---
library_name: transformers
language:
- en
license: cc-by-nc-sa-4.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- sage-bergerson/ume_erj_processed
metrics:
- wer
model-index:
- name: Whisper Large UME-ERJ V2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: UME-ERJ
      type: sage-bergerson/ume_erj_processed
      args: 'config: en, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 0.049601737871107894
---

<!-- 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 UME-ERJ V2

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the UME-ERJ dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0568
- Wer: 0.0496

## 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: 5e-06
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.7362        | 0.1143 | 200  | 0.1780          | 0.1274 |
| 0.167         | 0.2286 | 400  | 0.1095          | 0.0852 |
| 0.1248        | 0.3429 | 600  | 0.0959          | 0.0776 |
| 0.0999        | 0.4571 | 800  | 0.0833          | 0.0669 |
| 0.0919        | 0.5714 | 1000 | 0.0821          | 0.0703 |
| 0.0839        | 0.6857 | 1200 | 0.0703          | 0.0623 |
| 0.0749        | 0.8    | 1400 | 0.0686          | 0.0611 |
| 0.0747        | 0.9143 | 1600 | 0.0689          | 0.0597 |
| 0.0624        | 1.0286 | 1800 | 0.0646          | 0.0586 |
| 0.0516        | 1.1429 | 2000 | 0.0638          | 0.0553 |
| 0.0497        | 1.2571 | 2200 | 0.0593          | 0.0521 |
| 0.0462        | 1.3714 | 2400 | 0.0634          | 0.0556 |
| 0.0454        | 1.4857 | 2600 | 0.0588          | 0.0516 |
| 0.0455        | 1.6    | 2800 | 0.0596          | 0.0540 |
| 0.0432        | 1.7143 | 3000 | 0.0622          | 0.0526 |
| 0.0401        | 1.8286 | 3200 | 0.0572          | 0.0524 |
| 0.0437        | 1.9429 | 3400 | 0.0569          | 0.0529 |
| 0.0344        | 2.0571 | 3600 | 0.0568          | 0.0496 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1