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
Update README.md
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README.md
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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**.
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It is designed to be used as a drop-in replacement of the [transformers implementation](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/encodec#overview) of [Encodec](https://github.com/facebookresearch/encodec), so that architectures that use Encodec can also be trained with DAC instead.
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The [Parler-TTS library](https://github.com/huggingface/parler-tts) is an example of how to use DAC to train high-quality TTS models.
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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**.
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It is designed to be used as a drop-in replacement of the [transformers implementation](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/encodec#overview) of [Encodec](https://github.com/facebookresearch/encodec), so that architectures that use Encodec can also be trained with DAC instead.
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The [Parler-TTS library](https://github.com/huggingface/parler-tts) is an example of how to use DAC to train high-quality TTS models.
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