Audio Upscaler

Audio Upscaler is a U-Net-style convolutional autoencoder trained to "upscale" audio using AI (for example, up-conversion from an 8-bit 11KHz IFF file to a 16-bit 44.1KHz WAV file). The alpha weights included here are a placeholder and are mostly useless at the moment—this is a running project, training on approximately 200,000 audio files. The weights will eventually be useful for improving sample rates and depth as well as fixing issues associated with audio quality for old and obscure low-quality audio formats.

Project Status

Alpha 0.001 - Early development stage

• Alpha weights included are mostly non-functional placeholders
• Training ongoing on ~200,000 audio files
• Weights will improve significantly in future releases
• Not recommended for production use yet

Description

Audio Upscaler uses deep learning to enhance audio quality by increasing sample rate and bit depth. The U-Net architecture allows the model to learn features at multiple scales, making it effective for audio restoration and quality improvement tasks.

Use Cases

• Convert old 8-bit, low sample rate audio to modern formats
• Enhance audio from obsolete formats (IFF, SoundBlaster, etc.)
• Improve quality of digitized cassettes, vinyl records, and old game audio
• Restore archived low-quality audio files

Architecture

The model uses a U-Net-style convolutional autoencoder consisting of:

• Encoder: Downsampling path to capture multi-scale features
• Bottleneck: Central feature representation
• Decoder: Upsampling path for reconstruction
• Skip connections: Preserve fine details from encoder to decoder

Requirements

• Python 3.7+
• PyTorch 1.9+
• NumPy
• SciPy
• librosa (for audio processing)

Installation

git clone https://github.com/yourusername/AudioUpscaler.git
cd AudioUpscaler
pip install -r requirements.txt

Or install dependencies manually:

pip install torch numpy scipy librosa

Usage

Loading the Model

import torch
from audio_upscaler import AudioUpscaler

# Load the pre-trained model
model = AudioUpscaler()
model.load_state_dict(torch.load('AudioUpscaler-alpha0.001.pt'))
model.eval()

Upscaling Audio

import librosa
import numpy as np

# Load audio
audio, sr = librosa.load('input.wav', sr=11025, mono=True)

# Prepare input (normalize to [-1, 1])
audio_tensor = torch.FloatTensor(audio).unsqueeze(0).unsqueeze(0)

# Upscale
with torch.no_grad():
    upscaled = model(audio_tensor)

# Convert back to numpy and save
output = upscaled.squeeze().numpy()
librosa.output.write_wav('output.wav', output, sr=44100)

Model Files

AudioUpscaler-alpha0.001.pt

• Size: ~181 MB
• Format: PyTorch state dictionary
• Status: Alpha weights (placeholder)
• Recommended: For testing and development only

Training Data

The model is being trained on approximately 200,000 audio files covering:

• Various sample rates (8kHz, 11.025kHz, 16kHz, 22.05kHz, 44.1kHz)
• Multiple bit depths (8-bit, 16-bit)
• Diverse audio formats (IFF, WAV, MP3, FLAC, etc.)
• Wide range of audio content (speech, music, effects, etc.)

Future Improvements

• Expand training dataset
• Improve model architecture
• Add multi-channel support
• Optimize inference speed
• Release beta weights (v0.5)
• Release production weights (v1.0)
• Support for real-time upscaling
• CLI tool for batch processing

Limitations

• Alpha weights are mostly non-functional - do not expect good results with current weights
• Current model: single-channel audio only
• Fixed input/output sample rates (work in progress)
• GPU recommended for faster processing
• Long audio files may require memory optimization

Contributing

This is an active research and development project. We welcome:

• Feedback on audio quality improvements
• Bug reports and feature requests
• Contributions to training data collection
• Performance optimization suggestions

References

U-Net architecture papers and implementations:

• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation
• Various audio upsampling and enhancement techniques

License

[Specify your license - e.g., MIT, GPL, Apache 2.0]

Roadmap

v0.001 (Current - Alpha)

• Initial model and weights
• Basic U-Net architecture

v0.5 (Beta - Planned)

• Improved training on full dataset
• Better generalization across formats
• Performance optimizations

v1.0 (Production - Planned)

• Production-grade weights
• Full feature support
• Comprehensive documentation
• Optimization and deployment guides

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A deep learning project for audio quality enhancement and restoration. Stay tuned for improved weights as training progresses.