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# Whisper Large SEIN - COES SEIN

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the SEIN COES dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2948
- Wer: 70.8908

## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.3416        | 5.06  | 100  | 1.2948          | 70.8908 |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

{"language": ["es"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large", "model-index": [{"name": "Whisper Large SEIN - COES SEIN", "results": []}]}
transformers pytorch whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer es base_model:openai/whisper-large license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #es #base_model-openai/whisper-large #license-apache-2.0 #endpoints_compatible #region-us 

Whisper Large SEIN - COES SEIN
==============================


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


* Loss: 1.2948
* Wer: 70.8908


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: 1
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 100


### Training results



### Framework versions


* Transformers 4.34.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 100 ### Training results ### Framework versions


* Transformers 4.34.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #es #base_model-openai/whisper-large #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 100 ### Training results ### Framework versions


* Transformers 4.34.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.14.5
* Tokenizers 0.13.3