from typing import Union from argparse import ArgumentParser from pathlib import Path import subprocess import librosa import os import time import random import matplotlib.pyplot as plt import numpy as np from PIL import Image, ImageDraw, ImageFont from moviepy.editor import * from moviepy.video.io.VideoFileClip import VideoFileClip import asyncio import json import hashlib from os import path, getenv from pydub import AudioSegment import gradio as gr import torch import edge_tts from datetime import datetime from scipy.io.wavfile import write 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 # Make MV def make_bars_image(height_values, index, new_height): # Define the size of the image width = 512 height = new_height # Create a new image with a transparent background image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0)) # Get the image drawing context draw = ImageDraw.Draw(image) # Define the rectangle width and spacing rect_width = 2 spacing = 2 # Define the list of height values for the rectangles #height_values = [20, 40, 60, 80, 100, 80, 60, 40] num_bars = len(height_values) # Calculate the total width of the rectangles and the spacing total_width = num_bars * rect_width + (num_bars - 1) * spacing # Calculate the starting position for the first rectangle start_x = int((width - total_width) / 2) # Define the buffer size buffer_size = 80 # Draw the rectangles from left to right x = start_x for i, height in enumerate(height_values): # Define the rectangle coordinates y0 = buffer_size y1 = height + buffer_size x0 = x x1 = x + rect_width # Draw the rectangle draw.rectangle([x0, y0, x1, y1], fill='white') # Move to the next rectangle position if i < num_bars - 1: x += rect_width + spacing # Rotate the image by 180 degrees image = image.rotate(180) # Mirror the image image = image.transpose(Image.FLIP_LEFT_RIGHT) # Save the image image.save('audio_bars_'+ str(index) + '.png') return 'audio_bars_'+ str(index) + '.png' def db_to_height(db_value): # Scale the dB value to a range between 0 and 1 scaled_value = (db_value + 80) / 80 # Convert the scaled value to a height between 0 and 100 height = scaled_value * 50 return height def infer(title, audio_in, image_in): # Load the audio file audio_path = audio_in audio_data, sr = librosa.load(audio_path) # Get the duration in seconds duration = librosa.get_duration(y=audio_data, sr=sr) # Extract the audio data for the desired time start_time = 0 # start time in seconds end_time = duration # end time in seconds start_index = int(start_time * sr) end_index = int(end_time * sr) audio_data = audio_data[start_index:end_index] # Compute the short-time Fourier transform hop_length = 512 stft = librosa.stft(audio_data, hop_length=hop_length) spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max) # Get the frequency values freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0]) # Select the indices of the frequency values that correspond to the desired frequencies n_freqs = 114 freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int) # Extract the dB values for the desired frequencies db_values = [] for i in range(spectrogram.shape[1]): db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i]))) # Print the dB values for the first time frame print(db_values[0]) proportional_values = [] for frame in db_values: proportional_frame = [db_to_height(db) for f, db in frame] proportional_values.append(proportional_frame) print(proportional_values[0]) print("AUDIO CHUNK: " + str(len(proportional_values))) # Open the background image background_image = Image.open(image_in) # Resize the image while keeping its aspect ratio bg_width, bg_height = background_image.size aspect_ratio = bg_width / bg_height new_width = 512 new_height = int(new_width / aspect_ratio) resized_bg = background_image.resize((new_width, new_height)) # Apply black cache for better visibility of the white text bg_cache = Image.open('black_cache.png') resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache) # Create a new ImageDraw object draw = ImageDraw.Draw(resized_bg) # Define the text to be added text = title font = ImageFont.truetype("Lato-Regular.ttf", 16) text_color = (255, 255, 255) # white color # Calculate the position of the text text_width, text_height = draw.textsize(text, font=font) x = 30 y = new_height - 70 # Draw the text on the image draw.text((x, y), text, fill=text_color, font=font) # Save the resized image resized_bg.save('resized_background.jpg') generated_frames = [] for i, frame in enumerate(proportional_values): bars_img = make_bars_image(frame, i, new_height) bars_img = Image.open(bars_img) # Paste the audio bars image on top of the background image fresh_bg = Image.open('resized_background.jpg') fresh_bg.paste(bars_img, (0, 0), mask=bars_img) # Save the image fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg') generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg') print(generated_frames) # Create a video clip from the images clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time)) audio_clip = AudioFileClip(audio_in) clip = clip.set_audio(audio_clip) # Set the output codec codec = 'libx264' audio_codec = 'aac' # Save the video to a file clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec) retimed_clip = VideoFileClip("my_video.mp4") # Set the desired frame rate new_fps = 25 # Create a new clip with the new frame rate new_clip = retimed_clip.set_fps(new_fps) # Save the new clip as a new video file new_clip.write_videofile("my_video_retimed.mp4", codec=codec, audio_codec=audio_codec) return "my_video_retimed.mp4" # mix vocal and non-vocal def mix(audio1, audio2): sound1 = AudioSegment.from_file(audio1) sound2 = AudioSegment.from_file(audio2) length = len(sound1) mixed = sound1[:length].overlay(sound2) mixed.export("song.wav", format="wav") return "song.wav" # 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) > 600 and in_hf_space: return (None, 'Input audio is longer than 600 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.HTML("
" "

🥳🎶🎡 - AI歌手,RVC歌声转换 + AI变声

" "
") gr.Markdown("###
🦄 - 能够自动提取视频中的声音,并去除背景音;Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)
") gr.Markdown("###
更多精彩应用,敬请关注[滔滔AI](http://www.talktalkai.com);滔滔AI,为爱滔滔!💕
") 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(可直接填写相应的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="歌曲人声部分") as_audio_no_vocals = gr.Audio(label="Music only", type="filepath", visible=False) 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('🎶 - 歌声转换'): input_audio = as_audio_vocals vc_convert_btn = gr.Button('进行歌声转换吧!', variant='primary') full_song = gr.Button("加入歌曲伴奏吧!", variant="primary") new_song = gr.Audio(label="AI歌手+伴奏", type="filepath") with gr.Tab('🎙️ - 文本转语音'): tts_input = gr.Textbox( label='请填写您想要转换的文本(中英皆可)', lines=3 ) tts_speaker = gr.Dropdown( [ '%s (%s)' % ( s['FriendlyName'], s['Gender'] ) for s in tts_speakers_list ], label='请选择一个相应语言的说话人', type='index' ) tts_convert_btn = gr.Button('进行AI变声吧', variant='primary') with gr.Tab("📺 - 音乐视频"): with gr.Row(): with gr.Column(): inp1 = gr.Textbox(label="为视频配上精彩的文案吧(选填;英文)") inp2 = new_song inp3 = gr.Image(source='upload', type='filepath', label="上传一张背景图片吧") btn = gr.Button("生成您的专属音乐视频吧", variant="primary") with gr.Column(): out1 = gr.Video(label='您的专属音乐视频') btn.click(fn=infer, inputs=[inp1, inp2, inp3], outputs=[out1]) 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('更多设置', 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='请选择您的AI歌手(必选)', type='index' ) # Model info with gr.Box(): model_info = gr.Markdown( '### AI歌手信息\n' 'Please select a model from dropdown above.', elem_id='model_info' ) output_audio = gr.Audio(label='AI歌手(无伴奏)', type="filepath") 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' ) full_song.click(fn=mix, inputs=[output_audio, as_audio_no_vocals], outputs=[new_song]) model_index.change( update_model_info, inputs=[model_index], outputs=[model_info], show_progress=False, queue=False ) gr.Markdown("###
注意❗:请不要生成会对个人以及组织造成侵害的内容,此程序仅供科研、学习及个人娱乐使用。
") gr.Markdown("###
🧸 - 如何使用此程序:填写视频网址和视频起止时间后,依次点击“提取声音文件吧”、“去除背景音吧”、“进行歌声转换吧!”、“加入歌曲伴奏吧!”四个按键即可。
") gr.HTML(''' ''') app.queue( concurrency_count=1, max_size=20, api_open=args.api ).launch(show_error=True)