File size: 6,357 Bytes
ed1cdd1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
import time

import numpy as np
import torch
import torchaudio
from scipy.ndimage import maximum_filter1d, uniform_filter1d


def timeit(func):
    def run(*args, **kwargs):
        t = time.time()
        res = func(*args, **kwargs)
        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
        return res

    return run


# @timeit
def _window_maximum(arr, win_sz):
    return maximum_filter1d(arr, size=win_sz)[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]


# @timeit
def _window_rms(arr, win_sz):
    filtered = np.sqrt(uniform_filter1d(np.power(arr, 2), win_sz) - np.power(uniform_filter1d(arr, win_sz), 2))
    return filtered[win_sz // 2: win_sz // 2 + arr.shape[0] - win_sz + 1]


def level2db(levels, eps=1e-12):
    return 20 * np.log10(np.clip(levels, a_min=eps, a_max=1))


def _apply_slice(audio, begin, end):
    if len(audio.shape) > 1:
        return audio[:, begin: end]
    else:
        return audio[begin: end]


class Slicer:
    def __init__(self,
                 sr: int,
                 db_threshold: float = -40,
                 min_length: int = 5000,
                 win_l: int = 300,
                 win_s: int = 20,
                 max_silence_kept: int = 500):
        self.db_threshold = db_threshold
        self.min_samples = round(sr * min_length / 1000)
        self.win_ln = round(sr * win_l / 1000)
        self.win_sn = round(sr * win_s / 1000)
        self.max_silence = round(sr * max_silence_kept / 1000)
        if not self.min_samples >= self.win_ln >= self.win_sn:
            raise ValueError('The following condition must be satisfied: min_length >= win_l >= win_s')
        if not self.max_silence >= self.win_sn:
            raise ValueError('The following condition must be satisfied: max_silence_kept >= win_s')

    @timeit
    def slice(self, audio):
        samples = audio
        if samples.shape[0] <= self.min_samples:
            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
        # get absolute amplitudes
        abs_amp = np.abs(samples - np.mean(samples))
        # calculate local maximum with large window
        win_max_db = level2db(_window_maximum(abs_amp, win_sz=self.win_ln))
        sil_tags = []
        left = right = 0
        while right < win_max_db.shape[0]:
            if win_max_db[right] < self.db_threshold:
                right += 1
            elif left == right:
                left += 1
                right += 1
            else:
                if left == 0:
                    split_loc_l = left
                else:
                    sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
                    rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
                    split_win_l = left + np.argmin(rms_db_left)
                    split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
                if len(sil_tags) != 0 and split_loc_l - sil_tags[-1][1] < self.min_samples and right < win_max_db.shape[
                    0] - 1:
                    right += 1
                    left = right
                    continue
                if right == win_max_db.shape[0] - 1:
                    split_loc_r = right + self.win_ln
                else:
                    sil_right_n = min(self.max_silence, (right + self.win_ln - left) // 2)
                    rms_db_right = level2db(_window_rms(samples[right + self.win_ln - sil_right_n: right + self.win_ln],
                                                        win_sz=self.win_sn))
                    split_win_r = right + self.win_ln - sil_right_n + np.argmin(rms_db_right)
                    split_loc_r = split_win_r + np.argmin(abs_amp[split_win_r: split_win_r + self.win_sn])
                sil_tags.append((split_loc_l, split_loc_r))
                right += 1
                left = right
        if left != right:
            sil_left_n = min(self.max_silence, (right + self.win_ln - left) // 2)
            rms_db_left = level2db(_window_rms(samples[left: left + sil_left_n], win_sz=self.win_sn))
            split_win_l = left + np.argmin(rms_db_left)
            split_loc_l = split_win_l + np.argmin(abs_amp[split_win_l: split_win_l + self.win_sn])
            sil_tags.append((split_loc_l, samples.shape[0]))
        if len(sil_tags) == 0:
            return {"0": {"slice": False, "split_time": f"0,{len(audio)}"}}
        else:
            chunks = []
            # 第一段静音并非从头开始,补上有声片段
            if sil_tags[0][0]:
                chunks.append({"slice": False, "split_time": f"0,{sil_tags[0][0]}"})
            for i in range(0, len(sil_tags)):
                # 标识有声片段(跳过第一段)
                if i:
                    chunks.append({"slice": False, "split_time": f"{sil_tags[i - 1][1]},{sil_tags[i][0]}"})
                # 标识所有静音片段
                chunks.append({"slice": True, "split_time": f"{sil_tags[i][0]},{sil_tags[i][1]}"})
            # 最后一段静音并非结尾,补上结尾片段
            if sil_tags[-1][1] != len(audio):
                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1]},{len(audio)}"})
            chunk_dict = {}
            for i in range(len(chunks)):
                chunk_dict[str(i)] = chunks[i]
            return chunk_dict


def cut(audio_path, db_thresh=-30, min_len=5000, win_l=300, win_s=20, max_sil_kept=500):
    audio, sr = torchaudio.load(audio_path)
    if len(audio.shape) == 2 and audio.shape[1] >= 2:
        audio = torch.mean(audio, dim=0).unsqueeze(0)
    audio = audio.cpu().numpy()[0]

    slicer = Slicer(
        sr=sr,
        db_threshold=db_thresh,
        min_length=min_len,
        win_l=win_l,
        win_s=win_s,
        max_silence_kept=max_sil_kept
    )
    chunks = slicer.slice(audio)
    return chunks


def chunks2audio(audio_path, chunks):
    chunks = dict(chunks)
    audio, sr = torchaudio.load(audio_path)
    if len(audio.shape) == 2 and audio.shape[1] >= 2:
        audio = torch.mean(audio, dim=0).unsqueeze(0)
    audio = audio.cpu().numpy()[0]
    result = []
    for k, v in chunks.items():
        tag = v["split_time"].split(",")
        result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
    return result, sr