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Delete app-spaces.py
Browse files- app-spaces.py +0 -496
app-spaces.py
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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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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_audio_mode,
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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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spk_item,
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f0_up_key,
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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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vc_input = "tts.mp3"
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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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spk_item,
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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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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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# Spotify doesnt work.
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# Need to find other solution soon.
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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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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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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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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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# Input & Upload
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gr.Textbox.update(visible=True),
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gr.Audio.update(visible=False),
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# Youtube
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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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# TTS
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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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# Input & Upload
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gr.Textbox.update(visible=False),
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gr.Audio.update(visible=True),
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# Youtube
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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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# TTS
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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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# Input & Upload
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gr.Textbox.update(visible=False),
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gr.Audio.update(visible=False),
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# Youtube
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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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# TTS
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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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# Input & Upload
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gr.Textbox.update(visible=False),
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gr.Audio.update(visible=False),
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# Youtube
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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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# TTS
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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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# Input & Upload
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gr.Textbox.update(visible=False),
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gr.Audio.update(visible=True),
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# Youtube
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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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# TTS
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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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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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298 |
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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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301 |
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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302 |
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if_f0 = cpt.get("f0", 1)
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303 |
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version = cpt.get("version", "v1")
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304 |
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if version == "v1":
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305 |
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if if_f0 == 1:
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306 |
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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307 |
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else:
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308 |
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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309 |
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nodel_version = "V1"
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310 |
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elif version == "v2":
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311 |
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if if_f0 == 1:
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312 |
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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313 |
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else:
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314 |
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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315 |
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nodel_version = "V2"
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316 |
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del net_g.enc_q
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317 |
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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318 |
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net_g.eval().to(config.device)
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319 |
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if config.is_half:
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320 |
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net_g = net_g.half()
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321 |
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else:
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322 |
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net_g = net_g.float()
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323 |
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vc = VC(tgt_sr, config)
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324 |
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print(f"Model loaded: {model_name}")
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325 |
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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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326 |
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categories.append([category_title, category_folder, description, models])
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327 |
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with gr.Blocks() as app:
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328 |
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gr.Markdown(
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329 |
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"# <center> Multi Model RVC Inference\n"
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330 |
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"#### [Google Collab RVC Blue Archives](https://colab.research.google.com/drive/1Hl0zix92NYI5HsylyjAy_7XmuXTBC-J9?usp=sharing)\n"
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331 |
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"[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/ArkanDash/Multi-Model-RVC-Inference)"
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332 |
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)
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333 |
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for (folder_title, folder, description, models) in categories:
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334 |
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with gr.TabItem(folder_title):
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335 |
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if description:
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336 |
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gr.Markdown(f"### <center> {description}")
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337 |
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with gr.Tabs():
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338 |
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if not models:
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339 |
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gr.Markdown("# <center> No Model Loaded.")
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340 |
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gr.Markdown("## <center> Please add model or fix your model path.")
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341 |
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continue
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342 |
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for (name, title, author, cover, model_version, vc_fn) in models:
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343 |
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with gr.TabItem(name):
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344 |
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with gr.Row():
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345 |
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gr.Markdown(
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346 |
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'<div align="center">'
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347 |
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f'<div>{title}</div>\n'+
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348 |
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f'<div>RVC {model_version} Model</div>\n'+
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349 |
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(f'<div>Model author: {author}</div>' if author else "")+
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350 |
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(f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
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351 |
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'</div>'
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352 |
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)
|
353 |
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with gr.Row():
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354 |
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with gr.Column():
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355 |
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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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356 |
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# Input and Upload
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357 |
-
vc_input = gr.Textbox(label="Input audio path", visible=False)
|
358 |
-
vc_upload = gr.Audio(label="Upload audio file", visible=True, interactive=True)
|
359 |
-
# Youtube
|
360 |
-
vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
|
361 |
-
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=...")
|
362 |
-
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)")
|
363 |
-
vc_split = gr.Button("Split Audio", variant="primary", visible=False)
|
364 |
-
vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
|
365 |
-
vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
|
366 |
-
vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
|
367 |
-
# TTS
|
368 |
-
tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
|
369 |
-
tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
|
370 |
-
with gr.Column():
|
371 |
-
spk_item = gr.Slider(
|
372 |
-
minimum=0,
|
373 |
-
maximum=2333,
|
374 |
-
step=1,
|
375 |
-
label="Speaker ID",
|
376 |
-
info="(Default: 0)",
|
377 |
-
value=0,
|
378 |
-
interactive=True,
|
379 |
-
)
|
380 |
-
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')
|
381 |
-
f0method0 = gr.Radio(
|
382 |
-
label="Pitch extraction algorithm",
|
383 |
-
info="PM is fast, Harvest is good but extremely slow (Default: PM)",
|
384 |
-
choices=["pm", "harvest"],
|
385 |
-
value="pm",
|
386 |
-
interactive=True,
|
387 |
-
)
|
388 |
-
index_rate1 = gr.Slider(
|
389 |
-
minimum=0,
|
390 |
-
maximum=1,
|
391 |
-
label="Retrieval feature ratio",
|
392 |
-
info="(Default: 0.6)",
|
393 |
-
value=0.6,
|
394 |
-
interactive=True,
|
395 |
-
)
|
396 |
-
filter_radius0 = gr.Slider(
|
397 |
-
minimum=0,
|
398 |
-
maximum=7,
|
399 |
-
label="Apply Median Filtering",
|
400 |
-
info="The value represents the filter radius and can reduce breathiness.",
|
401 |
-
value=3,
|
402 |
-
step=1,
|
403 |
-
interactive=True,
|
404 |
-
)
|
405 |
-
resample_sr0 = gr.Slider(
|
406 |
-
minimum=0,
|
407 |
-
maximum=48000,
|
408 |
-
label="Resample the output audio",
|
409 |
-
info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
|
410 |
-
value=0,
|
411 |
-
step=1,
|
412 |
-
interactive=True,
|
413 |
-
)
|
414 |
-
rms_mix_rate0 = gr.Slider(
|
415 |
-
minimum=0,
|
416 |
-
maximum=1,
|
417 |
-
label="Volume Envelope",
|
418 |
-
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",
|
419 |
-
value=1,
|
420 |
-
interactive=True,
|
421 |
-
)
|
422 |
-
protect0 = gr.Slider(
|
423 |
-
minimum=0,
|
424 |
-
maximum=0.5,
|
425 |
-
label="Voice Protection",
|
426 |
-
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",
|
427 |
-
value=0.35,
|
428 |
-
step=0.01,
|
429 |
-
interactive=True,
|
430 |
-
)
|
431 |
-
with gr.Column():
|
432 |
-
vc_log = gr.Textbox(label="Output Information", interactive=False)
|
433 |
-
vc_output = gr.Audio(label="Output Audio", interactive=False)
|
434 |
-
vc_convert = gr.Button("Convert", variant="primary")
|
435 |
-
vc_volume = gr.Slider(
|
436 |
-
minimum=0,
|
437 |
-
maximum=10,
|
438 |
-
label="Vocal volume",
|
439 |
-
value=4,
|
440 |
-
interactive=True,
|
441 |
-
step=1,
|
442 |
-
info="Adjust vocal volume (Default: 4}",
|
443 |
-
visible=False
|
444 |
-
)
|
445 |
-
vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
|
446 |
-
vc_combine = gr.Button("Combine",variant="primary", visible=False)
|
447 |
-
vc_convert.click(
|
448 |
-
fn=vc_fn,
|
449 |
-
inputs=[
|
450 |
-
vc_audio_mode,
|
451 |
-
vc_input,
|
452 |
-
vc_upload,
|
453 |
-
tts_text,
|
454 |
-
tts_voice,
|
455 |
-
spk_item,
|
456 |
-
vc_transform0,
|
457 |
-
f0method0,
|
458 |
-
index_rate1,
|
459 |
-
filter_radius0,
|
460 |
-
resample_sr0,
|
461 |
-
rms_mix_rate0,
|
462 |
-
protect0,
|
463 |
-
],
|
464 |
-
outputs=[vc_log ,vc_output]
|
465 |
-
)
|
466 |
-
vc_split.click(
|
467 |
-
fn=cut_vocal_and_inst,
|
468 |
-
inputs=[vc_link, vc_download_audio, vc_split_model],
|
469 |
-
outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
|
470 |
-
)
|
471 |
-
vc_combine.click(
|
472 |
-
fn=combine_vocal_and_inst,
|
473 |
-
inputs=[vc_output, vc_volume, vc_split_model],
|
474 |
-
outputs=[vc_combined_output]
|
475 |
-
)
|
476 |
-
vc_audio_mode.change(
|
477 |
-
fn=change_audio_mode,
|
478 |
-
inputs=[vc_audio_mode],
|
479 |
-
outputs=[
|
480 |
-
vc_input,
|
481 |
-
vc_upload,
|
482 |
-
vc_download_audio,
|
483 |
-
vc_link,
|
484 |
-
vc_split_model,
|
485 |
-
vc_split,
|
486 |
-
vc_vocal_preview,
|
487 |
-
vc_inst_preview,
|
488 |
-
vc_audio_preview,
|
489 |
-
vc_volume,
|
490 |
-
vc_combined_output,
|
491 |
-
vc_combine,
|
492 |
-
tts_text,
|
493 |
-
tts_voice
|
494 |
-
]
|
495 |
-
)
|
496 |
-
app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
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