Retrieval-based-Voice-Conversion-WebUI/modules/spark_ui/main.py

import os
import torch
import soundfile as sf
import logging
import argparse
import gradio as gr
import platform

from datetime import datetime
from spark.cli.SparkTTS import SparkTTS
from spark.sparktts.utils.token_parser import LEVELS_MAP_UI


def initialize_model(model_dir, device):
    """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="spark/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_spark_ui():
    model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
    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('<h1 style="text-align: center;">Spark-TTS by SparkAudio</h1>')
        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", 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", streaming=True
                )
                create_button.click(
                    voice_creation,
                    inputs=[text_input_creation, gender, pitch, speed],
                    outputs=[audio_output],
                )

    return demo

if __name__ == "__main__":
    build_spark_ui()