语言：印度尼西亚
数据集：

• common_voice

标签：

• 语音
• 音频
• 自动语音识别
• xlsr微调周

许可证：apache-2.0

在Common Voice印尼语测试集上的评估

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

model_name = "munggok/xlsr_indonesia"  
device = "cuda"  
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"]'  # noqa: W605  

model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)  
processor = Wav2Vec2Processor.from_pretrained(model_name)  
ds = load_dataset("common_voice", "id", 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().replace("’", "'")  
    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=16, remove_columns=list(ds.features.keys()))  
wer = load_metric("wer")  
print(wer.compute(predictions=result["predicted"], references=result["target"]))  

结果：25.7%