app.py

# Install required packages
!pip install transformers datasets torchaudio TTS huggingface_hub

# Import libraries
from datasets import load_dataset, DatasetDict
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
import torch

# Load existing TTS model (you can choose other models too)
model_id = "microsoft/speecht5_tts"  # Example model
processor = SpeechT5Processor.from_pretrained(model_id)
model = SpeechT5ForTextToSpeech.from_pretrained(model_id)

# Load your conlang dataset
dataset = load_dataset("csv", data_files={"train": "./dataset/train.csv"}, delimiter=",")

# Preprocessing: convert text to tokens and load audio
# You can define your own tokenizer for your conlang here
def preprocess(example):
    input_ids = processor.tokenizer(example["text"], return_tensors="pt").input_ids[0]
    return {"input_ids": input_ids}

dataset = dataset.map(preprocess)

# Prepare DataLoader
from torch.utils.data import DataLoader

def collate_fn(batch):
    input_ids = torch.nn.utils.rnn.pad_sequence([b["input_ids"] for b in batch], batch_first=True)
    return {"input_ids": input_ids}

train_loader = DataLoader(dataset["train"], batch_size=4, shuffle=True, collate_fn=collate_fn)

# Fine-tune the model
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
model.train()

for epoch in range(10):  # example: 10 epochs
    for batch in train_loader:
        outputs = model(input_ids=batch["input_ids"], labels=batch["input_ids"])
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
    print(f"Epoch {epoch+1}, Loss: {loss.item()}")

# Save model to Hugging Face Hub (optional)
from huggingface_hub import HfApi, HfFolder, Repository

repo = Repository(local_dir="./conlang-tts", clone_from="your-username/conlang-tts")
model.save_pretrained("./conlang-tts")
processor.save_pretrained("./conlang-tts")
repo.push_to_hub()