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# whisper-tiny-oshiwambo-speech

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1409
- Wer: 44.7619
- Cer: 30.8962
- Word Acc: 64.4444
- Sent Acc: 2.8571
- Precision: 0.6444
- Recall: 0.5524
- F1 Score: 0.5949

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- 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: 100
- training_steps: 10000

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     | Cer     | Word Acc | Sent Acc | Precision | Recall | F1 Score |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|:--------:|:--------:|:---------:|:------:|:--------:|
| 0.0098        | 117.65  | 1000  | 0.0976          | 37.1429 | 29.0094 | 66.6667  | 8.5714   | 0.6538    | 0.6476 | 0.6507   |
| 0.0105        | 235.29  | 2000  | 0.1061          | 41.9048 | 33.0189 | 63.6364  | 2.8571   | 0.6238    | 0.6    | 0.6117   |
| 0.0105        | 352.94  | 3000  | 0.1134          | 37.1429 | 26.8868 | 66.6667  | 5.7143   | 0.6667    | 0.6286 | 0.6471   |
| 0.0091        | 470.59  | 4000  | 0.1222          | 37.1429 | 25.7075 | 66.6667  | 5.7143   | 0.6667    | 0.6286 | 0.6471   |
| 0.0098        | 588.24  | 5000  | 0.1265          | 40.0    | 28.3019 | 65.625   | 2.8571   | 0.6562    | 0.6    | 0.6269   |
| 0.0094        | 705.88  | 6000  | 0.1314          | 42.8571 | 30.8962 | 64.5161  | 2.8571   | 0.6452    | 0.5714 | 0.6061   |
| 0.0093        | 823.53  | 7000  | 0.1366          | 42.8571 | 29.2453 | 64.5161  | 2.8571   | 0.6452    | 0.5714 | 0.6061   |
| 0.0094        | 941.18  | 8000  | 0.1360          | 45.7143 | 31.8396 | 63.3333  | 0.0      | 0.6333    | 0.5429 | 0.5846   |
| 0.01          | 1058.82 | 9000  | 0.1394          | 44.7619 | 30.8962 | 64.4444  | 2.8571   | 0.6444    | 0.5524 | 0.5949   |
| 0.0087        | 1176.47 | 10000 | 0.1409          | 44.7619 | 30.8962 | 64.4444  | 2.8571   | 0.6444    | 0.5524 | 0.5949   |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer", "precision", "recall"], "model-index": [{"name": "whisper-tiny-oshiwambo-speech", "results": []}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer en license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #en #license-apache-2.0 #endpoints_compatible #region-us 

whisper-tiny-oshiwambo-speech
=============================


This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1409
* Wer: 44.7619
* Cer: 30.8962
* Word Acc: 64.4444
* Sent Acc: 2.8571
* Precision: 0.6444
* Recall: 0.5524
* F1 Score: 0.5949


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 4
* 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: 100
* training\_steps: 10000


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0
* 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: 4
* 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: 100
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #en #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: 4
* 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: 100
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0
* Datasets 2.12.0
* Tokenizers 0.13.3