---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-large-es-cv11-2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: es
      split: validation[:1000]
      args: es
    metrics:
    - name: Wer
      type: wer
      value: 3.7010962486171173
---

<!-- 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. -->

# whisper-large-es-cv11-2

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1320
- Wer: 3.7011
- Cer: 1.0555

## 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-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.1837        | 0.32  | 1000 | 0.1669          | 4.2442 | 1.2488 |
| 0.1343        | 0.64  | 2000 | 0.1444          | 4.0833 | 1.2084 |
| 0.1312        | 0.96  | 3000 | 0.1362          | 3.9324 | 1.1933 |
| 0.1206        | 1.28  | 4000 | 0.1333          | 3.8520 | 1.1748 |
| 0.1143        | 1.6   | 5000 | 0.1321          | 3.6508 | 1.0572 |
| 0.1202        | 1.92  | 6000 | 0.1291          | 3.8017 | 1.1311 |
| 0.0856        | 2.24  | 7000 | 0.1325          | 3.7011 | 1.0841 |
| 0.1005        | 2.56  | 8000 | 0.1320          | 3.7011 | 1.0555 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2