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# Whisper Base Lingala - BrainTheos

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7265
- Wer: 25.0509

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0081        | 21.0  | 1000 | 0.6218          | 29.8710 |
| 0.0016        | 42.01 | 2000 | 0.6865          | 25.1188 |
| 0.0009        | 63.01 | 3000 | 0.7152          | 24.9151 |
| 0.0007        | 85.0  | 4000 | 0.7265          | 25.0509 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.1.dev0
- Tokenizers 0.13.3

{"language": ["ln"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["google/fleurs"], "metrics": ["wer"], "model-index": [{"name": "Whisper Base Lingala - BrainTheos", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Fleurs", "type": "google/fleurs", "config": "ln_cd", "split": "validation", "args": "ln_cd"}, "metrics": [{"type": "wer", "value": 25.050916496945007, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer ln dataset:google/fleurs license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #ln #dataset-google/fleurs #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Whisper Base Lingala - BrainTheos
=================================


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


* Loss: 0.7265
* Wer: 25.0509


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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.1.dev0
* 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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #ln #dataset-google/fleurs #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.12.1.dev0
* Tokenizers 0.13.3