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import gradio as gr
#from __future__ import unicode_literals
import yt_dlp
import ffmpeg
import subprocess
import numpy as np
import librosa
import soundfile
# Function to download audio from YouTube
def download_audio(url, audio_name):
ydl_opts = {
'format': 'bestaudio/best',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'wav',
}],
"outtmpl": f'youtubeaudio/{audio_name}',
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
# Function to separate vocals using demucs
def separate_vocals(audio_path, audio_name):
command = f"demucs --two-stems=vocals {audio_path}"
result = subprocess.run(command.split(), stdout=subprocess.PIPE)
print(result.stdout.decode())
subprocess.run(f"!mkdir -p /content/audio/{audio_name}", shell=True)
subprocess.run(f"!cp -r /content/separated/htdemucs/{audio_name}/* /content/audio/{audio_name}", shell=True)
subprocess.run(f"!cp -r /content/youtubeaudio/{audio_name}.wav /content/audio/{audio_name}", shell=True)
# RMS function from librosa
def get_rms(y, frame_length=2048, hop_length=512, pad_mode="constant"):
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
out_strides = y.strides + tuple([y.strides[axis]])
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
target_axis = axis + 1 if axis >= 0 else axis - 1
xw = np.moveaxis(xw, -1, target_axis)
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
# Slicer class to split audio
class Slicer:
def __init__(self, sr, threshold=-40., min_length=5000, min_interval=300, hop_size=20, max_sil_kept=5000):
if not min_length >= min_interval >= hop_size:
raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
if not max_sil_kept >= hop_size:
raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.)
self.hop_size = round(sr * hop_size / 1000)
self.win_size = min(round(min_interval), 4 * self.hop_size)
self.min_length = round(sr * min_length / 1000 / self.hop_size)
self.min_interval = round(min_interval / self.hop_size)
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
def _apply_slice(self, waveform, begin, end):
if len(waveform.shape) > 1:
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
else:
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
def slice(self, waveform):
if len(waveform.shape) > 1:
samples = waveform.mean(axis=0)
else:
samples = waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
for i, rms in enumerate(rms_list):
if rms < self.threshold:
if silence_start is None:
silence_start = i
continue
if silence_start is None:
continue
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
if i - silence_start <= self.max_sil_kept:
pos = rms_list[silence_start: i + 1].argmin() + silence_start
if silence_start == 0:
sil_tags.append((0, pos))
else:
sil_tags.append((pos, pos))
clip_start = pos
elif i - silence_start <= self.max_sil_kept * 2:
pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
pos += i - self.max_sil_kept
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
clip_start = pos_r
else:
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
clip_start = max(pos_r, pos)
else:
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
else:
sil_tags.append((pos_l, pos_r))
clip_start = pos_r
silence_start = None
total_frames = rms_list.shape[0]
if silence_start is not None and total_frames - silence_start >= self.min_interval:
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
if len(sil_tags) == 0:
return [waveform]
else:
chunks = []
if sil_tags[0][0] > 0:
chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
for i in range(len(sil_tags) - 1):
chunks.append(self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]))
if sil_tags[-1][1] < total_frames:
chunks.append(self._apply_slice(waveform, sil_tags[-1][1], total_frames))
return chunks
def process_audio(mode, dataset, url, drive_path, audio_name):
if dataset == "Drive":
print("Dataset is set to Drive. Skipping this section")
elif dataset == "Youtube":
download_audio(url, audio_name)
audio_input = f"/content/youtubeaudio/{audio_name}.wav"
if dataset == "Drive":
command = f"demucs --two-stems=vocals {drive_path}"
elif dataset == "Youtube":
command = f"demucs --two-stems=vocals {audio_input}"
subprocess.run(command.split(), stdout=subprocess.PIPE)
if mode == "Splitting":
audio, sr = librosa.load(f'/content/separated/htdemucs/{audio_name}/vocals.wav', sr=None, mono=False)
slicer = Slicer(
sr=sr,
threshold=-40,
min_length=5000,
min_interval=500,
hop_size=10,
max_sil_kept=500
)
chunks = slicer.slice(audio)
for i, chunk in enumerate(chunks):
if len(chunk.shape) > 1:
chunk = chunk.T
soundfile.write(f'/content/dataset/{audio_name}/split_{i}.wav', chunk, sr)
return f"Processing complete for {audio_name}"
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("# Dataset Maker")
mode = gr.Dropdown(choices=["Splitting", "Separate"], label="Mode")
dataset = gr.Dropdown(choices=["Youtube", "Drive"], label="Dataset")
url = gr.Textbox(label="URL")
drive_path = gr.Textbox(label="Drive Path")
audio_name = gr.Textbox(label="Audio Name")
output = gr.Textbox(label="Output")
process_button = gr.Button("Process")
process_button.click(
process_audio,
inputs=[mode, dataset, url, drive_path, audio_name],
outputs=[output]
)
demo.launch()
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