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 infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from vc_infer_pipeline import VC from config import Config config = Config() logging.getLogger("numba").setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index): def vc_fn( vc_audio_mode, vc_input, vc_upload, tts_text, tts_voice, f0_up_key, vc_transform, f0_method, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, ): try: 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 "You need to upload an audio", None sampling_rate, audio = vc_upload duration = audio.shape[0] / sampling_rate if duration > 20 and limitation: return "Please upload an audio file that is less than 20 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 limitation: 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 asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3")) audio, sr = librosa.load("tts.mp3", sr=16000, mono=True) vc_input = "tts.mp3" times = [0, 0, 0] f0_up_key = int(f0_up_key) audio_opt = vc.pipeline( hubert_model, net_g, vc_transform, audio, vc_input, times, f0_up_key, f0_method, file_index, 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(info) return info, (tgt_sr, audio_opt) except: info = traceback.format_exc() print(info) return info, (None, None) return vc_fn 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), # TTS gr.Textbox.update(visible=False), gr.Dropdown.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), # TTS gr.Textbox.update(visible=False), gr.Dropdown.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) ) elif vc_audio_mode == "TTS 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=True), gr.Dropdown.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), gr.Dropdown.update(visible=False) ) if __name__ == '__main__': load_hubert() categories = [] tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] 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 model_name, info in models_info.items(): if not info['enable']: continue model_title = info['title'] model_author = info.get("author", None) model_cover = f"weights/{category_folder}/{model_name}/{info['cover']}" model_index = f"weights/{category_folder}/{model_name}/{info['feature_retrieval_library']}" cpt = torch.load(f"weights/{category_folder}/{model_name}/{model_name}.pth", 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"]) nodel_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"]) nodel_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() vc = VC(tgt_sr, config) print(f"Model loaded: {model_name}") models.append((model_name, model_title, model_author, model_cover, nodel_version, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index))) categories.append([category_title, category_folder, description, models]) with gr.Blocks() as app: gr.Markdown( "#
RVC Genshin Impact Inference\n" "#### From [Retrieval-based-Voice-Conversion](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)\n" "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)" ) for (folder_title, folder, 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 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(): with gr.Column(): vc_audio_mode = gr.Dropdown(label="Input voice", choices=["Upload audio", "TTS Audio"], allow_custom_value=False, value="Upload audio") # Input and Upload vc_input = gr.Textbox(label="Input audio path", visible=False) vc_upload = gr.Audio(label="Upload audio file", 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_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=False, label="TTS text", info="Text to speech input") tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") with gr.Column(): spk_item = gr.Slider( minimum=0, maximum=2333, step=1, label="Speaker ID", info="(Default: 0)", value=0, interactive=True, ) 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="PM is fast, Harvest is good but extremely slow (Default: PM)", choices=["pm", "harvest"], value="pm", interactive=True, ) index_rate1 = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", info="(Default: 0.6)", value=0.6, 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=0.35, 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_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=vc_fn, inputs=[ vc_audio_mode, vc_input, vc_upload, tts_text, tts_voice, spk_item, vc_transform0, f0method0, index_rate1, filter_radius0, resample_sr0, rms_mix_rate0, protect0, ], 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_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 ] ) app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)