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# la-whisper-small-covost2

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5845
- Sacrebleu: 2090.6716
- Wer: 73.0006

## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|
| 2.6943        | 0.11  | 50   | 2.1667          | 118.2640  | 686.6897 |
| 1.5505        | 0.23  | 100  | 1.6016          | 259.9307  | 165.6116 |
| 1.4093        | 0.34  | 150  | 1.5858          | 496.7335  | 197.0106 |
| 1.3209        | 0.45  | 200  | 1.5648          | 724.2491  | 121.8795 |
| 1.2941        | 0.56  | 250  | 1.5596          | 820.1241  | 161.7159 |
| 1.2078        | 0.68  | 300  | 1.5074          | 1022.0043 | 140.3875 |
| 1.1532        | 0.79  | 350  | 1.4972          | 174.8350  | 610.3716 |
| 1.0167        | 0.9   | 400  | 1.4551          | 1904.0921 | 82.7635  |
| 0.8842        | 1.01  | 450  | 1.4296          | 1883.6113 | 81.3906  |
| 0.5619        | 1.13  | 500  | 1.4333          | 1817.9440 | 84.9312  |
| 0.5523        | 1.24  | 550  | 1.4237          | 1517.1744 | 104.0918 |
| 0.4881        | 1.35  | 600  | 1.4413          | 1650.1807 | 97.2067  |
| 0.471         | 1.46  | 650  | 1.3961          | 1885.0014 | 82.2664  |
| 0.4412        | 1.58  | 700  | 1.3986          | 2145.9786 | 72.0469  |
| 0.4625        | 1.69  | 750  | 1.3885          | 1837.7812 | 87.4472  |
| 0.4195        | 1.8   | 800  | 1.4095          | 1909.2655 | 78.6920  |
| 0.4532        | 1.91  | 850  | 1.3891          | 1925.2238 | 82.0162  |
| 0.3201        | 2.03  | 900  | 1.4415          | 1919.2020 | 80.4437  |
| 0.1955        | 2.14  | 950  | 1.4410          | 1540.5046 | 101.0145 |
| 0.2111        | 2.25  | 1000 | 1.4345          | 1735.9648 | 90.9269  |
| 0.1981        | 2.36  | 1050 | 1.4597          | 1730.3250 | 91.5356  |
| 0.2052        | 2.48  | 1100 | 1.4439          | 2143.3630 | 72.4933  |
| 0.1886        | 2.59  | 1150 | 1.4702          | 1965.5005 | 77.7519  |
| 0.1918        | 2.7   | 1200 | 1.4518          | 2057.4517 | 75.4929  |
| 0.1755        | 2.81  | 1250 | 1.4788          | 1954.2237 | 78.2997  |
| 0.1769        | 2.93  | 1300 | 1.4588          | 1774.1464 | 91.9279  |
| 0.1104        | 3.04  | 1350 | 1.5281          | 1838.1999 | 84.7317  |
| 0.0718        | 3.15  | 1400 | 1.5133          | 2058.0955 | 76.0306  |
| 0.0855        | 3.26  | 1450 | 1.5271          | 1720.1072 | 89.1346  |
| 0.0717        | 3.38  | 1500 | 1.5289          | 2007.5163 | 75.9291  |
| 0.0707        | 3.49  | 1550 | 1.5366          | 2149.6478 | 71.9523  |
| 0.0704        | 3.6   | 1600 | 1.5355          | 2179.5147 | 69.8759  |
| 0.0676        | 3.71  | 1650 | 1.5393          | 2086.2197 | 73.2474  |
| 0.0748        | 3.83  | 1700 | 1.5398          | 1879.1610 | 80.7277  |
| 0.0695        | 3.94  | 1750 | 1.5351          | 2001.8476 | 78.8306  |
| 0.033         | 4.05  | 1800 | 1.5807          | 1892.0435 | 82.2630  |
| 0.0317        | 4.16  | 1850 | 1.5843          | 1967.1172 | 78.7765  |
| 0.0302        | 4.28  | 1900 | 1.5848          | 1969.6753 | 79.1248  |
| 0.0337        | 4.39  | 1950 | 1.5808          | 2062.9546 | 74.1537  |
| 0.0306        | 4.5   | 2000 | 1.5845          | 2090.6716 | 73.0006  |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["sacrebleu", "wer"], "model-index": [{"name": "la-whisper-small-covost2", "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 

la-whisper-small-covost2
========================


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


* Loss: 1.5845
* Sacrebleu: 2090.6716
* Wer: 73.0006


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: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 2000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 2000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2
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: 3e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 2000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.13.2