🚀 希腊语Wav2Vec2-Large-XLSR-53模型

本项目基于Common Voice数据集，对facebook/wav2vec2-large-xlsr-53模型进行了希腊语微调。该模型可用于自动语音识别任务。使用此模型时，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），以下是使用示例。

💻 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", "el", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1") 

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

# Preprocessing the datasets.
# We need to read the aduio 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])

🔧 评估

可以按照以下方式在Common Voice的希腊语测试数据上对模型进行评估。

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

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

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]' 
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays

def speech_file_to_array_fn(batch):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  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)

# Preprocessing the datasets.
# We need to read the aduio files as arrays

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：34.006258 %

📦 训练

训练使用了Common Voice的train和validation数据集。

训练脚本可在此处找到。（注：需补充训练脚本的链接。如果是在Colab中训练模型，直接填写Colab链接；如果是本地训练，建议将训练脚本上传到GitHub并在此处粘贴链接。）

📄 许可证

本项目采用Apache-2.0许可证。

📚 详细文档

模型信息

重要提示

⚠️ 重要提示

使用此模型时，请确保输入的语音采样率为16kHz。

使用建议

💡 使用建议

在评估和训练时，可根据实际情况调整批量大小和其他参数，以获得更好的性能。