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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- wtimit_asr
metrics:
- wer
model-index:
- name: wtimit-base-normal-all-nofreeze
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: wtimit_asr
type: wtimit_asr
config: clean
split: None
args: clean
metrics:
- name: Wer
type: wer
value: 0.09987953700309014
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wtimit-base-normal-all-nofreeze
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the wtimit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3190
- Wer: 0.0999
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.5076 | 0.4 | 1000 | 1.1220 | 0.6793 |
| 0.4102 | 0.81 | 2000 | 0.7851 | 0.4338 |
| 0.2278 | 1.21 | 3000 | 0.6897 | 0.3203 |
| 0.1723 | 1.61 | 4000 | 0.5668 | 0.2890 |
| 0.1407 | 2.02 | 5000 | 0.4399 | 0.2362 |
| 0.117 | 2.42 | 6000 | 0.4853 | 0.2508 |
| 0.098 | 2.83 | 7000 | 0.6732 | 0.2871 |
| 0.0862 | 3.23 | 8000 | 0.5802 | 0.2680 |
| 0.0806 | 3.63 | 9000 | 0.4730 | 0.2488 |
| 0.0706 | 4.04 | 10000 | 0.4001 | 0.1953 |
| 0.061 | 4.44 | 11000 | 0.4108 | 0.1971 |
| 0.063 | 4.84 | 12000 | 0.4544 | 0.2056 |
| 0.0527 | 5.25 | 13000 | 0.4235 | 0.1938 |
| 0.049 | 5.65 | 14000 | 0.4375 | 0.2054 |
| 0.0489 | 6.06 | 15000 | 0.5451 | 0.2522 |
| 0.0473 | 6.46 | 16000 | 0.3939 | 0.1868 |
| 0.0442 | 6.86 | 17000 | 0.5662 | 0.2548 |
| 0.0428 | 7.27 | 18000 | 0.6695 | 0.2755 |
| 0.0379 | 7.67 | 19000 | 0.3929 | 0.1947 |
| 0.0398 | 8.07 | 20000 | 0.4446 | 0.2066 |
| 0.0336 | 8.48 | 21000 | 0.5409 | 0.2260 |
| 0.0316 | 8.88 | 22000 | 0.3819 | 0.1715 |
| 0.0322 | 9.29 | 23000 | 0.3861 | 0.1711 |
| 0.0352 | 9.69 | 24000 | 0.4063 | 0.1728 |
| 0.0315 | 10.09 | 25000 | 0.4992 | 0.2146 |
| 0.0254 | 10.5 | 26000 | 0.5838 | 0.2158 |
| 0.0243 | 10.9 | 27000 | 0.3458 | 0.1523 |
| 0.0245 | 11.3 | 28000 | 0.5121 | 0.1953 |
| 0.0231 | 11.71 | 29000 | 0.3773 | 0.1616 |
| 0.0202 | 12.11 | 30000 | 0.4110 | 0.1715 |
| 0.0261 | 12.52 | 31000 | 0.5376 | 0.2116 |
| 0.0243 | 12.92 | 32000 | 0.4066 | 0.1569 |
| 0.0201 | 13.32 | 33000 | 0.5944 | 0.2276 |
| 0.0211 | 13.73 | 34000 | 0.4670 | 0.1997 |
| 0.0249 | 14.13 | 35000 | 0.5521 | 0.2254 |
| 0.021 | 14.53 | 36000 | 0.4602 | 0.2061 |
| 0.0169 | 14.94 | 37000 | 0.4870 | 0.1690 |
| 0.0184 | 15.34 | 38000 | 0.6038 | 0.2208 |
| 0.0207 | 15.74 | 39000 | 0.5266 | 0.2068 |
| 0.0209 | 16.15 | 40000 | 0.5197 | 0.2083 |
| 0.0175 | 16.55 | 41000 | 0.5074 | 0.1927 |
| 0.0164 | 16.96 | 42000 | 0.4594 | 0.1615 |
| 0.0164 | 17.36 | 43000 | 0.2956 | 0.1151 |
| 0.0142 | 17.76 | 44000 | 0.3834 | 0.1580 |
| 0.0139 | 18.17 | 45000 | 0.5316 | 0.2175 |
| 0.0181 | 18.57 | 46000 | 0.5226 | 0.1890 |
| 0.0159 | 18.97 | 47000 | 0.4914 | 0.1689 |
| 0.0127 | 19.38 | 48000 | 0.5454 | 0.1957 |
| 0.0136 | 19.78 | 49000 | 0.5530 | 0.2172 |
| 0.0129 | 20.19 | 50000 | 0.6980 | 0.2636 |
| 0.0131 | 20.59 | 51000 | 0.3984 | 0.1379 |
| 0.0123 | 20.99 | 52000 | 0.4925 | 0.1843 |
| 0.0095 | 21.4 | 53000 | 0.5367 | 0.1931 |
| 0.0124 | 21.8 | 54000 | 0.4299 | 0.1763 |
| 0.0115 | 22.2 | 55000 | 0.4797 | 0.1803 |
| 0.0136 | 22.61 | 56000 | 0.6638 | 0.2300 |
| 0.0121 | 23.01 | 57000 | 0.4292 | 0.1530 |
| 0.0097 | 23.42 | 58000 | 0.4064 | 0.1520 |
| 0.0143 | 23.82 | 59000 | 0.4691 | 0.1771 |
| 0.0092 | 24.22 | 60000 | 0.5134 | 0.2009 |
| 0.0097 | 24.63 | 61000 | 0.6165 | 0.2281 |
| 0.0078 | 25.03 | 62000 | 0.4828 | 0.1863 |
| 0.0114 | 25.43 | 63000 | 0.4817 | 0.1868 |
| 0.0089 | 25.84 | 64000 | 0.5137 | 0.2003 |
| 0.0083 | 26.24 | 65000 | 0.4194 | 0.1524 |
| 0.01 | 26.65 | 66000 | 0.3416 | 0.1332 |
| 0.0102 | 27.05 | 67000 | 0.3834 | 0.1475 |
| 0.0076 | 27.45 | 68000 | 0.3390 | 0.1277 |
| 0.0085 | 27.86 | 69000 | 0.4708 | 0.1843 |
| 0.0074 | 28.26 | 70000 | 0.4434 | 0.1530 |
| 0.0078 | 28.66 | 71000 | 0.2942 | 0.1104 |
| 0.0075 | 29.07 | 72000 | 0.3623 | 0.1442 |
| 0.0066 | 29.47 | 73000 | 0.4709 | 0.1547 |
| 0.0073 | 29.87 | 74000 | 0.5198 | 0.1750 |
| 0.0056 | 30.28 | 75000 | 0.3083 | 0.1211 |
| 0.0066 | 30.68 | 76000 | 0.3204 | 0.1243 |
| 0.0048 | 31.09 | 77000 | 0.3713 | 0.1326 |
| 0.0047 | 31.49 | 78000 | 0.3121 | 0.1018 |
| 0.0066 | 31.89 | 79000 | 0.4510 | 0.1473 |
| 0.0053 | 32.3 | 80000 | 0.3599 | 0.1130 |
| 0.0058 | 32.7 | 81000 | 0.4256 | 0.1463 |
| 0.0056 | 33.1 | 82000 | 0.4393 | 0.1605 |
| 0.0046 | 33.51 | 83000 | 0.6327 | 0.2056 |
| 0.0049 | 33.91 | 84000 | 0.4069 | 0.1360 |
| 0.0031 | 34.32 | 85000 | 0.4359 | 0.1458 |
| 0.0052 | 34.72 | 86000 | 0.2825 | 0.1032 |
| 0.0039 | 35.12 | 87000 | 0.3545 | 0.1256 |
| 0.003 | 35.53 | 88000 | 0.3674 | 0.1252 |
| 0.004 | 35.93 | 89000 | 0.3849 | 0.1288 |
| 0.0029 | 36.33 | 90000 | 0.3465 | 0.1130 |
| 0.003 | 36.74 | 91000 | 0.4034 | 0.1294 |
| 0.0036 | 37.14 | 92000 | 0.3456 | 0.1209 |
| 0.0033 | 37.55 | 93000 | 0.3882 | 0.1407 |
| 0.0037 | 37.95 | 94000 | 0.3372 | 0.1094 |
| 0.0025 | 38.35 | 95000 | 0.3601 | 0.1137 |
| 0.0037 | 38.76 | 96000 | 0.2804 | 0.1027 |
| 0.0022 | 39.16 | 97000 | 0.4160 | 0.1354 |
| 0.0027 | 39.56 | 98000 | 0.3379 | 0.1202 |
| 0.002 | 39.97 | 99000 | 0.3462 | 0.1171 |
| 0.0021 | 40.37 | 100000 | 0.3694 | 0.1272 |
| 0.0014 | 40.78 | 101000 | 0.3315 | 0.1048 |
| 0.0025 | 41.18 | 102000 | 0.3316 | 0.1088 |
| 0.002 | 41.58 | 103000 | 0.3776 | 0.1319 |
| 0.0028 | 41.99 | 104000 | 0.3024 | 0.1028 |
| 0.0015 | 42.39 | 105000 | 0.3087 | 0.1102 |
| 0.0018 | 42.79 | 106000 | 0.3254 | 0.1067 |
| 0.0028 | 43.2 | 107000 | 0.3305 | 0.1081 |
| 0.002 | 43.6 | 108000 | 0.3445 | 0.1120 |
| 0.0019 | 44.0 | 109000 | 0.3264 | 0.1082 |
| 0.0019 | 44.41 | 110000 | 0.3650 | 0.1202 |
| 0.001 | 44.81 | 111000 | 0.3415 | 0.1133 |
| 0.0015 | 45.22 | 112000 | 0.3194 | 0.1044 |
| 0.0011 | 45.62 | 113000 | 0.3302 | 0.1085 |
| 0.0013 | 46.02 | 114000 | 0.3083 | 0.1053 |
| 0.0008 | 46.43 | 115000 | 0.2976 | 0.0982 |
| 0.0019 | 46.83 | 116000 | 0.3212 | 0.1057 |
| 0.0006 | 47.23 | 117000 | 0.3415 | 0.1089 |
| 0.0025 | 47.64 | 118000 | 0.3188 | 0.1043 |
| 0.0009 | 48.04 | 119000 | 0.3136 | 0.1025 |
| 0.0015 | 48.45 | 120000 | 0.3180 | 0.1050 |
| 0.0013 | 48.85 | 121000 | 0.3439 | 0.1110 |
| 0.0007 | 49.25 | 122000 | 0.3286 | 0.1048 |
| 0.0014 | 49.66 | 123000 | 0.3190 | 0.0999 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
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