from typing import Union from argparse import ArgumentParser from pathlib import Path import subprocess import librosa import os import time import asyncio import json import hashlib from os import path, getenv import gradio as gr import torch import numpy as np import librosa import edge_tts import config import util from infer_pack.models import ( SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono ) from vc_infer_pipeline import VC # Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa in_hf_space = getenv('SYSTEM') == 'spaces' high_quality = True # Argument parsing arg_parser = ArgumentParser() arg_parser.add_argument( '--hubert', default=getenv('RVC_HUBERT', 'hubert_base.pt'), help='path to hubert base model (default: hubert_base.pt)' ) arg_parser.add_argument( '--config', default=getenv('RVC_MULTI_CFG', 'multi_config.json'), help='path to config file (default: multi_config.json)' ) arg_parser.add_argument( '--api', action='store_true', help='enable api endpoint' ) arg_parser.add_argument( '--cache-examples', action='store_true', help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa ) args = arg_parser.parse_args() app_css = ''' #model_info img { max-width: 100px; max-height: 100px; float: right; } #model_info p { margin: unset; } ''' app = gr.Blocks( theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"), css=app_css, analytics_enabled=False ) # Load hubert model hubert_model = util.load_hubert_model(config.device, args.hubert) hubert_model.eval() # Load models multi_cfg = json.load(open(args.config, 'r')) loaded_models = [] for model_name in multi_cfg.get('models'): print(f'Loading model: {model_name}') # Load model info model_info = json.load( open(path.join('model', model_name, 'config.json'), 'r') ) # Load RVC checkpoint cpt = torch.load( path.join('model', model_name, model_info['model']), 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) net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono] if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt['config'], is_half=util.is_half(config.device) ) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config']) del net_g.enc_q # According to original code, this thing seems necessary. print(net_g.load_state_dict(cpt['weight'], strict=False)) net_g.eval().to(config.device) net_g = net_g.half() if util.is_half(config.device) else net_g.float() vc = VC(tgt_sr, config) loaded_models.append(dict( name=model_name, metadata=model_info, vc=vc, net_g=net_g, if_f0=if_f0, target_sr=tgt_sr )) print(f'Models loaded: {len(loaded_models)}') # Edge TTS speakers tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa # Bilibili def youtube_downloader( video_identifier, start_time, end_time, output_filename="track.wav", num_attempts=5, url_base="", quiet=False, force=True, ): output_path = Path(output_filename) if output_path.exists(): if not force: return output_path else: output_path.unlink() quiet = "--quiet --no-warnings" if quiet else "" command = f""" yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}" # noqa: E501 """.strip() attempts = 0 while True: try: _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT) except subprocess.CalledProcessError: attempts += 1 if attempts == num_attempts: return None else: break if output_path.exists(): return output_path else: return None def audio_separated(audio_input, progress=gr.Progress()): # start progress progress(progress=0, desc="Starting...") time.sleep(0.1) # check file input if audio_input is None: # show progress for i in progress.tqdm(range(100), desc="Please wait..."): time.sleep(0.01) return (None, None, 'Please input audio.') # create filename filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S") # progress progress(progress=0.10, desc="Please wait...") # make dir output os.makedirs("output", exist_ok=True) # progress progress(progress=0.20, desc="Please wait...") # write if high_quality: write(filename+".wav", audio_input[0], audio_input[1]) else: write(filename+".mp3", audio_input[0], audio_input[1]) # progress progress(progress=0.50, desc="Please wait...") # demucs process if high_quality: command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output" else: command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output" os.system(command_demucs) # progress progress(progress=0.70, desc="Please wait...") # remove file audio if high_quality: command_delete = "rm -v ./"+filename+".wav" else: command_delete = "rm -v ./"+filename+".mp3" os.system(command_delete) # progress progress(progress=0.80, desc="Please wait...") # progress for i in progress.tqdm(range(80,100), desc="Please wait..."): time.sleep(0.1) if high_quality: return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..." else: return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..." # https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa def vc_func( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): if input_audio is None: return (None, 'Please provide input audio.') if model_index is None: return (None, 'Please select a model.') model = loaded_models[model_index] # Reference: so-vits (audio_samp, audio_npy) = input_audio # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49 # Can be change well, we will see if (audio_npy.shape[0] / audio_samp) > 60 and in_hf_space: return (None, 'Input audio is longer than 60 secs.') # Bloody hell: https://stackoverflow.com/questions/26921836/ if audio_npy.dtype != np.float32: # :thonk: audio_npy = ( audio_npy / np.iinfo(audio_npy.dtype).max ).astype(np.float32) if len(audio_npy.shape) > 1: audio_npy = librosa.to_mono(audio_npy.transpose(1, 0)) if audio_samp != 16000: audio_npy = librosa.resample( audio_npy, orig_sr=audio_samp, target_sr=16000 ) pitch_int = int(pitch_adjust) resample = ( 0 if resample_option == 'Disable resampling' else int(resample_option) ) times = [0, 0, 0] checksum = hashlib.sha512() checksum.update(audio_npy.tobytes()) output_audio = model['vc'].pipeline( hubert_model, model['net_g'], model['metadata'].get('speaker_id', 0), audio_npy, checksum.hexdigest(), times, pitch_int, f0_method, path.join('model', model['name'], model['metadata']['feat_index']), feat_ratio, model['if_f0'], filter_radius, model['target_sr'], resample, rms_mix_rate, 'v2' ) out_sr = ( resample if resample >= 16000 and model['target_sr'] != resample else model['target_sr'] ) print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s') return ((out_sr, output_audio), 'Success') async def edge_tts_vc_func( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): if input_text is None: return (None, 'Please provide TTS text.') if tts_speaker is None: return (None, 'Please select TTS speaker.') if model_index is None: return (None, 'Please select a model.') speaker = tts_speakers_list[tts_speaker]['ShortName'] (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text) return vc_func( (tts_sr, tts_np), model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) def update_model_info(model_index): if model_index is None: return str( '### Model info\n' 'Please select a model from dropdown above.' ) model = loaded_models[model_index] model_icon = model['metadata'].get('icon', '') return str( '### Model info\n' '![model icon]({icon})' '**{name}**\n\n' 'Author: {author}\n\n' 'Source: {source}\n\n' '{note}' ).format( name=model['metadata'].get('name'), author=model['metadata'].get('author', 'Anonymous'), source=model['metadata'].get('source', 'Unknown'), note=model['metadata'].get('note', ''), icon=( model_icon if model_icon.startswith(('http://', 'https://')) else '/file/model/%s/%s' % (model['name'], model_icon) ) ) def _example_vc( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): (audio, message) = vc_func( input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) return ( audio, message, update_model_info(model_index) ) async def _example_edge_tts( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ): (audio, message) = await edge_tts_vc_func( input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_option ) return ( audio, message, update_model_info(model_index) ) with app: gr.Markdown( '## A simplistic Web interface\n' 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa ) with gr.Tab("🤗 - B站视频提取声音"): with gr.Row(): with gr.Column(): ydl_url_input = gr.Textbox(label="B站视频网址(请填写相应的BV号)", value = "https://www.bilibili.com/video/BV...") start = gr.Number(value=0, label="起始时间 (秒)") end = gr.Number(value=15, label="结束时间 (秒)") ydl_url_submit = gr.Button("提取声音文件吧", variant="primary") as_audio_submit = gr.Button("去除背景音吧", variant="primary") with gr.Column(): ydl_audio_output = gr.Audio(label="Audio from Bilibili") as_audio_input = ydl_audio_output as_audio_vocals = gr.Audio(label="Vocal only") as_audio_no_vocals = gr.Audio(label="Music only", type="filepath") as_audio_message = gr.Textbox(label="Message", visible=False) ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end], outputs=[ydl_audio_output]) as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True) with gr.Row(): with gr.Column(): with gr.Tab('Audio conversion'): input_audio = gr.Audio(label='Input audio') vc_convert_btn = gr.Button('Convert', variant='primary') with gr.Tab('TTS conversion'): tts_input = gr.TextArea( label='TTS input text' ) tts_speaker = gr.Dropdown( [ '%s (%s)' % ( s['FriendlyName'], s['Gender'] ) for s in tts_speakers_list ], label='TTS speaker', type='index' ) tts_convert_btn = gr.Button('Convert', variant='primary') pitch_adjust = gr.Slider( label='Pitch', minimum=-24, maximum=24, step=1, value=0 ) f0_method = gr.Radio( label='f0 methods', choices=['pm', 'harvest'], value='pm', interactive=True ) with gr.Accordion('Advanced options', open=False): feat_ratio = gr.Slider( label='Feature ratio', minimum=0, maximum=1, step=0.1, value=0.6 ) filter_radius = gr.Slider( label='Filter radius', minimum=0, maximum=7, step=1, value=3 ) rms_mix_rate = gr.Slider( label='Volume envelope mix rate', minimum=0, maximum=1, step=0.1, value=1 ) resample_rate = gr.Dropdown( [ 'Disable resampling', '16000', '22050', '44100', '48000' ], label='Resample rate', value='Disable resampling' ) with gr.Column(): # Model select model_index = gr.Dropdown( [ '%s - %s' % ( m['metadata'].get('source', 'Unknown'), m['metadata'].get('name') ) for m in loaded_models ], label='Model', type='index' ) # Model info with gr.Box(): model_info = gr.Markdown( '### Model info\n' 'Please select a model from dropdown above.', elem_id='model_info' ) output_audio = gr.Audio(label='Output audio') output_msg = gr.Textbox(label='Output message') multi_examples = multi_cfg.get('examples') if ( multi_examples and multi_examples.get('vc') and multi_examples.get('tts_vc') ): with gr.Accordion('Sweet sweet examples', open=False): with gr.Row(): # VC Example if multi_examples.get('vc'): gr.Examples( label='Audio conversion examples', examples=multi_examples.get('vc'), inputs=[ input_audio, model_index, pitch_adjust, f0_method, feat_ratio ], outputs=[output_audio, output_msg, model_info], fn=_example_vc, cache_examples=args.cache_examples, run_on_click=args.cache_examples ) # Edge TTS Example if multi_examples.get('tts_vc'): gr.Examples( label='TTS conversion examples', examples=multi_examples.get('tts_vc'), inputs=[ tts_input, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio ], outputs=[output_audio, output_msg, model_info], fn=_example_edge_tts, cache_examples=args.cache_examples, run_on_click=args.cache_examples ) vc_convert_btn.click( vc_func, [ input_audio, model_index, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_rate ], [output_audio, output_msg], api_name='audio_conversion' ) tts_convert_btn.click( edge_tts_vc_func, [ tts_input, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, filter_radius, rms_mix_rate, resample_rate ], [output_audio, output_msg], api_name='tts_conversion' ) model_index.change( update_model_info, inputs=[model_index], outputs=[model_info], show_progress=False, queue=False ) app.queue( concurrency_count=1, max_size=20, api_open=args.api ).launch(show_error=True)