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 config = Config() logging.getLogger("numba").setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" #limitation=True language_dict = tts_order_voice audio_mode = [] f0method_mode = [] if limitation is True: f0method_info = "PM is better for testing, RMVPE is better for finalized generations. (Default: RMVPE)" audio_mode = ["Edge-TTS", "Upload audio", "Record Audio"] 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: RMVPE)" audio_mode = ["Edge-TTS", "Youtube", "Upload audio", "Record Audio"] f0method_mode = ["pm", "crepe", "harvest", "rmvpe"] #if os.path.isfile("rmvpe.pt"): # f0method_mode.append("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_audio_mode, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, record_button): try: #Setup audio if vc_audio_mode == "Input path" or "Youtube" and vc_input != "": audio, sr = librosa.load(vc_input, sr=16000, mono=True) elif vc_audio_mode == "Upload audio": if vc_upload is None: return "Please upload an audio file.", None 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" elif vc_audio_mode == "Edge-TTS": 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] asyncio.run(edge_tts.Communicate(tts_text, voice).save("tts.mp3")) 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" elif vc_audio_mode == "Record Audio": if record_button is None: return "Please record some audio.", None sampling_rate, audio = record_button duration = audio.shape[0] / sampling_rate if duration > 60 and limitation: return "Too long! Please record 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 = "Recorded Audio" 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 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 combine_vocal_and_inst(audio_data, audio_volume, split_model): if not os.path.exists("output/result"): os.mkdir("output/result") vocal_path = "output/result/output.wav" output_path = "output/result/combine.mp3" if split_model == "htdemucs": inst_path = "output/htdemucs/youtube_audio/no_vocals.wav" else: inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav" with wave.open(vocal_path, "w") as wave_file: wave_file.setnchannels(1) wave_file.setsampwidth(2) wave_file.setframerate(audio_data[0]) wave_file.writeframes(audio_data[1].tobytes()) command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}' result = subprocess.run(command.split(), stdout=subprocess.PIPE) print(result.stdout.decode()) return output_path 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() def change_audio_mode(vc_audio_mode): if vc_audio_mode == "Input path": return ( # Input & Upload gr.Textbox.update(visible=True), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Slider.update(visible=False), gr.Audio.update(visible=False), gr.Button.update(visible=False), # EdgeTTS gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), # Record Own gr.Audio.update(visible=False) ) elif vc_audio_mode == "Upload audio": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Audio.update(visible=True), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Slider.update(visible=False), gr.Audio.update(visible=False), gr.Button.update(visible=False), # EdgeTTS gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), # Record Own gr.Audio.update(visible=False) ) elif vc_audio_mode == "Youtube": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=True), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), gr.Button.update(visible=True), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Slider.update(visible=True), gr.Audio.update(visible=True), gr.Button.update(visible=True), # TTS gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), # Record Own gr.Audio.update(visible=False) ) elif vc_audio_mode == "Edge-TTS": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Slider.update(visible=False), gr.Audio.update(visible=False), gr.Button.update(visible=False), # TTS gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True), # Record Own gr.Audio.update(visible=False) ) elif vc_audio_mode == "Record Audio": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Slider.update(visible=False), gr.Audio.update(visible=False), gr.Button.update(visible=False), # TTS gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), # Record Own gr.Audio.update(visible=True) ) else: return ( # Input & Upload gr.Textbox.update(visible=False), gr.Audio.update(visible=True), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=False), gr.Slider.update(visible=False), gr.Audio.update(visible=False), gr.Button.update(visible=False), # TTS gr.Textbox.update(visible=False, interactive=True), gr.Dropdown.update(visible=False, interactive=True), # Record Own gr.Audio.update(visible=False) ) if __name__ == '__main__': load_hubert() categories = load_model() voices = list(language_dict.keys()) with gr.Blocks(theme=gr.themes.Base()) as app: gr.Markdown( "#
VTuber RVC Models\n" "###
Space by Kit Lemonfoot / Noel Shirogane's High Flying Birds" "
Original space by megaaziib & zomehwh\n" "###
Please credit the original model authors if you use this Space." "
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" ) for (folder_title, folder, models) in categories: with gr.TabItem(folder_title): with gr.Tabs(): if not models: gr.Markdown("#
No Model Loaded.") gr.Markdown("##
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(): gr.Markdown( '
' f'
{title}
\n'+ f'
RVC {model_version} Model
\n'+ (f'
Model author: {author}
' if author else "")+ (f'' if cover else "")+ '
' ) with gr.Row(): with gr.Column(): vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Edge-TTS") # Input and Upload vc_input = gr.Textbox(label="Input audio path", visible=False) vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True) # Youtube vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)") vc_link = gr.Textbox(label="Youtube URL", visible=False, 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, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)") vc_split = gr.Button("Split Audio", variant="primary", visible=False) vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False) vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False) vc_audio_preview = gr.Audio(label="Audio Preview", visible=False) # TTS tts_text = gr.Textbox(visible=True, 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, visible=True, allow_custom_value=False, value="English-Ana (Female)", interactive=True) # Record Own record_button = gr.Audio(source="microphone", label="Record your own audio", visible=False, interactive=True) with gr.Column(): 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="rmvpe", interactive=True ) if model_index: 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, ) else: index_rate1 = gr.Number(value=0.4, interactive=False, visible=False) with gr.Accordion("Advanced Options", open=False): 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.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_volume = gr.Slider( minimum=0, maximum=10, label="Vocal volume", value=4, interactive=True, step=1, info="Adjust vocal volume (Default: 4}", visible=False ) vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False) vc_combine = gr.Button("Combine",variant="primary", visible=False) vc_convert.click( fn=infer, inputs=[ vc_name, vc_mp, vc_mi, vc_audio_mode, vc_input, vc_upload, tts_text, tts_voice, vc_transform0, f0method0, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, record_button ], outputs=[vc_log, vc_output] ) 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] ) vc_combine.click( fn=combine_vocal_and_inst, inputs=[vc_output, vc_volume, vc_split_model], outputs=[vc_combined_output] ) vc_audio_mode.change( fn=change_audio_mode, inputs=[vc_audio_mode], outputs=[ vc_input, vc_upload, vc_download_audio, vc_link, vc_split_model, vc_split, vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_volume, vc_combined_output, vc_combine, tts_text, tts_voice, record_button ] ) gr.Markdown( "##
Credit to:\n" "####
Original devs:\n" "
the RVC Project, lj1995, zomehwh, sysf\n\n" "####
Model creators:\n" "
dacoolkid44, Hijack, Maki Ligon, megaaziib, KitLemonfoot, yeey5, Sui, MahdeenSky, Itaxhix, Acato, Kyuubical, Listra92, IshimaIshimsky, ZomballTH, Jotape91, RigidSpinner, RandomAssBettel, Mimizukari, Oida, Shu-Kun, Nhat Minh, Ardha27, Legitdark, TempoHawk, 0x3e9, Kaiaya, Skeetawn, Sonphantrung, Pianissimo, RavenCutie21, HinaBl, Brazurl, PetroOne, Rubinlord, Gloomwastragic, Sunesu, Aimbo, Act8113, Blyxeen\n" ) if limitation is True: app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab) else: app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=False)