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# wav2vec2-tiny-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: 1.3174
- Mean Acc: 41.4052
- Median Acc: 54.7558

## 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: linear
- num_epochs: 100.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Mean Acc | Median Acc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|
| No log        | 1.0   | 250   | 1.2708          | 0.0      | 0.0        |
| 1.2586        | 2.0   | 500   | 1.2401          | 0.0      | 0.0        |
| 1.2586        | 3.0   | 750   | 1.2212          | 0.0      | 0.0        |
| 1.1999        | 4.0   | 1000  | 1.1985          | 0.0026   | 0.0        |
| 1.1999        | 5.0   | 1250  | 1.1824          | 0.0122   | 0.0        |
| 1.1635        | 6.0   | 1500  | 1.1716          | 0.0301   | 0.0        |
| 1.1635        | 7.0   | 1750  | 1.1562          | 0.0296   | 0.0        |
| 1.1361        | 8.0   | 2000  | 1.1507          | 0.0088   | 0.0        |
| 1.1361        | 9.0   | 2250  | 1.1393          | 0.0649   | 0.0        |
| 1.1142        | 10.0  | 2500  | 1.1311          | 0.0150   | 0.0        |
| 1.1142        | 11.0  | 2750  | 1.0849          | 0.0534   | 0.0        |
| 1.0648        | 12.0  | 3000  | 1.0644          | 0.0912   | 0.0        |
| 1.0648        | 13.0  | 3250  | 1.0415          | 0.1348   | 0.0        |
| 1.0185        | 14.0  | 3500  | 1.0390          | 0.0443   | 0.0        |
| 1.0185        | 15.0  | 3750  | 1.0214          | 0.1120   | 0.0        |
| 0.9951        | 16.0  | 4000  | 1.0152          | 0.1647   | 0.0        |
| 0.9951        | 17.0  | 4250  | 1.0193          | 0.1194   | 0.0        |
| 0.9813        | 18.0  | 4500  | 1.0092          | 0.1182   | 0.0        |
| 0.9813        | 19.0  | 4750  | 1.0165          | 0.0672   | 0.0        |
| 0.9625        | 20.0  | 5000  | 0.9430          | 9.0082   | 0.0        |
| 0.9625        | 21.0  | 5250  | 0.9301          | 13.2452  | 0.0        |
| 0.8958        | 22.0  | 5500  | 0.9261          | 8.9878   | 0.0        |
| 0.8958        | 23.0  | 5750  | 0.9174          | 15.3641  | 0.0        |
| 0.8756        | 24.0  | 6000  | 0.9116          | 14.9011  | 0.0        |
| 0.8756        | 25.0  | 6250  | 0.9130          | 12.2681  | 0.0        |
| 0.8607        | 26.0  | 6500  | 0.9114          | 15.2874  | 0.0        |
| 0.8607        | 27.0  | 6750  | 0.9105          | 24.1205  | 0.0        |
| 0.8482        | 28.0  | 7000  | 0.9083          | 18.6607  | 0.0        |
| 0.8482        | 29.0  | 7250  | 0.9196          | 17.7246  | 0.0        |
| 0.8359        | 30.0  | 7500  | 0.9149          | 19.7733  | 0.0        |
| 0.8359        | 31.0  | 7750  | 0.9133          | 18.6297  | 0.0        |
| 0.8232        | 32.0  | 8000  | 0.9479          | 12.2703  | 0.0        |
| 0.8232        | 33.0  | 8250  | 0.9303          | 19.9043  | 0.0        |
| 0.8092        | 34.0  | 8500  | 0.9300          | 22.3510  | 0.0        |
| 0.8092        | 35.0  | 8750  | 0.9295          | 27.8118  | 0.0        |
| 0.7951        | 36.0  | 9000  | 0.9439          | 23.2963  | 0.0        |
| 0.7951        | 37.0  | 9250  | 0.9620          | 20.9074  | 0.0        |
| 0.7803        | 38.0  | 9500  | 0.9571          | 28.7828  | 52.3026    |
| 0.7803        | 39.0  | 9750  | 0.9814          | 25.2679  | 0.0        |
| 0.7669        | 40.0  | 10000 | 0.9787          | 31.2820  | 53.3865    |
| 0.7669        | 41.0  | 10250 | 0.9765          | 28.6333  | 52.2034    |
| 0.7529        | 42.0  | 10500 | 1.0038          | 27.5154  | 0.0        |
| 0.7529        | 43.0  | 10750 | 1.0338          | 28.4940  | 52.1368    |
| 0.7411        | 44.0  | 11000 | 1.0279          | 28.7206  | 52.2782    |
| 0.7411        | 45.0  | 11250 | 1.0177          | 29.0583  | 52.4116    |
| 0.7299        | 46.0  | 11500 | 1.0148          | 32.9230  | 53.6       |
| 0.7299        | 47.0  | 11750 | 1.0400          | 33.5174  | 53.8136    |
| 0.7198        | 48.0  | 12000 | 1.0478          | 30.9309  | 53.2544    |
| 0.7198        | 49.0  | 12250 | 1.0538          | 29.2445  | 52.5692    |
| 0.7109        | 50.0  | 12500 | 1.0524          | 27.2312  | 0.0        |
| 0.7109        | 51.0  | 12750 | 1.0862          | 33.3504  | 53.7234    |
| 0.7036        | 52.0  | 13000 | 1.0744          | 31.8779  | 53.3679    |
| 0.7036        | 53.0  | 13250 | 1.0628          | 28.3728  | 51.7123    |
| 0.6963        | 54.0  | 13500 | 1.0587          | 30.9822  | 53.125     |
| 0.6963        | 55.0  | 13750 | 1.0834          | 33.2130  | 53.5714    |
| 0.69          | 56.0  | 14000 | 1.1077          | 36.3846  | 54.2857    |
| 0.69          | 57.0  | 14250 | 1.1150          | 32.3586  | 53.4954    |
| 0.6855        | 58.0  | 14500 | 1.1352          | 36.9014  | 54.4061    |
| 0.6855        | 59.0  | 14750 | 1.1557          | 33.8871  | 53.8462    |
| 0.6811        | 60.0  | 15000 | 1.1315          | 33.5959  | 53.7736    |
| 0.6811        | 61.0  | 15250 | 1.0957          | 32.9908  | 53.5714    |
| 0.6768        | 62.0  | 15500 | 1.1236          | 32.7653  | 53.6122    |
| 0.6768        | 63.0  | 15750 | 1.1153          | 34.8283  | 53.9432    |
| 0.6722        | 64.0  | 16000 | 1.1300          | 35.0110  | 53.9683    |
| 0.6722        | 65.0  | 16250 | 1.1826          | 35.9229  | 54.1463    |
| 0.6682        | 66.0  | 16500 | 1.1534          | 38.5511  | 54.4850    |
| 0.6682        | 67.0  | 16750 | 1.1636          | 35.8289  | 54.0984    |
| 0.6653        | 68.0  | 17000 | 1.1404          | 34.6543  | 53.8462    |
| 0.6653        | 69.0  | 17250 | 1.1473          | 36.2493  | 54.1209    |
| 0.6624        | 70.0  | 17500 | 1.1532          | 39.5140  | 54.5775    |
| 0.6624        | 71.0  | 17750 | 1.1715          | 36.2354  | 54.1254    |
| 0.6597        | 72.0  | 18000 | 1.1875          | 35.5023  | 54.0856    |
| 0.6597        | 73.0  | 18250 | 1.1643          | 34.5047  | 53.9024    |
| 0.657         | 74.0  | 18500 | 1.1894          | 38.7561  | 54.4928    |
| 0.657         | 75.0  | 18750 | 1.2082          | 38.2157  | 54.4601    |
| 0.6543        | 76.0  | 19000 | 1.1843          | 34.2857  | 53.8835    |
| 0.6543        | 77.0  | 19250 | 1.1689          | 38.4264  | 54.4355    |
| 0.652         | 78.0  | 19500 | 1.2085          | 37.7495  | 54.3750    |
| 0.652         | 79.0  | 19750 | 1.1985          | 39.1444  | 54.5190    |
| 0.6497        | 80.0  | 20000 | 1.2331          | 40.0190  | 54.6125    |
| 0.6497        | 81.0  | 20250 | 1.2403          | 39.0511  | 54.5205    |
| 0.6476        | 82.0  | 20500 | 1.1910          | 37.2936  | 54.2453    |
| 0.6476        | 83.0  | 20750 | 1.2035          | 41.2572  | 54.7244    |
| 0.6457        | 84.0  | 21000 | 1.2124          | 38.7565  | 54.4892    |
| 0.6457        | 85.0  | 21250 | 1.2327          | 39.1286  | 54.4959    |
| 0.6437        | 86.0  | 21500 | 1.2252          | 39.8797  | 54.5775    |
| 0.6437        | 87.0  | 21750 | 1.2346          | 38.1128  | 54.3860    |
| 0.642         | 88.0  | 22000 | 1.2441          | 40.4358  | 54.6512    |
| 0.642         | 89.0  | 22250 | 1.2488          | 39.9912  | 54.5977    |
| 0.6403        | 90.0  | 22500 | 1.2483          | 39.9116  | 54.6053    |
| 0.6403        | 91.0  | 22750 | 1.2674          | 40.9458  | 54.7009    |
| 0.6387        | 92.0  | 23000 | 1.2694          | 40.4423  | 54.6358    |
| 0.6387        | 93.0  | 23250 | 1.2717          | 40.7397  | 54.6763    |
| 0.6371        | 94.0  | 23500 | 1.2820          | 40.5719  | 54.6392    |
| 0.6371        | 95.0  | 23750 | 1.2883          | 40.3534  | 54.6667    |
| 0.6358        | 96.0  | 24000 | 1.3039          | 40.6046  | 54.6667    |
| 0.6358        | 97.0  | 24250 | 1.3068          | 41.4525  | 54.7739    |
| 0.6347        | 98.0  | 24500 | 1.3132          | 41.4247  | 54.7541    |
| 0.6347        | 99.0  | 24750 | 1.3124          | 41.2563  | 54.7445    |
| 0.6339        | 100.0 | 25000 | 1.3174          | 41.4052  | 54.7558    |


### 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-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-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: 1.3174
* Mean Acc: 41.4052
* Median Acc: 54.7558


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: linear
* num\_epochs: 100.0


### 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: linear
* num\_epochs: 100.0 ### 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: linear
* num\_epochs: 100.0 ### 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: linear
* num\_epochs: 100.0### Training results### Framework versions


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