add timit w2vu recipe (facebookresearch#1991)

Summary:
## What does this PR do?
Add TIMIT data preparation scripts for wav2vec-U

Pull Request resolved: fairinternal/fairseq-py#1991

Reviewed By: alexeib

Differential Revision: D29284481

Pulled By: wnhsu

fbshipit-source-id: dccd75159a9de4f3cd95f9e4a90ce4bdf9264f2b

examples/wav2vec/unsupervised/README.md

@@ -48,6 +48,16 @@ The fifth argument is which phonemizer to use. Supported values are [espeak](htt
 
 Pre-trained fasttext LID models can be downloaded [here](https://fasttext.cc/docs/en/language-identification.html).
 
+### Prepare TIMIT data
+TIMIT transcripts include silence. Therefore VAD is not used for audio preprocessing, and we do not wrap transcripts with silences or insert random silence in between words.
+
+To prepare TIMIT data for both the matched an unmatched setup:
+```shell
+bash scripts/prepare_timit.sh /dir/to/timit/raw/data /output/dir /path/to/wav2vec2/model.pt
+```
+
+Note that we assume the TIMIT distribution with capitalized directories and filenames are used (e.g., `TRAIN/DR1/FCJF0/SA1.PHN`).
+
 ## Generative adversarial training (GAN)
 
 We then use a GAN model to build a first unsupervised ASR model. The data preparation above of both speech features and text data is a necessary procedure that enables the generator to match speech to text in an unsupervised way. 

examples/wav2vec/unsupervised/config/timit_matched/test.uid

@@ -0,0 +1,192 @@
+FDHC0_SI1559
+FDHC0_SI2189
+FDHC0_SI929
+FDHC0_SX119
+FDHC0_SX209
+FDHC0_SX29
+FDHC0_SX299
+FDHC0_SX389
+FELC0_SI1386
+FELC0_SI2016
+FELC0_SI756
+FELC0_SX126
+FELC0_SX216
+FELC0_SX306
+FELC0_SX36
+FELC0_SX396
+FJLM0_SI1043
+FJLM0_SI1673
+FJLM0_SI2303
+FJLM0_SX143
+FJLM0_SX233
+FJLM0_SX323
+FJLM0_SX413
+FJLM0_SX53
+FMGD0_SI1564
+FMGD0_SI2194
+FMGD0_SI934
+FMGD0_SX124
+FMGD0_SX214
+FMGD0_SX304
+FMGD0_SX34
+FMGD0_SX394
+FMLD0_SI2185
+FMLD0_SI822
+FMLD0_SI925
+FMLD0_SX115
+FMLD0_SX205
+FMLD0_SX25
+FMLD0_SX295
+FMLD0_SX385
+FNLP0_SI1308
+FNLP0_SI1938
+FNLP0_SI678
+FNLP0_SX138
+FNLP0_SX228
+FNLP0_SX318
+FNLP0_SX408
+FNLP0_SX48
+FPAS0_SI1272
+FPAS0_SI2204
+FPAS0_SI944
+FPAS0_SX134
+FPAS0_SX224
+FPAS0_SX314
+FPAS0_SX404
+FPAS0_SX44
+FPKT0_SI1538
+FPKT0_SI2168
+FPKT0_SI908
+FPKT0_SX188
+FPKT0_SX278
+FPKT0_SX368
+FPKT0_SX8
+FPKT0_SX98
+MBPM0_SI1577
+MBPM0_SI1584
+MBPM0_SI947
+MBPM0_SX137
+MBPM0_SX227
+MBPM0_SX317
+MBPM0_SX407
+MBPM0_SX47
+MCMJ0_SI1094
+MCMJ0_SI464
+MCMJ0_SI602
+MCMJ0_SX104
+MCMJ0_SX14
+MCMJ0_SX194
+MCMJ0_SX284
+MCMJ0_SX374
+MDAB0_SI1039
+MDAB0_SI1669
+MDAB0_SI2299
+MDAB0_SX139
+MDAB0_SX229
+MDAB0_SX319
+MDAB0_SX409
+MDAB0_SX49
+MGRT0_SI1450
+MGRT0_SI2080
+MGRT0_SI820
+MGRT0_SX10
+MGRT0_SX100
+MGRT0_SX190
+MGRT0_SX280
+MGRT0_SX370
+MJDH0_SI1354
+MJDH0_SI1984
+MJDH0_SI724
+MJDH0_SX184
+MJDH0_SX274
+MJDH0_SX364
+MJDH0_SX4
+MJDH0_SX94
+MJLN0_SI1449
+MJLN0_SI2079
+MJLN0_SI819
+MJLN0_SX189
+MJLN0_SX279
+MJLN0_SX369
+MJLN0_SX9
+MJLN0_SX99
+MJMP0_SI1535
+MJMP0_SI1791
+MJMP0_SI905
+MJMP0_SX185
+MJMP0_SX275
+MJMP0_SX365
+MJMP0_SX5
+MJMP0_SX95
+MKLT0_SI1213
+MKLT0_SI1843
+MKLT0_SI583
+MKLT0_SX133
+MKLT0_SX223
+MKLT0_SX313
+MKLT0_SX403
+MKLT0_SX43
+MLLL0_SI1363
+MLLL0_SI1993
+MLLL0_SI733
+MLLL0_SX103
+MLLL0_SX13
+MLLL0_SX193
+MLLL0_SX283
+MLLL0_SX373
+MLNT0_SI1574
+MLNT0_SI1902
+MLNT0_SI642
+MLNT0_SX102
+MLNT0_SX12
+MLNT0_SX192
+MLNT0_SX282
+MLNT0_SX372
+MNJM0_SI1580
+MNJM0_SI2210
+MNJM0_SI950
+MNJM0_SX140
+MNJM0_SX230
+MNJM0_SX320
+MNJM0_SX410
+MNJM0_SX50
+MPAM0_SI1189
+MPAM0_SI1819
+MPAM0_SI1961
+MPAM0_SX109
+MPAM0_SX19
+MPAM0_SX199
+MPAM0_SX289
+MPAM0_SX379
+MTAS1_SI1473
+MTAS1_SI2098
+MTAS1_SI838
+MTAS1_SX118
+MTAS1_SX208
+MTAS1_SX28
+MTAS1_SX298
+MTAS1_SX388
+MTLS0_SI1370
+MTLS0_SI2000
+MTLS0_SI740
+MTLS0_SX110
+MTLS0_SX20
+MTLS0_SX200
+MTLS0_SX290
+MTLS0_SX380
+MWBT0_SI1553
+MWBT0_SI2183
+MWBT0_SI923
+MWBT0_SX113
+MWBT0_SX203
+MWBT0_SX23
+MWBT0_SX293
+MWBT0_SX383
+MWEW0_SI1361
+MWEW0_SI1991
+MWEW0_SI731
+MWEW0_SX101
+MWEW0_SX11
+MWEW0_SX191
+MWEW0_SX281
+MWEW0_SX371