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import os |
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import glob |
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import json |
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import traceback |
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import logging |
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import gradio as gr |
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import numpy as np |
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import librosa |
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import torch |
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import asyncio |
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import edge_tts |
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import yt_dlp |
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import ffmpeg |
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import subprocess |
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import sys |
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import io |
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import wave |
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from datetime import datetime |
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from fairseq import checkpoint_utils |
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from infer_pack.models import ( |
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SynthesizerTrnMs256NSFsid, |
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SynthesizerTrnMs256NSFsid_nono, |
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SynthesizerTrnMs768NSFsid, |
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SynthesizerTrnMs768NSFsid_nono, |
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) |
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from vc_infer_pipeline import VC |
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from config import Config |
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config = Config() |
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logging.getLogger("numba").setLevel(logging.WARNING) |
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limitation = os.getenv("SYSTEM") == "spaces" |
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|
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def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index): |
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def vc_fn( |
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vc_input, |
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vc_upload, |
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tts_text, |
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tts_voice, |
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f0_up_key, |
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vc_transform, |
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f0_method, |
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index_rate, |
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filter_radius, |
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resample_sr, |
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rms_mix_rate, |
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protect, |
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): |
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try: |
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if vc_audio_mode == "Input path" or "Youtube" and vc_input != "": |
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audio, sr = librosa.load(vc_input, sr=16000, mono=True) |
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elif vc_audio_mode == "Upload audio": |
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if vc_upload is None: |
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return "You need to upload an audio", None |
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sampling_rate, audio = vc_upload |
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duration = audio.shape[0] / sampling_rate |
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if duration > 20 and limitation: |
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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 |
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audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) |
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if len(audio.shape) > 1: |
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audio = librosa.to_mono(audio.transpose(1, 0)) |
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if sampling_rate != 16000: |
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) |
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elif vc_audio_mode == "TTS Audio": |
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if len(tts_text) > 100 and limitation: |
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return "Text is too long", None |
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if tts_text is None or tts_voice is None: |
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return "You need to enter text and select a voice", None |
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asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3")) |
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audio, sr = librosa.load("tts.mp3", sr=16000, mono=True) |
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times = [0, 0, 0] |
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f0_up_key = int(f0_up_key) |
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audio_opt = vc.pipeline( |
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hubert_model, |
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net_g, |
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vc_transform, |
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audio, |
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vc_input, |
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times, |
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f0_up_key, |
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f0_method, |
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file_index, |
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index_rate, |
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if_f0, |
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filter_radius, |
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tgt_sr, |
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resample_sr, |
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rms_mix_rate, |
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version, |
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protect, |
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f0_file=None, |
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) |
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info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s" |
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print(info) |
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return info, (tgt_sr, audio_opt) |
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except: |
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info = traceback.format_exc() |
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print(info) |
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return info, (None, None) |
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return vc_fn |
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|
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def cut_vocal_and_inst(url, audio_provider, split_model): |
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if url != "": |
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if not os.path.exists("dl_audio"): |
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os.mkdir("dl_audio") |
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if audio_provider == "Youtube": |
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ydl_opts = { |
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'format': 'bestaudio/best', |
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'postprocessors': [{ |
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'key': 'FFmpegExtractAudio', |
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'preferredcodec': 'wav', |
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}], |
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"outtmpl": 'dl_audio/youtube_audio', |
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} |
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with yt_dlp.YoutubeDL(ydl_opts) as ydl: |
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ydl.download([url]) |
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audio_path = "dl_audio/youtube_audio.wav" |
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else: |
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|
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|
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''' |
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command = f"spotdl download {url} --output dl_audio/.wav" |
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result = subprocess.run(command.split(), stdout=subprocess.PIPE) |
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print(result.stdout.decode()) |
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audio_path = "dl_audio/spotify_audio.wav" |
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''' |
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if split_model == "htdemucs": |
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command = f"demucs --two-stems=vocals {audio_path} -o output" |
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result = subprocess.run(command.split(), stdout=subprocess.PIPE) |
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print(result.stdout.decode()) |
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return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav" |
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else: |
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command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output" |
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result = subprocess.run(command.split(), stdout=subprocess.PIPE) |
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print(result.stdout.decode()) |
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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" |
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else: |
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raise gr.Error("URL Required!") |
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return None, None, None, None |
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|
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def combine_vocal_and_inst(audio_data, audio_volume, split_model): |
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if not os.path.exists("output/result"): |
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os.mkdir("output/result") |
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vocal_path = "output/result/output.wav" |
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output_path = "output/result/combine.mp3" |
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if split_model == "htdemucs": |
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inst_path = "output/htdemucs/youtube_audio/no_vocals.wav" |
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else: |
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inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav" |
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with wave.open(vocal_path, "w") as wave_file: |
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wave_file.setnchannels(1) |
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wave_file.setsampwidth(2) |
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wave_file.setframerate(audio_data[0]) |
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wave_file.writeframes(audio_data[1].tobytes()) |
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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}' |
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result = subprocess.run(command.split(), stdout=subprocess.PIPE) |
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print(result.stdout.decode()) |
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return output_path |
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|
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def load_hubert(): |
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global hubert_model |
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task( |
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["hubert_base.pt"], |
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suffix="", |
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) |
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hubert_model = models[0] |
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hubert_model = hubert_model.to(config.device) |
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if config.is_half: |
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hubert_model = hubert_model.half() |
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else: |
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hubert_model = hubert_model.float() |
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hubert_model.eval() |
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|
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def change_audio_mode(vc_audio_mode): |
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if vc_audio_mode == "Input path": |
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return ( |
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|
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gr.Textbox.update(visible=True), |
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gr.Audio.update(visible=False), |
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|
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gr.Dropdown.update(visible=False), |
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False), |
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gr.Button.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Slider.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Button.update(visible=False), |
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|
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False) |
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) |
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elif vc_audio_mode == "Upload audio": |
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return ( |
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|
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gr.Textbox.update(visible=False), |
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gr.Audio.update(visible=True), |
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|
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gr.Dropdown.update(visible=False), |
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False), |
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gr.Button.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Slider.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Button.update(visible=False), |
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|
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False) |
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) |
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elif vc_audio_mode == "Youtube": |
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return ( |
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|
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gr.Textbox.update(visible=False), |
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gr.Audio.update(visible=False), |
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|
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gr.Dropdown.update(visible=True), |
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gr.Textbox.update(visible=True), |
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gr.Dropdown.update(visible=True), |
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gr.Button.update(visible=True), |
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gr.Audio.update(visible=True), |
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gr.Audio.update(visible=True), |
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gr.Audio.update(visible=True), |
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gr.Slider.update(visible=True), |
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gr.Audio.update(visible=True), |
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gr.Button.update(visible=True), |
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|
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False) |
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) |
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elif vc_audio_mode == "TTS Audio": |
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return ( |
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|
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gr.Textbox.update(visible=False), |
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gr.Audio.update(visible=False), |
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|
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gr.Dropdown.update(visible=False), |
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False), |
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gr.Button.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Slider.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Button.update(visible=False), |
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|
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gr.Textbox.update(visible=True), |
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gr.Dropdown.update(visible=True) |
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) |
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else: |
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return ( |
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|
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gr.Textbox.update(visible=False), |
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gr.Audio.update(visible=True), |
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|
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gr.Dropdown.update(visible=False), |
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False), |
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gr.Button.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Slider.update(visible=False), |
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gr.Audio.update(visible=False), |
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gr.Button.update(visible=False), |
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|
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gr.Textbox.update(visible=False), |
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gr.Dropdown.update(visible=False) |
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) |
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|
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if __name__ == '__main__': |
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load_hubert() |
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categories = [] |
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tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) |
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voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list] |
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with open("weights/folder_info.json", "r", encoding="utf-8") as f: |
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folder_info = json.load(f) |
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for category_name, category_info in folder_info.items(): |
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if not category_info['enable']: |
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continue |
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category_title = category_info['title'] |
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category_folder = category_info['folder_path'] |
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description = category_info['description'] |
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models = [] |
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with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f: |
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models_info = json.load(f) |
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for model_name, info in models_info.items(): |
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if not info['enable']: |
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continue |
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model_title = info['title'] |
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model_author = info.get("author", None) |
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model_cover = f"weights/{category_folder}/{model_name}/{info['cover']}" |
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model_index = f"weights/{category_folder}/{model_name}/{info['feature_retrieval_library']}" |
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cpt = torch.load(f"weights/{category_folder}/{model_name}/{model_name}.pth", map_location="cpu") |
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tgt_sr = cpt["config"][-1] |
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] |
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if_f0 = cpt.get("f0", 1) |
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version = cpt.get("version", "v1") |
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if version == "v1": |
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if if_f0 == 1: |
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) |
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else: |
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
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nodel_version = "V1" |
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elif version == "v2": |
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if if_f0 == 1: |
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) |
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else: |
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) |
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nodel_version = "V2" |
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del net_g.enc_q |
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print(net_g.load_state_dict(cpt["weight"], strict=False)) |
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net_g.eval().to(config.device) |
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if config.is_half: |
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net_g = net_g.half() |
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else: |
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net_g = net_g.float() |
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vc = VC(tgt_sr, config) |
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print(f"Model loaded: {model_name}") |
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models.append((model_name, model_title, model_author, model_cover, nodel_version, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index))) |
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categories.append([category_title, category_folder, description, models]) |
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with gr.Blocks() as app: |
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gr.Markdown( |
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"# <center> RVC Genshin Impact Inference\n" |
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"#### From [Retrieval-based-Voice-Conversion](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)\n" |
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"[](https://github.com/ArkanDash/Multi-Model-RVC-Inference)" |
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) |
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for (folder_title, folder, description, models) in categories: |
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with gr.TabItem(folder_title): |
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if description: |
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gr.Markdown(f"### <center> {description}") |
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with gr.Tabs(): |
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if not models: |
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gr.Markdown("# <center> No Model Loaded.") |
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gr.Markdown("## <center> Please add model or fix your model path.") |
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continue |
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for (name, title, author, cover, model_version, vc_fn) in models: |
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with gr.TabItem(name): |
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with gr.Row(): |
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gr.Markdown( |
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'<div align="center">' |
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f'<div>{title}</div>\n'+ |
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f'<div>RVC {model_version} Model</div>\n'+ |
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(f'<div>Model author: {author}</div>' if author else "")+ |
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(f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+ |
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'</div>' |
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) |
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with gr.Row(): |
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with gr.Column(): |
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vc_audio_mode = gr.Dropdown(label="Input voice", choices=["Upload audio", "TTS Audio"], allow_custom_value=False, value="Upload audio") |
|
|
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vc_input = gr.Textbox(label="Input audio path", visible=False) |
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vc_upload = gr.Audio(label="Upload audio file", visible=True, interactive=True) |
|
|
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vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)") |
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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=...") |
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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)") |
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vc_split = gr.Button("Split Audio", variant="primary", visible=False) |
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vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False) |
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vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False) |
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vc_audio_preview = gr.Audio(label="Audio Preview", visible=False) |
|
|
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tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input") |
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tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female") |
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with gr.Column(): |
|
spk_item = gr.Slider( |
|
minimum=0, |
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maximum=2333, |
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step=1, |
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label="Speaker ID", |
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info="(Default: 0)", |
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value=0, |
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interactive=True, |
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) |
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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( |
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label="Pitch extraction algorithm", |
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info="PM is fast, Harvest is good but extremely slow (Default: PM)", |
|
choices=["pm", "harvest"], |
|
value="pm", |
|
interactive=True, |
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) |
|
index_rate1 = gr.Slider( |
|
minimum=0, |
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maximum=1, |
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label="Retrieval feature ratio", |
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info="(Default: 0.6)", |
|
value=0.6, |
|
interactive=True, |
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) |
|
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_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) |
