app.py

print("Starting up. Please be patient...")

import os
import glob
import json
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import yt_dlp
import ffmpeg
import subprocess
import sys
import io
import wave
from datetime import datetime
from fairseq import checkpoint_utils
from lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)
from vc_infer_pipeline import VC
from config import Config
from edgetts_db import tts_order_voice

config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces"
#limitation=True
language_dict = tts_order_voice

authors = ["dacoolkid44", "Hijack", "Maki Ligon", "megaaziib", "KitLemonfoot", "yeey5", "Sui", "MahdeenSky"]
authorskip = ["dacoolkid44 & Hijack", "dacoolkid44 & Hijack & Maki Ligon", "Kit Lemonfoot / NSHFB"]

audio_mode = []
f0method_mode = []
if limitation is True:
    f0method_info = "PM is better for testing, RMVPE is better for finalized generations. (Default: RMVPE)"
    audio_mode = ["Edge-TTS", "Upload audio", "Record Audio"]
    f0method_mode = ["pm", "rmvpe"]
else:
    f0method_info = "PM is fast but low quality, crepe and harvest are slow but good quality, RMVPE is the best of both worlds. (Default: RMVPE)"
    audio_mode = ["Edge-TTS", "Youtube", "Upload audio", "Record Audio"]
    f0method_mode = ["pm", "crepe", "harvest", "rmvpe"]

#if os.path.isfile("rmvpe.pt"):
#    f0method_mode.append("rmvpe")


#Eagerload VCs
print("Preloading VCs...")
vcArr=[]
vcArr.append(VC(32000, config))
vcArr.append(VC(40000, config))
vcArr.append(VC(48000, config))

def infer(name, path, index, vc_audio_mode, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, record_button):
    try:
        #Setup audio
        audio=None
        if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
            audio, sr = librosa.load(vc_input, sr=16000, mono=True)
            tts_text = "YouTube Audio"
        elif vc_audio_mode == "Upload audio":
            if vc_upload is None:
                return "Please upload an audio file.", None
            sampling_rate, audio = vc_upload
            duration = audio.shape[0] / sampling_rate
            if duration > 60 and limitation:
                return "Too long! Please upload an audio file that is less than 1 minute.", None
            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
            if len(audio.shape) > 1:
                audio = librosa.to_mono(audio.transpose(1, 0))
            if sampling_rate != 16000:
                audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
            tts_text = "Uploaded Audio"
        elif vc_audio_mode == "Edge-TTS":
            if len(tts_text) > 250 and limitation:
                return "Text is too long.", None
            if tts_text is None or tts_voice is None or tts_text=="":
                return "You need to enter text and select a voice.", None
            voice = language_dict[tts_voice]
            try:
                asyncio.run(edge_tts.Communicate(tts_text, voice).save("tts.mp3"))
            except:
                print("Failed to get E-TTS handle. A restart may be needed soon.")
                return "ERROR: Failed to communicate with Edge-TTS. The Edge-TTS service may be down or cannot communicate. Please try another method or try again later.", None
            try:
                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
            except:
                return "ERROR: Invalid characters for the chosen TTS speaker. (Change your TTS speaker to one that supports your language!)", None
            duration = audio.shape[0] / sr
            if duration > 30 and limitation:
                return "Your text generated an audio that was too long.", None
            vc_input = "tts.mp3"
        elif vc_audio_mode == "Record Audio":
            if record_button is None:
                return "Please record some audio.", None
            sampling_rate, audio = record_button
            duration = audio.shape[0] / sampling_rate
            if duration > 60 and limitation:
                return "Too long! Please record an audio file that is less than 1 minute.", None
            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
            if len(audio.shape) > 1:
                audio = librosa.to_mono(audio.transpose(1, 0))
            if sampling_rate != 16000:
                audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
            tts_text = "Recorded Audio"

        if audio is None:
            if vc_audio_mode == "Edge-TTS":
                print("Failed to get E-TTS handle. A restart may be needed soon.")
                return "ERROR: Failed to obtain a correct response from Edge-TTS. The Edge-TTS service may be down or unable to communicate. Please try another method or try again later.", None
            return "ERROR: Unknown audio error. Please try again.", None
            
        times = [0, 0, 0]
        f0_up_key = int(f0_up_key)

        #Setup model
        cpt = torch.load(f"{path}", map_location="cpu")
        tgt_sr = cpt["config"][-1]
        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
        if_f0 = cpt.get("f0", 1)
        version = cpt.get("version", "v1")
        if version == "v1":
            if if_f0 == 1:
                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
            else:
                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            model_version = "V1"
        elif version == "v2":
            if if_f0 == 1:
                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
            else:
                net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            model_version = "V2"
        del net_g.enc_q
        print(net_g.load_state_dict(cpt["weight"], strict=False))
        net_g.eval().to(config.device)
        if config.is_half:
            net_g = net_g.half()
        else:
            net_g = net_g.float()
        vcIdx = int((tgt_sr/8000)-4)

        #Gen audio
        audio_opt = vcArr[vcIdx].pipeline(
            hubert_model,
            net_g,
            0,
            audio,
            vc_input,
            times,
            f0_up_key,
            f0_method,
            index,
            # file_big_npy,
            index_rate,
            if_f0,
            filter_radius,
            tgt_sr,
            resample_sr,
            rms_mix_rate,
            version,
            protect,
            f0_file=None,
        )
        info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
        print(f"Successful inference with model {name} | {tts_text} | {info}")
        del net_g, cpt
        return info, (tgt_sr, audio_opt)
    except:
        info = traceback.format_exc()
        print(info)
        return info, (None, None)

def load_model():
    categories = []
    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
        folder_info = json.load(f)
    for category_name, category_info in folder_info.items():
        if not category_info['enable']:
            continue
        category_title = category_info['title']
        category_folder = category_info['folder_path']
        models = []
        print(f"Creating category {category_title}...")
        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
            models_info = json.load(f)
        for character_name, info in models_info.items():
            if not info['enable']:
                continue
            model_title = info['title']
            model_name = info['model_path']
            model_author = info.get("author", None)
            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
            if info['feature_retrieval_library'] == "None":
                model_index = None
            if not (model_author in authors or model_author in authorskip):
                authors.append(model_author)
            model_path =  f"weights/{category_folder}/{character_name}/{model_name}"
            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
            model_version = cpt.get("version", "v1")
            print(f"Indexed model {model_title} by {model_author} ({model_version})")
            models.append((character_name, model_title, model_author, model_cover, model_version, model_path, model_index))
            del cpt
        categories.append([category_title, category_folder, models])
    return categories

def cut_vocal_and_inst(url, audio_provider, split_model):
    if url != "":
        if not os.path.exists("dl_audio"):
            os.mkdir("dl_audio")
        if audio_provider == "Youtube":
            ydl_opts = {
            'format': 'bestaudio/best',
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            "outtmpl": 'dl_audio/youtube_audio',
            }
            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                ydl.download([url])
            audio_path = "dl_audio/youtube_audio.wav"
        else:
            # Spotify doesnt work.
            # Need to find other solution soon.
            ''' 
            command = f"spotdl download {url} --output dl_audio/.wav"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            audio_path = "dl_audio/spotify_audio.wav"
            '''
        if split_model == "htdemucs":
            command = f"demucs --two-stems=vocals {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
        else:
            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
            print(result.stdout.decode())
            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
    else:
        raise gr.Error("URL Required!")
        return None, None, None, None

def combine_vocal_and_inst(audio_data, audio_volume, split_model):
    if not os.path.exists("output/result"):
        os.mkdir("output/result")
    vocal_path = "output/result/output.wav"
    output_path = "output/result/combine.mp3"
    if split_model == "htdemucs":
        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
    else:
        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
    with wave.open(vocal_path, "w") as wave_file:
        wave_file.setnchannels(1) 
        wave_file.setsampwidth(2)
        wave_file.setframerate(audio_data[0])
        wave_file.writeframes(audio_data[1].tobytes())
    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
    print(result.stdout.decode())
    return output_path

def load_hubert():
    global hubert_model
    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
        ["hubert_base.pt"],
        suffix="",
    )
    hubert_model = models[0]
    hubert_model = hubert_model.to(config.device)
    if config.is_half:
        hubert_model = hubert_model.half()
    else:
        hubert_model = hubert_model.float()
    hubert_model.eval()

def change_audio_mode(vc_audio_mode):
    if vc_audio_mode == "Input path":
        return (
            # Input & Upload
            gr.Textbox.update(visible=True),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            # EdgeTTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            # Record Own
            gr.Audio.update(visible=False)
        )
    elif vc_audio_mode == "Upload audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            # EdgeTTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            # Record Own
            gr.Audio.update(visible=False)
        )
    elif vc_audio_mode == "Youtube":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=True),
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True),
            gr.Button.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            gr.Audio.update(visible=True),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            # Record Own
            gr.Audio.update(visible=False)
        )
    elif vc_audio_mode == "Edge-TTS":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            # TTS
            gr.Textbox.update(visible=True),
            gr.Dropdown.update(visible=True),
            # Record Own
            gr.Audio.update(visible=False)
        )
    elif vc_audio_mode == "Record Audio":
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Audio.update(visible=False),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            # Record Own
            gr.Audio.update(visible=True)
        )
    else:
        return (
            # Input & Upload
            gr.Textbox.update(visible=False),
            gr.Audio.update(visible=True),
            # Youtube
            gr.Dropdown.update(visible=False),
            gr.Textbox.update(visible=False),
            gr.Dropdown.update(visible=False),
            gr.Button.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            gr.Audio.update(visible=False),
            # TTS
            gr.Textbox.update(visible=False, interactive=True),
            gr.Dropdown.update(visible=False, interactive=True),
            # Record Own
            gr.Audio.update(visible=False)
        )

if __name__ == '__main__':
    load_hubert()
    categories = load_model()
    voices = list(language_dict.keys())

    #Gradio preloading
    vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Edge-TTS")
    # Input and Upload
    vc_input = gr.Textbox(label="Input audio path", visible=False)
    vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True)
    # Youtube
    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
    vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
    vc_split = gr.Button("Split Audio", variant="primary", visible=False)
    vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
    vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
    vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
    # TTS
    tts_text = gr.Textbox(visible=True, label="TTS text", info="Text to speech input (There is a limit of 250 characters)", interactive=True)
    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=True, allow_custom_value=False, value="English-Ana (Female)", interactive=True)
    # Record Own
    record_button = gr.Audio(source="microphone", label="Record your own audio", visible=False, interactive=True)
    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
    f0method0 = gr.Radio(
        label="Pitch extraction algorithm",
        info=f0method_info,
        choices=f0method_mode,
        value="pm",
        interactive=True
    )
    index_rate1 = gr.Slider(
        minimum=0,
        maximum=1,
        label="Retrieval feature ratio",
        info="Accent control. Too high will usually sound too robotic. (Default: 0.4)",
        value=0.4,
        interactive=True,
    )
    filter_radius0 = gr.Slider(
        minimum=0,
        maximum=7,
        label="Apply Median Filtering",
        info="The value represents the filter radius and can reduce breathiness.",
        value=1,
        step=1,
        interactive=True,
    )
    resample_sr0 = gr.Slider(
        minimum=0,
        maximum=48000,
        label="Resample the output audio",
        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling.",
        value=0,
        step=1,
        interactive=True,
    )
    rms_mix_rate0 = gr.Slider(
        minimum=0,
        maximum=1,
        label="Volume Envelope",
        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
        value=1,
        interactive=True,
    )
    protect0 = gr.Slider(
        minimum=0,
        maximum=0.5,
        label="Voice Protection",
        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
        value=0.23,
        step=0.01,
        interactive=True,
    )

    with gr.Blocks(theme=gr.themes.Base()) as app:
        gr.Markdown(
            "# <center> VTuber RVC Models\n"
            "### <center> Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds"
            "<center> Original space by megaaziib & zomehwh\n"
            "### <center> Please credit the original model authors if you use this Space."
            "<center>Do no evil.\n\n"
            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Til3SY7-X0x3Wss3YXlgfq8go39DzWHk)\n\n"
        )
        for (folder_title, folder, models) in categories:
            with gr.TabItem(folder_title):
                with gr.Tabs():
                    if not models:
                        gr.Markdown("# <center> No Model Loaded.")
                        gr.Markdown("## <center> Please add model or fix your model path.")
                        continue
                    for (name, title, author, cover, model_version, model_path, model_index) in models:
                        with gr.TabItem(name):
                            with gr.Row():
                                gr.Markdown(
                                    '<div align="center">'
                                    f'<div>{title}</div>\n'+
                                    f'<div>RVC {model_version} Model</div>\n'+
                                    (f'<div>Model author: {author}</div>' if author else "")+
                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
                                    '</div>'
                                )
                                with gr.Column():
                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
                                    #This is a fucking stupid solution but Gradio refuses to pass in values unless I do this.
                                    vc_name = gr.Textbox(value=title, visible=False, interactive=False)
                                    vc_mp = gr.Textbox(value=model_path, visible=False, interactive=False)
                                    vc_mi = gr.Textbox(value=model_index, visible=False, interactive=False)
                                    vc_convert = gr.Button("Convert", variant="primary")

                                    vc_convert.click(
                                        fn=infer,
                                        inputs=[
                                            vc_name,
                                            vc_mp,
                                            vc_mi,
                                            vc_audio_mode,
                                            vc_input, 
                                            vc_upload,
                                            tts_text,
                                            tts_voice,
                                            vc_transform0,
                                            f0method0,
                                            index_rate1,
                                            filter_radius0,
                                            resample_sr0,
                                            rms_mix_rate0,
                                            protect0,
                                            record_button
                                        ],
                                        outputs=[vc_log, vc_output]
                                    )

        with gr.Row():
            with gr.Column():
                vc_audio_mode.render()
                vc_input.render()
                vc_upload.render()
                # Youtube
                vc_download_audio.render()
                vc_link.render()
                vc_split_model.render()
                vc_split.render()
                vc_vocal_preview.render()
                vc_inst_preview.render()
                vc_audio_preview.render()
                # TTS
                tts_text.render()
                tts_voice.render()
                # Record Own
                record_button.render()
            with gr.Column():
                vc_transform0.render()
                f0method0.render()
                index_rate1.render()
                with gr.Accordion("Advanced Options", open=False):
                    filter_radius0.render()
                    resample_sr0.render()
                    rms_mix_rate0.render()
                    protect0.render() 

        vc_split.click(
            fn=cut_vocal_and_inst, 
            inputs=[vc_link, vc_download_audio, vc_split_model], 
            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
        )
        vc_audio_mode.change(
            fn=change_audio_mode,
            inputs=[vc_audio_mode],
            outputs=[
                vc_input, 
                vc_upload,
                vc_download_audio,
                vc_link,
                vc_split_model,
                vc_split,
                vc_vocal_preview,
                vc_inst_preview,
                vc_audio_preview,
                
                tts_text,
                tts_voice,
                record_button
            ]
        )
        authStr=", ".join(authors)
        gr.Markdown(
            "## <center>Credit to:\n"
            "#### <center>Original devs:\n"
            "<center>the RVC Project, lj1995, zomehwh, sysf\n\n"
            "#### <center>Model creators:\n"
            f"<center>{authStr}\n"
        )
if limitation is True:
    app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
else:
    app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=True)