AudioLM - Pytorch
Overview
The AudioLM - Pytorch project is an implementation of Google's AudioLM, which uses a language modeling approach to generate audio. This implementation is realized in Pytorch, a popular deep learning framework. AudioLM provides a method for creating audio content that can be conditioned using text, a feature enhanced in this implementation to allow text-to-speech (TTS) capabilities. This means that a model similar to Microsoft's VALL-E can be trained using this project.
Key Features
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SoundStream and Encodec Compatibility: This repository includes a version of SoundStream, an end-to-end neural audio codec licensed under MIT. Encodec compatibility allows users to seamlessly integrate audio encoding into their projects.
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Text-to-Audio Synthesis: With the use of classifier-free guidance such as T5, text can be converted to audio, expanding the possibilities for TTS applications.
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Hierarchical Transformer Models: The project employs three distinct transformers—Semantic, Coarse, and Fine Transformers—to handle different layers of audio transformation, providing a scalable and efficient approach to audio processing.
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Training and Usage: The library provides tools for training models on large datasets. Users can train SoundStream on audio data and utilize trained models for a range of tasks, including encoding, decoding, and tokenizing audio inputs.
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Multi-GPU Support: Thanks to integration with the Huggingface Accelerate library, users can perform multi-GPU training, enhancing the speed and efficiency of model training on large datasets.
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Future-Proofing: AudioLM models are designed to evolve over time, with the potential to fundamentally change how AI-generated audio is produced, making traditional audio clips obsolete.
Installation
To get started with AudioLM - Pytorch, users simply need to install the package via pip:
$ pip install audiolm-pytorch
Usage
Using SoundStream & Encodec
Users can choose between using the pretrained Encodec model or training a custom SoundStream model for audio coding tasks. For those wishing to stay true to the original model, SoundStream offers adaptability for diverse audio inputs.
from audiolm_pytorch import EncodecWrapper
encodec = EncodecWrapper()
Alternatively, SoundStream can be trained on a collection of audio files, unlocking its full potential as a versatile audio tokenizer.
from audiolm_pytorch import SoundStream, SoundStreamTrainer
soundstream = SoundStream(...)
trainer = SoundStreamTrainer(soundstream, ...)
trainer.train()
Trained models can be used to convert audio data into token sequences, suitable for various advanced audio processing tasks.
Hierarchical Transformers
For comprehensive audio modeling, users can train three types of transformers:
- Semantic Transformer: Processes audio into a sequence of semantic tokens.
- Coarse Transformer: Further refines the tokens into a coarse audio representation.
- Fine Transformer: Adds finer details to the audio representation, ensuring high-quality output.
Contributions and Support
This project is supported by several leading organizations such as Stability.ai, Huggingface, and MetaAI. Community contributions have significantly advanced the development, supported by professional advice and the collective expertise of numerous individuals.
Future Directions
The project aims to explore new domains, including hierarchical transformer models and other sophisticated techniques like forgetful causal masking and heterogeneous model adaptation, promising a robust future for AI-driven audio synthesis.
Citations
The project builds upon numerous research works and innovations in the field, acknowledging the efforts of many researchers in related studies, which can be found in the citations section.
In summary, AudioLM - Pytorch is a formidable tool for researchers and developers interested in the next generation of audio modeling and generation technology, offering expansive capabilities in text-to-audio synthesis and audio coding.