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 from vc_infer_pipeline import VC from config import Config config = Config() logging.getLogger("numba").setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index): def vc_fn( input_audio, upload_audio, upload_mode, f0_up_key, f0_method, index_rate, tts_mode, tts_text, tts_voice ): try: if tts_mode: 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) else: if upload_mode: if input_audio is None: return "You need to upload an audio", None sampling_rate, audio = upload_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) else: audio, sr = librosa.load(input_audio, sr=16000, mono=True) times = [0, 0, 0] f0_up_key = int(f0_up_key) audio_opt = vc.pipeline( hubert_model, net_g, 0, audio, times, f0_up_key, f0_method, file_index, index_rate, if_f0, f0_file=None, ) print( f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s" ) return "Success", (tgt_sr, audio_opt) except: info = traceback.format_exc() print(info) return info, (None, None) return vc_fn def cut_vocal_and_inst(yt_url): if yt_url != "": if not os.path.exists("youtube_audio"): os.mkdir("youtube_audio") ydl_opts = { 'format': 'bestaudio/best', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', }], "outtmpl": 'youtube_audio/audio', } with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([yt_url]) yt_audio_path = "youtube_audio/audio.wav" command = f"demucs --two-stems=vocals {yt_audio_path}" result = subprocess.run(command.split(), stdout=subprocess.PIPE) print(result.stdout.decode()) return ("separated/htdemucs/audio/vocals.wav", "separated/htdemucs/audio/no_vocals.wav", yt_audio_path, "separated/htdemucs/audio/vocals.wav") def combine_vocal_and_inst(audio_data, audio_volume): print(audio_data) if not os.path.exists("result"): os.mkdir("result") vocal_path = "result/output.wav" inst_path = "separated/htdemucs/audio/no_vocals.wav" output_path = "result/combine.mp3" 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) 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_to_tts_mode(tts_mode, upload_mode): if tts_mode: return gr.Textbox.update(visible=False), gr.Audio.update(visible=False), gr.Checkbox.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True) else: if upload_mode: return gr.Textbox.update(visible=False), gr.Audio.update(visible=True), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) else: return gr.Textbox.update(visible=True), gr.Audio.update(visible=False), gr.Checkbox.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) def change_to_upload_mode(upload_mode): if upload_mode: return gr.Textbox().update(visible=False), gr.Audio().update(visible=True) else: return gr.Textbox().update(visible=True), gr.Audio().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 name, info in models_info.items(): if not info['enable']: continue title = info['title'] author = info.get("author", None) cover = f"weights/{category_folder}/{name}/{info['cover']}" index = f"weights/{category_folder}/{name}/{info['feature_retrieval_library']}" cpt = torch.load(f"weights/{category_folder}/{name}/{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) if if_f0 == 1: net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) else: net_g = SynthesizerTrnMs256NSFsid_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) print(f"Model loaded: {name}") models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index))) categories.append([category_title, category_folder, description, models]) with gr.Blocks() as app: gr.Markdown( "#
RVC Models [(Latest Update)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/releases/tag/20230428updated)\n" "##
The input audio should be clean and pure voice without background music.\n" "###
This project was inspired by [zomehwh](https://huggingface.co/spaces/zomehwh/rvc-models) and [ardha27](https://huggingface.co/spaces/ardha27/rvc-models)\n" "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n" "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)" ) 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 added the model or fix your model path.") continue for (name, title, author, cover, vc_fn) in models: with gr.TabItem(name): with gr.Row(): gr.Markdown( '
' f'
{title}
\n'+ (f'
Model author: {author}
' if author else "")+ (f'' if cover else "")+ '
' ) with gr.Row(): with gr.Column(): vc_youtube = gr.Textbox(label="Youtube URL") vc_convert = gr.Button("Convert", variant="primary") vc_vocal_preview = gr.Audio(label="Vocal Preview") vc_inst_preview = gr.Audio(label="Instrumental Preview") vc_audio_preview = gr.Audio(label="Audio Preview") with gr.Column(): vc_input = gr.Textbox(label="Input audio path") vc_upload = gr.Audio(label="Upload audio file", visible=False, interactive=True) upload_mode = gr.Checkbox(label="Upload mode", value=False) vc_transpose = gr.Number(label="Transpose", value=0) vc_f0method = gr.Radio( label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies", choices=["pm", "harvest"], value="pm", interactive=True, ) vc_index_ratio = gr.Slider( minimum=0, maximum=1, label="Retrieval feature ratio", value=0.6, interactive=True, ) tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False) tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text") tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") vc_output1 = gr.Textbox(label="Output Message") vc_output2 = gr.Audio(label="Output Audio") vc_submit = gr.Button("Generate", variant="primary") with gr.Column(): vc_volume = gr.Slider( minimum=0, maximum=10, label="Vocal volume", value=4, interactive=True, step=1 ) vc_outputCombine = gr.Audio(label="Output Combined Audio") vc_combine = gr.Button("Combine",variant="primary") vc_submit.click(vc_fn, [vc_input, vc_upload, upload_mode, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2]) vc_convert.click(cut_vocal_and_inst, vc_youtube, [vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]) vc_combine.click(combine_vocal_and_inst, [vc_output2, vc_volume], vc_outputCombine) tts_mode.change(change_to_tts_mode, [tts_mode, upload_mode], [vc_input, vc_upload, upload_mode, tts_text, tts_voice]) upload_mode.change(change_to_upload_mode, [upload_mode], [vc_input, vc_upload]) app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)