Update app.py

app.py

@@ -1,12 +1,31 @@
-from module import *
+import os
+import re
+import random
+from scipy.io.wavfile import write
+from scipy.io.wavfile import read
+import numpy as np
+import gradio as gr
+import yt_dlp
+import subprocess
+from pydub import AudioSegment
+from audio_separator.separator import Separator
+from lib.infer import infer_audio
+import edge_tts
+import tempfile
+import anyio
+from pathlib import Path
+from lib.language_tts import language_dict
+import shutil
+import time
+from argparse import ArgumentParser
 from download_model import download_online_model
+
 main_dir = Path().resolve()
 print(main_dir)
 
 os.chdir(main_dir)
 models_dir = main_dir / "rvc_models"
 audio_separat_dir = main_dir / "audio_input"
-
 AUDIO_DIR = main_dir / 'audio_input'
 
 
@@ -16,35 +35,28 @@ def get_folders():
         return [folder.name for folder in models_dir.iterdir() if folder.is_dir()]
     return []
 
+
 # Function to refresh and return the list of folders
 def refresh_folders():
     return gr.Dropdown.update(choices=get_folders())
 
 
-
-
 # Function to get the list of audio files in the specified directory
 def get_audio_files():
     if not os.path.exists(AUDIO_DIR):
         os.makedirs(AUDIO_DIR)
-    # List all supported audio file formats
     return [f for f in os.listdir(AUDIO_DIR) if f.lower().endswith(('.mp3', '.wav', '.flac', '.ogg', '.aac'))]
 
+
 # Function to return the full path of audio files for playback
 def load_audio_files():
     audio_files = get_audio_files()
     return [os.path.join(AUDIO_DIR, f) for f in audio_files]
 
-# Refresh function to update the list of files
+
 def refresh_audio_list():
     audio_files = load_audio_files()
-    return gr.update(choices=audio_files)
-
-# Function to play selected audio file
-def play_audio(file_path):
-    return file_path
-
-
+    return gr.Dropdown.update(choices=audio_files)
 
 
 def download_audio(url):
@@ -61,88 +73,7 @@ def download_audio(url):
     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
-
-
-
-# Define a function to handle the entire separation process
-def separate_audio(input_audio, model_voc_inst, model_deecho, model_back_voc):
-    output_dir = audio_separat_dir
-    separator = Separator(output_dir=output_dir)
-
-    # Define output files
-    vocals = os.path.join(output_dir, 'Vocals.wav')
-    instrumental = os.path.join(output_dir, 'Instrumental.wav')
-    vocals_reverb = os.path.join(output_dir, 'Vocals (Reverb).wav')
-    vocals_no_reverb = os.path.join(output_dir, 'Vocals (No Reverb).wav')
-    lead_vocals = os.path.join(output_dir, 'Lead Vocals.wav')
-    backing_vocals = os.path.join(output_dir, 'Backing Vocals.wav')
-
-    # Splitting a track into Vocal and Instrumental
-    separator.load_model(model_filename=model_voc_inst)
-    voc_inst = separator.separate(input_audio)
-    os.rename(os.path.join(output_dir, voc_inst[0]), instrumental)  # Rename to “Instrumental.wav”
-    os.rename(os.path.join(output_dir, voc_inst[1]), vocals)        # Rename to “Vocals.wav”
-
-    # Applying DeEcho-DeReverb to Vocals
-    separator.load_model(model_filename=model_deecho)
-    voc_no_reverb = separator.separate(vocals)
-    os.rename(os.path.join(output_dir, voc_no_reverb[0]), vocals_no_reverb)  # Rename to “Vocals (No Reverb).wav”
-    os.rename(os.path.join(output_dir, voc_no_reverb[1]), vocals_reverb)     # Rename to “Vocals (Reverb).wav”
-
-    # Separating Back Vocals from Main Vocals
-    separator.load_model(model_filename=model_back_voc)
-    backing_voc = separator.separate(vocals_no_reverb)
-    os.rename(os.path.join(output_dir, backing_voc[0]), backing_vocals)  # Rename to “Backing Vocals.wav”
-    os.rename(os.path.join(output_dir, backing_voc[1]), lead_vocals)     # Rename to “Lead Vocals.wav”
-
-    return "separation done..."
-
-# Main function to process audio (Inference)
-def process_audio(MODEL_NAME, SOUND_PATH, 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, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio=None):
-
-    # If no sound path is given, use the uploaded file
-    if not SOUND_PATH and upload_audio is not None:
-        SOUND_PATH = os.path.join("uploaded_audio", upload_audio.name)
-        with open(SOUND_PATH, "wb") as f:
-            f.write(upload_audio.read())
-    
-    # Check if a model name is provided
-    if not MODEL_NAME:
-        return "Please provide a model name."
-
-    # Run the inference
-    os.system("chmod +x stftpitchshift")
-    inferred_audio = infer_audio(
-        MODEL_NAME,
-        SOUND_PATH,
-        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,
-        FORMANT_SHIFT,
-        QUEFRENCY,
-        TIMBRE,
-        F0_AUTOTUNE,
-        OUTPUT_FORMAT
-    )
-    
-    return inferred_audio
+        return file_path
 
 
 async def text_to_speech_edge(text, language_code):
@@ -154,152 +85,77 @@ async def text_to_speech_edge(text, language_code):
     return tmp_path
 
 
-
-
-
 if __name__ == '__main__':
-    parser = ArgumentParser(description='Generate a AI song in the song_output/id directory.', add_help=True)
-    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
-    parser.add_argument("--listen", action="store_true", default=False, help="Make the UI reachable from your local network.")
-    parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
-    parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
+    parser = ArgumentParser()
+    parser.add_argument("--share", action="store_true", dest="share_enabled", default=False)
+    parser.add_argument("--listen", action="store_true", default=False)
+    parser.add_argument('--listen-host', type=str)
+    parser.add_argument('--listen-port', type=int)
     args = parser.parse_args()
 
-
-
-
-# Gradio Blocks Interface with Tabs
+# Gradio Interface
 with gr.Blocks(title="Hex RVC", theme=gr.themes.Base(primary_hue="red", secondary_hue="pink")) as app:
     gr.Markdown("# Hex RVC")
-    gr.Markdown(" join [AIHub](https://discord.gg/aihub) to get the rvc model!")
+    gr.Markdown("Join [AIHub](https://discord.gg/aihub) to get the RVC model!")
     
     with gr.Tab("Inference"):
         with gr.Row():
             MODEL_NAME = gr.Dropdown(
                 label="Select a Model",
                 choices=get_folders(),
-                interactive=True,
-                elem_id="model_folder"
+                interactive=True
             )
             SOUND_PATH = gr.Dropdown(
                 choices=load_audio_files(),
                 label="Select an audio file",
-                interactive=True,
-                value=None,
+                interactive=True
             )
-        # Button to refresh the list of folders
-        
-        with gr.Row():
-        #     = gr.Textbox(label="Model Name", placeholder="Enter model name")
-        #    SOUND_PATH = gr.Textbox(label="Audio Path (Optional)", placeholder="Leave blank to upload audio")
-            upload_audio = gr.Audio(label="Upload Audio", type='filepath', visible=False)
-
-        
+            upload_audio = gr.Audio(label="Upload Audio", type='filepath')
 
-
-
-        
-        
-
-        with gr.Accordion("Conversion Settings"):
-            with gr.Row():
-                F0_CHANGE = gr.Number(label="Pitch Change (semitones)", value=0)
-                F0_METHOD = gr.Dropdown(choices=["crepe", "harvest", "mangio-crepe", "rmvpe", "rmvpe_legacy", "fcpe", "fcpe_legacy", "hybrid[rmvpe+fcpe]"], label="F0 Method", value="fcpe")
-            with gr.Row():
-                MIN_PITCH = gr.Textbox(label="Min Pitch", value="50")
-                MAX_PITCH = gr.Textbox(label="Max Pitch", value="1100")
-                CREPE_HOP_LENGTH = gr.Number(label="Crepe Hop Length", value=120)
-                INDEX_RATE = gr.Slider(label="Index Rate", minimum=0, maximum=1, value=0.75)
-                FILTER_RADIUS = gr.Number(label="Filter Radius", value=3)
-                RMS_MIX_RATE = gr.Slider(label="RMS Mix Rate", minimum=0, maximum=1, value=0.25)
-                PROTECT = gr.Slider(label="Protect", minimum=0, maximum=1, value=0.33)
-
-        with gr.Accordion("Hex TTS", open=False):
+        with gr.Accordion("Hex TTS"):
             input_text = gr.Textbox(lines=5, label="Input Text")
-            #output_text = gr.Textbox(label="Output Text")
-            #output_audio = gr.Audio(type="filepath", label="Exported Audio")
             language = gr.Dropdown(choices=list(language_dict.keys()), label="Choose the Voice Model")
             tts_convert = gr.Button("Convert")
-            tts_convert.click(fn=text_to_speech_edge, inputs=[input_text, language], outputs=[upload_audio])
-        with gr.Accordion("Advanced Settings", open=False):
-            SPLIT_INFER = gr.Checkbox(label="Enable Split Inference", value=False)
-            MIN_SILENCE = gr.Number(label="Min Silence (ms)", value=500)
-            SILENCE_THRESHOLD = gr.Number(label="Silence Threshold (dBFS)", value=-50)
-            SEEK_STEP = gr.Slider(label="Seek Step (ms)", minimum=1, maximum=10, value=1)
-            KEEP_SILENCE = gr.Number(label="Keep Silence (ms)", value=200)
-            FORMANT_SHIFT = gr.Checkbox(label="Enable Formant Shift", value=False)
-            QUEFRENCY = gr.Number(label="Quefrency", value=0)
-            TIMBRE = gr.Number(label="Timbre", value=1)
-            F0_AUTOTUNE = gr.Checkbox(label="Enable F0 Autotune", value=False)
-            OUTPUT_FORMAT = gr.Dropdown(choices=["wav", "flac", "mp3"], label="Output Format", value="wav")
+            tts_output = gr.Audio(label="Generated TTS Audio", type='filepath')
+            
+            tts_convert.click(
+                fn=text_to_speech_edge, 
+                inputs=[input_text, language], 
+                outputs=tts_output
+            )
 
         output_audio = gr.Audio(label="Generated Audio", type='filepath')
-
         with gr.Row():
             refresh_btn = gr.Button("Refresh")
             run_button = gr.Button("Convert")
-        
-        #ref_btn.click(update_models_list, None, outputs=MODEL_NAME)
+
         refresh_btn.click(
-            lambda: (refresh_audio_list(), refresh_folders()), 
+            lambda: (refresh_audio_list(), refresh_folders()),
             outputs=[SOUND_PATH, MODEL_NAME]
         )
-        run_button.click(
-            process_audio, 
-            inputs=[MODEL_NAME, SOUND_PATH, 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, FORMANT_SHIFT, QUEFRENCY, TIMBRE, F0_AUTOTUNE, OUTPUT_FORMAT, upload_audio], 
-            outputs=output_audio
-        )
 
     with gr.Tab("Download RVC Model"):
-        with gr.Row():
-            url = gr.Textbox(label="Your model URL")
-            dirname = gr.Textbox(label="Your Model name")
-        outout_pah = gr.Textbox(label="output download", interactive=False)
-        button_model = gr.Button("Download model")
-        
-        button_model.click(fn=download_online_model, inputs=[url, dirname], outputs=[outout_pah])
-    with gr.Tab("Audio Separation"):
-        with gr.Row():
-            input_audio = gr.Audio(type="filepath", label="Upload Audio File")
-            
-        with gr.Row():
-            with gr.Accordion("Separation by Link", open = False):
-                with gr.Row():
-                    roformer_link = gr.Textbox(
-                    label = "Link",
-                    placeholder = "Paste the link here",
-                    interactive = True
-                )
-                with gr.Row():
-                   gr.Markdown("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")
-                with gr.Row():
-                    roformer_download_button = gr.Button(
-                    "Download!",
-                    variant = "primary"
-                )
+        url = gr.Textbox(label="Your Model URL")
+        dirname = gr.Textbox(label="Your Model Name")
+        download_button = gr.Button("Download Model")
+        download_output = gr.Textbox(label="Download Status")
+
+        download_button.click(
+            download_online_model,
+            inputs=[url, dirname],
+            outputs=download_output
+        )
 
-            roformer_download_button.click(download_audio, [roformer_link], [input_audio])
-        
-        with gr.Row():
-            model_voc_inst = gr.Textbox(value='model_bs_roformer_ep_317_sdr_12.9755.ckpt', label="Vocal & Instrumental Model", visible=False)
-            model_deecho = gr.Textbox(value='UVR-DeEcho-DeReverb.pth', label="DeEcho-DeReverb Model", visible=False)
-            model_back_voc = gr.Textbox(value='mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', label="Backing Vocals Model", visible=False)
+    with gr.Tab("Audio Separation"):
+        input_audio = gr.Audio(type="filepath", label="Upload Audio")
+        roformer_link = gr.Textbox(label="Audio Link")
+        roformer_download_button = gr.Button("Download")
         
         separate_button = gr.Button("Separate Audio")
-        
-        with gr.Row():
-            outout_paht = gr.Textbox(label="output download", interactive=False)
-      
-        separate_button.click(
-            separate_audio,
-            inputs=[input_audio, model_voc_inst, model_deecho, model_back_voc],
-            outputs=[outout_paht]
-        )
+        separation_output = gr.Textbox(label="Separation Output Path")
 
+        roformer_download_button.click(download_audio, inputs=[roformer_link], outputs=[input_audio])
 
-# Launch the Gradio app
 app.launch(
     share=args.share_enabled,
     server_name=None if not args.listen else (args.listen_host or '0.0.0.0'),