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import torch
import torchaudio
import logging
import os
from demucs.pretrained import get_model
from demucs.apply import apply_model
from typing import Tuple
logger = logging.getLogger(__name__)
class DemucsProcessor:
def __init__(self, model_name="htdemucs"):
try:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {self.device}")
self.model = get_model(model_name)
print(f"Model name: {model_name}")
print(f"Model sources: {self.model.sources}") # This will show available stems
print(f"Model sample rate: {self.model.samplerate}")
self.model.to(self.device)
print(f"Model loaded successfully on {self.device}")
except Exception as e:
print(f"Error initializing model: {str(e)}")
raise
def separate_stems(self, audio_path: str, progress=None) -> Tuple[torch.Tensor, int]:
try:
if progress:
progress(0.1, "Loading audio file...")
# Load audio
waveform, sample_rate = torchaudio.load(audio_path)
print(f"Audio loaded - Shape: {waveform.shape}")
if progress:
progress(0.3, "Processing stems...")
# Input validation and logging: Check waveform dimensions
if waveform.dim() not in (1, 2):
raise ValueError(f"Invalid waveform dimensions: Expected 1D or 2D, got {waveform.dim()}")
# Handle mono input by duplicating to stereo
if waveform.dim() == 1:
waveform = waveform.unsqueeze(0)
if waveform.shape[0] == 1:
waveform = waveform.repeat(2, 1)
print("Converted mono to stereo by duplication")
# Ensure 3D tensor for apply_model (batch, channels, time)
waveform = waveform.unsqueeze(0)
print(f"Waveform shape before apply_model: {waveform.shape}")
# Process
with torch.no_grad():
sources = apply_model(self.model, waveform.to(self.device))
print(f"Sources shape after processing: {sources.shape}")
print(f"Available stems: {self.model.sources}")
if progress:
progress(0.8, "Finalizing separation...")
return sources, sample_rate
except Exception as e:
print(f"Error in stem separation: {str(e)}")
raise
def save_stem(self, stem: torch.Tensor, stem_name: str, output_path: str, sample_rate: int):
try:
torchaudio.save(
f"{output_path}/{stem_name}.wav",
stem.cpu(),
sample_rate
)
except Exception as e:
print(f"Error saving stem: {str(e)}")
raise |