Update app.py

app.py

@@ -1,441 +1,442 @@
-print("Starting up. Please be patient...")
-
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-from edgetts_db import tts_order_voice
-
-#fuck intel
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-#limitation=True
-language_dict = tts_order_voice
-
-authors = ["dacoolkid44", "Hijack", "Maki Ligon", "megaaziib", "Kit Lemonfoot", "yeey5", "Sui", "MahdeenSky"]
-
-f0method_mode = []
-if limitation is True:
-    f0method_info = "PM is better for testing, RMVPE is better for finalized generations. (Default: PM)"
-    f0method_mode = ["pm", "rmvpe"]
-else:
-    f0method_info = "PM is fast but low quality, crepe and harvest are slow but good quality, RMVPE is the best of both worlds. (Default: PM)"
-    f0method_mode = ["pm", "crepe", "harvest", "rmvpe"]
-
-#Eagerload VCs
-print("Preloading VCs...")
-vcArr=[]
-vcArr.append(VC(32000, config))
-vcArr.append(VC(40000, config))
-vcArr.append(VC(48000, config))
-
-def infer(name, path, index, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect):
-    try:
-        #Setup audio
-        audio=None
-        #Determine audio mode
-        #TTS takes priority over uploads.
-        #Uploads takes priority over paths.
-        vc_audio_mode = ""
-        #Edge-TTS
-        if(tts_text):
-            vc_audio_mode = "ETTS"
-            if len(tts_text) > 250 and limitation:
-                return "Text is too long.", None
-            if tts_text is None or tts_voice is None or tts_text=="":
-                return "You need to enter text and select a voice.", None
-            voice = language_dict[tts_voice]
-            try:
-                asyncio.run(edge_tts.Communicate(tts_text, voice).save("tts.mp3"))
-            except:
-                print("Failed to get E-TTS handle. A restart may be needed soon.")
-                return "ERROR: Failed to communicate with Edge-TTS. The Edge-TTS service may be down or cannot communicate. Please try another method or try again later.", None
-            try:
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            except:
-                return "ERROR: Invalid characters for the chosen TTS speaker. (Change your TTS speaker to one that supports your language!)", None
-            duration = audio.shape[0] / sr
-            if duration > 30 and limitation:
-                return "Your text generated an audio that was too long.", None
-            vc_input = "tts.mp3"
-        #File upload
-        elif(vc_upload):
-            vc_audio_mode = "Upload"
-            sampling_rate, audio = vc_upload
-            duration = audio.shape[0] / sampling_rate
-            if duration > 60 and limitation:
-                return "Too long! Please upload an audio file that is less than 1 minute.", None
-            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-            if len(audio.shape) > 1:
-                audio = librosa.to_mono(audio.transpose(1, 0))
-            if sampling_rate != 16000:
-                audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            tts_text = "Uploaded Audio"
-        #YouTube or path
-        elif(vc_input):
-            audio, sr = librosa.load(vc_input, sr=16000, mono=True)
-            vc_audio_mode = "YouTube"
-            tts_text = "YouTube Audio"
-        else:
-            return "Please upload or choose some type of audio.", None  
-
-        if audio is None:
-            if vc_audio_mode == "ETTS":
-                print("Failed to get E-TTS handle. A restart may be needed soon.")
-                return "ERROR: Failed to obtain a correct response from Edge-TTS. The Edge-TTS service may be down or unable to communicate. Please try another method or try again later.", None
-            return "ERROR: Unknown audio error. Please try again.", None
-            
-        times = [0, 0, 0]
-        f0_up_key = int(f0_up_key)
-
-        #Setup model
-        cpt = torch.load(f"{path}", map_location="cpu")
-        tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        if_f0 = cpt.get("f0", 1)
-        version = cpt.get("version", "v1")
-        if version == "v1":
-            if if_f0 == 1:
-                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            model_version = "V1"
-        elif version == "v2":
-            if if_f0 == 1:
-                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-            else:
-                net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            model_version = "V2"
-        del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))
-        net_g.eval().to(config.device)
-        if config.is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        vcIdx = int((tgt_sr/8000)-4)
-
-        #Gen audio
-        audio_opt = vcArr[vcIdx].pipeline(
-            hubert_model,
-            net_g,
-            0,
-            audio,
-            vc_input,
-            times,
-            f0_up_key,
-            f0_method,
-            index,
-            # file_big_npy,
-            index_rate,
-            if_f0,
-            filter_radius,
-            tgt_sr,
-            resample_sr,
-            rms_mix_rate,
-            version,
-            protect,
-            f0_file=None,
-        )
-        info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-        print(f"Successful inference with model {name} | {tts_text} | {info}")
-        del net_g, cpt
-        return info, (tgt_sr, audio_opt)
-    except:
-        info = traceback.format_exc()
-        print(info)
-        return info, (None, None)
-
-def load_model():
-    categories = []
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-        folder_info = json.load(f)
-    for category_name, category_info in folder_info.items():
-        if not category_info['enable']:
-            continue
-        category_title = category_info['title']
-        category_folder = category_info['folder_path']
-        models = []
-        print(f"Creating category {category_title}...")
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-            models_info = json.load(f)
-        for character_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_name = info['model_path']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-            if info['feature_retrieval_library'] == "None":
-                model_index = None
-            if model_index:
-               assert os.path.exists(model_index), f"Model {model_title} failed to load index."
-            if not (model_author in authors or "/" in model_author or "&" in model_author):
-                authors.append(model_author)
-            model_path =  f"weights/{category_folder}/{character_name}/{model_name}"
-            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-            model_version = cpt.get("version", "v1")
-            print(f"Indexed model {model_title} by {model_author} ({model_version})")
-            models.append((character_name, model_title, model_author, model_cover, model_version, model_path, model_index))
-            del cpt
-        categories.append([category_title, category_folder, models])
-    return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        else:
-            # Spotify doesnt work.
-            # Need to find other solution soon.
-            ''' 
-            command = f"spotdl download {url} --output dl_audio/.wav"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            audio_path = "dl_audio/spotify_audio.wav"
-            '''
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = load_model()
-    voices = list(language_dict.keys())
-
-    # Gradio preloading
-    # Input and Upload
-    vc_upload = gr.Audio(label="Upload or record an audio file", interactive=True)
-    # Youtube
-    vc_input = gr.Textbox(label="Input audio path", visible=False)
-    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, value="Youtube", info="Select provider (Default: Youtube)")
-    vc_link = gr.Textbox(label="Youtube URL", info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-    vc_split = gr.Button("Split Audio", variant="primary")
-    vc_vocal_preview = gr.Audio(label="Vocal Preview")
-    vc_inst_preview = gr.Audio(label="Instrumental Preview")
-    vc_audio_preview = gr.Audio(label="Audio Preview")
-    # TTS
-    tts_text = gr.Textbox(label="TTS text", info="Text to speech input (There is a limit of 250 characters)", interactive=True)
-    tts_voice = gr.Dropdown(label="Edge-TTS speaker", choices=voices, allow_custom_value=False, value="English-Ana (Female)", interactive=True)
-    # Other settings
-    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-    f0method0 = gr.Radio(
-        label="Pitch extraction algorithm",
-        info=f0method_info,
-        choices=f0method_mode,
-        value="pm",
-        interactive=True
-    )
-    index_rate1 = gr.Slider(
-        minimum=0,
-        maximum=1,
-        label="Retrieval feature ratio",
-        info="Accent control. Too high will usually sound too robotic. (Default: 0.4)",
-        value=0.4,
-        interactive=True,
-    )
-    filter_radius0 = gr.Slider(
-        minimum=0,
-        maximum=7,
-        label="Apply Median Filtering",
-        info="The value represents the filter radius and can reduce breathiness.",
-        value=1,
-        step=1,
-        interactive=True,
-    )
-    resample_sr0 = gr.Slider(
-        minimum=0,
-        maximum=48000,
-        label="Resample the output audio",
-        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling.",
-        value=0,
-        step=1,
-        interactive=True,
-    )
-    rms_mix_rate0 = gr.Slider(
-        minimum=0,
-        maximum=1,
-        label="Volume Envelope",
-        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-        value=1,
-        interactive=True,
-    )
-    protect0 = gr.Slider(
-        minimum=0,
-        maximum=0.5,
-        label="Voice Protection",
-        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-        value=0.23,
-        step=0.01,
-        interactive=True,
-    )
-
-    with gr.Blocks(theme=gr.themes.Base()) as app:
-        gr.Markdown(
-            "# <center> VTuber RVC Models\n"
-            "### <center> Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds"
-            "<center> Original space by megaaziib & zomehwh\n"
-            "### <center> Please credit the original model authors if you use this Space."
-            "<center>Do no evil.\n\n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Til3SY7-X0x3Wss3YXlgfq8go39DzWHk)\n\n"
-        )
-        gr.Markdown("<center> Looking for more models? <a href=\"https://docs.google.com/spreadsheets/d/1tvZSggOsZGAPjbMrWOAAaoJJFpJuQlwUEQCf5x1ssO8\">Check out the VTuber AI Model Tracking spreadsheet!</a>")
-        for (folder_title, folder, models) in categories:
-            with gr.TabItem(folder_title):
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please add model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, model_version, model_path, model_index) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                with gr.Column():
-                                    gr.Markdown(
-                                        '<div align="center">'
-                                        f'<div>{title}</div>\n'+
-                                        f'<div>RVC {model_version} Model</div>\n'+
-                                        (f'<div>Model author: {author}</div>' if author else "")+
-                                        (f'<img style="width:auto;height:300px;" src="file/{cover}"></img>' if cover else "")+
-                                        '</div>'
-                                    )
-                                with gr.Column():
-                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    #This is a fucking stupid solution but Gradio refuses to pass in values unless I do this.
-                                    vc_name = gr.Textbox(value=title, visible=False, interactive=False)
-                                    vc_mp = gr.Textbox(value=model_path, visible=False, interactive=False)
-                                    vc_mi = gr.Textbox(value=model_index, visible=False, interactive=False)
-                                    vc_convert = gr.Button("Convert", variant="primary")
-
-                                    vc_convert.click(
-                                        fn=infer,
-                                        inputs=[
-                                            vc_name,
-                                            vc_mp,
-                                            vc_mi,
-                                            vc_input, 
-                                            vc_upload,
-                                            tts_text,
-                                            tts_voice,
-                                            vc_transform0,
-                                            f0method0,
-                                            index_rate1,
-                                            filter_radius0,
-                                            resample_sr0,
-                                            rms_mix_rate0,
-                                            protect0
-                                        ],
-                                        outputs=[vc_log, vc_output]
-                                    )
-
-        with gr.Row():
-            with gr.Column():
-                with gr.Tab("Edge-TTS"):
-                    tts_text.render()
-                    tts_voice.render()
-                with gr.Tab("Upload/Record"):
-                    vc_input.render()
-                    vc_upload.render()
-                if(not limitation):
-                    with gr.Tab("YouTube"):
-                        vc_download_audio.render()
-                        vc_link.render()
-                        vc_split_model.render()
-                        vc_split.render()
-                        vc_vocal_preview.render()
-                        vc_inst_preview.render()
-                        vc_audio_preview.render()
-            with gr.Column():
-                vc_transform0.render()
-                f0method0.render()
-                index_rate1.render()
-                with gr.Accordion("Advanced Options", open=False):
-                    filter_radius0.render()
-                    resample_sr0.render()
-                    rms_mix_rate0.render()
-                    protect0.render() 
-
-        vc_split.click(
-            fn=cut_vocal_and_inst, 
-            inputs=[vc_link, vc_download_audio, vc_split_model], 
-            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
-        )
-        
-        authStr=", ".join(authors)
-        gr.Markdown(
-            "## <center>Credit to:\n"
-            "#### <center>Original devs:\n"
-            "<center>the RVC Project, lj1995, zomehwh, sysf\n\n"
-            "#### <center>Model creators:\n"
-            f"<center>{authStr}\n"
-        )
-
-if limitation is True:
-    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"])
-else:
-    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"], share=False)
+print("Starting up. Please be patient...")
+
+import os
+import glob
+import json
+import traceback
+import logging
+import gradio as gr
+import numpy as np
+import librosa
+import torch
+import asyncio
+import edge_tts
+import yt_dlp
+import ffmpeg
+import subprocess
+import sys
+import io
+import wave
+from datetime import datetime
+from fairseq import checkpoint_utils
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from vc_infer_pipeline import VC
+from config import Config
+from edgetts_db import tts_order_voice
+
+#fuck intel
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+config = Config()
+logging.getLogger("numba").setLevel(logging.WARNING)
+limitation = os.getenv("SYSTEM") == "spaces"
+#limitation=True
+language_dict = tts_order_voice
+
+authors = ["dacoolkid44", "Hijack", "Maki Ligon", "megaaziib", "Kit Lemonfoot", "yeey5", "Sui", "MahdeenSky"]
+
+f0method_mode = []
+if limitation is True:
+    f0method_info = "PM is better for testing, RMVPE is better for finalized generations. (Default: PM)"
+    f0method_mode = ["pm", "rmvpe"]
+else:
+    f0method_info = "PM is fast but low quality, crepe and harvest are slow but good quality, RMVPE is the best of both worlds. (Default: PM)"
+    f0method_mode = ["pm", "crepe", "harvest", "rmvpe"]
+
+#Eagerload VCs
+print("Preloading VCs...")
+vcArr=[]
+vcArr.append(VC(32000, config))
+vcArr.append(VC(40000, config))
+vcArr.append(VC(48000, config))
+
+def infer(name, path, index, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect):
+    try:
+        #Setup audio
+        audio=None
+        #Determine audio mode
+        #TTS takes priority over uploads.
+        #Uploads takes priority over paths.
+        vc_audio_mode = ""
+        #Edge-TTS
+        if(tts_text):
+            vc_audio_mode = "ETTS"
+            if len(tts_text) > 250 and limitation:
+                return "Text is too long.", None
+            if tts_text is None or tts_voice is None or tts_text=="":
+                return "You need to enter text and select a voice.", None
+            voice = language_dict[tts_voice]
+            try:
+                asyncio.run(edge_tts.Communicate(tts_text, voice).save("tts.mp3"))
+            except:
+                print("Failed to get E-TTS handle. A restart may be needed soon.")
+                return "ERROR: Failed to communicate with Edge-TTS. The Edge-TTS service may be down or cannot communicate. Please try another method or try again later.", None
+            try:
+                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
+            except:
+                return "ERROR: Invalid characters for the chosen TTS speaker. (Change your TTS speaker to one that supports your language!)", None
+            duration = audio.shape[0] / sr
+            if duration > 30 and limitation:
+                return "Your text generated an audio that was too long.", None
+            vc_input = "tts.mp3"
+        #File upload
+        elif(vc_upload):
+            vc_audio_mode = "Upload"
+            sampling_rate, audio = vc_upload
+            duration = audio.shape[0] / sampling_rate
+            if duration > 60 and limitation:
+                return "Too long! Please upload an audio file that is less than 1 minute.", None
+            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
+            if len(audio.shape) > 1:
+                audio = librosa.to_mono(audio.transpose(1, 0))
+            if sampling_rate != 16000:
+                audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+            tts_text = "Uploaded Audio"
+        #YouTube or path
+        elif(vc_input):
+            audio, sr = librosa.load(vc_input, sr=16000, mono=True)
+            vc_audio_mode = "YouTube"
+            tts_text = "YouTube Audio"
+        else:
+            return "Please upload or choose some type of audio.", None  
+
+        if audio is None:
+            if vc_audio_mode == "ETTS":
+                print("Failed to get E-TTS handle. A restart may be needed soon.")
+                return "ERROR: Failed to obtain a correct response from Edge-TTS. The Edge-TTS service may be down or unable to communicate. Please try another method or try again later.", None
+            return "ERROR: Unknown audio error. Please try again.", None
+            
+        times = [0, 0, 0]
+        f0_up_key = int(f0_up_key)
+
+        #Setup model
+        cpt = torch.load(f"{path}", map_location="cpu")
+        tgt_sr = cpt["config"][-1]
+        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+        if_f0 = cpt.get("f0", 1)
+        version = cpt.get("version", "v1")
+        if version == "v1":
+            if if_f0 == 1:
+                net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+            else:
+                net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+            model_version = "V1"
+        elif version == "v2":
+            if if_f0 == 1:
+                net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+            else:
+                net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+            model_version = "V2"
+        del net_g.enc_q
+        print(net_g.load_state_dict(cpt["weight"], strict=False))
+        net_g.eval().to(config.device)
+        if config.is_half:
+            net_g = net_g.half()
+        else:
+            net_g = net_g.float()
+        vcIdx = int((tgt_sr/8000)-4)
+
+        #Gen audio
+        audio_opt = vcArr[vcIdx].pipeline(
+            hubert_model,
+            net_g,
+            0,
+            audio,
+            vc_input,
+            times,
+            f0_up_key,
+            f0_method,
+            index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            f0_file=None,
+        )
+        info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
+        print(f"Successful inference with model {name} | {tts_text} | {info}")
+        del net_g, cpt
+        return info, (tgt_sr, audio_opt)
+    except:
+        info = traceback.format_exc()
+        print(info)
+        return info, (None, None)
+
+def load_model():
+    categories = []
+    with open("/weights/folder_info.json", "r", encoding="utf-8") as f:
+        folder_info = json.load(f)
+    for category_name, category_info in folder_info.items():
+        if not category_info['enable']:
+            continue
+        category_title = category_info['title']
+        category_folder = category_info['folder_path']
+        models = []
+        print(f"Creating category {category_title}...")
+        with open(f"/weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+            models_info = json.load(f)
+        for character_name, info in models_info.items():
+            if not info['enable']:
+                continue
+            model_title = info['title']
+            model_name = info['model_path']
+            model_author = info.get("author", None)
+            model_cover = f"/weights/{category_folder}/{character_name}/{info['cover']}"
+            model_index = f"/weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+            if info['feature_retrieval_library'] == "None":
+                model_index = None
+            if model_index:
+               assert os.path.exists(model_index), f"Model {model_title} failed to load index."
+            if not (model_author in authors or "/" in model_author or "&" in model_author):
+                authors.append(model_author)
+            model_path =  f"/weights/{category_folder}/{character_name}/{model_name}"
+            cpt = torch.load(f"/weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
+            model_version = cpt.get("version", "v1")
+            print(f"Indexed model {model_title} by {model_author} ({model_version})")
+            models.append((character_name, model_title, model_author, model_cover, model_version, model_path, model_index))
+            del cpt
+        categories.append([category_title, category_folder, models])
+    return categories
+
+
+def cut_vocal_and_inst(url, audio_provider, split_model):
+    if url != "":
+        if not os.path.exists("dl_audio"):
+            os.mkdir("dl_audio")
+        if audio_provider == "Youtube":
+            ydl_opts = {
+            'format': 'bestaudio/best',
+            'postprocessors': [{
+                'key': 'FFmpegExtractAudio',
+                'preferredcodec': 'wav',
+            }],
+            "outtmpl": 'dl_audio/youtube_audio',
+            }
+            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                ydl.download([url])
+            audio_path = "dl_audio/youtube_audio.wav"
+        else:
+            # Spotify doesnt work.
+            # Need to find other solution soon.
+            ''' 
+            command = f"spotdl download {url} --output dl_audio/.wav"
+            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+            print(result.stdout.decode())
+            audio_path = "dl_audio/spotify_audio.wav"
+            '''
+        if split_model == "htdemucs":
+            command = f"demucs --two-stems=vocals {audio_path} -o output"
+            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+            print(result.stdout.decode())
+            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
+        else:
+            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
+            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
+            print(result.stdout.decode())
+            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
+    else:
+        raise gr.Error("URL Required!")
+        return None, None, None, None
+
+def load_hubert():
+    global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    hubert_model.eval()
+
+if __name__ == '__main__':
+    load_hubert()
+    categories = load_model()
+    voices = list(language_dict.keys())
+
+    # Gradio preloading
+    # Input and Upload
+    vc_upload = gr.Audio(label="Upload or record an audio file", interactive=True)
+    # Youtube
+    vc_input = gr.Textbox(label="Input audio path", visible=False)
+    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, value="Youtube", info="Select provider (Default: Youtube)")
+    vc_link = gr.Textbox(label="Youtube URL", info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
+    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
+    vc_split = gr.Button("Split Audio", variant="primary")
+    vc_vocal_preview = gr.Audio(label="Vocal Preview")
+    vc_inst_preview = gr.Audio(label="Instrumental Preview")
+    vc_audio_preview = gr.Audio(label="Audio Preview")
+    # TTS
+    tts_text = gr.Textbox(label="TTS text", info="Text to speech input (There is a limit of 250 characters)", interactive=True)
+    tts_voice = gr.Dropdown(label="Edge-TTS speaker", choices=voices, allow_custom_value=False, value="English-Ana (Female)", interactive=True)
+    # Other settings
+    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
+    f0method0 = gr.Radio(
+        label="Pitch extraction algorithm",
+        info=f0method_info,
+        choices=f0method_mode,
+        value="pm",
+        interactive=True
+    )
+    index_rate1 = gr.Slider(
+        minimum=0,
+        maximum=1,
+        label="Retrieval feature ratio",
+        info="Accent control. Too high will usually sound too robotic. (Default: 0.4)",
+        value=0.4,
+        interactive=True,
+    )
+    filter_radius0 = gr.Slider(
+        minimum=0,
+        maximum=7,
+        label="Apply Median Filtering",
+        info="The value represents the filter radius and can reduce breathiness.",
+        value=1,
+        step=1,
+        interactive=True,
+    )
+    resample_sr0 = gr.Slider(
+        minimum=0,
+        maximum=48000,
+        label="Resample the output audio",
+        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling.",
+        value=0,
+        step=1,
+        interactive=True,
+    )
+    rms_mix_rate0 = gr.Slider(
+        minimum=0,
+        maximum=1,
+        label="Volume Envelope",
+        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
+        value=1,
+        interactive=True,
+    )
+    protect0 = gr.Slider(
+        minimum=0,
+        maximum=0.5,
+        label="Voice Protection",
+        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
+        value=0.23,
+        step=0.01,
+        interactive=True,
+    )
+
+    with gr.Blocks(theme=gr.themes.Base()) as app:
+        gr.Markdown(
+            "# <center> VTuber RVC Models\n"
+            "### <center> Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds"
+            "<center> Original space by megaaziib & zomehwh\n"
+            "### <center> Please credit the original model authors if you use this Space."
+            "<center>Do no evil.\n\n"
+            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Til3SY7-X0x3Wss3YXlgfq8go39DzWHk)\n\n"
+        )
+        gr.Markdown("<center> Looking for more models? <a href=\"https://docs.google.com/spreadsheets/d/1tvZSggOsZGAPjbMrWOAAaoJJFpJuQlwUEQCf5x1ssO8\">Check out the VTuber AI Model Tracking spreadsheet!</a>")
+        for (folder_title, folder, models) in categories:
+            with gr.TabItem(folder_title):
+                with gr.Tabs():
+                    if not models:
+                        gr.Markdown("# <center> No Model Loaded.")
+                        gr.Markdown("## <center> Please add model or fix your model path.")
+                        continue
+                    for (name, title, author, cover, model_version, model_path, model_index) in models:
+                        with gr.TabItem(name):
+                            with gr.Row():
+                                with gr.Column():
+                                    gr.Markdown(
+                                        '<div align="center">'
+                                        f'<div>{title}</div>\n'+
+                                        f'<div>RVC {model_version} Model</div>\n'+
+                                        (f'<div>Model author: {author}</div>' if author else "")+
+                                        (f'<img style="width:auto;height:300px;" src="file/{cover}"></img>' if cover else "")+
+                                        '</div>'
+                                    )
+                                with gr.Column():
+                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
+                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
+                                    #This is a fucking stupid solution but Gradio refuses to pass in values unless I do this.
+                                    vc_name = gr.Textbox(value=title, visible=False, interactive=False)
+                                    vc_mp = gr.Textbox(value=model_path, visible=False, interactive=False)
+                                    vc_mi = gr.Textbox(value=model_index, visible=False, interactive=False)
+                                    vc_convert = gr.Button("Convert", variant="primary")
+
+                                    vc_convert.click(
+                                        fn=infer,
+                                        inputs=[
+                                            vc_name,
+                                            vc_mp,
+                                            vc_mi,
+                                            vc_input, 
+                                            vc_upload,
+                                            tts_text,
+                                            tts_voice,
+                                            vc_transform0,
+                                            f0method0,
+                                            index_rate1,
+                                            filter_radius0,
+                                            resample_sr0,
+                                            rms_mix_rate0,
+                                            protect0
+                                        ],
+                                        outputs=[vc_log, vc_output]
+                                    )
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Tab("Edge-TTS"):
+                    tts_text.render()
+                    tts_voice.render()
+                with gr.Tab("Upload/Record"):
+                    vc_input.render()
+                    vc_upload.render()
+                if(not limitation):
+                    with gr.Tab("YouTube"):
+                        vc_download_audio.render()
+                        vc_link.render()
+                        vc_split_model.render()
+                        vc_split.render()
+                        vc_vocal_preview.render()
+                        vc_inst_preview.render()
+                        vc_audio_preview.render()
+            with gr.Column():
+                vc_transform0.render()
+                f0method0.render()
+                index_rate1.render()
+                with gr.Accordion("Advanced Options", open=False):
+                    filter_radius0.render()
+                    resample_sr0.render()
+                    rms_mix_rate0.render()
+                    protect0.render() 
+
+        vc_split.click(
+            fn=cut_vocal_and_inst, 
+            inputs=[vc_link, vc_download_audio, vc_split_model], 
+            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
+        )
+        
+        authStr=", ".join(authors)
+        gr.Markdown(
+            "## <center>Credit to:\n"
+            "#### <center>Original devs:\n"
+            "<center>the RVC Project, lj1995, zomehwh, sysf\n\n"
+            "#### <center>Model creators:\n"
+            f"<center>{authStr}\n"
+        )
+
+if limitation is True:
+    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"])
+else:
+    app.queue(max_size=20, api_open=config.api).launch(allowed_paths=["/"], share=False)