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CPU Upgrade
import os | |
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, | |
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 config.files: | |
audio, sr = librosa.load(input_audio, sr=16000, mono=True) | |
else: | |
if input_audio is None: | |
return "You need to upload an audio", None | |
sampling_rate, audio = input_audio | |
duration = audio.shape[0] / sampling_rate | |
if duration > 20 and limitation: | |
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 | |
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) | |
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("/content/youtube_audio"): | |
os.mkdir("/content/youtube_audio") | |
ydl_opts = { | |
'format': 'bestaudio/best', | |
'postprocessors': [{ | |
'key': 'FFmpegExtractAudio', | |
'preferredcodec': 'wav', | |
}], | |
"outtmpl": '/content/youtube_audio/audio', | |
} | |
with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
ydl.download([yt_url]) | |
yt_audio_path = "/content/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 ("/content/rvc-models/separated/htdemucs/audio/vocals.wav", "/content/rvc-models/separated/htdemucs/audio/no_vocals.wav", yt_audio_path, "/content/rvc-models/separated/htdemucs/audio/vocals.wav") | |
def combine_vocal_and_inst(audio_data, audio_volume): | |
print(audio_data) | |
if not os.path.exists("/content/result"): | |
os.mkdir("/content/result") | |
vocal_path = "/content/result/output.wav" | |
inst_path = "/content/rvc-models/separated/htdemucs/audio/no_vocals.wav" | |
output_path = "/content/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): | |
if tts_mode: | |
return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True) | |
else: | |
return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False) | |
''' | |
def change_audio_to_path_mode(audio_mode): | |
if audio_mode: | |
return gr.Audio.update(visible=False), gr.Textbox.update(visible=True) | |
else: | |
return gr.Audio.update(visible=True), gr.Textbox.update(visible=False) | |
''' | |
if __name__ == '__main__': | |
load_hubert() | |
models = [] | |
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] | |
folder_path = "weights" | |
for name in os.listdir(folder_path): | |
print("check folder: " + name) | |
if name.startswith("."): break | |
cover_path = glob.glob(f"{folder_path}/{name}/*.png") + glob.glob(f"{folder_path}/{name}/*.jpg") | |
index_path = glob.glob(f"{folder_path}/{name}/*.index") | |
checkpoint_path = glob.glob(f"{folder_path}/{name}/*.pth") | |
title = name | |
cover = cover_path[0] | |
index = index_path[0] | |
cpt = torch.load(checkpoint_path[0], 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)) # 不加这一行清不干净, 真奇葩 | |
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) | |
models.append((name, title, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index))) | |
with gr.Blocks() as app: | |
gr.Markdown( | |
"# <center> RVC Models (Latest Update)\n" | |
"## <center> The input audio should be clean and pure voice without background music.\n" | |
"### <center> More feature will be added soon... \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)" | |
) | |
with gr.Tabs(): | |
for (name, title, cover, vc_fn) in models: | |
with gr.TabItem(name): | |
with gr.Row(): | |
gr.Markdown( | |
'<div align="center">' | |
f'<div>{title}</div>\n'+ | |
(f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+ | |
'</div>' | |
) | |
with gr.Row(): | |
if config.files: | |
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_audio_mode = gr.Checkbox(label="Use audio path", value=False) | |
if config.files: | |
vc_input = gr.Textbox(label="Input audio path") | |
else: | |
vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '') | |
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_submit = gr.Button("Generate", variant="primary") | |
vc_output1 = gr.Textbox(label="Output Message") | |
vc_output2 = gr.Audio(label="Output Audio") | |
if config.files: | |
with gr.Column(): | |
vc_volume = gr.Slider( | |
minimum=0, | |
maximum=10, | |
label="Vocal volume", | |
value=5, | |
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_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2]) | |
tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice]) | |
# vc_audio_input(change_audio_to_path_mode, [vc_audio_mode]) | |
if config.files: | |
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) | |
app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.share) |