Wav2vec2 Base Japanese Asr

🚀 wav2vec2-base-asr

該模型是基於 rinna/japanese-wav2vec2-base 在 common_voice_11_0 數據集 上針對自動語音識別（ASR）任務進行微調後的版本。此模型僅能預測平假名。

✨ 主要特性

• 基於預訓練模型微調，適用於日語自動語音識別任務。
• 僅能預測平假名。

📚 詳細文檔

致謝

本模型的微調方法受到了 vumichien/wav2vec2-large-xlsr-japanese-hiragana 訓練方法的啟發並進行了參考。

訓練過程

在 common_voice_11_0 數據集上進行微調後，得到以下結果：

訓練超參數

在微調過程中，訓練超參數保持一致：

• 學習率（learning_rate）：1e-4
• 訓練批次大小（train_batch_size）：16
• 評估批次大小（eval_batch_size）：16
• 隨機種子（seed）：42
• 梯度累積步數（gradient_accumulation_steps）：2
• 訓練輪數（num_train_epochs）：20
• 熱身步數（warmup_steps）：2000
• 學習率調度器類型（lr_scheduler_type）：線性

如何評估模型

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import torch
import torchaudio
import librosa
import numpy as np
import re
import MeCab
import pykakasi
from evaluate import load

model = Wav2Vec2ForCTC.from_pretrained('TKU410410103/wav2vec2-base-japanese-asr')
processor = Wav2Vec2Processor.from_pretrained("TKU410410103/wav2vec2-base-japanese-asr")

# load dataset
test_dataset = load_dataset('mozilla-foundation/common_voice_11_0', 'ja', split='test')
remove_columns = [col for col in test_dataset.column_names if col not in ['audio', 'sentence']]
test_dataset = test_dataset.remove_columns(remove_columns)

# resample
def process_waveforms(batch):
    speech_arrays = []
    sampling_rates = []

    for audio_path in batch['audio']:
        speech_array, _ = torchaudio.load(audio_path['path'])
        speech_array_resampled = librosa.resample(np.asarray(speech_array[0].numpy()), orig_sr=48000, target_sr=16000)
        speech_arrays.append(speech_array_resampled)
        sampling_rates.append(16000)

    batch["array"] = speech_arrays
    batch["sampling_rate"] = sampling_rates

    return batch

# hiragana
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
          "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
          "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
          "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
          "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"

wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
kakasi.setMode("J","H")
kakasi.setMode("K","H")
kakasi.setMode("r","Hepburn")
conv = kakasi.getConverter()

def prepare_char(batch):
    batch["sentence"] = conv.do(wakati.parse(batch["sentence"]).strip())
    batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
    return batch


resampled_eval_dataset = test_dataset.map(process_waveforms, batched=True, batch_size=50, num_proc=4)
eval_dataset = resampled_eval_dataset.map(prepare_char, num_proc=4)

# begin the evaluation process
wer = load("wer")
cer = load("cer")

def evaluate(batch):
    inputs = processor(batch["array"], sampling_rate=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to(device), attention_mask=inputs.attention_mask.to(device)).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

columns_to_remove = [column for column in eval_dataset.column_names if column != "sentence"]
batch_size = 16
result = eval_dataset.map(evaluate, remove_columns=columns_to_remove, batched=True, batch_size=batch_size)

wer_result = wer.compute(predictions=result["pred_strings"], references=result["sentence"])
cer_result = cer.compute(predictions=result["pred_strings"], references=result["sentence"])

print("WER: {:2f}%".format(100 * wer_result))
print("CER: {:2f}%".format(100 * cer_result))

測試結果

最終模型的評估結果如下：

• 在 common_voice_11_0 數據集上：
  • 字錯率（WER）：14.177284%
  • 字符錯誤率（CER）：6.462501%
• 在 Reazonspeech（tiny）數據集上：
  • 字錯率（WER）：40.864413%
  • 字符錯誤率（CER）：29.367348%

框架版本

• Transformers 4.39.1
• Pytorch 2.2.1+cu118
• Datasets 2.17.1

📄 許可證

本項目採用 Apache-2.0 許可證。