🚀 印度尼西亞語音識別模型評估項目
本項目專注於在印度尼西亞語的Common Voice數據集上進行語音識別模型的評估,利用Wav2Vec2ForCTC
模型實現自動語音識別,為相關研究和應用提供了有效的評估方案。
🚀 快速開始
評估環境準備
在Common Voice印度尼西亞語測試集上進行評估,需要安裝相關依賴庫,以下是評估代碼示例:
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 = '[\,\?\.\!\-\;\:\"]'
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)
數據處理
對數據集進行處理,包括音頻重採樣和文本清理:
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)
模型預測
使用模型進行預測,並計算詞錯誤率(WER):
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"]))
評估結果
結果:25.7 %
📦 項目信息
屬性 |
詳情 |
數據集 |
Common Voice |
標籤 |
語音、音頻、自動語音識別、XLSR微調周 |
許可證 |
Apache-2.0 |