Hugging Face's logo

Datasets:
malaysia-ai
/
malay-conversational-speech-corpus

Modalities:
Audio
Text
Formats:
parquet
Languages:
Malay
Size:
1K - 10K
Libraries:
Datasets
pandas
Dataset card Viewer Files Community
1
huseinzol05 commited on
Commit
8ceda27
•
1 Parent(s): e5e97e9

Upload prepare-malay-conversational-speech-corpus.ipynb

Browse files
Files changed (1) hide show
  1. prepare-malay-conversational-speech-corpus.ipynb +461 -0
prepare-malay-conversational-speech-corpus.ipynb ADDED
@@ -0,0 +1,461 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "id": "6160ca56",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "# download from https://magichub.com/datasets/malay-conversational-speech-corpus/\n",
11
+ "# !unzip Malay_Conversational_Speech_Corpus.zip -d malay-speech"
12
+ ]
13
+ },
14
+ {
15
+ "cell_type": "code",
16
+ "execution_count": 3,
17
+ "id": "db1d7259",
18
+ "metadata": {
19
+ "scrolled": true
20
+ },
21
+ "outputs": [
22
+ {
23
+ "name": "stdout",
24
+ "output_type": "stream",
25
+ "text": [
26
+ "AUDIOINFO.txt README.txt SPKINFO.txt\tTXT WAV\r\n"
27
+ ]
28
+ }
29
+ ],
30
+ "source": [
31
+ "!ls malay-speech"
32
+ ]
33
+ },
34
+ {
35
+ "cell_type": "code",
36
+ "execution_count": 7,
37
+ "id": "c28c25c0",
38
+ "metadata": {},
39
+ "outputs": [
40
+ {
41
+ "name": "stdout",
42
+ "output_type": "stream",
43
+ "text": [
44
+ "A0004_S008_0_G0369.wav\tA0008_S003_0_G0598.wav\tA0009_S008_0_G0474.wav\r\n",
45
+ "A0004_S008_0_G0489.wav\tA0008_S004_0_G0397.wav\tA0009_S008_0_G0591.wav\r\n",
46
+ "A0006_S001_0_G0216.wav\tA0008_S004_0_G0598.wav\tA0010_S001_0_G0521.wav\r\n",
47
+ "A0006_S001_0_G0712.wav\tA0008_S005_0_G0397.wav\tA0010_S001_0_G0626.wav\r\n",
48
+ "A0006_S002_0_G0216.wav\tA0008_S005_0_G0598.wav\tA0010_S002_0_G0521.wav\r\n",
49
+ "A0006_S002_0_G0712.wav\tA0009_S004_0_G0474.wav\tA0010_S002_0_G0626.wav\r\n",
50
+ "A0008_S003_0_G0397.wav\tA0009_S004_0_G0591.wav\r\n"
51
+ ]
52
+ }
53
+ ],
54
+ "source": [
55
+ "!ls malay-speech/WAV"
56
+ ]
57
+ },
58
+ {
59
+ "cell_type": "code",
60
+ "execution_count": 9,
61
+ "id": "7e94aed2",
62
+ "metadata": {},
63
+ "outputs": [],
64
+ "source": [
65
+ "!mkdir audio-malay-speech"
66
+ ]
67
+ },
68
+ {
69
+ "cell_type": "code",
70
+ "execution_count": 27,
71
+ "id": "b7562ac7",
72
+ "metadata": {},
73
+ "outputs": [],
74
+ "source": [
75
+ "import soundfile as sf\n",
76
+ "from glob import glob\n",
77
+ "import os"
78
+ ]
79
+ },
80
+ {
81
+ "cell_type": "code",
82
+ "execution_count": 30,
83
+ "id": "f630adef",
84
+ "metadata": {},
85
+ "outputs": [
86
+ {
87
+ "data": {
88
+ "text/plain": [
89
+ "'A0010_S001_0_G0521'"
90
+ ]
91
+ },
92
+ "execution_count": 30,
93
+ "metadata": {},
94
+ "output_type": "execute_result"
95
+ }
96
+ ],
97
+ "source": [
98
+ "os.path.split(f)[1].replace('.txt', '')"
99
+ ]
100
+ },
101
+ {
102
+ "cell_type": "code",
103
+ "execution_count": 52,
104
+ "id": "e3480688",
105
+ "metadata": {},
106
+ "outputs": [
107
+ {
108
+ "name": "stderr",
109
+ "output_type": "stream",
110
+ "text": [
111
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 191/191 [00:02<00:00, 73.04it/s]\n",
112
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 109/109 [00:01<00:00, 74.40it/s]\n",
113
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 156/156 [00:03<00:00, 44.06it/s]\n",
114
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 214/214 [00:03<00:00, 62.35it/s]\n",
115
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 155/155 [00:02<00:00, 69.24it/s]\n",
116
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 224/224 [00:04<00:00, 53.28it/s]\n",
117
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 175/175 [00:02<00:00, 85.67it/s]\n",
118
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 140/140 [00:02<00:00, 59.89it/s]\n",
119
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 225/225 [00:03<00:00, 73.55it/s]\n",
120
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:02<00:00, 53.09it/s]\n",
121
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 188/188 [00:02<00:00, 72.32it/s]\n",
122
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 192/192 [00:03<00:00, 54.64it/s]\n",
123
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 166/166 [00:03<00:00, 51.48it/s]\n",
124
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 175/175 [00:02<00:00, 77.67it/s]\n",
125
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 143/143 [00:02<00:00, 65.63it/s]\n",
126
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 144/144 [00:02<00:00, 64.00it/s]\n",
127
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 126/126 [00:02<00:00, 42.88it/s]\n",
128
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 119/119 [00:02<00:00, 49.52it/s]\n",
129
+ "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 55/55 [00:00<00:00, 61.18it/s]\n",
130
+ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 212/212 [00:03<00:00, 64.23it/s]\n"
131
+ ]
132
+ }
133
+ ],
134
+ "source": [
135
+ "from tqdm import tqdm\n",
136
+ "\n",
137
+ "files = glob('malay-speech/TXT/*.txt')\n",
138
+ "data = []\n",
139
+ "for f in files:\n",
140
+ " new_f = os.path.split(f)[1].replace('.txt', '')\n",
141
+ " audio = f.replace('/TXT/', '/WAV/').replace('.txt', '.wav')\n",
142
+ " y, sr = sf.read(audio)\n",
143
+ " with open(f) as fopen:\n",
144
+ " texts = fopen.read().split('\\n')\n",
145
+ " for no in tqdm(range(len(texts))):\n",
146
+ " t = texts[no]\n",
147
+ " if not len(t):\n",
148
+ " continue\n",
149
+ " splitted = t.split('\\t')\n",
150
+ " start, end = splitted[0].replace('[', '').replace(']', '').split(',')\n",
151
+ " start = int(float(start) * sr)\n",
152
+ " end = int(float(end) * sr)\n",
153
+ " y_ = y[start:end]\n",
154
+ " new_f_ = f'audio-malay-speech/{new_f}-{no}.mp3'\n",
155
+ " sf.write(new_f_, y_, sr)\n",
156
+ " data.append({\n",
157
+ " 'filename': new_f_,\n",
158
+ " 'Y': splitted[-1],\n",
159
+ " 'gender': splitted[-2],\n",
160
+ " 'id': splitted[-3]\n",
161
+ " })"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "code",
166
+ "execution_count": 50,
167
+ "id": "274ac002",
168
+ "metadata": {},
169
+ "outputs": [
170
+ {
171
+ "data": {
172
+ "text/plain": [
173
+ "{'filename': 'audio-malay-speech/A0010_S001_0_G0521-1.mp3',\n",
174
+ " 'Y': 'ah makanan bagi aku macam struggle kan',\n",
175
+ " 'gender': 'female,Malaysia',\n",
176
+ " 'id': 'G0521'}"
177
+ ]
178
+ },
179
+ "execution_count": 50,
180
+ "metadata": {},
181
+ "output_type": "execute_result"
182
+ }
183
+ ],
184
+ "source": [
185
+ "data[1]"
186
+ ]
187
+ },
188
+ {
189
+ "cell_type": "code",
190
+ "execution_count": 51,
191
+ "id": "3a9a57b6",
192
+ "metadata": {},
193
+ "outputs": [
194
+ {
195
+ "data": {
196
+ "text/html": [
197
+ "\n",
198
+ " <audio controls=\"controls\" >\n",
199
+ " <source src=\"data:audio/mpeg;base64,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\" type=\"audio/mpeg\" />\n",
200
+ " Your browser does not support the audio element.\n",
201
+ " </audio>\n",
202
+ " "
203
+ ],
204
+ "text/plain": [
205
+ "<IPython.lib.display.Audio object>"
206
+ ]
207
+ },
208
+ "execution_count": 51,
209
+ "metadata": {},
210
+ "output_type": "execute_result"
211
+ }
212
+ ],
213
+ "source": [
214
+ "import IPython.display as ipd\n",
215
+ "ipd.Audio(data[1]['filename'])"
216
+ ]
217
+ },
218
+ {
219
+ "cell_type": "code",
220
+ "execution_count": 53,
221
+ "id": "12e41983",
222
+ "metadata": {},
223
+ "outputs": [
224
+ {
225
+ "data": {
226
+ "text/plain": [
227
+ "3241"
228
+ ]
229
+ },
230
+ "execution_count": 53,
231
+ "metadata": {},
232
+ "output_type": "execute_result"
233
+ }
234
+ ],
235
+ "source": [
236
+ "len(data)"
237
+ ]
238
+ },
239
+ {
240
+ "cell_type": "code",
241
+ "execution_count": 55,
242
+ "id": "c495bc09",
243
+ "metadata": {},
244
+ "outputs": [],
245
+ "source": [
246
+ "from datasets import load_dataset, Audio\n",
247
+ "import json"
248
+ ]
249
+ },
250
+ {
251
+ "cell_type": "code",
252
+ "execution_count": 56,
253
+ "id": "181ee061",
254
+ "metadata": {},
255
+ "outputs": [],
256
+ "source": [
257
+ "with open('malay-speech.json', 'w') as fopen:\n",
258
+ " json.dump(data, fopen)"
259
+ ]
260
+ },
261
+ {
262
+ "cell_type": "code",
263
+ "execution_count": 57,
264
+ "id": "b14e9b4e",
265
+ "metadata": {},
266
+ "outputs": [
267
+ {
268
+ "name": "stdout",
269
+ "output_type": "stream",
270
+ "text": [
271
+ "Downloading and preparing dataset json/default to /home/husein/.cache/huggingface/datasets/json/default-0bb280d8793bbee0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n"
272
+ ]
273
+ },
274
+ {
275
+ "data": {
276
+ "application/vnd.jupyter.widget-view+json": {
277
+ "model_id": "7b2c25161eaa4f3b9febf9ca68da069f",
278
+ "version_major": 2,
279
+ "version_minor": 0
280
+ },
281
+ "text/plain": [
282
+ "Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
283
+ ]
284
+ },
285
+ "metadata": {},
286
+ "output_type": "display_data"
287
+ },
288
+ {
289
+ "data": {
290
+ "application/vnd.jupyter.widget-view+json": {
291
+ "model_id": "3caeaea4399241f4b6bb7caa97b3837b",
292
+ "version_major": 2,
293
+ "version_minor": 0
294
+ },
295
+ "text/plain": [
296
+ "Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
297
+ ]
298
+ },
299
+ "metadata": {},
300
+ "output_type": "display_data"
301
+ },
302
+ {
303
+ "data": {
304
+ "application/vnd.jupyter.widget-view+json": {
305
+ "model_id": "",
306
+ "version_major": 2,
307
+ "version_minor": 0
308
+ },
309
+ "text/plain": [
310
+ "Generating train split: 0 examples [00:00, ? examples/s]"
311
+ ]
312
+ },
313
+ "metadata": {},
314
+ "output_type": "display_data"
315
+ },
316
+ {
317
+ "name": "stdout",
318
+ "output_type": "stream",
319
+ "text": [
320
+ "Dataset json downloaded and prepared to /home/husein/.cache/huggingface/datasets/json/default-0bb280d8793bbee0/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.\n"
321
+ ]
322
+ }
323
+ ],
324
+ "source": [
325
+ "dataset = load_dataset('json', data_files = 'malay-speech.json', keep_in_memory = False, split = 'train')"
326
+ ]
327
+ },
328
+ {
329
+ "cell_type": "code",
330
+ "execution_count": 58,
331
+ "id": "bdf06b7d",
332
+ "metadata": {},
333
+ "outputs": [
334
+ {
335
+ "data": {
336
+ "text/plain": [
337
+ "{'Y': 'hai weh',\n",
338
+ " 'id': 'G0521',\n",
339
+ " 'gender': 'female,Malaysia',\n",
340
+ " 'filename': 'audio-malay-speech/A0010_S001_0_G0521-0.mp3'}"
341
+ ]
342
+ },
343
+ "execution_count": 58,
344
+ "metadata": {},
345
+ "output_type": "execute_result"
346
+ }
347
+ ],
348
+ "source": [
349
+ "dataset[0]"
350
+ ]
351
+ },
352
+ {
353
+ "cell_type": "code",
354
+ "execution_count": 59,
355
+ "id": "36432ba1",
356
+ "metadata": {},
357
+ "outputs": [],
358
+ "source": [
359
+ "dataset = dataset.cast_column('filename', Audio(sampling_rate = 16000))"
360
+ ]
361
+ },
362
+ {
363
+ "cell_type": "code",
364
+ "execution_count": 61,
365
+ "id": "d01099f1",
366
+ "metadata": {},
367
+ "outputs": [
368
+ {
369
+ "data": {
370
+ "text/plain": [
371
+ "array([ 0.00693709, 0.01167124, -0.00172221, ..., 0.00830214,\n",
372
+ " 0.00946393, 0.00854981])"
373
+ ]
374
+ },
375
+ "execution_count": 61,
376
+ "metadata": {},
377
+ "output_type": "execute_result"
378
+ }
379
+ ],
380
+ "source": [
381
+ "dataset[100]['filename']['array']"
382
+ ]
383
+ },
384
+ {
385
+ "cell_type": "code",
386
+ "execution_count": null,
387
+ "id": "cc3f2a3f",
388
+ "metadata": {
389
+ "scrolled": true
390
+ },
391
+ "outputs": [
392
+ {
393
+ "data": {
394
+ "application/vnd.jupyter.widget-view+json": {
395
+ "model_id": "",
396
+ "version_major": 2,
397
+ "version_minor": 0
398
+ },
399
+ "text/plain": [
400
+ "Map: 0%| | 0/3241 [00:00<?, ? examples/s]"
401
+ ]
402
+ },
403
+ "metadata": {},
404
+ "output_type": "display_data"
405
+ },
406
+ {
407
+ "data": {
408
+ "application/vnd.jupyter.widget-view+json": {
409
+ "model_id": "d1082320fe004f7699ab7f3521fd1942",
410
+ "version_major": 2,
411
+ "version_minor": 0
412
+ },
413
+ "text/plain": [
414
+ "Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]"
415
+ ]
416
+ },
417
+ "metadata": {},
418
+ "output_type": "display_data"
419
+ },
420
+ {
421
+ "data": {
422
+ "application/vnd.jupyter.widget-view+json": {
423
+ "model_id": "23b7ffea61524e12a9ce61d1782106ff",
424
+ "version_major": 2,
425
+ "version_minor": 0
426
+ },
427
+ "text/plain": [
428
+ "Creating parquet from Arrow format: 0%| | 0/33 [00:00<?, ?ba/s]"
429
+ ]
430
+ },
431
+ "metadata": {},
432
+ "output_type": "display_data"
433
+ }
434
+ ],
435
+ "source": [
436
+ "dataset.push_to_hub('malaysia-ai/malay-conversational-speech-corpus')"
437
+ ]
438
+ }
439
+ ],
440
+ "metadata": {
441
+ "kernelspec": {
442
+ "display_name": "Python 3 (ipykernel)",
443
+ "language": "python",
444
+ "name": "python3"
445
+ },
446
+ "language_info": {
447
+ "codemirror_mode": {
448
+ "name": "ipython",
449
+ "version": 3
450
+ },
451
+ "file_extension": ".py",
452
+ "mimetype": "text/x-python",
453
+ "name": "python",
454
+ "nbconvert_exporter": "python",
455
+ "pygments_lexer": "ipython3",
456
+ "version": "3.8.10"
457
+ }
458
+ },
459
+ "nbformat": 4,
460
+ "nbformat_minor": 5
461
+ }