import os import argparse import torch import soundfile as sf import logging from datetime import datetime from cli.SparkTTS import SparkTTS def parse_args(): """Parse command-line arguments.""" parser = argparse.ArgumentParser(description="Run TTS inference.") parser.add_argument("--model_dir", type=str, default="pretrained_models/Spark-TTS-0.5B", help="Path to the model directory") parser.add_argument("--save_dir", type=str, default="example/results", help="Directory to save generated audio files") parser.add_argument("--device", type=int, default=0, help="CUDA device number") parser.add_argument("--text", type=str, required=True, help="Text for TTS generation") parser.add_argument("--prompt_text", type=str, help="Transcript of prompt audio") parser.add_argument("--prompt_speech_path", type=str, required=True, help="Path to the prompt audio file") return parser.parse_args() def run_tts(args): """Perform TTS inference and save the generated audio.""" logging.info(f"Using model from: {args.model_dir}") logging.info(f"Saving audio to: {args.save_dir}") # Ensure the save directory exists os.makedirs(args.save_dir, exist_ok=True) # Convert device argument to torch.device device = torch.device(f"cuda:{args.device}") # Initialize the model model = SparkTTS(args.model_dir, device) # Generate unique filename using timestamp timestamp = datetime.now().strftime("%Y%m%d%H%M%S") save_path = os.path.join(args.save_dir, f"{timestamp}.wav") logging.info("Starting inference...") # Perform inference and save the output audio with torch.no_grad(): wav = model.inference(args.text, args.prompt_speech_path, prompt_text=args.prompt_text) sf.write(save_path, wav, samplerate=16000) logging.info(f"Audio saved at: {save_path}") if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") args = parse_args() run_tts(args)