File size: 12,834 Bytes
0844354
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
from multiprocess.pool import ThreadPool
from speaker_encoder.params_data import *
from speaker_encoder.config import librispeech_datasets, anglophone_nationalites
from datetime import datetime
from speaker_encoder import audio
from pathlib import Path
from tqdm import tqdm
import numpy as np


class DatasetLog:
    """
    Registers metadata about the dataset in a text file.
    """
    def __init__(self, root, name):
        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
        self.sample_data = dict()
        
        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
        self.write_line("Creating dataset %s on %s" % (name, start_time))
        self.write_line("-----")
        self._log_params()
        
    def _log_params(self):
        from speaker_encoder import params_data
        self.write_line("Parameter values:")
        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
            value = getattr(params_data, param_name)
            self.write_line("\t%s: %s" % (param_name, value))
        self.write_line("-----")
    
    def write_line(self, line):
        self.text_file.write("%s\n" % line)
        
    def add_sample(self, **kwargs):
        for param_name, value in kwargs.items():
            if not param_name in self.sample_data:
                self.sample_data[param_name] = []
            self.sample_data[param_name].append(value)
            
    def finalize(self):
        self.write_line("Statistics:")
        for param_name, values in self.sample_data.items():
            self.write_line("\t%s:" % param_name)
            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
        self.write_line("-----")
        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
        self.write_line("Finished on %s" % end_time)
        self.text_file.close()
       
        
def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
    dataset_root = datasets_root.joinpath(dataset_name)
    if not dataset_root.exists():
        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
        return None, None
    return dataset_root, DatasetLog(out_dir, dataset_name)


def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
                             skip_existing, logger):
    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
    
    # Function to preprocess utterances for one speaker
    def preprocess_speaker(speaker_dir: Path):
        # Give a name to the speaker that includes its dataset
        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
        
        # Create an output directory with that name, as well as a txt file containing a 
        # reference to each source file.
        speaker_out_dir = out_dir.joinpath(speaker_name)
        speaker_out_dir.mkdir(exist_ok=True)
        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
        
        # There's a possibility that the preprocessing was interrupted earlier, check if 
        # there already is a sources file.
        if sources_fpath.exists():
            try:
                with sources_fpath.open("r") as sources_file:
                    existing_fnames = {line.split(",")[0] for line in sources_file}
            except:
                existing_fnames = {}
        else:
            existing_fnames = {}
        
        # Gather all audio files for that speaker recursively
        sources_file = sources_fpath.open("a" if skip_existing else "w")
        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
            # Check if the target output file already exists
            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
            out_fname = out_fname.replace(".%s" % extension, ".npy")
            if skip_existing and out_fname in existing_fnames:
                continue
                
            # Load and preprocess the waveform
            wav = audio.preprocess_wav(in_fpath)
            if len(wav) == 0:
                continue
            
            # Create the mel spectrogram, discard those that are too short
            frames = audio.wav_to_mel_spectrogram(wav)
            if len(frames) < partials_n_frames:
                continue
            
            out_fpath = speaker_out_dir.joinpath(out_fname)
            np.save(out_fpath, frames)
            logger.add_sample(duration=len(wav) / sampling_rate)
            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
        
        sources_file.close()
    
    # Process the utterances for each speaker
    with ThreadPool(8) as pool:
        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
                  unit="speakers"))
    logger.finalize()
    print("Done preprocessing %s.\n" % dataset_name)


# Function to preprocess utterances for one speaker
def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, skip_existing: bool):
        # Give a name to the speaker that includes its dataset
        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
        
        # Create an output directory with that name, as well as a txt file containing a 
        # reference to each source file.
        speaker_out_dir = out_dir.joinpath(speaker_name)
        speaker_out_dir.mkdir(exist_ok=True)
        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
        
        # There's a possibility that the preprocessing was interrupted earlier, check if 
        # there already is a sources file.
        # if sources_fpath.exists():
        #     try:
        #         with sources_fpath.open("r") as sources_file:
        #             existing_fnames = {line.split(",")[0] for line in sources_file}
        #     except:
        #         existing_fnames = {}
        # else:
        #     existing_fnames = {}
        existing_fnames = {}
        # Gather all audio files for that speaker recursively
        sources_file = sources_fpath.open("a" if skip_existing else "w")

        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
            # Check if the target output file already exists
            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
            out_fname = out_fname.replace(".%s" % extension, ".npy")
            if skip_existing and out_fname in existing_fnames:
                continue
                
            # Load and preprocess the waveform
            wav = audio.preprocess_wav(in_fpath)
            if len(wav) == 0:
                continue
            
            # Create the mel spectrogram, discard those that are too short
            frames = audio.wav_to_mel_spectrogram(wav)
            if len(frames) < partials_n_frames:
                continue
            
            out_fpath = speaker_out_dir.joinpath(out_fname)
            np.save(out_fpath, frames)
            # logger.add_sample(duration=len(wav) / sampling_rate)
            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
        
        sources_file.close()
        return len(wav)

def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
                             skip_existing, logger):
    # from multiprocessing import Pool, cpu_count
    from pathos.multiprocessing import ProcessingPool as Pool
    # Function to preprocess utterances for one speaker
    def __preprocess_speaker(speaker_dir: Path):
        # Give a name to the speaker that includes its dataset
        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
        
        # Create an output directory with that name, as well as a txt file containing a 
        # reference to each source file.
        speaker_out_dir = out_dir.joinpath(speaker_name)
        speaker_out_dir.mkdir(exist_ok=True)
        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
        
        existing_fnames = {}
        # Gather all audio files for that speaker recursively
        sources_file = sources_fpath.open("a" if skip_existing else "w")
        wav_lens = []
        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
            # Check if the target output file already exists
            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
            out_fname = out_fname.replace(".%s" % extension, ".npy")
            if skip_existing and out_fname in existing_fnames:
                continue
                
            # Load and preprocess the waveform
            wav = audio.preprocess_wav(in_fpath)
            if len(wav) == 0:
                continue
            
            # Create the mel spectrogram, discard those that are too short
            frames = audio.wav_to_mel_spectrogram(wav)
            if len(frames) < partials_n_frames:
                continue
            
            out_fpath = speaker_out_dir.joinpath(out_fname)
            np.save(out_fpath, frames)
            # logger.add_sample(duration=len(wav) / sampling_rate)
            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
            wav_lens.append(len(wav))
        sources_file.close()
        return wav_lens

    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
    # Process the utterances for each speaker
    # with ThreadPool(8) as pool:
    #     list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
    #               unit="speakers"))
    pool = Pool(processes=20)
    for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1):
        for wav_len in wav_lens:
            logger.add_sample(duration=wav_len / sampling_rate)
        print(f'{i}/{len(speaker_dirs)} \r')

    logger.finalize()
    print("Done preprocessing %s.\n" % dataset_name)


def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
    for dataset_name in librispeech_datasets["train"]["other"]:
        # Initialize the preprocessing
        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
        if not dataset_root:
            return 
        
        # Preprocess all speakers
        speaker_dirs = list(dataset_root.glob("*"))
        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
                                 skip_existing, logger)


def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
    # Initialize the preprocessing
    dataset_name = "VoxCeleb1"
    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
    if not dataset_root:
        return

    # Get the contents of the meta file
    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
        metadata = [line.split("\t") for line in metafile][1:]
    
    # Select the ID and the nationality, filter out non-anglophone speakers
    nationalities = {line[0]: line[3] for line in metadata}
    # keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
    #                     nationality.lower() in anglophone_nationalites]
    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()]                        
    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
          (len(keep_speaker_ids), len(nationalities)))
    
    # Get the speaker directories for anglophone speakers only
    speaker_dirs = dataset_root.joinpath("wav").glob("*")
    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
                    speaker_dir.name in keep_speaker_ids]
    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))

    # Preprocess all speakers
    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
                             skip_existing, logger)


def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
    # Initialize the preprocessing
    dataset_name = "VoxCeleb2"
    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
    if not dataset_root:
        return
    
    # Get the speaker directories
    # Preprocess all speakers
    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
    _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
                             skip_existing, logger)