Wav2vec2 Large Xlsr Mongolian

🚀 Wav2Vec2-Large-XLSR-53-蒙古語模型

該項目是在蒙古語數據集上對 facebook/wav2vec2-large-xlsr-53 模型進行微調得到的。使用該模型時，請確保輸入的語音採樣率為 16kHz。

🚀 快速開始

本模型可直接使用（無需語言模型），具體使用方法如下：

💻 使用示例

基礎用法

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

model_name = "sammy786/wav2vec2-large-xlsr-mongolian"
device = "cuda"
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]'

model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
processor = Wav2Vec2Processor.from_pretrained(model_name)

ds = load_dataset("common_voice", "mn", split="test", data_dir="./cv-corpus-6.1-2020-12-11")

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

def map_to_array(batch):
    speech, _ = torchaudio.load(batch["path"])
    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
    batch["sampling_rate"] = resampler.new_freq
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
    return batch

ds = ds.map(map_to_array)

def map_to_pred(batch):
    features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt")
    input_values = features.input_values.to(device)
    attention_mask = features.attention_mask.to(device)
    with torch.no_grad():
        logits = model(input_values, attention_mask=attention_mask).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["predicted"] = processor.batch_decode(pred_ids)
    batch["target"] = batch["sentence"]
    return batch

result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))

wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))

測試結果：38.14 %

📄 許可證

本項目採用 Apache 2.0 許可證。

📚 詳細文檔

模型名稱為 XLSR Wav2Vec2 Mongolian by Salim Shaikh，在 Common Voice 蒙古語測試集上進行評估。