app.py

import os
import re
import random
from scipy.io.wavfile import write
from scipy.io.wavfile import read
import numpy as np
import gradio as gr
import yt_dlp
import subprocess
from pydub import AudioSegment
from audio_separator.separator import Separator
from lib.infer import infer_audio
import edge_tts
import tempfile
import anyio
from pathlib import Path
from lib.language_tts import language_dict
import os
import zipfile
import shutil
import urllib.request
import gdown
import subprocess
from argparse import ArgumentParser
main_dir = Path().resolve()
print(main_dir)

os.chdir(main_dir)
models_dir = "models"
audio_separat_dir = main_dir / "audio_input"



def download_audio(url):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': 'ytdl/%(title)s.%(ext)s',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'wav',
            'preferredquality': '192',
        }],
    }

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        info_dict = ydl.extract_info(url, download=True)
        file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav'
        sample_rate, audio_data = read(file_path)
        audio_array = np.asarray(audio_data, dtype=np.int16)

        return sample_rate, audio_array




# Define a function to handle the entire separation process
def separate_audio(input_audio, model_voc_inst, model_deecho, model_back_voc):
    output_dir = audio_separat_dir
    separator = Separator(output_dir=output_dir)

    # Define output files
    vocals = os.path.join(output_dir, 'Vocals.wav')
    instrumental = os.path.join(output_dir, 'Instrumental.wav')
    vocals_reverb = os.path.join(output_dir, 'Vocals (Reverb).wav')
    vocals_no_reverb = os.path.join(output_dir, 'Vocals (No Reverb).wav')
    lead_vocals = os.path.join(output_dir, 'Lead Vocals.wav')
    backing_vocals = os.path.join(output_dir, 'Backing Vocals.wav')

    # Splitting a track into Vocal and Instrumental
    separator.load_model(model_filename=model_voc_inst)
    voc_inst = separator.separate(input_audio)
    os.rename(os.path.join(output_dir, voc_inst[0]), instrumental)  # Rename to “Instrumental.wav”
    os.rename(os.path.join(output_dir, voc_inst[1]), vocals)        # Rename to “Vocals.wav”

    # Applying DeEcho-DeReverb to Vocals
    separator.load_model(model_filename=model_deecho)
    voc_no_reverb = separator.separate(vocals)
    os.rename(os.path.join(output_dir, voc_no_reverb[0]), vocals_no_reverb)  # Rename to “Vocals (No Reverb).wav”
    os.rename(os.path.join(output_dir, voc_no_reverb[1]), vocals_reverb)     # Rename to “Vocals (Reverb).wav”

    # Separating Back Vocals from Main Vocals
    separator.load_model(model_filename=model_back_voc)
    backing_voc = separator.separate(vocals_no_reverb)
    os.rename(os.path.join(output_dir, backing_voc[0]), backing_vocals)  # Rename to “Backing Vocals.wav”
    os.rename(os.path.join(output_dir, backing_voc[1]), lead_vocals)     # Rename to “Lead Vocals.wav”

    return instrumental, vocals, vocals_reverb, vocals_no_reverb, lead_vocals, backing_vocals


# Main function to process audio (Inference)
def process_audio(MODEL_NAME, SOUND_PATH, F0_CHANGE, F0_METHOD, MIN_PITCH, MAX_PITCH, CREPE_HOP_LENGTH, INDEX_RATE, 
                  FILTER_RADIUS, RMS_MIX_RATE, PROTECT, SPLIT_INFER, MIN_SILENCE, SILENCE_THRESHOLD, SEEK_STEP, 
                  KEEP_SILENCE, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio=None):

    # If no sound path is given, use the uploaded file
    if not SOUND_PATH and upload_audio is not None:
        SOUND_PATH = os.path.join("uploaded_audio", upload_audio.name)
        with open(SOUND_PATH, "wb") as f:
            f.write(upload_audio.read())
    
    # Check if a model name is provided
    if not MODEL_NAME:
        return "Please provide a model name."

    # Run the inference
    os.system("chmod +x stftpitchshift")
    inferred_audio = infer_audio(
        MODEL_NAME,
        SOUND_PATH,
        F0_CHANGE,
        F0_METHOD,
        MIN_PITCH,
        MAX_PITCH,
        CREPE_HOP_LENGTH,
        INDEX_RATE,
        FILTER_RADIUS,
        RMS_MIX_RATE,
        PROTECT,
        SPLIT_INFER,
        MIN_SILENCE,
        SILENCE_THRESHOLD,
        SEEK_STEP,
        KEEP_SILENCE,
        FORMANT_SHIFT,
        QUEFRENCY,
        TIMBRE,
        F0_AUTOTUNE,
        OUTPUT_FORMAT
    )
    
    return inferred_audio


async def text_to_speech_edge(text, language_code):
    voice = language_dict.get(language_code, "default_voice")
    communicate = edge_tts.Communicate(text, voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path




def extract_zip(extraction_folder, zip_name):
    os.makedirs(extraction_folder)
    with zipfile.ZipFile(zip_name, 'r') as zip_ref:
        zip_ref.extractall(extraction_folder)
    os.remove(zip_name)

    index_filepath, model_filepath = None, None
    for root, dirs, files in os.walk(extraction_folder):
        for name in files:
            if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100:
                index_filepath = os.path.join(root, name)

            if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40:
                model_filepath = os.path.join(root, name)

    if not model_filepath:
        raise Exception(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.')

    # move model and index file to extraction folder
    os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath)))
    if index_filepath:
        os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath)))

    # remove any unnecessary nested folders
    for filepath in os.listdir(extraction_folder):
        if os.path.isdir(os.path.join(extraction_folder, filepath)):
            shutil.rmtree(os.path.join(extraction_folder, filepath))

def download_online_model(url, dir_name):
    try:
        print(f'[~] Downloading voice model with name {dir_name}...')
        zip_name = url.split('/')[-1]
        extraction_folder = os.path.join(models_dir, dir_name)
        if os.path.exists(extraction_folder):
            raise Exception(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.')

        if 'pixeldrain.com' in url:
            url = f'https://pixeldrain.com/api/file/{zip_name}'
        if 'drive.google.com' in url:
          zip_name = dir_name + ".zip"
          gdown.download(url, output=zip_name, use_cookies=True, quiet=True, fuzzy=True)
        else:
            urllib.request.urlretrieve(url, zip_name)

        print(f'[~] Extracting zip file...')
        extract_zip(extraction_folder, zip_name)
        print(f'[+] {dir_name} Model successfully downloaded!')

    except Exception as e:
        raise Exception(str(e))




if __name__ == '__main__':
    parser = ArgumentParser(description='Generate a AI song in the song_output/id directory.', add_help=True)
    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
    parser.add_argument("--listen", action="store_true", default=False, help="Make the UI reachable from your local network.")
    parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
    parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
    args = parser.parse_args()




# Gradio Blocks Interface with Tabs
with gr.Blocks(title="Hex RVC", theme=gr.themes.Default(primary_hue="red", secondary_hue="pink")) as app:
    gr.Markdown("# Hex RVC")
    gr.Markdown(" join [AIHub](https://discord.gg/aihub) to get the rvc model!")
    
    with gr.Tab("Inference"):
        with gr.Row():
            MODEL_NAME = gr.Textbox(label="Model Name", placeholder="Enter model name")
            SOUND_PATH = gr.Textbox(label="Audio Path (Optional)", placeholder="Leave blank to upload audio")
            upload_audio = gr.Audio(label="Upload Audio", type='filepath')
        
        with gr.Row():
            F0_CHANGE = gr.Number(label="Pitch Change (semitones)", value=0)
            F0_METHOD = gr.Dropdown(choices=["crepe", "harvest", "mangio-crepe", "rmvpe", "rmvpe+", "fcpe", "hybrid[rmvpe+fcpe]"], 
                                    label="F0 Method", value="fcpe")
        
        with gr.Row():
            MIN_PITCH = gr.Textbox(label="Min Pitch", value="50")
            MAX_PITCH = gr.Textbox(label="Max Pitch", value="1100")
            CREPE_HOP_LENGTH = gr.Number(label="Crepe Hop Length", value=120)
            INDEX_RATE = gr.Slider(label="Index Rate", minimum=0, maximum=1, value=0.75)
            FILTER_RADIUS = gr.Number(label="Filter Radius", value=3)
            RMS_MIX_RATE = gr.Slider(label="RMS Mix Rate", minimum=0, maximum=1, value=0.25)
            PROTECT = gr.Slider(label="Protect", minimum=0, maximum=1, value=0.33)

        with gr.Accordion("Hex TTS"):
            input_text = gr.Textbox(lines=5, label="Input Text")
            #output_text = gr.Textbox(label="Output Text")
            #output_audio = gr.Audio(type="filepath", label="Exported Audio")
            language = gr.Dropdown(choices=list(language_dict.keys()), label="Choose the Voice Model")
            tts_convert = gr.Button("Convert")
            tts_convert.click(fn=text_to_speech_edge, inputs=[input_text, language], outputs=[upload_audio])
        with gr.Accordion("Advanced Settings", open=False):
            SPLIT_INFER = gr.Checkbox(label="Enable Split Inference", value=False)
            MIN_SILENCE = gr.Number(label="Min Silence (ms)", value=500)
            SILENCE_THRESHOLD = gr.Number(label="Silence Threshold (dBFS)", value=-50)
            SEEK_STEP = gr.Slider(label="Seek Step (ms)", minimum=1, maximum=10, value=1)
            KEEP_SILENCE = gr.Number(label="Keep Silence (ms)", value=200)
            FORMANT_SHIFT = gr.Checkbox(label="Enable Formant Shift", value=False)
            QUEFRENCY = gr.Number(label="Quefrency", value=0)
            TIMBRE = gr.Number(label="Timbre", value=1)
            F0_AUTOTUNE = gr.Checkbox(label="Enable F0 Autotune", value=False)
            OUTPUT_FORMAT = gr.Dropdown(choices=["wav", "flac", "mp3"], label="Output Format", value="wav")
        
        run_button = gr.Button("Run Inference")
        output_audio = gr.Audio(label="Generated Audio", type='filepath')

        run_button.click(
            process_audio, 
            inputs=[MODEL_NAME, SOUND_PATH, F0_CHANGE, F0_METHOD, MIN_PITCH, MAX_PITCH, CREPE_HOP_LENGTH, INDEX_RATE, 
                    FILTER_RADIUS, RMS_MIX_RATE, PROTECT, SPLIT_INFER, MIN_SILENCE, SILENCE_THRESHOLD, SEEK_STEP, 
                    KEEP_SILENCE, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio], 
            outputs=output_audio
        )

    with gr.Tab("Download RVC Model"):
        url = gr.Textbox(label="Your model URL")
        dirname = gr.Textbox(label="Your Model name")
        button_model = gr.Button("Download model")
        button_model.click(fn=download_online_model, inputs=[url, dirname], outputs=[dirname])
    with gr.Tab("Audio Separation"):
        with gr.Row():
            input_audio = gr.Audio(type="filepath", label="Upload Audio File")
            
        with gr.Row():
            with gr.Accordion("Separation by Link", open = False):
                with gr.Row():
                    roformer_link = gr.Textbox(
                    label = "Link",
                    placeholder = "Paste the link here",
                    interactive = True
                )
                with gr.Row():
                   gr.Markdown("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")
                with gr.Row():
                    roformer_download_button = gr.Button(
                    "Download!",
                    variant = "primary"
                )

            roformer_download_button.click(download_audio, [roformer_link], [input_audio])
        
        with gr.Row():
            model_voc_inst = gr.Textbox(value='model_bs_roformer_ep_317_sdr_12.9755.ckpt', label="Vocal & Instrumental Model", visible=False)
            model_deecho = gr.Textbox(value='UVR-DeEcho-DeReverb.pth', label="DeEcho-DeReverb Model", visible=False)
            model_back_voc = gr.Textbox(value='mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', label="Backing Vocals Model", visible=False)
        
        separate_button = gr.Button("Separate Audio")
        
        with gr.Row():
            instrumental_out = gr.Audio(label="Instrumental")
            vocals_out = gr.Audio(label="Vocals")
            vocals_reverb_out = gr.Audio(label="Vocals (Reverb)")
            vocals_no_reverb_out = gr.Audio(label="Vocals (No Reverb)")
            lead_vocals_out = gr.Audio(label="Lead Vocals")
            backing_vocals_out = gr.Audio(label="Backing Vocals")
        
        separate_button.click(
            separate_audio,
            inputs=[input_audio, model_voc_inst, model_deecho, model_back_voc],
            outputs=[instrumental_out, vocals_out, vocals_reverb_out, vocals_no_reverb_out, lead_vocals_out, backing_vocals_out]
        )


# Launch the Gradio app
app.launch(
    share=args.share_enabled,
    server_name=None if not args.listen else (args.listen_host or '0.0.0.0'),
    server_port=args.listen_port,
)