---
license: mit
base_model: distil-whisper/distil-large-v2
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: distil-whisper-large-v2-pt
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0
      type: mozilla-foundation/common_voice_13_0
      config: pt
      split: test
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 0.11035717806328657
---

<!-- 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. -->

# distil-whisper-large-v2-pt

This model is a fine-tuned version of [distil-whisper/distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) on the mozilla-foundation/common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3028
- Wer Ortho: 0.1649
- Wer: 0.1104

## 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: 7e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 1.6148        | 0.5   | 900  | 0.4448          | 0.2227    | 0.1690 |
| 0.3709        | 0.99  | 1800 | 0.3524          | 0.1927    | 0.1367 |
| 0.2619        | 1.49  | 2700 | 0.3266          | 0.1751    | 0.1213 |
| 0.2143        | 1.98  | 3600 | 0.3085          | 0.1726    | 0.1168 |
| 0.1219        | 2.48  | 4500 | 0.3070          | 0.1639    | 0.1112 |
| 0.1256        | 2.98  | 5400 | 0.3028          | 0.1649    | 0.1104 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1