일본어 및 베트남어 ASR 모델 파인튜닝

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python -m pip install -U pip
pip install evaluate pandas numpy huggingface_hub pydub tqdm spacy ginza audiomentations
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install datasets>=2.6.1
pip install git+https://github.com/huggingface/transformers
pip install librosa
pip install evaluate>=0.30
pip install jiwer
pip install gradio
pip install -q bitsandbytes datasets accelerate loralib
pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main

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from datasets import load_dataset, DatasetDict

common_voice = DatasetDict()
common_voice["train"] = load_dataset(
    "mozilla-foundation/common_voice_11_0", "ja",
    split="train+validation", use_auth_token=True
)
common_voice["test"] = load_dataset(
    "mozilla-foundation/common_voice_11_0", "ja",
    split="test", use_auth_token=True
)
common_voice = common_voice.remove_columns([
    "accent", "age", "client_id", "down_votes",
    "gender", "locale", "path", "segment", "up_votes"
])

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import os, csv, codecs

def text_change_csv(input_path, output_path):
    file_csv = os.path.splitext(output_path)[0] + ".csv"
    output_dir = os.path.dirname(input_path)
    output_file = os.path.join(output_dir, file_csv)
    encodings = ["utf-8", "latin-1"]

    for encoding in encodings:
        try:
            with open(input_path, 'r', encoding=encoding) as rf:
                with codecs.open(output_file, 'w', encoding=encoding, errors='replace') as wf:
                    readfile = rf.readlines()
                    for read_text in readfile:
                        read_text = read_text.split('|')
                        writer = csv.writer(wf, delimiter=',')
                        writer.writerow(read_text)
            print(f"CSV has been created using encoding: {encoding}")
            return True
        except UnicodeDecodeError:
            continue

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from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift

common_voice = common_voice.cast_column("audio", Audio(sampling_rate=16000))

augment_waveform = Compose([
    AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.2),
    TimeStretch(min_rate=0.8, max_rate=1.25, p=0.2, leave_length_unchanged=False),
    PitchShift(min_semitones=-4, max_semitones=4, p=0.2)
])

def augment_dataset(batch):
    audio = batch["audio"]["array"]
    augmented_audio = augment_waveform(samples=audio, sample_rate=16000)
    batch["audio"]["array"] = augmented_audio
    return batch

common_voice['train'] = common_voice['train'].map(augment_dataset, keep_in_memory=True)

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import string

def remove_punctuation(sentence):
    translator = str.maketrans('', '', string.punctuation)
    modified_sentence = sentence.translate(translator)
    return modified_sentence

def fix_sentence(sentence):
    transcription = sentence
    if transcription.startswith('"') and transcription.endswith('"'):
        transcription = transcription[1:-1]
    transcription = remove_punctuation(transcription)
    transcription = transcription.lower()
    return transcription

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def prepare_dataset(batch):
    audio = batch["audio"]
    batch["input_features"] = processor.feature_extractor(
        audio["array"], sampling_rate=audio["sampling_rate"]
    ).input_features[0]
    batch["input_length"] = len(audio["array"]) / audio["sampling_rate"]

    transcription = fix_sentence(batch["transcription"])
    batch["labels"] = processor.tokenizer(
        transcription, max_length=225, truncation=True
    ).input_ids
    return batch

common_voice = common_voice.map(
    prepare_dataset,
    remove_columns=common_voice.column_names['train'],
    num_proc=1,
    keep_in_memory=True
)

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import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Union

@dataclass
class DataCollatorSpeechSeq2SeqWithPadding:
    processor: Any

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        input_features = [{"input_features": feature["input_features"]} for feature in features]
        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")

        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

data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)

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import evaluate

metric = evaluate.load("wer")

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)

    wer = 100 * metric.compute(predictions=pred_str, references=label_str)
    return {"wer": wer}

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import spacy, ginza

nlp = spacy.load("ja_ginza")
ginza.set_split_mode(nlp, "C")

def compute_metrics(pred):
    pred_ids = pred.predictions
    label_ids = pred.label_ids
    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id

    pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
    label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)

    # 일본어 텍스트를 적절히 토큰화하여 평가
    pred_str = [" ".join([str(i) for i in nlp(j)]) for j in pred_str]
    label_str = [" ".join([str(i) for i in nlp(j)]) for j in label_str]

    wer = 100 * metric.compute(predictions=pred_str, references=label_str)
    return {"wer": wer}

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from transformers import Seq2SeqTrainingArguments

model.config.dropout = 0.05

training_args = Seq2SeqTrainingArguments(
    output_dir="./whisper-fine-tuned",
    per_device_train_batch_size=16,
    gradient_accumulation_steps=1,
    learning_rate=1e-6,
    lr_scheduler_type='linear',
    optim="adamw_bnb_8bit",
    warmup_steps=200,
    num_train_epochs=5,
    gradient_checkpointing=True,
    evaluation_strategy="steps",
    fp16=True,
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=255,
    eval_steps=500,
    logging_steps=500,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=False,
    save_total_limit=1
)

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from transformers import WhisperForConditionalGeneration, prepare_model_for_int8_training
from peft import LoraConfig, get_peft_model

model = WhisperForConditionalGeneration.from_pretrained(
    model_name_or_path, load_in_8bit=True, device_map="auto"
)
model = prepare_model_for_int8_training(model)

def make_inputs_require_grad(module, input, output):
    output.requires_grad_(True)

model.model.encoder.conv1.register_forward_hook(make_inputs_require_grad)

config = LoraConfig(
    r=32,
    lora_alpha=64,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none"
)
model = get_peft_model(model, config)
model.print_trainable_parameters()
# Output: trainable params: 15728640 || all params: 1559033600 || trainable%: 1.01%

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from ctranslate2.converters import TransformersConverter

model_id = "./whisper-fine-tuned/checkpoint-5000"
output_dir = "whisper-ct2"

converter = TransformersConverter(model_id, load_as_float16=True)
converter.convert(output_dir, quantization="float16")
model = WhisperModel(output_dir, device="cuda", compute_type="float16")

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import os, evaluate
from azure.cognitiveservices.speech import SpeechConfig, SpeechRecognizer, AudioConfig

subscription_key = "your_subscription_key"
location = "japaneast"
endpoint = "your_endpoint"

config = SpeechConfig(subscription=subscription_key, region=location)
config.endpoint_id = endpoint
speech_config = SpeechConfig(
    subscription=subscription_key,
    region=location,
    speech_recognition_language="ja-JP"
)

predictions = []
for root, _, files in os.walk(wav_base_path):
    for file_name in files:
        if file_name.endswith(".wav"):
            audio_file_path = os.path.join(root, file_name)
            audio_config = AudioConfig(filename=audio_file_path)
            speech_recognizer = SpeechRecognizer(
                speech_config=speech_config,
                audio_config=audio_config
            )
            result = speech_recognizer.recognize_once()
            if result.text:
                predictions.append(result.text)