our previous conversation :

lets debug everything and see whats happening ill give you the output and then lets fix it rather than brute forcing solutions error : .
Error processing file: not enough values to unpack (expected 3, got 2)

script : import whisperx
import torch
import torchaudio
from demucs.pretrained import get_model
from demucs.apply import apply_model
import numpy as np
import soundfile as sf
import subprocess
from pathlib import Path
import os
from config import *
from torch.hub import load as torch_hub_load
from silero_vad import load_silero_vad, get_speech_timestamps

class AudioProcessor:
def init(self, model_size=DEFAULT_MODEL_SIZE, whisper_version=DEFAULT_WHISPER_VERSION):
torch.set_num_threads(CPU_THREADS)
print(f"Initializing AudioProcessor with {DEVICE}")

    self.init_models(model_size, whisper_version)

def init_models(self, model_size, whisper_version):
    try:
        print(f"Loading Whisper {whisper_version} model: {model_size}")
        self.whisper_model = whisperx.load_model(
            model_size,
            device=DEVICE,
            compute_type="float32",
            download_root=str(MODELS_DIR)
        )
        
        print("Loading Demucs model...")
        self.demucs_model = get_model(name="htdemucs_ft")
        self.demucs_model.to(DEVICE)
        
        print("Loading VAD model...")
        self.vad_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
        print(f"Utils contents: {utils}")
        self.get_speech_timestamps = utils[0]  # Access the function directly from the utils tuple
        self.vad_model = self.vad_model.to(DEVICE)
        
        print("Models loaded successfully!")
    
    except Exception as e:
        print(f"Error initializing models: {e}")
        raise

def process_file(self, input_file, output_file, beam_size=2, patience=1, temperature=0.0):
    """Main processing pipeline"""
    try:
        # 1. Extract vocals
        vocals, sr = self.extract_vocals(input_file)
        
        # 2. Normalize and remove silence
        normalized = self.normalize_audio(vocals, sr)
        
        # 3. Apply VAD
        vad_audio = self.apply_vad(normalized)
        
        # 4. Add markers
        marked_audio = self.add_markers(vad_audio)
        
        # 5. Apply compression
        compressed = self.apply_compression(marked_audio)
        
        # Try transcription with different approaches
        transcription = None
        
        # With markers
        transcription = self.transcribe(compressed, beam_size, patience, temperature)
        if self.verify_transcription(transcription):
            return self.save_transcription(transcription, output_file)
            
        # With inverted markers
        transcription = self.transcribe(-compressed, beam_size, patience, temperature)
        if self.verify_transcription(transcription):
            return self.save_transcription(transcription, output_file)
            
        # Without markers
        transcription = self.transcribe(vad_audio, beam_size, patience, temperature)
        return self.save_transcription(transcription, output_file)
        
    except Exception as e:
        print(f"Error processing file: {e}")
        raise
    finally:
        cleanup_temp()

def extract_vocals(self, audio_path):
    """Extract vocals using Demucs"""
    print("Extracting vocals...")
    audio, sr = torchaudio.load(audio_path)
    audio = audio.to(DEVICE)
    
    # Process in chunks
    chunk_samples = int(CHUNK_DURATION * sr)
    if audio.shape[1] > chunk_samples:
        vocals_chunks = []
        for i in range(0, audio.shape[1], chunk_samples):
            chunk = audio[:, i:min(i+chunk_samples, audio.shape[1])]
            sources = apply_model(self.demucs_model, chunk, device=DEVICE)
            vocals_chunks.append(sources['vocals'])
        vocals = torch.cat(vocals_chunks, dim=1)
    else:
        sources = apply_model(self.demucs_model, audio, device=DEVICE)
        vocals = sources['vocals']
    
    return vocals, sr

def normalize_audio(self, audio, sr):
    """Normalize audio and remove silence using ffmpeg"""
    temp_in = TEMP_DIR / "temp_in.wav"
    temp_out = TEMP_DIR / "temp_out.wav"
    
    try:
        sf.write(str(temp_in), audio.cpu().numpy(), sr)
        
        cmd = [
            'ffmpeg', '-y',
            '-i', str(temp_in),
            '-af', f'silenceremove=stop_periods=-1:stop_duration=1:stop_threshold={VAD_THRESHOLD}dB,loudnorm=I=-16:TP=-1.5:LRA=11',
            '-ar', str(sr),
            str(temp_out)
        ]
        
        subprocess.run(cmd, check=True, capture_output=True)
        normalized, sr = torchaudio.load(str(temp_out))
        return normalized
        
    except Exception as e:
        print(f"Error normalizing audio: {e}")
        raise

def apply_vad(self, audio):
    """Apply Voice Activity Detection"""
    print("Applying VAD...")
    audio_mono = audio.mean(dim=0) if audio.dim() > 1 else audio
    audio_numpy = audio_mono.numpy()
    
    # Process in chunks
    chunk_samples = int(CHUNK_DURATION * SAMPLE_RATE)
    speech_timestamps = []
    
    for i in range(0, len(audio_numpy), chunk_samples):
        chunk = audio_numpy[i:min(i + chunk_samples, len(audio_numpy))]
        chunk_tensor = torch.FloatTensor(chunk)
        
        # Get speech timestamps for the chunk
        chunk_timestamps = self.get_speech_timestamps(
            chunk_tensor,
            self.vad_model,
            sampling_rate=SAMPLE_RATE
        )
        
        # Adjust timestamps for chunk position
        for ts in chunk_timestamps:
            ts['start'] += i
            ts['end'] += i
        speech_timestamps.extend(chunk_timestamps)
    
    # Create masked audio
    masked_audio = torch.zeros_like(audio_mono)
    for ts in speech_timestamps:
        start_frame = ts['start']
        end_frame = ts['end']
        if start_frame < len(masked_audio) and end_frame <= len(masked_audio):
            masked_audio[start_frame:end_frame] = audio_mono[start_frame:end_frame]
    
    return masked_audio

def add_markers(self, audio):
    """Add voice markers at speech boundaries"""
    print("Adding markers...")
    marked_audio = audio.clone()
    
    # Generate marker signal
    t = np.arange(int(MARKER_DURATION * SAMPLE_RATE))
    marker = torch.from_numpy(
        0.1 * np.sin(2 * np.pi * MARKER_FREQUENCY * t / SAMPLE_RATE)
    ).float()
    
    # Find speech segments
    speech_segments = self.find_speech_segments(audio)
    
    # Add markers at segment boundaries
    for start, end in speech_segments:
        if start + len(marker) < len(marked_audio):
            marked_audio[start:start + len(marker)] += marker
        if end - len(marker) >= 0:
            marked_audio[end - len(marker):end] += marker
    
    return marked_audio

def find_speech_segments(self, audio, threshold=0.01):
    """Find speech segments in audio"""
    is_speech = torch.abs(audio) > threshold
    changes = torch.diff(is_speech.long())
    change_points = torch.nonzero(changes).flatten()
    
    segments = []
    for i in range(0, len(change_points)-1, 2):
        start = change_points[i]
        end = change_points[i+1] if i+1 < len(change_points) else len(audio)-1
        segments.append((start, end))
    
    return segments

def apply_compression(self, audio):
    """Apply speech compression using ffmpeg"""
    print("Applying compression...")
    temp_in = TEMP_DIR / "temp_compress_in.wav"
    temp_out = TEMP_DIR / "temp_compress_out.wav"
    
    try:
        sf.write(str(temp_in), audio.numpy(), SAMPLE_RATE)
        
        cmd = [
            'ffmpeg', '-y',
            '-i', str(temp_in),
            '-af', 'acompressor=threshold=-12dB:ratio=2:attack=200:release=1000',
            '-ar', str(SAMPLE_RATE),
            str(temp_out)
        ]
        
        subprocess.run(cmd, check=True, capture_output=True)
        compressed, _ = torchaudio.load(str(temp_out))
        return compressed.squeeze()
        
    except Exception as e:
        print(f"Error applying compression: {e}")
        raise

def transcribe(self, audio, beam_size=2, patience=1, temperature=0.0):
    """Transcribe audio using WhisperX"""
    print("Transcribing...")
    temp_file = TEMP_DIR / "temp_transcribe.wav"
    
    try:
        sf.write(str(temp_file), audio.numpy(), SAMPLE_RATE)
        
        # Load and transcribe
        audio_data = whisperx.load_audio(str(temp_file))
        result = self.whisper_model.transcribe(
            audio_data,
            batch_size=1,  # Optimized for CPU
            beam_size=beam_size,
            patience=patience,
            temperature=temperature
        )
        
        # Align timestamps
        model_a, metadata, _ = whisperx.load_align_model(
            language_code=result["language"],
            device=DEVICE
        )
        
        result = whisperx.align(
            result["segments"],
            model_a,
            metadata,
            audio_data,
            DEVICE,
            extend_duration=0.0
        )
        
        return result
        
    except Exception as e:
        print(f"Transcription error: {e}")
        return None

def verify_transcription(self, transcription):
    """Verify if transcription contains markers"""
    if not transcription or 'segments' not in transcription:
        return False
        
    marker_patterns = ["*", "beep", "tone", "[noise]", "[sound]"]
    return any(
        any(pattern in segment['text'].lower() for pattern in marker_patterns)
        for segment in transcription['segments']
    )

def save_transcription(self, transcription, output_file):
    """Save transcription to file"""
    if not transcription:
        return False
        
    try:
        with open(output_file, 'w', encoding='utf-8') as f:
            for segment in transcription['segments']:
                f.write(
                    f"[{segment['start']:.2f} - {segment['end']:.2f}] {segment['text']}\n"
                )
        return True
    except Exception as e:
        print(f"Error saving transcription: {e}")
        return False

Based on the error message "not enough values to unpack (expected 3, got 2)" and looking at your code, this error typically occurs when trying to unpack a tuple or list with fewer elements than expected. Let's identify and fix the likely source of this error.

The most probable location for this error is in the VAD model loading section. Let's fix the VAD model initialization:

# Original problematic code:
self.vad_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')

# Modified version:
(self.vad_model, utils) = torch_hub_load(source="snakers4/silero-vad",
                                        model="silero_vad",
                                        force_reload=True)

Also, let's modify how we handle the utils:

def init_models(self, model_size, whisper_version):
    try:
        print(f"Loading Whisper {whisper_version} model: {model_size}")
        self.whisper_model = whisperx.load_model(
            model_size,
            device=DEVICE,
            compute_type="float32",
            download_root=str(MODELS_DIR)
        )
        
        print("Loading Demucs model...")
        self.demucs_model = get_model(name="htdemucs_ft")
        self.demucs_model.to(DEVICE)
        
        print("Loading VAD model...")
        # Modified VAD loading
        self.vad_model, utils = torch_hub_load(source="snakers4/silero-vad",
                                             model="silero_vad",
                                             force_reload=True)
        
        # Correctly handle utils
        if isinstance(utils, (list, tuple)):
            self.get_speech_timestamps = utils[0] if len(utils) > 0 else None
            self.vad_model = self.vad_model.to(DEVICE)
        else:
            raise ValueError("Expected utils to be a list or tuple")
        
        print("Models loaded successfully!")
    
    except Exception as e:
        print(f"Error initializing models: {e}")
        raise

Additionally, make sure you have these imports at the top of your file:

import torch
from torch.hub import load as torch_hub_load

If you're still getting the error, please provide:

    The complete error traceback
    The content of your config.py file
    The exact line where the error occurs

This will help me provide a more specific solution to your problem.

Also, consider adding error handling for the VAD model initialization:

def init_vad_model(self):
    try:
        model, utils = torch_hub_load(source="snakers4/silero-vad",
                                    model="silero_vad",
                                    force_reload=True)
        return model, utils
    except Exception as e:
        print(f"Error loading VAD model: {e}")
        raise

And then call it from init_models:

self.vad_model, utils = self.init_vad_model()

This will help isolate and identify any issues specifically related to the VAD model initialization.

why wonder about might let make everything explicit and debug ill return the output to you so you can identify exactly where it is


Initializing AudioProcessor with cpu
=== Starting model initialization ===
1. Loading Whisper model...
- Model size: base
- Whisper version: v2
- Device: cpu
- Models directory: /home/ubuntu/Desktop/new/models
No language specified, language will be first be detected for each audio file (increases inference time).
Lightning automatically upgraded your loaded checkpoint from v1.5.4 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../../.cache/torch/whisperx-vad-segmentation.bin`
Model was trained with pyannote.audio 0.0.1, yours is 3.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.
Model was trained with torch 1.10.0+cu102, yours is 2.5.1. Bad things might happen unless you revert torch to 1.x.
✓ Whisper model loaded successfully

2. Loading Demucs model...
- Model name: htdemucs_ft
✓ Demucs model loaded successfully

3. Loading VAD model...
- Attempting to load from snakers4/silero-vad
! Error during VAD model loading: load() missing 1 required positional argument: 'repo_or_dir'
! Error type: <class 'TypeError'>

!!! Error in model initialization !!!
Error type: <class 'TypeError'>
Error message: load() missing 1 required positional argument: 'repo_or_dir'
Full traceback:
Traceback (most recent call last):
  File "/home/ubuntu/Desktop/new/audio_processor.py", line 54, in init_models
    vad_result = torch_hub_load(
TypeError: load() missing 1 required positional argument: 'repo_or_dir'


script :

import whisperx
import torch
import torchaudio
from demucs.pretrained import get_model
from demucs.apply import apply_model
import numpy as np
import soundfile as sf
import subprocess
from pathlib import Path
import os
from config import *
from torch.hub import load as torch_hub_load
from silero_vad import load_silero_vad, get_speech_timestamps

class AudioProcessor:
    def __init__(self, model_size=DEFAULT_MODEL_SIZE, whisper_version=DEFAULT_WHISPER_VERSION):
        torch.set_num_threads(CPU_THREADS)
        print(f"Initializing AudioProcessor with {DEVICE}")
        
        self.init_models(model_size, whisper_version)

    def init_models(self, model_size, whisper_version):
        try:
            print(f"Loading Whisper {whisper_version} model: {model_size}")
            self.whisper_model = whisperx.load_model(
                model_size,
                device=DEVICE,
                compute_type="float32",
                download_root=str(MODELS_DIR)
            )
            
            print("Loading Demucs model...")
            self.demucs_model = get_model(name="htdemucs_ft")
            self.demucs_model.to(DEVICE)
            
            print("Loading VAD model...")
            # Correct way to load Silero VAD
            self.vad_model, utils = torch.hub.load(
                repo_or_dir='snakers4/silero-vad',
                model='silero_vad',
                force_reload=True
            )
            
            # Unpack utils properly
            (self.get_speech_timestamps, 
            self.save_audio, 
            self.read_audio, 
            self.VADIterator, 
            self.collect_chunks) = utils
            
            self.vad_model = self.vad_model.to(DEVICE)
            print("Models loaded successfully!")
        
        except Exception as e:
            print(f"Error initializing models: {e}")
            raise

    def process_file(self, input_file, output_file, beam_size=2, patience=1, temperature=0.0):
        """Main processing pipeline"""
        try:
            # 1. Extract vocals
            vocals, sr = self.extract_vocals(input_file)
            
            # 2. Normalize and remove silence
            normalized = self.normalize_audio(vocals, sr)
            
            # 3. Apply VAD
            vad_audio = self.apply_vad(normalized)
            
            # 4. Add markers
            marked_audio = self.add_markers(vad_audio)
            
            # 5. Apply compression
            compressed = self.apply_compression(marked_audio)
            
            # Try transcription with different approaches
            transcription = None
            
            # With markers
            transcription = self.transcribe(compressed, beam_size, patience, temperature)
            if self.verify_transcription(transcription):
                return self.save_transcription(transcription, output_file)
                
            # With inverted markers
            transcription = self.transcribe(-compressed, beam_size, patience, temperature)
            if self.verify_transcription(transcription):
                return self.save_transcription(transcription, output_file)
                
            # Without markers
            transcription = self.transcribe(vad_audio, beam_size, patience, temperature)
            return self.save_transcription(transcription, output_file)
            
        except Exception as e:
            print(f"Error processing file: {e}")
            raise
        finally:
            cleanup_temp()

    def extract_vocals(self, audio_path):
        """Extract vocals using Demucs"""
        print("Extracting vocals...")
        audio, sr = torchaudio.load(audio_path)
        audio = audio.to(DEVICE)
        
        # Process in chunks
        chunk_samples = int(CHUNK_DURATION * sr)
        if audio.shape[1] > chunk_samples:
            vocals_chunks = []
            for i in range(0, audio.shape[1], chunk_samples):
                chunk = audio[:, i:min(i+chunk_samples, audio.shape[1])]
                sources = apply_model(self.demucs_model, chunk, device=DEVICE)
                vocals_chunks.append(sources['vocals'])
            vocals = torch.cat(vocals_chunks, dim=1)
        else:
            sources = apply_model(self.demucs_model, audio, device=DEVICE)
            vocals = sources['vocals']
        
        return vocals, sr

    def normalize_audio(self, audio, sr):
        """Normalize audio and remove silence using ffmpeg"""
        temp_in = TEMP_DIR / "temp_in.wav"
        temp_out = TEMP_DIR / "temp_out.wav"
        
        try:
            sf.write(str(temp_in), audio.cpu().numpy(), sr)
            
            cmd = [
                'ffmpeg', '-y',
                '-i', str(temp_in),
                '-af', f'silenceremove=stop_periods=-1:stop_duration=1:stop_threshold={VAD_THRESHOLD}dB,loudnorm=I=-16:TP=-1.5:LRA=11',
                '-ar', str(sr),
                str(temp_out)
            ]
            
            subprocess.run(cmd, check=True, capture_output=True)
            normalized, sr = torchaudio.load(str(temp_out))
            return normalized
            
        except Exception as e:
            print(f"Error normalizing audio: {e}")
            raise

    def apply_vad(self, audio):
        """Apply Voice Activity Detection"""
        print("Applying VAD...")
        audio_mono = audio.mean(dim=0) if audio.dim() > 1 else audio
        audio_numpy = audio_mono.numpy()
        
        # Process in chunks
        chunk_samples = int(CHUNK_DURATION * SAMPLE_RATE)
        speech_timestamps = []
        
        for i in range(0, len(audio_numpy), chunk_samples):
            chunk = audio_numpy[i:min(i + chunk_samples, len(audio_numpy))]
            chunk_tensor = torch.FloatTensor(chunk)
            
            # Get speech timestamps for the chunk
            chunk_timestamps = self.get_speech_timestamps(
                chunk_tensor,
                self.vad_model,
                sampling_rate=SAMPLE_RATE
            )
            
            # Adjust timestamps for chunk position
            for ts in chunk_timestamps:
                ts['start'] += i
                ts['end'] += i
            speech_timestamps.extend(chunk_timestamps)
        
        # Create masked audio
        masked_audio = torch.zeros_like(audio_mono)
        for ts in speech_timestamps:
            start_frame = ts['start']
            end_frame = ts['end']
            if start_frame < len(masked_audio) and end_frame <= len(masked_audio):
                masked_audio[start_frame:end_frame] = audio_mono[start_frame:end_frame]
        
        return masked_audio

    def add_markers(self, audio):
        """Add voice markers at speech boundaries"""
        print("Adding markers...")
        marked_audio = audio.clone()
        
        # Generate marker signal
        t = np.arange(int(MARKER_DURATION * SAMPLE_RATE))
        marker = torch.from_numpy(
            0.1 * np.sin(2 * np.pi * MARKER_FREQUENCY * t / SAMPLE_RATE)
        ).float()
        
        # Find speech segments
        speech_segments = self.find_speech_segments(audio)
        
        # Add markers at segment boundaries
        for start, end in speech_segments:
            if start + len(marker) < len(marked_audio):
                marked_audio[start:start + len(marker)] += marker
            if end - len(marker) >= 0:
                marked_audio[end - len(marker):end] += marker
        
        return marked_audio

    def find_speech_segments(self, audio, threshold=0.01):
        """Find speech segments in audio"""
        is_speech = torch.abs(audio) > threshold
        changes = torch.diff(is_speech.long())
        change_points = torch.nonzero(changes).flatten()
        
        segments = []
        for i in range(0, len(change_points)-1, 2):
            start = change_points[i]
            end = change_points[i+1] if i+1 < len(change_points) else len(audio)-1
            segments.append((start, end))
        
        return segments

    def apply_compression(self, audio):
        """Apply speech compression using ffmpeg"""
        print("Applying compression...")
        temp_in = TEMP_DIR / "temp_compress_in.wav"
        temp_out = TEMP_DIR / "temp_compress_out.wav"
        
        try:
            sf.write(str(temp_in), audio.numpy(), SAMPLE_RATE)
            
            cmd = [
                'ffmpeg', '-y',
                '-i', str(temp_in),
                '-af', 'acompressor=threshold=-12dB:ratio=2:attack=200:release=1000',
                '-ar', str(SAMPLE_RATE),
                str(temp_out)
            ]
            
            subprocess.run(cmd, check=True, capture_output=True)
            compressed, _ = torchaudio.load(str(temp_out))
            return compressed.squeeze()
            
        except Exception as e:
            print(f"Error applying compression: {e}")
            raise

    def transcribe(self, audio, beam_size=2, patience=1, temperature=0.0):
        """Transcribe audio using WhisperX"""
        print("Transcribing...")
        temp_file = TEMP_DIR / "temp_transcribe.wav"
        
        try:
            sf.write(str(temp_file), audio.numpy(), SAMPLE_RATE)
            
            # Load and transcribe
            audio_data = whisperx.load_audio(str(temp_file))
            result = self.whisper_model.transcribe(
                audio_data,
                batch_size=1,  # Optimized for CPU
                beam_size=beam_size,
                patience=patience,
                temperature=temperature
            )
            
            # Align timestamps
            model_a, metadata, _ = whisperx.load_align_model(
                language_code=result["language"],
                device=DEVICE
            )
            
            result = whisperx.align(
                result["segments"],
                model_a,
                metadata,
                audio_data,
                DEVICE,
                extend_duration=0.0
            )
            
            return result
            
        except Exception as e:
            print(f"Transcription error: {e}")
            return None

    def verify_transcription(self, transcription):
        """Verify if transcription contains markers"""
        if not transcription or 'segments' not in transcription:
            return False
            
        marker_patterns = ["*", "beep", "tone", "[noise]", "[sound]"]
        return any(
            any(pattern in segment['text'].lower() for pattern in marker_patterns)
            for segment in transcription['segments']
        )

    def save_transcription(self, transcription, output_file):
        """Save transcription to file"""
        if not transcription:
            return False
            
        try:
            with open(output_file, 'w', encoding='utf-8') as f:
                for segment in transcription['segments']:
                    f.write(
                        f"[{segment['start']:.2f} - {segment['end']:.2f}] {segment['text']}\n"
                    )
            return True
        except Exception as e:
            print(f"Error saving transcription: {e}")
            return False


ubuntu@ubuntu-virtual-machine:~/Desktop/new$ ./main.py
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/core/io.py:43: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("soundfile")
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:43: UserWarning: torchaudio._backend.get_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  backend = torchaudio.get_audio_backend()
INFO:speechbrain.utils.quirks:Applied quirks (see `speechbrain.utils.quirks`): [allow_tf32, disable_jit_profiling]
INFO:speechbrain.utils.quirks:Excluded quirks specified by the `SB_DISABLE_QUIRKS` environment (comma-separated list): []
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:45: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0
  from speechbrain.pretrained import (
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:53: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend(backend)
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/tasks/segmentation/mixins.py:37: UserWarning: `torchaudio.backend.common.AudioMetaData` has been moved to `torchaudio.AudioMetaData`. Please update the import path.
  from torchaudio.backend.common import AudioMetaData
Initializing AudioProcessor with cpu
Loading Whisper v2 model: base
No language specified, language will be first be detected for each audio file (increases inference time).
Lightning automatically upgraded your loaded checkpoint from v1.5.4 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../../.cache/torch/whisperx-vad-segmentation.bin`
Model was trained with pyannote.audio 0.0.1, yours is 3.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.
Model was trained with torch 1.10.0+cu102, yours is 2.5.1. Bad things might happen unless you revert torch to 1.x.
Loading Demucs model...
Loading VAD model...
Downloading: "https://github.com/snakers4/silero-vad/zipball/master" to /home/ubuntu/.cache/torch/hub/master.zip
Models loaded successfully!
Extracting vocals...
Error processing file: not enough values to unpack (expected 3, got 2)

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S
S

Based on the error message and the context, it seems the error is occurring during the extract_vocals method. The line indicating the error suggests there's an issue with unpacking values returned by apply_model.

Let's modify the extract_vocals method to handle this more robustly:

python

def extract_vocals(self, audio_path):
    """Extract vocals using Demucs"""
    print("Extracting vocals...")
    audio, sr = torchaudio.load(audio_path)
    audio = audio.to(DEVICE)
    
    # Process in chunks
    chunk_samples = int(CHUNK_DURATION * sr)
    if audio.shape[1] > chunk_samples:
        vocals_chunks = []
        for i in range(0, audio.shape[1], chunk_samples):
            chunk = audio[:, i:min(i+chunk_samples, audio.shape[1])]
            # Modify this part to handle potential return variations
            sources = apply_model(self.demucs_model, chunk, device=DEVICE)
            
            # Check the type of sources and extract vocals accordingly
            if isinstance(sources, dict):
                vocals_chunk = sources.get('vocals')
            elif isinstance(sources, (list, tuple)):
                # Assuming vocals might be the first or last item
                vocals_chunk = sources[0] if 'vocals' not in sources else sources['vocals']
            else:
                raise ValueError(f"Unexpected return type from apply_model: {type(sources)}")
            
            if vocals_chunk is None:
                raise ValueError("Could not extract vocals from chunk")
            
            vocals_chunks.append(vocals_chunk)
        
        vocals = torch.cat(vocals_chunks, dim=1)
    else:
        sources = apply_model(self.demucs_model, audio, device=DEVICE)
        
        # Same handling as above for full audio
        if isinstance(sources, dict):
            vocals = sources.get('vocals')
        elif isinstance(sources, (list, tuple)):
            vocals = sources[0] if 'vocals' not in sources else sources['vocals']
        else:
            raise ValueError(f"Unexpected return type from apply_model: {type(sources)}")
        
        if vocals is None:
            raise ValueError("Could not extract vocals from full audio")
    
    return vocals, sr

The key changes are:

    Added robust handling for different return types from apply_model
    Added explicit checks and error handling for extracting vocals
    Handled potential scenarios where vocals might not be directly accessible

Additionally, could you confirm the following:

    Are you using the latest version of demucs?
    Can you print out the exact type and content of sources returned by apply_model in your environment?

You can add a debug print before returning vocals:

python

print("Sources type:", type(sources))
print("Sources content:", sources)

This will help me understand exactly what's being returned by the model and why the unpacking is failing.

Also, make sure in your config.py or wherever you define CHUNK_DURATION that it's a reasonable value (e.g., 10-30 seconds) that doesn't cause issues with model processing.

Would you like me to help you debug further by adding more detailed logging?
S

why guess and instead debug everything and ill provide you output

You're absolutely right. Instead of making assumptions, let's systematically debug the code. I'll modify the extract_vocals method to add comprehensive logging and error tracking:

python

def extract_vocals(self, audio_path):
    """Extract vocals using Demucs"""
    print("=== Extracting Vocals Debug Information ===")
    print(f"Input audio path: {audio_path}")
    
    try:
        # Load audio
        print("1. Loading audio file...")
        audio, sr = torchaudio.load(audio_path)
        print(f"- Audio loaded successfully")
        print(f"- Audio shape: {audio.shape}")
        print(f"- Sample rate: {sr}")
        
        # Move to device
        audio = audio.to(DEVICE)
        print(f"- Audio moved to device: {DEVICE}")
        
        # Chunk processing setup
        chunk_samples = int(CHUNK_DURATION * sr)
        print(f"- Chunk duration: {CHUNK_DURATION} seconds")
        print(f"- Chunk samples: {chunk_samples}")
        print(f"- Total audio samples: {audio.shape[1]}")
        
        # Process chunked or full audio
        if audio.shape[1] > chunk_samples:
            print("2. Processing audio in chunks...")
            vocals_chunks = []
            for i in range(0, audio.shape[1], chunk_samples):
                chunk = audio[:, i:min(i+chunk_samples, audio.shape[1])]
                print(f"  - Processing chunk {i//chunk_samples + 1}")
                print(f"  - Chunk shape: {chunk.shape}")
                
                try:
                    # Detailed logging for sources
                    sources = apply_model(self.demucs_model, chunk, device=DEVICE)
                    print("  - apply_model result:")
                    print(f"    - Type of sources: {type(sources)}")
                    print(f"    - Keys if dict: {sources.keys() if isinstance(sources, dict) else 'N/A'}")
                    print(f"    - Length if list/tuple: {len(sources) if isinstance(sources, (list, tuple)) else 'N/A'}")
                    
                    # Extract vocals
                    if isinstance(sources, dict):
                        vocals_chunk = sources.get('vocals')
                    elif isinstance(sources, (list, tuple)):
                        vocals_chunk = sources[0] if 'vocals' not in sources else sources['vocals']
                    else:
                        raise ValueError(f"Unexpected sources type: {type(sources)}")
                    
                    if vocals_chunk is None:
                        raise ValueError("No vocals extracted from chunk")
                    
                    print(f"    - Vocals chunk shape: {vocals_chunk.shape}")
                    vocals_chunks.append(vocals_chunk)
                
                except Exception as chunk_error:
                    print(f"  !! Error processing chunk {i//chunk_samples + 1}: {chunk_error}")
                    raise
            
            vocals = torch.cat(vocals_chunks, dim=1)
            print(f"- Concatenated vocals shape: {vocals.shape}")
        
        else:
            print("2. Processing full audio...")
            sources = apply_model(self.demucs_model, audio, device=DEVICE)
            print("  - apply_model result:")
            print(f"  - Type of sources: {type(sources)}")
            print(f"  - Keys if dict: {sources.keys() if isinstance(sources, dict) else 'N/A'}")
            print(f"  - Length if list/tuple: {len(sources) if isinstance(sources, (list, tuple)) else 'N/A'}")
            
            # Extract vocals for full audio
            if isinstance(sources, dict):
                vocals = sources.get('vocals')
            elif isinstance(sources, (list, tuple)):
                vocals = sources[0] if 'vocals' not in sources else sources['vocals']
            else:
                raise ValueError(f"Unexpected sources type: {type(sources)}")
            
            if vocals is None:
                raise ValueError("No vocals extracted from full audio")
            
            print(f"  - Vocals shape: {vocals.shape}")
        
        return vocals, sr
    
    except Exception as e:
        print("=== FATAL ERROR IN EXTRACT_VOCALS ===")
        print(f"Error details: {e}")
        import traceback
        traceback.print_exc()
        raise

This version will:

    Print detailed information about audio loading
    Log chunk processing details
    Capture and log exact details about sources from apply_model
    Provide comprehensive error tracking
    Help identify exactly where and why the extraction might fail

Before running, please confirm:

    The exact version of demucs you're using
    The version of PyTorch
    The content of your config.py, especially CHUNK_DURATION and related settings

Could you run this and share the complete output? This will help us pinpoint the exact issue with vocal extraction.

ubuntu@ubuntu-virtual-machine:~/Desktop/new$ ./main.py
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/core/io.py:43: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend("soundfile")
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:43: UserWarning: torchaudio._backend.get_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  backend = torchaudio.get_audio_backend()
INFO:speechbrain.utils.quirks:Applied quirks (see `speechbrain.utils.quirks`): [disable_jit_profiling, allow_tf32]
INFO:speechbrain.utils.quirks:Excluded quirks specified by the `SB_DISABLE_QUIRKS` environment (comma-separated list): []
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:45: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0
  from speechbrain.pretrained import (
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/pipelines/speaker_verification.py:53: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
  torchaudio.set_audio_backend(backend)
/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/tasks/segmentation/mixins.py:37: UserWarning: `torchaudio.backend.common.AudioMetaData` has been moved to `torchaudio.AudioMetaData`. Please update the import path.
  from torchaudio.backend.common import AudioMetaData
Initializing AudioProcessor with cpu
Loading Whisper v2 model: base
No language specified, language will be first be detected for each audio file (increases inference time).
Lightning automatically upgraded your loaded checkpoint from v1.5.4 to v2.4.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../../.cache/torch/whisperx-vad-segmentation.bin`
Model was trained with pyannote.audio 0.0.1, yours is 3.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.
Model was trained with torch 1.10.0+cu102, yours is 2.5.1. Bad things might happen unless you revert torch to 1.x.
Loading Demucs model...
Loading VAD model...
Downloading: "https://github.com/snakers4/silero-vad/zipball/master" to /home/ubuntu/.cache/torch/hub/master.zip
Models loaded successfully!
=== Extracting Vocals Debug Information ===
Input audio path: /home/ubuntu/Downloads/audio.mp3
1. Loading audio file...
- Audio loaded successfully
- Audio shape: torch.Size([2, 599040])
- Sample rate: 48000
- Audio moved to device: cpu
- Chunk duration: 30 seconds
- Chunk samples: 1440000
- Total audio samples: 599040
2. Processing full audio...
=== FATAL ERROR IN EXTRACT_VOCALS ===
Error details: not enough values to unpack (expected 3, got 2)
Traceback (most recent call last):
  File "/home/ubuntu/Desktop/new/audio_processor.py", line 162, in extract_vocals
    sources = apply_model(self.demucs_model, audio, device=DEVICE)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/demucs/apply.py", line 189, in apply_model
    out = apply_model(sub_model, mix, **kwargs)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/demucs/apply.py", line 205, in apply_model
    batch, channels, length = mix.shape
ValueError: not enough values to unpack (expected 3, got 2)
Error processing file: not enough values to unpack (expected 3, got 2)
^CTraceback (most recent call last):
  File "/home/ubuntu/Desktop/new/./main.py", line 69, in <module>
    main()
  File "/home/ubuntu/Desktop/new/./main.py", line 60, in main
    root.mainloop()
  File "/usr/lib/python3.10/tkinter/__init__.py", line 1458, in mainloop
    self.tk.mainloop(n)