metadata

language: en
datasets:
  - librispeech_asr
tags:
  - speech
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
license: apache-2.0
model-index:
  - name: wav2vec2-large-960h-lv60
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (clean)
          type: librispeech_asr
          config: clean
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 1.9
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (other)
          type: librispeech_asr
          config: other
          split: test
          args:
            language: en
        metrics:
          - name: Test WER
            type: wer
            value: 3.9

Wav2Vec2-Large-960h-Lv60 + Self-Training

Facebook's Wav2Vec2

The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with Self-Training objective. When using the model make sure that your speech input is also sampled at 16Khz.

Paper

Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli

Abstract

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

Usage

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

 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 from datasets import load_dataset
 import torch
 
 # load model and processor
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
     
 # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 
 # tokenize
 input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
 
 # retrieve logits
 logits = model(input_values).logits
 
 # take argmax and decode
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)

Evaluation

First, ensure the required Python packages are installed. We'll require transformers for running the Wav2Vec2 model, datasets for loading the LibriSpeech dataset, and evaluate plus jiwer for computing the word-error rate (WER):

pip install --upgrade pip
pip install --upgrade transformers datasets evaluate jiwer

The following code snippet shows how to evaluate facebook/wav2vec2-large-960h-lv60-self on LibriSpeech's "clean" and "other" test data. The batch size can be set according to your device, and is set to 8 by default:

import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from evaluate import load

librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")

model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")

def map_to_pred(batch):
    audios = [audio["array"] for audio in batch["audio"]]
    sampling_rate = batch["audio"][0]["sampling_rate"]
    input_values = processor(audios, sampling_rate=sampling_rate, return_tensors="pt", padding="longest").input_values
    with torch.no_grad():
        logits = model(input_values.to("cuda")).logits

    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
    batch["transcription"] = [t for t in transcription]
    return batch

result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["audio"])
wer = load("wer")

print("WER:", wer.compute(references=result["text"], predictions=result["transcription"]))

Result (WER):

"clean"	"other"
1.9	3.9