---
library_name: transformers
language:
- es
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fixie-ai/common_voice_17_0
metrics:
- wer
model-index:
- name: "Whisper Medium CV17 Es 5000 steps with the same training configuration and\
    \ processing of text as 500-steps_proc3-def3 -with filtering 30sec, customised\
    \ optimizer, and processing of text using method 3- ; but with the values for\
    \ some training_args scaled in proportion to 5000 - Mar\xEDa Marr\xF3n"
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: fixie-ai/common_voice_17_0
      args: 'config: es, split:test'
    metrics:
    - name: Wer
      type: wer
      value: 5.149797262084832
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Medium CV17 Es 5000 steps with the same training configuration and processing of text as 500-steps_proc3-def3 -with filtering 30sec, customised optimizer, and processing of text using method 3- ; but with the values for some training_args scaled in proportion to 5000 - María Marrón

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1402
- Wer Ortho: 9.1690
- Wer: 5.1498

## 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: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Wer Ortho |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|
| 0.1806        | 0.5   | 1000 | 0.1703          | 6.2760 | 10.5261   |
| 0.1607        | 1.0   | 2000 | 0.1536          | 5.4669 |  9.6349   |
| 0.1599        | 0.6   | 3000 | 0.1525          | 5.3907 |  9.4987   |
| 0.1468        | 0.8   | 4000 | 0.1448          | 5.1333 |  9.2110   |
| 0.1508        | 1.0   | 5000 | 0.1402          | 5.1498 |  9.1690   |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.9.1+cu128
- Datasets 2.14.4
- Tokenizers 0.21.4