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README.md

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----
-title: Aud2Stm2Mdi
-emoji: 👁
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.4.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: 'Convert audio files into separate stems and MIDI '
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Audio Processing Pipeline: Stem Separation and MIDI Conversion
+
+## Project Overview
+A production-ready web application that separates audio stems and converts them to MIDI using state-of-the-art deep learning models. Built with Gradio and deployed on LightningAI, this pipeline provides an intuitive interface for audio processing tasks.
+
+## Technical Requirements
+
+### Dependencies
+```bash
+pip install gradio>=4.0.0
+pip install demucs>=4.0.0
+pip install basic-pitch>=0.4.0
+pip install torch>=2.0.0 torchaudio>=2.0.0
+pip install soundfile>=0.12.1
+pip install numpy>=1.26.4
+pip install pretty_midi>=0.2.10
+```
+
+### File Structure
+```
+project/
+├── app.py                 # Main Gradio interface and processing logic
+├── demucs_handler.py      # Audio stem separation handler
+├── basic_pitch_handler.py # MIDI conversion handler
+├── validators.py          # Audio file validation utilities
+└── requirements.txt
+```
+
+## Implementation Details
+
+### demucs_handler.py
+Handles audio stem separation using the Demucs model:
+- Supports mono and stereo input
+- Automatic stereo conversion for mono inputs
+- Efficient tensor processing with PyTorch
+- Proper error handling and logging
+- Progress tracking during processing
+
+### basic_pitch_handler.py
+Manages MIDI conversion using Spotify's Basic Pitch:
+- Optimized parameters for music transcription
+- Support for polyphonic audio
+- Pitch bend detection
+- Configurable note duration and frequency ranges
+- Robust MIDI file generation
+
+### validators.py
+Provides comprehensive audio file validation:
+- Format verification (WAV, MP3, FLAC)
+- File size limits (30MB default)
+- Sample rate validation (8kHz-48kHz)
+- Audio integrity checking
+- Detailed error reporting
+
+### app.py
+Main application interface featuring:
+- Clean, intuitive Gradio UI
+- Multi-file upload support
+- Stem type selection (vocals, drums, bass, other)
+- Optional MIDI conversion
+- Persistent file handling
+- Progress tracking
+- Comprehensive error handling
+
+## Key Features
+
+### Audio Processing
+- High-quality stem separation using Demucs
+- Support for multiple audio formats
+- Automatic audio format conversion
+- Efficient memory management
+- Progress tracking during processing
+
+### MIDI Conversion
+- Accurate note detection
+- Polyphonic transcription
+- Configurable parameters:
+  - Note duration threshold
+  - Frequency range
+  - Onset detection sensitivity
+  - Frame-level pitch activation
+
+### User Interface
+- Simple, intuitive design
+- Real-time processing feedback
+- Preview capabilities
+- File download options
+
+## Deployment
+
+### Local Development
+```bash
+# Clone repository
+git clone https://github.com/eyov7/Aud2Stm2Mdi.git
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run application
+python app.py
+```
+
+### Lightning.ai Deployment
+1. Create new Lightning App
+2. Upload project files
+3. Configure compute instance (CPU or GPU)
+4. Deploy
+
+## Error Handling
+Implemented comprehensive error handling for:
+- Invalid file formats
+- File size limits
+- Processing failures
+- Memory constraints
+- File system operations
+- Model inference errors
+
+
+## Production Features
+- Robust file validation
+- Persistent storage management
+- Proper error logging
+- Progress tracking
+- Clean user interface
+- Download capabilities
+- Multi-format support
+
+## Limitations
+- Maximum file size: 30MB
+- Supported formats: WAV, MP3, FLAC
+- Single file processing (no batch)
+- CPU-only processing by default
+
+## Notes
+- Ensure proper audio codec support
+- Monitor system resources
+- Regular temporary file cleanup
+- Consider implementing rate limiting
+- Add user session management
+
+## Closing Note
+This implementation is currently running successfully on Lightning.ai, providing reliable audio stem separation and MIDI conversion capabilities through an intuitive web interface.
+
+
+
+
+
+

app.py

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+import gradio as gr
+import os
+import tempfile
+from pathlib import Path
+from typing import List, Tuple, Optional
+from concurrent.futures import ThreadPoolExecutor
+import logging
+import soundfile as sf
+import numpy as np
+import shutil
+from validators import AudioValidator
+from demucs_handler import DemucsProcessor
+from basic_pitch_handler import BasicPitchConverter
+
+# Suppress TF logging
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+logging.getLogger('tensorflow').setLevel(logging.ERROR)
+
+logger = logging.getLogger(__name__)
+
+# Create a persistent directory for outputs
+OUTPUT_DIR = Path("/tmp/audio_processor")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+def process_single_audio(audio_path: str, stem_type: str, convert_midi: bool) -> Tuple[Tuple[int, np.ndarray], Optional[str]]:
+    try:
+        # Create unique subdirectory for this processing
+        process_dir = OUTPUT_DIR / str(hash(audio_path))
+        process_dir.mkdir(parents=True, exist_ok=True)
+        
+        processor = DemucsProcessor()
+        converter = BasicPitchConverter()
+        
+        print(f"Starting processing of file: {audio_path}")
+        
+        # Process stems
+        sources, sample_rate = processor.separate_stems(audio_path)
+        print(f"Number of sources returned: {sources.shape}")
+        print(f"Stem type requested: {stem_type}")
+        
+        # Get the requested stem
+        stem_index = ["drums", "bass", "other", "vocals"].index(stem_type)
+        selected_stem = sources[0, stem_index]
+        
+        # Save stem
+        stem_path = process_dir / f"{stem_type}.wav"
+        processor.save_stem(selected_stem, stem_type, str(process_dir), sample_rate)
+        print(f"Saved stem to: {stem_path}")
+        
+        # Load the saved audio file for Gradio
+        audio_data, sr = sf.read(str(stem_path))
+        if len(audio_data.shape) > 1:
+            audio_data = audio_data.mean(axis=1)  # Convert to mono if stereo
+        
+        # Convert to int16 format
+        audio_data = (audio_data * 32767).astype(np.int16)
+        
+        # Convert to MIDI if requested
+        midi_path = None
+        if convert_midi:
+            midi_path = process_dir / f"{stem_type}.mid"
+            converter.convert_to_midi(str(stem_path), str(midi_path))
+            print(f"Saved MIDI to: {midi_path}")
+                
+        return (sr, audio_data), str(midi_path) if midi_path else None
+    except Exception as e:
+        print(f"Error in process_single_audio: {str(e)}")
+        raise
+
+def create_interface():
+    processor = DemucsProcessor()
+    converter = BasicPitchConverter()
+    validator = AudioValidator()
+
+    def process_audio(
+        audio_files: List[str],
+        stem_type: str,
+        convert_midi: bool = True,
+        progress=gr.Progress()
+    ) -> Tuple[Tuple[int, np.ndarray], Optional[str]]:
+        try:
+            print(f"Starting processing of {len(audio_files)} files")
+            print(f"Selected stem type: {stem_type}")
+            
+            # Process single file for now
+            if len(audio_files) > 0:
+                audio_path = audio_files[0]  # Take first file
+                print(f"Processing file: {audio_path}")
+                return process_single_audio(audio_path, stem_type, convert_midi)
+            else:
+                raise ValueError("No audio files provided")
+            
+        except Exception as e:
+            print(f"Error in audio processing: {str(e)}")
+            raise gr.Error(str(e))
+
+    interface = gr.Interface(
+        fn=process_audio,
+        inputs=[
+            gr.File(
+                file_count="multiple",
+                file_types=AudioValidator.SUPPORTED_FORMATS,
+                label="Upload Audio Files"
+            ),
+            gr.Dropdown(
+                choices=["vocals", "drums", "bass", "other"],
+                label="Select Stem",
+                value="vocals"
+            ),
+            gr.Checkbox(label="Convert to MIDI", value=True)
+        ],
+        outputs=[
+            gr.Audio(label="Separated Stems", type="numpy"),
+            gr.File(label="MIDI Files")
+        ],
+        title="Audio Stem Separator & MIDI Converter",
+        description="Upload audio files to separate stems and convert to MIDI",
+        cache_examples=True,
+        allow_flagging="never"
+    )
+    
+    return interface
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch(
+        share=False,
+        server_name="0.0.0.0",
+        server_port=7860,
+        auth=None,
+        ssl_keyfile=None,
+        ssl_certfile=None
+    )

basic_pitch_handler.py

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+import logging
+from basic_pitch.inference import predict
+from basic_pitch import ICASSP_2022_MODEL_PATH
+import pretty_midi
+from typing import Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+class BasicPitchConverter:
+    def __init__(self):
+        self.process_options = {
+            'onset_threshold': 0.5,
+            'frame_threshold': 0.3,
+            'minimum_note_length': 127.70,  # in milliseconds
+            'minimum_frequency': 32.7,  # C1
+            'maximum_frequency': 2093,  # C7
+            'multiple_pitch_bends': True,
+            'melodia_trick': True,
+            'midi_tempo': 120.0
+        }
+        print("Basic Pitch converter initialized")  # Keep using print for consistency
+
+    def convert_to_midi(self, audio_path: str, output_path: str, progress: Optional[callable] = None) -> str:
+        """
+        Convert audio to MIDI using Basic Pitch.
+        
+        Args:
+            audio_path: Path to input audio file
+            output_path: Path to save MIDI file
+            progress: Optional callback function for progress updates
+        
+        Returns:
+            str: Path to saved MIDI file
+        """
+        try:
+            print(f"Converting to MIDI: {audio_path}")  # Keep debugging output
+            if progress:
+                progress(0.1, "Loading audio for MIDI conversion...")
+            
+            # Predict using Basic Pitch with correct parameters
+            model_output, midi_data, note_events = predict(
+                audio_path=audio_path,
+                onset_threshold=self.process_options['onset_threshold'],
+                frame_threshold=self.process_options['frame_threshold'],
+                minimum_note_length=self.process_options['minimum_note_length'],
+                minimum_frequency=self.process_options['minimum_frequency'],
+                maximum_frequency=self.process_options['maximum_frequency'],
+                multiple_pitch_bends=self.process_options['multiple_pitch_bends'],
+                melodia_trick=self.process_options['melodia_trick'],
+                midi_tempo=self.process_options['midi_tempo']
+            )
+            
+            if progress:
+                progress(0.7, "Saving MIDI file...")
+            
+            print(f"Saving MIDI to: {output_path}")  # Keep debugging output
+            
+            # Save MIDI file with validation
+            if isinstance(midi_data, pretty_midi.PrettyMIDI):
+                midi_data.write(output_path)
+                print(f"Successfully saved MIDI to {output_path}")  # Keep using print
+                return output_path
+            else:
+                raise ValueError("MIDI conversion failed: Invalid MIDI data")
+            
+        except Exception as e:
+            print(f"Error in MIDI conversion: {str(e)}")  # Keep using print
+            raise
+
+    def set_process_options(self, **kwargs):
+        """Update processing options"""
+        self.process_options.update(kwargs)

demucs_handler.py

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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

requirements.txt

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+gradio>=4.0.0
+demucs>=4.0.0
+basic-pitch>=0.2.6
+torch>=2.0.0
+torchaudio>=2.0.0
+transformers>=4.30.0

validators.py

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+import os
+import logging
+import torchaudio
+from typing import Tuple
+
+logger = logging.getLogger(__name__)
+
+class AudioValidator:
+    SUPPORTED_FORMATS = ['.mp3', '.wav', '.flac']
+    MAX_FILE_SIZE = 30 * 1024 * 1024  # 30MB
+    
+    @staticmethod
+    def validate_audio_file(file_path: str) -> Tuple[bool, str]:
+        try:
+            if not os.path.exists(file_path):
+                return False, "File does not exist"
+                
+            file_size = os.path.getsize(file_path)
+            if file_size > AudioValidator.MAX_FILE_SIZE:
+                return False, f"File too large. Maximum size: {AudioValidator.MAX_FILE_SIZE // 1024 // 1024}MB"
+                
+            file_ext = os.path.splitext(file_path)[1].lower()
+            if file_ext not in AudioValidator.SUPPORTED_FORMATS:
+                return False, f"Unsupported format. Supported formats: {', '.join(AudioValidator.SUPPORTED_FORMATS)}"
+                
+            # Validate audio file integrity
+            try:
+                waveform, sample_rate = torchaudio.load(file_path)
+                if sample_rate < 8000 or sample_rate > 48000:
+                    return False, "Invalid sample rate"
+            except Exception as e:
+                return False, f"Invalid audio file: {str(e)}"
+                
+            return True, "Valid audio file"
+        except Exception as e:
+            logger.error(f"Error validating audio file: {str(e)}")
+            return False, str(e)