language:
- de
thumbnail: null
pipeline_tag: automatic-speech-recognition
tags:
- whisper
- pytorch
- speechbrain
- Transformer
- hf-asr-leaderboard
license: apache-2.0
datasets:
- RescueSpeech
metrics:
- wer
- cer
model-index:
- name: rescuespeech_whisper
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER
type: wer
value: '23.14'
inference: false
Whisper large-v2 fine-tuned on RescueSpeech dataset.
This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end whisper model fine-tuned on the RescueSpeech dataset within SpeechBrain. For a better experience, we encourage you to learn more about SpeechBrain.
The performance of the model is the following:
| Release | Test CER | Test WER | GPUs |
|---|---|---|---|
| 01-07-23 | 10.82 | 23.14 | 1xA100 80 GB |
Pipeline description
This ASR system is composed of whisper encoder-decoder blocks:
- The pretrained whisper-large-v2 encoder is frozen.
- The pretrained Whisper tokenizer is used.
- A pretrained Whisper-large-v2 decoder (openai/whisper-large-v2) is finetuned on RescueSpeech dataset. The obtained final acoustic representation is given to the greedy decoder.
The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed.
Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
pip install speechbrain
Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.
Transcribing your own audio files (in German)
from speechbrain.inference.ASR import WhisperASR
asr_model = WhisperASR.from_hparams(source="speechbrain/rescuespeech_whisper", savedir="pretrained_models/rescuespeech_whisper")
asr_model.transcribe_file("speechbrain/rescuespeech_whisper/example_de.wav")
Inference on GPU
To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.
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}},
}
Referencing RescueSpeech
@misc{sagar2023rescuespeech,
title={RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain},
author={Sangeet Sagar and Mirco Ravanelli and Bernd Kiefer and Ivana Kruijff Korbayova and Josef van Genabith},
year={2023},
eprint={2306.04054},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
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/