Downloads · 30 days
4.6K
5% of all-time downloads
slprl/mhubert-base-25hz
mhubert-base-25hz is a feature extraction model from slprl. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
This is a version of Hubert by Meta. This version was introduced in TWIST and showed lots of value as a speech tokeniser for training SpeechLMs.
Downloads · 30 days
4.6K
5% of all-time downloads
All-time downloads
86.1K
Public
Parameters
94.9M
759 MB on disk
Likes
4
Public
Click a slice to open those files.
.safetensors380 MB · 100%
From the Hugging Face model README
This is a version of Hubert by Meta. This version was introduced in TWIST and showed lots of value as a speech tokeniser for training SpeechLMs.
These model weights were converted by SLP-RL from the original Textlesslib release.
This Hubert model was introduced in TWIST we encourage you to look there for the full details.
It was trained on a varied mixture of datasets: Multilingual LS, Vox Populi, Common Voice, Spotify, and Fisher. This Hubert base model was trained for 3 iterations with the default 50Hz features rate. For the 4-th iteration, they add an additional convolutional layer at the CNN Encoder with the stride 2, resulting in features of 25Hz.
We converted the original Fairseq release to Huggingface🤗 using the conversion script, after adding support, and asserted that the results are identical.
transformers.HubertModelThis is a base HubertModel and as such is useful as a feature extractor for speech tokenisation for usages such as Spoken Language Modelling or Speaking Style Conversion.
This model requires a new version of transformers - transformers>=4.48, so make sure you have it installed.
Afterwards it can be used as follows:
from transformers import HubertModel
model = HubertModel.from_pretrained('slprl/mhubert-base-25hz')
BibTeX:
@article{hassid2024textually,
title={Textually pretrained speech language models},
author={Hassid, Michael and Remez, Tal and Nguyen, Tu Anh and Gat, Itai and Conneau, Alexis and Kreuk, Felix and Copet, Jade and Defossez, Alexandre and Synnaeve, Gabriel and Dupoux, Emmanuel and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2024}
}