Create app.py

app.py

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+# 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()
+