import torch, os, traceback, sys, warnings, shutil, numpy as np import gradio as gr import librosa import asyncio import rarfile import edge_tts import yt_dlp import ffmpeg import gdown import subprocess import wave import soundfile as sf from scipy.io import wavfile from datetime import datetime from urllib.parse import urlparse from mega import Mega from Applio import * os.system("aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d . -o hubert_base.pt") os.system("aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d . -o rmvpe.pt") os.system("aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/sail-rvc/yoimiya-jp/resolve/main/model.pth -d ./weights -o yoimiya.pth") os.system("aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/sail-rvc/yoimiya-jp/resolve/main/model.index -d ./weights/index -o yoimiya.index") now_dir = os.getcwd() tmp = os.path.join(now_dir, "TEMP") shutil.rmtree(tmp, ignore_errors=True) os.makedirs(tmp, exist_ok=True) os.environ["TEMP"] = tmp from lib.infer_pack.models import ( SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono, SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono, ) from fairseq import checkpoint_utils from vc_infer_pipeline import VC from config import Config config = Config() 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] hubert_model = None f0method_mode = ["pm", "harvest", "crepe"] f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)" if os.path.isfile("rmvpe.pt"): f0method_mode.insert(2, "rmvpe") 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)" 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() load_hubert() weight_root = "weights" index_root = "weights/index" weights_model = [] weights_index = [] for _, _, model_files in os.walk(weight_root): for file in model_files: if file.endswith(".pth"): weights_model.append(file) for _, _, index_files in os.walk(index_root): for file in index_files: if file.endswith('.index') and "trained" not in file: weights_index.append(os.path.join(index_root, file)) def check_models(): weights_model = [] weights_index = [] for _, _, model_files in os.walk(weight_root): for file in model_files: if file.endswith(".pth"): weights_model.append(file) for _, _, index_files in os.walk(index_root): for file in index_files: if file.endswith('.index') and "trained" not in file: weights_index.append(os.path.join(index_root, file)) return ( gr.Dropdown.update(choices=sorted(weights_model), value=weights_model[0]), gr.Dropdown.update(choices=sorted(weights_index)) ) def clean(): return ( gr.Dropdown.update(value=""), gr.Slider.update(visible=False) ) def vc_single( sid, vc_audio_mode, input_audio_path, input_upload_audio, vocal_audio, tts_text, tts_voice, f0_up_key, f0_file, f0_method, file_index, index_rate, filter_radius, resample_sr, rms_mix_rate, protect ): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 global tgt_sr, net_g, vc, hubert_model, version, cpt try: logs = [] print(f"Converting...") logs.append(f"Converting...") yield "\n".join(logs), None if vc_audio_mode == "Input path" or "Youtube" and input_audio_path != "": audio, sr = librosa.load(input_audio_path, sr=16000, mono=True) elif vc_audio_mode == "Upload audio": selected_audio = input_upload_audio if vocal_audio: selected_audio = vocal_audio elif input_upload_audio: selected_audio = input_upload_audio sampling_rate, audio = selected_audio duration = audio.shape[0] / sampling_rate 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 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) input_audio_path = "tts.mp3" f0_up_key = int(f0_up_key) times = [0, 0, 0] if hubert_model == None: load_hubert() if_f0 = cpt.get("f0", 1) audio_opt = vc.pipeline( hubert_model, net_g, sid, audio, input_audio_path, 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=f0_file ) if resample_sr >= 16000 and tgt_sr != resample_sr: tgt_sr = resample_sr index_info = ( "Using index:%s." % file_index if os.path.exists(file_index) else "Index not used." ) print("Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( index_info, times[0], times[1], times[2], )) info = f"{index_info}\n[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s" logs.append(info) yield "\n".join(logs), (tgt_sr, audio_opt) except: info = traceback.format_exc() print(info) logs.append(info) yield "\n".join(logs), None def get_vc(sid, to_return_protect0): global n_spk, tgt_sr, net_g, vc, cpt, version, weights_index if sid == "" or sid == []: global hubert_model if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 print("clean_empty_cache") del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None if torch.cuda.is_available(): torch.cuda.empty_cache() ###楼下不这么折腾清理不干净 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid( *cpt["config"], is_half=config.is_half ) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) del net_g, cpt if torch.cuda.is_available(): torch.cuda.empty_cache() cpt = None return ( gr.Slider.update(maximum=2333, visible=False), gr.Slider.update(visible=True), gr.Dropdown.update(choices=sorted(weights_index), value=""), gr.Markdown.update(value="#
No model selected") ) print(f"Loading {sid} model...") selected_model = sid[:-4] cpt = torch.load(os.path.join(weight_root, sid), map_location="cpu") tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] if_f0 = cpt.get("f0", 1) if if_f0 == 0: to_return_protect0 = { "visible": False, "value": 0.5, "__type__": "update", } else: to_return_protect0 = { "visible": True, "value": to_return_protect0, "__type__": "update", } 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"]) elif version == "v2": if if_f0 == 1: net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) 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) n_spk = cpt["config"][-3] weights_index = [] for _, _, index_files in os.walk(index_root): for file in index_files: if file.endswith('.index') and "trained" not in file: weights_index.append(os.path.join(index_root, file)) if weights_index == []: selected_index = gr.Dropdown.update(value="") else: selected_index = gr.Dropdown.update(value=weights_index[0]) for index, model_index in enumerate(weights_index): if selected_model in model_index: selected_index = gr.Dropdown.update(value=weights_index[index]) break return ( gr.Slider.update(maximum=n_spk, visible=True), to_return_protect0, selected_index, gr.Markdown.update( f'##
{selected_model}\n'+ f'###
RVC {version} Model' ) ) def find_audio_files(folder_path, extensions): audio_files = [] for root, dirs, files in os.walk(folder_path): for file in files: if any(file.endswith(ext) for ext in extensions): audio_files.append(file) return audio_files def vc_multi( spk_item, vc_input, vc_output, vc_transform0, f0method0, file_index, index_rate, filter_radius, resample_sr, rms_mix_rate, protect, ): global tgt_sr, net_g, vc, hubert_model, version, cpt logs = [] logs.append("Converting...") yield "\n".join(logs) print() try: if os.path.exists(vc_input): folder_path = vc_input extensions = [".mp3", ".wav", ".flac", ".ogg"] audio_files = find_audio_files(folder_path, extensions) for index, file in enumerate(audio_files, start=1): audio, sr = librosa.load(os.path.join(folder_path, file), sr=16000, mono=True) input_audio_path = folder_path, file f0_up_key = int(vc_transform0) times = [0, 0, 0] if hubert_model == None: load_hubert() if_f0 = cpt.get("f0", 1) audio_opt = vc.pipeline( hubert_model, net_g, spk_item, audio, input_audio_path, times, f0_up_key, f0method0, file_index, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, f0_file=None ) if resample_sr >= 16000 and tgt_sr != resample_sr: tgt_sr = resample_sr output_path = f"{os.path.join(vc_output, file)}" os.makedirs(os.path.join(vc_output), exist_ok=True) sf.write( output_path, audio_opt, tgt_sr, ) info = f"{index} / {len(audio_files)} | {file}" print(info) logs.append(info) yield "\n".join(logs) else: logs.append("Folder not found or path doesn't exist.") yield "\n".join(logs) except: info = traceback.format_exc() print(info) logs.append(info) yield "\n".join(logs) def download_audio(url, audio_provider): logs = [] os.makedirs("dl_audio", exist_ok=True) if url == "": logs.append("URL required!") yield None, "\n".join(logs) return None, "\n".join(logs) if audio_provider == "Youtube": logs.append("Downloading the audio...") yield None, "\n".join(logs) ydl_opts = { 'noplaylist': True, 'format': 'bestaudio/best', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', }], "outtmpl": 'result/dl_audio/audio', } audio_path = "result/dl_audio/audio.wav" with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([url]) logs.append("Download Complete.") yield audio_path, "\n".join(logs) def cut_vocal_and_inst_yt(split_model): logs = [] logs.append("Starting the audio splitting process...") yield "\n".join(logs), None, None, None command = f"demucs --two-stems=vocals -n {split_model} result/dl_audio/audio.wav -o output" result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True) for line in result.stdout: logs.append(line) yield "\n".join(logs), None, None, None print(result.stdout) vocal = f"output/{split_model}/audio/vocals.wav" inst = f"output/{split_model}/audio/no_vocals.wav" logs.append("Audio splitting complete.") yield "\n".join(logs), vocal, inst, vocal def cut_vocal_and_inst(split_model, audio_data): logs = [] vocal_path = "output/result/audio.wav" os.makedirs("output/result", exist_ok=True) wavfile.write(vocal_path, audio_data[0], audio_data[1]) logs.append("Starting the audio splitting process...") yield "\n".join(logs), None, None command = f"demucs --two-stems=vocals -n {split_model} {vocal_path} -o output" result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True) for line in result.stdout: logs.append(line) yield "\n".join(logs), None, None print(result.stdout) vocal = f"output/{split_model}/audio/vocals.wav" inst = f"output/{split_model}/audio/no_vocals.wav" logs.append("Audio splitting complete.") yield "\n".join(logs), vocal, inst def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model): os.makedirs("output/result", exist_ok=True) vocal_path = "output/result/output.wav" output_path = "output/result/combine.mp3" inst_path = f"output/{split_model}/audio/no_vocals.wav" wavfile.write(vocal_path, audio_data[0], audio_data[1]) command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}' result = subprocess.run(command.split(), stdout=subprocess.PIPE) print(result.stdout.decode()) return output_path def download_and_extract_models(urls): logs = [] os.makedirs("zips", exist_ok=True) os.makedirs(os.path.join("zips", "extract"), exist_ok=True) os.makedirs(os.path.join(weight_root), exist_ok=True) os.makedirs(os.path.join(index_root), exist_ok=True) for link in urls.splitlines(): url = link.strip() if not url: raise gr.Error("URL Required!") return "No URLs provided." model_zip = urlparse(url).path.split('/')[-2] + '.zip' model_zip_path = os.path.join('zips', model_zip) logs.append(f"Downloading...") yield "\n".join(logs) if "drive.google.com" in url: gdown.download(url, os.path.join("zips", "extract"), quiet=False) elif "mega.nz" in url: m = Mega() m.download_url(url, 'zips') else: os.system(f"wget {url} -O {model_zip_path}") logs.append(f"Extracting...") yield "\n".join(logs) for filename in os.listdir("zips"): archived_file = os.path.join("zips", filename) if filename.endswith(".zip"): shutil.unpack_archive(archived_file, os.path.join("zips", "extract"), 'zip') elif filename.endswith(".rar"): with rarfile.RarFile(archived_file, 'r') as rar: rar.extractall(os.path.join("zips", "extract")) for _, dirs, files in os.walk(os.path.join("zips", "extract")): logs.append(f"Searching Model and Index...") yield "\n".join(logs) model = False index = False if files: for file in files: if file.endswith(".pth"): basename = file[:-4] shutil.move(os.path.join("zips", "extract", file), os.path.join(weight_root, file)) model = True if file.endswith('.index') and "trained" not in file: shutil.move(os.path.join("zips", "extract", file), os.path.join(index_root, file)) index = True else: logs.append("No model in main folder.") yield "\n".join(logs) logs.append("Searching in subfolders...") yield "\n".join(logs) for sub_dir in dirs: for _, _, sub_files in os.walk(os.path.join("zips", "extract", sub_dir)): for file in sub_files: if file.endswith(".pth"): basename = file[:-4] shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(weight_root, file)) model = True if file.endswith('.index') and "trained" not in file: shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(index_root, file)) index = True shutil.rmtree(os.path.join("zips", "extract", sub_dir)) if index is False: logs.append("Model only file, no Index file detected.") yield "\n".join(logs) logs.append("Download Completed!") yield "\n".join(logs) logs.append("Successfully download all models! Refresh your model list to load the model") yield "\n".join(logs) def use_microphone(microphone): if microphone == True: return gr.Audio.update(source="microphone") else: return gr.Audio.update(source="upload") def change_audio_mode(vc_audio_mode): if vc_audio_mode == "Input path": return ( # Input & Upload gr.Textbox.update(visible=True), gr.Checkbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Textbox.update(visible=False), gr.Button.update(visible=False), # Splitter gr.Dropdown.update(visible=True), gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Button.update(visible=False), gr.Audio.update(visible=False), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Slider.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 == "Upload audio": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Checkbox.update(visible=True), gr.Audio.update(visible=True), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Textbox.update(visible=False), gr.Button.update(visible=False), # Splitter gr.Dropdown.update(visible=True), gr.Textbox.update(visible=True), gr.Button.update(visible=False), gr.Button.update(visible=True), gr.Audio.update(visible=False), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Slider.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 == "Youtube": return ( # Input & Upload gr.Textbox.update(visible=False), gr.Checkbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=True), gr.Textbox.update(visible=True), gr.Textbox.update(visible=True), gr.Button.update(visible=True), # Splitter gr.Dropdown.update(visible=True), gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Button.update(visible=False), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Audio.update(visible=True), gr.Slider.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.Checkbox.update(visible=False), gr.Audio.update(visible=False), # Youtube gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Textbox.update(visible=False), gr.Button.update(visible=False), # Splitter gr.Dropdown.update(visible=False), gr.Textbox.update(visible=False), gr.Button.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.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) ) with gr.Blocks(theme=applio) as app: gr.Markdown( "#
RVC V2\n" ) with gr.Row(): sid = gr.Dropdown( label="Weight", choices=sorted(weights_model), ) file_index = gr.Dropdown( label="List of index file", choices=sorted(weights_index), interactive=True, ) spk_item = gr.Slider( minimum=0, maximum=2333, step=1, label="Speaker ID", value=0, visible=False, interactive=True, ) refresh_model = gr.Button("Refresh model list", variant="primary") clean_button = gr.Button("Clear Model from memory", variant="primary") refresh_model.click( fn=check_models, inputs=[], outputs=[sid, file_index] ) vc_transform0 = gr.Number( label="Transpose", info='Type "12" to change from male to female convertion or Type "-12" to change female to male convertion.', value=0 ) clean_button.click(fn=clean, inputs=[], outputs=[sid, spk_item]) with gr.TabItem("Inference"): selected_model = gr.Markdown(value="#
No model selected") with gr.Row(): with gr.Column(): vc_audio_mode = gr.Dropdown(label="Input voice", choices=["Input path", "Upload audio", "Youtube", "TTS Audio"], allow_custom_value=False, value="Upload audio") # Input vc_input = gr.Textbox(label="Input audio path", visible=False) # Upload vc_microphone_mode = gr.Checkbox(label="Use Microphone", value=False, visible=True, interactive=True) vc_upload = gr.Audio(label="Upload audio file", source="upload", 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="Downloaded 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") # 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=True, value="htdemucs", info="Select the splitter model (Default: htdemucs)") vc_split_log = gr.Textbox(label="Output Information", visible=True, interactive=False) vc_split_yt = gr.Button("Split Audio", variant="primary", visible=False) vc_split = gr.Button("Split Audio", variant="primary", visible=True) vc_vocal_preview = gr.Audio(label="Vocal Preview", interactive=False, visible=True) vc_inst_preview = gr.Audio(label="Instrumental Preview", interactive=False, visible=True) with gr.Accordion('settings', open=False): with gr.Column(): f0method0 = gr.Radio( label="Pitch extraction algorithm", info=f0method_info, choices=f0method_mode, value="pm", interactive=True, ) index_rate0 = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", 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, ) f0_file0 = gr.File( label="F0 curve file (Optional)", info="One pitch per line, Replace the default F0 and pitch modulation" ) with gr.Column(): 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=True ) 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=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") with gr.Accordion('combine audio output (optional)', open=False): vc_combined_output = gr.Audio(label="Output Combined Audio", visible=True) vc_combine = gr.Button("Combine",variant="primary", visible=True) vc_convert.click( vc_single, [ spk_item, vc_audio_mode, vc_input, vc_upload, vc_vocal_preview, tts_text, tts_voice, vc_transform0, f0_file0, f0method0, file_index, index_rate0, filter_radius0, resample_sr0, rms_mix_rate0, protect0, ], [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_yt.click( fn=cut_vocal_and_inst_yt, inputs=[vc_split_model], outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input] ) vc_split.click( fn=cut_vocal_and_inst, inputs=[vc_split_model, vc_upload], outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview] ) 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_microphone_mode.change( fn=use_microphone, inputs=vc_microphone_mode, outputs=vc_upload ) vc_audio_mode.change( fn=change_audio_mode, inputs=[vc_audio_mode], outputs=[ # Input & Upload vc_input, vc_microphone_mode, vc_upload, # Youtube vc_download_audio, vc_link, vc_log_yt, vc_download_button, # Splitter vc_split_model, vc_split_log, vc_split_yt, vc_split, vc_audio_preview, vc_vocal_preview, vc_inst_preview, vc_vocal_volume, vc_inst_volume, vc_combined_output, vc_combine, # TTS tts_text, tts_voice ] ) sid.change(fn=get_vc, inputs=[sid, protect0], outputs=[spk_item, protect0, file_index, selected_model]) with gr.TabItem("Batch Inference"): with gr.Row(): with gr.Column(): vc_input_bat = gr.Textbox(label="Input audio path (folder)", visible=True) vc_output_bat = gr.Textbox(label="Output audio path (folder)", value="result/batch", visible=True) with gr.Column(): vc_transform0_bat = gr.Number( label="Transpose", info='Type "12" to change from male to female convertion or Type "-12" to change female to male convertion.', value=0 ) f0method0_bat = gr.Radio( label="Pitch extraction algorithm", info=f0method_info, choices=f0method_mode, value="pm", interactive=True, ) index_rate0_bat = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", value=0.7, interactive=True, ) filter_radius0_bat = 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_bat = 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_bat = 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_bat = 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_bat = gr.Textbox(label="Output Information", interactive=False) vc_convert_bat = gr.Button("Convert", variant="primary") vc_convert_bat.click( vc_multi, [ spk_item, vc_input_bat, vc_output_bat, vc_transform0_bat, f0method0_bat, file_index, index_rate0_bat, filter_radius0_bat, resample_sr0_bat, rms_mix_rate0_bat, protect0_bat, ], [vc_log_bat], ) with gr.TabItem("Model Downloader"): gr.Markdown( "#
Model Downloader (Beta)\n"+ "####
To download multi link you have to put your link to the textbox and every link separated by space\n"+ "####
Support Direct Link, Mega, Google Drive, etc" ) with gr.Column(): md_text = gr.Textbox(label="URL") with gr.Row(): md_download = gr.Button(label="Convert", variant="primary") with gr.Row(): md_download_logs = gr.Textbox(label="Output information", interactive=False) md_download.click( fn=download_and_extract_models, inputs=[md_text], outputs=[md_download_logs] ) app.launch(share=config.colab)