在英语语音识别中的经过优化的XLSR-53大型模型

使用Common Voice 6.1的训练和验证数据对facebook/wav2vec2-large-xlsr-53进行优化。使用该模型时，请确保你的语音输入采样率为16kHz。

此模型经过OVHcloud慷慨赠送的GPU积分进行了优化:)

训练所使用的脚本在这里可以找到：https://github.com/jonatasgrosman/wav2vec2-sprint

用法

该模型可以直接使用（不需要语言模型），如下所示...

使用HuggingSound库：

from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english")
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 = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
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
"SHE'LL BE ALL RIGHT."	SHE'LL BE ALL RIGHT
SIX	SIX
"ALL'S WELL THAT ENDS WELL."	ALL AS WELL THAT ENDS WELL
DO YOU MEAN IT?	DO YOU MEAN IT
THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS.	THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESSION
HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE?	HOW IS MOSLILLAR GOING TO HANDLE ANDBEWOOTH HIS LIKE Q AND Q
"I GUESS YOU MUST THINK I'M KINDA BATTY."	RUSTIAN WASTIN PAN ONTE BATTLY
NO ONE NEAR THE REMOTE MACHINE YOU COULD RING?	NO ONE NEAR THE REMOTE MACHINE YOU COULD RING
SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER.	SAUCE FOR THE GUICE IS SAUCE FOR THE GONDER
GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD.	GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD

评估

要在mozilla-foundation/common_voice_6_0上进行评估，使用分割测试

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

要在speech-recognition-community-v2/dev_data上进行评估

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0

引用

如果您想引用这个模型，可以使用以下引用：

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

作者:

Jonatas Grosman

数据集大小:

4.34 GB