Whisper tiny SpeechBrain
This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end whisper model within SpeechBrain. Please note that this is not an official Speechbrain repository.
Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
pip install speechbrain transformers==4.28.0
Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.
Transcribing your own audio files
from speechbrain.pretrained import WhisperASR
asr_model = WhisperASR.from_hparams(source="chaanks/asr-whisper-tiny-sb", savedir="pretrained_models/asr-whisper-tiny-sb")
asr_model.transcribe_file("chaanks/asr-whisper-tiny-sb/example.wav")
Inference on GPU
To perform inference on the GPU, add run_opts={"device":"cuda"}
when calling the from_hparams
method.
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/
- Downloads last month
- 7
Evaluation results
- Test WER on LibriSpeech (clean)test set self-reported7.540
- Test WER on LibriSpeech (other)test set self-reported17.150