🚀 Wav2Vec2-Large-XLSR-布列塔尼语模型
该项目基于facebook/wav2vec2-large-xlsr-53模型,在布列塔尼语Common Voice数据集上进行了微调。使用此模型时,请确保语音输入的采样率为16kHz。
模型信息
属性 |
详情 |
语言 |
布列塔尼语 |
数据集 |
Common Voice |
评估指标 |
词错误率(WER) |
标签 |
音频、自动语音识别、语音、XLSR微调周 |
许可证 |
Apache-2.0 |
模型名称 |
XLSR Wav2Vec2布列塔尼语模型(由Cahya训练) |
任务 |
语音识别(自动语音识别类型) |
测试数据集 |
Common Voice br |
测试WER |
41.71 |
🚀 快速开始
模型使用
模型可以直接使用(无需语言模型),示例代码如下:
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "br", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = batch["sentence"].replace("ʼ", "'")
batch["sentence"] = batch["sentence"].replace("’", "'")
batch["sentence"] = batch["sentence"].replace('‘', "'")
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["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)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
上述代码对前两个样本的预测结果如下:
Prediction: ["ne' ler ket don a-benn us netra pa vez zer nic'hed evel-si", 'an eil hag egile']
Reference: ['"n\'haller ket dont a-benn eus netra pa vezer nec\'het evel-se." ', 'an eil hag egile. ']
模型评估
可以在Common Voice的布列塔尼语测试数据上对模型进行评估,代码如下:
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "br", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model.to("cuda")
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = batch["sentence"].replace("ʼ", "'")
batch["sentence"] = batch["sentence"].replace("’", "'")
batch["sentence"] = batch["sentence"].replace('‘', "'")
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
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)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
测试结果:41.71 %
模型训练
使用了Common Voice的train
、validation
等数据集进行训练。训练脚本可在此处找到(即将发布)。
📄 许可证
本项目采用Apache-2.0许可证。