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Fine-tuned XLSR-53 large model for speech recognition in Portuguese

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

Usage

The model can be used directly (without a language model) as follows...

Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

transcriptions = model.transcribe(audio_paths)

Writing your own inference script:

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "pt"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)
Reference Prediction
NEM O RADAR NEM OS OUTROS INSTRUMENTOS DETECTARAM O BOMBARDEIRO STEALTH. NEMHUM VADAN OS OLTWES INSTRUMENTOS DE TTÉÃN UM BOMBERDEIRO OSTER
PEDIR DINHEIRO EMPRESTADO ÀS PESSOAS DA ALDEIA E DIR ENGINHEIRO EMPRESTAR AS PESSOAS DA ALDEIA
OITO OITO
TRANCÁ-LOS TRANCAUVOS
REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA
O YOUTUBE AINDA É A MELHOR PLATAFORMA DE VÍDEOS. YOUTUBE AINDA É A MELHOR PLATAFOMA DE VÍDEOS
MENINA E MENINO BEIJANDO NAS SOMBRAS MENINA E MENINO BEIJANDO NAS SOMBRAS
EU SOU O SENHOR EU SOU O SENHOR
DUAS MULHERES QUE SENTAM-SE PARA BAIXO LENDO JORNAIS. DUAS MIERES QUE SENTAM-SE PARA BAICLANE JODNÓI
EU ORIGINALMENTE ESPERAVA EU ORIGINALMENTE ESPERAVA

Evaluation

  1. To evaluate on mozilla-foundation/common_voice_6_0 with split test
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset mozilla-foundation/common_voice_6_0 --config pt --split test
  1. To evaluate on speech-recognition-community-v2/dev_data
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset speech-recognition-community-v2/dev_data --config pt --split validation --chunk_length_s 5.0 --stride_length_s 1.0

Citation

If you want to cite this model you can use this:

@misc{grosman2021xlsr53-large-portuguese,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}ortuguese},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese}},
  year={2021}
}
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