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import os.path
import time
from tempfile import NamedTemporaryFile
import gradio
import numpy
import soundfile
import torch
import torchaudio
import torchaudio.functional as F
import librosa
import webui.ui.tabs.rvc as rvc
from webui.modules.implementations.ffmpeg_utils import ffmpeg_utils_tab
def denoise_tab():
with gradio.Row():
audio_in = gradio.Audio(label='Input audio')
audio_out = gradio.Audio(label='Denoised audio', interactive=False)
denoise_button = gradio.Button('Denoise', variant='primary')
def denoise_func(audio):
sr, wav = audio
import noisereduce.noisereduce as noisereduce
wav = noisereduce.reduce_noise(wav, sr)
return sr, wav
with gradio.Row():
with gradio.Column():
in_directory = gradio.Textbox(label='Input directory')
out_directory = gradio.Textbox(label='Output directory')
batch_progress = gradio.Textbox(label='Batch processing progress')
denoise_batch = gradio.Button('Denoise batch', variant='primary')
def batch_denoise(in_dir, out_dir):
import noisereduce.noisereduce as noisereduce
if not os.path.isdir(in_dir):
yield 'Error: input directory is not a directory'
return
os.makedirs(out_dir, exist_ok=True)
output = f'Processing directory {in_dir}'
yield output
for f in os.listdir(in_dir):
if os.path.splitext(f)[-1] not in ['.wav', '.mp3']:
continue
output += f'\nProcessing {f}'
yield output
full_path = os.path.join(in_dir, f)
wav, sr = torchaudio.load(full_path)
wav = wav.detach().cpu().numpy()
wav = noisereduce.reduce_noise(wav, sr)
wav = torch.tensor(wav)
torchaudio.save(os.path.join(out_dir, f), wav, sr)
output += '\nCompleted!'
yield output
denoise_button.click(fn=denoise_func, inputs=audio_in, outputs=audio_out)
denoise_batch.click(fn=batch_denoise, inputs=[in_directory, out_directory], outputs=batch_progress)
def music_split_tab():
with gradio.Row():
audio_in = gradio.Audio(label='Input audio')
with gradio.Column():
audio_vocal = gradio.Audio(label='Vocals', interactive=False)
audio_background = gradio.Audio(label='Other audio', interactive=False)
def music_split_func(audio):
sr, wav = audio
wav = torch.tensor(wav).float() / 32767.0
if wav.shape[0] == 2:
wav = wav.mean(0)
import webui.modules.implementations.rvc.split_audio as split_audio
vocal, background, sr = split_audio.split(sr, wav)
if vocal.shape[0] == 2:
vocal = vocal.mean(0)
if background.shape[0] == 2:
background = background.mean(0)
return [(sr, vocal.squeeze().detach().numpy()), (sr, background.squeeze().detach().numpy())]
split_button = gradio.Button('Split', variant='primary')
split_button.click(fn=music_split_func, inputs=audio_in, outputs=[audio_vocal, audio_background])
with gradio.Row():
with gradio.Column():
in_directory = gradio.Textbox(label='Input directory')
out_directory = gradio.Textbox(label='Output directory')
batch_progress = gradio.Textbox(label='Batch processing progress')
split_batch = gradio.Button('Split batch', variant='primary')
def batch_music_split(in_dir, out_dir):
if not os.path.isdir(in_dir):
yield 'Error: input directory is not a directory'
return
os.makedirs(os.path.join(out_dir, 'vocal'), exist_ok=True)
os.makedirs(os.path.join(out_dir, 'background'), exist_ok=True)
output = f'Processing directory {in_dir}'
yield output
for f in os.listdir(in_dir):
split = os.path.splitext(f)
extension = split[-1]
if extension not in ['.wav', '.mp3']:
continue
output += f'\nProcessing {f}'
yield output
full_path = os.path.join(in_dir, f)
wav, sr = torchaudio.load(full_path)
# Split
if wav.dtype == numpy.int16:
wav = wav.float() / 32767.0
if wav.shape[0] == 2:
wav = wav.mean(0)
import webui.modules.implementations.rvc.split_audio as split_audio
vocal, background, sr = split_audio.split(sr, wav)
if vocal.shape[0] == 2:
vocal = vocal.mean(0)
if background.shape[0] == 2:
background = background.mean(0)
if len(vocal.shape) == 1:
vocal = vocal.unsqueeze(0)
if len(background.shape) == 1:
background = background.unsqueeze(0)
torchaudio.save(os.path.join(out_dir, 'vocal', f), vocal, sr)
torchaudio.save(os.path.join(out_dir, 'background', f), background, sr)
output += '\nCompleted!'
yield output
split_batch.click(fn=batch_music_split, inputs=[in_directory, out_directory], outputs=batch_progress)
with gradio.Row():
with gradio.Column():
# audio_combine_1 = gradio.Audio(label='Input audio 1', type='filepath')
audio_combine_1 = gradio.File(label='Input audio 1')
# audio_combine_2 = gradio.Audio(label='Input audio 2', type='filepath')
audio_combine_2 = gradio.File(label='Input audio 2')
audio_out = gradio.Audio(label='Combined audio', interactive=False)
def music_merge_func(audio1, audio2):
x, sr = torchaudio.load(audio1.name)
y, sry = torchaudio.load(audio2.name)
if x.shape[0] == 2:
x = x.mean(0)
if y.shape[0] == 2:
y = y.mean(0)
if x.shape[-1] == 2:
x = x.mean(-1)
if y.shape[-1] == 2:
y = y.mean(-1)
len_x = x.shape[-1] / sr
len_y = y.shape[-1] / sry
y = F.resample(y, sry, sr)
y = F.resample(y, sr, int(sr * len_x/len_y))
y = y.flatten()
x = x.flatten()
if x.shape[0] > y.shape[0]:
x = x[-y.shape[0]:]
else:
y = y[-x.shape[0]:]
return sr, x.add(y).flatten().cpu().detach().numpy()
split_button = gradio.Button('Merge', variant='primary')
split_button.click(fn=music_merge_func, inputs=[audio_combine_1, audio_combine_2], outputs=audio_out)
def audio_download_tab():
import webui.modules.implementations.audio_download as ad
with gradio.Row():
with gradio.Column():
url_type = gradio.Dropdown(['youtube'], value='youtube', label='Type')
url = gradio.Textbox(max_lines=1, label='Url')
file_out = gradio.File(label='Downloaded audio')
download_button = gradio.Button('Download', variant='primary')
download_button.click(fn=ad.download_audio, inputs=[url_type, url], outputs=file_out)
def waveform_tab():
def create_waveform(audio):
import webui.modules.util as u
return u.make_waveform(audio)
with gradio.Row():
audio_in = gradio.Audio(label='Input audio')
video_out = gradio.PlayableVideo(label='Output waveform video', interactive=False)
create_waveform_button = gradio.Button('Create waveform video', variant='primary')
create_waveform_button.click(fn=create_waveform, inputs=audio_in, outputs=video_out)
def enhance_tab():
def enhance_audio(file_in):
output_file = NamedTemporaryFile(suffix='.wav', delete=False).name
sampling_rate = 44100
y, sr = librosa.load(file_in, sr=sampling_rate)
y_shifted = librosa.effects.pitch_shift(y, sr=sr, n_steps=12, res_type="soxr_vhq")
soundfile.write(output_file, y + y_shifted, sampling_rate)
return output_file
with gradio.Row():
audio_in = gradio.Audio(label='Input audio', type='filepath')
audio_out = gradio.Audio(label='Output audio', interactive=False)
create_waveform_button = gradio.Button('Enhance audio quality (Mainly for audioldm).', variant='primary')
create_waveform_button.click(fn=enhance_audio, inputs=audio_in, outputs=audio_out)
def utils_tab():
with gradio.Tabs():
with gradio.Tab('🧹 Denoise'):
denoise_tab()
with gradio.Tab('🔊▶🗣/🎵 Music splitting'):
music_split_tab()
with gradio.Tab('🔎 Enhance'):
enhance_tab()
with gradio.Tab('📈 Audio waveforms'):
waveform_tab()
with gradio.Tab('👽 FFMPEG'):
ffmpeg_utils_tab()
with gradio.Tab('🔽 Audio downloads'):
audio_download_tab()
import webui.extensionlib.callbacks as cb
cb.get_manager('webui.tabs.utils')()
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