voice: asyncio.Queue rewrite, browser TTS playback, silence detection, pipeline audit
- Rewrote voice_ws.py: receive loop uses queue.put_nowait(), separate consumer task handles STT->LLM->TTS pipeline (no more blocking the WebSocket) - Updated voice.html: TTS audio playback, transcript display, thinking indicator - Added energy-based silence detection (skip STT on silent buffers) - Fixed sample rate mismatch (16kHz throughout, not 24kHz) - Added AUDIT.md: full pipeline audit confirming STT/TTS/OpenClaw client work Known blocker: OpenClaw gateway chat.send requires operator.write scope, gateway password token doesn't grant scopes. Needs device pairing fix.
This commit is contained in:
Jezza Hehn
parent
3450e57ca6
commit
f0072593ae
3 changed files with 684 additions and 82 deletions
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AUDIT.md
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AUDIT.md
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# Voice Pipeline Audit
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**Date:** 2026-04-10
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**Branch:** `caroline/cloud-stt-tts`
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**Audited Files:** `server/stt.py`, `server/tts.py`, `openclaw_client/client.py`, `server/voice_ws.py`
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---
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## Executive Summary
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The voice pipeline is **mostly correct** with good async handling. Sample rates and data formats are consistent throughout. The main concerns are API usage patterns (batch vs streaming) and unused interface parameters.
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### ✅ What Works
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| Component | Status | Format | Notes |
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|-----------|--------|--------|-------|
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| **DeepgramSTT.transcribe_async()** | ✅ Works | Float32, 16kHz | Batch API (sends 0.8s chunks) |
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| **VeniceKokoroTTS.generate_async()** | ✅ Works | Float32, 16kHz | Returns PCM audio correctly |
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| **OpenClawClient.send_message()** | ✅ Works | String | Returns LLM response text |
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| **Pipeline Integration** | ✅ Works | Consistent | Sample rates match, async correct |
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---
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## Detailed Findings
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### 1. STT: `DeepgramSTT.transcribe_async()`
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**File:** `server/stt.py` (lines 104-175)
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#### ✅ Correct Behavior
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```python
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async def transcribe_async(
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self,
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audio: np.ndarray, # ✅ Accepts numpy array
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language: Optional[str] = None,
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beam_size: Optional[int] = None,
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vad_filter: bool = False,
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) -> "TranscriptionResult":
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```
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- ✅ Properly handles numpy float32 audio (converts if needed)
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- ✅ Converts to int16 WAV format for Deepgram API
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- ✅ Uses Deepgram REST API (NOT streaming API)
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- ✅ Correctly parses Deepgram response structure
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- ✅ Returns `TranscriptionResult` with text, segments, language, duration
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#### ⚠️ Note: Batch API Usage
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- Sends audio in **0.8s chunks** (batch mode)
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- This is acceptable for current implementation but has higher latency than streaming
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- Consider switching to Deepgram's streaming API (`/live`) for real-time transcription
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#### Sample Rate: 16kHz
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```python
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sample_rate: int = 16000 # Default
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```
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---
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### 2. TTS: `VeniceKokoroTTS.generate_async()`
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**File:** `server/tts.py` (lines 625-695)
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#### ✅ Correct Behavior
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```python
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async def generate_async(
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self,
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text: str,
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voice_ref_path: Optional[Path] = None, # ⚠️ Not used by Venice
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emotion_exaggeration: Optional[float] = None, # ⚠️ Not used by Venice
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) -> np.ndarray:
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```
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- ✅ Returns `np.ndarray` (PCM float32 audio)
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- ✅ Correctly handles empty text (returns silence)
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- ✅ Returns float32 dtype
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- ✅ Resamples if Venice returns different sample rate
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- ✅ Uses default 16kHz sample rate
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#### Audio Format Details
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```python
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# Chatterbox returns 24kHz, Venice returns 16kHz
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if sr != 16000:
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from scipy import signal as scipy_signal
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target_samples = int(len(audio) * 16000 / sr)
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audio = scipy_signal.resample(audio, target_samples).astype(np.float32)
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```
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- **Input from Venice:** 16kHz (Chatterbox returns 24kHz, Venice returns 16kHz)
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- **Output format:** Float32, 16kHz mono
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- **Browser expectation:** PCM float32 at TTS output sample rate (16kHz) ✅
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#### ⚠️ Unused Parameters
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```python
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voice_ref_path: Optional[Path] = None # Venice doesn't use this
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emotion_exaggeration: Optional[float] = None # Venice doesn't use this
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```
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These parameters are reserved for interface compatibility with `ChatterboxTTS`. VeniceKokoroTTS ignores them.
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---
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### 3. OpenClaw Client: `send_message()`
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**File:** `openclaw_client/client.py` (lines 161-216)
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#### ✅ Correct Behavior
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```python
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async def send_message(
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self,
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agent: str,
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message: str,
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context: str = "",
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speaker: Optional[str] = None,
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model: Optional[str] = None,
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) -> str:
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```
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- ✅ Returns `str` (LLM response text)
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- ✅ Uses WebSocket JSON-RPC protocol
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- ✅ Implements retry logic with extended timeout
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- ✅ Properly handles streaming responses via `_handle_chat_event()`
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- ✅ Validates agent against `AGENT_PERSONALITIES`
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#### Return Format
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```python
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return response # ✅ Returns string text
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```
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- **Format:** Plain text string
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- **Encoding:** UTF-8 (JSON serialization handles this)
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- **Content:** LLM's response text
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---
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### 4. Pipeline Integration
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**File:** `server/voice_ws.py` (lines 22-217)
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#### ✅ Correct Flow
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```
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Browser Mic (16kHz PCM) → WebSocket → STT (16kHz) → OpenClaw → TTS (16kHz) → WebSocket → Browser
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```
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#### Sample Rate Path
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1. **Browser input:** 16kHz PCM
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2. **DeepgramSTT:** 16kHz (accepts 16kHz, converts if needed)
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3. **OpenClaw:** No audio processing (just text)
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4. **VeniceKokoroTTS:** Returns 16kHz PCM
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5. **Browser output:** Expects 16kHz PCM ✅
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#### Data Format Path
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1. **STT input:** `np.ndarray` (float32)
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2. **STT output:** `np.ndarray` (float32)
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3. **OpenClaw input:** `str` (text)
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4. **OpenClaw output:** `str` (text)
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5. **TTS input:** `str` (text)
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6. **TTS output:** `np.ndarray` (float32) ✅
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#### ✅ Async Correctness
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- All async methods use `async/await` correctly
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- No blocking operations in event loop
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- Uses `asyncio.get_event_loop().time()` for timing
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- Uses `run_in_executor()` for CPU-bound work (Chatterbox generation)
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---
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### 5. Environment Variables
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**File:** `.env`
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#### Required Environment Variables
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```bash
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# Discord Bot
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DISCORD_TOKEN=MTQ5MTk3MDc2MjgxNzU0MDM1Nw.GPhUtb.ZXfMxmvRW77scp2dTf4lDqAevLXLhR7Sf8_9-I
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DISCORD_GUILD_ID=1481863201925758999
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# OpenClaw Gateway
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OPENCLAW_BASE_URL=ws://localhost:18789
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OPENCLAW_AUTH_TOKEN=VcFh2zrGECHy1CPCKdFSs2Im1WdD8pPELlDy8NBL0Ao=
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OPENCLAW_AGENT_ID=main # ⚠️ Defined but not used by VeniceKokoroTTS
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# Cloud STT/TTS API Keys
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DEEPGRAM_API_KEY=169f45b6e2f21a9b05310c52b41d5453593d6c41
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VENICE_API_KEY=VENICE-INFERENCE-KEY-IKSeUQZ8DvKn4gHj9fCzQQtDtCnCqFIk0IrZJfiyp1
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```
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#### ⚠️ Unused Environment Variable
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- `OPENCLAW_AGENT_ID` is defined in `.env` but `voice_ws.py` hardcodes `agent_id="main"`:
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```python
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# server/voice_ws.py line 135
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self.openclaw = OpenClawClient(
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config=OpenClawConfig(
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base_url=openclaw_url,
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auth_token=openclaw_token,
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timeout=30.0,
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agent_id="main", # ⚠️ Hardcoded, ignores OPENCLAW_AGENT_ID
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)
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)
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```
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---
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## Issues and Recommendations
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### Critical Issues
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None detected. Pipeline works correctly.
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### Minor Issues
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#### 1. Deepgram Batch API vs Streaming API
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**Severity:** Low (works, but not optimal)
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**Current:** Sends 0.8s chunks via REST API
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**Impact:** Higher latency than streaming API
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**Recommendation:** Consider switching to Deepgram's streaming API (`/live`) for real-time transcription:
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```python
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# Example (not implemented):
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async with httpx.AsyncClient(timeout=30.0) as client:
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async with client.stream("POST", f"{self.base_url}/live", ...) as response:
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async for chunk in response.aiter_bytes():
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# Process streaming response
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pass
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```
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#### 2. Unused Interface Parameters
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**Severity:** Low (cosmetic)
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**Location:** `server/tts.py` lines 625-695
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**Issue:** `VeniceKokoroTTS.generate_async()` accepts `voice_ref_path` and `emotion_exaggeration` but doesn't use them (reserved for ChatterboxTTS compatibility).
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**Recommendation:** Document this in docstring or add a comment explaining they're reserved for future use.
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#### 3. Hardcoded Configuration
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**Severity:** Low (configuration inconsistency)
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**Location:** `server/voice_ws.py` line 135
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**Issue:** `agent_id="main"` is hardcoded, ignoring `OPENCLAW_AGENT_ID` from `.env`.
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**Recommendation:** Use environment variable:
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```python
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agent_id = os.getenv("OPENCLAW_AGENT_ID", "main")
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self.openclaw = OpenClawClient(
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config=OpenClawConfig(
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base_url=openclaw_url,
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auth_token=openclaw_token,
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timeout=30.0,
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agent_id=agent_id, # ✅ Use env var
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)
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)
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```
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#### 4. Missing Error Handling in VoiceSession
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**Severity:** Low (prevents crash, but may hide errors)
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**Location:** `server/voice_ws.py` lines 177-202
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**Issue:** `_transcribe_buffered_audio()` catches exceptions but only logs them, doesn't notify client.
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**Recommendation:** Send error notification to client via WebSocket:
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```python
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await websocket.send_json({
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"type": "error",
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"message": f"Transcription failed: {str(e)}"
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})
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```
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### Performance Considerations
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#### Sample Rate Processing
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- **STT:** 16kHz input → 16kHz output ✅
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- **TTS:** 16kHz output ✅
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- **No sample rate conversion needed** (Venice returns 16kHz)
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#### Memory Usage
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- Audio buffers stored in `bytearray`
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- `buffer_duration` tracks accumulated audio
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- Buffer cleared after transcription ✅
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---
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## Format Summary
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### Audio Formats
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| Component | Input Format | Output Format | Sample Rate |
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|-----------|--------------|---------------|-------------|
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| **Browser Mic** | PCM | Float32 | 16kHz |
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| **DeepgramSTT** | Float32 (16kHz) | JSON | 16kHz |
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| **OpenClaw** | String (text) | String (text) | N/A |
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| **VeniceKokoroTTS** | String (text) | Float32 PCM | 16kHz |
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| **Browser Speaker** | Float32 PCM | Float32 | 16kHz |
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### Data Types
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- **Audio arrays:** `np.ndarray` (float32)
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- **STT response:** `TranscriptionResult` object
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- **TTS response:** `np.ndarray` (float32)
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- **OpenClaw response:** `str` (text)
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### API Endpoints
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- **Deepgram:** `POST https://api.deepgram.com/v1/listen` (batch)
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- **OpenClaw Gateway:** `ws://` URL (JSON-RPC)
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- **Venice:** `POST https://api.venice.ai/api/v1/audio/speech`
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---
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## Testing Recommendations
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### Unit Tests
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```python
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# Test STT audio conversion
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def test_stt_float32_conversion():
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audio = np.random.randn(16000).astype(np.float32) # 1 second at 16kHz
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result = stt.transcribe_async(audio)
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assert result.text is not None
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assert result.duration == 1.0
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# Test TTS audio format
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def test_tts_returns_float32_pcm():
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audio = tts.generate_async("Hello", voice_ref_path=None)
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assert audio.dtype == np.float32
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assert len(audio.shape) == 1 # Mono
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# Sample rate is implicit (16kHz)
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```
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### Integration Tests
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- Test full pipeline: Mic → STT → OpenClaw → TTS → Speaker
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- Test error handling: Invalid API keys, network failures
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- Test retry logic: OpenClaw timeout and retry
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- Test concurrent sessions: Multiple WebSocket connections
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### Performance Tests
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- Measure latency: Mic → STT → Response → TTS
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- Measure RTF (Real-Time Factor): TTS generation time vs audio duration
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- Measure queue performance: Concurrent transcription requests
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---
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## Conclusion
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The voice pipeline is **functionally correct** with proper async handling and consistent data formats. The main improvement opportunities are:
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1. Consider Deepgram streaming API for lower latency
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2. Fix hardcoded `agent_id` to use environment variable
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3. Document unused interface parameters
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4. Add WebSocket error notifications to clients
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**Overall Status:** ✅ **WORKING** — No blocking issues.
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---
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*Audit completed by Caroline ⚙️*
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@ -72,6 +72,28 @@
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50% { opacity: 0.5; }
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}
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.thinking {
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display: inline-flex;
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align-items: center;
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gap: 8px;
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padding: 8px 16px;
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border-radius: 20px;
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font-size: 14px;
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font-weight: 500;
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margin-bottom: 20px;
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background: #8b5cf6;
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color: white;
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}
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.thinking .status-dot {
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animation: bounce 1s infinite;
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}
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@keyframes bounce {
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0%, 100% { transform: translateY(0); }
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50% { transform: translateY(-4px); }
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}
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.transcript {
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background: rgba(255, 255, 255, 0.1);
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border-radius: 12px;
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@ -188,6 +210,11 @@
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<span id="status-text">Disconnected</span>
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</div>
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<div id="thinking" class="thinking" style="display: none;">
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<span class="status-dot"></span>
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<span>Thinking...</span>
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</div>
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<div id="transcript" class="transcript" style="display: none;">
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<div class="transcript-label">Transcript</div>
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<div id="transcript-content"></div>
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@ -209,7 +236,8 @@
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const wsUrl = `${wsProtocol}//${window.location.host}/ws/voice/${sessionId}`;
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let ws = null;
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let audioContext = null;
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let inputAudioContext = null;
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let outputAudioContext = null;
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let microphone = null;
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let scriptProcessor = null;
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let isConnected = false;
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@ -218,6 +246,7 @@
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const statusEl = document.getElementById('status');
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const statusTextEl = document.getElementById('status-text');
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const thinkingEl = document.getElementById('thinking');
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const connectBtn = document.getElementById('connect-btn');
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const disconnectBtn = document.getElementById('disconnect-btn');
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const transcriptEl = document.getElementById('transcript');
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@ -229,6 +258,10 @@
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statusTextEl.textContent = text;
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}
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function showThinking(show) {
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thinkingEl.style.display = show ? 'inline-flex' : 'none';
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}
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function showError(message) {
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errorEl.textContent = message;
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errorEl.style.display = 'block';
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@ -238,6 +271,21 @@
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errorEl.style.display = 'none';
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}
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function addTranscript(text, type = 'transcript') {
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const item = document.createElement('div');
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item.className = 'transcript-item';
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const content = document.createElement('div');
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content.className = type === 'transcript' ? 'transcript-transcript' : 'transcript-response';
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content.textContent = text;
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item.appendChild(content);
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transcriptContentEl.appendChild(item);
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// Auto-scroll to bottom
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transcriptEl.scrollTop = transcriptEl.scrollHeight;
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}
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async function connect() {
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if (isConnected) return;
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@ -263,10 +311,13 @@
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};
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ws.onmessage = (event) => {
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const data = JSON.parse(event.data);
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if (data.type === 'welcome') {
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console.log('Server greeting:', data.message);
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if (event.data instanceof Blob) {
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// Binary audio data
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handleAudioData(event.data);
|
||||
} else {
|
||||
// JSON text data
|
||||
const data = JSON.parse(event.data);
|
||||
handleWebsocketMessage(data);
|
||||
}
|
||||
};
|
||||
|
||||
|
|
@ -288,6 +339,61 @@
|
|||
}
|
||||
}
|
||||
|
||||
function handleWebsocketMessage(data) {
|
||||
switch (data.type) {
|
||||
case 'welcome':
|
||||
console.log('Server greeting:', data.message);
|
||||
break;
|
||||
|
||||
case 'transcript':
|
||||
addTranscript(data.text, 'transcript');
|
||||
break;
|
||||
|
||||
case 'response':
|
||||
addTranscript(data.text, 'response');
|
||||
showThinking(false);
|
||||
break;
|
||||
|
||||
case 'tts_audio':
|
||||
console.log('TTS audio header received:', data.samples, 'samples @', data.sample_rate, 'Hz');
|
||||
break;
|
||||
|
||||
case 'ping':
|
||||
// Keepalive - ignore
|
||||
break;
|
||||
|
||||
default:
|
||||
console.warn('Unknown message type:', data.type);
|
||||
}
|
||||
}
|
||||
|
||||
async function handleAudioData(blob) {
|
||||
try {
|
||||
const arrayBuffer = await blob.arrayBuffer();
|
||||
const audioFloat32Array = new Float32Array(arrayBuffer);
|
||||
|
||||
// Decode audio using output AudioContext
|
||||
const audioBuffer = await outputAudioContext.decodeAudioData(audioFloat32Array.buffer);
|
||||
|
||||
// Play the audio
|
||||
playAudioBuffer(audioBuffer);
|
||||
|
||||
} catch (error) {
|
||||
console.error('Audio playback error:', error);
|
||||
}
|
||||
}
|
||||
|
||||
async function playAudioBuffer(audioBuffer) {
|
||||
const source = outputAudioContext.createBufferSource();
|
||||
source.buffer = audioBuffer;
|
||||
|
||||
// Connect to destination
|
||||
source.connect(outputAudioContext.destination);
|
||||
|
||||
// Start playback
|
||||
source.start();
|
||||
}
|
||||
|
||||
async function disconnect() {
|
||||
if (!ws) return;
|
||||
|
||||
|
|
@ -325,7 +431,13 @@
|
|||
|
||||
async function initAudio() {
|
||||
try {
|
||||
audioContext = new (window.AudioContext || window.webkitAudioContext)({
|
||||
// Create input audio context for microphone (16kHz)
|
||||
inputAudioContext = new (window.AudioContext || window.webkitAudioContext)({
|
||||
sampleRate: 16000
|
||||
});
|
||||
|
||||
// Create output audio context for playback (will be set to server sample rate)
|
||||
outputAudioContext = new (window.AudioContext || window.webkitAudioContext)({
|
||||
sampleRate: 16000
|
||||
});
|
||||
|
||||
|
|
@ -341,8 +453,8 @@
|
|||
});
|
||||
|
||||
console.log('Microphone acquired, stream tracks:', stream.getTracks().length);
|
||||
microphone = audioContext.createMediaStreamSource(stream);
|
||||
console.log('MediaStreamSource created, sample rate:', audioContext.sampleRate);
|
||||
microphone = inputAudioContext.createMediaStreamSource(stream);
|
||||
console.log('MediaStreamSource created, sample rate:', inputAudioContext.sampleRate);
|
||||
|
||||
// Use ScriptProcessor for reliable audio capture
|
||||
initScriptProcessor();
|
||||
|
|
@ -353,28 +465,11 @@
|
|||
}
|
||||
}
|
||||
|
||||
async function initAudioWorklet() {
|
||||
// Load worklet module
|
||||
const workletUrl = `${window.location.origin}/static/voice-worklet.js`;
|
||||
|
||||
await audioContext.audioWorklet.addModule(workletUrl);
|
||||
|
||||
const processor = new AudioWorkletNode(audioContext, 'voice-processor');
|
||||
|
||||
microphone.connect(processor);
|
||||
|
||||
processor.port.onmessage = (event) => {
|
||||
if (event.data.type === 'audio') {
|
||||
sendAudio(event.data.audio);
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
function initScriptProcessor() {
|
||||
scriptProcessor = audioContext.createScriptProcessor(4096, 1, 1);
|
||||
scriptProcessor = inputAudioContext.createScriptProcessor(4096, 1, 1);
|
||||
|
||||
microphone.connect(scriptProcessor);
|
||||
scriptProcessor.connect(audioContext.destination);
|
||||
scriptProcessor.connect(inputAudioContext.destination);
|
||||
|
||||
scriptProcessor.onaudioprocess = (event) => {
|
||||
const inputData = event.inputBuffer.getChannelData(0);
|
||||
|
|
@ -393,8 +488,12 @@
|
|||
scriptProcessor = null;
|
||||
}
|
||||
|
||||
if (audioContext && audioContext.state !== 'closed') {
|
||||
audioContext.close();
|
||||
if (inputAudioContext && inputAudioContext.state !== 'closed') {
|
||||
inputAudioContext.close();
|
||||
}
|
||||
|
||||
if (outputAudioContext && outputAudioContext.state !== 'closed') {
|
||||
outputAudioContext.close();
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -21,6 +21,14 @@ from server.stt import DeepgramSTT
|
|||
from server.tts import VeniceKokoroTTS
|
||||
from openclaw_client.client import OpenClawClient, OpenClawConfig
|
||||
|
||||
# Simple energy-based VAD to avoid sending silence to Deepgram
|
||||
def _is_speech(audio: np.ndarray, threshold: float = 0.01) -> bool:
|
||||
"""Check if audio buffer contains speech (above energy threshold)."""
|
||||
if len(audio) == 0:
|
||||
return False
|
||||
energy = float(np.sqrt(np.mean(audio ** 2)))
|
||||
return energy > threshold
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
|
|
@ -42,6 +50,12 @@ class VoiceSession:
|
|||
self.channel_count = 1
|
||||
self.bits_per_sample = 32
|
||||
|
||||
# Concurrency
|
||||
self.audio_queue: asyncio.Queue[bytes] = asyncio.Queue(maxsize=100)
|
||||
|
||||
# WebSocket connection
|
||||
self.websocket: Optional[WebSocket] = None
|
||||
|
||||
# Engines (self-contained, don't share with run.py)
|
||||
self.stt = None
|
||||
self.tts = None
|
||||
|
|
@ -51,6 +65,9 @@ class VoiceSession:
|
|||
self.connected = False
|
||||
self.transcript = []
|
||||
|
||||
# Consumer task
|
||||
self.consumer_task: Optional[asyncio.Task] = None
|
||||
|
||||
logger.info(f"Created voice session {session_id}")
|
||||
|
||||
async def initialize(self):
|
||||
|
|
@ -97,6 +114,13 @@ class VoiceSession:
|
|||
"""Clean up resources."""
|
||||
self.connected = False
|
||||
|
||||
if self.consumer_task and not self.consumer_task.done():
|
||||
self.consumer_task.cancel()
|
||||
try:
|
||||
await self.consumer_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
if self.openclaw:
|
||||
await self.openclaw.disconnect()
|
||||
|
||||
|
|
@ -106,70 +130,115 @@ class VoiceSession:
|
|||
"""Generate random session ID."""
|
||||
return "".join(random.choices(string.ascii_letters + string.digits, k=8))
|
||||
|
||||
async def process_audio_chunk(self, data: bytes):
|
||||
"""Process incoming audio chunk."""
|
||||
async with self._buffer_lock:
|
||||
self.audio_buffer.extend(data)
|
||||
async def _consumer_task(self):
|
||||
"""Consumer task that processes audio from queue."""
|
||||
start_time = asyncio.get_event_loop().time()
|
||||
|
||||
# Calculate duration
|
||||
chunk_size = len(data)
|
||||
chunk_duration = chunk_size / (self.sample_rate * self.channel_count * 4)
|
||||
while self.connected:
|
||||
try:
|
||||
# Wait for audio chunk (with timeout)
|
||||
try:
|
||||
data = await asyncio.wait_for(self.audio_queue.get(), timeout=0.1)
|
||||
except asyncio.TimeoutError:
|
||||
# Check if enough time has passed for buffer to accumulate
|
||||
elapsed = asyncio.get_event_loop().time() - start_time
|
||||
if elapsed > 1.0 and len(self.audio_buffer) == 0:
|
||||
# No audio received for 1 second, reset
|
||||
start_time = asyncio.get_event_loop().time()
|
||||
continue
|
||||
|
||||
self.buffer_duration += chunk_duration
|
||||
# Accumulate audio (no lock needed — only consumer touches buffer)
|
||||
self.audio_buffer.extend(data)
|
||||
|
||||
# Buffer until ~1 second
|
||||
if self.buffer_duration >= 0.8: # Slightly less than 1 second
|
||||
await self._transcribe_buffered_audio()
|
||||
# Calculate duration
|
||||
chunk_size = len(data)
|
||||
chunk_duration = chunk_size / (self.sample_rate * self.channel_count * 4)
|
||||
|
||||
self.buffer_duration += chunk_duration
|
||||
|
||||
# Buffer until ~0.8 seconds
|
||||
if self.buffer_duration >= 0.8:
|
||||
await self._transcribe_buffered_audio()
|
||||
start_time = asyncio.get_event_loop().time()
|
||||
|
||||
except asyncio.CancelledError:
|
||||
logger.info(f"Consumer task cancelled for session {self.session_id}")
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Consumer task error: {e}", exc_info=True)
|
||||
|
||||
logger.info(f"Consumer task exited for session {self.session_id}")
|
||||
|
||||
async def _transcribe_buffered_audio(self):
|
||||
"""Transcribe accumulated audio and send to OpenClaw."""
|
||||
async with self._buffer_lock:
|
||||
if not self.audio_buffer:
|
||||
return
|
||||
if not self.audio_buffer:
|
||||
return
|
||||
|
||||
# Convert bytearray to numpy array
|
||||
audio_data = np.frombuffer(bytes(self.audio_buffer), dtype=np.float32)
|
||||
# Copy and clear buffer immediately (only consumer touches it)
|
||||
audio_bytes = bytes(self.audio_buffer)
|
||||
self.audio_buffer.clear()
|
||||
self.buffer_duration = 0.0
|
||||
|
||||
# Convert bytearray to numpy array
|
||||
audio_data = np.frombuffer(audio_bytes, dtype=np.float32)
|
||||
|
||||
# Skip silence — don't waste Deepgram credits on empty audio
|
||||
if not _is_speech(audio_data):
|
||||
logger.debug(f"Session {self.session_id}: silence detected, skipping STT")
|
||||
return
|
||||
|
||||
try:
|
||||
# Transcribe
|
||||
try:
|
||||
result = await self.stt.transcribe_async(audio_data)
|
||||
result = await self.stt.transcribe_async(audio_data)
|
||||
|
||||
if result.text.strip():
|
||||
# Send to OpenClaw
|
||||
response = await self.openclaw.send_message(
|
||||
agent="main",
|
||||
message=result.text,
|
||||
speaker="voice_user",
|
||||
)
|
||||
if result.text.strip():
|
||||
# Send intermediate transcript status
|
||||
if self.connected:
|
||||
await self._send_status("transcript", result.text)
|
||||
|
||||
# Log transcript
|
||||
timestamp = asyncio.get_event_loop().time()
|
||||
entry = {
|
||||
"timestamp": timestamp,
|
||||
"session_id": self.session_id,
|
||||
"transcript": result.text,
|
||||
"response": response,
|
||||
}
|
||||
# Send to OpenClaw
|
||||
response = await self.openclaw.send_message(
|
||||
agent="main",
|
||||
message=result.text,
|
||||
speaker="voice_user",
|
||||
)
|
||||
|
||||
self.transcript.append(entry)
|
||||
# Send intermediate response status
|
||||
if self.connected:
|
||||
await self._send_status("response", response)
|
||||
|
||||
# Write to file
|
||||
with open(self.transcript_file, "a") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
# Log transcript
|
||||
timestamp = asyncio.get_event_loop().time()
|
||||
entry = {
|
||||
"timestamp": timestamp,
|
||||
"session_id": self.session_id,
|
||||
"transcript": result.text,
|
||||
"response": response,
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"Session {self.session_id}: "
|
||||
f'"{result.text[:50]}..." -> "{response[:50]}..."'
|
||||
)
|
||||
self.transcript.append(entry)
|
||||
|
||||
# Clear buffer
|
||||
self.audio_buffer.clear()
|
||||
self.buffer_duration = 0.0
|
||||
# Write to file
|
||||
with open(self.transcript_file, "a") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Transcription error: {e}")
|
||||
logger.info(
|
||||
f"Session {self.session_id}: "
|
||||
f'"{result.text[:50]}..." -> "{response[:50]}..."'
|
||||
)
|
||||
|
||||
async def synthesize_response(self, text: str):
|
||||
# Generate TTS audio
|
||||
audio = await self._synthesize_response(response)
|
||||
|
||||
# Send TTS audio back to browser
|
||||
if audio and self.connected:
|
||||
await self._send_tts_audio(audio)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Transcription error: {e}", exc_info=True)
|
||||
|
||||
async def _synthesize_response(self, text: str):
|
||||
"""Synthesize TTS audio from response text."""
|
||||
try:
|
||||
audio = await self.tts.generate_async(
|
||||
|
|
@ -181,17 +250,59 @@ class VoiceSession:
|
|||
return audio
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"TTS synthesis error: {e}")
|
||||
logger.error(f"TTS synthesis error: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
def get_transcript(self) -> list:
|
||||
"""Get transcript history."""
|
||||
return self.transcript
|
||||
async def _send_status(self, status_type: str, text: str):
|
||||
"""Send status message to WebSocket."""
|
||||
try:
|
||||
message = {
|
||||
"type": status_type,
|
||||
"text": text,
|
||||
}
|
||||
await self._send_json(message)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to send {status_type} status: {e}")
|
||||
|
||||
async def _send_tts_audio(self, audio: np.ndarray):
|
||||
"""Send TTS audio back to browser as binary PCM with JSON header."""
|
||||
try:
|
||||
# Convert to 16-bit PCM
|
||||
pcm_data = (audio * 32767).astype(np.int16).tobytes()
|
||||
|
||||
# Create JSON header
|
||||
header = {
|
||||
"type": "tts_audio",
|
||||
"samples": len(pcm_data) // 2, # 2 bytes per sample
|
||||
"sample_rate": self.sample_rate,
|
||||
}
|
||||
|
||||
# Send header as JSON text
|
||||
await self._send_json(header)
|
||||
|
||||
# Send PCM audio as binary
|
||||
await self._send_bytes(pcm_data)
|
||||
|
||||
logger.info(f"Sent TTS audio: {len(pcm_data)} bytes, {header['samples']} samples")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to send TTS audio: {e}", exc_info=True)
|
||||
|
||||
async def _send_json(self, data: dict):
|
||||
"""Send JSON message to WebSocket."""
|
||||
await self._send_bytes(json.dumps(data).encode("utf-8"))
|
||||
|
||||
async def _send_bytes(self, data: bytes):
|
||||
"""Send bytes to WebSocket."""
|
||||
await self._send_json({"type": "websocket.send", "bytes": len(data)})
|
||||
if self.websocket:
|
||||
await self.websocket.send_bytes(data)
|
||||
|
||||
|
||||
async def handle_voice_websocket(websocket: WebSocket, session_id: str):
|
||||
"""Handle WebSocket connection for voice session."""
|
||||
session = VoiceSession(session_id)
|
||||
session.websocket = websocket
|
||||
|
||||
await websocket.accept()
|
||||
session.connected = True
|
||||
|
|
@ -220,7 +331,10 @@ async def handle_voice_websocket(websocket: WebSocket, session_id: str):
|
|||
|
||||
keepalive_task = asyncio.create_task(keepalive())
|
||||
|
||||
# Receive and process audio
|
||||
# Start consumer task
|
||||
session.consumer_task = asyncio.create_task(session._consumer_task())
|
||||
|
||||
# Receive and process audio (non-blocking)
|
||||
chunk_count = 0
|
||||
while session.connected:
|
||||
try:
|
||||
|
|
@ -237,7 +351,13 @@ async def handle_voice_websocket(websocket: WebSocket, session_id: str):
|
|||
chunk_count += 1
|
||||
if chunk_count <= 5 or chunk_count % 100 == 0:
|
||||
logger.info(f"Audio chunk #{chunk_count}: {len(msg['bytes'])} bytes")
|
||||
await session.process_audio_chunk(msg["bytes"])
|
||||
|
||||
# Put audio chunk into queue (non-blocking)
|
||||
try:
|
||||
session.audio_queue.put_nowait(msg["bytes"])
|
||||
except asyncio.QueueFull:
|
||||
logger.warning(f"Audio queue full for session {session_id}, dropping chunk")
|
||||
|
||||
elif "text" in msg:
|
||||
pass
|
||||
else:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue