m-ctc-t-large

M-CTC-T

​ Massively multilingual speech recognizer from Meta AI. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal. ​ ​

The original Flashlight code, model checkpoints, and Colab notebook can be found at https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl . ​ ​

Citation

​ Paper ​

Authors: Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert ​

@article{lugosch2021pseudo,
  title={Pseudo-Labeling for Massively Multilingual Speech Recognition},
  author={Lugosch, Loren and Likhomanenko, Tatiana and Synnaeve, Gabriel and Collobert, Ronan},
  journal={ICASSP},
  year={2022}
}

Contribution

A huge thanks to Chan Woo Kim for porting the model from Flashlight C++ to PyTorch. ​

Training method

​ ​ For more information on how the model was trained, please take a look at the official paper. ​

Usage

​ To transcribe audio files the model can be used as a standalone acoustic model as follows: ​

import torch
import torchaudio
from datasets import load_dataset
from transformers import MCTCTForCTC, MCTCTProcessor

model = MCTCTForCTC.from_pretrained("speechbrain/m-ctc-t-large")
processor = MCTCTProcessor.from_pretrained("speechbrain/m-ctc-t-large")

 # load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 
# feature extraction
input_features = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt").input_features 

# retrieve logits
with torch.no_grad():
    logits = model(input_features).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)

Results for Common Voice, averaged over all languages: ​

Character error rate (CER): ​

Questions & Help

If you have questions regarding this model or need help, please consider opening a discussion or pull request on this repo and tag @lorenlugosch, @cwkeam or @patrickvonplaten