---
base_model: openai/whisper-large-v3
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-genbed-f-model
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: genbed
      type: genbed
      config: en
      split: test
    metrics:
    - type: wer
      value: 48.07
      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. -->

# whisper-large-v3-genbed-f-model

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5346
- Wer: 33.8051

## 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: 1.75e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.0784        | 0.6605 | 250  | 0.5140          | 48.6274 |
| 0.4405        | 1.3210 | 500  | 0.4665          | 40.7746 |
| 0.3641        | 1.9815 | 750  | 0.4253          | 37.1462 |
| 0.215         | 2.6420 | 1000 | 0.4413          | 35.1990 |
| 0.1871        | 3.3025 | 1250 | 0.4725          | 37.4548 |
| 0.1425        | 3.9630 | 1500 | 0.4407          | 34.2520 |
| 0.0918        | 4.6235 | 1750 | 0.4618          | 33.9860 |
| 0.0821        | 5.2840 | 2000 | 0.4980          | 33.8689 |
| 0.0665        | 5.9445 | 2250 | 0.5042          | 32.3367 |
| 0.048         | 6.6050 | 2500 | 0.4927          | 33.9860 |
| 0.0441        | 7.2655 | 2750 | 0.5449          | 32.0919 |
| 0.0387        | 7.9260 | 3000 | 0.5235          | 31.6876 |
| 0.0307        | 8.5865 | 3250 | 0.5227          | 31.7408 |
| 0.0282        | 9.2470 | 3500 | 0.5682          | 32.3792 |
| 0.0288        | 9.9075 | 3750 | 0.5346          | 33.8051 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1