wav2vec2-large-xlsr-53-portuguese

jonatasgrosman automatic-speech-recognition transformers pt

jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

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apache-2.0

许可

简介

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

模型卡片

许可协议 apache-2.0

语言

pt

数据集

common_voice common_voice_6_0

audio automatic-speech-recognition hf-asr-leaderboard mozilla-foundation/common_voice_6_0 pt robust-speech-event speech xlsr-fine-tuning-week

模型配置

模型类型 wav2vec2

架构 Wav2Vec2ForCTC

模型详情

已翻译

针对葡萄牙语语音识别微调的 XLSR-53 大型模型

基于 Common Voice 6.1 的训练集和验证集，对 facebook/wav2vec2-large-xlsr-53 进行了葡萄牙语微调。
使用该模型时，请确保语音输入的采样率为 16kHz。

该模型的微调得益于 OVHcloud 慷慨提供的 GPU 积分 :)

训练所用的脚本可在此处找到：https://github.com/jonatasgrosman/wav2vec2-sprint

使用方法

该模型可以直接使用（无需语言模型），操作如下……

使用 HuggingSound 库：

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)

编写自己的推理脚本：

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)

参考文本	预测结果
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

评估

在 mozilla-foundation/common_voice_6_0 数据集上使用 test 分割进行评估

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset mozilla-foundation/common_voice_6_0 --config pt --split test

在 speech-recognition-community-v2/dev_data 数据集上进行评估

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

引用

如果您想引用此模型，可以使用以下内容：

@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}
}