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# whisper-speech-small

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Google speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0021
- Wer: 0.3861


### 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: 250
- training_steps: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0095        | 0.2   | 250  | 0.0042          | 0.7239 |
| 0.0068        | 0.4   | 500  | 0.0051          | 1.0135 |
| 0.0045        | 0.6   | 750  | 0.0021          | 0.3861 |
| 0.0056        | 0.8   | 1000 | 0.0018          | 1.5927 |
| 0.0021        | 1.0   | 1250 | 0.0023          | 8.8803 |
| 0.0081        | 1.2   | 1500 | 0.0033          | 2.0270 |
| 0.0056        | 1.4   | 1750 | 0.0023          | 6.1293 |
| 0.0028        | 1.6   | 2000 | 0.0017          | 0.8687 |
| 0.0064        | 1.81  | 2250 | 0.0011          | 0.8687 |
| 0.0005        | 2.01  | 2500 | 0.0014          | 2.0270 |
| 0.0015        | 2.21  | 2750 | 0.0013          | 1.4961 |
| 0.0012        | 2.41  | 3000 | 0.0014          | 1.8822 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-speech-small", "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-speech-small
====================


This model is a fine-tuned version of openai/whisper-small on the Google speech dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0021
* Wer: 0.3861


### 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: 250
* training\_steps: 3000


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 250
* training\_steps: 3000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 250
* training\_steps: 3000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3