Delete app.py

app.py

@@ -1,384 +0,0 @@
-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)
-
-# Ensure the correct path to the models directory
-model_dir = os.path.join(os.path.dirname(__file__), "models")
-
-converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
-
-title = "<center><strong><font size='7'>Vodex AI</font></strong></center>"
-theme = "aliabid94/new-theme"
-
-def find_files(directory):
-    file_paths = []
-    for filename in os.listdir(directory):
-        if filename.endswith('.pth') or filename.endswith('.zip') or filename.endswith('.index'):
-            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:
-        t = threading.Thread(target=clear_files, args=(directory,))
-        t.start()
-
-def convert_now(audio_files, random_tag, converter):
-    return converter(
-        audio_files,
-        random_tag,
-        overwrite=False,
-        parallel_workers=8
-    )
-
-def apply_noisereduce(audio_list):
-    print("Applying noise reduction")
-
-    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())
-
-            reduced_noise = nr.reduce_noise(samples, sr=audio.frame_rate, prop_decrease=0.6)
-
-
-            reduced_audio = AudioSegment(
-                reduced_noise.tobytes(),
-                frame_rate=audio.frame_rate,
-                sample_width=audio.sample_width,
-                channels=audio.channels
-            )
-
-
-            reduced_audio.export(out_path, format="wav")
-            result.append(out_path)
-
-        except Exception as e:
-            traceback.print_exc()
-            print(f"Error in noise reduction: {str(e)}")
-            result.append(audio_path)
-
-    return result
-
-def run(audio_files, file_m, file_index):
-    if not audio_files:
-        raise ValueError("Please provide an audio file.")
-
-    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)
-
-    file_m = os.path.join(model_dir, file_m)
-    file_index = os.path.join(model_dir, file_index) if file_index else None
-
-    random_tag = "USER_" + str(random.randint(10000000, 99999999))
-
-    converter.apply_conf(
-        tag=random_tag,
-        file_model=file_m,
-        pitch_algo="rmvpe+",
-        pitch_lvl=0,
-        file_index=file_index,
-        index_influence=0.75,
-        respiration_median_filtering=3,
-        envelope_ratio=0.25,
-        consonant_breath_protection=0.5,
-        resample_sr=44100 if audio_files[0].endswith('.mp3') else 0,
-    )
-    time.sleep(0.1)
-
-    result = convert_now(audio_files, random_tag, converter)
-    result = apply_noisereduce(result)
-
-    return result, result[0]  # Assuming result is a list of file paths
-
-
-def process_audio(audio_file, uploaded_files, file_m, file_index):
-    if audio_file is not None:
-        result, _ = run([audio_file], file_m, file_index)
-    elif uploaded_files is not None:
-        result, _ = run(uploaded_files, file_m, file_index)
-
-    # Create a mapping from filenames to full paths
-    file_mapping = {os.path.basename(path): path for path in result}
-    filenames = list(file_mapping.keys())
-    
-    # Return the file_mapping, updated dropdown, initial playback file, and list of all files for download
-    return file_mapping, gr.update(choices=filenames, value=filenames[0], visible=True), file_mapping[filenames[0]], result
-
-def update_audio_selection(selected_filename, file_mapping):
-    if file_mapping is None:
-        raise ValueError("File mapping is not available.")
-    if selected_filename not in file_mapping:
-        raise ValueError(f"Selected filename {selected_filename} not found in file mapping.")
-    
-    # Use the selected filename to find the full path from the mapping
-    full_path = file_mapping[selected_filename]
-    return gr.update(value=full_path, visible=True)
-
-
-def switch_input(input_type):
-    if input_type == "Record Audio":
-        return gr.update(visible=True), gr.update(visible=False)
-    else:
-        return gr.update(visible=False), gr.update(visible=True)
-
-
-def model_conf():
-    model_files = [f for f in os.listdir(model_dir) if f.endswith(".pth")]
-    return gr.Dropdown(
-        label="Select Model File",
-        choices=model_files,
-        value=model_files[0] if model_files else None,
-        interactive=True,
-    )
-
-def index_conf():
-    index_files = [f for f in os.listdir(model_dir) if f.endswith(".index")]
-    return gr.Dropdown(
-        label="Select Index File",
-        choices=index_files,
-        value=index_files[0] if index_files else None,
-        interactive=True,
-    )
-
-def audio_conf():
-    return gr.Audio(
-        label="Upload or Record Audio",
-        sources=["upload", "microphone"],  # Allows recording via microphone
-        type="filepath",
-        
-    )
-
-def button_conf():
-    return gr.Button(
-        "Inference",
-        variant="primary",
-    )
-
-def output_conf():
-    return gr.File(label="Result", file_count="multiple", interactive=False), gr.Audio(label="Play Result",visible=False,show_share_button=False)
-
-def get_gui(theme):
-    with gr.Blocks(theme=theme, delete_cache=(3200, 3200)) as app:
-        gr.Markdown(title)
-
-
-        input_type = gr.Radio(["Record Audio", "Upload Files"], label="Select Input Method", value="Record Audio")
-    
-        audio = gr.Audio(label="Record Audio", sources="microphone", type="filepath", visible=True)
-        files = gr.File(label="Upload Audio Files", type="filepath", file_count="multiple", visible=False)
-        
-        input_type.change(switch_input, inputs=[input_type], outputs=[audio, files])
-
-        model = model_conf()
-        indx = index_conf()
-        button_base = button_conf()
-
-        dropdown = gr.Dropdown(choices=[], label="Select Processed Audio", visible=False)
-        output_audio = gr.Audio(label="Play Selected Audio", visible=False,show_share_button=False)
-        output_files = gr.File(label="Download Processed Audio", file_count="multiple", interactive=False)
-        
-        # output_file, output_audio = output_conf()
-
-        file_mapping_state = gr.State()
-
-        button_base.click(
-            process_audio,
-            inputs=[audio, files, model, indx],
-            outputs=[file_mapping_state, dropdown, output_audio, output_files],  # Store file mapping in state
-        )
-
-        dropdown.change(
-            update_audio_selection,
-            inputs=[dropdown, file_mapping_state],  # Pass the state (file_mapping) and dropdown selection
-            outputs=output_audio,  # Play the selected audio file using the full path
-        )
-
-        # gr.Examples(
-        #     examples=[
-        #         ["./test.ogg", "./model.pth", "./model.index"],
-        #         ["./example2/test2.ogg", "./example2/model.pth", "./example2/model.index"],
-        #     ],
-        #     fn=process_audio,
-        #     inputs=[audio, files, model, indx],
-        #     outputs=[output_file, output_audio],
-        #     cache_examples=False,
-        # )
-
-    return app
-
-if __name__ == "__main__":
-    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/"],
-    )
-
-