from transformers import ParlerTTSForConditionalGeneration, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Download model
model_name = "parler-tts/parler-tts-mini-v1"
model = ParlerTTSForConditionalGeneration.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load dataset (replace with your dataset)
dataset = load_dataset("lj_speech")  # Example dataset; adjust as needed

# Preprocess function (customize based on your dataset)
def preprocess_function(examples):
    # Tokenize text and prepare audio (example; adjust for your data)
    inputs = tokenizer(examples["text"], return_tensors="pt", padding=True, truncation=True)
    # Add audio processing if needed
    return {"input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"]}

train_dataset = dataset["train"].map(preprocess_function, batched=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./tts_finetuned",
    per_device_train_batch_size=8,
    num_train_epochs=3,
    save_steps=500,
    logging_steps=10,
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Fine-tune
trainer.train()

# Save fine-tuned model
trainer.save_model("./tts_finetuned")
tokenizer.save_pretrained("./tts_finetuned")

print("TTS model fine-tuned and saved to './tts_finetuned'. Upload to models/tts_model in your Space.")