Demo Fixes 19
This commit is contained in:
1066
Backend/index.html
1066
Backend/index.html
File diff suppressed because it is too large
Load Diff
@@ -14,8 +14,8 @@ import huggingface_hub
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from generator import load_csm_1b, Segment
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import threading
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import queue
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from flask import stream_with_context, Response
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import time
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import asyncio
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import json
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# Configure environment with longer timeouts
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os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "600" # 10 minutes timeout for downloads
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@@ -26,7 +26,7 @@ os.makedirs("models", exist_ok=True)
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app = Flask(__name__)
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app.config['SECRET_KEY'] = 'your-secret-key'
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socketio = SocketIO(app, cors_allowed_origins="*")
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socketio = SocketIO(app, cors_allowed_origins="*", async_mode='eventlet')
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# Explicitly check for CUDA and print more detailed info
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print("\n=== CUDA Information ===")
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@@ -128,8 +128,7 @@ def load_models():
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# Store conversation context
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conversation_context = {} # session_id -> context
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CHUNK_SIZE = 24000 # Number of audio samples per chunk (1 second at 24kHz)
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audio_stream_queues = {} # session_id -> queue for audio chunks
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active_audio_streams = {} # session_id -> stream status
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@app.route('/')
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def index():
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@@ -143,9 +142,14 @@ def handle_connect():
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'speakers': [0, 1], # 0 = user, 1 = bot
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'audio_buffer': deque(maxlen=10), # Store recent audio chunks
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'is_speaking': False,
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'silence_start': None
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'last_activity': time.time(),
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'active_session': True,
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'transcription_buffer': [] # For real-time transcription
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}
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emit('ready', {'message': 'Connection established'})
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emit('ready', {
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'message': 'Connection established',
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'sample_rate': getattr(csm_generator, 'sample_rate', 24000) if csm_generator else 24000
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})
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@socketio.on('disconnect')
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def handle_disconnect():
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@@ -154,56 +158,130 @@ def handle_disconnect():
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# Clean up resources
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if session_id in conversation_context:
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conversation_context[session_id]['active_session'] = False
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del conversation_context[session_id]
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if session_id in audio_stream_queues:
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del audio_stream_queues[session_id]
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if session_id in active_audio_streams:
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active_audio_streams[session_id]['active'] = False
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del active_audio_streams[session_id]
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@socketio.on('start_speaking')
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def handle_start_speaking():
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if request.sid in conversation_context:
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conversation_context[request.sid]['is_speaking'] = True
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conversation_context[request.sid]['audio_buffer'].clear()
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print(f"User {request.sid} started speaking")
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@socketio.on('audio_stream')
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def handle_audio_stream(data):
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"""Handle incoming audio stream from client"""
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session_id = request.sid
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@socketio.on('audio_chunk')
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def handle_audio_chunk(data):
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if request.sid not in conversation_context:
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if session_id not in conversation_context:
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return
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context = conversation_context[request.sid]
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context = conversation_context[session_id]
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context['last_activity'] = time.time()
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# Decode audio data
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audio_data = base64.b64decode(data['audio'])
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audio_numpy = np.frombuffer(audio_data, dtype=np.float32)
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audio_tensor = torch.tensor(audio_numpy)
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# Process different stream events
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if data.get('event') == 'start':
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# Client is starting to send audio
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context['is_speaking'] = True
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context['audio_buffer'].clear()
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context['transcription_buffer'] = []
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print(f"User {session_id} started streaming audio")
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# Add to buffer
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context['audio_buffer'].append(audio_tensor)
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# If AI was speaking, interrupt it
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if session_id in active_audio_streams and active_audio_streams[session_id]['active']:
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active_audio_streams[session_id]['active'] = False
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emit('ai_stream_interrupt', {}, room=session_id)
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# Check for silence to detect end of speech
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if context['is_speaking'] and is_silence(audio_tensor):
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if context['silence_start'] is None:
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context['silence_start'] = time.time()
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elif time.time() - context['silence_start'] > 1.0: # 1 second of silence
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elif data.get('event') == 'data':
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# Audio data received
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if not context['is_speaking']:
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return
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# Decode audio chunk
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try:
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audio_data = base64.b64decode(data.get('audio', ''))
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if not audio_data:
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return
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audio_numpy = np.frombuffer(audio_data, dtype=np.float32)
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# Apply a simple noise gate
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if np.mean(np.abs(audio_numpy)) < 0.01: # Very quiet
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return
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audio_tensor = torch.tensor(audio_numpy)
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# Add to audio buffer
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context['audio_buffer'].append(audio_tensor)
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# Real-time transcription (periodic)
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if len(context['audio_buffer']) % 3 == 0: # Process every 3 chunks
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threading.Thread(
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target=process_realtime_transcription,
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args=(session_id,),
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daemon=True
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).start()
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except Exception as e:
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print(f"Error processing audio chunk: {e}")
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elif data.get('event') == 'end':
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# Client has finished sending audio
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context['is_speaking'] = False
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if len(context['audio_buffer']) > 0:
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# Process the complete utterance
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process_user_utterance(request.sid)
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else:
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context['silence_start'] = None
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threading.Thread(
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target=process_complete_utterance,
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args=(session_id,),
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daemon=True
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).start()
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@socketio.on('stop_speaking')
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def handle_stop_speaking():
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if request.sid in conversation_context:
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conversation_context[request.sid]['is_speaking'] = False
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process_user_utterance(request.sid)
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print(f"User {request.sid} stopped speaking")
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print(f"User {session_id} stopped streaming audio")
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def is_silence(audio_tensor, threshold=0.02):
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"""Check if an audio chunk is silence based on amplitude threshold"""
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return torch.mean(torch.abs(audio_tensor)) < threshold
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def process_realtime_transcription(session_id):
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"""Process incoming audio for real-time transcription"""
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if session_id not in conversation_context or not conversation_context[session_id]['active_session']:
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return
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def process_user_utterance(session_id):
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context = conversation_context[session_id]
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if not context['audio_buffer'] or not context['is_speaking']:
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return
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try:
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# Combine current buffer for transcription
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buffer_copy = list(context['audio_buffer'])
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if not buffer_copy:
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return
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full_audio = torch.cat(buffer_copy, dim=0)
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# Save audio to temporary WAV file for transcription
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temp_audio_path = f"temp_rt_{session_id}.wav"
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torchaudio.save(
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temp_audio_path,
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full_audio.unsqueeze(0),
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44100 # Assuming 44.1kHz from client
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)
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# Transcribe with Whisper if available
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if whisper_model is not None:
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segments, _ = whisper_model.transcribe(temp_audio_path, beam_size=5)
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text = " ".join([segment.text for segment in segments])
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if text.strip():
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context['transcription_buffer'].append(text)
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# Send partial transcription to client
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emit('partial_transcription', {'text': text}, room=session_id)
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except Exception as e:
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print(f"Error in realtime transcription: {e}")
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finally:
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# Clean up
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if os.path.exists(temp_audio_path):
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os.remove(temp_audio_path)
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def process_complete_utterance(session_id):
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"""Process completed user utterance, generate response and stream audio back"""
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if session_id not in conversation_context or not conversation_context[session_id]['active_session']:
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return
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context = conversation_context[session_id]
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if not context['audio_buffer']:
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@@ -212,8 +290,6 @@ def process_user_utterance(session_id):
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# Combine audio chunks
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full_audio = torch.cat(list(context['audio_buffer']), dim=0)
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context['audio_buffer'].clear()
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context['is_speaking'] = False
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context['silence_start'] = None
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# Save audio to temporary WAV file for transcription
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temp_audio_path = f"temp_audio_{session_id}.wav"
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@@ -255,23 +331,23 @@ def process_user_utterance(session_id):
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# Generate and stream audio response if CSM is available
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if csm_generator is not None:
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# Set up streaming queue for this session
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if session_id not in audio_stream_queues:
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audio_stream_queues[session_id] = queue.Queue()
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else:
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# Clear any existing items in the queue
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while not audio_stream_queues[session_id].empty():
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audio_stream_queues[session_id].get()
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# Create stream state object
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active_audio_streams[session_id] = {
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'active': True,
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'text': bot_response
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}
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# Start audio generation in a separate thread to not block the server
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# Send initial response to prepare client
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emit('ai_stream_start', {
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'text': bot_response
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}, room=session_id)
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# Start audio generation in a separate thread
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threading.Thread(
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target=generate_and_stream_audio,
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target=generate_and_stream_audio_realtime,
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args=(bot_response, context['segments'], session_id),
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daemon=True
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).start()
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# Initial response with text
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emit('start_streaming_response', {'text': bot_response}, room=session_id)
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else:
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# Send text-only response if audio generation isn't available
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emit('text_response', {'text': bot_response}, room=session_id)
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@@ -378,8 +454,11 @@ def fallback_response(user_text):
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else:
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return "I understand you said something about that. Unfortunately, I'm running in fallback mode with limited capabilities. Please try again later when the main model is available."
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def generate_audio_response(text, conversation_segments):
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"""Generate audio response using CSM"""
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def generate_and_stream_audio_realtime(text, conversation_segments, session_id):
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"""Generate audio response using CSM and stream it in real-time to client"""
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if session_id not in active_audio_streams or not active_audio_streams[session_id]['active']:
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return
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try:
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# Use the last few conversation segments as context
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context_segments = conversation_segments[-4:] if len(conversation_segments) > 4 else conversation_segments
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@@ -394,40 +473,23 @@ def generate_audio_response(text, conversation_segments):
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topk=50
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)
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return audio
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except Exception as e:
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print(f"Error generating audio: {e}")
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# Return silence as fallback
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return torch.zeros(csm_generator.sample_rate * 3) # 3 seconds of silence
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def generate_and_stream_audio(text, conversation_segments, session_id):
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"""Generate audio response using CSM and stream it in chunks"""
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try:
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# Use the last few conversation segments as context
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context_segments = conversation_segments[-4:] if len(conversation_segments) > 4 else conversation_segments
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# Generate full audio for bot response
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audio = csm_generator.generate(
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text=text,
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speaker=1, # Bot is speaker 1
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context=context_segments,
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max_audio_length_ms=10000, # 10 seconds max
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temperature=0.9,
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topk=50
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)
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# Store the full audio for conversation history
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bot_segment = Segment(
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text=text,
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speaker=1, # Bot is speaker 1
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audio=audio
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)
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if session_id in conversation_context:
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if session_id in conversation_context and conversation_context[session_id]['active_session']:
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conversation_context[session_id]['segments'].append(bot_segment)
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# Split audio into chunks for streaming
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chunk_size = CHUNK_SIZE
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# Stream audio in small chunks for more responsive playback
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chunk_size = 4800 # 200ms at 24kHz
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for i in range(0, len(audio), chunk_size):
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if session_id not in active_audio_streams or not active_audio_streams[session_id]['active']:
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print("Audio streaming interrupted or session ended")
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break
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chunk = audio[i:i+chunk_size]
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# Convert audio chunk to base64 for streaming
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@@ -436,61 +498,33 @@ def generate_and_stream_audio(text, conversation_segments, session_id):
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audio_bytes.seek(0)
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audio_b64 = base64.b64encode(audio_bytes.read()).decode('utf-8')
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# Send the chunk to the client
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if session_id in audio_stream_queues:
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audio_stream_queues[session_id].put({
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'audio': audio_b64,
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'is_last': i + chunk_size >= len(audio)
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})
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else:
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# Session was disconnected before we finished generating
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break
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# Send chunk to client
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socketio.emit('ai_stream_data', {
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'audio': audio_b64,
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'is_last': i + chunk_size >= len(audio)
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}, room=session_id)
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# Signal the end of streaming if queue still exists
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if session_id in audio_stream_queues:
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# Add an empty chunk as a sentinel to signal end of streaming
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audio_stream_queues[session_id].put(None)
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# Simulate real-time speech by adding a small delay
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# Remove this in production for faster response
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time.sleep(0.15) # Slight delay for more natural timing
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# Signal end of stream
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if session_id in active_audio_streams and active_audio_streams[session_id]['active']:
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socketio.emit('ai_stream_end', {}, room=session_id)
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active_audio_streams[session_id]['active'] = False
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except Exception as e:
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print(f"Error generating or streaming audio: {e}")
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# Send error message to client
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if session_id in conversation_context:
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if session_id in conversation_context and conversation_context[session_id]['active_session']:
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socketio.emit('error', {
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'message': f'Error generating audio: {str(e)}'
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}, room=session_id)
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# Send a final message to unblock the client
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if session_id in audio_stream_queues:
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audio_stream_queues[session_id].put(None)
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@socketio.on('request_audio_chunk')
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def handle_request_audio_chunk():
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"""Send the next audio chunk in the queue to the client"""
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session_id = request.sid
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if session_id not in audio_stream_queues:
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emit('error', {'message': 'No audio stream available'})
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return
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# Get the next chunk or wait for it to be available
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try:
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if not audio_stream_queues[session_id].empty():
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chunk = audio_stream_queues[session_id].get(block=False)
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# If chunk is None, we're done streaming
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if chunk is None:
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emit('end_streaming')
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# Clean up the queue
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if session_id in audio_stream_queues:
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del audio_stream_queues[session_id]
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else:
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emit('audio_chunk', chunk)
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else:
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# If the queue is empty but we're still generating, tell client to wait
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emit('wait_for_chunk')
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except Exception as e:
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print(f"Error sending audio chunk: {e}")
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emit('error', {'message': f'Error streaming audio: {str(e)}'})
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# Signal stream end to unblock client
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socketio.emit('ai_stream_end', {}, room=session_id)
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if session_id in active_audio_streams:
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active_audio_streams[session_id]['active'] = False
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if __name__ == '__main__':
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# Ensure the existing index.html file is in the correct location
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@@ -500,10 +534,10 @@ if __name__ == '__main__':
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if os.path.exists('index.html') and not os.path.exists('templates/index.html'):
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os.rename('index.html', 'templates/index.html')
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# Load models asynchronously before starting the server
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# Load models before starting the server
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print("Starting model loading...")
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load_models()
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# Start the server
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# Start the server with eventlet for better WebSocket performance
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print("Starting Flask SocketIO server...")
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socketio.run(app, host='0.0.0.0', port=5000, debug=False)
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