app.py

import nest_asyncio
nest_asyncio.apply()

import asyncio
import datetime
import logging
import os
import time
import traceback

import edge_tts
import gradio as gr
import librosa
import torch
from fairseq import checkpoint_utils

from config import Config
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from rmvpe import RMVPE
from vc_infer_pipeline import VC

# Set logging levels
logging.getLogger("fairseq").setLevel(logging.WARNING)
logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

limitation = os.getenv("SYSTEM") == "spaces"

config = Config()

# Edge TTS
edge_output_filename = "edge_output.mp3"
tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
tts_voices = ["mn-MN-BataaNeural", "mn-MN-YesuiNeural"]  # Specific voices

# RVC models
model_root = "weights"
models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
models.sort()


def model_data(model_name):
    # ... (rest of your model_data function)
    pass  # Keep the existing code here


def load_hubert():
    # ... (rest of your load_hubert function)
    pass  # Keep the existing code here


def tts(
    model_name,
    speed,
    tts_text,
    tts_voice,
    f0_up_key,
    f0_method,
    index_rate,
    protect,
    filter_radius=3,
    resample_sr=0,
    rms_mix_rate=0.25,
):
    # ... (rest of your tts function)
    pass  # Keep the existing code here


print("Loading hubert model...")
hubert_model = load_hubert()
print("Hubert model loaded.")

print("Loading rmvpe model...")
rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
print("rmvpe model loaded.")

initial_md = """
# RVC text-to-speech demo
This is a text-to-speech demo of RVC moe models of [rvc_okiba](https://huggingface.co/litagin/rvc_okiba) using [edge-tts](https://github.com/rany2/edge-tts).
Input text ➡[(edge-tts)](https://github.com/rany2/edge-tts)➡ Speech mp3 file ➡[(RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)➡ Final output
This runs on the 🤗 server's cpu, so it may be slow.
Although the models are trained on Japanese voices and intended for Japanese text, they can also be used with other languages with the corresponding edge-tts speaker (but possibly with a Japanese accent).
Input characters are limited to 280 characters, and the speech audio is limited to 20 seconds in this 🤗 space.
[Visit this GitHub repo](https://github.com/litagin02/rvc-tts-webui) for running locally with your models and GPU!
"""

app = gr.Blocks()
with app:
    with gr.Row():
        with gr.Column():
            model_name = gr.Dropdown(
                label="Model (all models except man-_ are girl models)",
                choices=models,
                value=models[0],
            )
            f0_key_up = gr.Number(
                label="Tune (+12 = 1 octave up from edge-tts, the best value depends on the models and speakers)",
                value=0,
            )
        with gr.Column():
            f0_method = gr.Radio(
                label="Pitch extraction method (pm: very fast, low quality, rmvpe: a little slow, high quality)",
                choices=["pm", "rmvpe"],
                value="rmvpe",
                interactive=True,
            )
            index_rate = gr.Slider(
                minimum=0,
                maximum=1,
                label="Slang rate",
                value=0.75,
                interactive=True,
            )
            protect0 = gr.Slider(
                minimum=0,
                maximum=0.5,
                label="Protect",
                value=0.33,
                step=0.01,
                interactive=True,
            )
    with gr.Row():
        with gr.Column():
            tts_voice = gr.Dropdown(
                label="Edge-tts speaker (format: language-Country-Name-Gender)",
                choices=tts_voices,
                allow_custom_value=False,
                value="mn-MN-BataaNeural",
            )
            speed = gr.Slider(
                minimum=-100,
                maximum=100,
                label="Speech speed (%)",
                value=0,
                step=10,
                interactive=True,
            )
            tts_text = gr.Textbox(label="Input Text", value="Текстыг оруулна уу.")
        with gr.Column():
            but0 = gr.Button("Convert", variant="primary")
            info_text = gr.Textbox(label="Output info")
        with gr.Column():
            edge_tts_output = gr.Audio(label="Edge Voice", type="filepath")
            tts_output = gr.Audio(label="Result")
        but0.click(
            tts,
            [
                model_name,
                speed,
                tts_text,
                tts_voice,
                f0_key_up,
                f0_method,
                index_rate,
                protect0,
            ],
            [info_text, edge_tts_output, tts_output],
        )

# Modify the launch line to enable more concurrent jobs
app.queue(concurrency_count=20, max_size=100).launch(server_name="0.0.0.0", share=True, inbrowser=True)