Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from rvc_infer import download_online_model, infer_audio
+from rvc_infer import infer_audio
 import os
 import re
 import random
@@ -13,13 +13,67 @@ print("downloading RVC models")
 os.system("python dowoad_param.py")
 
 
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
+rvc_models_dir = os.path.join(BASE_DIR, 'models')
 
 
-def download_model(url, dir_name):
-    output_models = download_online_model(url, dir_name)
-    return output_models
 
+def update_models_list():
+    models_l = get_current_models(rvc_models_dir)
+    return gr.update(choices=models_l)
+
+
+
+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 gr.Error(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, progress=gr.Progress()):
+    try:
+        progress(0, desc=f'[~] Downloading voice model with name {dir_name}...')
+        zip_name = url.split('/')[-1]
+        extraction_folder = os.path.join(rvc_models_dir, dir_name)
+        if os.path.exists(extraction_folder):
+            raise gr.Error(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}'
+
+        urllib.request.urlretrieve(url, zip_name)
+
+        progress(0.5, desc='[~] Extracting zip...')
+        extract_zip(extraction_folder, zip_name)
+        return f'[+] {dir_name} Model successfully downloaded!'
+
+    except Exception as e:
+        raise gr.Error(str(e))
 
 def download_audio(url):
     ydl_opts = {
@@ -46,37 +100,39 @@ CSS = """
 
 with gr.Blocks(theme="Hev832/Applio", fill_width=True, css=CSS) as demo:
     gr.Markdown("# RVC INFER DEMOS ")
+    gr.Markdown(f"# recommended using colab version with more feature!<br> [![Open In Collab](https://img.shields.io/badge/google_colab-F9AB00?style=flat-square&logo=googlecolab&logoColor=white)](https://colab.research.google.com/drive/1bM1LB2__WNFxX8pyZmUPQZYq7dg58YWG?usp=sharing) ")
     with gr.Tab("Inferenece"):
         gr.Markdown("in progress")
-        model_name = gr.Textbox(label="Model Name #", lines=1, value="")
-        input_audio = gr.Audio(label="Input Audio #", type="filepath")
+        model_name = gr.Dropdown(voice_models, label='Voice Models', info='Models folder "rvc_infer --> models". After new models are added into this folder, click the refresh button')
+        ref_btn = gr.Button('Refresh Models', variant='primary')
+        input_audio = gr.Audio(label="Input Audio", type="filepath")
         with gr.Accordion("Settings", open=False):
-            f0_change = gr.Slider(label="f0 change #", minimum=-12, maximum=12, step=1, value=0)
-            f0_method = gr.Dropdown(label="f0 method #", choices=["rmvpe+", "rmvpe", "fcpe", " hybrid[rmvpe+fcpe]"], value="rmvpe+")
-            min_pitch = gr.Textbox(label="min pitch #", lines=1, value="-12")
-            max_pitch = gr.Textbox(label="max pitch #", lines=1, value="12")
-            crepe_hop_length = gr.Slider(label="crepe_hop_length #", minimum=0, maximum=256, step=1, value=128)
-            index_rate = gr.Slider(label="index_rate #", minimum=0, maximum=1.0, step=0.01, value=0.75)
-            filter_radius = gr.Slider(label="filter_radius #", minimum=0, maximum=10.0, step=0.01, value=3)
-            rms_mix_rate = gr.Slider(label="rms_mix_rate #", minimum=0, maximum=1.0, step=0.01, value=0.25)
-            protect = gr.Slider(label="protect #", minimum=0, maximum=1.0, step=0.01, value=0.33)
+            f0_change = gr.Slider(label="f0 change", minimum=-12, maximum=12, step=1, value=0)
+            f0_method = gr.Dropdown(label="f0 method", choices=["rmvpe+", "rmvpe", "fcpe", " hybrid[rmvpe+fcpe]"], value="rmvpe+")
+            min_pitch = gr.Textbox(label="min pitch", lines=1, value="-12")
+            max_pitch = gr.Textbox(label="max pitch", lines=1, value="12")
+            crepe_hop_length = gr.Slider(label="crepe_hop_length", minimum=0, maximum=256, step=1, value=128)
+            index_rate = gr.Slider(label="index_rate", minimum=0, maximum=1.0, step=0.01, value=0.75)
+            filter_radius = gr.Slider(label="filter_radius", minimum=0, maximum=10.0, step=0.01, value=3)
+            rms_mix_rate = gr.Slider(label="rms_mix_rate", minimum=0, maximum=1.0, step=0.01, value=0.25)
+            protect = gr.Slider(label="protect", minimum=0, maximum=1.0, step=0.01, value=0.33)
         with gr.Accordion("Advanced Settings", open=False):
-            split_infer = gr.Checkbox(label="split_infer #", value=False)
-            min_silence = gr.Slider(label="min_silence #", minimum=0, maximum=1000, step=1, value=500)
-            silence_threshold = gr.Slider(label="silence_threshold #", minimum=-1000, maximum=1000, step=1, value=-50)
-            seek_step = gr.Slider(label="seek_step #", minimum=0, maximum=100, step=1, value=0)
-            keep_silence = gr.Slider(label="keep_silence #", minimum=-1000, maximum=1000, step=1, value=100)
-            do_formant = gr.Checkbox(label="do_formant #", value=False)
-            quefrency = gr.Slider(label="quefrency #", minimum=0, maximum=100, step=1, value=0)
-            timbre = gr.Slider(label="timbre #", minimum=0, maximum=100, step=1, value=1)
-            f0_autotune = gr.Checkbox(label="f0_autotune #", value=False)
-            audio_format = gr.Dropdown(label="audio_format #", choices=["wav"], value="wav", visible=False)
-            resample_sr = gr.Slider(label="resample_sr #", minimum=0, maximum=100, step=1, value=0)
-            hubert_model_path = gr.Textbox(label="hubert_model_pathe #", lines=1, value="hubert_base.pt", visible=False)
-            rmvpe_model_path = gr.Textbox(label="rmvpe_model_path #", lines=1, value="rmvpe.pt", visible=False)
-            fcpe_model_path = gr.Textbox(label="fcpe_model_path #", lines=1, value="fcpe.pt", visible=False)
-        submit_inference = gr.Button('Inference #', variant='primary')
-        result_audio = gr.Audio("Output Audio #")
+            split_infer = gr.Checkbox(label="split_infer", value=False)
+            min_silence = gr.Slider(label="min_silence", minimum=0, maximum=1000, step=1, value=500)
+            silence_threshold = gr.Slider(label="silence_threshold", minimum=-1000, maximum=1000, step=1, value=-50)
+            seek_step = gr.Slider(label="seek_step", minimum=0, maximum=100, step=1, value=0)
+            keep_silence = gr.Slider(label="keep_silence", minimum=-1000, maximum=1000, step=1, value=100)
+            do_formant = gr.Checkbox(label="do_formant", value=False)
+            quefrency = gr.Slider(label="quefrency", minimum=0, maximum=100, step=1, value=0)
+            timbre = gr.Slider(label="timbre", minimum=0, maximum=100, step=1, value=1)
+            f0_autotune = gr.Checkbox(label="f0_autotune", value=False)
+            audio_format = gr.Dropdown(label="audio_format", choices=["wav"], value="wav", visible=False)
+            resample_sr = gr.Slider(label="resample_sr", minimum=0, maximum=100, step=1, value=0)
+            hubert_model_path = gr.Textbox(label="hubert_model_path", lines=1, value="hubert_base.pt", visible=False)
+            rmvpe_model_path = gr.Textbox(label="rmvpe_model_path", lines=1, value="rmvpe.pt", visible=False)
+            fcpe_model_path = gr.Textbox(label="fcpe_model_path", lines=1, value="fcpe.pt", visible=False)
+        submit_inference = gr.Button('Inference', variant='primary')
+        result_audio = gr.Audio("Output Audio")
 
     with gr.Tab("Download Model"):
         gr.Markdown("## Download Model for infernece")
@@ -84,7 +140,7 @@ with gr.Blocks(theme="Hev832/Applio", fill_width=True, css=CSS) as demo:
         dir_name_input = gr.Textbox(label="Directory Name", placeholder="Enter the directory name")
         output = gr.Textbox(label="Output Models")
         download_button = gr.Button("Download Model")
-        download_button.click(download_model, inputs=[url_input, dir_name_input], outputs=output)
+        download_button.click(download_online_model, inputs=[url_input, dir_name_input], outputs=output)
 
         with gr.Tab(" Credits"):
             gr.Markdown(
@@ -93,6 +149,8 @@ with gr.Blocks(theme="Hev832/Applio", fill_width=True, css=CSS) as demo:
                 """)
 
 
+
+    ref_btn.click(update_models_list, None, outputs=rvc_model)
     gr.on(
         triggers=[submit_inference.click],
         fn=infer_audio,