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Demo Fixes 11

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This commit is contained in:
GamerBoss101
2025-03-30 07:30:23 -04:00
parent 263127ed18
commit 30388d816f
1 changed files with 32 additions and 13 deletions
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37
Backend/server.py
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@@ -24,14 +24,26 @@ app = Flask(__name__)
app.config['SECRET_KEY'] = 'your-secret-key' app.config['SECRET_KEY'] = 'your-secret-key'
socketio = SocketIO(app, cors_allowed_origins="*") socketio = SocketIO(app, cors_allowed_origins="*")
# Select the best available device # Check for CUDA availability and handle potential CUDA/cuDNN issues
if torch.cuda.is_available(): try:
cuda_available = torch.cuda.is_available()
# Try to initialize CUDA to check if libraries are properly loaded
if cuda_available:
_ = torch.zeros(1).cuda()
device = "cuda" device = "cuda"
whisper_compute_type = "float16" whisper_compute_type = "float16"
elif torch.backends.mps.is_available(): print("CUDA is available and initialized successfully")
elif torch.backends.mps.is_available():
device = "mps" device = "mps"
whisper_compute_type = "float32" whisper_compute_type = "float32"
else: print("MPS is available (Apple Silicon)")
else:
device = "cpu"
whisper_compute_type = "int8"
print("Using CPU (CUDA/MPS not available)")
except Exception as e:
print(f"Error initializing CUDA: {e}")
print("Falling back to CPU")
device = "cpu" device = "cpu"
whisper_compute_type = "int8" whisper_compute_type = "int8"
@@ -51,7 +63,9 @@ def load_models():
print("Loading Whisper model...") print("Loading Whisper model...")
# Import here to avoid immediate import errors if package is missing # Import here to avoid immediate import errors if package is missing
from faster_whisper import WhisperModel from faster_whisper import WhisperModel
whisper_model = WhisperModel("base", device=device, compute_type=whisper_compute_type, download_root="./models/whisper") # Force CPU for Whisper if we had CUDA issues
whisper_device = device if device != "cpu" else "cpu"
whisper_model = WhisperModel("base", device=whisper_device, compute_type=whisper_compute_type, download_root="./models/whisper")
print("Whisper model loaded successfully") print("Whisper model loaded successfully")
except Exception as e: except Exception as e:
print(f"Error loading Whisper model: {e}") print(f"Error loading Whisper model: {e}")
@@ -60,7 +74,9 @@ def load_models():
# Initialize CSM model for audio generation # Initialize CSM model for audio generation
try: try:
print("Loading CSM model...") print("Loading CSM model...")
csm_generator = load_csm_1b(device=device) # Force CPU for CSM if we had CUDA issues
csm_device = device if device != "cpu" else "cpu"
csm_generator = load_csm_1b(device=csm_device)
print("CSM model loaded successfully") print("CSM model loaded successfully")
except Exception as e: except Exception as e:
print(f"Error loading CSM model: {e}") print(f"Error loading CSM model: {e}")
@@ -71,11 +87,14 @@ def load_models():
print("Loading Llama 3.2 model...") print("Loading Llama 3.2 model...")
llm_model_id = "meta-llama/Llama-3.2-1B" # Choose appropriate size based on resources llm_model_id = "meta-llama/Llama-3.2-1B" # Choose appropriate size based on resources
llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_id, cache_dir="./models/llama") llm_tokenizer = AutoTokenizer.from_pretrained(llm_model_id, cache_dir="./models/llama")
# Force CPU for LLM if we had CUDA issues
llm_device = device if device != "cpu" else "cpu"
llm_model = AutoModelForCausalLM.from_pretrained( llm_model = AutoModelForCausalLM.from_pretrained(
llm_model_id, llm_model_id,
torch_dtype=torch.bfloat16, torch_dtype=torch.bfloat16 if llm_device != "cpu" else torch.float32,
device_map=device, device_map=llm_device,
cache_dir="./models/llama" cache_dir="./models/llama",
low_cpu_mem_usage=True
) )
print("Llama 3.2 model loaded successfully") print("Llama 3.2 model loaded successfully")
except Exception as e: except Exception as e: