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Model Details
Model Description
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- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
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Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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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
1pip install "huggingface-hub[cli]"
2huggingface-cli login # Paste access token w/ read access to this repository.
3 # Tokens look like this: hf_*****
4export TEMP_DIR=$(mktemp -d)
5huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR
6pip install "file://$TEMP_DIR"
Or for an automated approach:
1pip install "huggingface-hub[cli]"
2export HF_TOKEN=hf_******
3export TEMP_DIR=$(mktemp -d)
4huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR
5pip install "file://$TEMP_DIR"
If you want to hardcode your token for some reason:
1pip install "huggingface-hub[cli]"
2export TEMP_DIR=$(mktemp -d)
3huggingface-cli download optimizerai/vocos --exclude "*.safetensors" --local-dir $TEMP_DIR --token hf_*****
4pip install "file://$TEMP_DIR"
Example usage
1import torch
2from vocos import get_voco
3
4mel_voco = get_voco("mel")
5encodec_voco = get_voco("encodec")
6dac_voco = get_voco("dac")
7dac_vae_voco = get_voco("dacvae")
8oobleck_voco = get_voco("oobleck")
9
10audio = torch.randn(1, 44100, 2) # [batch, audio_length, audio_channels]
11latents = oobleck_voco.encode(audio) # [batch, encoded_length, latent_dim]
12recon = oobleck_voco.decode(latents) # [batch, recon_length, audio_channels]
Sampling rate: oobleck_voco.sampling_rate
Audio channels: oobleck_voco.channel
Length conversion:
1import torch
2from vocos import get_voco
3
4oobleck_voco = get_voco("oobleck")
5
6audio_length = 44100
7encode_length = oobleck_voco.encode_length(audio_length)
8recon_length = oobleck_voco.decode_length(encode_length)
9
10audio = torch.randn(1, audio_length, oobleck_voco.channel)
11latent = oobleck_voco.encode(audio)
12recon = oobleck_voco.decode(latent)
13
14assert encode_length == latent.shape[1]
15assert recon_length == recon.shape[1]
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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