Instructions to use MingHuiFang/dac_16khz_8kbps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MingHuiFang/dac_16khz_8kbps with Transformers:
# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("MingHuiFang/dac_16khz_8kbps") model = AutoModel.from_pretrained("MingHuiFang/dac_16khz_8kbps") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
tags:
- DAC
- Audio
license: mit
This repository offers 16khzDAC with 9 codebooks and 8kbps bitrate.
For integration into ParlerTTS, you first need to install the Parler-TTS library with (to do once):
pip install git+https://github.com/huggingface/parler-tts.git
Descript Audio Codec (.dac): High-Fidelity Audio Compression with Improved RVQGAN
This repository is a wrapper around the original Descript Audio Codec model, a high fidelity general neural audio codec, introduced in the paper titled High-Fidelity Audio Compression with Improved RVQGAN.
It is designed to be used as a drop-in replacement of the transformers implementation of Encodec, so that architectures that use Encodec can also be trained with DAC instead. The Parler-TTS library is an example of how to use DAC to train high-quality TTS models.