Spaces:
Sleeping
Sleeping
File size: 6,303 Bytes
5fbd01c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
import os
import soundfile as sf
import torch
import evaluate
import numpy as np
from dataclasses import dataclass
from datasets import Dataset, DatasetDict
import evaluate
from transformers import (
WhisperFeatureExtractor,
WhisperTokenizer,
WhisperProcessor,
WhisperForConditionalGeneration,
Seq2SeqTrainer,
Seq2SeqTrainingArguments
)
from typing import Any, Dict, List, Union
# 加载特征提取器
def load_feature_extractor():
return WhisperFeatureExtractor.from_pretrained("openai/whisper-tiny")
def load_dataset(directory, train_ratio):
def load_audio_data(dir):
data_dict = {'audio': [], 'sentence': []}
for filename in os.listdir(dir):
if filename.endswith('.wav'):
path = os.path.join(dir, filename)
data, samplerate = sf.read(path)
audio_dict = {'path': path, 'array': data, 'sampling_rate': samplerate}
data_dict['audio'].append(audio_dict)
sentence = filename.split('_')[0] # 获取文件名中的第一个部分作为句子
data_dict['sentence'].append(sentence)
return data_dict
def split_dataset(data_dict, train_ratio):
total_size = len(data_dict['audio'])
train_size = int(total_size * train_ratio)
indices = np.arange(total_size)
np.random.shuffle(indices)
train_indices, test_indices = indices[:train_size], indices[train_size:]
train_dict = {key: [value[i] for i in train_indices] for key, value in data_dict.items()}
test_dict = {key: [value[i] for i in test_indices] for key, value in data_dict.items()}
return Dataset.from_dict(train_dict), Dataset.from_dict(test_dict)
data_dict = load_audio_data(directory)
train_dataset, test_dataset = split_dataset(data_dict, train_ratio)
return DatasetDict({
'train': train_dataset,
'test': test_dataset
})
# 加载语音转换器
def load_tokenizer():
return WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="zh", task="transcribe")
# 准备数据集
def prepare_dataset(batch):
audio = batch["audio"]
batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0]
batch["labels"] = tokenizer(batch["sentence"]).input_ids
return batch
# 数据集整理
@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
processor: Any
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# 整理特征
input_features = [{"input_features": feature["input_features"]} for feature in features]
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
# 整理标签
label_features = [{"input_ids": feature["labels"]} for feature in features]
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():
labels = labels[:, 1:]
batch["labels"] = labels
return batch
metric = evaluate.load("cer")
# 计算指标
def compute_metrics(pred):
pred_ids = pred.predictions
label_ids = pred.label_ids
label_ids[label_ids == -100] = tokenizer.pad_token_id
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
print("pred_str", pred_str)
print("label_str", label_str)
cer = 100 * metric.compute(predictions=pred_str, references=label_str)
return {"cer": cer}
# 训练模型
def train_model(train_dataset, eval_dataset, model, processor, output_dir):
training_args = Seq2SeqTrainingArguments(
output_dir=output_dir,
per_device_train_batch_size=16,
gradient_accumulation_steps=1,
learning_rate=1e-5,
warmup_steps=5,
max_steps=50,
gradient_checkpointing=True,
fp16=True,
evaluation_strategy="steps",
per_device_eval_batch_size=8,
predict_with_generate=True,
generation_max_length=225,
save_steps=10,
eval_steps=10,
logging_steps=5,
report_to=["tensorboard"],
load_best_model_at_end=True,
metric_for_best_model="cer",
greater_is_better=False,
push_to_hub=True
)
trainer = Seq2SeqTrainer(
args=training_args,
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=DataCollatorSpeechSeq2SeqWithPadding(processor=processor),
compute_metrics=compute_metrics,
tokenizer=processor.feature_extractor
)
processor.save_pretrained(training_args.output_dir)
trainer.train()
def load_my_dataset_with_cache():
import os
import pickle
cache_file = 'dataset_cache.pkl'
if os.path.exists(cache_file):
# 如果缓存文件存在,就直接从缓存中加载数据集
print("WAIN: load dataset from cache: {cache_file}")
with open(cache_file, 'rb') as f:
dataset = pickle.load(f)
return dataset
else:
# 否则,就加载并处理数据集,然后将其保存到缓存文件中
dataset = load_dataset('audios', 0.8)
dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names["train"])
with open(cache_file, 'wb') as f:
pickle.dump(dataset, f)
return dataset
# 以下是主程序
if __name__ == "__main__":
# 加载模型和工具
feature_extractor = load_feature_extractor()
tokenizer = load_tokenizer()
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", language="zh", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
# 加载数据集
dataset = load_my_dataset_with_cache()
# 训练模型
train_model(dataset["train"], dataset["test"], model, processor, "./whisper-tiny-zh4") |