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
- 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
- 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}
}
