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import yt_dlp
import numpy as np
import librosa
import soundfile as sf
import os
import zipfile
# Function to download audio from YouTube and save it as a WAV file
def download_youtube_audio(url, audio_name):
ydl_opts = {
"format": "bestaudio/best",
"postprocessors": [
{
"key": "FFmpegExtractAudio",
"preferredcodec": "wav",
}
],
"outtmpl": f"youtubeaudio/{audio_name}", # Output template
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
return f"youtubeaudio/{audio_name}.wav"
# Function to calculate RMS
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)
if axis < 0:
target_axis = axis - 1
else:
target_axis = 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
class Slicer:
def __init__(
self,
sr,
threshold=-40.0,
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 / 20.0)
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
# Function to slice and save audio chunks
def slice_audio(file_path, audio_name):
audio, sr = librosa.load(file_path, sr=None, mono=False)
os.makedirs(f"dataset/{audio_name}", exist_ok=True)
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
sf.write(f"dataset/{audio_name}/split_{i}.wav", chunk, sr)
return f"dataset/{audio_name}"
# Function to zip the dataset directory
def zip_directory(directory_path, audio_name):
zip_file = f"dataset/{audio_name}.zip"
os.makedirs(os.path.dirname(zip_file), exist_ok=True) # Ensure the directory exists
with zipfile.ZipFile(zip_file, "w", zipfile.ZIP_DEFLATED) as zipf:
for root, dirs, files in os.walk(directory_path):
for file in files:
file_path = os.path.join(root, file)
arcname = os.path.relpath(file_path, start=directory_path)
zipf.write(file_path, arcname)
return zip_file
# Gradio interface
def process_audio(url, audio_name):
file_path = download_youtube_audio(url, audio_name)
dataset_path = slice_audio(file_path, audio_name)
zip_file = zip_directory(dataset_path, audio_name)
return zip_file, print(f"{zip_file} successfully processed")
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