import subprocess
import numpy as np
import librosa
import soundfile

# Set your variables here
Mode = “Splitting”
dataset = “Youtube”
url = “”
drive_path = “”
AUDIO_NAME = “”

if dataset == “Youtube”:
    !pip install yt_dlp
    !pip install ffmpeg
    !mkdir youtubeaudio

from future import unicode_literals

if dataset == “Youtube”:
    import yt_dlp
    import ffmpeg
    import sys

    ydl_opts = {
        ‘format’: ‘bestaudio/best’,
        ‘postprocessors’: [{
            ‘key’: ‘FFmpegExtractAudio’,
            ‘preferredcodec’: ‘wav’,
        }],
        “outtmpl”: f’youtubeaudio/{AUDIO_NAME}‘,
    }
    def download_from_url(url):
        ydl.download([url])

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        download_from_url(url)

!python3 -m pip install -U demucs

if dataset == “Drive”:
    command = f"demucs --two-stems=vocals {drive_path}“
elif dataset == “Youtube”:
    command = f"demucs --two-stems=vocals youtubeaudio/{AUDIO_NAME}.wav”

result = subprocess.run(command.split(), stdout=subprocess.PIPE)
print(result.stdout.decode())

if Mode == “Separate”:
    print(“Mode is set to Separate. Skipping this section”)
elif Mode ==  “Splitting”:
    !pip install numpy
    !pip install librosa
    !pip install soundfile
    !mkdir -p dataset/{AUDIO_NAME}

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)


class Slicer:
    def init(self,
                 sr: int,
                 threshold: float = -40.,
                 min_length: int= 5000,
                 min_interval: int = 300,
                 hop_size: int = 20,
                 max_sil_kept: int = 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= 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]
        slices = []
        for tag in sil_tags:
            slices.append(self.apply_slice(waveform, tag[0], tag[1]))
        return slices

# Example usage
sr, waveform = soundfile.read(‘audio.wav’)
slicer = Slicer(sr)
slices = slicer.slice(waveform)
for i, slice in enumerate(slices):
    soundfile.write(f’slice{i}.wav’, slice, sr)