297 lines
12 KiB
Python
297 lines
12 KiB
Python
import os
|
|
import base64
|
|
import json
|
|
import asyncio
|
|
import torch
|
|
import torchaudio
|
|
import numpy as np
|
|
from io import BytesIO
|
|
from typing import List, Dict, Any, Optional
|
|
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
from pydantic import BaseModel
|
|
from generator import load_csm_1b, Segment
|
|
import uvicorn
|
|
import time
|
|
from collections import deque
|
|
|
|
# Select device
|
|
if torch.cuda.is_available():
|
|
device = "cuda"
|
|
else:
|
|
device = "cpu"
|
|
print(f"Using device: {device}")
|
|
|
|
# Initialize the model
|
|
generator = load_csm_1b(device=device)
|
|
|
|
app = FastAPI()
|
|
|
|
# Add CORS middleware to allow cross-origin requests
|
|
app.add_middleware(
|
|
CORSMiddleware,
|
|
allow_origins=["*"], # Allow all origins in development
|
|
allow_credentials=True,
|
|
allow_methods=["*"],
|
|
allow_headers=["*"],
|
|
)
|
|
|
|
# Connection manager to handle multiple clients
|
|
class ConnectionManager:
|
|
def __init__(self):
|
|
self.active_connections: List[WebSocket] = []
|
|
|
|
async def connect(self, websocket: WebSocket):
|
|
await websocket.accept()
|
|
self.active_connections.append(websocket)
|
|
|
|
def disconnect(self, websocket: WebSocket):
|
|
self.active_connections.remove(websocket)
|
|
|
|
manager = ConnectionManager()
|
|
|
|
# Silence detection parameters
|
|
SILENCE_THRESHOLD = 0.01 # Adjust based on your audio normalization
|
|
SILENCE_DURATION_SEC = 1.0 # How long silence must persist to be considered "stopped talking"
|
|
|
|
# Helper function to convert audio data
|
|
async def decode_audio_data(audio_data: str) -> torch.Tensor:
|
|
"""Decode base64 audio data to a torch tensor"""
|
|
try:
|
|
# Decode base64 audio data
|
|
binary_data = base64.b64decode(audio_data.split(',')[1] if ',' in audio_data else audio_data)
|
|
|
|
# Save to a temporary WAV file first
|
|
temp_file = BytesIO(binary_data)
|
|
|
|
# Load audio from binary data, explicitly specifying the format
|
|
audio_tensor, sample_rate = torchaudio.load(temp_file, format="wav")
|
|
|
|
# Resample if needed
|
|
if sample_rate != generator.sample_rate:
|
|
audio_tensor = torchaudio.functional.resample(
|
|
audio_tensor.squeeze(0),
|
|
orig_freq=sample_rate,
|
|
new_freq=generator.sample_rate
|
|
)
|
|
else:
|
|
audio_tensor = audio_tensor.squeeze(0)
|
|
|
|
return audio_tensor
|
|
except Exception as e:
|
|
print(f"Error decoding audio: {str(e)}")
|
|
# Return a small silent audio segment as fallback
|
|
return torch.zeros(generator.sample_rate // 2) # 0.5 seconds of silence
|
|
|
|
|
|
async def encode_audio_data(audio_tensor: torch.Tensor) -> str:
|
|
"""Encode torch tensor audio to base64 string"""
|
|
buf = BytesIO()
|
|
torchaudio.save(buf, audio_tensor.unsqueeze(0).cpu(), generator.sample_rate, format="wav")
|
|
buf.seek(0)
|
|
audio_base64 = base64.b64encode(buf.read()).decode('utf-8')
|
|
return f"data:audio/wav;base64,{audio_base64}"
|
|
|
|
|
|
@app.websocket("/ws")
|
|
async def websocket_endpoint(websocket: WebSocket):
|
|
await manager.connect(websocket)
|
|
context_segments = [] # Store conversation context
|
|
streaming_buffer = [] # Buffer for streaming audio chunks
|
|
is_streaming = False
|
|
|
|
# Variables for silence detection
|
|
last_active_time = time.time()
|
|
is_silence = False
|
|
energy_window = deque(maxlen=10) # For tracking recent audio energy
|
|
|
|
try:
|
|
while True:
|
|
# Receive JSON data from client
|
|
data = await websocket.receive_text()
|
|
request = json.loads(data)
|
|
|
|
action = request.get("action")
|
|
|
|
if action == "generate":
|
|
try:
|
|
text = request.get("text", "")
|
|
speaker_id = request.get("speaker", 0)
|
|
|
|
# Generate audio response
|
|
print(f"Generating audio for: '{text}' with speaker {speaker_id}")
|
|
audio_tensor = generator.generate(
|
|
text=text,
|
|
speaker=speaker_id,
|
|
context=context_segments,
|
|
max_audio_length_ms=10_000,
|
|
)
|
|
|
|
# Add to conversation context
|
|
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
|
|
|
|
# Convert audio to base64 and send back to client
|
|
audio_base64 = await encode_audio_data(audio_tensor)
|
|
await websocket.send_json({
|
|
"type": "audio_response",
|
|
"audio": audio_base64
|
|
})
|
|
except Exception as e:
|
|
print(f"Error generating audio: {str(e)}")
|
|
await websocket.send_json({
|
|
"type": "error",
|
|
"message": f"Error generating audio: {str(e)}"
|
|
})
|
|
|
|
elif action == "add_to_context":
|
|
try:
|
|
text = request.get("text", "")
|
|
speaker_id = request.get("speaker", 0)
|
|
audio_data = request.get("audio", "")
|
|
|
|
# Convert received audio to tensor
|
|
audio_tensor = await decode_audio_data(audio_data)
|
|
|
|
# Add to conversation context
|
|
context_segments.append(Segment(text=text, speaker=speaker_id, audio=audio_tensor))
|
|
|
|
await websocket.send_json({
|
|
"type": "context_updated",
|
|
"message": "Audio added to context"
|
|
})
|
|
except Exception as e:
|
|
print(f"Error adding to context: {str(e)}")
|
|
await websocket.send_json({
|
|
"type": "error",
|
|
"message": f"Error processing audio: {str(e)}"
|
|
})
|
|
|
|
elif action == "clear_context":
|
|
context_segments = []
|
|
await websocket.send_json({
|
|
"type": "context_updated",
|
|
"message": "Context cleared"
|
|
})
|
|
|
|
elif action == "stream_audio":
|
|
try:
|
|
speaker_id = request.get("speaker", 0)
|
|
audio_data = request.get("audio", "")
|
|
|
|
# Convert received audio to tensor
|
|
audio_chunk = await decode_audio_data(audio_data)
|
|
|
|
# Start streaming mode if not already started
|
|
if not is_streaming:
|
|
is_streaming = True
|
|
streaming_buffer = []
|
|
energy_window.clear()
|
|
is_silence = False
|
|
last_active_time = time.time()
|
|
await websocket.send_json({
|
|
"type": "streaming_status",
|
|
"status": "started"
|
|
})
|
|
|
|
# Calculate audio energy for silence detection
|
|
chunk_energy = torch.mean(torch.abs(audio_chunk)).item()
|
|
energy_window.append(chunk_energy)
|
|
avg_energy = sum(energy_window) / len(energy_window)
|
|
|
|
# Check if audio is silent
|
|
current_silence = avg_energy < SILENCE_THRESHOLD
|
|
|
|
# Track silence transition
|
|
if not is_silence and current_silence:
|
|
# Transition to silence
|
|
is_silence = True
|
|
last_active_time = time.time()
|
|
elif is_silence and not current_silence:
|
|
# User started talking again
|
|
is_silence = False
|
|
|
|
# Add chunk to buffer regardless of silence state
|
|
streaming_buffer.append(audio_chunk)
|
|
|
|
# Check if silence has persisted long enough to consider "stopped talking"
|
|
silence_elapsed = time.time() - last_active_time
|
|
|
|
if is_silence and silence_elapsed >= SILENCE_DURATION_SEC and len(streaming_buffer) > 0:
|
|
# User has stopped talking - process the collected audio
|
|
full_audio = torch.cat(streaming_buffer, dim=0)
|
|
|
|
# Process with speech-to-text (you would need to implement this)
|
|
# For now, just use a placeholder text
|
|
text = f"User audio from speaker {speaker_id}"
|
|
|
|
print(f"Detected end of speech, processing {len(streaming_buffer)} chunks")
|
|
|
|
# Add to conversation context
|
|
context_segments.append(Segment(text=text, speaker=speaker_id, audio=full_audio))
|
|
|
|
# Generate response
|
|
response_text = "This is a response to what you just said"
|
|
audio_tensor = generator.generate(
|
|
text=response_text,
|
|
speaker=1 if speaker_id == 0 else 0, # Use opposite speaker
|
|
context=context_segments,
|
|
max_audio_length_ms=10_000,
|
|
)
|
|
|
|
# Convert audio to base64 and send back to client
|
|
audio_base64 = await encode_audio_data(audio_tensor)
|
|
await websocket.send_json({
|
|
"type": "audio_response",
|
|
"audio": audio_base64
|
|
})
|
|
|
|
# Clear buffer and reset silence detection
|
|
streaming_buffer = []
|
|
energy_window.clear()
|
|
is_silence = False
|
|
last_active_time = time.time()
|
|
|
|
# If buffer gets too large without silence, process it anyway
|
|
# This prevents memory issues with very long streams
|
|
elif len(streaming_buffer) >= 30: # ~6 seconds of audio at 5 chunks/sec
|
|
print("Buffer limit reached, processing audio")
|
|
full_audio = torch.cat(streaming_buffer, dim=0)
|
|
text = f"Continued speech from speaker {speaker_id}"
|
|
context_segments.append(Segment(text=text, speaker=speaker_id, audio=full_audio))
|
|
streaming_buffer = []
|
|
|
|
except Exception as e:
|
|
print(f"Error processing streaming audio: {str(e)}")
|
|
await websocket.send_json({
|
|
"type": "error",
|
|
"message": f"Error processing streaming audio: {str(e)}"
|
|
})
|
|
|
|
elif action == "stop_streaming":
|
|
is_streaming = False
|
|
if streaming_buffer:
|
|
# Process any remaining audio in the buffer
|
|
full_audio = torch.cat(streaming_buffer, dim=0)
|
|
text = f"Final streaming audio from speaker {request.get('speaker', 0)}"
|
|
context_segments.append(Segment(text=text, speaker=request.get("speaker", 0), audio=full_audio))
|
|
|
|
streaming_buffer = []
|
|
await websocket.send_json({
|
|
"type": "streaming_status",
|
|
"status": "stopped"
|
|
})
|
|
|
|
except WebSocketDisconnect:
|
|
manager.disconnect(websocket)
|
|
print("Client disconnected")
|
|
except Exception as e:
|
|
print(f"Error: {str(e)}")
|
|
await websocket.send_json({
|
|
"type": "error",
|
|
"message": str(e)
|
|
})
|
|
manager.disconnect(websocket)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
uvicorn.run(app, host="localhost", port=8000) |