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import os
import re
import random
from scipy.io.wavfile import write
from scipy.io.wavfile import read
import numpy as np
import gradio as gr
import yt_dlp
import subprocess
mdxnet_models = [
'UVR-MDX-NET-Inst_full_292.onnx',
'UVR-MDX-NET_Inst_187_beta.onnx',
'UVR-MDX-NET_Inst_82_beta.onnx',
'UVR-MDX-NET_Inst_90_beta.onnx',
'UVR-MDX-NET_Main_340.onnx',
'UVR-MDX-NET_Main_390.onnx',
'UVR-MDX-NET_Main_406.onnx',
'UVR-MDX-NET_Main_427.onnx',
'UVR-MDX-NET_Main_438.onnx',
'UVR-MDX-NET-Inst_HQ_1.onnx',
'UVR-MDX-NET-Inst_HQ_2.onnx',
'UVR-MDX-NET-Inst_HQ_3.onnx',
'UVR-MDX-NET-Inst_HQ_4.onnx',
'UVR_MDXNET_Main.onnx',
'UVR-MDX-NET-Inst_Main.onnx',
'UVR_MDXNET_1_9703.onnx',
'UVR_MDXNET_2_9682.onnx',
'UVR_MDXNET_3_9662.onnx',
'UVR-MDX-NET-Inst_1.onnx',
'UVR-MDX-NET-Inst_2.onnx',
'UVR-MDX-NET-Inst_3.onnx',
'UVR_MDXNET_KARA.onnx',
'UVR_MDXNET_KARA_2.onnx',
'UVR_MDXNET_9482.onnx',
'UVR-MDX-NET-Voc_FT.onnx',
'Kim_Vocal_1.onnx',
'Kim_Vocal_2.onnx',
'Kim_Inst.onnx',
'Reverb_HQ_By_FoxJoy.onnx',
'UVR-MDX-NET_Crowd_HQ_1.onnx',
'kuielab_a_vocals.onnx',
'kuielab_a_other.onnx',
'kuielab_a_bass.onnx',
'kuielab_a_drums.onnx',
'kuielab_b_vocals.onnx',
'kuielab_b_other.onnx',
'kuielab_b_bass.onnx',
'kuielab_b_drums.onnx',
]
output_format = [
'wav',
'flac',
'mp3',
]
mdxnet_overlap_values = [
'0.25',
'0.5',
'0.75',
'0.99',
]
def download_audio(url):
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': 'ytdl/%(title)s.%(ext)s',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'wav',
'preferredquality': '192',
}],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
info_dict = ydl.extract_info(url, download=True)
file_path = ydl.prepare_filename(info_dict).rsplit('.', 1)[0] + '.wav'
sample_rate, audio_data = read(file_path)
audio_array = np.asarray(audio_data, dtype=np.int16)
return sample_rate, audio_array
def mdxnet_separator(mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_segment_size, mdxnet_overlap, mdxnet_denoise):
files_list = []
files_list.clear()
directory = "./outputs"
random_id = str(random.randint(10000, 99999))
pattern = f"{random_id}"
os.makedirs("outputs", exist_ok=True)
write(f'{random_id}.wav', mdxnet_audio[0], mdxnet_audio[1])
prompt = f"audio-separator {random_id}.wav --model_filename {mdxnet_model} --output_dir=./outputs --output_format={mdxnet_output_format} --normalization=0.9 --mdx_segment_size={mdxnet_segment_size} --mdx_overlap={mdxnet_overlap}"
if mdxnet_denoise:
prompt += " --mdx_enable_denoise"
os.system(prompt)
for file in os.listdir(directory):
if re.search(pattern, file):
files_list.append(os.path.join(directory, file))
stem1_file = files_list[0]
stem2_file = files_list[1]
return stem1_file, stem2_file
def mdxnet_batch(path_input, path_output, model, output_format, overlap, segment_size, denoise):
found_files = []
logs = []
logs.clear()
extensions = (".mp3", ".wav", ".flac")
for audio_files in os.listdir(path_input):
if audio_files.endswith(extensions):
found_files.append(audio_files)
total_files = len(found_files)
if total_files == 0:
logs.append("No valid audio files.")
yield "\n".join(logs)
else:
logs.append(f"{total_files} audio files found")
found_files.sort()
for audio_files in found_files:
file_path = os.path.join(path_input, audio_files)
prompt = ["audio-separator", file_path, "-m", f"{model}", f"--output_dir={path_output}", f"--output_format={output_format}", "--normalization=0.9", f"--mdx_overlap={overlap}", f"--mdx_segment_size={segment_size}"]
if denoise:
prompt.append("--mdx_enable_denoise")
logs.append(f"Processing file: {audio_files}")
yield "\n".join(logs)
subprocess.run(prompt)
logs.append(f"File: {audio_files} processed!")
yield "\n".join(logs)
with gr.Blocks(theme="Blane187/fuchsia", title="🎵 UVR5 MDX 🎵") as app:
gr.Markdown("<h1> 🎵 UVR MDX 🎵 </h1>")
gr.Markdown("If you liked this HF Space you can give me a ❤️")
gr.Markdown("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)")
with gr.Tabs():
with gr.TabItem("MDX-NET"):
with gr.Row():
mdxnet_model = gr.Dropdown(
label = "Select the Model",
choices = mdxnet_models,
interactive = True
)
with gr.Row():
mdxnet_output_format = gr.Dropdown(
label = "Select the Output Format",
choices = output_format,
interactive = True
)
with gr.Row():
mdxnet_segment_size = gr.Slider(
minimum = 32,
maximum = 4000,
step = 32,
label = "Segment Size",
info = "Larger consumes more resources, but may give better results.",
value = 256,
interactive = True
)
with gr.Row():
mdxnet_overlap = gr.Dropdown(
label = "Overlap",
choices = mdxnet_overlap_values,
value = mdxnet_overlap_values[0],
interactive = True
)
mdxnet_denoise = gr.Checkbox(
label = "Denoise",
info = "Enable denoising during separation.",
value = True,
interactive = True
)
with gr.Row():
mdxnet_audio = gr.Audio(
label = "Input Audio",
type = "numpy",
interactive = True
)
with gr.Accordion("Separation by Link", open = False):
with gr.Row():
mdxnet_link = gr.Textbox(
label = "Link",
placeholder = "Paste the link here",
interactive = True
)
with gr.Row():
gr.Markdown("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")
with gr.Row():
mdxnet_download_button = gr.Button(
"Download!",
variant = "primary"
)
mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio])
with gr.Accordion("Batch Separation", open = False):
with gr.Row():
mdxnet_input_path = gr.Textbox(
label = "Input Path",
placeholder = "Place the input path here",
interactive = True
)
mdxnet_output_path = gr.Textbox(
label = "Output Path",
placeholder = "Place the output path here",
interactive = True
)
with gr.Row():
mdxnet_bath_button = gr.Button("Separate!", variant = "primary")
with gr.Row():
mdxnet_info = gr.Textbox(
label = "Output Information",
interactive = False
)
mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_overlap, mdxnet_segment_size, mdxnet_denoise], [mdxnet_info])
with gr.Row():
mdxnet_button = gr.Button("Separate!", variant = "primary")
with gr.Row():
mdxnet_stem1 = gr.Audio(
show_download_button = True,
interactive = False,
label = "instrumental",
type = "filepath"
)
mdxnet_stem2 = gr.Audio(
show_download_button = True,
interactive = False,
label = "vocal",
type = "filepath"
)
mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_segment_size, mdxnet_overlap, mdxnet_denoise], [mdxnet_stem1, mdxnet_stem2])
with gr.TabItem("Credits"):
gr.Markdown(
"""
UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community.
* python-audio-separator by [beveradb](https://github.com/beveradb).
* Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help.
* Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
* Thanks to [Nick088](https://huggingface.co./Nick088) for the help to fix roformers.
* Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
* Separation by link source code and improvements by [Blane187](https://huggingface.co./Blane187).
You can donate to the original UVR5 project here:
[](https://www.buymeacoffee.com/uvr5)
"""
)
app.launch(share=True, debug=True) |