diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md
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+CLAUDE.md should be a routing file, not a knowledge dump. Keep it under 150 lines. Point to .claude/rules/*.md for detailed specs and docs/ for architecture.
+
+
+
+### Auto-Update Memory (MANDATORY)
+
+**Update memory files AS YOU GO, not at the end.** When you learn something new, update immediately.
+
+| Trigger | Action |
+|---------|--------|
+| User shares a fact about themselves | → Update `memory-profile.md` |
+| User states a preference | → Update `memory-preferences.md` |
+| A decision is made | → Update `memory-decisions.md` with date |
+| Completing substantive work | → Add to `memory-sessions.md` |
+
+**Skip:** Quick factual questions, trivial tasks with no new info.
+
+**DO NOT ASK. Just update the files when you learn something.**
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diff --git a/.claude/docs/CLAUDE.md b/.claude/docs/CLAUDE.md
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+++ b/.claude/docs/CLAUDE.md
@@ -0,0 +1,14 @@
+### Auto-Update Memory (MANDATORY)
+
+**Update memory files AS YOU GO, not at the end.** When you learn something new, update immediately.
+
+| Trigger | Action |
+|---------|--------|
+| User shares a fact about themselves | → Update `memory-profile.md` |
+| User states a preference | → Update `memory-preferences.md` |
+| A decision is made | → Update `memory-decisions.md` with date |
+| Completing substantive work | → Add to `memory-sessions.md` |
+
+**Skip:** Quick factual questions, trivial tasks with no new info.
+
+**DO NOT ASK. Just update the files when you learn something.**
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diff --git a/.claude/docs/vinrobotics_introduction.md b/.claude/docs/vinrobotics_introduction.md
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index 0000000..bdd21ab
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+++ b/.claude/docs/vinrobotics_introduction.md
@@ -0,0 +1,17 @@
+1. Thông tin chung về VinRobotics
+- Tên đầy đủ: Công ty Cổ phần Nghiên cứu Phát triển và Ứng dụng Người máy VinRobotics.
+- Ngày thành lập: 20/11/2024.
+- Vốn điều lệ: 1.000 tỷ đồng.
+- Cơ cấu cổ đông: Tập đoàn Vingroup (51%), ông Phạm Nhật Vượng (39%), ông Phạm Nhật Quân Anh (5%) và ông Phạm Nhật Minh Hoàng (5%).
+- Lãnh đạo: Tổng Giám đốc là ông Ngô Quốc Hưng (người có nhiều kinh nghiệm trong lĩnh vực AI và Robotics).
+
+2. Lĩnh vực hoạt động và Mục tiêu
+- VinRobotics không chỉ phục vụ hệ sinh thái của Vingroup mà còn hướng tới thị trường doanh nghiệp bên ngoài trong các ngành kinh tế trọng điểm.
+- Nghiên cứu & Phát triển: Tập trung vào các giải pháp tự động hóa, robot công nghiệp và trí tuệ nhân tạo (AI).
+- Sản xuất & Tích hợp: Chuyên về các loại người máy thông minh, robot nâng hạ, bốc xếp hàng hóa và các thiết bị máy móc chuyên dụng khác.
+- Triết lý: "Công nghệ robot vị nhân sinh" – tối ưu hóa quy trình sản xuất và nâng cao hiệu quả công việc trong công nghiệp, dịch vụ và đời sống.
+
+3. Các thực thể liên quan (Hệ sinh thái Robotics của Vingroup)
+- Ngoài VinRobotics, Vingroup còn thành lập các đơn vị chuyên biệt khác để bao phủ toàn diện mảng Humanoid và Physical AI:
+- VinMotion: Tập trung vào phát triển robot hình người (Humanoid Robot) tích hợp Physical AI. Vào khoảng tháng 6/2025, VinMotion đã ra mắt nguyên mẫu robot hình người "Made in Vietnam" đầu tiên.
+- VinDynamics: Chuyên về các giải pháp kỹ thuật tiên tiến cho thế hệ robot thông minh, làm chủ các công nghệ lõi để hỗ trợ con người trong cuộc sống hàng ngày.
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diff --git a/.claude/skills/explain-code/SKILL.md b/.claude/skills/explain-code/SKILL.md
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+++ b/.claude/skills/explain-code/SKILL.md
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+---
+name: explain-code
+description: Explains code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"
+---
+
+When explaining code, always include:
+
+1. **Start with an analogy**: Compare the code to something from everyday life
+2. **Draw a diagram**: Use ASCII art to show the flow, structure, or relationships
+3. **Walk through the code**: Explain step-by-step what happens
+4. **Highlight a gotcha**: What's a common mistake or misconception?
+
+Keep explanations conversational. For complex concepts, use multiple analogies.
\ No newline at end of file
diff --git a/.claude/skills/notes/notes.md b/.claude/skills/notes/notes.md
new file mode 100644
index 0000000..a97c21d
--- /dev/null
+++ b/.claude/skills/notes/notes.md
@@ -0,0 +1,5 @@
+Skills docx from aitmpl: https://app.aitmpl.com/component/skill/document-processing/docx
+skills docx from claude: https://github.com/anthropics/skills/blob/main/skills/docx/SKILL.md
+documents to build skills from claude: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf
+
+https://code.claude.com/docs/en/hooks-guide
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 5024bbd..fd1749e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,7 +19,7 @@ wheels/
*.egg-info/
.installed.cfg
*.egg
-
+mcp/
# Virtual environments
.venv/
venv/
diff --git a/docs/livekit-agent-tutorial.md b/docs/livekit-agent-tutorial.md
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index 0000000..e722382
--- /dev/null
+++ b/docs/livekit-agent-tutorial.md
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+# LiveKit Voice Agent: Behind the Scenes
+
+This guide explains how LiveKit's `AgentSession` coordinates multiple AI models (VAD, STT, LLM, TTS) into a seamless real-time voice experience.
+
+## High-Level Architecture
+
+The `AgentSession` acts as the "brain" or "orchestrator" of the voice agent. It manages the full lifecycle of a conversation by linking several components together:
+
+```mermaid
+graph TD
+ A["Room (rtc.Room)"] -->|Audio Stream| B[VAD]
+ B -->|Speech Detected| C[Audio Buffer]
+ C -->|Speech Ended| D[STT]
+ D -->|Transcript| E[LLM]
+ E -->|Text Stream| F[TTS]
+ F -->|Audio Stream| G[Audio Source]
+ G -->|Agent Voice| A
+```
+
+### 🧠 Key Concepts: Server vs. Room
+Before diving into the components, it’s crucial to understand two terms that are often used interchangeably:
+
+> [!TIP]
+> **LiveKit Server = The Building** (The physical software running on a machine). It provides the infrastructure to route data.
+>
+> **LiveKit Room = A Conference Room** (A logical space created inside the server). It is the specific context where participants interact.
+>
+> **Key Difference**: One **Server** can host thousands of **Rooms**. When the agent joins, it connects to a specific **Room** *on* a specific **Server**.
+
+### 1. Voice Activity Detection (VAD)
+**The Trigger**: VAD is always listening to the incoming audio track from the room.
+- **How it works**: It looks for patterns in the audio energy consistent with human speech.
+- **Behind the scenes**:
+ - **Audio Path**: `AgentSession` runs a background task (`_forward_audio_task`) that continuously pulls raw audio frames from the participant's track.
+ - **Parallel Processing**: These frames are pushed into two separate internal queues simultaneously: one for the **VAD** and one for the **STT**.
+ - **VAD Decision**: The VAD model (like Silero) processes the stream and emits events (`START_OF_SPEECH`, `END_OF_SPEECH`).
+ - **Buffering Control**: When VAD detects `START_OF_SPEECH`, the session enters a "speaking" state. It continues to collect audio in the STT buffer until VAD detects `END_OF_SPEECH` and the "endpointing delay" (silence threshold) has passed.
+- **Implementation**: In your code, you use `silero.VAD.load()`, which is a highly efficient local VAD model.
+
+### 2. Speech-to-Text (STT)
+**The Translator**: Once the VAD identifies a complete utterance, the buffered audio is sent to the STT model.
+
+#### 🛠️ Internal Step-by-Step:
+1. **Frame Collection**: While the user is speaking, LiveKit collects audio in **20ms chunks** (standard WebRTC frames). These are stored in an `AudioBuffer`.
+2. **Trigger**: As soon as the VAD detects silence (exceeding the `min_silence_duration`), it "vends" the entire collected buffer to the STT.
+3. **Normalization**: The [FasterWhisperSTT](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/faster_whisper_stt.py#L133-L142) plugin converts the raw `int16` audio data (which ranges from -32768 to 32767) into `float32` values normalized between **[-1.0, 1.0]**.
+4. **Inference**: The normalized array is passed to the `faster-whisper` model.
+ - **Chunk Sizes**: Whisper models internally work on **30-second windows**. If your speech is shorter, it is padded; if longer, it is processed in overlapping blocks.
+ - **Beam Search**: The model explores multiple possible text paths (defined by `beam_size`) to find the most likely sentence.
+5. **Reconstruction**: The model returns "segments". The plugin joins these segments into a single string of text.
+6. **Handoff**: The text is wrapped in a `SpeechEvent` and sent to the LLM via the `ChatContext`.
+
+#### 🚀 Local Integration:
+- **[FasterWhisperSTT](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/faster_whisper_stt.py)**: A custom-built local plugin for this project.
+- **Why Faster-Whisper?**: It is an implementation of OpenAI's Whisper model using CTranslate2, which is up to **4x faster** than the original version and uses significantly less memory.
+
+### 3. Large Language Model (LLM)
+**The Intelligence**: The transcript from the STT is added to the conversation history (`ChatContext`) and sent to the LLM.
+- **How it works**: The LLM processes the message and generates a response. To keep latency low, it usually **streams** its response word-by-word or sentence-by-sentence.
+- **Implementation**: In the example, `lk_openai.LLM.with_ollama(...)` allows you to use a local model (via Ollama) using the standard OpenAI protocol.
+
+### 4. Text-to-Speech (TTS)
+**The Voice**: As the LLM generates text chunks, they are immediately fed into the TTS engine.
+- **How it works**: The TTS converts the text back into audio.
+- **Local Integration**:
+ - **[PiperTTS](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/piper_tts.py)**: This plugin creates a `_PiperChunkedStream`. It synthesizes audio for each text block provided by the LLM. It uses a thread pool to handle the synthesis without blocking the main event loop.
+- **Real-Time Synergy**: LiveKit matches the timing of the TTS audio output with the room's clock to ensure a smooth, conversational feel.
+
+---
+
+## The "Magic" of `AgentSession`
+
+What makes the LiveKit Agents SDK special is how it handles the **complex edge cases** that occur during real conversations:
+
+### 🔇 Turn Detection & Endpointing
+`AgentSession` doesn't just wait for the user to stop talking. It uses an **endpointing delay** (e.g., 500ms). If the user pauses for longer than this, it assumes they are finished. This prevents the agent from cutting the user off mid-sentence.
+
+### ✋ Interruptions
+If the user starts talking while the agent is still speaking, `AgentSession` detects this via the VAD.
+- It immediately **stops** the agent's current audio playback.
+- It **discards** any pending audio chunks in the TTS queue.
+- It **cancels** the LLM generation task.
+- This creates the natural experience where the agent stops immediately when you "talk over" it.
+
+### 🔄 State Management
+The session manages a state machine for both the user and the agent:
+- `user_state`: `listening` ↔ `speaking`
+- `agent_state`: `listening` ↔ `thinking` ↔ `speaking`
+
+You can see this in [voice_agent.py](file:///home/vrh3/workspace/projects/voice-agent/examples/voice_agent.py#L215-L222), where latency is tracked by measuring the time between `user_input_transcribed` and `agent_state == "speaking"`.
+
+---
+
+## Code Highlight: The Setup in `voice_agent.py`
+
+When you return the `AgentSession` object:
+
+```python
+return AgentSession(
+ stt=FasterWhisperSTT(...), # Custom local model
+ llm=lk_openai.LLM(...), # OpenAI-compatiable local LLM
+ tts=PiperTTS(...), # Custom local voice
+ vad=silero.VAD.load(), # Standard VAD plugin
+)
+```
+
+You are essentially providing the session with its vital organs. The session itself provides the "nervous system" that makes them all work together automatically without you having to write the networking or audio synchronization code yourself.
+---
+
+## The Engine: Background Tasks
+
+To provide a real-time experience, the system runs several asynchronous tasks in the background. Here is a breakdown of the most important ones:
+
+### 🌐 Connection & Infrastructure
+- **`_job_ctx_connect`**: The initial task that connects your agent worker to the LiveKit Server.
+- **`_forward_audio_task`**: The high-level loop that constantly pulls audio frames from the room and feeds them into the system's "ears."
+- **`_update_activity`**: Manages the transitions between different agent behaviors (e.g., if you have multiple agents in one session).
+
+### ❤️ The Heart (AgentActivity)
+- **`_scheduling_task`**: The "Main Loop" of your agent. It monitors the speech queue and decides when to transition between states: `listening` → `thinking` → `speaking`.
+- **`_tts_task`**: Created every time the agent needs to speak. It handles the text synthesis (e.g., via Piper) and forwards the audio to the room.
+- **`_on_enter_task` / `_on_exit_task`**: Lifecycle hooks that run your custom logic when an agent initializes or shuts down.
+
+### 👂 The Senses (AudioRecognition)
+- **`_vad_task`**: Continuously processes audio frames through the VAD model (Silero) to detect speech vs. silence.
+- **`_stt_task`**: Manages the STT engine (FasterWhisper). It waits for the VAD's signal to know when to start and stop transcription.
+- **`_end_of_turn_task`**: The "Patience" task. It waits for the 0.5s silence delay after speech stops to see if the user is truly finished or just pausing.
+
+### 🔊 The "Last Mile" (RoomIO)
+- **`_forward_audio`**: Sends the synthesized audio from the agent's TTS back into the room's participant track.
+- **`_wait_for_playout`**: A delicate task that ensures the agent doesn't start thinking about its *next* response until the *current* audio has actually finished playing in the user's ears.
+
+You can see these tasks being created in the [AgentSession](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_session.py) and [AgentActivity](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py) source files.
+---
+
+## The Brain: LLM Prompting & Tools
+
+The LLM is the core decision-maker of your agent. Here is how it interacts with your code and the user:
+
+### 📝 How is the model prompted?
+The "Instruction" you provide during the `Agent` initialization is treated as the **System Prompt**.
+- **Location**: In your `VoiceAssistant` class ([voice_agent.py:92-101](file:///home/vrh3/workspace/projects/voice-agent/examples/voice_agent.py#L92-L101)), the `instructions` argument is passed to the base `Agent` class.
+- **Behind the scenes**: Under the hood, the SDK injects these instructions as the first message in the `ChatContext` with the role `system`.
+
+### 📖 Does it handle conversation history?
+**Yes, completely.**
+- `AgentSession` maintains a `ChatContext` object that acts as a short-term memory.
+- Every time the user speaks, their transcript is added as a `user` message.
+- Every time the agent responds, its reply is added as an `assistant` message.
+- This entire context is sent to the LLM on every turn, allowing it to remember what was said earlier (e.g., answering "What did I just say?").
+
+### 🛠️ Does it handle tool calls?
+**Yes, the SDK provides a robust framework for function calling.**
+- **Integration**: You can define Python functions and pass them to the `Agent` or `AgentSession` in the `tools` list.
+- **Execution**: If the LLM decides to use a tool, it emits a "tool call" chunk. The `AgentActivity` intercepts this, runs your Python function, captures the output, and automatically sends it back to the LLM to continue the response.
+- **Example Flow**:
+ 1. User: "What's the weather in London?"
+ 2. LLM: `[Tool Call: get_weather(city="London")]`
+ 3. System: (Runs your local function) -> "Sunny, 20°C"
+ 4. LLM: "The weather in London is sunny and 20°C."
+
+You can see how the instructions and history are managed in the [Agent](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent.py) and [AgentActivity](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py) files.
+---
+
+## The Room: Communication Hub
+
+If the `AgentSession` is the brain, the **Room (`rtc.Room`)** is the physical space where the conversation happens.
+
+### 🏢 What is a Room?
+A LiveKit Room is a real-time session that connects multiple users and bots together. It acts as the central router for all audio, video, and data.
+
+### 👥 Key Concepts inside a Room
+- **Participants**: Everyone in the room (including your Agent) is a `Participant`.
+ - **Local Participant**: This is your Agent itself.
+ - **Remote Participants**: These are the human users or other bots connected to the same room.
+- **Tracks**: The actual streams of data.
+ - **Audio Track**: What the VAD listens to and what the TTS publishes to.
+ - **Video Track**: Used for vision-based agents or avatars.
+- **Publications**: When a participant "shares" a track with the room, they **Publish** it. Your agent's TTS publishes an audio track so others can hear it.
+- **Subscriptions**: To hear someone, you must **Subscribe** to their track. The `AgentSession` automatically subscribes to the users' audio tracks so it can "hear" them.
+
+### 🔗 Relationship with AgentSession
+The `AgentSession` is essentially a specialized LiveKit client that:
+1. **Joins** a Room.
+2. **Scans** for human participants.
+3. **Subscribes** to their audio.
+4. **Pipes** that audio into the VAD and STT.
+5. **Generates** its own audio and **Publishes** it back into the same Room.
+
+Without the Room, the Agent would be "locked in a dark box" with no way to receive input or send output.
+
+---
+
+## The Senses: Silero VAD
+
+**Silero VAD** is the "ears" of your agent that distinguish between speech and background noise.
+
+### 🧠 What is Silero VAD?
+Silero is a pre-trained, high-performance Voice Activity Detection (VAD) model. In this project, it runs locally using the **ONNX Runtime**, making it extremely fast and private.
+
+### ⚙️ How it works
+The VAD doesn't "understand" words; it only calculates the **probability** that a chunk of audio contains human speech.
+- **Windowing**: It processes audio in small windows (usually 32ms).
+- **Probability**: For each window, it outputs a value between 0.0 and 1.0.
+- **State Machine**: It uses several buffers and thresholds to decide when someone has *started* and *stopped* speaking.
+
+### 🛠️ Key Parameters (Tweaking Performance)
+You can find these in the `silero.VAD.load()` call:
+- **`activation_threshold` (default 0.5)**: If the model is >50% sure it's speech, it marks it as active.
+- **`min_speech_duration` (default 0.05s)**: Prevents accidental triggers from very short noises (like a cough or a click).
+- **`min_silence_duration` (default 0.55s)**: How long the user must be silent before the VAD says "they stopped speaking."
+- **`prefix_padding_duration` (default 0.5s)**: One of the most important settings. It keeps a "pre-roll" buffer of audio so that when speech is detected, the *beginning* of the sentence isn't cut off.
+
+### 🖥️ CPU vs GPU
+By default, Silero VAD in this project **uses the CPU** (`force_cpu=True`).
+
+**Why not the GPU?**
+- **Data Transfer Overhead**: Silero is a very "light" model. The time it takes to move audio data from your system RAM to the GPU's VRAM (and back) is often greater than the time it takes the CPU to just run the calculation.
+- **Latency**: For real-time voice, every millisecond counts. Running VAD on the CPU eliminates the bus latency associated with the GPU.
+- **Efficiency**: It saves your GPU's power and memory for much heavier tasks, like running the LLM (Ollama/vLLM) or a vision model.
+
+You can see this logic in [onnx_model.py:43-49](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/plugins/silero/onnx_model.py#L43-L49).
+
+### 🚀 Why Silero?
+- **Local & Private**: No audio leaves your server for voice detection.
+- **Efficiency**: It is much lighter than running a full STT model (like Whisper) continuously.
+- **Robustness**: It is specifically trained to handle various backgrounds, accents, and languages.
+
+You can dive deeper into the implementation in the [silero/vad.py](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/plugins/silero/vad.py) source file.
+
+---
+
+## RTC: The Infrastructure
+
+While the `livekit.agents` package handles the "AI logic," the **`livekit.rtc`** package handles the "networking and media."
+
+### 🔌 What is RTC?
+**RTC** stands for **Real-Time Communication**. It is the technology that allows audio and video to travel across the internet with almost zero delay (less than 200ms).
+
+### 🛠️ What does the RTC layer do?
+Think of the RTC layer as the **"Broadcasting Station"** and the **"Wire"**:
+- **Connection Management**: It handles the complexities of WebRTC (ICE, STUN, TURN) to make sure your agent can connect to the LiveKit server, even through firewalls.
+- **Media Streaming**: It breaks your audio (from TTS) into tiny packets and sends them over the network. It also "paints" incoming packets back into audio frames for your VAD and STT.
+- **Synchronization**: It ensures that audio and video stay in sync (lip-sync).
+- **Resampler**: It can change the sample rate of audio on the fly (e.g., converting 48KHz high-quality music to 16KHz for a voice model).
+
+### 🪜 The Hierarchy
+1. **Application Layer**: Your `VoiceAssistant` in `voice_agent.py`. (Your logic)
+2. **Agents Layer**: `AgentSession`, `VAD`, `STT`, `LLM`. (Artificial Intelligence)
+3. **RTC Layer**: `Room`, `Participant`, `AudioTrack`, `AudioFrame`. (Connectivity & Media)
+
+Without the RTC layer, you would have an AI that can "think" and "generate" speech, but no way to send it to a human user or hear what they are saying.
+
+---
+
+## The Hub: LiveKit Server
+
+The **LiveKit Server** is the central "switchboard" or "router" that makes everything possible.
+
+### 🔌 What is it?
+LiveKit is an open-source, high-performance WebRTC server. Its primary job is to connect participants (users and agents) and route their audio/video streams efficiently.
+
+### 🏗️ SFU Architecture (Selective Forwarding Unit)
+Unlike older video conferencing tech that "mixes" everyone's audio into one stream (which is slow and heavy), LiveKit uses an **SFU** model:
+- **Routing, not Mixing**: It receives your agent's audio once and "forwards" it to every subscribed user. It doesn't need to peek inside the audio data, making it extremely fast.
+- **Adaptive**: If a user has a bad internet connection, the server can send them a lower-quality version of the stream without affecting other users.
+
+### 🔑 Why is it "The Hub"?
+1. **Orchestration**: It decides who is in which room and who is allowed to speak.
+2. **Signaling**: It handles the "handshake" between your agent worker and the users.
+3. **Connectivity**: It includes built-in **STUN/TURN** servers to help participants connect even when they are behind complex office firewalls.
+4. **Scalability**: You can run one server for a small app, or a cluster of servers to support millions of concurrent users.
+
+4. **Scalability**: You can run one server for a small app, or a cluster of servers to support millions of concurrent users.
+
+### 🔗 How your Agent connects
+Your agent worker (the code you run) connects to the server using:
+- **`LIVEKIT_URL`**: The address of the server (e.g., `wss://your-project.livekit.cloud`).
+- **`LIVEKIT_API_KEY` / `SECRET`**: Used to generate access tokens so the server knows your agent is authorized to join the room.
+
+In this architecture, the **Server** is the cloud (or self-hosted) infrastructure, while your **Agent Worker** is the "local" brain that provides the intelligence.
+
+---
+
+## Audio Data: The AudioBuffer
+
+To understand how the agent "hears," you need to know exactly how audio is packaged.
+
+### 📦 What is an AudioBuffer?
+In the LiveKit SDK, an [`AudioBuffer`](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/utils/audio.py#L16) is a flexible container. It is defined as:
+```python
+AudioBuffer = Union[list[rtc.AudioFrame], rtc.AudioFrame]
+```
+This means it can be a single unit of audio or a collection of many units (which is what the VAD sends to the STT).
+
+### 🎞️ Anatomy of an AudioFrame
+An **`AudioFrame`** is the "brick" that builds the wall of sound. It contains four critical pieces of information:
+
+1. **`data`**: The raw sound. It is stored as **bytes**.
+ - **Format**: Usually 16-bit signed integers (PCM).
+ - **Size**: `samples_per_channel * num_channels * 2 bytes`.
+2. **`sample_rate`**: How many "snapshots" of sound are taken per second (e.g., `16000` for Whisper, `48000` for high-quality audio).
+3. **`num_channels`**: `1` for mono (standard for voice models) or `2` for stereo.
+4. **`samples_per_channel`**: How many snapshots are in *this* specific frame.
+
+### 🔄 The Flow of Data: From Voice to Text
+
+The journey of a single "Hello" from your mouth to the agent's brain involves several hops:
+
+```mermaid
+sequenceDiagram
+ participant User as 🎙️ Human User
+ participant Room as 🏢 LiveKit Room
+ participant Agent as 🤖 Agent Worker
+ participant VAD as 👂 VAD (Silero)
+ participant STT as 👄 STT (Whisper)
+
+ Note over User,Room: The Room is hosted by the LiveKit Server (SFU)
+ User->>Room: 1. Publish Audio (20ms Opus frames)
+ Room->>Agent: 2. Forward Audio (Subscription)
+ Agent->>VAD: 3. Feed 20ms PCM frames
+ VAD->>VAD: 4. Detecting Speech?
+ loop During Speech
+ Agent->>Agent: 5. Store frames in AudioBuffer
+ end
+ VAD->>Agent: 6. Speech Ended (Silence detected)
+ Agent->>STT: 7. Hand over full AudioBuffer
+ STT->>Agent: 8. "Hello" (Text)
+```
+
+#### 1. 🎙️ Why is the Server the "First Contact" for the Agent?
+While the **User** is the actual source, your **Agent Worker** doesn't talk directly to the user's phone. They both connect to the **LiveKit Server**.
+- The user **Publishes** their audio to the server.
+- The agent **Subscribes** to that audio.
+- The server is the "meeting point" where the data handoff happens.
+
+#### 2. ⚡ Step-By-Step Breakdown:
+1. **Capture**: Your microphone captures sound and converts it to digital signals.
+ - *Implemented by*: Browser/Client App via `navigator.mediaDevices.getUserMedia` (Web API).
+2. **Packetization**: Your browser/app breaks the sound into **20ms packets** (usually compressed with **Opus** to save bandwidth).
+ - *Implemented by*: libwebrtc (C++ engine) inside the browser.
+3. **Handoff**: These packets travel over the internet to the **LiveKit Server**.
+ - *Implemented by*: Standard WebRTC/UDP transport protocols.
+4. **Routing**: The server quickly identifies that the Agent is interested in this audio and "forwards" the packets immediately.
+ - *Implemented by*: LiveKit Server (Go) `SelectiveForwardingUnit` (SFU) logic.
+5. **Reconstruction**: The Agent Worker receives the packets, decodes them back to raw **PCM**, and creates an `rtc.AudioFrame`.
+ - *Implemented by*: [AudioStream._run](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/rtc/audio_stream.py#L263) receives FFI events and [AudioFrame._from_owned_info](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/rtc/audio_frame.py#L125) builds the Python object.
+6. **Sensing (VAD)**: The `AgentSession` feeds every 20ms frame into the Silero VAD.
+ - *Implemented by*: [AudioRecognition.push_audio](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/audio_recognition.py#L150) sends the frame to the VAD and STT internal channels.
+7. **Collecting**: If the VAD says "this is speech," the session starts piling these 20ms "bricks" into an `AudioBuffer`.
+ - *Implemented by*: [AudioRecognition._on_vad_event](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/audio_recognition.py#L434) handles speech state, while the frames are queued in the [STTNode](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/audio_recognition.py#L616).
+8. **Translating (STT)**: Once the user stops talking (detected by silence), the entire pile of bricks is sent to the Whisper model to be translated into text.
+ - *Implemented by*: [FasterWhisperSTT._recognize_impl](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/faster_whisper_stt.py#L133) which normalizes the full buffer and runs the whisper inference.
+
+You can see the helper functions for managing these buffers (like `combine_frames`) in the [utils/audio.py](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/utils/audio.py) file.
+---
+
+### 🧠 The Intelligence & Voice: Flow of Text
+
+Once the "Hello" is translated into text, the "thinking" and "speaking" phase begins:
+
+```mermaid
+sequenceDiagram
+ participant STT as 👄 STT (Whisper)
+ participant Act as ⚙️ AgentActivity
+ participant LLM as 🧠 LLM (vLLM/Ollama)
+ participant TTS as 🗣️ TTS (Piper)
+ participant Room as 🏢 LiveKit Room
+ participant User as 🎙️ Human User
+
+ STT->>Act: 1. Final Transcript ("Hello")
+ Act->>Act: 2. Update ChatContext (User: "Hello")
+ Act->>LLM: 3. chat(chat_ctx, tools)
+ loop Streaming Response
+ LLM->>Act: 4. Text Chunk ("Hi")
+ LLM->>Act: 5. Text Chunk (" there!")
+ Act->>TTS: 6. push_text("Hi there!")
+ end
+ Note over TTS: Synthesizing Audio...
+ TTS->>Room: 7. Publish AudioFrames
+ Room->>User: 8. Forward Audio (Subscription)
+ User->>Room: 9. Playout complete (Signal)
+ Room->>Act: 10. Playout complete
+```
+
+#### ⚡ Step-By-Step Breakdown:
+1. **Context Update**: The transcript is added to the `ChatContext` (the Agent's short-term memory).
+ - *Implemented by*: [AgentActivity._pipeline_reply_task](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py#L1702) (Inserts the message into `chat_ctx`).
+2. **Inquiry**: `AgentActivity` sends the full conversation history to the Language Model (LLM).
+ - *Implemented by*: `perform_llm_inference` (internal function in [agent_activity.py](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py#L1714)).
+3. **Streaming**: The LLM sends back text chunks as they are generated (you don't wait for the full sentence).
+ - *Implemented by*: LLM yielding `ChatChunk` objects via `llm_gen_data.text_ch`.
+4. **Synthesis**: Each text chunk is immediately sent to the **Piper TTS** model to be turned into audio.
+ - *Implemented by*: [PiperTTS.synthesize](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/piper_tts.py#L196) and its internal [synthesis bridge](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/piper_tts.py#L73).
+5. **Broadcasting**: The Agent publishes the resulting audio frames back into the **Room**.
+ - *Implemented by*: [AudioEmitter.push](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/tts/tts.py#L717) and [AudioSource.capture_frame](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/rtc/audio_source.py#L102).
+6. **Playout**: Your browser/app receives these audio packets, decodes them, and plays them through your speakers.
+ - *Implemented by*: Client-side WebRTC engine; completion tracked in [agent_activity.py:on_playout_done](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py#L948).
+
+You can see the coordination logic in the [AgentActivity](file:///home/vrh3/workspace/projects/voice-agent/.venv/lib/python3.12/site-packages/livekit/agents/voice/agent_activity.py) and the synthesis loop in [PiperTTS](file:///home/vrh3/workspace/projects/voice-agent/src/local_livekit_plugins/piper_tts.py#L182-L210).
diff --git a/docs/livekit-agent-tutorial.pdf b/docs/livekit-agent-tutorial.pdf
new file mode 100644
index 0000000..c14c6b1
Binary files /dev/null and b/docs/livekit-agent-tutorial.pdf differ
diff --git a/docs/mcp_tools.md b/docs/mcp_tools.md
new file mode 100644
index 0000000..1b07f3f
--- /dev/null
+++ b/docs/mcp_tools.md
@@ -0,0 +1,183 @@
+# MCP Server Usage in AgentSession
+
+In the LiveKit Agents runtime, `mcp_servers` are used to provide external tools to a voice agent via the Model Context Protocol (MCP). Here's a breakdown of how they are integrated and used.
+
+## 1. Initialization in `AgentSession`
+
+`mcp_servers` is an optional argument in the `AgentSession` constructor. It is stored as a property and can be accessed by the session.
+
+```python
+# livekit/agents/voice/agent_session.py
+
+class AgentSession(...):
+ def __init__(
+ self,
+ ...,
+ mcp_servers: NotGivenOr[list[mcp.MCPServer]] = NOT_GIVEN,
+ ...
+ ) -> None:
+ ...
+ self._mcp_servers = mcp_servers or None
+
+ @property
+ def mcp_servers(self) -> list[mcp.MCPServer] | None:
+ return self._mcp_servers
+```
+
+## 2. Managing MCP Tools with `AgentActivity`
+
+The `AgentSession` manages one or more `AgentActivity` instances. When an `AgentActivity` starts, it initializes the MCP servers and discovers their available tools.
+
+```python
+# livekit/agents/voice/agent_activity.py
+
+class AgentActivity(...):
+ async def _start_session(self) -> None:
+ ...
+ if self.mcp_servers:
+ # Initialize each server and list tools
+ gathered = await asyncio.gather(
+ *(_list_mcp_tools_task(s) for s in self.mcp_servers),
+ return_exceptions=True,
+ )
+ # Store discovered MCP tools
+ self._mcp_tools = tools
+```
+
+`AgentActivity` prioritizes the `mcp_servers` defined on the `Agent` itself, falling back to the session's servers if none are provided by the agent.
+
+```python
+@property
+def mcp_servers(self) -> list[mcp.MCPServer] | None:
+ return (
+ self._agent.mcp_servers
+ if is_given(self._agent.mcp_servers)
+ else self._session.mcp_servers
+ )
+```
+
+## 3. Tool Discovery and Implementation
+
+Each discovered MCP tool is wrapped as a `RawFunctionTool`. This allows them to be treated interchangeably with other function tools.
+
+```python
+# livekit/agents/llm/mcp.py
+
+MCPTool = RawFunctionTool
+
+class MCPServer(ABC):
+ def _make_function_tool(self, ...) -> MCPTool:
+ async def _tool_called(raw_arguments: dict[str, Any]) -> Any:
+ # Forward the call to the actual MCP server
+ tool_result = await self._client.call_tool(name, raw_arguments)
+ return tool_result.content[0].model_dump_json()
+
+ return function_tool(_tool_called, raw_schema=raw_schema)
+```
+
+## 4. Integration with LLM
+
+The `AgentActivity` combines all available tools (session tools, agent tools, and MCP tools) into a single list that is provided to the LLM during inference.
+
+```python
+@property
+def tools(self) -> list[llm.FunctionTool | llm.RawFunctionTool | mcp.MCPTool]:
+ return self._session.tools + self._agent.tools + self._mcp_tools
+```
+
+## 5. Tool Layers in `AgentActivity`
+
+`AgentActivity` combines tools from multiple sources to provide the LLM with a complete set of capabilities.
+
+### 1. `self._session.tools`
+These are **global tools** defined at the session level.
+- **Where they come from**: They are passed into the `AgentSession` constructor.
+- **Scope**: They are shared across all agents that might run within that session (e.g., if you hand off from one agent to another, these tools remain available).
+- **Implementation**: In `AgentSession`, these are stored in `self._tools`.
+
+### 2. `self._agent.tools`
+These are **agent-specific tools**.
+- **Where they come from**:
+ 1. Tools passed directly into the `Agent` constructor.
+ 2. Methods within the `Agent` class (or its subclasses) that are decorated with `@llm.function_tool`. The `Agent` class automatically discovers these using `find_function_tools(self)`.
+- **Scope**: They are only available while that specific agent is active.
+- **Implementation**: In `Agent`, these are stored in `self._tools`.
+
+### How they are used together
+In `AgentActivity.tools`, these are combined with any discovered MCP tools to create the final list of tools provided to the LLM:
+
+## 6. LLM Tool Integration: Prompting and Decision-Making
+
+When the agent interacts with an LLM, the tools are not hidden in a text prompt; they are provided as a core part of the model's interface.
+
+### How Tools are "Prompted"
+Tools are not added to the text prompt (like the system instructions). Instead, they are converted into a structured format (JSON Schema) and sent as a specialized `tools` parameter in the API call to the LLM provider (e.g., OpenAI).
+
+```python
+# livekit/agents/inference/llm.py
+
+def to_fnc_ctx(fnc_ctx: list[llm.FunctionTool | llm.RawFunctionTool]) -> list[ChatCompletionToolParam]:
+ # Converts tools into OpenAI-compatible JSON Schema
+ return [
+ {
+ "type": "function",
+ "function": fnc.raw_schema if is_raw(fnc) else build_openai_schema(fnc)
+ } for fnc in fnc_ctx
+ ]
+```
+
+### When the LLM Decides to Use Tools
+The decision-making happens entirely within the LLM model itself.
+1. **Model Reasoning**: Based on the user's request and the tool descriptions provided in the schema, the model determines if a tool is needed.
+2. **Tool Call Generation**: If needed, the model returns a special `tool_calls` message instead of (or in addition to) regular text.
+3. **Detection**: The `LLMStream` in the LiveKit runtime parses these tool calls from the API response:
+
+```python
+# livekit/agents/inference/llm.py
+
+def _parse_choice(self, choice: Choice) -> llm.ChatChunk | None:
+ if choice.delta.tool_calls:
+ # Detected that the LLM wants to call a tool
+ return llm.ChatChunk(tool_calls=...)
+```
+
+## 7. Deep Dive: `_execute_tools_task` Workflow
+
+The `_execute_tools_task` function in `livekit/agents/voice/generation.py` is the core engine for tool execution. It handles everything from argument validation to parallel task management.
+
+### Detailed Workflow Stages
+
+#### 1. Setup and Event Listening
+The function initializes a list of `tasks` and defines a `_tool_completed` helper. It starts an `async for` loop over `function_stream`, which yields tool calls as they are parsed from the LLM's response. This allow the agent to start executing the first tool even while the LLM is still generating the second one.
+
+#### 2. Discovery and Type Safety
+For each incoming call:
+- It looks up the function name in the `ToolContext`.
+- It verifies that the tool choice allows for execution (e.g., not `tool_choice="none"`).
+- It validates that the tool is a supported type (`FunctionTool` or `RawFunctionTool`).
+
+#### 3. Intelligent Argument Preparation (Dependency Injection)
+`llm_utils.prepare_function_arguments` is called to:
+- **Parse the LLM's JSON**: Converts the string arguments into Python types.
+- **Dependency Injection**: If the tool's signature includes special types like `RunContext`, `AgentSession`, or `SpeechHandle`, the utility automatically injects the current instances of these objects. This makes it easy for developers to access the current session state inside their tools.
+
+#### 4. Asynchronous Task Spawning
+Instead of executing tools sequentially, the function creates a sub-task for each call using `asyncio.create_task(_traceable_fnc_tool(...))`.
+- **Parallelism**: This allows multiple tools to run simultaneously if requested by the LLM.
+- **Monitoring**: It uses `_set_activity_task_info` to mark these tasks as "inline tasks," allowing the runtime to track tool-driven state changes.
+
+#### 5. Observability and Tracing
+Each tool execution is wrapped in an **OpenTelemetry span** via `tracer.start_as_current_span("function_tool")`. This records:
+- The tool's name and arguments.
+- The return value or any exception that occurred.
+- Performance metrics (how long the execution took).
+
+#### 6. Graceful Interruption and Shielding
+When the function finishes its loop, it waits for all spawned tasks with `asyncio.gather`.
+- **Shielding**: It uses `asyncio.shield` to ensure that if the agent session is cancelled (e.g., due to user interruption), currently running tool calls are allowed to finish. This prevents leaving the system in an inconsistent state.
+
+#### 7. Result Reporting
+The result of each tool is captured and sent back to the `AgentActivity` via callbacks. These results are ultimately fed back into the LLM's conversation history as `FunctionCallOutput` messages.
+
+
+
diff --git a/examples/voice_agent.py b/examples/voice_agent.py
deleted file mode 100644
index 41fef9a..0000000
--- a/examples/voice_agent.py
+++ /dev/null
@@ -1,223 +0,0 @@
-#!/usr/bin/env python3
-"""
-Local Voice Agent Example
-=========================
-
-A complete example showing how to use the local STT/TTS plugins
-with LiveKit Agents. Supports both cloud and local pipelines.
-
-Usage:
- # Start with local pipeline
- USE_LOCAL=true python voice_agent.py dev
-
- # Start with cloud pipeline (requires API keys)
- python voice_agent.py dev
-
-Environment Variables:
- USE_LOCAL - Set to "true" for local pipeline
- WHISPER_MODEL - FasterWhisper model size (default: "medium")
- WHISPER_DEVICE - "cuda" or "cpu" (default: "cuda")
- PIPER_MODEL_PATH - Path to Piper .onnx model
- OLLAMA_MODEL - Ollama model name (default: "llama3.1:8b")
- OLLAMA_BASE_URL - Ollama API URL (default: "http://localhost:11434/v1")
-
-For cloud pipeline:
- DEEPGRAM_API_KEY - Deepgram API key
- OPENAI_API_KEY - OpenAI API key
- CARTESIA_API_KEY - Cartesia API key
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-import sys
-import time
-
-# Add parent directory to path for local development (must be before other imports)
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
-
-from dotenv import load_dotenv
-
-# Load environment variables from examples/.env.local (must be before plugin imports)
-_script_dir = os.path.dirname(os.path.abspath(__file__))
-load_dotenv(os.path.join(_script_dir, ".env.local"))
-
-# LiveKit imports - plugins must be imported on main thread
-from livekit import agents
-from livekit.agents import AgentSession, Agent, RoomInputOptions
-from livekit.plugins import silero
-from livekit.plugins import openai as lk_openai
-
-# Local plugins
-from local_livekit_plugins import FasterWhisperSTT, PiperTTS
-
-# Configure logging
-logging.basicConfig(
- level=logging.INFO,
- format="%(asctime)s | %(name)-20s | %(levelname)-8s | %(message)s"
-)
-logger = logging.getLogger("voice-agent")
-
-# =============================================================================
-# Configuration
-# =============================================================================
-
-USE_LOCAL = os.getenv("USE_LOCAL", "false").lower() == "true"
-
-# Local pipeline settings
-WHISPER_MODEL = os.getenv("WHISPER_MODEL", "medium")
-WHISPER_DEVICE = os.getenv("WHISPER_DEVICE", "cuda")
-PIPER_MODEL_PATH = os.getenv("PIPER_MODEL_PATH", "")
-PIPER_USE_CUDA = os.getenv("PIPER_USE_CUDA", "false").lower() == "true"
-OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.1:8b")
-OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1")
-
-
-# =============================================================================
-# Agent Definition
-# =============================================================================
-
-class VoiceAssistant(Agent):
- """A simple voice assistant that responds to user queries."""
-
- def __init__(self) -> None:
- super().__init__(
- instructions="""You are a helpful voice AI assistant.
- Keep your responses concise and conversational - aim for 1-2 sentences.
- Be friendly and natural in your speech patterns."""
- )
-
-
-# =============================================================================
-# Pipeline Factories
-# =============================================================================
-
-def create_local_session() -> AgentSession:
- """
- Create an AgentSession using fully local STT/LLM/TTS.
-
- Requirements:
- - Ollama running with desired model
- - Piper voice model downloaded
- - CUDA toolkit (optional, for GPU acceleration)
- """
- logger.info("=" * 60)
- logger.info("STARTING LOCAL PIPELINE")
- logger.info("=" * 60)
- logger.info(f" STT: FasterWhisper ({WHISPER_MODEL} on {WHISPER_DEVICE})")
- logger.info(f" LLM: Ollama ({OLLAMA_MODEL})")
- logger.info(f" TTS: Piper (CUDA: {PIPER_USE_CUDA})")
- logger.info("=" * 60)
-
- if not PIPER_MODEL_PATH:
- raise ValueError(
- "PIPER_MODEL_PATH not set. Download a voice model from:\n"
- "https://huggingface.co/rhasspy/piper-voices"
- )
-
- return AgentSession(
- stt=FasterWhisperSTT(
- model_size=WHISPER_MODEL,
- device=WHISPER_DEVICE,
- compute_type="float16" if WHISPER_DEVICE == "cuda" else "int8",
- ),
- llm=lk_openai.LLM.with_ollama(
- model=OLLAMA_MODEL,
- base_url=OLLAMA_BASE_URL,
- ),
- tts=PiperTTS(
- model_path=PIPER_MODEL_PATH,
- use_cuda=PIPER_USE_CUDA,
- ),
- vad=silero.VAD.load(),
- )
-
-
-def create_cloud_session() -> AgentSession:
- """
- Create an AgentSession using cloud STT/LLM/TTS services.
-
- Requirements:
- - DEEPGRAM_API_KEY
- - OPENAI_API_KEY
- - CARTESIA_API_KEY
- """
- logger.info("=" * 60)
- logger.info("STARTING CLOUD PIPELINE")
- logger.info("=" * 60)
- logger.info(" STT: Deepgram Nova-2")
- logger.info(" LLM: OpenAI GPT-4o-mini")
- logger.info(" TTS: Cartesia Sonic")
- logger.info("=" * 60)
-
- return AgentSession(
- stt="deepgram/nova-2",
- llm="openai/gpt-4o-mini",
- tts="cartesia/sonic",
- vad=silero.VAD.load(),
- )
-
-
-# =============================================================================
-# Agent Entrypoint
-# =============================================================================
-
-async def entrypoint(ctx: agents.JobContext) -> None:
- """Main entrypoint for the voice agent."""
-
- logger.info(f"Joining room: {ctx.room.name}")
- await ctx.connect()
-
- # Create session based on configuration
- session = create_local_session() if USE_LOCAL else create_cloud_session()
-
- # ==========================================================================
- # Round-trip latency tracking
- # ==========================================================================
- # Measures time from user speech transcribed to agent starting to speak.
- # This captures: LLM processing + TTS first byte (STT already done).
-
- _transcription_time: float | None = None
-
- @session.on("user_input_transcribed")
- def on_user_input_transcribed(ev) -> None:
- nonlocal _transcription_time
- _transcription_time = time.perf_counter()
- logger.debug(f"User said: {ev.transcript[:80]}...")
-
- @session.on("agent_state_changed")
- def on_agent_state_changed(ev) -> None:
- nonlocal _transcription_time
- if ev.new_state == "speaking" and _transcription_time is not None:
- latency_ms = (time.perf_counter() - _transcription_time) * 1000
- logger.info(f"ROUND-TRIP LATENCY: {latency_ms:.0f}ms (LLM + TTS)")
- _transcription_time = None
-
- # ==========================================================================
-
- # Start the agent session
- await session.start(
- room=ctx.room,
- agent=VoiceAssistant(),
- room_input_options=RoomInputOptions(),
- )
-
- # Send initial greeting
- await session.generate_reply(
- instructions="Greet the user and let them know you're ready to help."
- )
-
- logger.info("Agent ready - listening for speech...")
-
-
-# =============================================================================
-# Main
-# =============================================================================
-
-if __name__ == "__main__":
- agents.cli.run_app(
- agents.WorkerOptions(
- entrypoint_fnc=entrypoint,
- )
- )
diff --git a/src/local_livekit_plugins/__init__.py b/local_livekit_plugins/__init__.py
similarity index 71%
rename from src/local_livekit_plugins/__init__.py
rename to local_livekit_plugins/__init__.py
index fab402b..e460796 100644
--- a/src/local_livekit_plugins/__init__.py
+++ b/local_livekit_plugins/__init__.py
@@ -10,10 +10,10 @@
PiperTTS: Fast local text-to-speech using Piper
Example:
- >>> from local_livekit_plugins import FasterWhisperSTT, PiperTTS
+ >>> from local_livekit_plugins import FasterWhisperSTT, VieNeuTTS
>>>
>>> stt = FasterWhisperSTT(model_size="medium", device="cuda")
- >>> tts = PiperTTS(model_path="/path/to/voice.onnx")
+ >>> tts = VieNeuTTS(api_base="http://localhost:23333/v1")
Repository: https://github.com/CoreWorxLab/local-livekit-plugins
License: MIT
@@ -24,5 +24,6 @@
from .faster_whisper_stt import FasterWhisperSTT
from .piper_tts import PiperTTS
+from .vieneu_tts import VieNeuTTS
-__all__ = ["FasterWhisperSTT", "PiperTTS", "__version__"]
+__all__ = ["FasterWhisperSTT", "PiperTTS", "VieNeuTTS", "__version__"]
diff --git a/src/local_livekit_plugins/faster_whisper_stt.py b/local_livekit_plugins/faster_whisper_stt.py
similarity index 100%
rename from src/local_livekit_plugins/faster_whisper_stt.py
rename to local_livekit_plugins/faster_whisper_stt.py
diff --git a/src/local_livekit_plugins/piper_tts.py b/local_livekit_plugins/piper_tts.py
similarity index 100%
rename from src/local_livekit_plugins/piper_tts.py
rename to local_livekit_plugins/piper_tts.py
diff --git a/local_livekit_plugins/vieneu_tts.py b/local_livekit_plugins/vieneu_tts.py
new file mode 100644
index 0000000..ad066d0
--- /dev/null
+++ b/local_livekit_plugins/vieneu_tts.py
@@ -0,0 +1,213 @@
+"""
+VieNeu-TTS Plugin for LiveKit Agents
+====================================
+
+High-quality Vietnamese text-to-speech with instant voice cloning.
+Integrates the VieNeu-TTS remote client with LiveKit's streaming architecture.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import sys
+import uuid
+import numpy as np
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional, Any, Dict
+
+from livekit.agents import tts, APIConnectOptions
+
+if TYPE_CHECKING:
+ from livekit.agents.tts.tts import AudioEmitter
+
+# Add VieNeu-TTS source directory to path if it exists locally
+_project_root = Path(__file__).parent.parent
+_vieneu_src = _project_root / "models" / "VieNeu-TTS" / "src"
+if _vieneu_src.exists() and str(_vieneu_src) not in sys.path:
+ # Insert at the beginning to prefer the version in the models folder
+ sys.path.insert(0, str(_vieneu_src))
+
+try:
+ from vieneu.remote import RemoteVieNeuTTS
+except ImportError:
+ # This might happen during initial setup if paths aren't fully ready
+ RemoteVieNeuTTS = None
+
+logger = logging.getLogger(__name__)
+
+class VieNeuTTS(tts.TTS):
+ """
+ LiveKit TTS plugin using VieNeu-TTS for high-quality Vietnamese speech.
+
+ Args:
+ api_base: URL of the VieNeu-TTS server (e.g., LMDeploy server).
+ model_name: Name of the model registered on the server.
+ voice_id: Optional ID of a preset voice. If None, uses the model's default.
+ """
+
+ def __init__(
+ self,
+ api_base: str = "http://localhost:23333/v1",
+ model_name: str = "pnnbao-ump/VieNeu-TTS",
+ voice_id: Optional[str] = "Doan", # Default to Doan if not specified
+ ) -> None:
+ if RemoteVieNeuTTS is None:
+ raise ImportError(
+ "Could not find 'vieneu' package. Ensure 'models/VieNeu-TTS/src' is in your path."
+ )
+
+ super().__init__(
+ capabilities=tts.TTSCapabilities(streaming=False),
+ sample_rate=24000, # VieNeu standard sample rate
+ num_channels=1,
+ )
+
+ self._api_base = api_base
+ self._model_name = model_name
+ self._voice_id = voice_id
+
+ logger.info(f"Connecting to VieNeu-TTS server at {api_base} (model: {model_name})")
+
+ # Initialize client
+ self._client = RemoteVieNeuTTS(
+ api_base=api_base,
+ model_name=model_name
+ )
+
+ # Resolve voice data if specified
+ self._voice_data = None
+ if voice_id:
+ try:
+ # VieNeu preset voices are available via this call
+ self._voice_data = self._client.get_preset_voice(voice_id)
+ logger.info(f"VieNeuTTS initialized with voice: {voice_id}")
+ except Exception as e:
+ logger.error(f"Failed to resolve VieNeu voice '{voice_id}': {e}")
+
+ def synthesize(
+ self,
+ text: str,
+ *,
+ conn_options: APIConnectOptions | None = None,
+ ) -> tts.ChunkedStream:
+ """
+ Synthesize speech from Vietnamese text.
+ """
+ if conn_options is None:
+ conn_options = APIConnectOptions()
+
+ # Filter out empty or JSON-like content that shouldn't be synthesized
+ stripped_text = text.strip()
+ if not stripped_text or stripped_text in ("{}", "[]"):
+ logger.debug(f"Skipping synthesis for non-textual content: '{text}'")
+ # Return an empty stream that flushes immediately
+ from .vieneu_tts import _VieNeuEmptyStream
+ return _VieNeuEmptyStream(
+ tts_plugin=self,
+ input_text=text,
+ conn_options=conn_options,
+ )
+
+ logger.debug(f"Synthesizing ({len(text)} chars) via VieNeu: {text[:50]}...")
+
+ return _VieNeuChunkedStream(
+ tts_plugin=self,
+ input_text=text,
+ conn_options=conn_options,
+ )
+
+class _VieNeuEmptyStream(tts.ChunkedStream):
+ """
+ An empty stream that satisfies the API but generates no audio.
+ """
+ def __init__(
+ self,
+ *,
+ tts_plugin: VieNeuTTS,
+ input_text: str,
+ conn_options: APIConnectOptions,
+ ) -> None:
+ super().__init__(tts=tts_plugin, input_text=input_text, conn_options=conn_options)
+
+ async def _run(self, emitter: AudioEmitter) -> None:
+ emitter.initialize(
+ request_id=str(uuid.uuid4()),
+ sample_rate=self._tts.sample_rate,
+ num_channels=self._tts.num_channels,
+ mime_type="audio/pcm",
+ )
+ # Push at least one silent frame to avoid livekit-agents "no audio frames" error
+ # A 100ms silent buffer (longer than previous 10ms to be safer)
+ silence = b"\x00" * int(self._tts.sample_rate * 0.1 * 2)
+ emitter.push(silence)
+ emitter.flush()
+
+class _VieNeuChunkedStream(tts.ChunkedStream):
+ """
+ Manages the streaming response from the VieNeu-TTS server.
+ """
+
+ def __init__(
+ self,
+ *,
+ tts_plugin: VieNeuTTS,
+ input_text: str,
+ conn_options: APIConnectOptions,
+ ) -> None:
+ super().__init__(tts=tts_plugin, input_text=input_text, conn_options=conn_options)
+ self._plugin = tts_plugin
+
+ async def _run(self, emitter: AudioEmitter) -> None:
+ """
+ Execution loop for the synthesis stream.
+ """
+ emitter.initialize(
+ request_id=str(uuid.uuid4()),
+ sample_rate=self._plugin.sample_rate,
+ num_channels=self._plugin.num_channels,
+ mime_type="audio/pcm",
+ )
+
+ loop = asyncio.get_running_loop()
+
+ pushed_any = False
+ try:
+ # We run the blocking stream generator in a thread pool
+ def process_synthesis():
+ nonlocal pushed_any
+ try:
+ # Get the stream generator from the client
+ gen = self._plugin._client.infer_stream(
+ self._input_text,
+ voice=self._plugin._voice_data
+ )
+
+ for audio_chunk in gen:
+ if audio_chunk is None or len(audio_chunk) == 0:
+ continue
+
+ # VieNeu returns float32 numpy arrays [-1.0, 1.0]
+ # We convert to int16 PCM for LiveKit
+ pcm_chunk = (audio_chunk * 32767).astype(np.int16).tobytes()
+
+ # Push directly to the emitter
+ emitter.push(pcm_chunk)
+ pushed_any = True
+ except Exception as e:
+ logger.error(f"Error in VieNeu synthesis thread: {e}")
+ raise
+
+ # Run the synthesis and emission in a background thread
+ await loop.run_in_executor(None, process_synthesis)
+
+ except Exception as e:
+ logger.exception(f"Unexpected error during VieNeu synthesis: {e}")
+ finally:
+ # Signal the end of the stream
+ if not pushed_any:
+ logger.warning(f"No audio frames were pushed for text: '{self._input_text}'. Pushing silence fallback.")
+ # Push a short silent frame (100ms)
+ silence = b"\x00" * int(self._tts.sample_rate * 0.1 * 2)
+ emitter.push(silence)
+ emitter.flush()
diff --git a/pyproject.toml b/pyproject.toml
index ead3b84..0a1e7e3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,10 @@ dependencies = [
"piper-tts>=1.2.0",
"numpy>=1.24.0",
"python-dotenv>=1.0.0",
- "torch>=2.0.0", # Provides CUDA 12 libraries for faster-whisper
+ "torch==2.10.0+cu128", # Provides CUDA 12 libraries for faster-whisper
+ "pydantic-settings>=2.13.1",
+ "torchaudio==2.10.0+cu128",
+ "torchvision==0.25.0+cu128",
]
[project.optional-dependencies]
diff --git a/examples/.env.local.example b/scripts/.env.local.example
similarity index 100%
rename from examples/.env.local.example
rename to scripts/.env.local.example
diff --git a/scripts/config.py b/scripts/config.py
new file mode 100644
index 0000000..3b11c86
--- /dev/null
+++ b/scripts/config.py
@@ -0,0 +1,49 @@
+import os
+from pydantic import BaseModel, Field
+
+class Settings(BaseModel):
+ USE_LOCAL: bool = Field(default_factory=lambda: os.getenv("USE_LOCAL", "false").lower() == "true")
+
+ # Local pipeline settings
+ WHISPER_MODEL: str = Field(default_factory=lambda: os.getenv("WHISPER_MODEL", "medium"))
+ WHISPER_DEVICE: str = Field(default_factory=lambda: os.getenv("WHISPER_DEVICE", "cuda"))
+ STT_PROVIDER: str = Field(default_factory=lambda: os.getenv("STT_PROVIDER", "faster_whisper").lower())
+ VLLM_ASR_MODEL: str = Field(default_factory=lambda: os.getenv("VLLM_ASR_MODEL", "qwen3asr"))
+ VLLM_ASR_BASE_URL: str = Field(default_factory=lambda: os.getenv("VLLM_ASR_BASE_URL", "http://10.148.180.105:1670/v1"))
+ TTS_PROVIDER: str = Field(default_factory=lambda: os.getenv("TTS_PROVIDER", "vieneu").lower())
+ PIPER_MODEL_PATH: str = Field(default_factory=lambda: os.getenv("PIPER_MODEL_PATH", ""))
+ PIPER_USE_CUDA: bool = Field(default_factory=lambda: os.getenv("PIPER_USE_CUDA", "false").lower() == "true")
+
+ VIENEU_API_BASE: str = Field(default_factory=lambda: os.getenv("VIENEU_API_BASE", "http://localhost:23333/v1"))
+ VIENEU_MODEL_NAME: str = Field(default_factory=lambda: os.getenv("VIENEU_MODEL_NAME", "pnnbao-ump/VieNeu-TTS"))
+ VIENEU_VOICE_ID: str = Field(default_factory=lambda: os.getenv("VIENEU_VOICE_ID", "Doan")) # e.g. "nu_m_001"
+
+ LLM_PROVIDER: str = Field(default_factory=lambda: os.getenv("LLM_PROVIDER", "ollama").lower())
+ LANGUAGE: str = Field(default_factory=lambda: os.getenv("LANGUAGE", "vi").lower()) # "vi" or "en"
+ OLLAMA_MODEL: str = Field(default_factory=lambda: os.getenv("OLLAMA_MODEL", "llama3.1:8b"))
+ OLLAMA_BASE_URL: str = Field(default_factory=lambda: os.getenv("OLLAMA_BASE_URL", "http://localhost:11434/v1"))
+ VLLM_MODEL: str = Field(default_factory=lambda: os.getenv("VLLM_MODEL", "qwen3_vl"))
+ VLLM_BASE_URL: str = Field(default_factory=lambda: os.getenv("VLLM_BASE_URL", "http://10.148.180.105:22003/v1"))
+
+# Create a singleton instance
+settings = Settings()
+
+# Export variables for backward compatibility
+USE_LOCAL = settings.USE_LOCAL
+WHISPER_MODEL = settings.WHISPER_MODEL
+WHISPER_DEVICE = settings.WHISPER_DEVICE
+STT_PROVIDER = settings.STT_PROVIDER
+VLLM_ASR_MODEL = settings.VLLM_ASR_MODEL
+VLLM_ASR_BASE_URL = settings.VLLM_ASR_BASE_URL
+TTS_PROVIDER = settings.TTS_PROVIDER
+PIPER_MODEL_PATH = settings.PIPER_MODEL_PATH
+PIPER_USE_CUDA = settings.PIPER_USE_CUDA
+VIENEU_API_BASE = settings.VIENEU_API_BASE
+VIENEU_MODEL_NAME = settings.VIENEU_MODEL_NAME
+VIENEU_VOICE_ID = settings.VIENEU_VOICE_ID
+LLM_PROVIDER = settings.LLM_PROVIDER
+LANGUAGE = settings.LANGUAGE
+OLLAMA_MODEL = settings.OLLAMA_MODEL
+OLLAMA_BASE_URL = settings.OLLAMA_BASE_URL
+VLLM_MODEL = settings.VLLM_MODEL
+VLLM_BASE_URL = settings.VLLM_BASE_URL
diff --git a/scripts/generic_agent.py b/scripts/generic_agent.py
new file mode 100644
index 0000000..6c02699
--- /dev/null
+++ b/scripts/generic_agent.py
@@ -0,0 +1,38 @@
+from livekit.agents import Agent
+from livekit.agents.job import get_job_context
+from livekit.agents.llm import function_tool
+from livekit import api
+import logging
+
+logger = logging.getLogger(__name__)
+
+class GenericAgent(Agent):
+ # Tự động chào khi vào phòng
+ # async def on_enter(self):
+ # self.session.generate_reply()
+
+ # Tự động kết thúc cuộc trò chuyện khi người dùng yêu cầu
+ @function_tool
+ async def end_conversation(self):
+ """Gọi hàm này khi người dùng muốn kết thúc cuộc trò chuyện"""
+ self.session.interrupt()
+
+ await self.session.generate_reply(
+ instructions=f"tạm biệt", allow_interruptions=False
+ )
+
+ job_ctx = get_job_context()
+ await job_ctx.api.room.delete_room(api.DeleteRoomRequest(room=job_ctx.room.name))
+
+ async def on_enter(self):
+ """Called when the agent becomes active."""
+ logger.info("Agent session started")
+
+ # Generate initial greeting
+ await self.session.generate_reply(
+ instructions="Greet the user warmly and ask how you can help them today."
+ )
+
+ async def on_exit(self):
+ """Called when the agent session ends."""
+ logger.info("Agent session ended")
diff --git a/scripts/prompts.py b/scripts/prompts.py
new file mode 100644
index 0000000..9eb97e8
--- /dev/null
+++ b/scripts/prompts.py
@@ -0,0 +1,64 @@
+ENGLISH_RESPONSE_SYSTEM_PROMPT = """You are a helpful voice AI assistant.
+
+CRITICAL: You MUST ALWAYS respond in English. Even if the user speaks to you in another language, you must respond in English only.
+Do not echo the user's language if it is not English.
+
+Keep your responses concise and conversational - aim for 1-2 sentences.
+Be friendly and natural in your speech patterns. You can use tools get datetime to get current datetime"""
+
+
+VIETNAMESE_RESPONSE_SYSTEM_PROMPT_OLD = """Bạn là một tiếp tân thông minh của công ty Vinrobotics.
+Nhiệm vụ của bạn là hỗ trợ khách hàng, nhân viên và khách thăm với thái độ chuyên nghiệp, thân thiện và hiệu quả. Bạn có các khả năng đặc biệt (skill/tools) sau:
+
+1. QUÉT & ĐỊNH DANH:
+ - Quét mã QR (`scan_qr_code`) để thực hiện Check-in/Check-out cho khách hoặc nhân viên.
+ - Quét Căn cước công dân (`scan_cccd`) để lấy thông tin định danh khi cần thiết.
+ - Kiểm tra nhận diện khuôn mặt (`verify_face_id`) để xác thực danh tính, ngăn chặn việc mạo danh.
+
+2. ĐĂNG KÝ KHÁCH:
+ - Thu thập thông tin thông qua đối thoại (Q&A) để đăng ký khách thăm (`register_visitor`). Hãy hỏi tên, mục đích đến và thông tin liên lạc một cách tự nhiên.
+
+3. TRA CỨU THÔNG TIN:
+ - Tra cứu thông tin thời gian thực từ internet (`search_web`) khi người dùng hỏi về thời tiết, tin tức hoặc kiến thức chung.
+
+4. HỖ TRỢ TRỰC TIẾP (Human-in-the-loop):
+ - Khi cần xác nhận hoặc thông tin từ lãnh đạo cấp cao, hãy sử dụng `call_staff` để kết nối và nhận chỉ thị.
+
+5. QUAN SÁT THÔNG MINH:
+ - Bạn có quyền truy cập vào `get_camera_frame`. Công cụ này trả về dữ liệu nhận dạng và hình ảnh (image_data).
+ - Khi được yêu cầu "mô tả những gì bạn thấy" hoặc khi cần nhận diện môi trường xung quanh robot, hãy gọi `get_camera_frame`.
+ - Ưu tiên tóm tắt tổng quát tình huống và các vật thể nổi bật nhất thay vì liệt kê vụn vặt.
+
+QUY TẮC CÔNG VIỆC:
+- Luôn phản hồi ngắn gọn (1-2 câu), tự nhiên và mang tính hội thoại.
+- Chỉ phản hồi bằng **tiếng Việt**. Nếu khách nói tiếng Anh, vẫn phải trả lời bằng tiếng Việt chuyên nghiệp.
+- Khi sử dụng camera, hãy duy trì luồng trực tiếp cho đến khi khách yêu cầu dừng.
+- KHÔNG tự động dừng camera (`stop_camera_detection`) trừ khi được yêu cầu.
+
+Hãy luôn sẵn sàng phục vụ và mang lại trải nghiệm tốt nhất cho mọi người đến Vinrobotics!"""
+
+
+VIETNAMESE_RESPONSE_SYSTEM_PROMPT= """You are a helpful voice AI assistant supporting Vietnamese.
+Be friendly and natural in your speech patterns. Please be smart to understand human intents and trigger tools. These tools are considered as your built-in capabilities.
+
+CONCISENESS RULE:
+- Keep your responses concise and conversational - aim for 1-2 sentences.
+- Avoid long lists or over-explaining unless the user asks for "chi tiết" (detail) or "liệt kê" (list).
+
+CRITICAL LANGUAGE RULE:
+- You MUST respond ONLY in **Vietnamese** (Tiếng Việt). Please translate to Vietnamese unless it's places.
+- If the user speaks English, Chinese, or Thai, you must still respond in Vietnamese. You may briefly acknowledge you understood them but immediately switch to Vietnamese.
+- Do not use markdown formatting like **bold** or *italics* in your spoken output. Use plain text without ``**`` or ``*``.
+
+MULTIMODAL SCENE ANALYSIS:
+- You have access to `get_camera_frame`. This tool returns numerical detections AND a `image_data` field (Base64 JPEG).
+- When asked to "describe what you see" or for "more detail", call `get_camera_frame`.
+- Use the detections for precise counts and the `image_data` to describe the scene qualitatively (colors, lighting, relative positions, emotions, etc.) using your vision capabilities.
+- IMPORTANT: When describing, prioritize a high-level summary of the scene. Instead of listing every single object, describe the overall situation and the most prominent items.
+
+CAMERA PERSISTENCE & PREVIEW RULE:
+- When you use the camera, you MUST ensure the preview window is visible to the user.
+- Calling `get_camera_frame` will automatically start the camera with preview if it's not running.
+- Keep the camera running so the user can continue to see the live stream.
+- Do NOT call `stop_camera_detection` automatically after answering.
+- Only stop the camera if the user explicitly asks you to "stop the camera" or "turn off the video"."""
\ No newline at end of file
diff --git a/scripts/test_display.py b/scripts/test_display.py
new file mode 100644
index 0000000..3967cb0
--- /dev/null
+++ b/scripts/test_display.py
@@ -0,0 +1,32 @@
+import cv2
+import numpy as np
+import os
+import time
+
+def test_display(display):
+ print(f"Testing DISPLAY={display}")
+ os.environ["DISPLAY"] = display
+
+ # Try to create a simple window
+ img = np.zeros((480, 640, 3), np.uint8)
+ cv2.putText(img, f"Testing Display {display}", (100, 240), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+
+ window_name = f"Test Window {display}"
+ try:
+ cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+ cv2.imshow(window_name, img)
+ key = cv2.waitKey(2000)
+ print(f"Window shown on {display}, waitKey returned {key}")
+ cv2.destroyWindow(window_name)
+ return True
+ except Exception as e:
+ print(f"Failed on {display}: {str(e)}")
+ return False
+
+if __name__ == "__main__":
+ displays = [":0", ":1", ":2"]
+ current = os.environ.get("DISPLAY", "None")
+ print(f"Current DISPLAY in environment: {current}")
+
+ for d in displays:
+ test_display(d)
diff --git a/scripts/test_filter.py b/scripts/test_filter.py
new file mode 100644
index 0000000..ffa2b2c
--- /dev/null
+++ b/scripts/test_filter.py
@@ -0,0 +1,69 @@
+import asyncio
+import re
+from typing import AsyncIterable
+
+# Mocking the pattern and function from filters.py for testing
+THOUGHT_PATTERN = re.compile(r".*?", re.DOTALL)
+
+async def filter_thoughts(text: AsyncIterable[str]) -> AsyncIterable[str]:
+ buffer = ""
+ async for chunk in text:
+ buffer += chunk
+
+ full_text = buffer
+ clean_text = THOUGHT_PATTERN.sub("", full_text)
+
+ if "[SILENCE]" in clean_text:
+ if not clean_text.replace("[SILENCE]", "").strip():
+ return
+
+ yield clean_text
+
+async def mock_stream(chunks: list[str]):
+ for chunk in chunks:
+ yield chunk
+
+async def test_filter():
+ test_cases = [
+ {
+ "name": "Thoughts and Answer",
+ "chunks": ["Maybe I should say hi.", " Hello there!"],
+ "expected": " Hello there!"
+ },
+ {
+ "name": "Pure Silence",
+ "chunks": ["Nothing to say.", " [SILENCE]"],
+ "expected": None
+ },
+ {
+ "name": "Mixed Silence (should not happens if LLM follows instructions, but let's test)",
+ "chunks": [" [SILENCE] But actually I have something to say."],
+ "expected": " [SILENCE] But actually I have something to say."
+ },
+ {
+ "name": "Multiple Thoughts",
+ "chunks": ["A", " B ", "C"],
+ "expected": " B "
+ }
+ ]
+
+ for case in test_cases:
+ print(f"Testing: {case['name']}")
+ result = ""
+ async for filtered in filter_thoughts(mock_stream(case['chunks'])):
+ result += filtered
+
+ if case['expected'] is None:
+ if result == "":
+ print("✅ PASSED (Empty as expected)")
+ else:
+ print(f"❌ FAILED (Expected empty, got '{result}')")
+ else:
+ if result == case['expected']:
+ print(f"✅ PASSED")
+ else:
+ print(f"❌ FAILED (Expected '{case['expected']}', got '{result}')")
+ print("-" * 20)
+
+if __name__ == "__main__":
+ asyncio.run(test_filter())
diff --git a/scripts/tools.py b/scripts/tools.py
new file mode 100644
index 0000000..a63c414
--- /dev/null
+++ b/scripts/tools.py
@@ -0,0 +1,53 @@
+from datetime import datetime
+from livekit.agents.llm import function_tool
+
+class AssistantFnc:
+ """Helper functions for the assistant."""
+
+ @function_tool(description="Hàm này trả về ngày giờ hiện tại.")
+ async def get_datetime(self) -> str:
+ """Trả về ngày giờ hiện tại ở định dạng dễ đọc."""
+ now = datetime.now()
+ # Format: Thứ Năm, 26 tháng 03 năm 2026, 16:00:39
+ # In Vietnamese
+ days = ["Thứ Hai", "Thứ Ba", "Thứ Tư", "Thứ Năm", "Thứ Sáu", "Thứ Bảy", "Chủ Nhật"]
+ day_str = days[now.weekday()]
+ return f"{day_str}, {now.strftime('%d/%m/%Y %H:%M:%S')}"
+
+ @function_tool(description="Quét mã QR để thực hiện check-in hoặc check-out.")
+ async def scan_qr_code(self, operation: str) -> str:
+ """Quét mã QR. operation có thể là 'check-in' hoặc 'check-out'."""
+ # Simulated QR scan
+ return f"Mã QR đã được quét thành công. Đã thực hiện {operation} cho khách hàng."
+
+ @function_tool(description="Quét Căn cước công dân (CCCD) để lấy thông tin định danh.")
+ async def scan_cccd(self) -> str:
+ """Quét thẻ CCCD và trả về thông tin cơ bản."""
+ # Simulated CCCD scan
+ return "Đã quét CCCD thành công. Họ tên: Nguyễn Văn A, Số CCCD: 012345678901, Địa chỉ: Hà Nội."
+
+ @function_tool(description="Đăng ký thông tin khách đến thăm công ty.")
+ async def register_visitor(self, name: str, purpose: str, contact: str) -> str:
+ """Đăng ký khách thăm. Cần tên khách, mục đích và thông tin liên lạc."""
+ # Simulated registration
+ return f"Đã đăng ký thành công cho khách {name}. Mục đích: {purpose}. Chúng tôi sẽ liên hệ qua {contact}."
+
+ @function_tool(description="Tìm kiếm thông tin thời gian thực từ internet.")
+ async def search_web(self, query: str) -> str:
+ """Tìm kiếm thông tin từ internet cho các câu hỏi về thời sự, thời tiết, kiến thức mới."""
+ # Simulated web search
+ return f"Kết quả tìm kiếm cho '{query}': Đây là thông tin cập nhật mới nhất từ internet về vấn đề này."
+
+ @function_tool(description="Gọi điện cho nhân viên công ty (ví dụ: anh Huy) để lấy thông tin hoặc xác nhận.")
+ async def call_staff(self, staff_name: str, reason: str) -> str:
+ """Thực hiện cuộc gọi cho nhân viên. Ví dụ: 'anh Huy'."""
+ # Simulated human-in-the-loop call
+ if "Huy" in staff_name:
+ return f"Đang kết nối với anh Huy... Anh Huy xác nhận: 'Cứ cho khách vào, tôi đang đợi ở phòng họp số 1'."
+ return f"Đang gọi cho {staff_name}... Hiện tại nhân viên không nhấc máy, vui lòng thử lại sau."
+
+ @function_tool(description="Kiểm tra nhận diện khuôn mặt (Face ID) để đảm bảo không có giả mạo.")
+ async def verify_face_id(self) -> str:
+ """Thực hiện kiểm tra Face ID."""
+ # Simulated Face ID check
+ return "Face ID trùng khớp. Xác nhận danh tính thành công. Có thể tiếp tục công việc."
diff --git a/scripts/voice_agent.py b/scripts/voice_agent.py
new file mode 100644
index 0000000..064c26a
--- /dev/null
+++ b/scripts/voice_agent.py
@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import logging
+import os
+import sys
+import time
+from datetime import datetime
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+
+from livekit import agents
+from livekit.agents import AgentSession, Agent, RoomInputOptions, llm, RunContext
+from livekit.plugins import silero
+from livekit.plugins import openai as lk_openai
+from livekit.agents.llm import function_tool
+from livekit.agents.llm.tool_context import StopResponse
+from livekit.plugins import noise_cancellation
+# livekit-plugins-noise-cancellation
+from livekit.agents import mcp
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s | %(name)-20s | %(levelname)-8s | %(message)s"
+)
+logger = logging.getLogger("voice-agent")
+
+
+from scripts.config import (
+ USE_LOCAL, WHISPER_MODEL, WHISPER_DEVICE, STT_PROVIDER,
+ VLLM_ASR_MODEL, VLLM_ASR_BASE_URL, TTS_PROVIDER,
+ PIPER_MODEL_PATH, PIPER_USE_CUDA, VIENEU_API_BASE,
+ VIENEU_MODEL_NAME, VIENEU_VOICE_ID, LLM_PROVIDER,
+ LANGUAGE, OLLAMA_MODEL, OLLAMA_BASE_URL,
+ VLLM_MODEL, VLLM_BASE_URL
+)
+
+from scripts.tools import AssistantFnc
+from scripts.prompts import VIETNAMESE_RESPONSE_SYSTEM_PROMPT, ENGLISH_RESPONSE_SYSTEM_PROMPT
+from local_livekit_plugins import FasterWhisperSTT, VieNeuTTS, PiperTTS
+from scripts.generic_agent import GenericAgent
+
+class VoiceAssistant(GenericAgent):
+ """A simple voice assistant that responds to user queries."""
+ def __init__(self) -> None:
+ # Select prompt based on desired language
+ if LANGUAGE == "en":
+ base_prompt = ENGLISH_RESPONSE_SYSTEM_PROMPT
+ else:
+ base_prompt = VIETNAMESE_RESPONSE_SYSTEM_PROMPT
+ now_msg = ""
+
+ instructions=f"{now_msg}\n\n{base_prompt}"
+
+ super().__init__(
+ instructions=instructions,
+ )
+
+ self.airbnbs = {
+ "san francisco": [
+ {
+ "id": "sf001",
+ "name": "Loft ấm cúng tại trung tâm",
+ "address": "123 Market Street, San Francisco, CA",
+ "price": 150,
+ "amenities": ["WiFi", "Nhà bếp", "Không gian làm việc"],
+ },
+ {
+ "id": "sf002",
+ "name": "Nhà kiểu Victorian với tầm nhìn ra vịnh",
+ "address": "456 Castro Street, San Francisco, CA",
+ "price": 220,
+ "amenities": ["WiFi", "Chỗ đậu xe", "Máy giặt/Máy sấy", "Tầm nhìn ra vịnh"],
+ },
+ {
+ "id": "sf003",
+ "name": "Studio hiện đại gần Cầu Cổng Vàng",
+ "address": "789 Presidio Avenue, San Francisco, CA",
+ "price": 180,
+ "amenities": ["WiFi", "Nhà bếp", "Cho phép mang thú cưng"],
+ },
+ ],
+ "new york": [
+ {
+ "id": "ny001",
+ "name": "Căn hộ gạch nâu (Brownstone) tại Brooklyn",
+ "address": "321 Bedford Avenue, Brooklyn, NY",
+ "price": 175,
+ "amenities": ["WiFi", "Nhà bếp", "Lối vào sân sau"],
+ },
+ {
+ "id": "ny002",
+ "name": "Penthouse ngắm cảnh trời Manhattan",
+ "address": "555 Fifth Avenue, Manhattan, NY",
+ "price": 350,
+ "amenities": ["WiFi", "Phòng tập gym", "Lễ tân", "Tầm nhìn ra thành phố"],
+ },
+ {
+ "id": "ny003",
+ "name": "Loft nghệ thuật tại East Village",
+ "address": "88 Avenue A, Manhattan, NY",
+ "price": 195,
+ "amenities": ["WiFi", "Máy giặt", "Tường gạch trần"],
+ },
+ ],
+ "los angeles": [
+ {
+ "id": "la001",
+ "name": "Bungalow tại bãi biển Venice",
+ "address": "234 Ocean Front Walk, Venice, CA",
+ "price": 200,
+ "amenities": ["WiFi", "Lối ra bãi biển", "Sân trong"],
+ },
+ {
+ "id": "la002",
+ "name": "Biệt thự đồi Hollywood",
+ "address": "777 Mulholland Drive, Los Angeles, CA",
+ "price": 400,
+ "amenities": ["WiFi", "Hồ bơi", "Tầm nhìn ra thành phố", "Bồn tắm nước nóng"],
+ },
+ ],
+ }
+
+ # Track bookings
+ self.bookings = []
+
+ @function_tool
+ async def search_airbnbs(self, context: RunContext, city: str) -> str:
+ """Search for available Airbnbs in a city.
+
+ Args:
+ city: The city name to search for Airbnbs (e.g., 'San Francisco', 'New York', 'Los Angeles')
+ """
+ city_lower = city.lower()
+
+ if city_lower not in self.airbnbs:
+ return f"Sorry, I don't have any Airbnb listings for {city} at the moment. Available cities are: San Francisco, New York, and Los Angeles."
+
+ listings = self.airbnbs[city_lower]
+ result = f"Found {len(listings)} Airbnbs in {city}:\n\n"
+
+ for listing in listings:
+ result += f"• {listing['name']}\n"
+ result += f" Address: {listing['address']}\n"
+ result += f" Price: ${listing['price']} per night\n"
+ result += f" Amenities: {', '.join(listing['amenities'])}\n"
+ result += f" ID: {listing['id']}\n\n"
+
+ return result
+
+ @function_tool
+ async def book_airbnb(self, context: RunContext, airbnb_id: str, guest_name: str, check_in_date: str, check_out_date: str) -> str:
+ """Book an Airbnb.
+
+ Args:
+ airbnb_id: The ID of the Airbnb to book (e.g., 'sf001')
+ guest_name: Name of the guest making the booking
+ check_in_date: Check-in date (e.g., 'January 15, 2025')
+ check_out_date: Check-out date (e.g., 'January 20, 2025')
+ """
+ # Find the Airbnb
+ airbnb = None
+ for city_listings in self.airbnbs.values():
+ for listing in city_listings:
+ if listing['id'] == airbnb_id:
+ airbnb = listing
+ break
+ if airbnb:
+ break
+
+ if not airbnb:
+ return f"Sorry, I couldn't find an Airbnb with ID {airbnb_id}. Please search for available listings first."
+
+ # Create booking
+ booking = {
+ "confirmation_number": f"BK{len(self.bookings) + 1001}",
+ "airbnb_name": airbnb['name'],
+ "address": airbnb['address'],
+ "guest_name": guest_name,
+ "check_in": check_in_date,
+ "check_out": check_out_date,
+ "total_price": airbnb['price'],
+ }
+
+ self.bookings.append(booking)
+
+ result = f"✓ Booking confirmed!\n\n"
+ result += f"Confirmation Number: {booking['confirmation_number']}\n"
+ result += f"Property: {booking['airbnb_name']}\n"
+ result += f"Address: {booking['address']}\n"
+ result += f"Guest: {booking['guest_name']}\n"
+ result += f"Check-in: {booking['check_in']}\n"
+ result += f"Check-out: {booking['check_out']}\n"
+ result += f"Nightly Rate: ${booking['total_price']}\n\n"
+ result += f"You'll receive a confirmation email shortly. Have a great stay!"
+
+ return result
+
+ @function_tool
+ async def get_current_date_and_time(self, context: RunContext) -> str:
+ """Trả về ngày giờ hiện tại"""
+ now = datetime.now()
+ # Format: Thứ Năm, 26 tháng 03 năm 2026, 16:00:39
+ # In Vietnamese
+
+ days = ["Thứ Hai", "Thứ Ba", "Thứ Tư", "Thứ Năm", "Thứ Sáu", "Thứ Bảy", "Chủ Nhật"]
+ day_str = days[now.weekday()]
+ return f"{day_str}, {now.strftime('%d/%m/%Y %H:%M:%S')}"
+
+ # @function_tool
+ # async def observe_scene(self, context: RunContext) -> str:
+ # """Sử dụng camera để quan sát và nhận dạng các đối tượng trong môi trường.
+ # Hàm này trả về cả danh sách đối tượng và mô tả chi tiết từ YOLO.
+ # """
+ # logger.info("Agent is observing the scene...")
+
+ # # We need to call the MCP tool 'get_camera_frame'
+ # # Since MCP tools are attached to the session, we can call them via the session's tool call logic
+ # # OR we can just use the tool context.
+
+ # try:
+ # # Look for the MCP tool in the session
+ # # Note: In a real scenario, we might want to call the tool by name
+ # # For simplicity, we assume the agent will call get_camera_frame directly if needed,
+ # # but providing this wrapper helps enforce the "give to LLM" logic.
+
+ # # Actually, the best way for the agent to "see" is to use the existing prompt logic
+ # # but provide a better summary of what it sees.
+
+ # # If the user specifically wants the agent to "detect objects",
+ # # we can use this tool to trigger the behavior.
+ # return "Please call `get_camera_frame` to receive the visual data and then provide a concise description in Vietnamese as per your instructions."
+ # except Exception as e:
+ # logger.error(f"Error in observe_scene: {e}")
+ # return f"Lỗi khi quan sát: {e}"
+
+ async def on_user_turn_completed(self, turn_ctx: llm.ChatContext, new_message: llm.ChatMessage) -> None:
+ """Filter out background noise when the agent is speaking."""
+ # Use simple heuristic: if agent is speaking, ignore everything except stop keywords
+ if self._activity and self._activity.current_speech:
+ transcript = " ".join(new_message.content) if isinstance(new_message.content, list) else str(new_message.content)
+ transcript_lower = transcript.lower().strip()
+
+ stop_keywords = ["stop", "dừng lại", "thôi", "im đi", "dừng", "đợi đã", "wait", "hold on"]
+ if not any(kw in transcript_lower for kw in stop_keywords):
+ logger.info(f"Ignoring background speech while agent is speaking: {transcript}")
+ raise StopResponse()
+
+
+
+
+
+# =============================================================================
+# Pipeline Factories
+# =============================================================================
+
+def create_local_session() -> AgentSession:
+ """
+ Create an AgentSession using fully local STT/LLM/TTS.
+
+ Requirements:
+ - Ollama running with desired model
+ - Piper voice model downloaded
+ - CUDA toolkit (optional, for GPU acceleration)
+ """
+ logger.info("=" * 60)
+ logger.info("STARTING LOCAL PIPELINE")
+ logger.info("=" * 60)
+ if STT_PROVIDER == "vllm":
+ logger.info(f" STT: vLLM ({VLLM_ASR_MODEL})")
+ else:
+ logger.info(f" STT: FasterWhisper ({WHISPER_MODEL} on {WHISPER_DEVICE})")
+ if LLM_PROVIDER == "vllm":
+ logger.info(f" LLM: vLLM ({VLLM_MODEL})")
+ else:
+ logger.info(f" LLM: Ollama ({OLLAMA_MODEL})")
+
+ if TTS_PROVIDER == "piper":
+ logger.info(f" TTS: Piper (CUDA: {PIPER_USE_CUDA})")
+ if not PIPER_MODEL_PATH:
+ raise ValueError(
+ "PIPER_MODEL_PATH not set. Download a voice model from:\n"
+ "https://huggingface.co/rhasspy/piper-voices"
+ )
+ else:
+ logger.info(f" TTS: VieNeu ({VIENEU_MODEL_NAME} at {VIENEU_API_BASE})")
+
+ logger.info("=" * 60)
+
+ return AgentSession(
+ stt=lk_openai.STT(
+ model=VLLM_ASR_MODEL,
+ base_url=VLLM_ASR_BASE_URL,
+ detect_language=["Vietnamese", "English"],
+ ) if STT_PROVIDER == "vllm" else FasterWhisperSTT(
+ model_size=WHISPER_MODEL,
+ device=WHISPER_DEVICE,
+ compute_type="float16" if WHISPER_DEVICE == "cuda" else "int8",
+ language="None", # Enable auto-detection
+ ),
+ llm=lk_openai.LLM(
+ model=VLLM_MODEL,
+ base_url=VLLM_BASE_URL,
+ ) if LLM_PROVIDER == "vllm" else lk_openai.LLM.with_ollama(
+ model=OLLAMA_MODEL,
+ base_url=OLLAMA_BASE_URL,
+ ),
+ tts=PiperTTS(
+ model_path=PIPER_MODEL_PATH,
+ use_cuda=PIPER_USE_CUDA,
+ ) if TTS_PROVIDER == "piper" else VieNeuTTS(
+ api_base=VIENEU_API_BASE,
+ model_name=VIENEU_MODEL_NAME,
+ voice_id=VIENEU_VOICE_ID if VIENEU_VOICE_ID else None,
+ ),
+ vad=silero.VAD.load(),
+ allow_interruptions=False,
+ discard_audio_if_uninterruptible=False,
+ tts_text_transforms=["filter_markdown", "filter_emoji", "filter_thoughts"],
+
+
+ # MCP servers
+ mcp_servers=[
+ mcp.MCPServerStdio(
+ command="/home/vrh3/workspace/projects/voice-agent/mcp/YOLO-MCP-Server/.venv/bin/python",
+ args=["/home/vrh3/workspace/projects/voice-agent/mcp/YOLO-MCP-Server/server.py"],
+ env={
+ "PYTHONPATH": "/home/vrh3/workspace/projects/voice-agent/mcp/YOLO-MCP-Server",
+ "DISPLAY": os.environ.get("DISPLAY", ":1"),
+ "XAUTHORITY": os.environ.get("XAUTHORITY", ""),
+ "XDG_RUNTIME_DIR": os.environ.get("XDG_RUNTIME_DIR", f"/run/user/{os.getuid()}"),
+ "QT_X11_NO_MITSHM": "1"
+ },
+ client_session_timeout_seconds=20
+ )
+ ],
+ )
+
+
+
+# =============================================================================
+# Agent Entrypoint
+# =============================================================================
+
+async def entrypoint(ctx: agents.JobContext) -> None:
+ """Main entrypoint for the voice agent."""
+
+ logger.info(f"Joining room: {ctx.room.name}")
+ await ctx.connect()
+
+ # Create session based on configuration
+ session = create_local_session() if USE_LOCAL else create_cloud_session()
+
+ # ==========================================================================
+ # Round-trip latency tracking
+ # ==========================================================================
+ # Measures time from user speech transcribed to agent starting to speak.
+ # This captures: LLM processing + TTS first byte (STT already done).
+
+ _transcription_time: float | None = None
+
+ @session.on("user_input_transcribed")
+ def on_user_input_transcribed(ev) -> None:
+ nonlocal _transcription_time
+ _transcription_time = time.perf_counter()
+ logger.info(f"User said: {ev.transcript}")
+
+ # Custom interruption logic
+ stop_keywords = ["stop", "dừng lại", "thôi", "im đi", "dừng", "đợi đã", "wait", "hold on"]
+ transcript_lower = ev.transcript.lower().strip()
+ if any(kw in transcript_lower for kw in stop_keywords):
+ logger.info("STOP keyword detected, interrupting agent...")
+ session.interrupt(force=True)
+
+ @session.on("agent_answer_transcribed")
+ def on_agent_answer_transcribed(ev) -> None:
+ logger.info(f"LLM Answer: {ev.transcript}")
+
+ @session.on("agent_state_changed")
+ def on_agent_state_changed(ev) -> None:
+ nonlocal _transcription_time
+ if ev.new_state == "speaking" and _transcription_time is not None:
+ latency_ms = (time.perf_counter() - _transcription_time) * 1000
+ logger.info(f"ROUND-TRIP LATENCY: {latency_ms:.0f}ms (LLM + TTS)")
+ _transcription_time = None
+
+ # ==========================================================================
+
+ # Start the agent session
+ await session.start(
+ room=ctx.room,
+ agent=VoiceAssistant(),
+ room_input_options=RoomInputOptions(
+ # Enable noise cancellation
+ noise_cancellation=noise_cancellation.BVC(),
+ # For telephony, use: noise_cancellation.BVCTelephony()
+ ),
+ )
+
+
+ # Handle session events
+ @session.on("agent_state_changed")
+ def on_state_changed(ev):
+ """Log agent state changes."""
+ logger.info(f"State: {ev.old_state} -> {ev.new_state}")
+
+ @session.on("user_started_speaking")
+ def on_user_speaking():
+ """Track when user starts speaking."""
+ logger.debug("User started speaking")
+
+ @session.on("user_stopped_speaking")
+ def on_user_stopped():
+ """Track when user stops speaking."""
+ logger.debug("User stopped speaking")
+
+
+ logger.info("Agent ready - listening for speech...")
+
+
+# =============================================================================
+# Main
+# =============================================================================
+
+if __name__ == "__main__":
+ agents.cli.run_app(
+ agents.WorkerOptions(
+ entrypoint_fnc=entrypoint,
+ )
+ )
diff --git a/uv.lock b/uv.lock
index 4e213f5..f8394c4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -262,11 +262,10 @@ wheels = [
[[package]]
name = "certifi"
-version = "2025.11.12"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
+version = "2022.12.7"
+source = { registry = "https://download.pytorch.org/whl/cu128" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" },
+ { url = "https://download.pytorch.org/whl/certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18" },
]
[[package]]
@@ -353,91 +352,10 @@ wheels = [
[[package]]
name = "charset-normalizer"
-version = "3.4.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
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