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mlx-community/
mlx_bark

Name: mlx_bark
Availability: InStock

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Model Summary

Bark is a transformer based text-to-audio model that can generate speech and miscellaneous audio i.e. background noise / music.

This is a port of Suno's Bark model in Apple's ML Framework, MLX. The intention of the port is to explore the potential in making fast on-device TTS inference possible.

This repository contains the Bark weights in npz format suitable for use with Apple's MLX Framework.

Repo links

Usage

bash

# Setup
pip install transformers huggingface_hub hf_transfer
git clone https://github.com/j-csc/mlx_bark
cd mlx_bark
pip install -r requirements.txt

# Download model
export HF_HUB_ENABLE_HF_TRANSFER=1
huggingface-cli download --local-dir-use-symlinks False --local-dir weights/ mlx-community/mlx_bark

# Run example (large model)
python model.py --text="Hello world!" --path weights/ --model large

The rest of the model card was copied from the original Bark repository

Model Details

The following is additional information about the models released here.

Bark is a series of three transformer models that turn text into audio.

Text to semantic tokens

Input: text, tokenized with BERT tokenizer from Hugging Face
Output: semantic tokens that encode the audio to be generated

Semantic to coarse tokens

Input: semantic tokens
Output: tokens from the first two codebooks of the EnCodec Codec from facebook

Coarse to fine tokens

Input: the first two codebooks from EnCodec
Output: 8 codebooks from EnCodec

Architecture

Model	Parameters	Attention	Output Vocab size
Text to semantic tokens	80/300 M	Causal	10,000
Semantic to coarse tokens	80/300 M	Causal	2x 1,024
Coarse to fine tokens	80/300 M	Non-causal	6x 1,024

Release date

April 2023

Broader Implications

We anticipate that this model's text to audio capabilities can be used to improve accessbility tools in a variety of languages.

While we hope that this release will enable users to express their creativity and build applications that are a force for good, we acknowledge that any text to audio model has the potential for dual use. While it is not straightforward to voice clone known people with Bark, it can still be used for nefarious purposes. To further reduce the chances of unintended use of Bark, we also release a simple classifier to detect Bark-generated audio with high accuracy (see notebooks section of the main repository).