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# wav2vec2-tiny-fp16-demo

This model is a fine-tuned version of [yenpolin/wav2vec2-tiny](https://huggingface.co/yenpolin/wav2vec2-tiny) on the DNA_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5995
- Mean Acc: 51.7421
- Median Acc: 56.8345

## 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: 0.0003
- train_batch_size: 400
- eval_batch_size: 800
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Mean Acc | Median Acc |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:----------:|
| 1.1754        | 1.0   | 1250   | 1.0956          | 1.8729   | 0.0        |
| 1.0286        | 2.0   | 2500   | 0.9805          | 12.8456  | 0.0        |
| 0.921         | 3.0   | 3750   | 0.9232          | 12.3399  | 0.0        |
| 0.8819        | 4.0   | 5000   | 0.8857          | 18.0811  | 0.0        |
| 0.8574        | 5.0   | 6250   | 0.8691          | 17.4596  | 0.0        |
| 0.8419        | 6.0   | 7500   | 0.8578          | 25.5205  | 0.0        |
| 0.8291        | 7.0   | 8750   | 0.8441          | 23.7467  | 0.0        |
| 0.8167        | 8.0   | 10000  | 0.8346          | 22.1622  | 0.0        |
| 0.801         | 9.0   | 11250  | 0.8108          | 31.3152  | 53.4606    |
| 0.7768        | 10.0  | 12500  | 0.7868          | 30.8526  | 53.2143    |
| 0.7533        | 11.0  | 13750  | 0.7642          | 34.3817  | 53.9589    |
| 0.7385        | 12.0  | 15000  | 0.7637          | 39.9523  | 54.6539    |
| 0.7273        | 13.0  | 16250  | 0.7412          | 38.2095  | 54.5455    |
| 0.7103        | 14.0  | 17500  | 0.7212          | 39.3217  | 54.6032    |
| 0.6876        | 15.0  | 18750  | 0.7128          | 33.8479  | 53.8226    |
| 0.6744        | 16.0  | 20000  | 0.6916          | 36.8302  | 54.0541    |
| 0.6664        | 17.0  | 21250  | 0.6825          | 39.4562  | 54.4928    |
| 0.6598        | 18.0  | 22500  | 0.6753          | 36.5260  | 53.9130    |
| 0.654         | 19.0  | 23750  | 0.6748          | 33.4539  | 53.6058    |
| 0.6492        | 20.0  | 25000  | 0.6702          | 37.7485  | 54.1209    |
| 0.6457        | 21.0  | 26250  | 0.6694          | 40.0633  | 54.4343    |
| 0.6413        | 22.0  | 27500  | 0.6609          | 36.9515  | 53.9062    |
| 0.6377        | 23.0  | 28750  | 0.6576          | 39.6921  | 54.2857    |
| 0.6341        | 24.0  | 30000  | 0.6527          | 41.3149  | 54.5455    |
| 0.6312        | 25.0  | 31250  | 0.6516          | 39.5467  | 54.2587    |
| 0.6279        | 26.0  | 32500  | 0.6487          | 38.4877  | 54.0541    |
| 0.6246        | 27.0  | 33750  | 0.6444          | 43.1149  | 54.7619    |
| 0.6219        | 28.0  | 35000  | 0.6439          | 40.7786  | 54.5151    |
| 0.6191        | 29.0  | 36250  | 0.6418          | 41.0059  | 54.5161    |
| 0.6167        | 30.0  | 37500  | 0.6359          | 42.2812  | 54.7425    |
| 0.6136        | 31.0  | 38750  | 0.6346          | 41.2515  | 54.5977    |
| 0.6111        | 32.0  | 40000  | 0.6332          | 41.7222  | 54.6667    |
| 0.6087        | 33.0  | 41250  | 0.6333          | 41.3847  | 54.5455    |
| 0.6062        | 34.0  | 42500  | 0.6279          | 43.7450  | 55.0000    |
| 0.6042        | 35.0  | 43750  | 0.6269          | 42.5222  | 54.7619    |
| 0.6021        | 36.0  | 45000  | 0.6288          | 43.8255  | 54.9133    |
| 0.6004        | 37.0  | 46250  | 0.6292          | 43.5378  | 55.0186    |
| 0.5987        | 38.0  | 47500  | 0.6233          | 43.8364  | 55.0000    |
| 0.5969        | 39.0  | 48750  | 0.6213          | 44.3750  | 55.1155    |
| 0.595         | 40.0  | 50000  | 0.6207          | 40.1240  | 54.5732    |
| 0.5934        | 41.0  | 51250  | 0.6198          | 44.3429  | 55.0885    |
| 0.5914        | 42.0  | 52500  | 0.6178          | 44.0396  | 55.1181    |
| 0.5895        | 43.0  | 53750  | 0.6155          | 45.7166  | 55.4017    |
| 0.5874        | 44.0  | 55000  | 0.6159          | 46.3816  | 55.4745    |
| 0.5853        | 45.0  | 56250  | 0.6136          | 45.7973  | 55.4810    |
| 0.5834        | 46.0  | 57500  | 0.6142          | 44.1374  | 55.2       |
| 0.5819        | 47.0  | 58750  | 0.6204          | 46.3006  | 55.4945    |
| 0.5802        | 48.0  | 60000  | 0.6093          | 47.6637  | 55.7276    |
| 0.5785        | 49.0  | 61250  | 0.6098          | 46.7728  | 55.625     |
| 0.5771        | 50.0  | 62500  | 0.6075          | 47.4424  | 55.7423    |
| 0.5751        | 51.0  | 63750  | 0.6066          | 46.8072  | 55.7377    |
| 0.5734        | 52.0  | 65000  | 0.6053          | 48.7600  | 56.0109    |
| 0.5719        | 53.0  | 66250  | 0.6048          | 48.8645  | 55.9557    |
| 0.5705        | 54.0  | 67500  | 0.6030          | 48.4536  | 55.9557    |
| 0.5693        | 55.0  | 68750  | 0.6046          | 49.0295  | 56.0870    |
| 0.5677        | 56.0  | 70000  | 0.6034          | 47.9864  | 55.9767    |
| 0.5665        | 57.0  | 71250  | 0.6033          | 48.4370  | 56.0241    |
| 0.5653        | 58.0  | 72500  | 0.6016          | 49.2084  | 56.1069    |
| 0.5635        | 59.0  | 73750  | 0.6021          | 49.6844  | 56.2648    |
| 0.5621        | 60.0  | 75000  | 0.6001          | 50.2077  | 56.3218    |
| 0.5607        | 61.0  | 76250  | 0.6007          | 49.1341  | 56.1753    |
| 0.5592        | 62.0  | 77500  | 0.6011          | 48.9626  | 56.2016    |
| 0.5582        | 63.0  | 78750  | 0.6008          | 49.6105  | 56.2982    |
| 0.557         | 64.0  | 80000  | 0.6012          | 49.5089  | 56.2814    |
| 0.5557        | 65.0  | 81250  | 0.5994          | 49.9531  | 56.3246    |
| 0.5542        | 66.0  | 82500  | 0.6003          | 50.2286  | 56.4706    |
| 0.5533        | 67.0  | 83750  | 0.5990          | 49.9327  | 56.3725    |
| 0.5521        | 68.0  | 85000  | 0.5995          | 50.1928  | 56.4516    |
| 0.5511        | 69.0  | 86250  | 0.5979          | 50.2263  | 56.4444    |
| 0.55          | 70.0  | 87500  | 0.6005          | 50.4875  | 56.5341    |
| 0.5484        | 71.0  | 88750  | 0.5984          | 50.0615  | 56.4626    |
| 0.5475        | 72.0  | 90000  | 0.5991          | 50.4546  | 56.5598    |
| 0.5467        | 73.0  | 91250  | 0.5972          | 50.8276  | 56.5728    |
| 0.5458        | 74.0  | 92500  | 0.5987          | 51.1359  | 56.5934    |
| 0.5445        | 75.0  | 93750  | 0.5986          | 51.0738  | 56.6474    |
| 0.5439        | 76.0  | 95000  | 0.5991          | 50.7110  | 56.6102    |
| 0.5431        | 77.0  | 96250  | 0.5986          | 51.0240  | 56.6667    |
| 0.5423        | 78.0  | 97500  | 0.5992          | 51.0228  | 56.6901    |
| 0.5413        | 79.0  | 98750  | 0.5992          | 50.8097  | 56.6343    |
| 0.5407        | 80.0  | 100000 | 0.5993          | 51.2619  | 56.7251    |
| 0.5399        | 81.0  | 101250 | 0.5975          | 51.4024  | 56.7282    |
| 0.5393        | 82.0  | 102500 | 0.5982          | 51.2136  | 56.7358    |
| 0.5386        | 83.0  | 103750 | 0.5983          | 51.4384  | 56.7376    |
| 0.538         | 84.0  | 105000 | 0.5988          | 51.6233  | 56.7568    |
| 0.5374        | 85.0  | 106250 | 0.5983          | 51.4155  | 56.7867    |
| 0.5369        | 86.0  | 107500 | 0.5987          | 51.4146  | 56.7568    |
| 0.5365        | 87.0  | 108750 | 0.5995          | 51.4874  | 56.8       |
| 0.5361        | 88.0  | 110000 | 0.5994          | 51.5007  | 56.7775    |
| 0.5357        | 89.0  | 111250 | 0.5991          | 51.5986  | 56.7901    |
| 0.5353        | 90.0  | 112500 | 0.5990          | 51.4792  | 56.7742    |
| 0.535         | 91.0  | 113750 | 0.5993          | 51.8824  | 56.8528    |
| 0.5348        | 92.0  | 115000 | 0.5993          | 51.6289  | 56.8116    |
| 0.5346        | 93.0  | 116250 | 0.5993          | 51.6716  | 56.8254    |
| 0.5344        | 94.0  | 117500 | 0.5991          | 51.5984  | 56.8       |
| 0.5342        | 95.0  | 118750 | 0.5995          | 51.8793  | 56.8245    |
| 0.5341        | 96.0  | 120000 | 0.5994          | 51.7306  | 56.8254    |
| 0.534         | 97.0  | 121250 | 0.5994          | 51.7991  | 56.8306    |
| 0.5339        | 98.0  | 122500 | 0.5993          | 51.8468  | 56.8233    |
| 0.5339        | 99.0  | 123750 | 0.5994          | 51.8060  | 56.8389    |
| 0.5338        | 100.0 | 125000 | 0.5995          | 51.7421  | 56.8345    |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["automatic-speech-recognition", "dna_r9.4.1", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-tiny-fp16-demo", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition dna_r9.4.1 generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 

wav2vec2-tiny-fp16-demo
=======================


This model is a fine-tuned version of yenpolin/wav2vec2-tiny on the DNA\_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5995
* Mean Acc: 51.7421
* Median Acc: 56.8345


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: 0.0003
* train\_batch\_size: 400
* eval\_batch\_size: 800
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 400
* eval\_batch\_size: 800
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 400
* eval\_batch\_size: 800
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dna_r9.4.1 #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 400
* eval\_batch\_size: 800
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.13.2