train_orpheus.py

import os
import torch
from transformers import Trainer, TrainingArguments
from datasets import load_dataset
import subprocess

# Install required packages
subprocess.run("pip install git+https://github.com/canopyai/Orpheus-TTS.git", shell=True)
subprocess.run("pip install orpheus-speech vllm==0.7.3", shell=True)

# Load the dataset
dataset = load_dataset("Emotional_Speech_Dataset_(ESD)")

# Get the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("canopylabs/orpheus-3b-0.1-pretrained")
tokenizer = AutoTokenizer.from_pretrained("canopylabs/orpheus-3b-0.1-pretrained")

# Setup training arguments
training_args = TrainingArguments(
    output_dir="./orpheus-finetuned",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    learning_rate=5e-5,
    num_train_epochs=3,
    save_strategy="steps",
    save_steps=500,
)

# Start training
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
)

trainer.train()

# Save the model
model.save_pretrained("./orpheus-finetuned-model")
tokenizer.save_pretrained("./orpheus-finetuned-model")