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# Whisper Small spanish - ROGRANMAR

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the minds dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 100.0

## 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: 0.1
- train_batch_size: 64
- 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: 1
- training_steps: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer       |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log        | 5.0   | 10   | 141.9978        | 1248.1793 |
| No log        | 10.0  | 20   | nan             | 100.0     |
| 77.0413       | 15.0  | 30   | nan             | 100.0     |
| 77.0413       | 20.0  | 40   | nan             | 100.0     |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"language": ["es"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small spanish - ROGRANMAR", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer es license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #es #license-apache-2.0 #endpoints_compatible #region-us 

Whisper Small spanish - ROGRANMAR
=================================


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


* Loss: nan
* Wer: 100.0


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: 0.1
* train\_batch\_size: 64
* 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: 1
* training\_steps: 40
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.1
* train\_batch\_size: 64
* 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: 1
* training\_steps: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #es #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.1
* train\_batch\_size: 64
* 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: 1
* training\_steps: 40
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3