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import gradio as gr
from rvc_remind import *
import wave
import yt_dlp
import shutil, glob
from easyfuncs import download_from_url, CachedModels
os.makedirs("dataset",exist_ok=True)
model_library = CachedModels()
import os
def get_training_info(audio_file):
if audio_file is None:
return 'Please provide an audio file!'
duration = get_audio_duration(audio_file)
sample_rate = wave.open(audio_file, 'rb').getframerate()
training_info = {
(0, 2): (150, 'OV2'),
(2, 3): (200, 'OV2'),
(3, 5): (250, 'OV2'),
(5, 10): (300, 'Normal'),
(10, 25): (500, 'Normal'),
(25, 45): (700, 'Normal'),
(45, 60): (1000, 'Normal')
}
for (min_duration, max_duration), (epochs, pretrain) in training_info.items():
if min_duration <= duration < max_duration:
break
else:
return 'Duration is not within the specified range!'
return f'You should use the **{pretrain}** pretrain with **{epochs}** epochs at **{sample_rate/1000}khz** sample rate.'
def if_done(done, p):
while 1:
if p.poll() is None:
sleep(0.5)
else:
break
done[0] = True
def on_button_click(audio_file_path):
return get_training_info(audio_file_path)
def downloader_yt(url, save_path, audio_name):
ydl_opts = {
'format': 'bestaudio/best',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': "wav",
}],
'outtmpl': f"audios/{save_path}",
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
def download_audio(video_url, save_folder, audio_name):
save_path = os.path.join(audio_name)
downloader(video_url, save_path, audio_name)
return "Download Complete!"
assets_folder = "./assets/pretrained_v2/"
if not os.path.exists(assets_folder):
os.makedirs(assets_folder)
files = {
# Ov2 Super
"f0Ov2Super32kD.pth",
"f0Ov2Super40kG.pth",
# TITAN
"D-f040k-TITAN-Medium.pth",
"G-f032k-TITAN-Medium.pth",
# Snowie V3
"D_SnowieV3.1_40k.pth",
"G_SnowieV3.1_48k.pth",
# RIN E3
"RIN_E3_G.pth",
"RIN_E3_D.pth",
}
theme = gr.themes.Base()
with gr.Blocks(theme=theme,title="Blane rvc backup") as app:
with gr.Row():
gr.Markdown(
"""
# Blane RVC
"Please read Credits!"
"""
)
with gr.Tabs():
with gr.TabItem("Inference"):
with gr.Row():
voice_model = gr.Dropdown(label="Model Voice", choices=sorted(names), value=lambda:sorted(names)[0] if len(sorted(names)) > 0 else '', interactive=True)
spk_item = gr.Slider(
minimum=0,
maximum=2333,
step=1,
label="Speaker ID",
value=0,
#visible=False,
interactive=True,
)
vc_transform0 = gr.Number(
label="Pitch",
value=0
)
refresh_button = gr.Button("Refresh", variant="primary")
but0 = gr.Button(value="Convert", variant="primary")
with gr.Row():
with gr.Column():
with gr.Row():
dropbox = gr.File(label="Drop your audio here & hit the Reload button.")
with gr.Row():
paths_for_files = lambda path:[os.path.abspath(os.path.join(path, f)) for f in os.listdir(path) if os.path.splitext(f)[1].lower() in ('.mp3', '.wav', '.flac', '.ogg')]
input_audio0 = gr.Dropdown(
label="Input Path",
value=paths_for_files('audios')[0] if len(paths_for_files('audios')) > 0 else '',
choices=paths_for_files('audios'), # Only show absolute paths for audio files ending in .mp3, .wav, .flac or .ogg
allow_custom_value=True
)
with gr.Row():
audio_player = gr.Audio()
input_audio0.change(
inputs=[input_audio0],
outputs=[audio_player],
fn=lambda path: {"value":path,"__type__":"update"} if os.path.exists(path) else None
)
dropbox.upload(
fn=lambda audio:audio.name,
inputs=[dropbox],
outputs=[input_audio0])
with gr.Column():
with gr.Accordion("Change Index", open=False):
file_index2 = gr.Dropdown(
label="Change Index",
choices=sorted(index_paths),
interactive=True,
value=sorted(index_paths)[0] if len(sorted(index_paths)) > 0 else ''
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label="Index Strength",
value=0.5,
interactive=True,
)
vc_output2 = gr.Audio(label="Output")
with gr.Accordion("General Settings", open=False):
f0method0 = gr.Radio(
label="Method",
choices=["pm", "harvest", "crepe", "rmvpe"],
value="rmvpe",
interactive=True,
)
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label="Breathiness Reduction (Harvest only)",
value=3,
step=1,
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label="Resample",
value=0,
step=1,
interactive=True,
visible=False
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label="Volume Normalization",
value=0,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label="Breathiness Protection (0 is enabled, 0.5 is disabled)",
value=0.33,
step=0.01,
interactive=True,
)
if voice_model != None: vc.get_vc(voice_model.value,protect0,protect0)
file_index1 = gr.Textbox(
label="Index Path",
interactive=True,
visible=False#Not used here
)
refresh_button.click(
fn=change_choices,
inputs=[],
outputs=[voice_model, file_index2],
api_name="infer_refresh",
)
refresh_button.click(
fn=lambda:{"choices":paths_for_files('audios'),"__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
inputs=[],
outputs = [input_audio0],
)
refresh_button.click(
fn=lambda:{"value":paths_for_files('audios')[0],"__type__":"update"} if len(paths_for_files('audios')) > 0 else {"value":"","__type__":"update"}, #TODO check if properly returns a sorted list of audio files in the 'audios' folder that have the extensions '.wav', '.mp3', '.ogg', or '.flac'
inputs=[],
outputs = [input_audio0],
)
with gr.Row():
f0_file = gr.File(label="F0 Path", visible=False)
with gr.Row():
vc_output2 = gr.Audio(label="Information")
with gr.Row():
vc_output1 = gr.Textbox(label="Information") #visible=False
but0.click(
vc.vc_single,
[
spk_item,
input_audio0,
vc_transform0,
f0_file,
f0method0,
file_index1,
file_index2,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
],
[vc_output1, vc_output2],
api_name="infer_convert",
)
voice_model.change(
fn=vc.get_vc,
inputs=[voice_model, protect0, protect0],
outputs=[spk_item, protect0, protect0, file_index2, file_index2],
api_name="infer_change_voice",
)
with gr.TabItem("Download Models"):
with gr.Row():
url_input = gr.Textbox(label="URL to model", value="",placeholder="https://...", scale=6)
name_output = gr.Textbox(label="Save as", value="",placeholder="MyModel",scale=2)
url_download = gr.Button(value="Download Model",scale=2)
url_download.click(
inputs=[url_input,name_output],
outputs=[url_input],
fn=download_from_url,
)
with gr.Row():
model_browser = gr.Dropdown(choices=list(model_library.models.keys()),label="OR Search Models (Quality UNKNOWN)",scale=5)
download_from_browser = gr.Button(value="Get Voice models",scale=2)
download_from_browser.click(
inputs=[model_browser],
outputs=[model_browser],
fn=lambda model: download_from_url(model_library.models[model],model),
)
with gr.TabItem(" vocal separation"):
with gr.Group():
gr.Markdown(
value=(
"Batch processing of vocal accompaniment separation using the UVR5 model. <br>Example of a valid folder path format: E:\\codes\\py39\\vits_vc_gpu\\White Egret Frost Testing Samples (just copy from the file manager address bar). <br>The model is divided into three categories: <br>1. Preserve vocals: Choose this for audio without background vocals, it preserves the main vocals better than HP5. It includes two built-in models, HP2 and HP3. HP3 may slightly leak accompaniment but preserves the main vocals slightly better than HP2; <br>2. Only preserve main vocals: Choose this for audio with background vocals, it may weaken the main vocals. It includes one built-in model, HP5; <br>3. Dereverberation and delay removal model (by FoxJoy):<br>(1)MDX-Net(onnx_dereverb): The best choice for dual-channel reverberation, cannot remove single-channel reverberation:"
)
)
with gr.Row():
with gr.Column():
dir_wav_input = gr.Textbox(
label=("Enter the path of the audio folder to be processed"),
placeholder="C:\\Users\\Desktop\\todo-songs",
)
wav_inputs = gr.File(
file_count="multiple",
label=("You can also input audio files in batches, choose one of the two options, and prioritize reading the folder."),
)
with gr.Column():
model_choose = gr.Dropdown(
label=("choices"), choices=uvr5_names
)
agg = gr.Slider(
minimum=0,
maximum=20,
step=1,
label="Vocal extraction aggressiveness",
value=10,
interactive=True,
visible=False, # 先不开放调整
)
opt_vocal_root = gr.Textbox(
label=("Specify the output folder for the lead vocals"), value="opt"
)
opt_ins_root = gr.Textbox(
label=("Specify the folder for outputting non-lead vocals"), value="opt"
)
format0 = gr.Radio(
label=i18n("Export file formats"),
choices=["wav", "flac", "mp3"],
value="wav",
interactive=True,
)
but2uvr5 = gr.Button("Conversion", variant="primary")
with gr.Row():
uvrc_output4 = gr.Textbox(label="Output information")
but2uvr5.click(
uvr,
[
model_choose,
dir_wav_input,
opt_vocal_root,
wav_inputs,
opt_ins_root,
agg,
format0,
],
[uvrc_output4],
api_name="uvr_convert",
)
with gr.TabItem("Train"):
with gr.Row():
with gr.Column():
training_name = gr.Textbox(label="Name your model", value="My-Voice",placeholder="My-Voice")
np7 = gr.Slider(
minimum=0,
maximum=config.n_cpu,
step=1,
label="Number of CPU processes used to extract pitch features",
value=int(np.ceil(config.n_cpu / 1.5)),
interactive=True,
)
sr2 = gr.Radio(
label="Sampling Rate",
choices=["40k", "32k"],
value="32k",
interactive=True,
visible=False
)
if_f0_3 = gr.Radio(
label="Will your model be used for singing? If not, you can ignore this.",
choices=[True, False],
value=True,
interactive=True,
visible=False
)
version19 = gr.Radio(
label="Version",
choices=["v1", "v2"],
value="v2",
interactive=True,
visible=False,
)
dataset_folder = gr.Textbox(
label="dataset folder", value='dataset'
)
easy_uploader = gr.Files(label="Drop your audio files here",file_types=['audio'])
but1 = gr.Button("1. Process", variant="primary")
info1 = gr.Textbox(label="Information", value="",visible=True)
easy_uploader.upload(inputs=[dataset_folder],outputs=[],fn=lambda folder:os.makedirs(folder,exist_ok=True))
easy_uploader.upload(
fn=lambda files,folder: [shutil.copy2(f.name,os.path.join(folder,os.path.split(f.name)[1])) for f in files] if folder != "" else gr.Warning('Please enter a folder name for your dataset'),
inputs=[easy_uploader, dataset_folder],
outputs=[])
gpus6 = gr.Textbox(
label="Enter the GPU numbers to use separated by -, (e.g. 0-1-2)",
value=gpus,
interactive=True,
visible=F0GPUVisible,
)
gpu_info9 = gr.Textbox(
label="GPU Info", value=gpu_info, visible=F0GPUVisible
)
spk_id5 = gr.Slider(
minimum=0,
maximum=4,
step=1,
label="Speaker ID",
value=0,
interactive=True,
visible=False
)
but1.click(
preprocess_dataset,
[dataset_folder, training_name, sr2, np7],
[info1],
api_name="train_preprocess",
)
with gr.Column():
f0method8 = gr.Radio(
label="F0 extraction method",
choices=["pm", "harvest", "dio", "rmvpe", "rmvpe_gpu"],
value="rmvpe_gpu",
interactive=True,
)
gpus_rmvpe = gr.Textbox(
label="GPU numbers to use separated by -, (e.g. 0-1-2)",
value="%s-%s" % (gpus, gpus),
interactive=True,
visible=F0GPUVisible,
)
but2 = gr.Button("2. Extract Features", variant="primary")
info2 = gr.Textbox(label="Information", value="", max_lines=8)
f0method8.change(
fn=change_f0_method,
inputs=[f0method8],
outputs=[gpus_rmvpe],
)
but2.click(
extract_f0_feature,
[
gpus6,
np7,
f0method8,
if_f0_3,
training_name,
version19,
gpus_rmvpe,
],
[info2],
api_name="train_extract_f0_feature",
)
with gr.Column():
total_epoch11 = gr.Slider(
minimum=2,
maximum=1000,
step=1,
label="Epochs (more epochs may improve quality but takes longer)",
value=150,
interactive=True,
)
but4 = gr.Button("3. Train Index", variant="primary")
but3 = gr.Button("4. Train Model", variant="primary")
info3 = gr.Textbox(label="Information", value="", max_lines=10)
with gr.Accordion(label="Change pretrains", open=False):
pretrained = lambda sr, letter: [os.path.abspath(os.path.join('assets/pretrained_v2', file)) for file in os.listdir('assets/pretrained_v2') if file.endswith('.pth') and sr in file and letter in file]
pretrained_G14 = gr.Dropdown(
label="pretrained G",
choices=pretrained(sr2.value, 'G'),
value=pretrained(sr2.value, 'G')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
interactive=True,
visible=True
)
pretrained_D15 = gr.Dropdown(
label="pretrained D",
choices=pretrained(sr2.value, 'D'),
value=pretrained(sr2.value, 'D')[0] if len(pretrained(sr2.value, 'G')) > 0 else '',
visible=True,
interactive=True
)
with gr.Accordion(label="General Settings", open=False):
gpus16 = gr.Textbox(
label="GPUs separated by -, (e.g. 0-1-2)",
value="0",
interactive=True,
visible=True
)
save_epoch10 = gr.Slider(
minimum=1,
maximum=50,
step=1,
label="Weight Saving Frequency",
value=25,
interactive=True,
)
batch_size12 = gr.Slider(
minimum=1,
maximum=40,
step=1,
label="Batch Size",
value=default_batch_size,
interactive=True,
)
if_save_latest13 = gr.Radio(
label="Only save the latest model",
choices=["yes", "no"],
value="yes",
interactive=True,
visible=False
)
if_cache_gpu17 = gr.Radio(
label="If your dataset is UNDER 10 minutes, cache it to train faster",
choices=["yes", "no"],
value="no",
interactive=True,
)
if_save_every_weights18 = gr.Radio(
label="Save small model at every save point",
choices=["yes", "no"],
value="yes",
interactive=True,
)
with gr.Row():
download_model = gr.Button('5.Download Model')
with gr.Row():
model_files = gr.Files(label='Your Model and Index file can be downloaded here:')
download_model.click(
fn=lambda name: os.listdir(f'assets/weights/{name}') + glob.glob(f'logs/{name.split(".")[0]}/added_*.index'),
inputs=[training_name],
outputs=[model_files, info3])
with gr.Row():
sr2.change(
change_sr2,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15],
)
version19.change(
change_version19,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15, sr2],
)
if_f0_3.change(
change_f0,
[if_f0_3, sr2, version19],
[f0method8, pretrained_G14, pretrained_D15],
)
with gr.Row():
but5 = gr.Button("1 Click Training", variant="primary", visible=True)
but3.click(
click_train,
[
training_name,
sr2,
if_f0_3,
spk_id5,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
],
info3,
api_name="train_start",
)
but4.click(train_index, [training_name, version19], info3)
but5.click(
train1key,
[
training_name,
sr2,
if_f0_3,
dataset_folder,
spk_id5,
np7,
f0method8,
save_epoch10,
total_epoch11,
batch_size12,
if_save_latest13,
pretrained_G14,
pretrained_D15,
gpus16,
if_cache_gpu17,
if_save_every_weights18,
version19,
gpus_rmvpe,
],
info3,
api_name="train_start_all",
)
with gr.TabItem("Extra"):
with gr.Accordion('Training Helper', open=True):
input_audio = gr.Audio(type="filepath", label="Upload your audio file")
gr.Textbox("Please note that these results are approximate and intended to provide a general idea for beginners.", label='Notice:')
training_info_text = gr.Markdown(label="Training Information:")
get_info_button = gr.Button("Get Training Info")
get_info_button.click(
fn=on_button_click,
inputs=[input_audio],
outputs=[training_info_text]
)
with gr.Accordion('audio downloader for inference (vocal only for this one!)', open=True):
url = gr.Textbox(label="input url")
audio_name = gr.Textbox(label="audio name for audio (no space)")
output_songs = gr.Audio(label="outputs songs")
get_info_button = gr.Button("Get audios")
get_info_button.click(
fn=downloader_yt,
inputs=[url, audio_name],
outputs=[output_songs]
)
with gr.TabItem('credits'):
with gr.Accordion('Credits', open=True):
gr.Markdown(
'''
# CREDITS
- **RVC Project**: Original Developers
- **blane187**: colab + GUI mod
- **rejetks**: Original EASY GUI Coder
- **eddycrack864**: Helper
''')
if config.iscolab:
app.launch(share=True)
else:
app.launch(
server_name="0.0.0.0",
inbrowser=not config.noautoopen,
server_port=config.listen_port,
quiet=True,
) |