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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() | |