Update app.py

app.py

@@ -1,27 +1,384 @@
+import os
 import gradio as gr
-import numpy as np
+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 add_to_stream(audio, instream):
-    time.sleep(1)
-    if audio is None:
-        return gr.update(), instream
-    if instream is None:
-        ret = audio
+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:
-        ret = (audio[0], np.concatenate((instream[1], audio[1])))
-    return ret, ret
+        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)
 
-with gr.Blocks() as demo:
-    inp = gr.Audio(sources="microphone")
-    out = gr.Audio()
-    stream = gr.State()
-    clear = gr.Button("Clear")
+def get_gui(theme):
+    with gr.Blocks(theme=theme, delete_cache=(3200, 3200)) as app:
+        gr.Markdown(title)
 
-    inp.stream(add_to_stream, [inp, stream], [out, stream])
-    clear.click(lambda: [None, None, None], None, [inp, out, stream])
 
+        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__":
-    demo.launch()
+    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/"],
+    )
+
+