---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- NbAiLab/NCC_S3
metrics:
- wer
model-index:
- name: Whisper Tiny GPU test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: NbAiLab/NCC_S3
      type: NbAiLab/NCC_S3
      config: 'no'
      split: validation
      args: 'no'
    metrics:
    - name: Wer
      type: wer
      value: 51.37028014616322
---

<!-- 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 Tiny GPU test

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the NbAiLab/NCC_S3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9375
- Wer: 51.3703

## 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: 3e-06
- train_batch_size: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 2.4574        | 0.1   | 200  | 1.4663          | 71.6504 |
| 1.9587        | 0.2   | 400  | 1.2581          | 64.7381 |
| 1.816         | 0.3   | 600  | 1.1672          | 60.9318 |
| 1.7199        | 0.4   | 800  | 1.1006          | 57.6736 |
| 1.6686        | 0.5   | 1000 | 1.0630          | 56.1815 |
| 1.621         | 0.6   | 1200 | 1.0273          | 55.4811 |
| 1.5846        | 0.7   | 1400 | 1.0017          | 53.9890 |
| 1.5482        | 0.8   | 1600 | 0.9773          | 53.0146 |
| 1.521         | 0.9   | 1800 | 0.9575          | 52.1011 |
| 1.4932        | 1.0   | 2000 | 0.9375          | 51.3703 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2