app.py

import os
import gradio as gr
import spaces
from infer_rvc_python import BaseLoader
import random
import logging
import time
import soundfile as sf
from infer_rvc_python.main import download_manager
import zipfile
import edge_tts
import asyncio
import librosa
import traceback
import soundfile as sf
from pedalboard import Pedalboard, Reverb, Compressor, HighpassFilter
from pedalboard.io import AudioFile
from pydub import AudioSegment
import noisereduce as nr
import numpy as np
import urllib.request
import shutil
import threading

logging.getLogger("infer_rvc_python").setLevel(logging.ERROR)

converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)

theme = "aliabid94/new-theme"

PITCH_ALGO_OPT = [
    "pm",
    "harvest",
    "crepe",
    "rmvpe",
    "rmvpe+",
]


def find_files(directory):
    file_paths = []
    for filename in os.listdir(directory):
        # Check if the file has the desired extension
        if filename.endswith('.pth') or filename.endswith('.zip') or filename.endswith('.index'):
            # If yes, add the file path to the list
            file_paths.append(os.path.join(directory, filename))

    return file_paths


def unzip_in_folder(my_zip, my_dir):
    with zipfile.ZipFile(my_zip) as zip:
        for zip_info in zip.infolist():
            if zip_info.is_dir():
                continue
            zip_info.filename = os.path.basename(zip_info.filename)
            zip.extract(zip_info, my_dir)


def find_my_model(a_, b_):

    if a_ is None or a_.endswith(".pth"):
        return a_, b_

    txt_files = []
    for base_file in [a_, b_]:
        if base_file is not None and base_file.endswith(".txt"):
            txt_files.append(base_file)

    directory = os.path.dirname(a_)

    for txt in txt_files:
        with open(txt, 'r') as file:
            first_line = file.readline()

        download_manager(
            url=first_line.strip(),
            path=directory,
            extension="",
        )

    for f in find_files(directory):
        if f.endswith(".zip"):
            unzip_in_folder(f, directory)

    model = None
    index = None
    end_files = find_files(directory)

    for ff in end_files:
        if ff.endswith(".pth"):
            model = os.path.join(directory, ff)
            gr.Info(f"Model found: {ff}")
        if ff.endswith(".index"):
            index = os.path.join(directory, ff)
            gr.Info(f"Index found: {ff}")

    if not model:
        gr.Error(f"Model not found in: {end_files}")

    if not index:
        gr.Warning("Index not found")

    return model, index


def get_file_size(url):

    if "huggingface" not in url:
        raise ValueError("Only downloads from Hugging Face are allowed")

    try:
        with urllib.request.urlopen(url) as response:
            info = response.info()
            content_length = info.get("Content-Length")

        file_size = int(content_length)
        if file_size > 500000000:
            raise ValueError("The file is too large. You can only download files up to 500 MB in size.")

    except Exception as e:
        raise e


def clear_files(directory):
    time.sleep(15)
    print(f"Clearing files: {directory}.")
    shutil.rmtree(directory)


def get_my_model(url_data):

    if not url_data:
        return None, None

    if "," in url_data:
        a_, b_ = url_data.split()
        a_, b_ = a_.strip().replace("/blob/", "/resolve/"), b_.strip().replace("/blob/", "/resolve/")
    else:
        a_, b_ = url_data.strip().replace("/blob/", "/resolve/"), None

    out_dir = "downloads"
    folder_download = str(random.randint(1000, 9999))
    directory = os.path.join(out_dir, folder_download)
    os.makedirs(directory, exist_ok=True)

    try:
        get_file_size(a_)
        if b_:
            get_file_size(b_)

        valid_url = [a_] if not b_ else [a_, b_]
        for link in valid_url:
            download_manager(
                url=link,
                path=directory,
                extension="",
            )

        for f in find_files(directory):
            if f.endswith(".zip"):
                unzip_in_folder(f, directory)

        model = None
        index = None
        end_files = find_files(directory)

        for ff in end_files:
            if ff.endswith(".pth"):
                model = ff
                gr.Info(f"Model found: {ff}")
            if ff.endswith(".index"):
                index = ff
                gr.Info(f"Index found: {ff}")

        if not model:
            raise ValueError(f"Model not found in: {end_files}")

        if not index:
            gr.Warning("Index not found")
        else:
            index = os.path.abspath(index)

        return os.path.abspath(model), index

    except Exception as e:
        raise e
    finally:
        # time.sleep(10)
        # shutil.rmtree(directory)
        t = threading.Thread(target=clear_files, args=(directory,))
        t.start()


def add_audio_effects(audio_list):
    print("Audio effects")

    result = []
    for audio_path in audio_list:
        try:
            output_path = f'{os.path.splitext(audio_path)[0]}_effects.wav'

            # Initialize audio effects plugins
            board = Pedalboard(
                [
                    HighpassFilter(),
                    Compressor(ratio=4, threshold_db=-15),
                    Reverb(room_size=0.10, dry_level=0.8, wet_level=0.2, damping=0.7)
                 ]
            )

            with AudioFile(audio_path) as f:
                with AudioFile(output_path, 'w', f.samplerate, f.num_channels) as o:
                    # Read one second of audio at a time, until the file is empty:
                    while f.tell() < f.frames:
                        chunk = f.read(int(f.samplerate))
                        effected = board(chunk, f.samplerate, reset=False)
                        o.write(effected)
            result.append(output_path)
        except Exception as e:
            traceback.print_exc()
            print(f"Error noisereduce: {str(e)}")
            result.append(audio_path)

    return result


def apply_noisereduce(audio_list):
    # https://github.com/sa-if/Audio-Denoiser
    print("Noice reduce")

    result = []
    for audio_path in audio_list:
        out_path = f'{os.path.splitext(audio_path)[0]}_noisereduce.wav'

        try:
            # Load audio file
            audio = AudioSegment.from_file(audio_path)

            # Convert audio to numpy array
            samples = np.array(audio.get_array_of_samples())

            # Reduce noise
            reduced_noise = nr.reduce_noise(samples, sr=audio.frame_rate, prop_decrease=0.6)

            # Convert reduced noise signal back to audio
            reduced_audio = AudioSegment(
                reduced_noise.tobytes(), 
                frame_rate=audio.frame_rate, 
                sample_width=audio.sample_width,
                channels=audio.channels
            )

            # Save reduced audio to file
            reduced_audio.export(out_path, format="wav")
            result.append(out_path)

        except Exception as e:
            traceback.print_exc()
            print(f"Error noisereduce: {str(e)}")
            result.append(audio_path)

    return result


@spaces.GPU()
def convert_now(audio_files, random_tag, converter):
    return converter(
        audio_files,
        random_tag,
        overwrite=False,
        parallel_workers=8
    )

@spaces.GPU()
def run(
    audio_files,
    file_m,
    pitch_alg,
    pitch_lvl,
    file_index,
    index_inf,
    r_m_f,
    e_r,
    c_b_p,
    active_noise_reduce,
    audio_effects,
):
    if not audio_files:
        raise ValueError("The audio pls")

    if isinstance(audio_files, str):
        audio_files = [audio_files]

    try:
        duration_base = librosa.get_duration(filename=audio_files[0])
        print("Duration:", duration_base)
    except Exception as e:
        print(e)

    if file_m is not None and file_m.endswith(".txt"):
        file_m, file_index = find_my_model(file_m, file_index)
        print(file_m, file_index)

    random_tag = "USER_"+str(random.randint(10000000, 99999999))

    converter.apply_conf(
        tag=random_tag,
        file_model=file_m,
        pitch_algo=pitch_alg,
        pitch_lvl=pitch_lvl,
        file_index=file_index,
        index_influence=index_inf,
        respiration_median_filtering=r_m_f,
        envelope_ratio=e_r,
        consonant_breath_protection=c_b_p,
        resample_sr=44100 if audio_files[0].endswith('.mp3') else 0, 
    )
    time.sleep(0.1)

    result = convert_now(audio_files, random_tag, converter)

    if active_noise_reduce:
        result = apply_noisereduce(result)

    if audio_effects:
        result = add_audio_effects(result)

    return result


def audio_conf():
    return gr.File(
        label="Audio files",
        file_count="multiple",
        type="filepath",
        container=True,
    )


def model_conf():
    return gr.File(
        label="Model file",
        type="filepath",
        height=130,
    )


def pitch_algo_conf():
    return gr.Dropdown(
        PITCH_ALGO_OPT,
        value=PITCH_ALGO_OPT[4],
        label="Pitch algorithm",
        visible=True,
        interactive=True,
    )


def pitch_lvl_conf():
    return gr.Slider(
        label="Pitch level",
        minimum=-24,
        maximum=24,
        step=1,
        value=0,
        visible=True,
        interactive=True,
    )


def index_conf():
    return gr.File(
        label="Index file",
        type="filepath",
        height=130,
    )


def index_inf_conf():
    return gr.Slider(
        minimum=0,
        maximum=1,
        label="Index influence",
        value=0.75,
    )


def respiration_filter_conf():
    return gr.Slider(
        minimum=0,
        maximum=7,
        label="Respiration median filtering",
        value=3,
        step=1,
        interactive=True,
    )


def envelope_ratio_conf():
    return gr.Slider(
        minimum=0,
        maximum=1,
        label="Envelope ratio",
        value=0.25,
        interactive=True,
    )


def consonant_protec_conf():
    return gr.Slider(
        minimum=0,
        maximum=0.5,
        label="Consonant breath protection",
        value=0.5,
        interactive=True,
    )


def button_conf():
    return gr.Button(
        "Inference",
        variant="primary",
    )


def output_conf():
    return gr.File(
        label="Result",
        file_count="multiple",
        interactive=False,
    )


def active_tts_conf():
    return gr.Checkbox(
        False,
        label="TTS",
        # info="",
        container=False,
    )


def tts_voice_conf():
    return gr.Dropdown(
        label="tts voice",
        choices=voices,
        visible=False,
        value="en-US-EmmaMultilingualNeural-Female",
    )


def tts_text_conf():
    return gr.Textbox(
        value="",
        placeholder="Write the text here...",
        label="Text",
        visible=False,
        lines=3,
    )


def tts_button_conf():
    return gr.Button(
        "Process TTS",
        variant="secondary",
        visible=False,
    )


def tts_play_conf():
    return gr.Checkbox(
        False,
        label="Play",
        # info="",
        container=False,
        visible=False,
    )


def sound_gui():
    return gr.Audio(
        value=None,
        type="filepath",
        # format="mp3",
        autoplay=True,
        visible=False,
    )


def denoise_conf():
    return gr.Checkbox(
        False,
        label="Denoise",
        # info="",
        container=False,
        visible=True,
    )


def effects_conf():
    return gr.Checkbox(
        False,
        label="Reverb",
        # info="",
        container=False,
        visible=True,
    )

@spaces.GPU
def infer_tts_audio(tts_voice, tts_text, play_tts):
    out_dir = "output"
    folder_tts = "USER_"+str(random.randint(10000, 99999))

    os.makedirs(out_dir, exist_ok=True)
    os.makedirs(os.path.join(out_dir, folder_tts), exist_ok=True)
    out_path = os.path.join(out_dir, folder_tts, "tts.mp3")

    asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(out_path))
    if play_tts:
        return [out_path], out_path
    return [out_path], None
   

def show_components_tts(value_active):
    return gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    )


def down_active_conf():
    return gr.Checkbox(
        False,
        label="URL-to-Model",
        # info="",
        container=False,
    )


def down_url_conf():
    return gr.Textbox(
        value="",
        placeholder="Write the url here...",
        label="Enter URL",
        visible=False,
        lines=1,
    )


def down_button_conf():
    return gr.Button(
        "Process",
        variant="secondary",
        visible=False,
    )


def show_components_down(value_active):
    return gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    ), gr.update(
        visible=value_active
    )


def get_gui(theme):
    with gr.Blocks(theme=theme, delete_cache=(3200, 3200)) as app:

        active_tts = active_tts_conf()
        with gr.Row():
            with gr.Column(scale=1):
                tts_text = tts_text_conf()
            with gr.Column(scale=2):
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
                            tts_voice = tts_voice_conf()
                            tts_active_play = tts_play_conf()

                tts_button = tts_button_conf()
                tts_play = sound_gui()

        active_tts.change(
            fn=show_components_tts,
            inputs=[active_tts],
            outputs=[tts_voice, tts_text, tts_button, tts_active_play],
        )

        aud = audio_conf()
        # gr.HTML("<hr>")

        tts_button.click(
            fn=infer_tts_audio,
            inputs=[tts_voice, tts_text, tts_active_play],
            outputs=[aud, tts_play],
        )

        down_active_gui = down_active_conf()
        down_info = gr.Markdown(
            "Provide a link to a zip file, like this one: `https://huggingface.co/mrmocciai/Models/resolve/main/Genshin%20Impact/ayaka-v2.zip?download=true`, or separate links with a comma for the .pth and .index files, like this: `https://huggingface.co/sail-rvc/ayaka-jp/resolve/main/model.pth?download=true, https://huggingface.co/sail-rvc/ayaka-jp/resolve/main/model.index?download=true`",
            visible=False
        )
        with gr.Row():
            with gr.Column(scale=3):
                down_url_gui = down_url_conf()
            with gr.Column(scale=1):
                down_button_gui = down_button_conf()

        with gr.Column():
            with gr.Row():
                model = model_conf()
                indx = index_conf()

        down_active_gui.change(
            show_components_down,
            [down_active_gui],
            [down_info, down_url_gui, down_button_gui]
        )

        down_button_gui.click(
            get_my_model,
            [down_url_gui],
            [model, indx]
        )

        algo = pitch_algo_conf()
        algo_lvl = pitch_lvl_conf()
        indx_inf = index_inf_conf()
        res_fc = respiration_filter_conf()
        envel_r = envelope_ratio_conf()
        const = consonant_protec_conf()
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    denoise_gui = denoise_conf()
                    effects_gui = effects_conf()
        button_base = button_conf()
        output_base = output_conf()

        button_base.click(
            run,
            inputs=[
                aud,
                model,
                algo,
                algo_lvl,
                indx,
                indx_inf,
                res_fc,
                envel_r,
                const,
                denoise_gui,
                effects_gui,
            ],
            outputs=[output_base],
        )

        gr.Examples(
            examples=[
                [
                    ["./test.ogg"],
                    "./model.pth",
                    "rmvpe+",
                    0,
                    "./model.index",
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],
                [
                    ["./example2/test2.ogg"],
                    "./example2/model_link.txt",
                    "rmvpe+",
                    0,
                    "./example2/index_link.txt",
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],
                [
                    ["./example3/test3.wav"],
                    "./example3/zip_link.txt",
                    "rmvpe+",
                    0,
                    None,
                    0.75,
                    3,
                    0.25,
                    0.50,
                ],

            ],
            fn=run,
            inputs=[
                aud,
                model,
                algo,
                algo_lvl,
                indx,
                indx_inf,
                res_fc,
                envel_r,
                const,
            ],
            outputs=[output_base],
            cache_examples=False,
        )

    return app


if __name__ == "__main__":

    tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
    voices = sorted([f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list])

    app = get_gui(theme)

    app.queue(default_concurrency_limit=40)

    app.launch(
        max_threads=40,
        share=False,
        show_error=True,
        quiet=False,
        debug=False,
        allowed_paths=["./downloads/"],
    )