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Demo Fixes 10

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This commit is contained in:
GamerBoss101
2025-03-30 07:23:39 -04:00
parent a4f282fbcc
commit 263127ed18
1 changed files with 203 additions and 90 deletions
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165
Backend/server.py
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@@ -8,9 +8,17 @@ import numpy as np
from flask import Flask, render_template, request
from flask_socketio import SocketIO, emit
from transformers import AutoModelForCausalLM, AutoTokenizer
from faster_whisper import WhisperModel
from generator import load_csm_1b, Segment
from collections import deque
import requests
import huggingface_hub
from generator import load_csm_1b, Segment
# Configure environment with longer timeouts
os.environ["HF_HUB_DOWNLOAD_TIMEOUT"] = "600" # 10 minutes timeout for downloads
requests.adapters.DEFAULT_TIMEOUT = 60 # Increase default requests timeout
# Create a models directory for caching
os.makedirs("models", exist_ok=True)
app = Flask(__name__)
app.config['SECRET_KEY'] = 'your-secret-key'
@@ -29,23 +37,50 @@ else:
print(f"Using device: {device}")
# Initialize models with proper error handling
whisper_model = None
csm_generator = None
llm_model = None
llm_tokenizer = None
def load_models():
global whisper_model, csm_generator, llm_model, llm_tokenizer
# Initialize Faster-Whisper for transcription
try:
print("Loading Whisper model...")
whisper_model = WhisperModel("base", device=device, compute_type=whisper_compute_type)
# Import here to avoid immediate import errors if package is missing
from faster_whisper import WhisperModel
whisper_model = WhisperModel("base", device=device, compute_type=whisper_compute_type, download_root="./models/whisper")
print("Whisper model loaded successfully")
except Exception as e:
print(f"Error loading Whisper model: {e}")
print("Will use backup speech recognition method if available")
# Initialize CSM model for audio generation
try:
print("Loading CSM model...")
csm_generator = load_csm_1b(device=device)
print("CSM model loaded successfully")
except Exception as e:
print(f"Error loading CSM model: {e}")
print("Audio generation will not be available")
# Initialize Llama 3.2 model for response generation
try:
print("Loading Llama 3.2 model...")
llm_model_id = "meta-llama/Llama-3.2-1B" # Choose appropriate size based on resources
llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_id)
llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_id, cache_dir="./models/llama")
llm_model = AutoModelForCausalLM.from_pretrained(
llm_model_id,
torch_dtype=torch.bfloat16,
device_map=device
device_map=device,
cache_dir="./models/llama"
)
print("Llama 3.2 model loaded successfully")
except Exception as e:
print(f"Error loading Llama 3.2 model: {e}")
print("Will use a fallback response generation method")
# Store conversation context
conversation_context = {} # session_id -> context
@@ -128,7 +163,7 @@ def process_user_utterance(session_id):
context['is_speaking'] = False
context['silence_start'] = None
# Save audio to temporary WAV file for Whisper transcription
# Save audio to temporary WAV file for transcription
temp_audio_path = f"temp_audio_{session_id}.wav"
torchaudio.save(
temp_audio_path,
@@ -136,25 +171,17 @@ def process_user_utterance(session_id):
44100 # Assuming 44.1kHz from client
)
# Transcribe speech using Faster-Whisper
try:
segments, info = whisper_model.transcribe(temp_audio_path, beam_size=5)
# Collect all text from segments
user_text = ""
for segment in segments:
segment_text = segment.text.strip()
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment_text}")
user_text += segment_text + " "
user_text = user_text.strip()
# Cleanup temp file
if os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
# Try using Whisper first if available
if whisper_model is not None:
user_text = transcribe_with_whisper(temp_audio_path)
else:
# Fallback to Google's speech recognition
user_text = transcribe_with_google(temp_audio_path)
if not user_text:
print("No speech detected.")
emit('error', {'message': 'No speech detected. Please try again.'}, room=session_id)
return
print(f"Transcribed: {user_text}")
@@ -171,6 +198,11 @@ def process_user_utterance(session_id):
bot_response = generate_llm_response(user_text, context['segments'])
print(f"Bot response: {bot_response}")
# Send transcribed text to client
emit('transcription', {'text': user_text}, room=session_id)
# Generate and send audio response if CSM is available
if csm_generator is not None:
# Convert to audio using CSM
bot_audio = generate_audio_response(bot_response, context['segments'])
@@ -188,24 +220,64 @@ def process_user_utterance(session_id):
)
context['segments'].append(bot_segment)
# Send transcribed text to client
emit('transcription', {'text': user_text}, room=session_id)
# Send audio response to client
emit('audio_response', {
'audio': audio_b64,
'text': bot_response
}, room=session_id)
else:
# Send text-only response if audio generation isn't available
emit('text_response', {'text': bot_response}, room=session_id)
# Add text-only bot response to conversation history
bot_segment = Segment(
text=bot_response,
speaker=1, # Bot is speaker 1
audio=torch.zeros(1) # Placeholder empty audio
)
context['segments'].append(bot_segment)
except Exception as e:
print(f"Error processing speech: {e}")
emit('error', {'message': f'Error processing speech: {str(e)}'}, room=session_id)
# Cleanup temp file in case of error
finally:
# Cleanup temp file
if os.path.exists(temp_audio_path):
os.remove(temp_audio_path)
def transcribe_with_whisper(audio_path):
"""Transcribe audio using Faster-Whisper"""
segments, info = whisper_model.transcribe(audio_path, beam_size=5)
# Collect all text from segments
user_text = ""
for segment in segments:
segment_text = segment.text.strip()
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment_text}")
user_text += segment_text + " "
return user_text.strip()
def transcribe_with_google(audio_path):
"""Fallback transcription using Google's speech recognition"""
import speech_recognition as sr
recognizer = sr.Recognizer()
with sr.AudioFile(audio_path) as source:
audio = recognizer.record(source)
try:
text = recognizer.recognize_google(audio)
return text
except sr.UnknownValueError:
return ""
except sr.RequestError:
# If Google API fails, try a basic energy-based VAD approach
# This is a very basic fallback and won't give good results
return "[Speech detected but transcription failed]"
def generate_llm_response(user_text, conversation_segments):
"""Generate text response using Llama 3.2"""
"""Generate text response using available model"""
if llm_model is not None and llm_tokenizer is not None:
# Format conversation history for the LLM
conversation_history = ""
for segment in conversation_segments[-5:]: # Use last 5 utterances for context
@@ -215,6 +287,7 @@ def generate_llm_response(user_text, conversation_segments):
# Add the current user query
conversation_history += f"User: {user_text}\nAssistant:"
try:
# Generate response
inputs = llm_tokenizer(conversation_history, return_tensors="pt").to(device)
output = llm_model.generate(
@@ -227,9 +300,38 @@ def generate_llm_response(user_text, conversation_segments):
response = llm_tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
return response.strip()
except Exception as e:
print(f"Error generating response with LLM: {e}")
return fallback_response(user_text)
else:
return fallback_response(user_text)
def fallback_response(user_text):
"""Generate a simple fallback response when LLM is not available"""
# Simple rule-based responses
user_text_lower = user_text.lower()
if "hello" in user_text_lower or "hi" in user_text_lower:
return "Hello! I'm a simple fallback assistant. The main language model couldn't be loaded, so I have limited capabilities."
elif "how are you" in user_text_lower:
return "I'm functioning within my limited capabilities. How can I assist you today?"
elif "thank" in user_text_lower:
return "You're welcome! Let me know if there's anything else I can help with."
elif "bye" in user_text_lower or "goodbye" in user_text_lower:
return "Goodbye! Have a great day!"
elif any(q in user_text_lower for q in ["what", "who", "where", "when", "why", "how"]):
return "I'm running in fallback mode and can't answer complex questions. Please try again when the main language model is available."
else:
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."
def generate_audio_response(text, conversation_segments):
"""Generate audio response using CSM"""
try:
# Use the last few conversation segments as context
context_segments = conversation_segments[-4:] if len(conversation_segments) > 4 else conversation_segments
@@ -244,6 +346,10 @@ def generate_audio_response(text, conversation_segments):
)
return audio
except Exception as e:
print(f"Error generating audio: {e}")
# Return silence as fallback
return torch.zeros(csm_generator.sample_rate * 3) # 3 seconds of silence
if __name__ == '__main__':
# Ensure the existing index.html file is in the correct location
@@ -253,4 +359,11 @@ if __name__ == '__main__':
if os.path.exists('index.html') and not os.path.exists('templates/index.html'):
os.rename('index.html', 'templates/index.html')
# Load models asynchronously before starting the server
print("Starting model loading...")
# In a production environment, you could load models in a separate thread
load_models()
# Start the server
print("Starting Flask SocketIO server...")
socketio.run(app, host='0.0.0.0', port=5000, debug=False)