optimizerai/vocos

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Model Details

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Installation

pip install "huggingface-hub[cli]"
huggingface-cli login # Paste access token w/ read access to this repository.
                      # Tokens look like this: hf_*****
export TEMP_DIR=$(mktemp -d)
huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR
pip install "file://$TEMP_DIR"

Or for an automated approach:

pip install "huggingface-hub[cli]"
export HF_TOKEN=hf_******
export TEMP_DIR=$(mktemp -d)
huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR
pip install "file://$TEMP_DIR"

If you want to hardcode your token for some reason:

pip install "huggingface-hub[cli]"
export TEMP_DIR=$(mktemp -d)
huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR --token hf_*****
pip install "file://$TEMP_DIR"

Example usage

import torch
from vocos import get_voco

mel_voco = get_voco("mel")
encodec_voco = get_voco("encodec")
dac_voco = get_voco("dac")
dac_vae_voco = get_voco("dacvae")
oobleck_voco = get_voco("oobleck")

audio = torch.randn(1, 44100, 2) # [batch, audio_length, audio_channels]
latents = oobleck_voco.encode(audio) # [batch, encoded_length, latent_dim]
recon = oobleck_voco.decode(latents) # [batch, recon_length, audio_channels]

Sampling rate: oobleck_voco.sampling_rate Audio channels: oobleck_voco.channel

Length conversion:

import torch
from vocos import get_voco

oobleck_voco = get_voco("oobleck")

audio_length = 44100
encode_length = oobleck_voco.encode_length(audio_length)
recon_length = oobleck_voco.decode_length(encode_length)

audio = torch.randn(1, audio_length, oobleck_voco.channel)
latent = oobleck_voco.encode(audio)
recon = oobleck_voco.decode(latent)

assert encode_length == latent.shape[1]
assert recon_length == recon.shape[1]

Training Details

Training Data

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Training Procedure

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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