metadata

language:
  - en
  - ca
datasets:
  - covost2
  - librispeech_asr
tags:
  - audio
  - speech-translation
  - automatic-speech-recognition
  - speech2text2
license: mit
pipeline_tag: automatic-speech-recognition
widget:
  - example_title: Common Voice 1
    src: >-
      https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
  - example_title: Common Voice 2
    src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99989.mp3
  - example_title: Common Voice 3
    src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_9999.mp3

S2T2-Wav2Vec2-CoVoST2-EN-CA-ST

s2t-wav2vec2-large-en-ca is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST). The S2T2 model was proposed in Large-Scale Self- and Semi-Supervised Learning for Speech Translation and officially released in Fairseq.

Model description

S2T2 is a transformer-based seq2seq (speech encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a pretrained Wav2Vec2 as the encoder and a transformer-based decoder. The model is trained with standard autoregressive cross-entropy loss and generates the translations autoregressively.

Intended uses & limitations

This model can be used for end-to-end English speech to Catalan text translation. See the model hub to look for other S2T2 checkpoints.

How to use

As this a standard sequence to sequence transformer model, you can use the generate method to generate the transcripts by passing the speech features to the model.

You can use the model directly via the ASR pipeline

from datasets import load_dataset
from transformers import pipeline

librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-ca", feature_extractor="facebook/s2t-wav2vec2-large-en-ca")

translation = asr(librispeech_en[0]["file"])

or step-by-step as follows:

import torch
from transformers import Speech2Text2Processor, SpeechEncoderDecoder
from datasets import load_dataset

import soundfile as sf
model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-ca")
processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-ca")

def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch
    
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)

inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
transcription = processor.batch_decode(generated_ids)

Evaluation results

CoVoST-V2 test results for en-ca (BLEU score): 34.1

For more information, please have a look at the official paper - especially row 10 of Table 2.

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2104-06678,
  author    = {Changhan Wang and
               Anne Wu and
               Juan Miguel Pino and
               Alexei Baevski and
               Michael Auli and
               Alexis Conneau},
  title     = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation},
  journal   = {CoRR},
  volume    = {abs/2104.06678},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.06678},
  archivePrefix = {arXiv},
  eprint    = {2104.06678},
  timestamp = {Thu, 12 Aug 2021 15:37:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-06678.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}