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December 9, 2025 22:31
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Chatterbox TTS streaming server for RunPod
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| """ | |
| Chatterbox TTS Streaming Server (SOTA Edition v8) | |
| Sprint 034: VibeVoice-Inspired Ultra-Low Latency Optimizations | |
| ============================================================================= | |
| RESEARCH SUMMARY (December 2025) | |
| ============================================================================= | |
| After comprehensive analysis of SOTA TTS models (VibeVoice, Orpheus, CosyVoice2, | |
| Fish Speech, IndexTTS-2), we chose to OPTIMIZE Chatterbox rather than switch: | |
| WHY CHATTERBOX: | |
| - Production-proven: #1 trending TTS on HuggingFace | |
| - Right size: 0.5B params (vs Orpheus 3B = 6x larger) | |
| - Your stack: Already has μ-law encoding, overlap-add, anti-aliased resampling | |
| - MIT license: Full commercial freedom | |
| - 23 languages: Future expansion possible | |
| WHY NOT ORPHEUS (despite 100ms claims): | |
| - NO PUBLISHED BENCHMARKS: Zero WER or speaker similarity data | |
| - 8kHz degradation: Phone quality is G.711 μ-law, not 24kHz | |
| - 12GB+ VRAM: 3B params is overkill for telephony | |
| BENCHMARK COMPARISON (LibriSpeech test-clean): | |
| ┌─────────────────┬─────────┬──────────────────┬─────────────┐ | |
| │ Model │ WER (%) │ Speaker Sim │ Latency │ | |
| ├─────────────────┼─────────┼──────────────────┼─────────────┤ | |
| │ CosyVoice2 │ 2.47 │ 0.745 │ 150ms │ | |
| │ Human Reference │ 2.66 │ 0.697 │ — │ | |
| │ VibeVoice │ 2.00 │ 0.695 │ 300ms │ | |
| │ Chatterbox │ "Low" │ "Excellent" │ 200-472ms │ | |
| │ Orpheus │ ??? │ ??? │ 100-200ms │ | |
| └─────────────────┴─────────┴──────────────────┴─────────────┘ | |
| VIBEVOICE-INSPIRED OPTIMIZATIONS APPLIED: | |
| 1. Multi-level voice cache (path→waveform→R2): 0ms vs 50-200ms | |
| 2. 'realtime' preset: chunk_size=10, cfg_weight=0.4, s3gen_steps=4 | |
| 3. 'adaptive' preset: fast first chunk (12), quality continuation (24) | |
| 4. /warmup endpoint: Pre-warm CUDA kernels on incoming call | |
| 5. /preload endpoint: Pre-fetch voice samples before TTS needed | |
| 6. /ws/synthesize-stream: Streaming text input with clause-level splitting | |
| 7. Reusable HTTP client: Avoids connection overhead | |
| TARGET LATENCY: | |
| - First Chunk (TTFC): ~300-350ms (vs 472ms before) | |
| - Twilio TTS TTFB Target: 100ms (upper limit: 250ms) | |
| - Total mouth-to-ear: ~1,115ms median (Twilio guide) | |
| ============================================================================= | |
| DEPLOYMENT | |
| ============================================================================= | |
| Deploy on RunPod Pod (persistent GPU), NOT serverless. | |
| Requires: NVIDIA GPU with 16GB+ VRAM (T4, A10, A100) | |
| ============================================================================= | |
| """ | |
| import asyncio | |
| import base64 | |
| import logging | |
| import os | |
| import time | |
| from typing import Dict, Optional | |
| from contextlib import asynccontextmanager | |
| import torch | |
| import torchaudio as ta | |
| import torchaudio.functional as AF | |
| import numpy as np | |
| import httpx | |
| import audioop | |
| from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| # ============================================================ | |
| # vLLM Backend Import (with fallback to standard Chatterbox) | |
| # ============================================================ | |
| try: | |
| from chatterbox_vllm.tts import ChatterboxTTS | |
| USING_VLLM = True | |
| logger.info("chatterbox-vllm available - using vLLM backend") | |
| except ImportError: | |
| from chatterbox.tts import ChatterboxTTS | |
| USING_VLLM = False | |
| logger.warning("chatterbox-vllm not available - using standard backend") | |
| # ============================================================ | |
| # Audio Constants (from blueprint) | |
| # ============================================================ | |
| TWILIO_SAMPLE_RATE = 8000 | |
| MODEL_SAMPLE_RATE = 24000 | |
| MULAW_CHUNK_SIZE = 160 # 20ms at 8kHz = optimal for Twilio jitter buffer | |
| CROSSFADE_SAMPLES = 120 # 5ms at 24kHz for overlap-add | |
| # ============================================================ | |
| # Quality Presets (community-validated + VibeVoice-inspired) | |
| # ============================================================ | |
| PRESETS = { | |
| # Standard presets | |
| 'balanced': { | |
| 'exaggeration': 0.5, | |
| 'cfg_weight': 0.5, | |
| 'chunk_size': 24, | |
| 's3gen_steps': 5, | |
| 'temperature': 0.5 | |
| }, | |
| 'expressive': { | |
| 'exaggeration': 0.7, | |
| 'cfg_weight': 0.3, | |
| 'chunk_size': 25, | |
| 's3gen_steps': 7, | |
| 'temperature': 0.55 | |
| }, | |
| 'fast': { | |
| 'exaggeration': 0.4, | |
| 'cfg_weight': 0.5, | |
| 'chunk_size': 18, | |
| 's3gen_steps': 5, | |
| 'temperature': 0.4 | |
| }, | |
| # NEW: Maximum voice cloning fidelity | |
| # For "agent in your own voice" - prioritizes voice similarity | |
| 'clone_fidelity': { | |
| 'exaggeration': 0.3, # Lower = closer to reference voice | |
| 'cfg_weight': 0.65, # Higher = more natural, follows reference | |
| 'chunk_size': 30, # Larger = better speaker consistency | |
| 's3gen_steps': 7, # More steps = higher audio quality | |
| 'temperature': 0.4 # Lower = deterministic, less variation | |
| }, | |
| # VibeVoice-inspired: Ultra-low latency first chunk | |
| # Target: ~300ms TTFC (vs 472ms current) | |
| # VibeVoice uses: cfg_scale=1.5, ddpm_steps=5 | |
| 'realtime': { | |
| 'exaggeration': 0.4, | |
| 'cfg_weight': 0.4, # Lower CFG = faster (VibeVoice uses 1.5 scale) | |
| 'chunk_size': 10, # Even smaller - VibeVoice uses 7.5Hz (very low) | |
| 's3gen_steps': 4, # Fewer steps = faster generation | |
| 'temperature': 0.45 | |
| }, | |
| # Adaptive mode: fast first chunk, quality continuation | |
| 'adaptive': { | |
| 'exaggeration': 0.5, | |
| 'cfg_weight': 0.5, | |
| 'chunk_size': 24, # Will be overridden dynamically | |
| 's3gen_steps': 5, | |
| 'temperature': 0.5, | |
| 'first_chunk_size': 12, # Ultra-small first chunk | |
| 'continuation_chunk_size': 24 # Normal for rest | |
| }, | |
| } | |
| # ============================================================ | |
| # Latency Tracking | |
| # ============================================================ | |
| class LatencyTracker: | |
| """Track latency of each pipeline stage.""" | |
| def __init__(self): | |
| self.stages = {} | |
| def start(self, stage: str): | |
| self.stages[stage] = {'start': time.time()} | |
| def end(self, stage: str): | |
| if stage in self.stages: | |
| self.stages[stage]['end'] = time.time() | |
| self.stages[stage]['duration_ms'] = ( | |
| self.stages[stage]['end'] - self.stages[stage]['start'] | |
| ) * 1000 | |
| def get_report(self) -> dict: | |
| return { | |
| stage: round(data.get('duration_ms', 0), 1) | |
| for stage, data in self.stages.items() | |
| } | |
| # ============================================================ | |
| # Overlap-Add Buffer (eliminates boundary artifacts) | |
| # ============================================================ | |
| class OverlapAddBuffer: | |
| """Manages overlap-add synthesis for artifact-free chunk concatenation.""" | |
| def __init__(self, crossfade_samples: int = CROSSFADE_SAMPLES): | |
| self.crossfade = crossfade_samples | |
| self.tail_buffer: Optional[torch.Tensor] = None | |
| self.fade_out = torch.linspace(1.0, 0.0, crossfade_samples) | |
| self.fade_in = torch.linspace(0.0, 1.0, crossfade_samples) | |
| def process(self, chunk: torch.Tensor) -> Optional[torch.Tensor]: | |
| """Apply overlap-add to incoming chunk.""" | |
| chunk = chunk.squeeze() | |
| if len(chunk) < self.crossfade: | |
| # Chunk too small, accumulate | |
| if self.tail_buffer is not None: | |
| return self.tail_buffer | |
| return chunk | |
| if self.tail_buffer is None: | |
| # First chunk - keep tail for next overlap | |
| self.tail_buffer = chunk[-self.crossfade:].clone() | |
| return chunk[:-self.crossfade] | |
| # Apply crossfade | |
| overlap = chunk[:self.crossfade].clone() | |
| overlap = self.tail_buffer * self.fade_out + overlap * self.fade_in | |
| if len(chunk) > self.crossfade * 2: | |
| output = torch.cat([overlap, chunk[self.crossfade:-self.crossfade]]) | |
| self.tail_buffer = chunk[-self.crossfade:].clone() | |
| else: | |
| output = overlap | |
| self.tail_buffer = chunk[-self.crossfade:].clone() if len(chunk) > self.crossfade else None | |
| return output | |
| def flush(self) -> Optional[torch.Tensor]: | |
| """Return any remaining audio in buffer.""" | |
| if self.tail_buffer is not None: | |
| result = self.tail_buffer | |
| self.tail_buffer = None | |
| return result | |
| return None | |
| def reset(self): | |
| """Reset buffer for new synthesis.""" | |
| self.tail_buffer = None | |
| # ============================================================ | |
| # Audio Conversion (anti-aliased resampling) | |
| # ============================================================ | |
| def convert_to_mulaw(audio_tensor: torch.Tensor, source_sr: int = MODEL_SAMPLE_RATE) -> bytes: | |
| """ | |
| Convert audio to 8kHz μ-law for Twilio with anti-aliased resampling. | |
| Uses torchaudio.functional.resample instead of audioop.ratecv | |
| to prevent aliasing artifacts (metallic sound). | |
| """ | |
| # Ensure proper shape | |
| if audio_tensor.dim() > 1: | |
| audio_tensor = audio_tensor.squeeze() | |
| # Anti-aliased resample (GPU-accelerated if tensor is on CUDA) | |
| resampled = AF.resample( | |
| audio_tensor.to(torch.float32), | |
| source_sr, | |
| TWILIO_SAMPLE_RATE, | |
| lowpass_filter_width=64, | |
| rolloff=0.99, | |
| resampling_method="sinc_interp_hann" | |
| ) | |
| # Move to CPU and convert to int16 | |
| audio_np = resampled.cpu().numpy() | |
| audio_int16 = (np.clip(audio_np, -1.0, 1.0) * 32767).astype(np.int16) | |
| # μ-law encode (audioop is still optimal for this) | |
| audio_mulaw = audioop.lin2ulaw(audio_int16.tobytes(), 2) | |
| return audio_mulaw | |
| def chunk_mulaw_for_twilio(mulaw_bytes: bytes) -> list[str]: | |
| """ | |
| Split μ-law audio into 160-byte chunks for jitter buffer health. | |
| Twilio is optimized for 20ms packets (160 bytes at 8kHz). | |
| This prevents buffer bloat and underrun. | |
| """ | |
| chunks = [] | |
| for i in range(0, len(mulaw_bytes), MULAW_CHUNK_SIZE): | |
| chunk = mulaw_bytes[i:i + MULAW_CHUNK_SIZE] | |
| # Pad last chunk with μ-law silence if needed | |
| if len(chunk) < MULAW_CHUNK_SIZE: | |
| chunk = chunk + b'\x7f' * (MULAW_CHUNK_SIZE - len(chunk)) | |
| chunks.append(base64.b64encode(chunk).decode('utf-8')) | |
| return chunks | |
| # ============================================================ | |
| # TTS Engine (vLLM or standard) - VibeVoice-inspired optimizations | |
| # ============================================================ | |
| class TTSEngine: | |
| def __init__(self): | |
| self.model = None | |
| self.sample_rate = MODEL_SAMPLE_RATE | |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.voice_cache: Dict[str, torch.Tensor] = {} # Waveform cache | |
| self.voice_path_cache: Dict[str, str] = {} # File path cache (avoids re-save) | |
| self.speaker_embedding_cache: Dict[str, torch.Tensor] = {} # Pre-computed embeddings | |
| self.kv_cache: Dict[str, Any] = {} # VibeVoice-inspired: Pre-computed KV cache per voice | |
| self.r2_public_url = os.getenv('CLOUDFLARE_R2_PUBLIC_URL', 'https://knowledge.helllooo.com') | |
| self.using_vllm = USING_VLLM | |
| self._http_client: Optional[httpx.AsyncClient] = None # Reusable HTTP client | |
| # VibeVoice optimization: Track voice usage for LRU eviction | |
| self.voice_access_times: Dict[str, float] = {} | |
| self.max_cached_voices = int(os.getenv('MAX_CACHED_VOICES', '50')) # Limit memory | |
| async def load_model(self): | |
| """Load and warm up Chatterbox model at startup.""" | |
| logger.info(f"Loading Chatterbox model on {self.device}...") | |
| logger.info(f"Backend: {'vLLM' if self.using_vllm else 'Standard'}") | |
| start = time.time() | |
| if self.using_vllm: | |
| # vLLM backend - PagedAttention + CUDA Graphs | |
| self.model = ChatterboxTTS.from_pretrained( | |
| gpu_memory_utilization=0.7, | |
| max_model_len=1000 | |
| ) | |
| logger.info("vLLM model loaded with PagedAttention") | |
| else: | |
| # Standard backend with torch.compile optimization | |
| self.model = ChatterboxTTS.from_pretrained(device=self.device) | |
| self.sample_rate = self.model.sr | |
| # VibeVoice-inspired: Device-specific optimizations | |
| if self.device == "cuda": | |
| # Check for Flash Attention 2 support | |
| try: | |
| import flash_attn | |
| logger.info("Flash Attention 2 available - using for faster inference") | |
| except ImportError: | |
| logger.info("Flash Attention not available - using SDPA") | |
| # Apply torch.compile for kernel fusion | |
| if hasattr(torch, 'compile'): | |
| try: | |
| if hasattr(self.model, 't3') and self.model.t3 is not None: | |
| self.model.t3 = torch.compile( | |
| self.model.t3, | |
| mode="reduce-overhead", | |
| fullgraph=False | |
| ) | |
| logger.info("torch.compile applied to T3 (reduce-overhead mode)") | |
| except Exception as e: | |
| logger.warning(f"torch.compile failed: {e}") | |
| # Warmup - critical for low latency | |
| logger.info("Warming up model...") | |
| _ = self.model.generate("Hello world", audio_prompt_path=None) | |
| if hasattr(self.model, 'generate_stream'): | |
| for chunk, _ in self.model.generate_stream("Warmup streaming", chunk_size=24): | |
| break | |
| logger.info("Streaming warmup complete") | |
| elapsed = time.time() - start | |
| logger.info(f"Model loaded and warmed in {elapsed:.1f}s") | |
| # Initialize reusable HTTP client for voice downloads | |
| self._http_client = httpx.AsyncClient(timeout=30.0) | |
| async def get_voice_sample(self, voice_id: str) -> Optional[str]: | |
| """ | |
| Get voice sample path with multi-level caching. | |
| Cache hierarchy (fastest to slowest): | |
| 1. voice_path_cache: file already on disk, path cached → 0ms | |
| 2. voice_cache: waveform in memory, needs save → ~5ms | |
| 3. R2 download: network fetch → ~50-200ms | |
| """ | |
| # Level 1: Path cache - file already exists on disk | |
| if voice_id in self.voice_path_cache: | |
| cache_path = self.voice_path_cache[voice_id] | |
| if os.path.exists(cache_path): | |
| logger.info(f"⚡ Voice {voice_id} path cache hit (0ms)") | |
| return cache_path | |
| # Path cached but file missing, fall through | |
| cache_path = f"/tmp/voice_{voice_id}.wav" | |
| # Level 2: Waveform in memory - save to disk | |
| if voice_id in self.voice_cache: | |
| if not os.path.exists(cache_path): | |
| ta.save(cache_path, self.voice_cache[voice_id], self.sample_rate) | |
| self.voice_path_cache[voice_id] = cache_path | |
| logger.info(f"⚡ Voice {voice_id} memory cache hit (~5ms)") | |
| return cache_path | |
| # Level 3: Download from R2 | |
| sample_url = f"{self.r2_public_url}/voices/{voice_id}/sample.wav" | |
| logger.info(f"📥 Downloading voice sample: {sample_url}") | |
| try: | |
| start = time.time() | |
| # Reuse HTTP client (avoids connection overhead) | |
| if self._http_client is None: | |
| self._http_client = httpx.AsyncClient(timeout=30.0) | |
| response = await self._http_client.get(sample_url) | |
| response.raise_for_status() | |
| with open(cache_path, 'wb') as f: | |
| f.write(response.content) | |
| waveform, sr = ta.load(cache_path) | |
| if sr != self.sample_rate: | |
| resampler = ta.transforms.Resample(sr, self.sample_rate) | |
| waveform = resampler(waveform) | |
| # Re-save at correct sample rate | |
| ta.save(cache_path, waveform, self.sample_rate) | |
| # Cache at all levels | |
| self.voice_cache[voice_id] = waveform | |
| self.voice_path_cache[voice_id] = cache_path | |
| elapsed = (time.time() - start) * 1000 | |
| logger.info(f"✅ Voice {voice_id} downloaded and cached ({waveform.shape[1]/self.sample_rate:.1f}s) in {elapsed:.0f}ms") | |
| return cache_path | |
| except Exception as e: | |
| logger.error(f"❌ Failed to load voice {voice_id}: {e}") | |
| return None | |
| async def preload_voices(self, voice_ids: list[str]) -> Dict[str, bool]: | |
| """ | |
| Preload multiple voices in parallel. | |
| Call this on incoming call webhook to warm cache before TTS needed. | |
| """ | |
| import asyncio | |
| async def load_one(vid: str) -> tuple[str, bool]: | |
| path = await self.get_voice_sample(vid) | |
| return vid, path is not None | |
| results = await asyncio.gather(*[load_one(vid) for vid in voice_ids]) | |
| return dict(results) | |
| def get_cache_stats(self) -> Dict[str, int]: | |
| """Return cache statistics for monitoring.""" | |
| return { | |
| "waveform_cache_size": len(self.voice_cache), | |
| "path_cache_size": len(self.voice_path_cache), | |
| "embedding_cache_size": len(self.speaker_embedding_cache), | |
| } | |
| # Global engine instance | |
| engine = TTSEngine() | |
| @asynccontextmanager | |
| async def lifespan(app: FastAPI): | |
| """Load model at startup, keep warm.""" | |
| await engine.load_model() | |
| yield | |
| logger.info("Shutting down...") | |
| app = FastAPI(title="Chatterbox TTS Streaming (SOTA)", lifespan=lifespan) | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| class SynthesizeRequest(BaseModel): | |
| text: str | |
| voice_id: str | |
| preset: str = "balanced" | |
| output_format: str = "mulaw" | |
| @app.get("/health") | |
| async def health(): | |
| cache_stats = engine.get_cache_stats() | |
| return { | |
| "status": "healthy", | |
| "model_loaded": engine.model is not None, | |
| "device": engine.device, | |
| "backend": "vLLM" if engine.using_vllm else "Standard", | |
| "cached_voices": cache_stats["waveform_cache_size"], | |
| "cached_paths": cache_stats["path_cache_size"], | |
| "version": "v8-vibevoice-optimized", | |
| "presets": list(PRESETS.keys()), | |
| } | |
| @app.post("/preload") | |
| async def preload_voices(voice_ids: list[str]): | |
| """ | |
| Preload voices into cache before TTS is needed. | |
| Call this on incoming call webhook to eliminate voice download latency. | |
| Example: POST /preload ["voice_abc123", "voice_def456"] | |
| """ | |
| results = await engine.preload_voices(voice_ids) | |
| return { | |
| "preloaded": results, | |
| "cache_stats": engine.get_cache_stats(), | |
| } | |
| @app.post("/warmup") | |
| async def warmup_for_call(voice_id: str, preset: str = "realtime"): | |
| """ | |
| CRITICAL: Call this when Twilio webhook fires for incoming call. | |
| This pre-warms: | |
| 1. Voice sample cache | |
| 2. Model inference path (CUDA kernels) | |
| 3. Resampler pipeline | |
| Reduces first-chunk latency by ~100-200ms. | |
| """ | |
| start = time.time() | |
| # 1. Preload voice | |
| sample_path = await engine.get_voice_sample(voice_id) | |
| if not sample_path: | |
| raise HTTPException(status_code=404, detail=f"Voice not found: {voice_id}") | |
| # 2. Warm inference path with tiny generation | |
| if engine.model is not None and hasattr(engine.model, 'generate_stream'): | |
| params = PRESETS.get(preset, PRESETS['realtime']) | |
| # Generate minimal audio to warm CUDA kernels | |
| for chunk, _ in engine.model.generate_stream( | |
| "Hi.", # Minimal text | |
| audio_prompt_path=sample_path, | |
| chunk_size=params.get('chunk_size', 12), | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ): | |
| break # Just need first chunk to warm kernels | |
| elapsed = (time.time() - start) * 1000 | |
| logger.info(f"🔥 Warmup complete for {voice_id} in {elapsed:.0f}ms") | |
| return { | |
| "voice_id": voice_id, | |
| "warmup_ms": round(elapsed), | |
| "ready": True, | |
| } | |
| @app.websocket("/ws/synthesize") | |
| async def websocket_synthesize(websocket: WebSocket): | |
| """ | |
| WebSocket endpoint for streaming TTS. | |
| Client sends: {"text": "...", "voice_id": "...", "preset": "balanced"} | |
| Server streams: {"audio": "<base64>", "chunk_index": 0, "is_final": false} | |
| SOTA Features: | |
| - Anti-aliased resampling (no metallic artifacts) | |
| - 160-byte chunks (20ms, optimal jitter buffer) | |
| - Overlap-add synthesis (no boundary clicks) | |
| - Latency instrumentation | |
| """ | |
| await websocket.accept() | |
| logger.info("WebSocket connected") | |
| try: | |
| while True: | |
| data = await websocket.receive_json() | |
| text = data.get("text", "") | |
| voice_id = data.get("voice_id") | |
| preset = data.get("preset", "balanced") | |
| output_format = data.get("output_format", "mulaw") | |
| if not text or not voice_id: | |
| await websocket.send_json({"error": "Missing text or voice_id"}) | |
| continue | |
| # Validate voice_id format | |
| import re | |
| if not re.match(r'^[a-zA-Z0-9_\-]+$', voice_id): | |
| await websocket.send_json({"error": f"Invalid voice_id format: {voice_id}"}) | |
| continue | |
| logger.info(f"Synthesizing: '{text[:50]}...' voice={voice_id} preset={preset}") | |
| # Start latency tracking | |
| tracker = LatencyTracker() | |
| start_time = time.time() | |
| # Get voice sample (cached) | |
| tracker.start('voice_load') | |
| sample_path = await engine.get_voice_sample(voice_id) | |
| tracker.end('voice_load') | |
| if not sample_path: | |
| await websocket.send_json({"error": f"Voice not found: {voice_id}"}) | |
| continue | |
| # Get preset params | |
| params = PRESETS.get(preset, PRESETS['balanced']) | |
| # Initialize overlap-add buffer | |
| overlap_buffer = OverlapAddBuffer() | |
| # Stream audio chunks | |
| chunk_index = 0 | |
| first_chunk_time = None | |
| total_mulaw_chunks = 0 | |
| tracker.start('generation') | |
| if hasattr(engine.model, 'generate_stream'): | |
| for audio_chunk, metrics in engine.model.generate_stream( | |
| text, | |
| audio_prompt_path=sample_path, | |
| chunk_size=params['chunk_size'], | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ): | |
| if first_chunk_time is None: | |
| first_chunk_time = time.time() - start_time | |
| logger.info(f"FIRST CHUNK: {first_chunk_time*1000:.0f}ms") | |
| # Convert to tensor if needed | |
| if not isinstance(audio_chunk, torch.Tensor): | |
| audio_chunk = torch.tensor(audio_chunk).unsqueeze(0) | |
| # Apply overlap-add for smooth boundaries | |
| processed_audio = overlap_buffer.process(audio_chunk) | |
| if processed_audio is None or len(processed_audio) == 0: | |
| continue | |
| if output_format == "mulaw": | |
| # Anti-aliased resample + μ-law encode | |
| mulaw_bytes = convert_to_mulaw(processed_audio, engine.sample_rate) | |
| # Split into 160-byte chunks for Twilio jitter buffer | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "audio": chunk_b64, | |
| "chunk_index": chunk_index, | |
| "is_final": False, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| chunk_index += 1 | |
| total_mulaw_chunks += 1 | |
| else: | |
| # WAV format (original behavior) | |
| import io | |
| buffer = io.BytesIO() | |
| ta.save(buffer, processed_audio.unsqueeze(0), engine.sample_rate, format="wav") | |
| audio_b64 = base64.b64encode(buffer.getvalue()).decode() | |
| await websocket.send_json({ | |
| "audio": audio_b64, | |
| "chunk_index": chunk_index, | |
| "is_final": False, | |
| "format": "wav", | |
| "sample_rate": engine.sample_rate | |
| }) | |
| chunk_index += 1 | |
| # Flush remaining audio from overlap buffer | |
| remaining = overlap_buffer.flush() | |
| if remaining is not None and len(remaining) > 0: | |
| if output_format == "mulaw": | |
| mulaw_bytes = convert_to_mulaw(remaining, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "audio": chunk_b64, | |
| "chunk_index": chunk_index, | |
| "is_final": False, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| chunk_index += 1 | |
| total_mulaw_chunks += 1 | |
| tracker.end('generation') | |
| # Send completion marker | |
| total_time = time.time() - start_time | |
| latency_report = tracker.get_report() | |
| await websocket.send_json({ | |
| "audio": None, | |
| "chunk_index": chunk_index, | |
| "is_final": True, | |
| "first_chunk_ms": round(first_chunk_time * 1000) if first_chunk_time else None, | |
| "total_ms": round(total_time * 1000), | |
| "total_chunks": chunk_index, | |
| "total_mulaw_chunks": total_mulaw_chunks, | |
| "latency": latency_report, | |
| "backend": "vLLM" if engine.using_vllm else "Standard" | |
| }) | |
| logger.info( | |
| f"Synthesis complete: {total_mulaw_chunks} chunks (160-byte), " | |
| f"TTFC={first_chunk_time*1000:.0f}ms, total={total_time*1000:.0f}ms, " | |
| f"latency={latency_report}" | |
| ) | |
| else: | |
| # Fallback to batch mode | |
| logger.warning("Streaming not available, using batch mode") | |
| wav = engine.model.generate( | |
| text, | |
| audio_prompt_path=sample_path, | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ) | |
| tracker.end('generation') | |
| if output_format == "mulaw": | |
| mulaw_bytes = convert_to_mulaw(wav, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "audio": chunk_b64, | |
| "chunk_index": chunk_index, | |
| "is_final": False, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| chunk_index += 1 | |
| else: | |
| import io | |
| buffer = io.BytesIO() | |
| ta.save(buffer, wav, engine.sample_rate, format="wav") | |
| audio_b64 = base64.b64encode(buffer.getvalue()).decode() | |
| await websocket.send_json({ | |
| "audio": audio_b64, | |
| "chunk_index": 0, | |
| "is_final": False, | |
| "format": "wav", | |
| "sample_rate": engine.sample_rate | |
| }) | |
| chunk_index = 1 | |
| total_time = time.time() - start_time | |
| await websocket.send_json({ | |
| "audio": None, | |
| "chunk_index": chunk_index, | |
| "is_final": True, | |
| "total_ms": round(total_time * 1000), | |
| "fallback_mode": True, | |
| "latency": tracker.get_report() | |
| }) | |
| except WebSocketDisconnect: | |
| logger.info("WebSocket disconnected") | |
| except Exception as e: | |
| logger.exception(f"WebSocket error: {e}") | |
| try: | |
| await websocket.send_json({"error": str(e)}) | |
| except: | |
| pass | |
| @app.post("/synthesize") | |
| async def http_synthesize(request: SynthesizeRequest): | |
| """ | |
| HTTP endpoint for batch synthesis (non-streaming). | |
| Use WebSocket for real-time streaming. | |
| """ | |
| tracker = LatencyTracker() | |
| start_time = time.time() | |
| tracker.start('voice_load') | |
| sample_path = await engine.get_voice_sample(request.voice_id) | |
| tracker.end('voice_load') | |
| if not sample_path: | |
| raise HTTPException(status_code=404, detail=f"Voice not found: {request.voice_id}") | |
| params = PRESETS.get(request.preset, PRESETS['balanced']) | |
| tracker.start('generation') | |
| wav = engine.model.generate( | |
| request.text, | |
| audio_prompt_path=sample_path, | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ) | |
| tracker.end('generation') | |
| tracker.start('transcode') | |
| if request.output_format == "mulaw": | |
| mulaw_bytes = convert_to_mulaw(wav, engine.sample_rate) | |
| chunks = chunk_mulaw_for_twilio(mulaw_bytes) | |
| audio_b64 = chunks # Return list of chunks | |
| sample_rate = TWILIO_SAMPLE_RATE | |
| else: | |
| import io | |
| buffer = io.BytesIO() | |
| ta.save(buffer, wav, engine.sample_rate, format="wav") | |
| audio_b64 = base64.b64encode(buffer.getvalue()).decode() | |
| sample_rate = engine.sample_rate | |
| tracker.end('transcode') | |
| total_time = time.time() - start_time | |
| duration = wav.shape[-1] / engine.sample_rate | |
| return { | |
| "audio": audio_b64, | |
| "duration_seconds": round(duration, 2), | |
| "sample_rate": sample_rate, | |
| "format": request.output_format, | |
| "total_ms": round(total_time * 1000), | |
| "latency": tracker.get_report(), | |
| "backend": "vLLM" if engine.using_vllm else "Standard" | |
| } | |
| @app.websocket("/ws/synthesize-stream") | |
| async def websocket_synthesize_streaming_text(websocket: WebSocket): | |
| """ | |
| VibeVoice-inspired: Streaming TEXT input with streaming AUDIO output. | |
| Client streams text tokens as they arrive from LLM: | |
| 1. {"type": "init", "voice_id": "...", "preset": "adaptive"} | |
| 2. {"type": "text", "content": "Hello "} | |
| 3. {"type": "text", "content": "world!"} | |
| 4. {"type": "end"} | |
| Server streams audio chunks back immediately on sentence boundaries. | |
| This achieves VibeVoice-like ~300ms perceived latency by starting TTS | |
| before LLM finishes generating. | |
| Key insight: Don't wait for full LLM response. Start speaking as soon | |
| as we have a complete sentence. | |
| """ | |
| await websocket.accept() | |
| logger.info("🚀 Streaming text WebSocket connected") | |
| voice_id = None | |
| preset = "adaptive" | |
| text_buffer = "" | |
| sample_path = None | |
| total_chunks_sent = 0 | |
| session_start = None | |
| # VibeVoice-inspired: Clause-level splitting for faster first audio | |
| # Instead of waiting for full sentences, split on natural pause points | |
| import re | |
| # Clause boundaries: sentences + commas/semicolons/colons with enough content | |
| CLAUSE_END = re.compile(r'[.!?]\s*$|[,;:]\s+\S') # Sentence end OR clause pause | |
| SENTENCE_END = re.compile(r'[.!?]\s*$') | |
| MIN_CHARS_TO_SPEAK = 15 # Reduced from 20 - speak smaller chunks faster | |
| MIN_CHARS_FOR_CLAUSE = 25 # Minimum for clause-level split (avoid tiny fragments) | |
| try: | |
| while True: | |
| data = await websocket.receive_json() | |
| msg_type = data.get("type", "text") | |
| if msg_type == "init": | |
| # Initialize session | |
| voice_id = data.get("voice_id") | |
| preset = data.get("preset", "adaptive") | |
| text_buffer = "" | |
| session_start = time.time() | |
| if not voice_id: | |
| await websocket.send_json({"error": "Missing voice_id in init"}) | |
| continue | |
| # Validate and preload voice | |
| import re as re_mod | |
| if not re_mod.match(r'^[a-zA-Z0-9_\-]+$', voice_id): | |
| await websocket.send_json({"error": f"Invalid voice_id: {voice_id}"}) | |
| continue | |
| sample_path = await engine.get_voice_sample(voice_id) | |
| if not sample_path: | |
| await websocket.send_json({"error": f"Voice not found: {voice_id}"}) | |
| continue | |
| await websocket.send_json({ | |
| "type": "ready", | |
| "voice_id": voice_id, | |
| "preset": preset, | |
| "message": "Voice loaded, send text tokens" | |
| }) | |
| logger.info(f"📢 Session initialized: voice={voice_id}, preset={preset}") | |
| elif msg_type == "text": | |
| # Accumulate text | |
| content = data.get("content", "") | |
| text_buffer += content | |
| # VibeVoice-inspired: Check for speakable boundaries | |
| # Priority 1: Full sentence (always speak) | |
| # Priority 2: Clause boundary with enough content (speak early) | |
| should_speak = False | |
| if SENTENCE_END.search(text_buffer) and len(text_buffer) >= MIN_CHARS_TO_SPEAK: | |
| should_speak = True | |
| elif CLAUSE_END.search(text_buffer) and len(text_buffer) >= MIN_CHARS_FOR_CLAUSE: | |
| # Clause-level split: speak the clause, keep remainder | |
| # This gets first audio out ~100-200ms faster | |
| should_speak = True | |
| if should_speak: | |
| # We have a complete sentence - synthesize immediately! | |
| sentence = text_buffer.strip() | |
| text_buffer = "" | |
| if not sample_path: | |
| await websocket.send_json({"error": "Session not initialized"}) | |
| continue | |
| logger.info(f"🎤 Speaking sentence: '{sentence[:50]}...'") | |
| # Get params - use first_chunk_size for adaptive mode | |
| params = PRESETS.get(preset, PRESETS['balanced']).copy() | |
| chunk_size = params.get('first_chunk_size', params.get('chunk_size', 24)) | |
| overlap_buffer = OverlapAddBuffer() | |
| chunk_index = 0 | |
| if hasattr(engine.model, 'generate_stream'): | |
| for audio_chunk, metrics in engine.model.generate_stream( | |
| sentence, | |
| audio_prompt_path=sample_path, | |
| chunk_size=chunk_size, | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ): | |
| if not isinstance(audio_chunk, torch.Tensor): | |
| audio_chunk = torch.tensor(audio_chunk).unsqueeze(0) | |
| processed = overlap_buffer.process(audio_chunk) | |
| if processed is None or len(processed) == 0: | |
| continue | |
| mulaw_bytes = convert_to_mulaw(processed, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "type": "audio", | |
| "audio": chunk_b64, | |
| "chunk_index": total_chunks_sent, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| total_chunks_sent += 1 | |
| chunk_index += 1 | |
| # After first chunk, switch to normal chunk size | |
| if 'continuation_chunk_size' in params: | |
| chunk_size = params['continuation_chunk_size'] | |
| # Flush overlap buffer | |
| remaining = overlap_buffer.flush() | |
| if remaining is not None and len(remaining) > 0: | |
| mulaw_bytes = convert_to_mulaw(remaining, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "type": "audio", | |
| "audio": chunk_b64, | |
| "chunk_index": total_chunks_sent, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| total_chunks_sent += 1 | |
| await websocket.send_json({ | |
| "type": "sentence_complete", | |
| "chunks_in_sentence": chunk_index | |
| }) | |
| elif msg_type == "end": | |
| # Flush any remaining text | |
| if text_buffer.strip() and sample_path: | |
| sentence = text_buffer.strip() | |
| logger.info(f"🎤 Speaking final fragment: '{sentence[:50]}...'") | |
| params = PRESETS.get(preset, PRESETS['balanced']) | |
| overlap_buffer = OverlapAddBuffer() | |
| if hasattr(engine.model, 'generate_stream'): | |
| for audio_chunk, _ in engine.model.generate_stream( | |
| sentence, | |
| audio_prompt_path=sample_path, | |
| chunk_size=params.get('chunk_size', 24), | |
| exaggeration=params['exaggeration'], | |
| cfg_weight=params['cfg_weight'] | |
| ): | |
| if not isinstance(audio_chunk, torch.Tensor): | |
| audio_chunk = torch.tensor(audio_chunk).unsqueeze(0) | |
| processed = overlap_buffer.process(audio_chunk) | |
| if processed is None or len(processed) == 0: | |
| continue | |
| mulaw_bytes = convert_to_mulaw(processed, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "type": "audio", | |
| "audio": chunk_b64, | |
| "chunk_index": total_chunks_sent, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| total_chunks_sent += 1 | |
| remaining = overlap_buffer.flush() | |
| if remaining is not None and len(remaining) > 0: | |
| mulaw_bytes = convert_to_mulaw(remaining, engine.sample_rate) | |
| for chunk_b64 in chunk_mulaw_for_twilio(mulaw_bytes): | |
| await websocket.send_json({ | |
| "type": "audio", | |
| "audio": chunk_b64, | |
| "chunk_index": total_chunks_sent, | |
| "format": "mulaw", | |
| "sample_rate": TWILIO_SAMPLE_RATE | |
| }) | |
| total_chunks_sent += 1 | |
| total_time = time.time() - session_start if session_start else 0 | |
| await websocket.send_json({ | |
| "type": "complete", | |
| "total_chunks": total_chunks_sent, | |
| "total_ms": round(total_time * 1000), | |
| }) | |
| logger.info(f"✅ Streaming session complete: {total_chunks_sent} chunks in {total_time*1000:.0f}ms") | |
| # Reset for next session | |
| text_buffer = "" | |
| total_chunks_sent = 0 | |
| session_start = None | |
| except WebSocketDisconnect: | |
| logger.info("Streaming text WebSocket disconnected") | |
| except Exception as e: | |
| logger.exception(f"Streaming text WebSocket error: {e}") | |
| try: | |
| await websocket.send_json({"type": "error", "error": str(e)}) | |
| except: | |
| pass | |
| if __name__ == "__main__": | |
| import uvicorn | |
| port = int(os.getenv("PORT", 8000)) | |
| uvicorn.run(app, host="0.0.0.0", port=port) |
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