File size: 13,196 Bytes
93c029f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
import os

import pandas as pd
from pydub import AudioSegment
import numpy as np
from moviepy.editor import *
import time
import pickle
import audioread
import librosa # install numba==0.49.1
# setup A: numba 0.51.2, librosa 0.6.3, llvmlite: 0.34.0
# setupB: numba==0.49.1, llvmlite-0.32.1
from src.music.config import RATE_AUDIO_SAVE
import hashlib
import unicodedata
import re

# from src.music.piano_detection_model.piano_detection_model import SR

def clean_removed_mp3_from_csv(path):
    print(f"Cleaning meta_data.csv using files from the folder, in {path}")
    files = os.listdir(path)
    indexes_to_remove = []
    meta_data = pd.read_csv(path + 'meta_data.csv')
    for i, fn in enumerate(meta_data['filename']):
        if fn not in files:
            indexes_to_remove.append(i)
    meta_data = meta_data.drop(indexes_to_remove)
    meta_data.to_csv(path + 'meta_data.csv', index=False)
    print('\tDone.')

def clean_removed_csv_from_folder(path):
    print(f"Cleaning files from folder using meta_data.csv listed file, in {path}")
    files = os.listdir(path)
    meta_data = pd.read_csv(path + 'meta_data.csv')
    hashes = set(meta_data['hash'])
    count = 0
    for f in files:
        if f not in ['meta_data.csv', 'url.txt']:
            if f[:-4] not in hashes:
                count += 1
                print(count)
                # os.remove(path + f)
            stop = 1
    print('\tDone.')

# def convert_mp3_to_mono_16k(path):
#     print(f"\n\n\t\tConverting mp3 to mono and 16k sample rate, in {path}\n")
#     if '.mp3' == path[-4:]:
#         audio = AudioFileClip(path)
#         audio.write_audiofile(path[:-4] + '.mp3',
#                               verbose=False,
#                               logger=None,
#                               fps=FPS,
#                               ffmpeg_params=["-ac", "1"])
#     else:
#         list_files = os.listdir(path)
#         for i, f in enumerate(list_files):
#             print(compute_progress(i, len(list_files)))
#             if ".mp3" in f:
#                 audio = AudioFileClip(path + f)
#                 audio.write_audiofile(path + f[:-4] + '.mp3',
#                                       verbose=False,
#                                       logger=None,
#                                       fps=FPS, # 16000 sr
#                                       ffmpeg_params=["-ac", "1"] # make it mono
#                                       )
#     print('\tDone.')



def load_audio(path, sr=22050, mono=True, offset=0.0, duration=None,
               dtype=np.float32, res_type='kaiser_best',
               backends=[audioread.ffdec.FFmpegAudioFile]):
    """Load audio. Copied from librosa.core.load() except that ffmpeg backend is
    always used in this function. Code from piano_transcription_inference"""

    y = []
    with audioread.audio_open(os.path.realpath(path), backends=backends) as input_file:
        sr_native = input_file.samplerate
        n_channels = input_file.channels

        s_start = int(np.round(sr_native * offset)) * n_channels

        if duration is None:
            s_end = np.inf
        else:
            s_end = s_start + (int(np.round(sr_native * duration))
                               * n_channels)

        n = 0

        for frame in input_file:
            frame = librosa.core.audio.util.buf_to_float(frame, dtype=dtype)
            n_prev = n
            n = n + len(frame)

            if n < s_start:
                # offset is after the current frame
                # keep reading
                continue

            if s_end < n_prev:
                # we're off the end.  stop reading
                break

            if s_end < n:
                # the end is in this frame.  crop.
                frame = frame[:s_end - n_prev]

            if n_prev <= s_start <= n:
                # beginning is in this frame
                frame = frame[(s_start - n_prev):]

            # tack on the current frame
            y.append(frame)

    if y:
        y = np.concatenate(y)

        if n_channels > 1:
            y = y.reshape((-1, n_channels)).T
            if mono:
                y = librosa.core.audio.to_mono(y)

        if sr is not None:
            y = librosa.core.audio.resample(y, sr_native, sr, res_type=res_type)

        else:
            sr = sr_native

    # Final cleanup for dtype and contiguity
    y = np.ascontiguousarray(y, dtype=dtype)

    return (y, sr)

def compute_progress(iter, total):
    return f"{int((iter+ 1) / total * 100)}%"

def compute_progress_and_eta(times, iter, total, n_av=3000):
    av_time = np.mean(times[-n_av:])
    progress = int(((iter + 1) / total) * 100)
    eta_h = int(av_time * (total - iter) // 3600)
    eta_m = int((av_time * (total - iter) - (eta_h * 3600)) // 60)
    eta_s = int((av_time * (total - iter) - (eta_h * 3600) - eta_m * 60))
    eta = f"Progress: {progress}%, ETA: {eta_h}H{eta_m}M{eta_s}S."
    return eta

def crop_mp3_from_meta_data_constraints(path, clean_constraints=True):
    print(f"Cropping mp3 using constraints from meta_data.csv, in {path}")
    meta_data = pd.read_csv(path + 'meta_data.csv')
    constraint_start = meta_data['constraint_start'].copy()
    length = meta_data['length'].copy()
    constraint_end = meta_data['constraint_end'].copy()
    filenames = meta_data['filename'].copy()
    times = [5]
    for i, c_start, c_end, fn, l in zip(range(len(constraint_start)), constraint_start, constraint_end, filenames, length):
        if c_start != 0 or c_end != l:
            i_time = time.time()
            print(compute_progress_and_eta(times, i, len(constraint_start), n_av=100))
            song = AudioSegment.from_mp3(path + fn)
            extract = song[c_start*1000:c_end*1000]
            extract.export(path + fn, format="mp3")
            if clean_constraints:
                constraint_start[i] = 0
                constraint_end[i] = length[i]
                meta_data['constraint_start'] = constraint_start
                meta_data['constraint_end'] = constraint_end
                meta_data.to_csv(path + 'meta_data.csv', index=False)
            times.append(time.time() - i_time)
    print('\tDone.')

def get_all_subfiles_with_extension(path, max_depth=3, extension='.*', current_depth=0):
    folders = [f for f in os.listdir(path) if os.path.isdir(path + f)]
    # get all files in current folder with a given extension
    if isinstance(extension, list):
        assert all([isinstance(e, str) for e in extension]), 'extension can be a str or a list'
        files = [path + f for f in os.listdir(path) if os.path.isfile(path + f) and any([ext == f[-len(ext):] for ext in extension])]
    elif isinstance(extension, str):
        assert extension[0] == '.', 'extension should be an extension or a list of extensions'
        if extension == '.*':
            files = [path + f for f in os.listdir(path) if os.path.isfile(path + f)]
        else:
            files = [path + f for f in os.listdir(path) if os.path.isfile(path + f) and f[-len(extension):]==extension]
    else:
        print('Error: extension should be either a str or a list')
        raise ValueError

    if current_depth < max_depth:
        for fold in folders:
            files += get_all_subfiles_with_extension(path + fold + '/', max_depth=max_depth, extension=extension, current_depth=current_depth+1)
    return files

def get_out_path(in_path, in_word, out_word, out_extension, exclude_paths=()):
    splitted_in_path = in_path.split('/')
    for i in range(len(splitted_in_path)):
        if splitted_in_path[i] == in_word:
            splitted_in_path[i] = out_word
            playlist_index = i + 1
    file_index = len(splitted_in_path) - 1
    if splitted_in_path[playlist_index] in exclude_paths:
        to_exclude = True
        return None, to_exclude, None
    else:
        to_exclude = False
        if out_word != 'midi':
            splitted_in_path[playlist_index] = '_'.join(splitted_in_path[playlist_index].split('_')[:-len(in_word.split('_'))]) + '_' + out_word
        else:
            splitted_in_path[playlist_index] += '_' + out_word
        if 'fake' not in splitted_in_path:
            os.makedirs('/'.join(splitted_in_path[:playlist_index + 1]), exist_ok=True)
        if out_word != 'midi':
            new_filename = '_'.join(splitted_in_path[file_index].split('_')[:-len(in_word.split('_'))]) + '_' + out_word + out_extension
        else:
            new_filename = '.'.join(splitted_in_path[file_index].split('.')[:-len(in_word.split('_'))]) + '_' + out_word + out_extension
        splitted_in_path[file_index] = new_filename
        splitted_in_path = splitted_in_path[:playlist_index + 1] + [splitted_in_path[file_index]]
        out_path = '/'.join(splitted_in_path)
        return out_path, to_exclude, splitted_in_path[playlist_index]

def set_all_seeds(seed):
    import random
    import numpy as np
    import torch
    torch.manual_seed(seed)
    random.seed(seed)
    np.random.seed(seed)

def get_paths_in_and_out(in_path, in_word, in_extension, out_word, out_extension, max_depth, exclude_paths=()):
    # find all files with the in_extension in subfolders of in_path up to max_depth.
    # for each, replace the in_word keyword in folders with the out_word, and append out_word to filenames.
    all_in_paths = get_all_subfiles_with_extension(in_path, max_depth=max_depth, extension=in_extension)
    indexes_not_transcribed = []
    all_out_paths = []
    all_playlists = []
    for i_path, in_path in enumerate(all_in_paths):
        out_path, to_exclude, playlist = get_out_path(in_path=in_path, in_word=in_word, out_word=out_word, out_extension=out_extension, exclude_paths=exclude_paths)
        if not to_exclude:
            indexes_not_transcribed.append(i_path)
            all_out_paths.append(out_path)
            all_playlists.append(playlist)
    all_in_paths = [in_path for i, in_path in enumerate(all_in_paths) if i in indexes_not_transcribed]
    assert len(all_out_paths) == len(all_in_paths)
    return all_in_paths, all_out_paths, all_playlists

def get_path_and_filter_existing(in_path, in_word, in_extension, out_word, out_extension, max_depth, exclude_paths=()):
    # find all files with the in_extension in subfolders of in_path up to max_depth.
    # for each, replace the in_word keyword in folders with the out_word, and append out_word to filenames.
    all_in_paths = get_all_subfiles_with_extension(in_path, max_depth=max_depth, extension=in_extension)
    indexes_to_process = []
    all_out_paths = []
    all_playlists = []
    for i_path, in_path in enumerate(all_in_paths):
        out_path, to_exclude, playlist = get_out_path(in_path=in_path, in_word=in_word, out_word=out_word, out_extension=out_extension, exclude_paths=exclude_paths)
        if not to_exclude:
            if not os.path.exists(out_path):
                indexes_to_process.append(i_path)
                all_out_paths.append(out_path)
                all_playlists.append(playlist)
    all_in_paths = list(np.array(all_in_paths)[indexes_to_process])#[in_path for i, in_path in enumerate(all_in_paths) if i in indexes_to_process]
    assert len(all_out_paths) == len(all_in_paths)
    return all_in_paths, all_out_paths, all_playlists

def md5sum(filename, blocksize=65536):
    hash = hashlib.md5()
    with open(filename, "rb") as f:
        for block in iter(lambda: f.read(blocksize), b""):
            hash.update(block)
    return hash.hexdigest()


emoji_pattern = re.compile("["
                           u"\U0001F600-\U0001F64F"  # emoticons
                           u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                           u"\U0001F680-\U0001F6FF"  # transport & map symbols
                           u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                           "]+", flags=re.UNICODE)
def slugify(value, allow_unicode=False):
    """
    Taken from https://github.com/django/django/blob/master/django/utils/text.py
    Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
    dashes to single dashes. Remove characters that aren't alphanumerics,
    underscores, or hyphens. Convert to lowercase. Also strip leading and
    trailing whitespace, dashes, and underscores.
    """
    value = str(value).lower()
    if allow_unicode:
        value = unicodedata.normalize('NFKC', value)
    else:
        value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')
    value = re.sub(r'[^\w\s-]', '', value.lower())
    value = emoji_pattern.sub(r'', value)
    value = re.sub(r'[-\s]+', '_', value).strip('-_')
    # if value == '':
    #     for i in range(10):
    #         value += str(np.random.choice(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']))
    return value

if __name__ == '__main__':
    path = "/home/cedric/Documents/pianocktail/data/midi/street_piano/"
    # for folder in ['my_sheet_music_transcriptions']:#os.listdir(path):
    #     print('\n\n\t\t', folder)
    #     convert_mp4_to_mp3(path + folder + '/')

    clean_removed_csv_from_folder(path)
    # folder = 'street_piano/'
    # for folder in ['street_piano/']:
    #     clean_removed_mp3_from_csv(path + folder)