license: apache-2.0
datasets:
- librispeech_asr
language:
- en
library_name: sklearn
K-means (Quantization)
This folder contains pre-trained K-means models for the LibriSpeech Dataset. The model serves to quantize self-supervised representations into discrete representation. Thus representations can be used as a discrete audio input for various tasks including classification, ASR and speech gneration. It supports kmeans models using the features from HuBERT, WAVLM or Wav2Vec.
Training
The model was trained with SpeechBrain. To train it from scratch follow these steps:
- Clone SpeechBrain:
git clone --branch unstable-v0.6 https://github.com/speechbrain/speechbrain/
- Install it:
cd speechbrain
pip install -r requirements.txt
pip install -e .
- Run Training:
cd recipes/LibriSpeech/quantization/
pip install -r rextra-requirements.txt
python train.py hparams/train_with_[ssl_model].yaml --data_folder=your_data_folder
You can find our training results (models, logs, etc) here.
Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
Referencing SpeechBrain
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
}
About SpeechBrain
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
Website: https://speechbrain.github.io/