---
language:
- en
license: mit
base_model: distil-whisper/distil-small.en
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: Distil Whisper Small finetuned on PolyAI Minds14 English US.
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Speech Transcription in English from e-banking domain.
      type: PolyAI/minds14
      config: en-US
      split: train
      args: en-US
    metrics:
    - name: Wer
      type: wer
      value: 0.3318442884492661
---

<!-- 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 Small finetuned on PolyAI Minds14 English US.

This model is a fine-tuned version of [distil-whisper/distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) on the Speech Transcription in English from e-banking domain. dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0182
- Wer Ortho: 0.3371
- Wer: 0.3318

## 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: 1e-05
- 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_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.2325        | 3.57  | 100  | 0.6222          | 0.3557    | 0.3472 |
| 0.0196        | 7.14  | 200  | 0.8475          | 0.3757    | 0.3689 |
| 0.0014        | 10.71 | 300  | 0.9729          | 0.3630    | 0.3555 |
| 0.0006        | 14.29 | 400  | 1.0182          | 0.3371    | 0.3318 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0