app.py

import gradio as gr
import requests
import random
import os
import zipfile 
import librosa
import time
from infer_rvc_python import BaseLoader
from pydub import AudioSegment
from tts_voice import tts_order_voice
import edge_tts
import tempfile
import anyio
import asyncio
from audio_separator.separator import Separator

language_dict = tts_order_voice

async def text_to_speech_edge(text, language_code):
    voice = language_dict[language_code]
    communicate = edge_tts.Communicate(text, voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name

    await communicate.save(tmp_path)

    return tmp_path

# fucking dogshit toggle
try:
    import spaces
    spaces_status = True
except ImportError:
    spaces_status = False

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

global pth_file
global index_file

pth_file = "model.pth"
index_file = "model.index"

#CONFIGS
TEMP_DIR = "temp"
MODEL_PREFIX = "model"
PITCH_ALGO_OPT = [
    "pm",
    "harvest",
    "crepe",
    "rmvpe",
    "rmvpe+",
]

os.makedirs(TEMP_DIR, exist_ok=True)

def unzip_file(file):
    filename = os.path.basename(file).split(".")[0] 
    with zipfile.ZipFile(file, 'r') as zip_ref:
        zip_ref.extractall(os.path.join(TEMP_DIR, filename))
    return True

def get_training_info(audio_file):
    if audio_file is None:
        return 'Please provide an audio file!'
    duration = get_audio_duration(audio_file)
    sample_rate = wave.open(audio_file, 'rb').getframerate()

    training_info = {
        (0, 2): (150, 'OV2'),
        (2, 3): (200, 'OV2'),
        (3, 5): (250, 'OV2'),
        (5, 10): (300, 'Normal'),
        (10, 25): (500, 'Normal'),
        (25, 45): (700, 'Normal'),
        (45, 60): (1000, 'Normal')
    }

    for (min_duration, max_duration), (epochs, pretrain) in training_info.items():
        if min_duration <= duration < max_duration:
            break
    else:
        return 'Duration is not within the specified range!'

    return f'You should use the **{pretrain}** pretrain with **{epochs}** epochs at **{sample_rate/1000}khz** sample rate.'

def on_button_click(audio_file_path):
    return get_training_info(audio_file_path)

def get_audio_duration(audio_file_path):
    audio_info = sf.info(audio_file_path)
    duration_minutes = audio_info.duration / 60
    return duration_minutes

def progress_bar(total, current): # best progress bar ever trust me sunglasses emoji 😎 
    return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"

def download_from_url(url, filename=None):
    if "/blob/" in url:
        url = url.replace("/blob/", "/resolve/") # made it delik proof 😎
    if "huggingface" not in url:
        return ["The URL must be from huggingface", "Failed", "Failed"]
    if filename is None:
        filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
    response = requests.get(url)
    total = int(response.headers.get('content-length', 0)) # bytes to download (length of the file)
    if total > 500000000:

        return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
    current = 0
    with open(filename, "wb") as f:
        for data in response.iter_content(chunk_size=4096):
            f.write(data)
            current += len(data)
            print(progress_bar(total, current), end="\r") 
    


    try:
        unzip_file(filename)
    except Exception as e:
        return ["Failed to unzip the file", "Failed", "Failed"]
    unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
    pth_files = []
    index_files = []
    for root, dirs, files in os.walk(unzipped_dir): 
        for file in files:
            if file.endswith(".pth"):
                pth_files.append(os.path.join(root, file))
            elif file.endswith(".index"):
                index_files.append(os.path.join(root, file))
    
    print(pth_files, index_files) 
    global pth_file
    global index_file
    pth_file = pth_files[0]
    index_file = index_files[0]

    pth_file_ui.value = pth_file
    index_file_ui.value = index_file
    print(pth_file_ui.value)
    print(index_file_ui.value)
    return ["Downloaded as " + filename, pth_files[0], index_files[0]]

def inference(audio, model_name):
        output_data = inf_handler(audio, model_name)
        vocals = output_data[0]
        inst = output_data[1]

        return vocals, inst

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

        
else:
    def convert_now(audio_files, random_tag, converter):
        return converter(
            audio_files,
            random_tag,
            overwrite=False,
            parallel_workers=8
        )

def calculate_remaining_time(epochs, seconds_per_epoch):
    total_seconds = epochs * seconds_per_epoch

    hours = total_seconds // 3600
    minutes = (total_seconds % 3600) // 60
    seconds = total_seconds % 60

    if hours == 0:
        return f"{int(minutes)} minutes"
    elif hours == 1:
        return f"{int(hours)} hour and {int(minutes)} minutes"
    else:
        return f"{int(hours)} hours and {int(minutes)} minutes"

def inf_handler(audio, model_name): 
    model_found = False
    for model_info in UVR_5_MODELS:
        if model_info["model_name"] == model_name:
            separator.load_model(model_info["checkpoint"])
            model_found = True
            break
    if not model_found:
        separator.load_model()
    output_files = separator.separate(audio)
    vocals = output_files[0]
    inst = output_files[1]
    return vocals, inst

    
def run(
    audio_files,
    pitch_alg,
    pitch_lvl,
    index_inf,
    r_m_f,
    e_r,
    c_b_p,
):
    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)

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

    file_m = pth_file_ui.value
    file_index = index_file_ui.value

    print("Random tag:", random_tag)
    print("File model:", file_m)
    print("Pitch algorithm:", pitch_alg)
    print("Pitch level:", pitch_lvl)
    print("File index:", file_index)
    print("Index influence:", index_inf)
    print("Respiration median filtering:", r_m_f)
    print("Envelope ratio:", e_r)

    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)
    print("Result:", result)

    return result[0]

def upload_model(index_file, pth_file):
    pth_file = pth_file.name
    index_file = index_file.name
    pth_file_ui.value = pth_file
    index_file_ui.value = index_file
    return "Uploaded!"  

with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as demo:
    gr.Markdown("## Ilaria RVC 💖")
    with gr.Tab("Inference"):
        sound_gui = gr.Audio(value=None, type="filepath", autoplay=False, visible=True)
        pth_file_ui = gr.Textbox(label="Model pth file", value=pth_file, visible=False, interactive=False)
        index_file_ui = gr.Textbox(label="Index pth file", value=index_file, visible=False, interactive=False)
    
        with gr.Accordion("Ilaria TTS", open=False):
            text_tts = gr.Textbox(label="Text", placeholder="Hello!", lines=3, interactive=True)
            dropdown_tts = gr.Dropdown(label="Language and Model", choices=list(language_dict.keys()), interactive=True, value=list(language_dict.keys())[0])
    
            button_tts = gr.Button("Speak", variant="primary")
    
            # Rimuovi l'output_tts e usa solo sound_gui come output
            button_tts.click(text_to_speech_edge, inputs=[text_tts, dropdown_tts], outputs=sound_gui)
            
        with gr.Accordion("Settings", open=False):
            pitch_algo_conf = gr.Dropdown(PITCH_ALGO_OPT, value=PITCH_ALGO_OPT[4], label="Pitch algorithm", visible=True, interactive=True)
            pitch_lvl_conf = gr.Slider(label="Pitch level (lower -> 'male' while higher -> 'female')", minimum=-24, maximum=24, step=1, value=0, visible=True, interactive=True)
            index_inf_conf = gr.Slider(minimum=0, maximum=1, label="Index influence -> How much accent is applied", value=0.75)
            respiration_filter_conf = gr.Slider(minimum=0, maximum=7, label="Respiration median filtering", value=3, step=1, interactive=True)
            envelope_ratio_conf = gr.Slider(minimum=0, maximum=1, label="Envelope ratio", value=0.25, interactive=True)
            consonant_protec_conf = gr.Slider(minimum=0, maximum=0.5, label="Consonant breath protection", value=0.5, interactive=True)
    
        button_conf = gr.Button("Convert", variant="primary")
        output_conf = gr.Audio(type="filepath", label="Output")
        
        button_conf.click(lambda: None, None, output_conf)
        button_conf.click(
            run,
            inputs=[
                sound_gui,
                pitch_algo_conf,
                pitch_lvl_conf,
                index_inf_conf,
                respiration_filter_conf,
                envelope_ratio_conf,
                consonant_protec_conf,
            ],
            outputs=[output_conf],
        )

    with gr.Tab("Model Loader (Download and Upload)"):
        with gr.Accordion("Model Downloader", open=False):
            gr.Markdown(
                "Download the model from the following URL and upload it here. (Hugginface RVC model)"
            )
            model = gr.Textbox(lines=1, label="Model URL")
            download_button = gr.Button("Download Model")
            status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
            model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
            index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
            download_button.click(download_from_url, model, outputs=[status, model_pth, index_pth])
        with gr.Accordion("Upload A Model", open=False):
            index_file_upload = gr.File(label="Index File (.index)")
            pth_file_upload = gr.File(label="Model File (.pth)")
            upload_button = gr.Button("Upload Model")
            upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)

            upload_button.click(upload_model, [index_file_upload, pth_file_upload], upload_status)
    
    with gr.Tab("Extra"):
        with gr.Accordion("Training Time Calculator", open=False):
            with gr.Column():
                epochs_input = gr.Number(label="Number of Epochs")
                seconds_input = gr.Number(label="Seconds per Epoch")
                calculate_button = gr.Button("Calculate Time Remaining")
                remaining_time_output = gr.Textbox(label="Remaining Time", interactive=False)
                
                calculate_button.click(
                    fn=calculate_remaining_time,
                    inputs=[epochs_input, seconds_input],
                    outputs=[remaining_time_output]
                )
        
        with gr.Accordion('Training Helper', open=False):
            with gr.Column():
                 audio_input = gr.Audio(type="filepath", label="Upload your audio file")
                 gr.Text("Please note that these results are approximate and intended to provide a general idea for beginners.", label='Notice:')
                 training_info_output = gr.Markdown(label="Training Information:")
                 get_info_button = gr.Button("Get Training Info")
                 get_info_button.click(
                  fn=on_button_click,
                  inputs=[audio_input],
                  outputs=[training_info_output]
                )

    with gr.Tab("Credits"):
        gr.Markdown(
            """
            Ilaria RVC made by [Ilaria](https://huggingface.co/TheStinger) suport her on [ko-fi](https://ko-fi.com/ilariaowo)
            
            The Inference code is made by [r3gm](https://huggingface.co/r3gm) (his module helped form this space 💖)

            made with ❤️ by [mikus](https://github.com/cappuch) - i make this ui........

            ## In loving memory of JLabDX 🕊️
            """
        )

demo.queue(api_open=False).launch(show_api=False)