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# Whisper Medium Bambara Fieldwork

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0725
- Wer: 157.6004

## 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: 2
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 13532
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6342        | 1.03  | 1000  | 2.5810          | 159.5293 |
| 0.2873        | 3.03  | 2000  | 2.9513          | 159.1140 |
| 0.1461        | 5.02  | 3000  | 3.6833          | 158.3941 |
| 0.049         | 7.02  | 4000  | 4.0725          | 157.6004 |
| 0.0218        | 9.01  | 5000  | 4.2531          | 158.3664 |
| 0.0071        | 11.0  | 6000  | 4.5944          | 157.9972 |
| 0.0057        | 12.04 | 7000  | 4.6659          | 161.4952 |
| 0.0061        | 14.03 | 8000  | 4.9162          | 161.0614 |
| 0.0042        | 16.03 | 9000  | 5.0205          | 158.9848 |
| 0.0007        | 18.02 | 10000 | 5.1463          | 159.1970 |
| 0.0015        | 20.01 | 11000 | 5.2150          | 159.0401 |
| 0.0001        | 22.01 | 12000 | 5.3008          | 159.5478 |
| 0.0001        | 24.0  | 13000 | 5.3800          | 159.0309 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["wer"], "model-index": [{"name": "Whisper Medium Bambara Fieldwork", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "wer", "value": 157.60036917397323, "name": "Wer"}]}]}]}
transformers pytorch whisper automatic-speech-recognition whisper-event generated_from_trainer dataset:audiofolder license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Medium Bambara Fieldwork
================================


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


* Loss: 4.0725
* Wer: 157.6004


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: 2
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 13532
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 13532
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 13532
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 13532
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2