access and modification\n29|default_config = {\n30|    'vocab_size': VOCAB_SIZE,\n31|    'context_length': CONTEXT_LENGTH,\n32|    'n_embed': N_EMBED,\n33|    'n_head': N_HEAD,\n34|    'n_blocks': N_BLOCKS,\n35|    'train_path': TRAIN_PATH,\n36|    'dev_path': DEV_PATH,\n37|    't_batch_size': T_BATCH_SIZE,\n38|    't_context_length': T_CONTEXT_LENGTH,\n39|    't_train_steps': T_TRAIN_STEPS,\n40|    't_eval_steps': T_EVAL_STEPS,\n41|    't_eval_iters': T_EVAL_ITERS,\n42|    't_lr_decay_step': T_LR_DECAY_STEP,\n43|    't_lr': T_LR,\n44|    't_lr_decayed': T_LR_DECAYED,\n45|    't_out_path': T_OUT_PATH,\n46|    'device': DEVICE,\n47|}", "total_lines": 46, "file_size": 1940, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|import torch\n2|import torch.nn.functional as F\n3|import os\n4|from tqdm import tqdm\n5|import numpy as np\n6|from config.config import default_config as config\n7|from src.models.transformer import Transformer\n8|from data_loader.data_loader import get_batch_iterator\n9|from typing import Dict\n10|\n11|# --- Initialize the Model and Print Parameters ---\n12|\n13|model = Transformer(\n14|    n_head=config['n_head'],\n15|    n_embed=config['n_embed'],\n16|    context_length=config['context_length'],\n17|    vocab_size=config['vocab_size'],\n18|    N_BLOCKS=config['n_blocks']\n19|).to(config['device'])\n20|\n21|# Print the total number of parameters\n22|total_params = sum(p.numel() for p in model.parameters())\n23|print(f\"Total number of parameters in the model: {total_params:,}\")\n24|\n25|# --- Optimizer Setup and Loss Tracking ---\n26|\n27|# Set up the AdamW optimizer with the specified learning rate.\n28|optimizer = torch.optim.AdamW(model.parameters(), lr=config['t_lr'])\n29|\n30|# List to track loss values during training.\n31|losses = []\n32|\n33|# Define a window size for averaging recent losses in the training loop.\n34|AVG_WINDOW = 64\n35|\n36|# Helper function to estimate the average loss for training and development data.\n37|@torch.no_grad()\n38|def estimate_loss(steps: int) -> Dict[str, float]:\n39|    \"\"\"\n40|    Evaluate the model on training and development datasets and calculate average loss.\n41|\n42|    Args:\n43|        steps (int): Number of steps to evaluate.\n44|\n45|    Returns:\n46|        dict: Dictionary containing average losses for 'train' and 'dev' splits.\n47|    \"\"\"\n48|    out = {}\n49|    model.eval()  # Set the model to evaluation mode.\n50|\n51|    for split in ['train', 'dev']:\n52|        # Select the appropriate data path for the current split.\n53|        data_path = config['train_path'] if split == 'train' else config['dev_path']\n54|\n55|        # Create a batch iterator for evaluation.\n56|        batch_iterator_eval = get_batch_iterator(\n57|            data_path, config['t_batch_size'], config['t_context_length'], device=config['device']\n58|        )\n59|\n60|        # Initialize a tensor to track loss values for each evaluation step.\n61|        losses_eval = torch.zeros(steps)\n62|        for k in range(steps):\n63|            try:\n64|                # Fetch a batch and calculate the loss.\n65|                xb, yb = next(batch_iterator_eval)\n66|                _, loss = model(xb, yb)\n67|                losses_eval[k] = loss.item()\n68|            except StopIteration:\n69|                # Handle the case where the data iterator ends early.\n70|                print(f\"Warning: Iterator for {split} ended early.\")\n71|                break\n72|\n73|        # Compute the mean loss for the current split.\n74|        out[split] = losses_eval[:k + 1].mean()\n75|\n76|    model.train()  # Restore the model to training mode.\n77|    return out\n78|\n79|# --- Training Loop ---\n80|\n81|# Create a batch iterator for the training data.\n82|batch_iterator = get_batch_iterator(\n83|    config['train_path'],\n84|    config['t_batch_size'],\n85|    config['t_context_length'],\n86|    device=config['device']\n87|)\n88|\n89|# Create a progress bar to monitor training progress.\n90|pbar = tqdm(range(config['t_train_steps']))\n91|for step in pbar:\n92|    try:\n93|        # Fetch a batch of input and target data.\n94|        xb, yb = next(batch_iterator)\n95|\n96|        # Perform a forward pass and compute the loss.\n97|        _, loss = model(xb, yb)\n98|\n99|        # Record the loss for tracking.\n100|        losses.append(loss.item())\n101|        pbar.set_description(f\"Train loss: {np.mean(losses[-AVG_WINDOW:]):.4f}\")\n102|\n103|        # Backpropagate the loss and update the model parameters.\n104|        optimizer.zero_grad(set_to_none=True)\n105|        loss.backward()\n106|        optimizer.step()\n107|\n108|        # Periodically evaluate the model on training and development data.\n109|        if step % config['t_eval_steps'] == 0:\n110|            evaluation_losses = estimate_loss(config['t_eval_iters'])\n111|            train_loss = evaluation_losses['train']\n112|            dev_loss = evaluation_losses['dev']\n113|            print(f\"Step: {step}, Train loss: {train_loss:.4f}, Dev loss: {dev_loss:.4f}\")\n114|\n115|        # Decay the learning rate at the specified step.\n116|        if step == config['t_lr_decay_step']:\n117|            print('Decaying learning rate')\n118|            for g in optimizer.param_groups:\n119|                g['lr'] = config['t_lr_decayed']\n120|    except StopIteration:\n121|        # Handle the case where the training data iterator ends early.\n122|        print(\"Training data iterator finished early.\")\n123|        break\n124|\n125|# --- Save Model and Final Evaluation ---\n126|\n127|# Create the output directory if it does not exist.\n128|os.makedirs(config['t_out_path'].split('/')[0], exist_ok=True)\n129|\n130|# Perform a final evaluation of the model on training and development datasets.\n131|evaluation_losses = estimate_loss(200)\n132|train_loss = evaluation_losses['train']\n133|dev_loss = evaluation_losses['dev']\n134|\n135|# Ensure unique model save path in case the file already exists.\n136|modified_model_out_path = config['t_out_path']\n137|save_tries = 0\n138|while os.path.exists(modified_model_out_path):\n139|    save_tries += 1\n140|    model_out_name = os.path.splitext(config['t_out_path'])[0]\n141|    modified_model_out_path = model_out_name + f\"_{save_tries}\" + \".pt\"\n142|\n143|# Save the model's state dictionary, optimizer state, and training metadata.\n144|torch.save(\n145|    {\n146|        'model_state_dict': model.state_dict(),\n147|        'optimizer_state_dict': optimizer.state_dict(),\n148|        'losses': losses,\n149|        'train_loss': train_loss,\n150|        'dev_loss': dev_loss,\n151|        'steps': len(losses),\n152|    },\n153|    modified_model_out_path\n154|)\n155|print(f\"Saved model to {modified_model_out_path}\")\n156|print(f\"Finished training. Train loss: {train_loss:.4f}, Dev loss: {dev_loss:.4f}\")", "total_lines": 155, "file_size": 5462, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  Análise do repositório **FareedKhan-dev/train-llm-from-scratch**:

  - **Problema**: Falta de material acessível que mostre de forma executável todo o fluxo de treinar um LLM — desde aquisição de dados até geração de texto — sem depender de infraestrutura industrial.
  - **Tema**: Treinamento de modelo de linguagem do zero com PyTorch, baseado na arquitetura Transformer do paper *Attention is All You Need*, usando o dataset The Pile e tokenizer tiktoken.
  - **Razão do hype**:
    - Promete reproduzibilidade “de verdade” (download → pré-processamento → treino → geração).
    - Foco em acessibilidade: sugere treino de modelos de ~13M de parâmetros em GPUs comuns.
    - Traz exemplo prático de saída gerada e comparativo de GPUs por capacidade de treino.
  - **Tecnologias**:
    - PyTorch
    - tiktoken (tokenizer r50k_base)
    - h5py, zstandard (armazenamento e compactação de dados)
    - Jupyter Notebook
    - The Pile dataset
    - CUDA/config para execução em GPU  


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c0:
  {"content": "1|# MOSS-TTS Family\n2|\n3|<br>\n4|\n5|<p align=\"center\">\n6|  <img src=\"./assets/OpenMOSS_Logo.png\" height=\"70\" align=\"middle\" />\n7|  &nbsp;&nbsp;&nbsp;&nbsp;\n8|  <img src=\"./assets/mosi-logo.png\" height=\"50\" align=\"middle\" />\n9|</p>\n10|\n11|<div align=\"center\">\n12|  <a href=\"https://trendshift.io/repositories/22854\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/22854\" alt=\"OpenMOSS%2FMOSS-TTS | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n13|</div>\n14|\n15|\n16|<div align=\"center\">\n17|  <a href=\"https://clawhub.ai/luogao2333/moss-tts-voice\"><img src=\"https://img.shields.io/badge/🦞_OpenClaw-Skills-8A2BE2\" alt=\"OpenClaw\"></a>\n18|  <a href=\"https://huggingface.co/collections/OpenMOSS-Team/moss-tts\"><img src=\"https://img.shields.io/badge/Huggingface-Models-orange?logo=huggingface&amp\"></a>\n19|  <a href=\"https://www.modelscope.cn/collections/openmoss/MOSS-TTS\"><img src=\"https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white\"></a>\n20|  <a href=\"https://mosi.cn/#models\"><img src=\"https://img.shields.io/badge/Blog-View-blue?logo=internet-explorer&amp\"></a>\n21|  <a href=\"https://arxiv.org/abs/2603.18090\"><img src=\"https://img.shields.io/badge/Arxiv-2603.18090-red?logo=Arxiv&amp\"></a>\n22|\n23|  <a href=\"https://studio.mosi.cn\"><img src=\"https://img.shields.io/badge/AIStudio-Try-green?logo=internet-explorer&amp\"></a>\n24|  <a href=\"https://studio.mosi.cn/docs/moss-tts\"><img src=\"https://img.shields.io/badge/API-Docs-00A3FF?logo=fastapi&amp\"></a>\n25|  <a href=\"https://x.com/Open_MOSS\"><img src=\"https://img.shields.io/badge/Twitter-Follow-black?logo=x&amp\"></a>\n26|  <a href=\"https://discord.gg/fvm5TaWjU3\"><img src=\"https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&amp\"></a>\n27|  <a href=\"./assets/wechat.jpg\"><img src=\"https://img.shields.io/badge/WeChat-Join-07C160?logo=wechat&amp;logoColor=white\" alt=\"WeChat\"></a>\n28|</div>\n29|\n30|\n31|[English](README.md) | [简体中文](README_zh.md)\n32|\n33|\n34|MOSS‑TTS Family is an open‑source **speech and sound generation model family** from [MOSI.AI](https://mosi.cn/#hero) and the [OpenMOSS team](https://www.open-moss.com/). It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS.\n35|\n36|\n37|## News\n38|* 2026.5.26: 🚀 Released [MOSS-SoundEffect-v2.0](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0), a new text-to-audio model using a **DiT backbone with the Flow Matching objective**, generating **48 kHz** bilingual sound effects up to **30 seconds** — see [`moss_soundeffect_v2/`](https://github.com/OpenMOSS/MOSS-TTS/tree/main/moss_soundeffect_v2).\n39|* 2026.5.26: 🚀 Released [MOSS-TTS-v1.5](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5), with stronger multilingual synthesis when language tags are provided, more stable voice cloning, better long-reference short-text cloning, punctuation-following prosody, and explicit pause control via `[pause X.Ys]`.\n40|* 2026.5.6: 🚀 MOSS-TTS and MOSS-Audio-Tokenizer now support `mlx-audio`. Visit the [mlx-audio GitHub repository](https://github.com/Blaizzy/mlx-audio) for details.\n41|* 2026.4.29: 📝 MOSS-TTS 2.0 is coming soon! We are collecting TTS feedback, suggestions, and feature requests via the [requirements collection form](https://acnc6zeentra.feishu.cn/share/base/form/shrcnyAe1LwqKWjCSuW4wiZ2Hef).\n42|* 2026.4.13: 🚀 MOSS-TTS-Nano, our ~100M-parameter model, is now available! It supports multilingual voice cloning, 48 kHz stereo input/output, and streaming output on just 4 CPU cores. Check the [GitHub repository](https://github.com/OpenMOSS/MOSS-TTS-Nano) and our [blog](https://openmoss.github.io/MOSS-TTS-Nano-Demo/) for more details.\n43|* 2026.3.31: 📄 Our technical reports for [MOSS-TTSD](https://arxiv.org/pdf/2603.19739) and [MOSS-VoiceGenerator](https://arxiv.org/pdf/2603.28086) are now available on arXiv!\n44|* 2026.3.26: 📘 Added a tutorial on fine-tuning the MOSS-TTS-Realtime!\n45|* 2026.3.20: 📄 Our [technical report](https://arxiv.org/pdf/2603.18090) is now available on arXiv!\n46|* 2026.3.18: 🚀 Added a first-class MOSS-TTS `llama.cpp` implementation in the companion repository [`OpenMOSS/llama.cpp`](https://github.com/OpenMOSS/llama.cpp/tree/moss-tts-firstclass), including end-to-end docs and a runnable pipeline for GGUF backbone inference plus ONNX audio codec decoding. See the [first-class e2e guide](https://github.com/OpenMOSS/llama.cpp/blob/moss-tts-firstclass/docs/moss-tts-firstclass-e2e.md).\n47|* 2026.3.16: 📘 Added a tutorial on fine-tuning the MossTTSLocal architecture, suitable for MOSS-TTS-Local-Transformer!\n48|* 2026.3.12: 🚀 Added SGLang backend support for the `MossTTSDelay` architecture, enabling efficient inference for MOSS-TTS (Delay) and MOSS-SoundEffect, with around **3× faster** generation throughput!\n49|* 2026.3.11: 📘 Added a tutorial on fine-tuning the MossTTSDelay architecture, suitable for MOSS-TTS(Delay), MOSS-TTSD, MOSS-VoiceGenerator, and MOSS-SoundEffect!\n50|* 2026.3.10: ⚡️ Significantly optimized the VRAM usage of llama.cpp inference pipeline. Now 8B model fits onto 8GB GPUs!\n51|* 2026.3.4: 🚀 Added **PyTorch-free inference support** — enabling lightweight on-device deployment via **llama.cpp + ONNX Runtime**. Quantized **GGUF weights** are released at [OpenMOSS-Team/MOSS-TTS-GGUF](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-GGUF), and the **ONNX audio tokenizer** is available at [OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX). See the [llama.cpp backend](#llamacpp-backend-torch-free-inference) for details.\n52|* 2026.3.4: 🎉 We add MOSS-TTS skills in [ClawHub](https://clawhub.ai) of 🦞 OpenClaw: [feishu-voice-tts](https://clawhub.ai/helloeveryworlds/feishu-voice-tts) and [moss-tts-voice](https://clawhub.ai/luogao2333/moss-tts-voice).\n53|* 2026.2.10: 🎉🎉🎉 We have released [MOSS-TTS Family](https://huggingface.co/collections/OpenMOSS-Team/moss-tts). Check our [Blog](https://mosi.cn/#models) for more details! Our **Huggingface Space** is here: [MOSS-TTS](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTS), [MOSS-TTSD-v1.0](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTSD-v1.0), [MOSS-VoiceGenerator](https://huggingface.co/spaces/OpenMOSS-Team/MOSS-VoiceGenerator).\n54|\n55|\n56|## Demo\n57|\n58|<div align=\"center\">\n59|  <video src=\"https://gist.github.com/user-attachments/assets/fdce9f66-20ec-45e8-9615-89606ae2fbe8\" width=\"70%\" poster=\"\"> </video>\n60|</div>\n61|\n62|## Contents\n63|\n64|- [MOSS-TTS Family](#moss-tts-family)\n65|  - [News](#news)\n66|  - [Demo](#demo)\n67|  - [Contents](#contents)\n68|  - [Introduction](#introduction)\n69|  - [Model Architecture](#model-architecture)\n70|  - [Released Models](#released-models)\n71|  - [Supported Languages](#supported-languages)\n72|  - [MOSS-TTS-v1.5](#moss-tts-v15)\n73|  - [Quickstart](#quickstart)\n74|    - [OpenClaw API Skills](#openclaw-api-skills)\n75|    - [Environment Setup](#environment-setup)\n76|      - [Using Conda](#using-conda)\n77|      - [Using `uv`](#using-uv)\n78|      - [(Optional) Install FlashAttention 2](#optional-install-flashattention-2)\n79|    - [MOSS‑TTS Basic Usage](#mosstts-basic-usage)\n80|  - [Fine-Tuning](#fine-tuning)\n81|  - [llama.cpp Backend (Torch-Free Inference)](#llamacpp-backend-torch-free-inference)\n82|    - [Quick Start](#quick-start)\n83|    - [Installation Profiles](#installation-profiles)\n84|    - [Model Weights](#model-weights)\n85|    - [Configuration](#configuration)\n86|  - [SGLang Backend (Accelerated Inference)](#sglang-backend-accelerated-inference)\n87|    - [Quick Start](#quick-start-1)\n88|    - [Request and Response](#request-and-response)\n89|      - [MOSS-TTS (Delay)](#moss-tts-delay)\n90|      - [MOSS-SoundEffect](#moss-soundeffect)\n91|      - [Response](#response)\n92|  - [Evaluation](#evaluation)\n93|    - [MOSS‑TTS](#mosstts)\n94|    - [MOSS‑TTSD](#mossttsd)\n95|      - [Objective Evaluation](#objective-evaluation)\n96|      - [Subjective Evaluation](#subjective-evaluation)\n97|    - [MOSS‑VoiceGenerator](#mossvoicegenerator)\n98|    - [MOSS‑TTS-Realtime](#mosstts-realtime)\n99|  - [MOSS-TTS-Nano](#moss-tts-nano)\n100|    - [Introduction](#introduction-1)\n101|    - [Model Weights](#model-weights-1)\n102|  - [MOSS-Audio-Tokenizer](#moss-audio-tokenizer)\n103|    - [Introduction](#introduction-2)\n104|    - [Model Weights](#model-weights-2)\n105|    - [Objective Reconstruction Evaluation](#objective-reconstruction-evaluation)\n106|  - [📚 More Information](#-more-information)\n107|    - [🌟 Community Projects](#-community-projects)\n108|  - [LICENSE](#license)\n109|  - [Citation](#citation)\n110|  - [Star History](#star-history)\n111|\n112|\n113|## Introduction\n114|\n115|<p align=\"center\">\n116|  <img src=\"./assets/moss_tts_family.jpeg\" width=\"85%\" />\n117|</p>\n118|\n119|When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline.\n120|\n121|- **MOSS‑TTS**: The flagship production model featuring high fidelity and optimal zero-shot voice cloning. It supports **long-speech generation**, **fine-grained control over Pinyin, phonemes, and duration**, as well as **multilingual/code-switched synthesis**.\n122|- **MOSS‑TTSD**: A spoken dialogue generation model for expressive, multi-speaker, and ultra-long dialogues. The new **v1.0 version** achieves **industry-leading performance on objective metrics** and **outperformed top closed-source models like Doubao and Gemini 2.5-pro** in subjective evaluations. You can visit the [MOSS-TTSD repository](https://github.com/OpenMOSS/MOSS-TTSD) for details.\n123|- **MOSS‑VoiceGenerator**: An open-source voice design model capable of generating diverse voices and styles directly from text prompts, **without any reference speech**. It unifies voice design, style control, and synthesis, functioning independently or as a design layer for downstream TTS. Its performance **surpasses other top-tier voice design models in arena ratings**.\n124|- **MOSS‑TTS‑Realtime**: A multi-turn context-aware model for real-time voice agents. It uses incremental synthesis to ensure natural and coherent replies, making it **ideal for building low-latency voice agents when paired with text models**. The TTFB (Time To First Byte) of MOSS-TTS-Realtime reaches 180 ms, and the $T_{\\text{LLM-first-sentence}} + T_{\\text{MOSS-TTS-Realtime-TTFB}}$ is 377 ms.\n125|- **MOSS‑SoundEffect**: A content creation model specialized in **sound effect generation** with wide category coverage and controllable duration. It generates audio for natural environments, urban scenes, biological sounds, human actions, and musical fragments, suitable for film, games, and interactive experiences.\n126|\n127|\n128|## Model Architecture\n129|\n130|We train **MossTTSDelay** and **MossTTSLocal** as complementary baselines under one training/evaluation setup: **Delay** emphasizes long-context stability, inference speed, and production readiness, while **Local** emphasizes lightweight flexibility and strong objective performance for streaming-oriented systems. Together they provide reproducible references for deployment and research.\n131|\n132|**MossTTSRealtime** is not a third comparison baseline; it is a capability-driven design for voice agents. By modeling multi-turn context from both prior text and user acoustics, it delivers low-latency streaming speech that stays coherent and voice-consistent across turns.\n133|\n134|\n135|| Architecture  | Core Mechanism | Arch Details |\n136||---|---|---|\n137|| `MossTTSDelay` |  Multi‑head parallel RVQ prediction with delay‑pattern scheduling | [![Arch Details](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](moss_tts_delay/README.md) |\n138|| `MossTTSLocal` | Time‑synchronous RVQ blocks with a depth transformer | [![Arch Details](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](moss_tts_local/README.md) |\n139|| `MossTTSRealtime` | Hierarchical text–audio inputs for realtime synthesis | [![Arch Details](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](moss_tts_realtime/README.md) |\n140|\n141|## Released Models\n142|\n143|\n144|| Model | Architecture | Size | Model Card | Hugging Face | ModelScope |\n145||---|---|---:|---|---|---|\n146|| **MOSS-TTS-v1.5** | `MossTTSDelay` | 8B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_tts_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-TTS-v1.5) |\n147|| **MOSS-TTS 1.0** | `MossTTSDelay` | 8B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_tts_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-TTS) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-TTS) |\n148|| **MOSS-TTS-Local-Transformer** | `MossTTSLocal` | 1.7B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_tts_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-TTS-Local-Transformer) |\n149|| **MOSS‑TTSD‑V1.0** | `MossTTSDelay` | 8B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_ttsd_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-TTSD-v1.0) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-TTSD-v1.0) |\n150|| **MOSS‑VoiceGenerator** | `MossTTSDelay` | 1.7B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_voice_generator_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-VoiceGenerator) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-VoiceGenerator) |\n151|| **MOSS‑SoundEffect** | `MossTTSDelay` | 8B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_sound_effect_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-SoundEffect) |\n152|| **MOSS‑SoundEffect‑v2.0** | `MossSoundEffectPipeline` | 1.3B DiT | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](moss_soundeffect_v2/README.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-SoundEffect-v2.0) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-SoundEffect-v2.0) |\n153|| **MOSS‑TTS‑Realtime** | `MossTTSRealtime` | 1.7B | [![Model Card](https://img.shields.io/badge/Model%20Card-View-blue?logo=markdown)](docs/moss_tts_realtime_model_card.md) | [![Hugging Face](https://img.shields.io/badge/Huggingface-Model-orange?logo=huggingface)](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Realtime) | [![ModelScope](https://img.shields.io/badge/ModelScope-Model-7B61FF?logo=modelscope&logoColor=white)](https://modelscope.cn/models/openmoss/MOSS-TTS-Realtime) |\n154|\n155|## Supported Languages\n156|\n157|MOSS-TTS-v1.5 currently supports **31 languages**. It keeps the 20 languages supported by [MOSS-TTS 1.0](https://huggingface.co/OpenMOSS-Team/MOSS-TTS) and extends multilingual continued training to Cantonese, Dutch, Finnish, Hindi, Macedonian, Malay, Romanian, Swahili, Tagalog, Thai, and Vietnamese.\n158|\n159|MOSS-TTSD and MOSS-TTS-Realtime follow their own model cards for supported language coverage.\n160|\n161|| Language | Code | Flag | Language | Code | Flag | Language | Code | Flag |\n162||---|---|---|---|---|---|---|---|---|\n163|| Chinese | zh | 🇨🇳 | Cantonese | yue | 🇭🇰 | English | en | 🇺🇸 |\n164|| Arabic | ar | 🇸🇦 | Czech | cs | 🇨🇿 | Danish | da | 🇩🇰 |\n165|| Dutch | nl | 🇳🇱 | Finnish | fi | 🇫🇮 | French | fr | 🇫🇷 |\n166|| German | de | 🇩🇪 | Greek | el | 🇬🇷 | Hebrew | he | 🇮🇱 |\n167|| Hindi | hi | 🇮🇳 | Hungarian | hu | 🇭🇺 | Italian | it | 🇮🇹 |\n168|| Japanese | ja | 🇯🇵 | Korean | ko | 🇰🇷 | Macedonian | mk | 🇲🇰 |\n169|| Malay | ms | 🇲🇾 | Persian (Farsi) | fa | 🇮🇷 | Polish | pl | 🇵🇱 |\n170|| Portuguese | pt | 🇵🇹 | Romanian | ro | 🇷🇴 | Russian | ru | 🇷🇺 |\n171|| Spanish | es | 🇪🇸 | Swahili | sw | 🇹🇿 | Swedish | sv | 🇸🇪 |\n172|| Tagalog | tl | 🇵🇭 | Thai | th | 🇹🇭 | Turkish | tr | 🇹🇷 |\n173|| Vietnamese | vi | 🇻🇳 | | | | | | |\n174|\n175|## MOSS-TTS-v1.5\n176|\n177|**MOSS-TTS-v1.5** is continued from [MOSS-TTS 1.0](https://huggingface.co/OpenMOSS-Team/MOSS-TTS). It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching.\n178|\n179|Compared with MOSS-TTS 1.0, v1.5 focuses on these improvements:\n180|\n181|- **Stronger multilingual synthesis with language tags**: when the language is known, set it in `processor.build_user_message(text=text, language=\"French\")` or the equivalent API field.\n182|- **More stable voice cloning**: v1.5 improves speaker similarity and reduces cloning variance across repeated generations.\n183|- **Better long-reference, short-text cloning**: v1.5 handles reference audio that is much longer than the target text more reliably.\n184|- **More stable punctuation-following prosody**: v1.5 follows punctuation-driven pauses more closely, especially in long sentences.\n185|- **Explicit pause control**: use inline pause markers such as `[pause 3.2s]`, for example `我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！`.\n186|\n187|\n188|## Quickstart\n189|\n190|### OpenClaw API Skills\n191|\n192|We add MOSS-TTS skills in [ClawHub](https://clawhub.ai) of 🦞 OpenClaw. You can get your API key from [MOSI AI Studio](https://studio.mosi.cn).\n193|\n194|| Skill | Description | Install |\n195||---|---|---|\n196|| [`feishu-voice-tts`](https://clawhub.ai/helloeveryworlds/feishu-voice-tts) | Send voice messages in Feishu | `clawhub install feishu-voice-tts` |\n197|| [`moss-tts-voice`](https://clawhub.ai/luogao2333/moss-tts-voice) | Call MOSS-TTS API to generate speech | `clawhub install moss-tts-voice` |\n198|\n199|### Environment Setup\n200|\n201|We recommend a clean, isolated Python environment with **Transformers 5.0.0** to avoid dependency conflicts.\n202|\n203|#### Using Conda\n204|\n205|```bash\n206|conda create -n moss-tts python=3.12 -y\n207|conda activate moss-tts\n208|```\n209|\n210|Install all required dependencies:\n211|\n212|```bash\n213|git clone https://github.com/OpenMOSS/MOSS-TTS.git\n214|cd MOSS-TTS\n215|pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e \".[torch-runtime]\"\n216|```\n217|\n218|#### Using `uv`\n219|\n220|```bash\n221|# Install uv first: https://docs.astral.sh/uv/getting-started/installation/\n222|git clone https://github.com/OpenMOSS/MOSS-TTS.git\n223|cd MOSS-TTS\n224|uv venv --python 3.12 .venv\n225|source .venv/bin/activate\n226|uv pip install --torch-backend cu128 -e \".[torch-runtime]\"\n227|```\n228|\n229|#### (Optional) Install FlashAttention 2\n230|\n231|For better speed and lower GPU memory usage, you can install FlashAttention 2 if your hardware supports it.\n232|\n233|If you use Conda/pip:\n234|\n235|```bash\n236|pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e \".[torch-runtime,flash-attn]\"\n237|```\n238|\n239|If your machine has limited RAM and many CPU cores, you can cap build parallelism:\n240|\n241|```bash\n242|MAX_JOBS=4 pip install --extra-index-url https://download.pytorch.org/whl/cu128 -e \".[torch-runtime,flash-attn]\"\n243|```\n244|\n245|If you use `uv`:\n246|\n247|```bash\n248|uv pip install --torch-backend cu128 -e \".[torch-runtime,flash-attn]\"\n249|```\n250|\n251|If your machine has limited RAM and many CPU cores, you can cap build parallelism:\n252|\n253|```bash\n254|MAX_JOBS=4 uv pip install --torch-backend cu128 -e \".[torch-runtime,flash-attn]\"\n255|```\n256|\n257|Notes:\n258|- Dependencies are managed in `pyproject.toml`, which currently pins `torch==2.9.1+cu128` and `torchaudio==2.9.1+cu128`.\n259|- In `uv`, `--torch-backend cu128` lets uv fetch compatible PyTorch CUDA wheels and resolve the rest from PyPI with the default safe index strategy.\n260|- If you need another backend, replace `cu128` with your target (for example, `cpu`, `cu126`).\n261|- If FlashAttention 2 fails to build on your machine, you can skip it and use the default attention backend.\n262|- FlashAttention 2 is only available on supported GPUs and is typically used with `torch.float16` or `torch.bfloat16`.\n263|\n264|\n265|<a id=\"moss-tts-basic-usage\"></a>\n266|### MOSS‑TTS Basic Usage\n267|\n268|If you prefer Gradio demos, we provide 4 scripts for the main models:\n269|\n270|| Model | Script |\n271||---|---|\n272|| MOSS-TTS | [clis/moss_tts_app.py](clis/moss_tts_app.py) |\n273|| MOSS-TTSD | [clis/moss_ttsd_app.py](clis/moss_ttsd_app.py) |\n274|| MOSS-VoiceGenerator | [clis/moss_voice_generator_app.py](clis/moss_voice_generator_app.py) |\n275|| MOSS-SoundEffect | [clis/moss_sound_effect_app.py](clis/moss_sound_effect_app.py) |\n276|\n277|For the MOSS-TTS-Realtime Gradio demo, please refer to [MOSS-TTS-Realtime Model Card](docs/moss_tts_realtime_model_card.md)\n278|\n279|> Tip: MOSS-TTS-v1.5 uses the same generation API as the 1.0 `MossTTSDelay-8B` checkpoint. For multilingual inputs, set `language` whenever the language is known.\n280|\n281|MOSS-TTS provides a convenient `generate` interface for rapid usage. The examples below cover:\n282|1. Direct generation (Chinese / English / multilingual text with language tags / Pinyin / IPA)\n283|2. Voice cloning\n284|3. Duration control\n285|4. Explicit pause control with `[pause X.Ys]`\n286|\n287|```python\n288|from pathlib import Path\n289|import importlib.util\n290|import torch\n291|import torchaudio\n292|from transformers import AutoModel, AutoProcessor\n293|# Disable the broken cuDNN SDPA backend\n294|torch.backends.cuda.enable_cudnn_sdp(False)\n295|# Keep these enabled as fallbacks\n296|torch.backends.cuda.enable_flash_sdp(True)\n297|torch.backends.cuda.enable_mem_efficient_sdp(True)\n298|torch.backends.cuda.enable_math_sdp(True)\n299|\n300|\n301|pretrained_model_name_or_path = \"OpenMOSS-Team/MOSS-TTS-v1.5\"\n302|device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n303|dtype = torch.bfloat16 if device == \"cuda\" else torch.float32\n304|\n305|def resolve_attn_implementation() -> str:\n306|    # Prefer FlashAttention 2 when package + device conditions are met.\n307|    if (\n308|        device == \"cuda\"\n309|        and importlib.util.find_spec(\"flash_attn\") is not None\n310|        and dtype in {torch.float16, torch.bfloat16}\n311|    ):\n312|        major, _ = torch.cuda.get_device_capability()\n313|        if major >= 8:\n314|            return \"flash_attention_2\"\n315|\n316|    # CUDA fallback: use PyTorch SDPA kernels.\n317|    if device == \"cuda\":\n318|        return \"sdpa\"\n319|\n320|    # CPU fallback.\n321|    return \"eager\"\n322|\n323|\n324|attn_implementation = resolve_attn_implementation()\n325|print(f\"[INFO] Using attn_implementation={attn_implementation}\")\n326|\n327|processor = AutoProcessor.from_pretrained(\n328|    pretrained_model_name_or_path,\n329|    trust_remote_code=True,\n330|)\n331|processor.audio_tokenizer = processor.audio_tokenizer.to(device)\n332|\n333|text_1 = \"亲爱的你，\\n你好呀。\\n\\n今天，我想用最认真、最温柔的声音，对你说一些重要的话。\\n这些话，像一颗小小的星星，希望能在你的心里慢慢发光。\\n\\n首先，我想祝你——\\n每天都能平平安安、快快乐乐。\\n\\n希望你早上醒来的时候，\\n窗外有光，屋子里很安静，\\n你的心是轻轻的，没有着急，也没有害怕。\\n\\n希望你吃饭的时候胃口很好，\\n走路的时候脚步稳稳，\\n晚上睡觉的时候，能做一个又一个甜甜的梦。\\n\\n我希望你能一直保持好奇心。\\n对世界充满问题，\\n对天空、星星、花草、书本和故事感兴趣。\\n当你问“为什么”的时候，\\n希望总有人愿意认真地听你说话。\\n\\n我也希望你学会温柔。\\n温柔地对待朋友，\\n温柔地对待小动物，\\n也温柔地对待自己。\\n\\n如果有一天你犯了错，\\n请不要太快责怪自己，\\n因为每一个认真成长的人，\\n都会在路上慢慢学会更好的方法。\\n\\n愿你拥有勇气。\\n当你站在陌生的地方时，\\n当你第一次举手发言时，\\n当你遇到困难、感到害怕的时候，\\n希望你能轻轻地告诉自己：\\n“我可以试一试。”\\n\\n就算没有一次成功，也没有关系。\\n失败不是坏事，\\n它只是告诉你，你正在努力。\\n\\n我希望你学会分享快乐。\\n把开心的事情告诉别人，\\n把笑声送给身边的人，\\n因为快乐被分享的时候，\\n会变得更大、更亮。\\n\\n如果有一天你感到难过，\\n我希望你知道——\\n难过并不丢脸，\\n哭泣也不是软弱。\\n\\n愿你能找到一个安全的地方，\\n慢慢把心里的话说出来，\\n然后再一次抬起头，看见希望。\\n\\n我还希望你能拥有梦想。\\n这个梦想也许很大，\\n也许很小，\\n也许现在还说不清楚。\\n\\n没关系。\\n梦想会和你一起长大，\\n在时间里慢慢变得清楚。\\n\\n最后，我想送你一个最最重要的祝福：\\n\\n愿你被世界温柔对待，\\n也愿你成为一个温柔的人。\\n\\n愿你的每一天，\\n都值得被记住，\\n都值得被珍惜。\\n\\n亲爱的你，\\n请记住，\\n你是独一无二的，\\n你已经很棒了，\\n而你的未来，\\n一定会慢慢变得闪闪发光。\\n\\n祝你健康、勇敢、幸福，\\n祝你永远带着笑容向前走。\"\n334|text_2 = \"We stand on the threshold of the AI era.\\nArtificial intelligence is no longer just a concept in laboratories, but is entering every industry, every creative endeavor, and every decision. It has learned to see, hear, speak, and think, and is beginning to become an extension of human capabilities. AI is not about replacing humans, but about amplifying human creativity, making knowledge more equitable, more efficient, and allowing imagination to reach further. A new era, jointly shaped by humans and intelligent systems, has arrived.\"\n335|text_3 = \"nin2 hao3，qing3 wen4 nin2 lai2 zi4 na3 zuo4 cheng2 shi4？\"\n336|text_4 = \"nin2 hao3，qing4 wen3 nin2 lai2 zi4 na4 zuo3 cheng4 shi3？\"\n337|text_5 = \"您好，请问您来自哪 zuo4 cheng2 shi4？\"\n338|text_6 = \"/həloʊ, meɪ aɪ æsk wɪtʃ sɪti juː ɑːr frʌm?/\"\n339|text_7 = \"Bonjour, je voudrais essayer une voix française naturelle et stable.\"\n340|text_8 = \"我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！\"\n341|\n342|# Use audio from ./assets/audio to avoid downloading from the cloud.\n343|ref_audio_1 = \"https://speech-demo.oss-cn-shanghai.aliyuncs.com/moss_tts_demo/tts_readme_demo/reference_zh.wav\"\n344|ref_audio_2 = \"https://speech-demo.oss-cn-shanghai.aliyuncs.com/moss_tts_demo/tts_readme_demo/reference_en.m4a\"\n345|\n346|conversations = [\n347|    # Direct TTS (no reference). Language tags are recommended in v1.5.\n348|    [processor.build_user_message(text=text_1)],\n349|    [processor.build_user_message(text=text_2)],\n350|    # Direct TTS (no reference). For languages other than Chinese and English,\n351|    # set the language tag whenever it is known.\n352|    [processor.build_user_message(text=text_7, language=\"French\")],\n353|    # Pinyin or IPA input\n354|    [processor.build_user_message(text=text_3)],\n355|    [processor.build_user_message(text=text_4)],\n356|    [processor.build_user_message(text=text_5)],\n357|    [processor.build_user_message(text=text_6)],\n358|    # Explicit pause control. Use [pause X.Ys], such as [pause 3.2s].\n359|    [processor.build_user_message(text=text_8)],\n360|    # Voice cloning (with reference)\n361|    [processor.build_user_message(text=text_1, reference=[ref_audio_1])],\n362|    [processor.build_user_message(text=text_2, reference=[ref_audio_2])],\n363|    # Duration control\n364|    [processor.build_user_message(text=text_2, tokens=325)],\n365|    [processor.build_user_message(text=text_2, tokens=600)],\n366|]\n367|\n368|model = AutoModel.from_pretrained(\n369|    pretrained_model_name_or_path,\n370|    trust_remote_code=True,\n371|    attn_implementation=attn_implementation,\n372|    torch_dtype=dtype,\n373|).to(device)\n374|model.eval()\n375|\n376|batch_size = 1\n377|\n378|save_dir = Path(\"inference_root\")\n379|save_dir.mkdir(exist_ok=True, parents=True)\n380|sample_idx = 0\n381|with torch.no_grad():\n382|    for start in range(0, len(conversations), batch_size):\n383|        batch_conversations = conversations[start : start + batch_size]\n384|        batch = processor(batch_conversations, mode=\"generation\")\n385|        input_ids = batch[\"input_ids\"].to(device)\n386|        attention_mask = batch[\"attention_mask\"].to(device)\n387|\n388|        outputs = model.generate(\n389|            input_ids=input_ids,\n390|            attention_mask=attention_mask,\n391|            max_new_tokens=4096,\n392|        )\n393|\n394|        for message in processor.decode(outputs):\n395|            audio = message.audio_codes_list[0]\n396|            out_path = save_dir / f\"sample{sample_idx}.wav\"\n397|            sample_idx += 1\n398|            torchaudio.save(out_path, audio.unsqueeze(0), processor.model_config.sampling_rate)\n399|\n400|```\n401|\n402|For each model’s full usage, please refer to its corresponding model card.\n403|\n404|<a id=\"fine-tuning\"></a>\n405|## Fine-Tuning\n406|\n407|Finetuning tutorials are organized by architecture.\n408|\n409|Currently available:\n410|\n411|- `MossTTSDelay` / `OpenMOSS-Team/MOSS-TTS-v1.5` (also compatible with `OpenMOSS-Team/MOSS-TTS`): [moss_tts_delay/finetuning/README.md](moss_tts_delay/finetuning/README.md)\n412|- `MossTTSLocal` / `OpenMOSS-Team/MOSS-TTS-Local-Transformer`: [moss_tts_local/finetuning/README.md](moss_tts_local/finetuning/README.md)\n413|- `Moss-TTS-Realtime` / `OpenMOSS-Team/MOSS-TTS-Realtime`: [moss_tts_realtime/finetuning/README.md](moss_tts_realtime/finetuning/README.md)\n414|\n415|Additional architecture-specific finetuning tutorials will be added under their corresponding directories.\n416|\n417|## llama.cpp Backend (Torch-Free Inference)\n418|\n419|For lightweight or edge deployment, MOSS-TTS supports a **torch-free** inference path using [llama.cpp](https://github.com/ggerganov/llama.cpp) for the Qwen3 backbone and ONNX Runtime / TensorRT for the audio tokenizer. No PyTorch installation required.\n420|\n421|We also maintain a newer first-class MOSS-TTS path in the companion repository [`OpenMOSS/llama.cpp`](https://github.com/OpenMOSS/llama.cpp/tree/moss-tts-firstclass). Unlike the legacy bridge backend documented below, it moves multi-channel embeddings, multi-head outputs, and delay-pattern decoding directly into `llama.cpp`.\n422|\n423|For that path, start from the [first-class e2e guide](https://github.com/OpenMOSS/llama.cpp/blob/moss-tts-firstclass/docs/moss-tts-firstclass-e2e.md).\n424|\n425|### Quick Start\n426|\n427|```bash\n428|# 1. Install (torch-free)\n429|pip install -e \".[llama-cpp-onnx]\"\n430|\n431|# 2. Download pre-quantized backbone + embedding/lm_head weights\n432|huggingface-cli download OpenMOSS-Team/MOSS-TTS-GGUF --local-dir weights/MOSS-TTS-GGUF\n433|\n434|# 3. Download ONNX audio tokenizer\n435|huggingface-cli download OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX --local-dir weights/MOSS-Audio-Tokenizer-ONNX\n436|\n437|# 4. Build the C bridge (one-time, requires llama.cpp compiled from source)\n438|cd moss_tts_delay/llama_cpp && bash build_bridge.sh /path/to/llama.cpp && cd ../..\n439|\n440|# 5. Run inference\n441|python -m moss_tts_delay.llama_cpp \\\n442|    --config configs/llama_cpp/default.yaml \\\n443|    --text \"Hello, world!\" --output output.wav\n444|\n445|# 6. (Optional) Low-memory mode for 8 GB GPUs — loads/unloads components per stage\n446|python -m moss_tts_delay.llama_cpp \\\n447|    --config configs/llama_cpp/trt-8gb.yaml \\\n448|    --text \"Hello, world!\" --output output.wav\n449|```\n450|\n451|### Installation Profiles\n452|\n453|| Profile | Install Command | Dependencies | Use Case |\n454||---------|----------------|--------------|----------|\n455|| **Torch-free (ONNX)** | `pip install -e \".[llama-cpp-onnx]\"` | numpy, onnxruntime-gpu, tokenizers | Recommended starting point |\n456|| **Torch-free (TRT)** | `pip install -e \".[llama-cpp-trt]\"` | numpy, tensorrt, cuda-python | Maximum audio tokenizer speed (build engines yourself) |\n457|| **Torch-accelerated** | `pip install -e \".[llama-cpp-onnx,llama-cpp-torch]\"` | + torch | GPU-accelerated LM heads (~30x faster) |\n458|\n459|> **Want to convert weights yourself?** See the [conversion guide](moss_tts_delay/llama_cpp/conversion/README.md) for step-by-step instructions on extracting, converting, and quantizing MOSS-TTS weights with llama.cpp.\n460|\n461|### Model Weights\n462|\n463|| Repository | Contents | Download |\n464||-----------|----------|----------|\n465|| [`OpenMOSS-Team/MOSS-TTS-GGUF`](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-GGUF) | Q4_K_M backbone `.gguf`, `embeddings/` (`.npy`), `lm_heads/` (`.npy`), tokenizer | `huggingface-cli download OpenMOSS-Team/MOSS-TTS-GGUF --local-dir weights/MOSS-TTS-GGUF` |\n466|| [`OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX`](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX) | Encoder & decoder ONNX models | `huggingface-cli download OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX --local-dir weights/MOSS-Audio-Tokenizer-ONNX` |\n467|\n468|> **Note:** We do **not** provide pre-built TensorRT engines, as they are tied to your specific GPU and TensorRT version. To use TRT, build engines from the ONNX models yourself — see `moss_audio_tokenizer/trt/build_engine.sh`.\n469|\n470|### Configuration\n471|\n472|Four pre-built configs are provided in `configs/llama_cpp/`:\n473|\n474|- `default.yaml` — ONNX audio + GGUF backbone (recommended start)\n475|- `trt.yaml` — TensorRT audio + GGUF backbone (max throughput, user-built engines)\n476|- `trt-8gb.yaml` — Low-memory mode for 8 GB GPUs (staged loading, TRT audio)\n477|- `cpu-only.yaml` — fully CPU-based (no GPU required)\n478|\n479|Key config options:\n480|- `heads_backend: auto | numpy | torch` — LM heads computation backend\n481|- `audio_backend: onnx | trt | torch` — audio tokenizer backend\n482|- `low_memory: true | false` — staged loading for limited VRAM (loads/unloads encoder, backbone, decoder per stage)\n483|- `kv_cache_type_k / kv_cache_type_v` — KV cache quantization (e.g. `q8_0`, `q4_0`) to reduce VRAM\n484|- `flash_attn: auto | enabled | disabled` — flash attention for lower peak VRAM during prefill\n485|\n486|For full documentation, see [moss_tts_delay/llama_cpp/README.md](moss_tts_delay/llama_cpp/README.md).\n487|\n488|## SGLang Backend (Accelerated Inference)\n489|\n490|MOSS-TTS (Delay) supports running the fused MOSS-TTS and MOSS-Audio-Tokenizer model with the deeply extended [SGLang](https://github.com/OpenMOSS/sglang) from OpenMOSS, enabling efficient inference for audio generation.\n491|\n492|### Quick Start\n493|\n494|```bash\n495|# 1. Clone the SGLang repository\n496|git clone https://github.com/OpenMOSS/sglang.git\n497|\n498|# 2. Install SGLang\n499|pip install -e ./sglang/python[all]\n500|\n501|", "total_lines": 777, "file_size": 50867, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 777 lines)", "is_binary": false, "is_image": false} read_file 


