Whisper Large V2 Japanese 5k Steps

🚀 whisper-large-v2-japanese-5k-steps

本模型是在日語CommonVoice數據集（v11）上對openai/whisper-large-v2進行微調後的版本。它在評估集上取得了以下成績：

• 損失值：0.4200
• 詞錯誤率（Wer）：0.7449

🚀 快速開始

本模型是為研究目的進行了5000步微調的版本，這意味著轉錄結果可能無法讓用戶完全滿意。

✨ 主要特性

• 基於openai/whisper-large-v2模型進行微調，適配日語語音識別任務。
• 在日語CommonVoice數據集（v11）上進行訓練和評估。

📦 安裝指南

文檔中未提及安裝步驟，故跳過此章節。

💻 使用示例

基礎用法

以下代碼展示瞭如何使用該模型進行轉錄：

from datasets import load_dataset, Audio
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# load the model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")

# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="validation", streaming=True)
commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
sample = next(iter(commonvoice_eval))["audio"]

# features and generate token ids
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)

# decode
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

print(transcription)

高級用法

以下代碼展示瞭如何在mozilla-foundation/common_voice_11_0測試集上評估該模型：

from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
import torch
import re
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# metric
wer_metric = evaluate.load("wer")

# model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")

# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="test", ) #cache_dir=args.cache_dir
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))

#for debuggings: it gets some examples
#dataset = dataset.shard(num_shards=7000, index=0)
#print(dataset)
   
def normalize(batch):
  batch["gold_text"] = whisper_norm(batch['sentence'])
  return batch

def map_wer(batch):
  model.to(device)
  forced_decoder_ids = processor.get_decoder_prompt_ids(language = "ja", task = "transcribe")
  inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
  with torch.no_grad():
    generated_ids = model.generate(inputs=inputs.to(device), forced_decoder_ids=forced_decoder_ids)
    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
  batch["predicted_text"] = whisper_norm(transcription)
  return batch

# process GOLD text
processed_dataset = dataset.map(normalize)
# get predictions
predicted = processed_dataset.map(map_wer)

# word error rate
wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
wer = round(100 * wer, 2)
print("WER:", wer)

📚 詳細文檔

訓練和評估數據

訓練過程

訓練超參數

訓練過程中使用了以下超參數：

• 學習率：1e-05
• 訓練批次大小：50
• 評估批次大小：16
• 隨機種子：42
• 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
• 學習率調度器類型：線性
• 學習率調度器熱身步數：500
• 訓練步數：5000
• 混合精度訓練：Native AMP

訓練結果

框架版本

• Transformers 4.26.0.dev0
• Pytorch 1.13.1
• Datasets 2.8.1.dev0
• Tokenizers 0.13.2

📄 許可證

本項目採用Apache-2.0許可證。