metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-ln-BibleTTS-1hr-v1
results: []
wav2vec2-large-xls-r-300m-ln-BibleTTS-1hr-v1
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6740
- Wer: 0.7140
- Cer: 0.1802
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 17.416 | 0.9091 | 5 | 17.5456 | 1.0 | 1.5562 |
| 14.5068 | 2.0 | 11 | 17.5120 | 1.0 | 1.4789 |
| 17.3472 | 2.9091 | 16 | 17.4189 | 1.0 | 1.1780 |
| 14.3268 | 4.0 | 22 | 17.2016 | 1.0 | 0.9674 |
| 17.0088 | 4.9091 | 27 | 17.0130 | 1.0 | 1.0293 |
| 13.9609 | 6.0 | 33 | 16.7075 | 1.0 | 0.8481 |
| 16.4587 | 6.9091 | 38 | 16.4284 | 1.0 | 0.8363 |
| 13.3164 | 8.0 | 44 | 15.9681 | 1.0 | 0.9944 |
| 15.3216 | 8.9091 | 49 | 15.3243 | 1.0 | 1.0 |
| 11.5548 | 10.0 | 55 | 14.0370 | 1.0 | 1.0 |
| 11.5967 | 10.9091 | 60 | 12.5247 | 1.0 | 1.0 |
| 8.2049 | 12.0 | 66 | 11.1587 | 1.0 | 1.0 |
| 8.4152 | 12.9091 | 71 | 10.0872 | 1.0 | 1.0 |
| 6.0687 | 14.0 | 77 | 8.8445 | 1.0 | 1.0 |
| 6.4176 | 14.9091 | 82 | 8.1147 | 1.0 | 1.0 |
| 4.8424 | 16.0 | 88 | 7.4897 | 1.0 | 1.0 |
| 5.3723 | 16.9091 | 93 | 6.9360 | 1.0 | 1.0 |
| 4.1999 | 18.0 | 99 | 6.3607 | 1.0 | 1.0 |
| 4.7866 | 18.9091 | 104 | 5.9263 | 1.0 | 1.0 |
| 3.8236 | 20.0 | 110 | 5.5028 | 1.0 | 1.0 |
| 4.4302 | 20.9091 | 115 | 5.0334 | 1.0 | 1.0 |
| 3.591 | 22.0 | 121 | 4.6869 | 1.0 | 1.0 |
| 4.1949 | 22.9091 | 126 | 4.4762 | 1.0 | 1.0 |
| 3.4191 | 24.0 | 132 | 4.2646 | 1.0 | 1.0 |
| 4.0108 | 24.9091 | 137 | 4.1448 | 1.0 | 1.0 |
| 3.28 | 26.0 | 143 | 3.9637 | 1.0 | 1.0 |
| 3.8588 | 26.9091 | 148 | 3.8742 | 1.0 | 1.0 |
| 3.158 | 28.0 | 154 | 3.7669 | 1.0 | 1.0 |
| 3.7243 | 28.9091 | 159 | 3.7039 | 1.0 | 1.0 |
| 3.0502 | 30.0 | 165 | 3.5948 | 1.0 | 1.0 |
| 3.6 | 30.9091 | 170 | 3.5306 | 1.0 | 1.0 |
| 2.9515 | 32.0 | 176 | 3.4187 | 1.0 | 1.0 |
| 3.4903 | 32.9091 | 181 | 3.3450 | 1.0 | 1.0 |
| 2.8763 | 34.0 | 187 | 3.2715 | 1.0 | 1.0 |
| 3.3998 | 34.9091 | 192 | 3.2082 | 1.0 | 1.0 |
| 2.7964 | 36.0 | 198 | 3.2021 | 1.0 | 1.0 |
| 3.3113 | 36.9091 | 203 | 3.1217 | 1.0 | 1.0 |
| 2.7362 | 38.0 | 209 | 3.0453 | 1.0 | 1.0 |
| 3.2274 | 38.9091 | 214 | 3.0072 | 1.0 | 1.0 |
| 2.6544 | 40.0 | 220 | 2.9557 | 1.0 | 1.0 |
| 3.1479 | 40.9091 | 225 | 2.9229 | 1.0 | 1.0 |
| 2.5983 | 42.0 | 231 | 2.8882 | 1.0 | 1.0 |
| 3.0854 | 42.9091 | 236 | 2.8638 | 1.0 | 1.0 |
| 2.5509 | 44.0 | 242 | 2.8354 | 1.0 | 1.0 |
| 3.0388 | 44.9091 | 247 | 2.8169 | 1.0 | 1.0 |
| 2.517 | 46.0 | 253 | 2.8000 | 1.0 | 1.0 |
| 3.0042 | 46.9091 | 258 | 2.7845 | 1.0 | 1.0 |
| 2.4924 | 48.0 | 264 | 2.7746 | 1.0 | 1.0 |
| 2.9774 | 48.9091 | 269 | 2.7673 | 1.0 | 1.0 |
| 2.4696 | 50.0 | 275 | 2.7573 | 1.0 | 1.0 |
| 2.9528 | 50.9091 | 280 | 2.7507 | 1.0 | 1.0 |
| 2.4516 | 52.0 | 286 | 2.7300 | 1.0 | 1.0 |
| 2.9192 | 52.9091 | 291 | 2.7201 | 1.0 | 1.0 |
| 2.4026 | 54.0 | 297 | 2.6965 | 1.0 | 1.0 |
| 2.8441 | 54.9091 | 302 | 2.6793 | 1.0 | 1.0 |
| 2.3344 | 56.0 | 308 | 2.6544 | 1.0 | 1.0 |
| 2.7453 | 56.9091 | 313 | 2.6112 | 1.0 | 1.0 |
| 2.223 | 58.0 | 319 | 2.5443 | 1.0 | 1.0 |
| 2.5596 | 58.9091 | 324 | 2.4781 | 1.0 | 0.9985 |
| 2.0074 | 60.0 | 330 | 2.3645 | 1.0 | 0.8402 |
| 2.2236 | 60.9091 | 335 | 2.2414 | 1.0 | 0.7652 |
| 1.703 | 62.0 | 341 | 2.0875 | 1.0 | 0.6468 |
| 1.8108 | 62.9091 | 346 | 2.0012 | 1.0 | 0.6570 |
| 1.2854 | 64.0 | 352 | 1.7562 | 0.9994 | 0.5339 |
| 1.289 | 64.9091 | 357 | 1.5530 | 0.9984 | 0.4409 |
| 0.8914 | 66.0 | 363 | 1.5710 | 0.9998 | 0.5147 |
| 0.8887 | 66.9091 | 368 | 1.3824 | 0.9547 | 0.4007 |
| 0.6132 | 68.0 | 374 | 1.3746 | 0.9759 | 0.4337 |
| 0.6176 | 68.9091 | 379 | 1.2084 | 0.9326 | 0.3478 |
| 0.4483 | 70.0 | 385 | 1.3181 | 0.9215 | 0.3787 |
| 0.4877 | 70.9091 | 390 | 1.1160 | 0.8970 | 0.3005 |
| 0.3614 | 72.0 | 396 | 1.1371 | 0.9054 | 0.3223 |
| 0.3889 | 72.9091 | 401 | 1.0581 | 0.8344 | 0.2827 |
| 0.2835 | 74.0 | 407 | 0.8882 | 0.7899 | 0.2292 |
| 0.3203 | 74.9091 | 412 | 0.9902 | 0.8447 | 0.2651 |
| 0.2494 | 76.0 | 418 | 1.0597 | 0.8338 | 0.2928 |
| 0.2693 | 76.9091 | 423 | 0.9066 | 0.8115 | 0.2517 |
| 0.2094 | 78.0 | 429 | 0.8811 | 0.7823 | 0.2311 |
| 0.2332 | 78.9091 | 434 | 0.9556 | 0.8330 | 0.2648 |
| 0.1874 | 80.0 | 440 | 0.9001 | 0.8103 | 0.2374 |
| 0.2155 | 80.9091 | 445 | 0.9119 | 0.8338 | 0.2499 |
| 0.1691 | 82.0 | 451 | 0.8158 | 0.7254 | 0.2066 |
| 0.1941 | 82.9091 | 456 | 0.8922 | 0.8010 | 0.2407 |
| 0.1537 | 84.0 | 462 | 0.8880 | 0.8137 | 0.2343 |
| 0.1801 | 84.9091 | 467 | 0.8078 | 0.7503 | 0.2021 |
| 0.141 | 86.0 | 473 | 0.7358 | 0.7340 | 0.1920 |
| 0.161 | 86.9091 | 478 | 0.8496 | 0.7928 | 0.2307 |
| 0.1317 | 88.0 | 484 | 0.7789 | 0.7491 | 0.2017 |
| 0.1435 | 88.9091 | 489 | 0.9110 | 0.8074 | 0.2435 |
| 0.1168 | 90.0 | 495 | 0.7392 | 0.7249 | 0.1908 |
| 0.1223 | 90.9091 | 500 | 0.7573 | 0.7177 | 0.1904 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1