Wav2vec2 Large Xlsr Arabic

🚀 用于阿拉伯语语音识别的微调Wav2Vec2-Large-XLSR-53大模型

本模型是在阿拉伯语上对 facebook/wav2vec2-large-xlsr-53 进行微调得到的，使用了 Common Voice 和 Arabic Speech Corpus 的 train 分割集。使用此模型时，请确保语音输入的采样率为 16kHz。

🚀 快速开始

✨ 主要特性

• 基于预训练的 facebook/wav2vec2-large-xlsr-53 模型进行微调，适用于阿拉伯语语音识别任务。
• 可直接使用，无需语言模型。

📦 安装指南

在运行模型之前，需要安装以下依赖库：

%%capture
!pip install datasets
!pip install transformers==4.4.0
!pip install torchaudio
!pip install jiwer
!pip install tnkeeh

💻 使用示例

基础用法

模型可以直接（无需语言模型）按如下方式使用：

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

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


processor = Wav2Vec2Processor.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic")
model = Wav2Vec2ForCTC.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic")

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("The predicted sentence is: ", processor.batch_decode(predicted_ids))
print("The original sentence is:", test_dataset["sentence"][:2])

输出结果如下：

The predicted sentence is : ['ألديك قلم', 'ليست نارك مكسافة على هذه الأرض أبعد من يوم أمس']
The original sentence is: ['ألديك قلم ؟', 'ليست هناك مسافة على هذه الأرض أبعد من يوم أمس.']

🔧 技术细节

评估方法

模型可以在 Common Voice 的阿拉伯语测试数据上按如下方式进行评估：

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
# creating a dictionary with all diacritics
dict = {
'ِ': '',
'ُ': '', 
'ٓ': '', 
'ٰ': '', 
'ْ': '', 
'ٌ': '', 
'ٍ': '', 
'ً': '', 
'ّ': '', 
'َ': '',
'~': '',
',': '',
'ـ': '',
'—': '',
'.': '',
'!': '',
'-': '',
';': '',
':': '',
'\'': '',
'"': '',
'☭': '',
'«': '',
'»': '',
'؛': '',
'ـ': '',
'_': '',
'،': '',
'“': '',
'%': '',
'‘': '',
'”': '',
'�': '',
'_': '',
',': '',
'?': '',
'#': '',
'‘': '',
'.': '',
'؛': '',
'get': '',
'؟': '',
'  ': ' ',
'\'ۖ ': '',
'\'': '',
 '\'ۚ' : '',
 ' \'': '',
 '31': '', 
 '24': '',
 '39': ''
} 

# replacing multiple diacritics using dictionary (stackoverflow is amazing)
def remove_special_characters(batch):
  # Create a regular expression  from the dictionary keys
  regex = re.compile("(%s)" % "|".join(map(re.escape, dict.keys())))
  # For each match, look-up corresponding value in dictionary
  batch["sentence"] = regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], batch["sentence"])
  return batch 
  

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

processor = Wav2Vec2Processor.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic") 
model = Wav2Vec2ForCTC.from_pretrained("mohammed/wav2vec2-large-xlsr-arabic")
model.to("cuda")


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)
test_dataset = test_dataset.map(remove_special_characters)
# Preprocessing the datasets.
# We need to read the audio 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：36.699%

📚 详细文档

未来工作

可以使用 数据增强、音译 或 注意力掩码 来提高模型的准确性。

📄 许可证

本项目采用 Apache-2.0 许可证。

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