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# Whisper Small Swahili - Badili

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4329
- Wer: 98.4012

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3563        | 0.35  | 1000  | 0.4938          | 100.5715 |
| 0.2853        | 0.69  | 2000  | 0.4143          | 100.7007 |
| 0.1612        | 1.04  | 3000  | 0.3910          | 100.9748 |
| 0.1399        | 1.38  | 4000  | 0.3762          | 98.4989  |
| 0.1657        | 1.73  | 5000  | 0.3700          | 90.3357  |
| 0.0818        | 2.08  | 6000  | 0.3775          | 98.0493  |
| 0.0749        | 2.42  | 7000  | 0.3768          | 97.9936  |
| 0.0637        | 2.77  | 8000  | 0.3822          | 92.9440  |
| 0.0355        | 3.11  | 9000  | 0.4036          | 93.8979  |
| 0.0299        | 3.46  | 10000 | 0.4141          | 97.9695  |
| 0.0277        | 3.8   | 11000 | 0.4175          | 98.2961  |
| 0.0147        | 4.15  | 12000 | 0.4329          | 98.4012  |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["sw"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Swahili - Badili", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 13.0", "type": "mozilla-foundation/common_voice_13_0", "config": "sw", "split": "test", "args": "config: sw, split: test"}, "metrics": [{"type": "wer", "value": 98.40119332745073, "name": "Wer"}]}]}]}
transformers pytorch whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer sw dataset:mozilla-foundation/common_voice_13_0 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #sw #dataset-mozilla-foundation/common_voice_13_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Whisper Small Swahili - Badili
==============================


This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4329
* Wer: 98.4012


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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 12000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 12000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #sw #dataset-mozilla-foundation/common_voice_13_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 12000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3