openclaw-voice/AUDIT.md

Voice Pipeline Audit

Date: 2026-04-10 Branch: caroline/cloud-stt-tts Audited Files: server/stt.py, server/tts.py, openclaw_client/client.py, server/voice_ws.py

---

Executive Summary

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.

✅ What Works

Component	Status	Format	Notes
DeepgramSTT.transcribe_async()	✅ Works	Float32, 16kHz	Batch API (sends 0.8s chunks)
VeniceKokoroTTS.generate_async()	✅ Works	Float32, 16kHz	Returns PCM audio correctly
OpenClawClient.send_message()	✅ Works	String	Returns LLM response text
Pipeline Integration	✅ Works	Consistent	Sample rates match, async correct

---

Detailed Findings

1. STT: DeepgramSTT.transcribe_async()

File: server/stt.py (lines 104-175)

✅ Correct Behavior

async def transcribe_async(
    self,
    audio: np.ndarray,  # ✅ Accepts numpy array
    language: Optional[str] = None,
    beam_size: Optional[int] = None,
    vad_filter: bool = False,
) -> "TranscriptionResult":

• ✅ Properly handles numpy float32 audio (converts if needed)
• ✅ Converts to int16 WAV format for Deepgram API
• ✅ Uses Deepgram REST API (NOT streaming API)
• ✅ Correctly parses Deepgram response structure
• ✅ Returns TranscriptionResult with text, segments, language, duration

⚠️ Note: Batch API Usage

• Sends audio in 0.8s chunks (batch mode)
• This is acceptable for current implementation but has higher latency than streaming
• Consider switching to Deepgram's streaming API (/live) for real-time transcription

Sample Rate: 16kHz

sample_rate: int = 16000  # Default

---

2. TTS: VeniceKokoroTTS.generate_async()

File: server/tts.py (lines 625-695)

✅ Correct Behavior

async def generate_async(
    self,
    text: str,
    voice_ref_path: Optional[Path] = None,  # ⚠️ Not used by Venice
    emotion_exaggeration: Optional[float] = None,  # ⚠️ Not used by Venice
) -> np.ndarray:

• ✅ Returns np.ndarray (PCM float32 audio)
• ✅ Correctly handles empty text (returns silence)
• ✅ Returns float32 dtype
• ✅ Resamples if Venice returns different sample rate
• ✅ Uses default 16kHz sample rate

Audio Format Details

# Chatterbox returns 24kHz, Venice returns 16kHz
if sr != 16000:
    from scipy import signal as scipy_signal
    target_samples = int(len(audio) * 16000 / sr)
    audio = scipy_signal.resample(audio, target_samples).astype(np.float32)

• Input from Venice: 16kHz (Chatterbox returns 24kHz, Venice returns 16kHz)
• Output format: Float32, 16kHz mono
• Browser expectation: PCM float32 at TTS output sample rate (16kHz) ✅

⚠️ Unused Parameters

voice_ref_path: Optional[Path] = None  # Venice doesn't use this
emotion_exaggeration: Optional[float] = None  # Venice doesn't use this

These parameters are reserved for interface compatibility with ChatterboxTTS. VeniceKokoroTTS ignores them.

---

3. OpenClaw Client: send_message()

File: openclaw_client/client.py (lines 161-216)

✅ Correct Behavior

async def send_message(
    self,
    agent: str,
    message: str,
    context: str = "",
    speaker: Optional[str] = None,
    model: Optional[str] = None,
) -> str:

• ✅ Returns str (LLM response text)
• ✅ Uses WebSocket JSON-RPC protocol
• ✅ Implements retry logic with extended timeout
• ✅ Properly handles streaming responses via _handle_chat_event()
• ✅ Validates agent against AGENT_PERSONALITIES

Return Format

return response  # ✅ Returns string text

• Format: Plain text string
• Encoding: UTF-8 (JSON serialization handles this)
• Content: LLM's response text

---

4. Pipeline Integration

File: server/voice_ws.py (lines 22-217)

✅ Correct Flow

Browser Mic (16kHz PCM) → WebSocket → STT (16kHz) → OpenClaw → TTS (16kHz) → WebSocket → Browser

Sample Rate Path

1. Browser input: 16kHz PCM
2. DeepgramSTT: 16kHz (accepts 16kHz, converts if needed)
3. OpenClaw: No audio processing (just text)
4. VeniceKokoroTTS: Returns 16kHz PCM
5. Browser output: Expects 16kHz PCM ✅

Data Format Path

1. STT input: np.ndarray (float32)
2. STT output: np.ndarray (float32)
3. OpenClaw input: str (text)
4. OpenClaw output: str (text)
5. TTS input: str (text)
6. TTS output: np.ndarray (float32) ✅

✅ Async Correctness

• All async methods use async/await correctly
• No blocking operations in event loop
• Uses asyncio.get_event_loop().time() for timing
• Uses run_in_executor() for CPU-bound work (Chatterbox generation)

---

5. Environment Variables

File: .env

Required Environment Variables

# Discord Bot
DISCORD_TOKEN=MTQ5MTk3MDc2MjgxNzU0MDM1Nw.GPhUtb.ZXfMxmvRW77scp2dTf4lDqAevLXLhR7Sf8_9-I
DISCORD_GUILD_ID=1481863201925758999

# OpenClaw Gateway
OPENCLAW_BASE_URL=ws://localhost:18789
OPENCLAW_AUTH_TOKEN=VcFh2zrGECHy1CPCKdFSs2Im1WdD8pPELlDy8NBL0Ao=
OPENCLAW_AGENT_ID=main  # ⚠️ Defined but not used by VeniceKokoroTTS

# Cloud STT/TTS API Keys
DEEPGRAM_API_KEY=169f45b6e2f21a9b05310c52b41d5453593d6c41
VENICE_API_KEY=VENICE-INFERENCE-KEY-IKSeUQZ8DvKn4gHj9fCzQQtDtCnCqFIk0IrZJfiyp1

⚠️ Unused Environment Variable

• OPENCLAW_AGENT_ID is defined in .env but voice_ws.py hardcodes agent_id="main":

# server/voice_ws.py line 135
self.openclaw = OpenClawClient(
    config=OpenClawConfig(
        base_url=openclaw_url,
        auth_token=openclaw_token,
        timeout=30.0,
        agent_id="main",  # ⚠️ Hardcoded, ignores OPENCLAW_AGENT_ID
    )
)

---

Issues and Recommendations

Critical Issues

None detected. Pipeline works correctly.

Minor Issues

1. Deepgram Batch API vs Streaming API

Severity: Low (works, but not optimal)

Current: Sends 0.8s chunks via REST API Impact: Higher latency than streaming API

Recommendation: Consider switching to Deepgram's streaming API (/live) for real-time transcription:

# Example (not implemented):
async with httpx.AsyncClient(timeout=30.0) as client:
    async with client.stream("POST", f"{self.base_url}/live", ...) as response:
        async for chunk in response.aiter_bytes():
            # Process streaming response
            pass

2. Unused Interface Parameters

Severity: Low (cosmetic)

Location: server/tts.py lines 625-695

Issue: VeniceKokoroTTS.generate_async() accepts voice_ref_path and emotion_exaggeration but doesn't use them (reserved for ChatterboxTTS compatibility).

Recommendation: Document this in docstring or add a comment explaining they're reserved for future use.

3. Hardcoded Configuration

Severity: Low (configuration inconsistency)

Location: server/voice_ws.py line 135

Issue: agent_id="main" is hardcoded, ignoring OPENCLAW_AGENT_ID from .env.

Recommendation: Use environment variable:

agent_id = os.getenv("OPENCLAW_AGENT_ID", "main")
self.openclaw = OpenClawClient(
    config=OpenClawConfig(
        base_url=openclaw_url,
        auth_token=openclaw_token,
        timeout=30.0,
        agent_id=agent_id,  # ✅ Use env var
    )
)

4. Missing Error Handling in VoiceSession

Severity: Low (prevents crash, but may hide errors)

Location: server/voice_ws.py lines 177-202

Issue: _transcribe_buffered_audio() catches exceptions but only logs them, doesn't notify client.

Recommendation: Send error notification to client via WebSocket:

await websocket.send_json({
    "type": "error",
    "message": f"Transcription failed: {str(e)}"
})

Performance Considerations

Sample Rate Processing

• STT: 16kHz input → 16kHz output ✅
• TTS: 16kHz output ✅
• No sample rate conversion needed (Venice returns 16kHz)

Memory Usage

• Audio buffers stored in bytearray
• buffer_duration tracks accumulated audio
• Buffer cleared after transcription ✅

---

Format Summary

Audio Formats

Component	Input Format	Output Format	Sample Rate
Browser Mic	PCM	Float32	16kHz
DeepgramSTT	Float32 (16kHz)	JSON	16kHz
OpenClaw	String (text)	String (text)	N/A
VeniceKokoroTTS	String (text)	Float32 PCM	16kHz
Browser Speaker	Float32 PCM	Float32	16kHz

Data Types

• Audio arrays: np.ndarray (float32)
• STT response: TranscriptionResult object
• TTS response: np.ndarray (float32)
• OpenClaw response: str (text)

API Endpoints

• Deepgram: POST https://api.deepgram.com/v1/listen (batch)
• OpenClaw Gateway: ws:// URL (JSON-RPC)
• Venice: POST https://api.venice.ai/api/v1/audio/speech

---

Testing Recommendations

Unit Tests

# Test STT audio conversion
def test_stt_float32_conversion():
    audio = np.random.randn(16000).astype(np.float32)  # 1 second at 16kHz
    result = stt.transcribe_async(audio)
    assert result.text is not None
    assert result.duration == 1.0

# Test TTS audio format
def test_tts_returns_float32_pcm():
    audio = tts.generate_async("Hello", voice_ref_path=None)
    assert audio.dtype == np.float32
    assert len(audio.shape) == 1  # Mono
    # Sample rate is implicit (16kHz)

Integration Tests

• Test full pipeline: Mic → STT → OpenClaw → TTS → Speaker
• Test error handling: Invalid API keys, network failures
• Test retry logic: OpenClaw timeout and retry
• Test concurrent sessions: Multiple WebSocket connections

Performance Tests

• Measure latency: Mic → STT → Response → TTS
• Measure RTF (Real-Time Factor): TTS generation time vs audio duration
• Measure queue performance: Concurrent transcription requests

---

Conclusion

The voice pipeline is functionally correct with proper async handling and consistent data formats. The main improvement opportunities are:

1. Consider Deepgram streaming API for lower latency
2. Fix hardcoded agent_id to use environment variable
3. Document unused interface parameters
4. Add WebSocket error notifications to clients

Overall Status: ✅ WORKING — No blocking issues.

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Audit completed by Caroline ⚙️