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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.") |