---
library_name: transformers
language:
- en
license: cc-by-sa-4.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- sage-bergerson/edacc_processed
metrics:
- wer
model-index:
- name: Whisper Large EdAcc V2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: EdAcc
      type: sage-bergerson/edacc_processed
      args: 'config: en, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 0.5855270257403117
---

<!-- 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 EdAcc V2

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the EdAcc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6378
- Wer: 0.5855

## 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: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.1515        | 0.3247 | 100  | 0.7869          | 0.3055 |
| 0.6272        | 0.6494 | 200  | 0.6171          | 0.4607 |
| 0.5614        | 0.9740 | 300  | 0.5925          | 0.6110 |
| 0.43          | 1.2987 | 400  | 0.5868          | 0.5105 |
| 0.4576        | 1.6234 | 500  | 0.5844          | 0.6095 |
| 0.4727        | 1.9481 | 600  | 0.5784          | 0.6796 |
| 0.3274        | 2.2727 | 700  | 0.6094          | 0.5416 |
| 0.2862        | 2.5974 | 800  | 0.6027          | 0.5609 |
| 0.2908        | 2.9221 | 900  | 0.6107          | 0.4607 |
| 0.2221        | 3.2468 | 1000 | 0.6378          | 0.5855 |


### Framework versions

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