README.md

metadata

language:
  - br
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - br
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: XLS-R-300M - Breton
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: br
        metrics:
          - name: Test WER
            type: wer
            value: 54.855
          - name: Test CER
            type: cer
            value: 17.865

XLS-R-300M - Breton

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set:

• Loss: NA
• Wer: NA

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

NA

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.0+cu102
• Datasets 1.17.1.dev0
• Tokenizers 0.10.3

Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 --dataset mozilla-foundation/common_voice_8_0 --config br --split test

2. To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8 --dataset speech-recognition-community-v2/dev_data --config br --split validation --chunk_length_s 5.0 --stride_length_s 1.0

Inference With LM

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F


model_id = "infinitejoy/wav2vec2-large-xls-r-300m-breton-cv8"

sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "br", split="test", streaming=True, use_auth_token=True))

sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()

model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)

input_values = processor(resampled_audio, return_tensors="pt").input_values

with torch.no_grad():
    logits = model(input_values).logits

transcription = processor.batch_decode(logits.numpy()).text

Eval results on Common Voice 7 "test" (WER):

NA