app.py

from KOKORO.models import build_model
from KOKORO.utils import tts,tts_file_name,podcast
import sys
sys.path.append('.')
import torch
import gc 
print("Loading model...")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using device: {device}')
MODEL = build_model('./KOKORO/kokoro-v0_19.pth', device)
print("Model loaded successfully.")

def tts_maker(text,voice_name="af_bella",speed = 0.8,trim=0,pad_between=0,save_path="temp.wav",remove_silence=False,minimum_silence=50):
    # Sanitize the save_path to remove any newline characters
    save_path = save_path.replace('\n', '').replace('\r', '')
    global MODEL
    audio_path=tts(MODEL,device,text,voice_name,speed=speed,trim=trim,pad_between_segments=pad_between,output_file=save_path,remove_silence=remove_silence,minimum_silence=minimum_silence)
    return audio_path


model_list = ["kokoro-v0_19.pth", "kokoro-v0_19-half.pth"]
current_model = model_list[0]

def update_model(model_name):
    """
    Updates the TTS model only if the specified model is not already loaded.
    """
    global MODEL, current_model
    if current_model == model_name:
        return f"Model already set to {model_name}"  # No need to reload
    model_path = f"./KOKORO/{model_name}"  # Default model path
    if model_name == "kokoro-v0_19-half.pth":
        model_path = f"./KOKORO/fp16/{model_name}"  # Update path for specific model
    # print(f"Loading new model: {model_name}")
    del MODEL  # Cleanup existing model
    gc.collect()
    torch.cuda.empty_cache()  # Ensure GPU memory is cleared
    MODEL = build_model(model_path, device)
    current_model = model_name
    return f"Model updated to {model_name}"


def text_to_speech(text, model_name, voice_name, speed, trim, pad_between_segments, remove_silence, minimum_silence):
    """
    Converts text to speech using the specified parameters and ensures the model is updated only if necessary.
    """
    update_status = update_model(model_name)  # Load the model only if required
    # print(update_status)  # Log model loading status
    if not minimum_silence:
        minimum_silence = 0.05
    keep_silence = int(minimum_silence * 1000)
    save_at = tts_file_name(text)
    audio_path = tts_maker(
        text, 
        voice_name, 
        speed, 
        trim, 
        pad_between_segments, 
        save_at, 
        remove_silence, 
        keep_silence
    )
    return audio_path




import gradio as gr

# voice_list = [
#     'af',  # Default voice is a 50-50 mix of af_bella & af_sarah
#     'af_bella', 'af_sarah', 'am_adam', 'am_michael',
#     'bf_emma', 'bf_isabella', 'bm_george', 'bm_lewis',
# ]



import os

# Get the list of voice names without file extensions
voice_list = [
    os.path.splitext(filename)[0]
    for filename in os.listdir("./KOKORO/voices")
    if filename.endswith('.pt')
]

# Sort the list based on the length of each name
voice_list = sorted(voice_list, key=len)

def toggle_autoplay(autoplay):
    return gr.Audio(interactive=False, label='Output Audio', autoplay=autoplay)

with gr.Blocks() as demo1:
    gr.Markdown("# Batched TTS")
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(
                label='Enter Text',
                lines=3,
                placeholder="Type your text here..."
            )
            with gr.Row():
                voice = gr.Dropdown(
                    voice_list, 
                    value='af', 
                    allow_custom_value=False, 
                    label='Voice', 
                    info='Starred voices are more stable'
                )
            with gr.Row():
                generate_btn = gr.Button('Generate', variant='primary')
            with gr.Accordion('Audio Settings', open=False):
                model_name=gr.Dropdown(model_list,label="Model",value=model_list[0])
                remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS')
                minimum_silence = gr.Number(
                    label="Keep Silence Upto (In seconds)", 
                    value=0.05
                )
                speed = gr.Slider(
                    minimum=0.25, maximum=2, value=1, step=0.1, 
                    label='⚡️Speed', info='Adjust the speaking speed'
                )
                trim = gr.Slider(
                    minimum=0, maximum=1, value=0, step=0.1, 
                    label='🔪 Trim', info='How much to cut from both ends of each segment'
                )   
                pad_between = gr.Slider(
                    minimum=0, maximum=2, value=0, step=0.1, 
                    label='🔇 Pad Between', info='Silent Duration between segments [For Large Text]'
                )
                
        with gr.Column():
            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
            with gr.Accordion('Enable Autoplay', open=False):
                autoplay = gr.Checkbox(value=True, label='Autoplay')
                autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])

    text.submit(
        text_to_speech, 
        inputs=[text, model_name,voice, speed, trim, pad_between, remove_silence, minimum_silence], 
        outputs=[audio]
    )
    generate_btn.click(
        text_to_speech, 
        inputs=[text,model_name, voice, speed, trim, pad_between, remove_silence, minimum_silence], 
        outputs=[audio]
    )

def podcast_maker(text,remove_silence=False,minimum_silence=50,model_name="kokoro-v0_19.pth"):
    global MODEL,device
    update_model(model_name)
    if not minimum_silence:
        minimum_silence = 0.05
    keep_silence = int(minimum_silence * 1000)
    podcast_save_at=podcast(MODEL, device,text,remove_silence=remove_silence, minimum_silence=keep_silence)
    return podcast_save_at
    


dummpy_example="""{af} Hello, I'd like to order a sandwich please.                                                         
{af_sky} What do you mean you're out of bread?                                                                      
{af_bella} I really wanted a sandwich though...                                                              
{af_nicole} You know what, darn you and your little shop!                                                                       
{bm_george} I'll just go back home and cry now.                                                                           
{am_adam} Why me?"""
with gr.Blocks() as demo2:
    gr.Markdown(
        """
    # Multiple Speech-Type Generation
    This section allows you to generate multiple speech types or multiple people's voices. Enter your text in the format shown below, and the system will generate speech using the appropriate type. If unspecified, the model will use "af" voice.
    Format:
    {voice_name} your text here
    """
    )
    with gr.Row():
        gr.Markdown(
            """
            **Example Input:**                                                                      
            {af} Hello, I'd like to order a sandwich please.                                                         
            {af_sky} What do you mean you're out of bread?                                                                      
            {af_bella} I really wanted a sandwich though...                                                              
            {af_nicole} You know what, darn you and your little shop!                                                                       
            {bm_george} I'll just go back home and cry now.                                                                           
            {am_adam} Why me?!                                                                         
            """
        )
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(
                label='Enter Text',
                lines=7,
                placeholder=dummpy_example
            )
            with gr.Row():
                generate_btn = gr.Button('Generate', variant='primary')
            with gr.Accordion('Audio Settings', open=False):
                remove_silence = gr.Checkbox(value=False, label='✂️ Remove Silence From TTS')
                minimum_silence = gr.Number(
                    label="Keep Silence Upto (In seconds)", 
                    value=0.20
                )
        with gr.Column():
            audio = gr.Audio(interactive=False, label='Output Audio', autoplay=True)
            with gr.Accordion('Enable Autoplay', open=False):
                autoplay = gr.Checkbox(value=True, label='Autoplay')
                autoplay.change(toggle_autoplay, inputs=[autoplay], outputs=[audio])

    text.submit(
        podcast_maker, 
        inputs=[text, remove_silence, minimum_silence], 
        outputs=[audio]
    )
    generate_btn.click(
        podcast_maker, 
        inputs=[text, remove_silence, minimum_silence], 
        outputs=[audio]
    )

display_text = "  \n".join(voice_list)

with gr.Blocks() as demo3:
    gr.Markdown(f"# Voice Names \n{display_text}")

import click
@click.command()
@click.option("--debug", is_flag=True, default=False, help="Enable debug mode.")
@click.option("--share", is_flag=True, default=False, help="Enable sharing of the interface.")
def main(debug, share):
    demo = gr.TabbedInterface([demo1, demo2,demo3], ["Batched TTS", "Multiple Speech-Type Generation","Available Voice Names"],title="Kokoro TTS")

    demo.queue().launch(debug=debug, share=share)
    #Run on local network
    # laptop_ip="192.168.0.30"
    # port=8080
    # demo.queue().launch(debug=debug, share=share,server_name=laptop_ip,server_port=port)

if __name__ == "__main__":
    main()    


##For client side
# from gradio_client import Client
# import shutil
# import os
# os.makedirs("temp_audio", exist_ok=True)
# from gradio_client import Client
# client = Client("http://127.0.0.1:7860/")
# result = client.predict(
# 		text="Hello!!",
# 		model_name="kokoro-v0_19.pth",
# 		voice_name="af_bella",
# 		speed=1,
# 		trim=0,
# 		pad_between_segments=0,
# 		remove_silence=False,
# 		minimum_silence=0.05,
# 		api_name="/text_to_speech"
# )

# save_at=f"./temp_audio/{os.path.basename(result)}"
# shutil.move(result, save_at)
# print(f"Saved at {save_at}")