Wav2vec2 Large Xlsr 53 Mongolian

🚀 蒙古語Wav2Vec2-Large-XLSR-53模型

本模型基於Common Voice數據集，對facebook/wav2vec2-large-xlsr-53進行了蒙古語微調。使用該模型時，請確保輸入的語音採樣率為16kHz。

✨ 主要特性

• 數據集：使用Common Voice數據集進行訓練和評估。
• 評估指標：使用字錯率（WER）進行評估。
• 適用場景：適用於蒙古語的自動語音識別任務。

📦 安裝指南

文檔未提及具體安裝步驟，可參考Hugging Face相關庫的安裝方法，如transformers、torch、torchaudio、datasets等庫：

pip install transformers torch torchaudio datasets

💻 使用示例

基礎用法

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

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

processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")

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

# 預處理數據集
# 需要將音頻文件讀取為數組
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("預測結果:", processor.batch_decode(predicted_ids))
print("參考結果:", test_dataset["sentence"][:2])

📚 詳細文檔

評估模型

可以使用以下代碼在Common Voice的蒙古語測試數據上評估模型：

import torch
import torchaudio
import urllib.request
import tarfile
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# 下載原始數據，而不是使用HF數據集以節省磁盤空間
data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/mn.tar.gz"
filestream = urllib.request.urlopen(data_url)
data_file = tarfile.open(fileobj=filestream, mode="r|gz")
data_file.extractall()

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-mongolian")
model.to("cuda")

cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/mn/test.tsv", sep='\t')
clips_path = "cv-corpus-6.1-2020-12-11/mn/clips/"

def clean_sentence(sent):
    sent = sent.lower()
    # 用空格替換非字母字符
    sent = "".join(ch if ch.isalpha() else " " for ch in sent)
    # 去除重複的空格
    sent = " ".join(sent.split())
    return sent

targets = []
preds = []

for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
    row["sentence"] = clean_sentence(row["sentence"])
    speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
    row["speech"] = resampler(speech_array).squeeze().numpy()

    inputs = processor(row["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)

    targets.append(row["sentence"])
    preds.append(processor.batch_decode(pred_ids)[0])

print("字錯率 (WER): {:2f}%".format(100 * wer.compute(predictions=preds, references=targets)))

測試結果：38.53 %

訓練信息

訓練使用了Common Voice的train和validation數據集。

📄 許可證

本項目採用Apache 2.0許可證。

📋 模型信息