Delete slicer2.py

slicer2.py

@@ -1,260 +0,0 @@
-import numpy as np
-
-
-# This function is obtained 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
-    # put our new within-frame axis at the end for now
-    out_strides = y.strides + tuple([y.strides[axis]])
-    # Reduce the shape on the framing 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)
-    # Downsample along the target axis
-    slices = [slice(None)] * xw.ndim
-    slices[axis] = slice(0, None, hop_length)
-    x = xw[tuple(slices)]
-
-    # Calculate power
-    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
-
-    return np.sqrt(power)
-
-
-class Slicer:
-    def __init__(
-        self,
-        sr: int,
-        threshold: float = -40.0,
-        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 / 20.0)
-        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)
-            ]
-
-    # @timeit
-    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):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            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
-            # Need slicing. Record the range of silent frames to be removed.
-            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
-        # Deal with trailing silence.
-        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))
-        # Apply and return slices.
-        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 main():
-    import os.path
-    from argparse import ArgumentParser
-
-    import librosa
-    import soundfile
-
-    parser = ArgumentParser()
-    parser.add_argument("audio", type=str, help="The audio to be sliced")
-    parser.add_argument(
-        "--out", type=str, help="Output directory of the sliced audio clips"
-    )
-    parser.add_argument(
-        "--db_thresh",
-        type=float,
-        required=False,
-        default=-40,
-        help="The dB threshold for silence detection",
-    )
-    parser.add_argument(
-        "--min_length",
-        type=int,
-        required=False,
-        default=5000,
-        help="The minimum milliseconds required for each sliced audio clip",
-    )
-    parser.add_argument(
-        "--min_interval",
-        type=int,
-        required=False,
-        default=300,
-        help="The minimum milliseconds for a silence part to be sliced",
-    )
-    parser.add_argument(
-        "--hop_size",
-        type=int,
-        required=False,
-        default=10,
-        help="Frame length in milliseconds",
-    )
-    parser.add_argument(
-        "--max_sil_kept",
-        type=int,
-        required=False,
-        default=500,
-        help="The maximum silence length kept around the sliced clip, presented in milliseconds",
-    )
-    args = parser.parse_args()
-    out = args.out
-    if out is None:
-        out = os.path.dirname(os.path.abspath(args.audio))
-    audio, sr = librosa.load(args.audio, sr=None, mono=False)
-    slicer = Slicer(
-        sr=sr,
-        threshold=args.db_thresh,
-        min_length=args.min_length,
-        min_interval=args.min_interval,
-        hop_size=args.hop_size,
-        max_sil_kept=args.max_sil_kept,
-    )
-    chunks = slicer.slice(audio)
-    if not os.path.exists(out):
-        os.makedirs(out)
-    for i, chunk in enumerate(chunks):
-        if len(chunk.shape) > 1:
-            chunk = chunk.T
-        soundfile.write(
-            os.path.join(
-                out,
-                f"%s_%d.wav"
-                % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
-            ),
-            chunk,
-            sr,
-        )
-
-
-if __name__ == "__main__":
-    main()