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# whisper_tn_hi

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6400
- Wer: 147.3885
- Cer: 295.3741

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      | Cer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.6414        | 1.0   | 409   | 0.8809          | 83.9795  | 248.4138 |
| 0.531         | 2.0   | 818   | 0.6346          | 75.4042  | 280.7284 |
| 0.3538        | 3.0   | 1227  | 0.5810          | 69.8595  | 259.1276 |
| 0.2679        | 4.0   | 1636  | 0.5639          | 84.1488  | 332.292  |
| 0.2074        | 5.0   | 2045  | 0.5715          | 85.9773  | 264.435  |
| 0.1599        | 6.0   | 2454  | 0.6074          | 86.481   | 262.8894 |
| 0.1216        | 7.0   | 2863  | 0.6402          | 110.0398 | 285.4649 |
| 0.0903        | 8.0   | 3272  | 0.6736          | 92.7961  | 278.7952 |
| 0.0663        | 9.0   | 3681  | 0.7023          | 96.3896  | 268.8475 |
| 0.0472        | 10.0  | 4090  | 0.7527          | 104.0041 | 276.3805 |
| 0.0335        | 11.0  | 4499  | 0.7907          | 99.0646  | 274.9479 |
| 0.0235        | 12.0  | 4908  | 0.8320          | 128.1004 | 282.2682 |
| 0.0169        | 13.0  | 5317  | 0.8741          | 116.1305 | 277.659  |
| 0.0124        | 14.0  | 5726  | 0.9090          | 137.6534 | 290.6719 |
| 0.0094        | 15.0  | 6135  | 0.9492          | 117.0405 | 285.2422 |
| 0.007         | 16.0  | 6544  | 0.9905          | 122.1663 | 280.3801 |
| 0.0061        | 17.0  | 6953  | 1.0199          | 125.0656 | 277.5049 |
| 0.0051        | 18.0  | 7362  | 1.0383          | 117.3368 | 278.8806 |
| 0.0044        | 19.0  | 7771  | 1.0617          | 110.8736 | 275.7667 |
| 0.0041        | 20.0  | 8180  | 1.0867          | 142.9061 | 291.1584 |
| 0.0037        | 21.0  | 8589  | 1.1224          | 119.377  | 273.63   |
| 0.0026        | 22.0  | 8998  | 1.1322          | 158.2452 | 295.0852 |
| 0.0024        | 23.0  | 9407  | 1.1619          | 134.9446 | 283.1038 |
| 0.0022        | 24.0  | 9816  | 1.1677          | 124.5789 | 283.5701 |
| 0.002         | 25.0  | 10225 | 1.1898          | 125.0275 | 288.5318 |
| 0.0019        | 26.0  | 10634 | 1.1994          | 138.0386 | 288.9011 |
| 0.0023        | 27.0  | 11043 | 1.2216          | 119.7071 | 279.7329 |
| 0.0021        | 28.0  | 11452 | 1.2521          | 96.3388  | 266.6656 |
| 0.0018        | 29.0  | 11861 | 1.2568          | 148.4932 | 288.7504 |
| 0.0018        | 30.0  | 12270 | 1.2541          | 115.3771 | 283.8205 |
| 0.0021        | 31.0  | 12679 | 1.2291          | 98.8995  | 271.6817 |
| 0.0014        | 32.0  | 13088 | 1.2821          | 130.6654 | 293.2197 |
| 0.0014        | 33.0  | 13497 | 1.2804          | 121.8954 | 287.2249 |
| 0.0013        | 34.0  | 13906 | 1.2802          | 137.5857 | 293.6275 |
| 0.0015        | 35.0  | 14315 | 1.3010          | 147.5789 | 296.7907 |
| 0.0014        | 36.0  | 14724 | 1.2945          | 139.6766 | 292.4335 |
| 0.0012        | 37.0  | 15133 | 1.3310          | 144.5653 | 288.2045 |
| 0.0011        | 38.0  | 15542 | 1.3200          | 160.6493 | 306.8297 |
| 0.0009        | 39.0  | 15951 | 1.3394          | 211.9783 | 341.8621 |
| 0.0013        | 40.0  | 16360 | 1.3367          | 133.4166 | 304.8621 |
| 0.0007        | 41.0  | 16769 | 1.3472          | 154.6601 | 319.8702 |
| 0.0005        | 42.0  | 17178 | 1.3617          | 149.0815 | 301.4669 |
| 0.0009        | 43.0  | 17587 | 1.3570          | 163.2312 | 319.4675 |
| 0.0009        | 44.0  | 17996 | 1.3723          | 149.9915 | 310.0088 |
| 0.0009        | 45.0  | 18405 | 1.3809          | 133.1118 | 289.5232 |
| 0.0009        | 46.0  | 18814 | 1.3664          | 166.6427 | 308.9287 |
| 0.0008        | 47.0  | 19223 | 1.3894          | 150.0127 | 304.3739 |
| 0.0005        | 48.0  | 19632 | 1.3632          | 129.4929 | 307.7766 |
| 0.0005        | 49.0  | 20041 | 1.3917          | 143.9304 | 313.3529 |
| 0.0005        | 50.0  | 20450 | 1.4006          | 113.0111 | 295.1966 |
| 0.0007        | 51.0  | 20859 | 1.3966          | 158.3129 | 303.4328 |
| 0.0009        | 52.0  | 21268 | 1.4149          | 138.1613 | 304.2098 |
| 0.0003        | 53.0  | 21677 | 1.3998          | 163.519  | 314.8466 |
| 0.0002        | 54.0  | 22086 | 1.4192          | 141.4035 | 302.2313 |
| 0.0001        | 55.0  | 22495 | 1.4183          | 150.2878 | 300.9336 |
| 0.0002        | 56.0  | 22904 | 1.4281          | 172.598  | 321.0298 |
| 0.0018        | 57.0  | 23313 | 1.4229          | 151.9597 | 309.6211 |
| 0.0009        | 58.0  | 23722 | 1.4263          | 128.9554 | 290.2265 |
| 0.0003        | 59.0  | 24131 | 1.4430          | 135.6599 | 301.7223 |
| 0.0002        | 60.0  | 24540 | 1.4487          | 156.1034 | 307.6167 |
| 0.0004        | 61.0  | 24949 | 1.4252          | 107.7161 | 272.8312 |
| 0.0001        | 62.0  | 25358 | 1.4254          | 123.5122 | 289.272  |
| 0.0           | 63.0  | 25767 | 1.4510          | 121.2901 | 280.6162 |
| 0.0002        | 64.0  | 26176 | 1.4407          | 111.6482 | 284.5364 |
| 0.0003        | 65.0  | 26585 | 1.4512          | 123.5207 | 285.948  |
| 0.0006        | 66.0  | 26994 | 1.4476          | 108.9224 | 280.1608 |
| 0.0005        | 67.0  | 27403 | 1.4721          | 153.8178 | 309.4788 |
| 0.0004        | 68.0  | 27812 | 1.4675          | 132.1341 | 289.9678 |
| 0.0001        | 69.0  | 28221 | 1.4712          | 135.9096 | 292.8338 |
| 0.0001        | 70.0  | 28630 | 1.4712          | 137.0228 | 294.8725 |
| 0.0           | 71.0  | 29039 | 1.4727          | 137.9582 | 292.8438 |
| 0.0           | 72.0  | 29448 | 1.4766          | 135.6514 | 291.9329 |
| 0.0           | 73.0  | 29857 | 1.4808          | 135.7784 | 292.3431 |
| 0.0           | 74.0  | 30266 | 1.4850          | 135.5414 | 291.5527 |
| 0.0           | 75.0  | 30675 | 1.4901          | 134.3224 | 290.6803 |
| 0.0           | 76.0  | 31084 | 1.4943          | 135.9562 | 291.6507 |
| 0.0           | 77.0  | 31493 | 1.4986          | 136.0069 | 291.0294 |
| 0.0           | 78.0  | 31902 | 1.5039          | 139.228  | 292.1162 |
| 0.0           | 79.0  | 32311 | 1.5092          | 138.6862 | 291.7796 |
| 0.0           | 80.0  | 32720 | 1.5146          | 139.8375 | 292.3959 |
| 0.0           | 81.0  | 33129 | 1.5208          | 138.9782 | 292.097  |
| 0.0           | 82.0  | 33538 | 1.5270          | 140.976  | 293.3127 |
| 0.0           | 83.0  | 33947 | 1.5334          | 141.3993 | 292.2359 |
| 0.0           | 84.0  | 34356 | 1.5401          | 141.2258 | 292.2309 |
| 0.0           | 85.0  | 34765 | 1.5472          | 140.7686 | 291.4648 |
| 0.0           | 86.0  | 35174 | 1.5550          | 140.6163 | 291.7997 |
| 0.0           | 87.0  | 35583 | 1.5617          | 142.9104 | 293.0816 |
| 0.0           | 88.0  | 35992 | 1.5700          | 140.9972 | 292.0618 |
| 0.0           | 89.0  | 36401 | 1.5781          | 141.5559 | 292.3054 |
| 0.0           | 90.0  | 36810 | 1.5855          | 142.4109 | 293.033  |
| 0.0           | 91.0  | 37219 | 1.5925          | 145.0436 | 293.8586 |
| 0.0           | 92.0  | 37628 | 1.6010          | 144.2648 | 293.2315 |
| 0.0           | 93.0  | 38037 | 1.6083          | 144.3833 | 293.3211 |
| 0.0           | 94.0  | 38446 | 1.6153          | 146.3007 | 294.4095 |
| 0.0           | 95.0  | 38855 | 1.6207          | 146.9864 | 295.1798 |
| 0.0           | 96.0  | 39264 | 1.6269          | 145.179  | 293.7054 |
| 0.0           | 97.0  | 39673 | 1.6321          | 148.0107 | 295.6043 |
| 0.0           | 98.0  | 40082 | 1.6358          | 147.088  | 295.2686 |
| 0.0           | 99.0  | 40491 | 1.6389          | 148.1503 | 295.822  |
| 0.0           | 100.0 | 40900 | 1.6400          | 147.3885 | 295.3741 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper_tn_hi", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

whisper\_tn\_hi
===============


This model is a fine-tuned version of openai/whisper-tiny on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6400
* Wer: 147.3885
* Cer: 295.3741


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0
* Datasets 2.6.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0
* Datasets 2.6.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0
* Datasets 2.6.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 1.13.0
* Datasets 2.6.1
* Tokenizers 0.11.0