Update app.py

app.py

@@ -1,162 +1,171 @@
-import gradio as gr
-from rvc_infer import infer_audio, get_current_models
-import os
-import re
-import random
-from scipy.io.wavfile import write
-from scipy.io.wavfile import read
-import numpy as np
-import yt_dlp
-import subprocess
-import zipfile
-import shutil
-import urllib
-
-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 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 = {
-        '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
-
-
-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.Dropdown(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)
-        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_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")
-        url_input = gr.Textbox(label="Model URL", placeholder="Enter the URL of the model")
-        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_online_model, inputs=[url_input, dir_name_input], outputs=output)
-
-        with gr.Tab(" Credits"):
-            gr.Markdown(
-                """
-                this project made by [Blane187](https://huggingface.co/Blane187) with Improvements by [John6666](https://huggingfce.co/John6666)
-                """)
-
-    ref_btn.click(update_models_list, None, outputs=model_name)
-    gr.on(
-        triggers=[submit_inference.click],
-        fn=infer_audio,
-        inputs=[model_name, input_audio, 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, do_formant, quefrency, timbre, f0_autotune, audio_format, resample_sr,
-                hubert_model_path, rmvpe_model_path, fcpe_model_path],
-        outputs=[result_audio],
-        queue=True,
-        show_api=True,
-        show_progress="full",
-    )
-
-demo.queue()
-demo.launch(debug=True,share=True,show_api=False)
+import gradio as gr
+from rvc_infer import infer_audio
+import os
+import re
+import random
+from scipy.io.wavfile import write
+from scipy.io.wavfile import read
+import numpy as np
+import yt_dlp
+import subprocess
+import zipfile
+import shutil
+import urllib
+
+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 get_current_models(models_dir):
+    models_list = os.listdir(models_dir)
+    items_to_remove = ['hubert_base.pt', 'MODELS.txt', 'public_models.json', 'rmvpe.pt']
+    return [item for item in models_list if item not in items_to_remove]
+
+
+
+
+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 = {
+        '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
+
+
+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.Dropdown(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)
+        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_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")
+        url_input = gr.Textbox(label="Model URL", placeholder="Enter the URL of the model")
+        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_online_model, inputs=[url_input, dir_name_input], outputs=output)
+
+        with gr.Tab(" Credits"):
+            gr.Markdown(
+                """
+                this project made by [Blane187](https://huggingface.co/Blane187) with Improvements by [John6666](https://huggingfce.co/John6666)
+                """)
+
+    ref_btn.click(update_models_list, None, outputs=model_name)
+    gr.on(
+        triggers=[submit_inference.click],
+        fn=infer_audio,
+        inputs=[model_name, input_audio, 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, do_formant, quefrency, timbre, f0_autotune, audio_format, resample_sr,
+                hubert_model_path, rmvpe_model_path, fcpe_model_path],
+        outputs=[result_audio],
+        queue=True,
+        show_api=True,
+        show_progress="full",
+    )
+
+demo.queue()
+demo.launch(debug=True,share=True,show_api=False)