File size: 44,381 Bytes
9e91ea9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91b21cf6",
   "metadata": {},
   "source": [
    "## Generate the datasets for uploading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a3d25b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa925968",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[]\n",
      "['kotiria000263.wav', 'kotiria000265.wav', 'kotiria000273.wav', 'kotiria000285.wav', 'kotiria000289.wav', 'kotiria000291.wav', 'kotiria000294.wav', 'kotiria000295.wav', 'kotiria000297.wav', 'kotiria000300.wav', 'kotiria000306.wav', 'kotiria000308.wav']\n",
      "[]\n",
      "['waikhana000740.wav', 'waikhana000745.wav', 'waikhana000746.wav']\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15adf9d48a44440dac871ce9f432294c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading the dataset shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa491992d4fa43688c71ea1e09b25ca0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f560606c9094daf92d9f5328f18b2dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "538b15ad0bbe4684a09ae610fce7ab8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "391fb889ea15447ca8ec509a04de2ebe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "960a0088fc564383a32d2f6f0816b215",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1583 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3355cb1c65d84a24b1a146a17e43b1c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<cell line: 38>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m a \u001b[38;5;241m=\u001b[39m flatten(a)\n\u001b[1;32m     32\u001b[0m audio_dataset \u001b[38;5;241m=\u001b[39m Dataset\u001b[38;5;241m.\u001b[39mfrom_dict({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile_name\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     33\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     34\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     35\u001b[0m                                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m: flatten(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget_raw\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mtolist()),\n\u001b[1;32m     36\u001b[0m                                   },\n\u001b[1;32m     37\u001b[0m                                  )\u001b[38;5;241m.\u001b[39mcast_column(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m, Audio())\n\u001b[0;32m---> 38\u001b[0m \u001b[43maudio_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_to_hub\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mivangtorre/second_americas_nlp_2022\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     42\u001b[0m df \u001b[38;5;241m=\u001b[39m generate_df(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquechua\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdev\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:5707\u001b[0m, in \u001b[0;36mDataset.push_to_hub\u001b[0;34m(self, repo_id, config_name, set_default, split, data_dir, commit_message, commit_description, private, token, revision, branch, create_pr, max_shard_size, num_shards, embed_external_files)\u001b[0m\n\u001b[1;32m   5705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(repo_info\u001b[38;5;241m.\u001b[39msplits) \u001b[38;5;241m!=\u001b[39m [split]:\n\u001b[1;32m   5706\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures \u001b[38;5;241m!=\u001b[39m repo_info\u001b[38;5;241m.\u001b[39mfeatures:\n\u001b[0;32m-> 5707\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   5708\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeatures of the new split don\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt match the features of the existing splits on the hub: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_info\u001b[38;5;241m.\u001b[39mfeatures\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   5709\u001b[0m         )\n\u001b[1;32m   5711\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m repo_info\u001b[38;5;241m.\u001b[39msplits:\n\u001b[1;32m   5712\u001b[0m         repo_info\u001b[38;5;241m.\u001b[39mdownload_size \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m deleted_size\n",
      "\u001b[0;31mValueError\u001b[0m: Features of the new split don't match the features of the existing splits on the hub: {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None), 'source_processed': Value(dtype='string', id=None), 'source_raw': Value(dtype='string', id=None), 'target_raw': Value(dtype='string', id=None)} != {'audio': Audio(sampling_rate=None, mono=True, decode=True, id=None)}"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datasets import Dataset, Audio\n",
    "\n",
    "def generate_df(language, split):\n",
    "    # QUECHUA TRAIN\n",
    "    with open(\"./../\"+language +\"_\"+split+\".tsv\") as f:\n",
    "        lines = f.read().splitlines()\n",
    "    lines2 = [l.split(\"\\t\") for l in lines if len(l.split(\"\\t\"))==4]\n",
    "    asd = [l.split(\"\\t\")[0] for l in lines if len(l.split(\"\\t\"))>4]\n",
    "    print(asd)\n",
    "    df1 = pd.DataFrame(lines2[1::], columns =lines2[0:1])\n",
    "    df1 = df1.assign(split=[split]*df1.shape[0])\n",
    "    df1 = df1.assign(subset=[language]*df1.shape[0])\n",
    "    df1 = df1.rename(columns={'wav': 'file_name'})\n",
    "    df1['file_name'] = 'data/' + language + '/' + split +'/' + df1['file_name'].astype(str)\n",
    "    return df1\n",
    "\n",
    "df = generate_df(\"quechua\", \"train\")\n",
    "df = pd.concat([df, generate_df(\"guarani\", \"train\")])\n",
    "df = pd.concat([df, generate_df(\"kotiria\", \"train\")])\n",
    "df = pd.concat([df, generate_df(\"bribri\", \"train\")])\n",
    "df = pd.concat([df, generate_df(\"waikhana\", \"train\")])\n",
    "cols = df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df = df[cols]\n",
    "\n",
    "def flatten(xss):\n",
    "    return [x for xs in xss for x in xs]\n",
    "\n",
    "a = flatten(df[\"file_name\"].values.tolist())\n",
    "a = flatten(a)\n",
    "audio_dataset = Dataset.from_dict({\"audio\": flatten(df[\"file_name\"].values.tolist()),\n",
    "                                   \"source_processed\": flatten(df[\"source_processed\"].values.tolist()),\n",
    "                                   \"source_raw\": flatten(df[\"source_raw\"].values.tolist()),\n",
    "                                   \"target_raw\": flatten(df[\"target_raw\"].values.tolist()),\n",
    "                                  },\n",
    "                                 ).cast_column(\"audio\", Audio())\n",
    "audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"train\")\n",
    "\n",
    "df.to_csv(\"train.csv\", sep='\\t', index=None)\n",
    "\n",
    "df = generate_df(\"quechua\", \"dev\")\n",
    "df = pd.concat([df, generate_df(\"guarani\", \"dev\")])\n",
    "df = pd.concat([df, generate_df(\"kotiria\", \"dev\")])\n",
    "df = pd.concat([df, generate_df(\"bribri\", \"dev\")])\n",
    "df = pd.concat([df, generate_df(\"waikhana\", \"dev\")])\n",
    "cols = df.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "df = df[cols]\n",
    "df.to_csv(\"dev.csv\", sep='\\t', index=None)\n",
    "\n",
    "a = df[\"file_name\"].values.tolist()\n",
    "a = flatten(a)\n",
    "#audio_dataset = Dataset.from_dict({\"audio\": a}).cast_column(\"audio\", Audio())\n",
    "#audio_dataset.push_to_hub(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ce2eeb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'audio': {'path': 'data/quechua/train/quechua000000.wav',\n",
       "  'array': array([0.00045776, 0.00042725, 0.00018311, ..., 0.00286865, 0.00186157,\n",
       "         0.00253296]),\n",
       "  'sampling_rate': 16000}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd39f2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subset</th>\n",
       "      <th>file_name</th>\n",
       "      <th>source_processed</th>\n",
       "      <th>source_raw</th>\n",
       "      <th>target_raw</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>quechua</td>\n",
       "      <td>data/quechua/train/quechua000000.wav</td>\n",
       "      <td>wañuchisunchu kay suwakunata</td>\n",
       "      <td>wañuchisunchu kay suwakunata</td>\n",
       "      <td>matemos a esos ladrones</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>quechua</td>\n",
       "      <td>data/quechua/train/quechua000001.wav</td>\n",
       "      <td>imaninkichikmi qamkuna</td>\n",
       "      <td>imaninkichikmi qamkuna</td>\n",
       "      <td>que dicen ustedes</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>quechua</td>\n",
       "      <td>data/quechua/train/quechua000002.wav</td>\n",
       "      <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
       "      <td>hatun urqukunapi kunturkunapas uyarirqan</td>\n",
       "      <td>en grandes montañas hasta los condores escuchaban</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>quechua</td>\n",
       "      <td>data/quechua/train/quechua000003.wav</td>\n",
       "      <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
       "      <td>ninsi winsislaw maqtaqa tumpa machasqaña</td>\n",
       "      <td>dice el joven wessceslao cuando ya estaba borr...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>quechua</td>\n",
       "      <td>data/quechua/train/quechua000004.wav</td>\n",
       "      <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
       "      <td>huk qilli chuspi chuspi misapi kimsantin suwak...</td>\n",
       "      <td>una sucia mosca en la mesa con los tres ladron...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>waikhana</td>\n",
       "      <td>data/waikhana/train/waikhana001414.wav</td>\n",
       "      <td>masiaha malia masinapea</td>\n",
       "      <td>masiaha malia masinapea, ()</td>\n",
       "      <td>Nos tambem sabemos (as historias antigas)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>waikhana</td>\n",
       "      <td>data/waikhana/train/waikhana001415.wav</td>\n",
       "      <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
       "      <td>a'lide mu:sale ya'uaha yu:'u:</td>\n",
       "      <td>Tudo isso estou explicando para voces.</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>waikhana</td>\n",
       "      <td>data/waikhana/train/waikhana001416.wav</td>\n",
       "      <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
       "      <td>a'lide tina a'likodo pekasonoko a'li gravaka'a...</td>\n",
       "      <td>Tudo isso essa branca vai gravar.</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>waikhana</td>\n",
       "      <td>data/waikhana/train/waikhana001417.wav</td>\n",
       "      <td>sayeotha ninokata mipe</td>\n",
       "      <td>sayeotha ninokata mipe</td>\n",
       "      <td>Ela disse que vai fazer tudo isso,</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>waikhana</td>\n",
       "      <td>data/waikhana/train/waikhana001418.wav</td>\n",
       "      <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
       "      <td>yu:'u:le ~o'o ihide yu:'u: akaye</td>\n",
       "      <td>Para mim, e' ate aqui, meus irmaos.</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4749 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        subset                               file_name  \\\n",
       "0      quechua    data/quechua/train/quechua000000.wav   \n",
       "1      quechua    data/quechua/train/quechua000001.wav   \n",
       "2      quechua    data/quechua/train/quechua000002.wav   \n",
       "3      quechua    data/quechua/train/quechua000003.wav   \n",
       "4      quechua    data/quechua/train/quechua000004.wav   \n",
       "...        ...                                     ...   \n",
       "1411  waikhana  data/waikhana/train/waikhana001414.wav   \n",
       "1412  waikhana  data/waikhana/train/waikhana001415.wav   \n",
       "1413  waikhana  data/waikhana/train/waikhana001416.wav   \n",
       "1414  waikhana  data/waikhana/train/waikhana001417.wav   \n",
       "1415  waikhana  data/waikhana/train/waikhana001418.wav   \n",
       "\n",
       "                                       source_processed  \\\n",
       "0                          wañuchisunchu kay suwakunata   \n",
       "1                                imaninkichikmi qamkuna   \n",
       "2              hatun urqukunapi kunturkunapas uyarirqan   \n",
       "3              ninsi winsislaw maqtaqa tumpa machasqaña   \n",
       "4     huk qilli chuspi chuspi misapi kimsantin suwak...   \n",
       "...                                                 ...   \n",
       "1411                            masiaha malia masinapea   \n",
       "1412                      a'lide mu:sale ya'uaha yu:'u:   \n",
       "1413  a'lide tina a'likodo pekasonoko a'li gravaka'a...   \n",
       "1414                             sayeotha ninokata mipe   \n",
       "1415                   yu:'u:le ~o'o ihide yu:'u: akaye   \n",
       "\n",
       "                                             source_raw  \\\n",
       "0                          wañuchisunchu kay suwakunata   \n",
       "1                                imaninkichikmi qamkuna   \n",
       "2              hatun urqukunapi kunturkunapas uyarirqan   \n",
       "3              ninsi winsislaw maqtaqa tumpa machasqaña   \n",
       "4     huk qilli chuspi chuspi misapi kimsantin suwak...   \n",
       "...                                                 ...   \n",
       "1411                        masiaha malia masinapea, ()   \n",
       "1412                      a'lide mu:sale ya'uaha yu:'u:   \n",
       "1413  a'lide tina a'likodo pekasonoko a'li gravaka'a...   \n",
       "1414                             sayeotha ninokata mipe   \n",
       "1415                   yu:'u:le ~o'o ihide yu:'u: akaye   \n",
       "\n",
       "                                             target_raw  split  \n",
       "0                               matemos a esos ladrones  train  \n",
       "1                                     que dicen ustedes  train  \n",
       "2     en grandes montañas hasta los condores escuchaban  train  \n",
       "3     dice el joven wessceslao cuando ya estaba borr...  train  \n",
       "4     una sucia mosca en la mesa con los tres ladron...  train  \n",
       "...                                                 ...    ...  \n",
       "1411          Nos tambem sabemos (as historias antigas)  train  \n",
       "1412             Tudo isso estou explicando para voces.  train  \n",
       "1413                  Tudo isso essa branca vai gravar.  train  \n",
       "1414                 Ela disse que vai fazer tudo isso,  train  \n",
       "1415                Para mim, e' ate aqui, meus irmaos.  train  \n",
       "\n",
       "[4749 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1f02703",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from datasets import load_dataset\n",
    "#dataset = load_dataset(\"audiofolder\", data_dir=\"second_americas_nlp_2022\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eaa7c93",
   "metadata": {},
   "source": [
    "# EVALUATE MODELS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4e15c9",
   "metadata": {},
   "source": [
    "## QUECHUA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e165f4bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c96f2ce38474bc990e57387acd56fc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/250 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "LibsndfileError",
     "evalue": "Error opening 'data/quechua/dev/quechua000573.wav': System error.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLibsndfileError\u001b[0m                           Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m     batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtranscription\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m processor\u001b[38;5;241m.\u001b[39mbatch_decode(predicted_ids)\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m batch\n\u001b[0;32m---> 25\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mquechua\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmap_to_pred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatched\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCER:\u001b[39m\u001b[38;5;124m\"\u001b[39m, cer(result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msource_processed\u001b[39m\u001b[38;5;124m\"\u001b[39m], result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtranscription\u001b[39m\u001b[38;5;124m\"\u001b[39m]))\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:602\u001b[0m, in \u001b[0;36mtransmit_tasks.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    600\u001b[0m     \u001b[38;5;28mself\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    601\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 602\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    603\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    604\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets:\n\u001b[1;32m    605\u001b[0m     \u001b[38;5;66;03m# Remove task templates if a column mapping of the template is no longer valid\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:567\u001b[0m, in \u001b[0;36mtransmit_format.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    560\u001b[0m self_format \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    561\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_type,\n\u001b[1;32m    562\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_kwargs,\n\u001b[1;32m    563\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_columns,\n\u001b[1;32m    564\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_all_columns\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_all_columns,\n\u001b[1;32m    565\u001b[0m }\n\u001b[1;32m    566\u001b[0m \u001b[38;5;66;03m# apply actual function\u001b[39;00m\n\u001b[0;32m--> 567\u001b[0m out: Union[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatasetDict\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    568\u001b[0m datasets: List[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(out\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m [out]\n\u001b[1;32m    569\u001b[0m \u001b[38;5;66;03m# re-apply format to the output\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3156\u001b[0m, in \u001b[0;36mDataset.map\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transformed_dataset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3151\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m hf_tqdm(\n\u001b[1;32m   3152\u001b[0m         unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m examples\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3153\u001b[0m         total\u001b[38;5;241m=\u001b[39mpbar_total,\n\u001b[1;32m   3154\u001b[0m         desc\u001b[38;5;241m=\u001b[39mdesc \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMap\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   3155\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m pbar:\n\u001b[0;32m-> 3156\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m rank, done, content \u001b[38;5;129;01min\u001b[39;00m Dataset\u001b[38;5;241m.\u001b[39m_map_single(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdataset_kwargs):\n\u001b[1;32m   3157\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m   3158\u001b[0m                 shards_done \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3547\u001b[0m, in \u001b[0;36mDataset._map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3543\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m   3544\u001b[0m     \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mslice\u001b[39m(i, i \u001b[38;5;241m+\u001b[39m batch_size)\u001b[38;5;241m.\u001b[39mindices(shard\u001b[38;5;241m.\u001b[39mnum_rows)))\n\u001b[1;32m   3545\u001b[0m )  \u001b[38;5;66;03m# Something simpler?\u001b[39;00m\n\u001b[1;32m   3546\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3547\u001b[0m     batch \u001b[38;5;241m=\u001b[39m \u001b[43mapply_function_on_filtered_inputs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3548\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3549\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3550\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_same_num_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mshard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3551\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3552\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3553\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m NumExamplesMismatchError:\n\u001b[1;32m   3554\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetTransformationNotAllowedError(\n\u001b[1;32m   3555\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3556\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py:3416\u001b[0m, in \u001b[0;36mDataset._map_single.<locals>.apply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_rank:\n\u001b[1;32m   3415\u001b[0m     additional_args \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (rank,)\n\u001b[0;32m-> 3416\u001b[0m processed_inputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfn_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed_inputs, LazyDict):\n\u001b[1;32m   3418\u001b[0m     processed_inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m   3419\u001b[0m         k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m processed_inputs\u001b[38;5;241m.\u001b[39mkeys_to_format\n\u001b[1;32m   3420\u001b[0m     }\n",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36mmap_to_pred\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmap_to_pred\u001b[39m(batch):\n\u001b[0;32m---> 16\u001b[0m     wav, curr_sample_rate \u001b[38;5;241m=\u001b[39m \u001b[43msf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfile_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfloat32\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m     feats \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(wav)\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     18\u001b[0m     feats \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mlayer_norm(feats, feats\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;66;03m# Normalization performed during finetuning\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:285\u001b[0m, in \u001b[0;36mread\u001b[0;34m(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread\u001b[39m(file, frames\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, stop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat64\u001b[39m\u001b[38;5;124m'\u001b[39m, always_2d\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    200\u001b[0m          fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, samplerate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    201\u001b[0m          \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, subtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, endian\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, closefd\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    202\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Provide audio data from a sound file as NumPy array.\u001b[39;00m\n\u001b[1;32m    203\u001b[0m \n\u001b[1;32m    204\u001b[0m \u001b[38;5;124;03m    By default, the whole file is read from the beginning, but the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    283\u001b[0m \n\u001b[1;32m    284\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 285\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mSoundFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msamplerate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    286\u001b[0m \u001b[43m                   \u001b[49m\u001b[43msubtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendian\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosefd\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    287\u001b[0m         frames \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39m_prepare_read(start, stop, frames)\n\u001b[1;32m    288\u001b[0m         data \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread(frames, dtype, always_2d, fill_value, out)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:658\u001b[0m, in \u001b[0;36mSoundFile.__init__\u001b[0;34m(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\u001b[0m\n\u001b[1;32m    655\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m    656\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info \u001b[38;5;241m=\u001b[39m _create_info_struct(file, mode, samplerate, channels,\n\u001b[1;32m    657\u001b[0m                                  \u001b[38;5;28mformat\u001b[39m, subtype, endian)\n\u001b[0;32m--> 658\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode_int\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosefd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mset\u001b[39m(mode)\u001b[38;5;241m.\u001b[39missuperset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseekable():\n\u001b[1;32m    660\u001b[0m     \u001b[38;5;66;03m# Move write position to 0 (like in Python file objects)\u001b[39;00m\n\u001b[1;32m    661\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/soundfile.py:1216\u001b[0m, in \u001b[0;36mSoundFile._open\u001b[0;34m(self, file, mode_int, closefd)\u001b[0m\n\u001b[1;32m   1213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file_ptr \u001b[38;5;241m==\u001b[39m _ffi\u001b[38;5;241m.\u001b[39mNULL:\n\u001b[1;32m   1214\u001b[0m     \u001b[38;5;66;03m# get the actual error code\u001b[39;00m\n\u001b[1;32m   1215\u001b[0m     err \u001b[38;5;241m=\u001b[39m _snd\u001b[38;5;241m.\u001b[39msf_error(file_ptr)\n\u001b[0;32m-> 1216\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LibsndfileError(err, prefix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError opening \u001b[39m\u001b[38;5;132;01m{0!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m   1217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode_int \u001b[38;5;241m==\u001b[39m _snd\u001b[38;5;241m.\u001b[39mSFM_WRITE:\n\u001b[1;32m   1218\u001b[0m     \u001b[38;5;66;03m# Due to a bug in libsndfile version <= 1.0.25, frames != 0\u001b[39;00m\n\u001b[1;32m   1219\u001b[0m     \u001b[38;5;66;03m# when opening a named pipe in SFM_WRITE mode.\u001b[39;00m\n\u001b[1;32m   1220\u001b[0m     \u001b[38;5;66;03m# See http://github.com/erikd/libsndfile/issues/77.\u001b[39;00m\n\u001b[1;32m   1221\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info\u001b[38;5;241m.\u001b[39mframes \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[0;31mLibsndfileError\u001b[0m: Error opening 'data/quechua/dev/quechua000573.wav': System error."
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n",
    "import torch\n",
    "from jiwer import cer\n",
    "import torch.nn.functional as F\n",
    "from datasets import load_dataset\n",
    "import soundfile as sf\n",
    "\n",
    "americasnlp = load_dataset(\"ivangtorre/second_americas_nlp_2022\", split=\"dev\")\n",
    "quechua = americasnlp.filter(lambda language: language['subset']=='quechua')\n",
    "\n",
    "model = Wav2Vec2ForCTC.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
    "processor = Wav2Vec2Processor.from_pretrained(\"ivangtorre/wav2vec2-xlsr-300m-quechua\")\n",
    "\n",
    "def map_to_pred(batch):\n",
    "    wav, curr_sample_rate = sf.read(batch[\"file_name\"][0], dtype=\"float32\")\n",
    "    feats = torch.from_numpy(wav).float()\n",
    "    feats = F.layer_norm(feats, feats.shape) # Normalization performed during finetuning\n",
    "    feats = torch.unsqueeze(feats, 0)\n",
    "    logits = model(feats).logits\n",
    "    predicted_ids = torch.argmax(logits, dim=-1)\n",
    "    batch[\"transcription\"] = processor.batch_decode(predicted_ids)\n",
    "    return batch\n",
    "\n",
    "result = quechua.map(map_to_pred, batched=True, batch_size=1)\n",
    "\n",
    "print(\"CER:\", cer(result[\"source_processed\"], result[\"transcription\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e29bc13",
   "metadata": {},
   "source": [
    "## BRIBRI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cdec414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/quechua/dev/quechua000573.wav'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quechua[0:1][\"file_name\"][0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}