Wav2vec2 Large Xlsr 53 Eu

🚀 Wav2Vec2-Large-XLSR-53-EU

本项目是在巴斯克语上使用Common Voice数据集对facebook/wav2vec2-large-xlsr-53进行微调的成果。使用该模型时，请确保输入的语音采样率为16kHz。

🚀 快速开始

本模型可直接使用（无需语言模型），以下是具体的使用步骤。

💻 使用示例

基础用法

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

test_dataset = load_dataset("common_voice", "eu", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")
model = Wav2Vec2ForCTC.from_pretrained("pcuenq/wav2vec2-large-xlsr-53-eu")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
	speech_array, sampling_rate = torchaudio.load(batch["path"])
	batch["speech"] = resampler(speech_array).squeeze().numpy()
	return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], 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)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])

高级用法

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")

model_name = "pcuenq/wav2vec2-large-xlsr-53-eu"

processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.to("cuda")

## Text pre-processing

chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
chars_to_ignore_pattern = re.compile(chars_to_ignore_regex)

def remove_special_characters(batch):
    batch["sentence"] = chars_to_ignore_pattern.sub('', batch["sentence"]).lower() + " "
    return batch

## Audio pre-processing

import librosa
def speech_file_to_array_fn(batch):
    speech_array, sample_rate = torchaudio.load(batch["path"])
    batch["speech"] = librosa.resample(speech_array.squeeze().numpy(), sample_rate, 16_000)
    return batch

# Text transformation and audio resampling
def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = speech_file_to_array_fn(batch)
    return batch

# Number of CPUs or None
num_proc = 16
test_dataset = test_dataset.map(cv_prepare, remove_columns=['path'], num_proc=num_proc)

def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

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

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

# WER Metric computation
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

评估结果

测试结果：15.34 %

📚 详细文档

训练信息

训练使用了Common Voice的train和validation数据集，共进行了22 + 20个epoch的训练，训练参数如下：

• 批量大小：16，梯度累积步数：2。
• 学习率：2.5e-4
• 激活丢弃率：0.05
• 注意力丢弃率：0.1
• 隐藏层丢弃率：0.05
• 特征投影丢弃率：0.05
• 掩码时间概率：0.08
• 层丢弃率：0.05

📄 许可证

本项目采用Apache-2.0许可证。

📦 模型信息