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# wav2vec2-tiny-1-cnn-fp16-demo

This model is a fine-tuned version of [yenpolin/wav2vec2-tiny-1-cnn](https://huggingface.co/yenpolin/wav2vec2-tiny-1-cnn) on the DNA_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2063
- Mean Acc: 2.4426
- Median Acc: 0.0

## 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: 100
- eval_batch_size: 200
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:----------:|
| 0.5146        | 1.0   | 1000   | 0.3673          | 0.0      | 0.0        |
| 0.3785        | 2.0   | 2000   | 0.3447          | 0.0      | 0.0        |
| 0.364         | 3.0   | 3000   | 0.3317          | 0.0      | 0.0        |
| 0.3523        | 4.0   | 4000   | 0.3249          | 0.0027   | 0.0        |
| 0.3447        | 5.0   | 5000   | 0.3181          | 0.0027   | 0.0        |
| 0.3386        | 6.0   | 6000   | 0.3123          | 0.0027   | 0.0        |
| 0.3334        | 7.0   | 7000   | 0.3085          | 0.0027   | 0.0        |
| 0.3294        | 8.0   | 8000   | 0.3046          | 0.0      | 0.0        |
| 0.326         | 9.0   | 9000   | 0.3027          | 0.0      | 0.0        |
| 0.323         | 10.0  | 10000  | 0.2978          | 0.0      | 0.0        |
| 0.32          | 11.0  | 11000  | 0.2963          | 0.0027   | 0.0        |
| 0.3061        | 12.0  | 12000  | 0.2616          | 0.0465   | 0.0        |
| 0.2818        | 13.0  | 13000  | 0.2575          | 0.0486   | 0.0        |
| 0.2768        | 14.0  | 14000  | 0.2517          | 0.1075   | 0.0        |
| 0.2731        | 15.0  | 15000  | 0.2492          | 0.0397   | 0.0        |
| 0.2697        | 16.0  | 16000  | 0.2448          | 0.0648   | 0.0        |
| 0.2658        | 17.0  | 17000  | 0.2444          | 0.1171   | 0.0        |
| 0.2619        | 18.0  | 18000  | 0.2385          | 0.1305   | 0.0        |
| 0.2585        | 19.0  | 19000  | 0.2389          | 0.0960   | 0.0        |
| 0.2546        | 20.0  | 20000  | 0.2326          | 0.0909   | 0.0        |
| 0.2505        | 21.0  | 21000  | 0.2287          | 0.0635   | 0.0        |
| 0.2462        | 22.0  | 22000  | 0.2240          | 0.1070   | 0.0        |
| 0.2408        | 23.0  | 23000  | 0.2190          | 0.1429   | 0.0        |
| 0.2334        | 24.0  | 24000  | 0.2109          | 0.7212   | 0.0        |
| 0.225         | 25.0  | 25000  | 0.2027          | 0.7305   | 0.0        |
| 0.2178        | 26.0  | 26000  | 0.1980          | 0.8186   | 0.0        |
| 0.2115        | 27.0  | 27000  | 0.1937          | 0.9743   | 0.0        |
| 0.2065        | 28.0  | 28000  | 0.1892          | 0.8266   | 0.0        |
| 0.2026        | 29.0  | 29000  | 0.1890          | 0.1615   | 0.0        |
| 0.1987        | 30.0  | 30000  | 0.1836          | 1.0021   | 0.0        |
| 0.1953        | 31.0  | 31000  | 0.1830          | 0.8009   | 0.0        |
| 0.1921        | 32.0  | 32000  | 0.1821          | 1.2837   | 0.0        |
| 0.1893        | 33.0  | 33000  | 0.1819          | 0.5987   | 0.0        |
| 0.1865        | 34.0  | 34000  | 0.1835          | 0.9360   | 0.0        |
| 0.1835        | 35.0  | 35000  | 0.1796          | 1.3452   | 0.0        |
| 0.1808        | 36.0  | 36000  | 0.1816          | 1.4669   | 0.0        |
| 0.1779        | 37.0  | 37000  | 0.1806          | 2.4269   | 0.0        |
| 0.1755        | 38.0  | 38000  | 0.1787          | 0.7843   | 0.0        |
| 0.1726        | 39.0  | 39000  | 0.1807          | 1.8650   | 0.0        |
| 0.1699        | 40.0  | 40000  | 0.1811          | 2.1893   | 0.0        |
| 0.167         | 41.0  | 41000  | 0.1799          | 1.7285   | 0.0        |
| 0.1644        | 42.0  | 42000  | 0.1792          | 1.5862   | 0.0        |
| 0.1617        | 43.0  | 43000  | 0.1785          | 1.5165   | 0.0        |
| 0.159         | 44.0  | 44000  | 0.1806          | 1.1542   | 0.0        |
| 0.1563        | 45.0  | 45000  | 0.1804          | 1.8334   | 0.0        |
| 0.1539        | 46.0  | 46000  | 0.1830          | 2.1450   | 0.0        |
| 0.1515        | 47.0  | 47000  | 0.1835          | 2.2905   | 0.0        |
| 0.1489        | 48.0  | 48000  | 0.1821          | 2.4879   | 0.0        |
| 0.1465        | 49.0  | 49000  | 0.1806          | 1.6113   | 0.0        |
| 0.1441        | 50.0  | 50000  | 0.1857          | 1.7132   | 0.0        |
| 0.1418        | 51.0  | 51000  | 0.1847          | 1.2830   | 0.0        |
| 0.1394        | 52.0  | 52000  | 0.1862          | 1.8548   | 0.0        |
| 0.137         | 53.0  | 53000  | 0.1815          | 2.1585   | 0.0        |
| 0.1345        | 54.0  | 54000  | 0.1896          | 1.1918   | 0.0        |
| 0.1325        | 55.0  | 55000  | 0.1892          | 1.5508   | 0.0        |
| 0.1304        | 56.0  | 56000  | 0.1879          | 1.5180   | 0.0        |
| 0.1282        | 57.0  | 57000  | 0.1868          | 0.8048   | 0.0        |
| 0.126         | 58.0  | 58000  | 0.1906          | 1.6521   | 0.0        |
| 0.1243        | 59.0  | 59000  | 0.1879          | 1.3255   | 0.0        |
| 0.1222        | 60.0  | 60000  | 0.1884          | 1.2970   | 0.0        |
| 0.1202        | 61.0  | 61000  | 0.1905          | 1.5370   | 0.0        |
| 0.1184        | 62.0  | 62000  | 0.1936          | 1.7408   | 0.0        |
| 0.1167        | 63.0  | 63000  | 0.1922          | 1.5556   | 0.0        |
| 0.1149        | 64.0  | 64000  | 0.1960          | 1.5176   | 0.0        |
| 0.1133        | 65.0  | 65000  | 0.1966          | 1.8577   | 0.0        |
| 0.1117        | 66.0  | 66000  | 0.1942          | 2.1886   | 0.0        |
| 0.1102        | 67.0  | 67000  | 0.1961          | 1.4547   | 0.0        |
| 0.1087        | 68.0  | 68000  | 0.1965          | 1.5482   | 0.0        |
| 0.1072        | 69.0  | 69000  | 0.1965          | 1.5644   | 0.0        |
| 0.1058        | 70.0  | 70000  | 0.1982          | 1.5436   | 0.0        |
| 0.1045        | 71.0  | 71000  | 0.1956          | 2.2319   | 0.0        |
| 0.1033        | 72.0  | 72000  | 0.2037          | 2.5393   | 0.0        |
| 0.1019        | 73.0  | 73000  | 0.1977          | 1.9089   | 0.0        |
| 0.1008        | 74.0  | 74000  | 0.1983          | 1.6957   | 0.0        |
| 0.0997        | 75.0  | 75000  | 0.1980          | 3.0164   | 0.0        |
| 0.0986        | 76.0  | 76000  | 0.2004          | 1.8043   | 0.0        |
| 0.0976        | 77.0  | 77000  | 0.2025          | 1.8234   | 0.0        |
| 0.0966        | 78.0  | 78000  | 0.2006          | 2.1719   | 0.0        |
| 0.0957        | 79.0  | 79000  | 0.1991          | 1.8594   | 0.0        |
| 0.0948        | 80.0  | 80000  | 0.2037          | 1.9660   | 0.0        |
| 0.094         | 81.0  | 81000  | 0.2000          | 2.2630   | 0.0        |
| 0.0932        | 82.0  | 82000  | 0.2068          | 2.3164   | 0.0        |
| 0.0925        | 83.0  | 83000  | 0.2004          | 2.0110   | 0.0        |
| 0.0918        | 84.0  | 84000  | 0.2034          | 2.4419   | 0.0        |
| 0.0912        | 85.0  | 85000  | 0.2031          | 2.0823   | 0.0        |
| 0.0906        | 86.0  | 86000  | 0.2039          | 2.3955   | 0.0        |
| 0.0901        | 87.0  | 87000  | 0.2042          | 2.4907   | 0.0        |
| 0.0897        | 88.0  | 88000  | 0.2052          | 2.3399   | 0.0        |
| 0.0892        | 89.0  | 89000  | 0.2050          | 2.3214   | 0.0        |
| 0.0888        | 90.0  | 90000  | 0.2052          | 2.1339   | 0.0        |
| 0.0884        | 91.0  | 91000  | 0.2050          | 2.3432   | 0.0        |
| 0.0881        | 92.0  | 92000  | 0.2052          | 2.3161   | 0.0        |
| 0.0878        | 93.0  | 93000  | 0.2050          | 2.4258   | 0.0        |
| 0.0876        | 94.0  | 94000  | 0.2051          | 2.2742   | 0.0        |
| 0.0874        | 95.0  | 95000  | 0.2065          | 2.3400   | 0.0        |
| 0.0872        | 96.0  | 96000  | 0.2063          | 2.4099   | 0.0        |
| 0.0871        | 97.0  | 97000  | 0.2060          | 2.4249   | 0.0        |
| 0.087         | 98.0  | 98000  | 0.2064          | 2.4826   | 0.0        |
| 0.0869        | 99.0  | 99000  | 0.2064          | 2.4447   | 0.0        |
| 0.0869        | 100.0 | 100000 | 0.2063          | 2.4426   | 0.0        |


### 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-1-cnn-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-1-cnn-fp16-demo
=============================


This model is a fine-tuned version of yenpolin/wav2vec2-tiny-1-cnn on the DNA\_R9.4.1 - NA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2063
* Mean Acc: 2.4426
* Median Acc: 0.0


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: 100
* eval\_batch\_size: 200
* 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: 100
* eval\_batch\_size: 200
* 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: 100
* eval\_batch\_size: 200
* 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: 100
* eval\_batch\_size: 200
* 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