File size: 7,671 Bytes
1e4a2ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
import numpy as np

class Slicer:
    def __init__(self, sr, threshold = -40.0, min_length = 5000, min_interval = 300, hop_size = 20, max_sil_kept = 5000):
        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):
        start_idx = begin * self.hop_size

        return waveform[:, start_idx:min(waveform.shape[1], end * self.hop_size)] if len(waveform.shape) > 1 else waveform[start_idx:min(waveform.shape[0], end * self.hop_size)]

    def slice(self, waveform):
        samples = waveform.mean(axis=0) if len(waveform.shape) > 1 else 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, clip_start = None, 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
                sil_tags.append((0, pos) if silence_start == 0 else (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_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((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), pos), max(pos_r, pos)))
                    clip_start = max(pos_r, pos)
            else:
                pos_r = (rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept)
                sil_tags.append((0, pos_r) if silence_start == 0 else ((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), 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): sil_tags.append((rms_list[silence_start : min(total_frames, silence_start + self.max_sil_kept) + 1].argmin() + silence_start, total_frames + 1))

        if not sil_tags: 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

class Slicer2(Slicer):
    def slice2(self, waveform):
        samples = waveform.mean(axis=0) if len(waveform.shape) > 1 else waveform

        if samples.shape[0] <= self.min_length: return [(waveform, 0, samples.shape[0])]
        rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)

        sil_tags = []
        silence_start, clip_start = None, 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
                sil_tags.append((0, pos) if silence_start == 0 else (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_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((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), pos), max(pos_r, pos)))
                    clip_start = max(pos_r, pos)
            else:
                pos_r = (rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept)
                sil_tags.append((0, pos_r) if silence_start == 0 else ((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), 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): sil_tags.append((rms_list[silence_start : min(total_frames, silence_start + self.max_sil_kept) + 1].argmin() + silence_start, total_frames + 1))

        if not sil_tags: return [(waveform, 0, samples.shape[-1])]
        else:
            chunks = []
            if sil_tags[0][0] > 0: chunks.append((self._apply_slice(waveform, 0, sil_tags[0][0]), 0, sil_tags[0][0] * self.hop_size))

            for i in range(len(sil_tags) - 1):
                chunks.append((self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]), sil_tags[i][1] * self.hop_size, sil_tags[i + 1][0] * self.hop_size))

            if sil_tags[-1][1] < total_frames: chunks.append((self._apply_slice(waveform, sil_tags[-1][1], total_frames), sil_tags[-1][1] * self.hop_size, samples.shape[-1]))
            return chunks
            
def get_rms(y, frame_length=2048, hop_length=512, pad_mode="constant"):
    y = np.pad(y, (int(frame_length // 2), int(frame_length // 2)), mode=pad_mode)
    axis = -1

    x_shape_trimmed = list(y.shape)
    x_shape_trimmed[axis] -= frame_length - 1
    xw = np.moveaxis(np.lib.stride_tricks.as_strided(y, shape=tuple(x_shape_trimmed) + tuple([frame_length]), strides=y.strides + tuple([y.strides[axis]])), -1, axis - 1 if axis < 0 else axis + 1)
    
    slices = [slice(None)] * xw.ndim
    slices[axis] = slice(0, None, hop_length)

    return np.sqrt(np.mean(np.abs(xw[tuple(slices)]) ** 2, axis=-2, keepdims=True))