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# apac_5sents_XLS-R_2_e-4

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4066
- Wer: 0.2188

## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 194.7445      | 11.11   | 200   | 81.6865         | 1.0    |
| 61.9115       | 22.22   | 400   | 39.3596         | 1.0    |
| 22.518        | 33.32   | 600   | 9.0332          | 1.0    |
| 6.0776        | 44.43   | 800   | 5.0634          | 1.0    |
| 4.555         | 55.54   | 1000  | 4.1552          | 1.0    |
| 2.402         | 66.65   | 1200  | 2.1760          | 0.7746 |
| 0.8349        | 77.76   | 1400  | 2.0442          | 0.8326 |
| 0.5484        | 88.86   | 1600  | 1.4219          | 0.8415 |
| 0.4242        | 99.97   | 1800  | 3.1197          | 0.8817 |
| 0.3173        | 111.11  | 2000  | 1.7508          | 0.7388 |
| 0.1989        | 122.22  | 2200  | 2.4075          | 0.6920 |
| 0.1169        | 133.32  | 2400  | 6.3769          | 0.5670 |
| 0.0925        | 144.43  | 2600  | 1.6440          | 0.4710 |
| 0.0681        | 155.54  | 2800  | 1.4864          | 0.3103 |
| 0.0561        | 166.65  | 3000  | 2.2973          | 0.3996 |
| 0.0445        | 177.76  | 3200  | 1.8107          | 0.4219 |
| 0.0336        | 188.86  | 3400  | 1.3867          | 0.3728 |
| 0.0344        | 199.97  | 3600  | 1.7830          | 0.3281 |
| 0.0345        | 211.11  | 3800  | 2.3773          | 0.3638 |
| 0.0304        | 222.22  | 4000  | 1.4448          | 0.1987 |
| 0.0357        | 233.32  | 4200  | 2.5893          | 0.3125 |
| 0.0253        | 244.43  | 4400  | 2.4619          | 0.3013 |
| 0.0212        | 255.54  | 4600  | 2.8144          | 0.2790 |
| 0.0186        | 266.65  | 4800  | 2.1155          | 0.2545 |
| 0.0196        | 277.76  | 5000  | 1.7306          | 0.2254 |
| 0.0153        | 288.86  | 5200  | 1.6247          | 0.2121 |
| 0.0146        | 299.97  | 5400  | 3.0580          | 0.4442 |
| 0.0296        | 311.11  | 5600  | 1.7865          | 0.2857 |
| 0.017         | 322.22  | 5800  | 5.4352          | 0.3795 |
| 0.0162        | 333.32  | 6000  | 1.9186          | 0.25   |
| 0.0139        | 344.43  | 6200  | 2.6566          | 0.2589 |
| 0.0139        | 355.54  | 6400  | 2.6532          | 0.2946 |
| 0.0111        | 366.65  | 6600  | 1.9131          | 0.2567 |
| 0.0119        | 377.76  | 6800  | 1.8914          | 0.3214 |
| 0.012         | 388.86  | 7000  | 2.2985          | 0.3371 |
| 0.0117        | 399.97  | 7200  | 3.3127          | 0.3393 |
| 0.0185        | 411.11  | 7400  | 3.1641          | 0.3862 |
| 0.0096        | 422.22  | 7600  | 2.3704          | 0.3973 |
| 0.007         | 433.32  | 7800  | 5.5839          | 0.4375 |
| 0.0104        | 444.43  | 8000  | 3.1441          | 0.3973 |
| 0.0098        | 455.54  | 8200  | 1.8188          | 0.2768 |
| 0.009         | 466.65  | 8400  | 1.8589          | 0.3058 |
| 0.0142        | 477.76  | 8600  | 3.9817          | 0.3772 |
| 0.0095        | 488.86  | 8800  | 2.1353          | 0.3237 |
| 0.0071        | 499.97  | 9000  | 1.5266          | 0.2902 |
| 0.0071        | 511.11  | 9200  | 1.4713          | 0.2746 |
| 0.0068        | 522.22  | 9400  | 2.2041          | 0.3125 |
| 0.0046        | 533.32  | 9600  | 1.4471          | 0.2522 |
| 0.0078        | 544.43  | 9800  | 1.6511          | 0.2946 |
| 0.0077        | 555.54  | 10000 | 1.7329          | 0.2121 |
| 0.004         | 566.65  | 10200 | 1.8652          | 0.2031 |
| 0.0049        | 577.76  | 10400 | 1.3661          | 0.2210 |
| 0.0066        | 588.86  | 10600 | 1.7544          | 0.2321 |
| 0.0072        | 599.97  | 10800 | 1.8081          | 0.2835 |
| 0.0055        | 611.11  | 11000 | 1.5139          | 0.2232 |
| 0.0053        | 622.22  | 11200 | 1.6138          | 0.2991 |
| 0.0052        | 633.32  | 11400 | 1.4865          | 0.2924 |
| 0.0067        | 644.43  | 11600 | 2.4807          | 0.3705 |
| 0.0044        | 655.54  | 11800 | 1.4097          | 0.3371 |
| 0.0026        | 666.65  | 12000 | 1.5313          | 0.3348 |
| 0.0055        | 677.76  | 12200 | 2.1968          | 0.3661 |
| 0.0034        | 688.86  | 12400 | 1.5198          | 0.3839 |
| 0.0028        | 699.97  | 12600 | 1.5379          | 0.3683 |
| 0.0033        | 711.11  | 12800 | 2.1355          | 0.3571 |
| 0.0044        | 722.22  | 13000 | 1.4440          | 0.3371 |
| 0.0024        | 733.32  | 13200 | 3.5154          | 0.3438 |
| 0.0012        | 744.43  | 13400 | 2.8505          | 0.3214 |
| 0.002         | 755.54  | 13600 | 2.9340          | 0.3304 |
| 0.0029        | 766.65  | 13800 | 2.8148          | 0.3214 |
| 0.0034        | 777.76  | 14000 | 2.7587          | 0.2835 |
| 0.0025        | 788.86  | 14200 | 2.8232          | 0.3638 |
| 0.0012        | 799.97  | 14400 | 2.6047          | 0.375  |
| 0.0015        | 811.11  | 14600 | 2.6364          | 0.3772 |
| 0.0018        | 822.22  | 14800 | 2.5143          | 0.3929 |
| 0.0032        | 833.32  | 15000 | 2.9826          | 0.5469 |
| 0.0035        | 844.43  | 15200 | 1.5761          | 0.4420 |
| 0.0023        | 855.54  | 15400 | 1.7465          | 0.4598 |
| 0.0016        | 866.65  | 15600 | 1.7740          | 0.4397 |
| 0.0007        | 877.76  | 15800 | 1.8296          | 0.4286 |
| 0.0012        | 888.86  | 16000 | 2.2368          | 0.3906 |
| 0.0027        | 899.97  | 16200 | 1.7112          | 0.3527 |
| 0.0009        | 911.11  | 16400 | 2.5084          | 0.375  |
| 0.001         | 922.22  | 16600 | 2.3311          | 0.3304 |
| 0.0019        | 933.32  | 16800 | 1.6653          | 0.3080 |
| 0.0018        | 944.43  | 17000 | 1.4620          | 0.2768 |
| 0.0007        | 955.54  | 17200 | 1.8509          | 0.2723 |
| 0.0005        | 966.65  | 17400 | 1.9279          | 0.2879 |
| 0.0009        | 977.76  | 17600 | 2.3558          | 0.2812 |
| 0.0004        | 988.86  | 17800 | 2.8907          | 0.2924 |
| 0.0011        | 999.97  | 18000 | 2.4847          | 0.2902 |
| 0.0016        | 1011.11 | 18200 | 2.2670          | 0.3058 |
| 0.0004        | 1022.22 | 18400 | 2.2399          | 0.3125 |
| 0.0004        | 1033.32 | 18600 | 2.4376          | 0.3192 |
| 0.001         | 1044.43 | 18800 | 2.4744          | 0.3214 |
| 0.0006        | 1055.54 | 19000 | 2.4975          | 0.3147 |
| 0.0006        | 1066.65 | 19200 | 2.6372          | 0.3259 |
| 0.0005        | 1077.76 | 19400 | 2.5817          | 0.3304 |
| 0.0004        | 1088.86 | 19600 | 2.5573          | 0.3326 |
| 0.0001        | 1099.97 | 19800 | 2.5579          | 0.3348 |
| 0.0003        | 1111.11 | 20000 | 2.5641          | 0.3326 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "apac_5sents_XLS-R_2_e-4", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us