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 sys import io from datetime import datetime from lib.config.config import Config from lib.vc.vc_infer_pipeline import VC from lib.vc.settings import change_audio_mode from lib.vc.audio import load_audio from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from lib.vc.utils import ( combine_vocal_and_inst, cut_vocal_and_inst, download_audio, load_hubert ) config = Config() logging.getLogger("numba").setLevel(logging.WARNING) logger = logging.getLogger(__name__) spaces = os.getenv("SYSTEM") == "spaces" force_support = None if config.unsupported is False: if config.device == "mps" or config.device == "cpu": force_support = False else: force_support = True audio_mode = [] f0method_mode = [] f0method_info = "" hubert_model = load_hubert(config) if force_support is False or spaces is True: if spaces is True: audio_mode = ["Upload audio", "TTS Audio"] else: audio_mode = ["Input path", "Upload audio", "TTS Audio"] f0method_mode = ["pm", "harvest"] f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)" else: audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"] f0method_mode = ["pm", "harvest", "crepe"] f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)" if os.path.isfile("rmvpe.pt"): f0method_mode.insert(2, "rmvpe") def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index): def vc_fn( 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, ): try: logs = [] logger.info(f"Converting using {model_name}...") logs.append(f"Converting using {model_name}...") yield "\n".join(logs), None if vc_audio_mode == "Input path" or "Youtube" and vc_input != "": audio = load_audio(vc_input, 16000) audio_max = np.abs(audio).max() / 0.95 if audio_max > 1: audio /= audio_max elif vc_audio_mode == "Upload audio": if vc_upload is None: return "You need to upload an audio", None sampling_rate, audio = vc_upload duration = audio.shape[0] / sampling_rate if duration > 90 and spaces: return "Please upload an audio file that is less than 90 seconds. If you need to generate a longer audio file, please use Colab.", 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) elif vc_audio_mode == "TTS Audio": if len(tts_text) > 100 and spaces: return "Text is too long", None if tts_text is None or tts_voice is None: return "You need to enter text and select a voice", None os.makedirs("output", exist_ok=True) os.makedirs(os.path.join("output", "tts"), exist_ok=True) asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(os.path.join("output", "tts", "tts.mp3"))) audio, sr = librosa.load(os.path.join("output", "tts", "tts.mp3"), sr=16000, mono=True) vc_input = os.path.join("output", "tts", "tts.mp3") times = [0, 0, 0] f0_up_key = int(f0_up_key) audio_opt = vc.pipeline( hubert_model, net_g, 0, audio, vc_input, times, f0_up_key, f0_method, file_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" logger.info(f"{model_name} | {info}") logs.append(f"Successfully Convert {model_name}\n{info}") yield "\n".join(logs), (tgt_sr, audio_opt) except Exception as err: info = traceback.format_exc() logger.error(info) logger.error(f"Error when using {model_name}.\n{str(err)}") yield info, None return vc_fn def load_model(): categories = [] category_count = 0 if os.path.isfile("weights/folder_info.json"): 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'] description = category_info['description'] models = [] 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']}" cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", 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 logger.info(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() vc = VC(tgt_sr, config) logger.info(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})") models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index))) category_count += 1 categories.append([category_title, description, models]) elif os.path.exists("weights"): models = [] for w_root, w_dirs, _ in os.walk("weights"): model_count = 1 for sub_dir in w_dirs: pth_files = glob.glob(f"weights/{sub_dir}/*.pth") index_files = glob.glob(f"weights/{sub_dir}/*.index") if pth_files == []: logger.debug(f"Model [{model_count}/{len(w_dirs)}]: No Model file detected, skipping...") continue cpt = torch.load(pth_files[0]) 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 logger.info(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() vc = VC(tgt_sr, config) if index_files == []: logger.warning("No Index file detected!") index_info = "None" model_index = "" else: index_info = index_files[0] model_index = index_files[0] logger.info(f"Model loaded [{model_count}/{len(w_dirs)}]: {index_files[0]} / {index_info} | ({model_version})") model_count += 1 models.append((index_files[0][:-4], index_files[0][:-4], "", "", model_version, create_vc_fn(index_files[0], tgt_sr, net_g, vc, if_f0, version, model_index))) categories.append(["Models", "", models]) else: categories = [] return categories if __name__ == '__main__': categories = load_model() tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices()) voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] with gr.Blocks() as app: gr.Markdown( "
\n\n"+ "# Model RVC\n\n"+ " kalau upload Audio sampai 90 ada lambat sikit\n\n"+ " guna pm saja kalau guna harvest slow betul\n\n"+ "[![Repository](https://img.shields.io/badge/Github-Multi%20Model%20RVC%20Inference-blue?style=for-the-badge&logo=github)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)\n\n"+ "
" ) if categories == []: gr.Markdown( "
\n\n"+ "## No model found, please add the model into weights folder\n\n"+ "
" ) for (folder_title, description, models) in categories: with gr.TabItem(folder_title): if description: gr.Markdown(f"###
{description}") with gr.Tabs(): if not models: gr.Markdown("#
No Model Loaded.") gr.Markdown("##
Please add the model or fix your model path.") continue for (name, title, author, cover, model_version, vc_fn) 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(): if spaces is False: with gr.TabItem("Input"): with gr.Row(): with gr.Column(): vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio") # Input vc_input = gr.Textbox(label="Input audio path", visible=False) # Upload vc_upload = gr.Audio(label="Upload audio file", sources=["upload", "microphone"], visible=True, 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_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False) vc_download_button = gr.Button("Download Audio", variant="primary", visible=False) vc_audio_preview = gr.Audio(label="Audio Preview", visible=False) # TTS tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False) tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") with gr.Column(): vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)") vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False) 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) with gr.TabItem("Convert"): with gr.Row(): 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="pm", interactive=True ) index_rate1 = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", info="(Default: 0.7)", value=0.7, 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=3, 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=0.5, 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) vc_convert = gr.Button("Convert", variant="primary") vc_vocal_volume = gr.Slider( minimum=0, maximum=10, label="Vocal volume", value=1, interactive=True, step=1, info="Adjust vocal volume (Default: 1}", visible=False ) vc_inst_volume = gr.Slider( minimum=0, maximum=10, label="Instrument volume", value=1, interactive=True, step=1, info="Adjust instrument volume (Default: 1}", visible=False ) vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False) vc_combine = gr.Button("Combine",variant="primary", visible=False) else: with gr.Column(): vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio") # Input vc_input = gr.Textbox(label="Input audio path", visible=False) # Upload vc_upload = gr.Audio(label="Upload audio file", sources=["upload", "microphone"], visible=True, 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_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False) vc_download_button = gr.Button("Download Audio", variant="primary", visible=False) vc_audio_preview = gr.Audio(label="Audio Preview", visible=False) # Splitter vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)") vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False) 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) # TTS tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False) tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") 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="pm", interactive=True ) index_rate1 = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", info="(Default: 0.7)", value=0.7, 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=3, 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=0.5, 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) vc_convert = gr.Button("Convert", variant="primary") vc_vocal_volume = gr.Slider( minimum=0, maximum=10, label="Vocal volume", value=1, interactive=True, step=1, info="Adjust vocal volume (Default: 1}", visible=False ) vc_inst_volume = gr.Slider( minimum=0, maximum=10, label="Instrument volume", value=1, interactive=True, step=1, info="Adjust instrument volume (Default: 1}", 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=vc_fn, inputs=[ vc_audio_mode, 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] ) vc_download_button.click( fn=download_audio, inputs=[vc_link, vc_download_audio], outputs=[vc_audio_preview, vc_log_yt] ) vc_split.click( fn=cut_vocal_and_inst, inputs=[vc_split_model], outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input] ) vc_combine.click( fn=combine_vocal_and_inst, inputs=[vc_output, vc_vocal_volume, vc_inst_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_log_yt, vc_download_button, vc_split_model, vc_split_log, vc_split, vc_audio_preview, vc_vocal_preview, vc_inst_preview, vc_vocal_volume, vc_inst_volume, vc_combined_output, vc_combine, tts_text, tts_voice ] ) app.queue( max_size=20, api_open=config.api, ).launch( share=config.share, max_threads=1, allowed_paths=["weights"] )