# Copyright (c) 2025 SparkAudio # 2025 Xinsheng Wang (w.xinshawn@gmail.com) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import torch import soundfile as sf import logging import argparse import gradio as gr import platform from datetime import datetime from cli.SparkTTS import SparkTTS from sparktts.utils.token_parser import LEVELS_MAP_UI def initialize_model(model_dir="pretrained_models/Spark-TTS-0.5B", device=0): """Load the model once at the beginning.""" logging.info(f"Loading model from: {model_dir}") # Determine appropriate device based on platform and availability if platform.system() == "Darwin": # macOS with MPS support (Apple Silicon) device = torch.device(f"mps:{device}") logging.info(f"Using MPS device: {device}") elif torch.cuda.is_available(): # System with CUDA support device = torch.device(f"cuda:{device}") logging.info(f"Using CUDA device: {device}") else: # Fall back to CPU device = torch.device("cpu") logging.info("GPU acceleration not available, using CPU") model = SparkTTS(model_dir, device) return model def run_tts( text, model, prompt_text=None, prompt_speech=None, gender=None, pitch=None, speed=None, save_dir="example/results", ): """Perform TTS inference and save the generated audio.""" logging.info(f"Saving audio to: {save_dir}") if prompt_text is not None: prompt_text = None if len(prompt_text) <= 1 else prompt_text # Ensure the save directory exists os.makedirs(save_dir, exist_ok=True) # Generate unique filename using timestamp timestamp = datetime.now().strftime("%Y%m%d%H%M%S") save_path = os.path.join(save_dir, f"{timestamp}.wav") logging.info("Starting inference...") # Perform inference and save the output audio with torch.no_grad(): wav = model.inference( text, prompt_speech, prompt_text, gender, pitch, speed, ) sf.write(save_path, wav, samplerate=16000) logging.info(f"Audio saved at: {save_path}") return save_path def build_ui(model_dir, device=0): # Initialize model model = initialize_model(model_dir, device=device) # Define callback function for voice cloning def voice_clone(text, prompt_text, prompt_wav_upload, prompt_wav_record): """ Gradio callback to clone voice using text and optional prompt speech. - text: The input text to be synthesised. - prompt_text: Additional textual info for the prompt (optional). - prompt_wav_upload/prompt_wav_record: Audio files used as reference. """ prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record prompt_text_clean = None if len(prompt_text) < 2 else prompt_text audio_output_path = run_tts( text, model, prompt_text=prompt_text_clean, prompt_speech=prompt_speech ) return audio_output_path # Define callback function for creating new voices def voice_creation(text, gender, pitch, speed): """ Gradio callback to create a synthetic voice with adjustable parameters. - text: The input text for synthesis. - gender: 'male' or 'female'. - pitch/speed: Ranges mapped by LEVELS_MAP_UI. """ pitch_val = LEVELS_MAP_UI[int(pitch)] speed_val = LEVELS_MAP_UI[int(speed)] audio_output_path = run_tts( text, model, gender=gender, pitch=pitch_val, speed=speed_val ) return audio_output_path with gr.Blocks() as demo: # Use HTML for centered title gr.HTML('

Spark-TTS by SparkAudio

') with gr.Tabs(): # Voice Clone Tab with gr.TabItem("Voice Clone"): gr.Markdown( "### Upload reference audio or recording （上传参考音频或者录音）" ) with gr.Row(): prompt_wav_upload = gr.Audio( sources="upload", type="filepath", label="Choose the prompt audio file, ensuring the sampling rate is no lower than 16kHz.", ) prompt_wav_record = gr.Audio( sources="microphone", type="filepath", label="Record the prompt audio file.", ) with gr.Row(): text_input = gr.Textbox( label="Text", lines=3, placeholder="Enter text here" ) prompt_text_input = gr.Textbox( label="Text of prompt speech (Optional; recommended for cloning in the same language.)", lines=3, placeholder="Enter text of the prompt speech.", ) audio_output = gr.Audio( label="Generated Audio", autoplay=True, streaming=True ) generate_buttom_clone = gr.Button("Generate") generate_buttom_clone.click( voice_clone, inputs=[ text_input, prompt_text_input, prompt_wav_upload, prompt_wav_record, ], outputs=[audio_output], ) # Voice Creation Tab with gr.TabItem("Voice Creation"): gr.Markdown( "### Create your own voice based on the following parameters" ) with gr.Row(): with gr.Column(): gender = gr.Radio( choices=["male", "female"], value="male", label="Gender" ) pitch = gr.Slider( minimum=1, maximum=5, step=1, value=3, label="Pitch" ) speed = gr.Slider( minimum=1, maximum=5, step=1, value=3, label="Speed" ) with gr.Column(): text_input_creation = gr.Textbox( label="Input Text", lines=3, placeholder="Enter text here", value="You can generate a customized voice by adjusting parameters such as pitch and speed.", ) create_button = gr.Button("Create Voice") audio_output = gr.Audio( label="Generated Audio", autoplay=True, streaming=True ) create_button.click( voice_creation, inputs=[text_input_creation, gender, pitch, speed], outputs=[audio_output], ) return demo def parse_arguments(): """ Parse command-line arguments such as model directory and device ID. """ parser = argparse.ArgumentParser(description="Spark TTS Gradio server.") parser.add_argument( "--model_dir", type=str, default="pretrained_models/Spark-TTS-0.5B", help="Path to the model directory." ) parser.add_argument( "--device", type=int, default=0, help="ID of the GPU device to use (e.g., 0 for cuda:0)." ) parser.add_argument( "--server_name", type=str, default="0.0.0.0", help="Server host/IP for Gradio app." ) parser.add_argument( "--server_port", type=int, default=7860, help="Server port for Gradio app." ) return parser.parse_args() if __name__ == "__main__": # Parse command-line arguments args = parse_arguments() # Build the Gradio demo by specifying the model directory and GPU device demo = build_ui( model_dir=args.model_dir, device=args.device ) # Launch Gradio with the specified server name and port demo.launch( server_name=args.server_name, server_port=args.server_port, share=True )