Spaces:
Sleeping
Sleeping
import json | |
import os | |
import shutil | |
import urllib.request | |
import zipfile | |
from argparse import ArgumentParser | |
import gradio as gr | |
from main import song_cover_pipeline | |
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
mdxnet_models_dir = os.path.join(BASE_DIR, 'mdxnet_models') | |
rvc_models_dir = os.path.join(BASE_DIR, 'rvc_models') | |
output_dir = os.path.join(BASE_DIR, 'song_output') | |
def get_current_models(models_dir): | |
models_list = os.listdir(models_dir) | |
items_to_remove = ['hubert_base.pt', 'MODELS.txt', 'public_models.json', 'rmvpe.pt'] | |
return [item for item in models_list if item not in items_to_remove] | |
def update_models_list(): | |
models_l = get_current_models(rvc_models_dir) | |
return gr.Dropdown.update(choices=models_l) | |
def load_public_models(): | |
models_table = [] | |
for model in public_models['voice_models']: | |
if not model['name'] in voice_models: | |
model = [model['name'], model['description'], model['credit'], model['url'], ', '.join(model['tags'])] | |
models_table.append(model) | |
tags = list(public_models['tags'].keys()) | |
return gr.DataFrame.update(value=models_table), gr.CheckboxGroup.update(choices=tags) | |
def extract_zip(extraction_folder, zip_name): | |
os.makedirs(extraction_folder) | |
with zipfile.ZipFile(zip_name, 'r') as zip_ref: | |
zip_ref.extractall(extraction_folder) | |
os.remove(zip_name) | |
index_filepath, model_filepath = None, None | |
for root, dirs, files in os.walk(extraction_folder): | |
for name in files: | |
if name.endswith('.index') and os.stat(os.path.join(root, name)).st_size > 1024 * 100: | |
index_filepath = os.path.join(root, name) | |
if name.endswith('.pth') and os.stat(os.path.join(root, name)).st_size > 1024 * 1024 * 40: | |
model_filepath = os.path.join(root, name) | |
if not model_filepath: | |
raise gr.Error(f'No .pth model file was found in the extracted zip. Please check {extraction_folder}.') | |
# move model and index file to extraction folder | |
os.rename(model_filepath, os.path.join(extraction_folder, os.path.basename(model_filepath))) | |
if index_filepath: | |
os.rename(index_filepath, os.path.join(extraction_folder, os.path.basename(index_filepath))) | |
# remove any unnecessary nested folders | |
for filepath in os.listdir(extraction_folder): | |
if os.path.isdir(os.path.join(extraction_folder, filepath)): | |
shutil.rmtree(os.path.join(extraction_folder, filepath)) | |
def download_online_model(url, dir_name, progress=gr.Progress()): | |
try: | |
progress(0, desc=f'[~] Downloading voice model with name {dir_name}...') | |
zip_name = url.split('/')[-1] | |
extraction_folder = os.path.join(rvc_models_dir, dir_name) | |
if os.path.exists(extraction_folder): | |
raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.') | |
if 'pixeldrain.com' in url: | |
url = f'https://pixeldrain.com/api/file/{zip_name}' | |
urllib.request.urlretrieve(url, zip_name) | |
progress(0.5, desc='[~] Extracting zip...') | |
extract_zip(extraction_folder, zip_name) | |
return f'[+] {dir_name} Model successfully downloaded!' | |
except Exception as e: | |
raise gr.Error(str(e)) | |
def upload_local_model(zip_path, dir_name, progress=gr.Progress()): | |
try: | |
extraction_folder = os.path.join(rvc_models_dir, dir_name) | |
if os.path.exists(extraction_folder): | |
raise gr.Error(f'Voice model directory {dir_name} already exists! Choose a different name for your voice model.') | |
zip_name = zip_path.name | |
progress(0.5, desc='[~] Extracting zip...') | |
extract_zip(extraction_folder, zip_name) | |
return f'[+] {dir_name} Model successfully uploaded!' | |
except Exception as e: | |
raise gr.Error(str(e)) | |
def filter_models(tags, query): | |
models_table = [] | |
# no filter | |
if len(tags) == 0 and len(query) == 0: | |
for model in public_models['voice_models']: | |
models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']]) | |
# filter based on tags and query | |
elif len(tags) > 0 and len(query) > 0: | |
for model in public_models['voice_models']: | |
if all(tag in model['tags'] for tag in tags): | |
model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower() | |
if query.lower() in model_attributes: | |
models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']]) | |
# filter based on only tags | |
elif len(tags) > 0: | |
for model in public_models['voice_models']: | |
if all(tag in model['tags'] for tag in tags): | |
models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']]) | |
# filter based on only query | |
else: | |
for model in public_models['voice_models']: | |
model_attributes = f"{model['name']} {model['description']} {model['credit']} {' '.join(model['tags'])}".lower() | |
if query.lower() in model_attributes: | |
models_table.append([model['name'], model['description'], model['credit'], model['url'], model['tags']]) | |
return gr.DataFrame.update(value=models_table) | |
def pub_dl_autofill(pub_models, event: gr.SelectData): | |
return gr.Text.update(value=pub_models.loc[event.index[0], 'URL']), gr.Text.update(value=pub_models.loc[event.index[0], 'Model Name']) | |
def swap_visibility(): | |
return gr.update(visible=True), gr.update(visible=False), gr.update(value=''), gr.update(value=None) | |
def process_file_upload(file): | |
return file.name, gr.update(value=file.name) | |
def show_hop_slider(pitch_detection_algo): | |
if pitch_detection_algo == 'mangio-crepe': | |
return gr.update(visible=True) | |
else: | |
return gr.update(visible=False) | |
if __name__ == '__main__': | |
parser = ArgumentParser(description='Generate a AI cover song in the song_output/id directory.', add_help=True) | |
parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing") | |
parser.add_argument("--listen", action="store_true", default=False, help="Make the WebUI reachable from your local network.") | |
parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.') | |
parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.') | |
args = parser.parse_args() | |
voice_models = get_current_models(rvc_models_dir) | |
with open(os.path.join(rvc_models_dir, 'public_models.json'), encoding='utf8') as infile: | |
public_models = json.load(infile) | |
with gr.Blocks(title='AICoverGenWebUI') as app: | |
gr.Label('AICoverGen WebUI created with โค๏ธ', show_label=False) | |
# main tab | |
with gr.Tab("Generate"): | |
with gr.Accordion('Main Options'): | |
with gr.Row(): | |
with gr.Column(): | |
rvc_model = gr.Dropdown(voice_models, label='Voice Models', info='Models folder "AICoverGen --> rvc_models". After new models are added into this folder, click the refresh button') | |
ref_btn = gr.Button('Refresh Models ๐', variant='primary') | |
with gr.Column() as yt_link_col: | |
song_input = gr.Text(label='Song input', info='Link to a song on YouTube or full path to a local file. For file upload, click the button below. Example: https://www.youtube.com/watch?v=M-mtdN6R3bQ') | |
show_file_upload_button = gr.Button('Upload file instead') | |
with gr.Column(visible=False) as file_upload_col: | |
local_file = gr.File(label='Audio file') | |
song_input_file = gr.UploadButton('Upload ๐', file_types=['audio'], variant='primary') | |
show_yt_link_button = gr.Button('Paste YouTube link/Path to local file instead') | |
song_input_file.upload(process_file_upload, inputs=[song_input_file], outputs=[local_file, song_input]) | |
with gr.Column(): | |
pitch = gr.Slider(-3, 3, value=0, step=1, label='Pitch Change (Vocals ONLY)', info='Generally, use 1 for male to female conversions and -1 for vice-versa. (Octaves)') | |
pitch_all = gr.Slider(-12, 12, value=0, step=1, label='Overall Pitch Change', info='Changes pitch/key of vocals and instrumentals together. Altering this slightly reduces sound quality. (Semitones)') | |
show_file_upload_button.click(swap_visibility, outputs=[file_upload_col, yt_link_col, song_input, local_file]) | |
show_yt_link_button.click(swap_visibility, outputs=[yt_link_col, file_upload_col, song_input, local_file]) | |
with gr.Accordion('Voice conversion options', open=False): | |
with gr.Row(): | |
index_rate = gr.Slider(0, 1, value=0.5, label='Index Rate', info="Controls how much of the AI voice's accent to keep in the vocals") | |
filter_radius = gr.Slider(0, 7, value=3, step=1, label='Filter radius', info='If >=3: apply median filtering median filtering to the harvested pitch results. Can reduce breathiness') | |
rms_mix_rate = gr.Slider(0, 1, value=0.25, label='RMS mix rate', info="Control how much to mimic the original vocal's loudness (0) or a fixed loudness (1)") | |
protect = gr.Slider(0, 0.5, value=0.33, label='Protect rate', info='Protect voiceless consonants and breath sounds. Set to 0.5 to disable.') | |
with gr.Column(): | |
f0_method = gr.Dropdown(['rmvpe', 'mangio-crepe'], value='rmvpe', label='Pitch detection algorithm', info='Best option is rmvpe (clarity in vocals), then mangio-crepe (smoother vocals)') | |
crepe_hop_length = gr.Slider(32, 320, value=128, step=1, visible=False, label='Crepe hop length', info='Lower values leads to longer conversions and higher risk of voice cracks, but better pitch accuracy.') | |
f0_method.change(show_hop_slider, inputs=f0_method, outputs=crepe_hop_length) | |
keep_files = gr.Checkbox(label='Keep intermediate files', info='Keep all audio files generated in the song_output/id directory, e.g. Isolated Vocals/Instrumentals. Leave unchecked to save space') | |
with gr.Accordion('Audio mixing options', open=False): | |
gr.Markdown('### Volume Change (decibels)') | |
with gr.Row(): | |
main_gain = gr.Slider(-20, 20, value=0, step=1, label='Main Vocals') | |
backup_gain = gr.Slider(-20, 20, value=0, step=1, label='Backup Vocals') | |
inst_gain = gr.Slider(-20, 20, value=0, step=1, label='Music') | |
gr.Markdown('### Reverb Control on AI Vocals') | |
with gr.Row(): | |
reverb_rm_size = gr.Slider(0, 1, value=0.15, label='Room size', info='The larger the room, the longer the reverb time') | |
reverb_wet = gr.Slider(0, 1, value=0.2, label='Wetness level', info='Level of AI vocals with reverb') | |
reverb_dry = gr.Slider(0, 1, value=0.8, label='Dryness level', info='Level of AI vocals without reverb') | |
reverb_damping = gr.Slider(0, 1, value=0.7, label='Damping level', info='Absorption of high frequencies in the reverb') | |
gr.Markdown('### Audio Output Format') | |
output_format = gr.Dropdown(['mp3', 'wav'], value='mp3', label='Output file type', info='mp3: small file size, decent quality. wav: Large file size, best quality') | |
with gr.Row(): | |
clear_btn = gr.ClearButton(value='Clear', components=[song_input, rvc_model, keep_files, local_file]) | |
generate_btn = gr.Button("Generate", variant='primary') | |
ai_cover = gr.Audio(label='AI Cover', show_share_button=False) | |
ref_btn.click(update_models_list, None, outputs=rvc_model) | |
is_webui = gr.Number(value=1, visible=False) | |
generate_btn.click(song_cover_pipeline, | |
inputs=[song_input, rvc_model, pitch, keep_files, is_webui, main_gain, backup_gain, | |
inst_gain, index_rate, filter_radius, rms_mix_rate, f0_method, crepe_hop_length, | |
protect, pitch_all, reverb_rm_size, reverb_wet, reverb_dry, reverb_damping, | |
output_format], | |
outputs=[ai_cover]) | |
clear_btn.click(lambda: [0, 0, 0, 0, 0.5, 3, 0.25, 0.33, 'rmvpe', 128, 0, 0.15, 0.2, 0.8, 0.7, 'mp3', None], | |
outputs=[pitch, main_gain, backup_gain, inst_gain, index_rate, filter_radius, rms_mix_rate, | |
protect, f0_method, crepe_hop_length, pitch_all, reverb_rm_size, reverb_wet, | |
reverb_dry, reverb_damping, output_format, ai_cover]) | |
# Download tab | |
with gr.Tab('Download model'): | |
with gr.Tab('From HuggingFace/Pixeldrain URL'): | |
with gr.Row(): | |
model_zip_link = gr.Text(label='Download link to model', info='Should be a zip file containing a .pth model file and an optional .index file.') | |
model_name = gr.Text(label='Name your model', info='Give your new model a unique name from your other voice models.') | |
with gr.Row(): | |
download_btn = gr.Button('Download ๐', variant='primary', scale=19) | |
dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20) | |
download_btn.click(download_online_model, inputs=[model_zip_link, model_name], outputs=dl_output_message) | |
gr.Markdown('## Input Examples') | |
gr.Examples( | |
[ | |
['https://huggingface.co/phant0m4r/LiSA/resolve/main/LiSA.zip', 'Lisa'], | |
['https://pixeldrain.com/u/3tJmABXA', 'Gura'], | |
['https://huggingface.co/Kit-Lemonfoot/kitlemonfoot_rvc_models/resolve/main/AZKi%20(Hybrid).zip', 'Azki'] | |
], | |
[model_zip_link, model_name], | |
[], | |
download_online_model, | |
) | |
with gr.Tab('From Public Index'): | |
gr.Markdown('## How to use') | |
gr.Markdown('- Click Initialize public models table') | |
gr.Markdown('- Filter models using tags or search bar') | |
gr.Markdown('- Select a row to autofill the download link and model name') | |
gr.Markdown('- Click Download') | |
with gr.Row(): | |
pub_zip_link = gr.Text(label='Download link to model') | |
pub_model_name = gr.Text(label='Model name') | |
with gr.Row(): | |
download_pub_btn = gr.Button('Download ๐', variant='primary', scale=19) | |
pub_dl_output_message = gr.Text(label='Output Message', interactive=False, scale=20) | |
filter_tags = gr.CheckboxGroup(value=[], label='Show voice models with tags', choices=[]) | |
search_query = gr.Text(label='Search') | |
load_public_models_button = gr.Button(value='Initialize public models table', variant='primary') | |
public_models_table = gr.DataFrame(value=[], headers=['Model Name', 'Description', 'Credit', 'URL', 'Tags'], label='Available Public Models', interactive=False) | |
public_models_table.select(pub_dl_autofill, inputs=[public_models_table], outputs=[pub_zip_link, pub_model_name]) | |
load_public_models_button.click(load_public_models, outputs=[public_models_table, filter_tags]) | |
search_query.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table) | |
filter_tags.change(filter_models, inputs=[filter_tags, search_query], outputs=public_models_table) | |
download_pub_btn.click(download_online_model, inputs=[pub_zip_link, pub_model_name], outputs=pub_dl_output_message) | |
# Upload tab | |
with gr.Tab('Upload model'): | |
gr.Markdown('## Upload locally trained RVC v2 model and index file') | |
gr.Markdown('- Find model file (weights folder) and optional index file (logs/[name] folder)') | |
gr.Markdown('- Compress files into zip file') | |
gr.Markdown('- Upload zip file and give unique name for voice') | |
gr.Markdown('- Click Upload model') | |
with gr.Row(): | |
with gr.Column(): | |
zip_file = gr.File(label='Zip file') | |
local_model_name = gr.Text(label='Model name') | |
with gr.Row(): | |
model_upload_button = gr.Button('Upload model', variant='primary', scale=19) | |
local_upload_output_message = gr.Text(label='Output Message', interactive=False, scale=20) | |
model_upload_button.click(upload_local_model, inputs=[zip_file, local_model_name], outputs=local_upload_output_message) | |
app.launch( | |
share=args.share_enabled, | |
enable_queue=True, | |
server_name=None if not args.listen else (args.listen_host or '0.0.0.0'), | |
server_port=args.listen_port, | |
) | |