---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
base_model: juancopi81/whisper-medium-es-train-valid
model-index:
- name: juancopi81/whisper-medium-es-train-valid
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: es
      split: test
      args: es
    metrics:
    - type: wer
      value: 6.15482563276337
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# juancopi81/whisper-medium-es-train-valid

This model is a fine-tuned version of [juancopi81/whisper-medium-es-train-valid](https://huggingface.co/juancopi81/whisper-medium-es-train-valid) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2227
- Wer: 6.1548

Using the script provided in the Whisper Sprint (Dec. 2022) the models achieves these results on the evaluation sets (WER):
- google/fleurs: 6.94
- mozilla-foundation/common_voice_11_0: XXXX

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0539        | 1.01  | 1000 | 0.2100          | 6.4465 |
| 0.0211        | 2.01  | 2000 | 0.2286          | 6.5082 |
| 0.0088        | 3.02  | 3000 | 0.2418          | 6.3848 |
| 0.0205        | 4.02  | 4000 | 0.2288          | 6.6603 |
| 0.1031        | 5.03  | 5000 | 0.2227          | 6.1548 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2