
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper-base-oshiwambo-speech

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on [meyabase/crowd-oshiwambo-speech-greetings](https://huggingface.co/datasets/meyabase/crowd-oshiwambo-speech-greetings) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0834
- Wer: 80.9524
- Cer: 58.9623
- Word Acc: 82.2917
- Sent Acc: 54.2857
- Precision: 0.5097
- Recall: 0.7524
- F1 Score: 0.6077

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|:--------:|:--------:|:---------:|:------:|:--------:|
| 0.0099        | 117.65  | 1000  | 0.0777          | 46.6667 | 31.6038 | 69.1358  | 11.4286  | 0.6914    | 0.5333 | 0.6022   |
| 0.0105        | 235.29  | 2000  | 0.0806          | 47.6190 | 33.2547 | 71.4286  | 11.4286  | 0.7143    | 0.5238 | 0.6044   |
| 0.0106        | 352.94  | 3000  | 0.0795          | 44.7619 | 34.6698 | 76.3158  | 25.7143  | 0.7632    | 0.5524 | 0.6409   |
| 0.0092        | 470.59  | 4000  | 0.0793          | 42.8571 | 35.8491 | 81.0811  | 31.4286  | 0.8108    | 0.5714 | 0.6704   |
| 0.0099        | 588.24  | 5000  | 0.0806          | 92.3810 | 69.8113 | 81.7073  | 42.8571  | 0.4752    | 0.6381 | 0.5447   |
| 0.0094        | 705.88  | 6000  | 0.0800          | 28.5714 | 22.1698 | 83.3333  | 48.5714  | 0.8333    | 0.7143 | 0.7692   |
| 0.0093        | 823.53  | 7000  | 0.0796          | 24.7619 | 16.2736 | 82.2917  | 54.2857  | 0.8229    | 0.7524 | 0.7861   |
| 0.0095        | 941.18  | 8000  | 0.0815          | 82.8571 | 59.1981 | 80.2083  | 51.4286  | 0.4968    | 0.7333 | 0.5923   |
| 0.01          | 1058.82 | 9000  | 0.0815          | 24.7619 | 16.5094 | 82.2917  | 54.2857  | 0.8229    | 0.7524 | 0.7861   |
| 0.0088        | 1176.47 | 10000 | 0.0834          | 80.9524 | 58.9623 | 82.2917  | 54.2857  | 0.5097    | 0.7524 | 0.6077   |


### Framework versions

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

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

whisper-base-oshiwambo-speech
=============================


This model is a fine-tuned version of openai/whisper-base on meyabase/crowd-oshiwambo-speech-greetings dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0834
* Wer: 80.9524
* Cer: 58.9623
* Word Acc: 82.2917
* Sent Acc: 54.2857
* Precision: 0.5097
* Recall: 0.7524
* F1 Score: 0.6077


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 #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