language: it
thumbnail: null
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
license: apache-2.0
datasets:
- common_voice
metrics:
- wer
- cer
CRDNN with CTC/Attention trained on CommonVoice Italian (No LM)
This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (IT) within SpeechBrain. For a better experience, we encourage you to learn more about SpeechBrain. The given ASR model performance are:
Release | Test CER | Test WER | GPUs |
---|---|---|---|
07-03-21 | 5.40 | 16.61 | 2xV100 16GB |
Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions (train.tsv) of CommonVoice (IT).
- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convolutional neural networks with normalization and pooling on the frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain the final acoustic representation that is given to the CTC and attention decoders.
Intended uses & limitations
This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model for the Italian language. Thanks to the flexibility of SpeechBrain, any of the 2 blocks detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is installed.
Install SpeechBrain
First of all, please install 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 Italian)
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-it", savedir="pretrained_models/asr-crdnn-commonvoice-it")
asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-it/example-it.wav")
Inference on GPU
To perform inference on the GPU, add run_opts={"device":"cuda"}
when calling the from_hparams
method.
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}},
}