transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer en dataset:PolyAI/minds14 base_model:openai/whisper-tiny license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: fine-tuned-Whisper-Tiny-en-US
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: minds14 - en(US)
      type: PolyAI/minds14
      config: en-US
      split: train
      args: 'config: en-US, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.3247210804462713
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# fine-tuned-Whisper-Tiny-en-US

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the minds14 - en(US) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7793
- Wer Ortho: 0.3222
- Wer: 0.3247

## 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: 400
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.0014        | 17.24  | 500  | 0.5901          | 0.3210    | 0.3188 |
| 0.0003        | 34.48  | 1000 | 0.6579          | 0.3124    | 0.3142 |
| 0.0002        | 51.72  | 1500 | 0.6892          | 0.3143    | 0.3165 |
| 0.0001        | 68.97  | 2000 | 0.7129          | 0.3167    | 0.3194 |
| 0.0001        | 86.21  | 2500 | 0.7330          | 0.3179    | 0.3206 |
| 0.0           | 103.45 | 3000 | 0.7511          | 0.3191    | 0.3218 |
| 0.0           | 120.69 | 3500 | 0.7653          | 0.3179    | 0.3206 |
| 0.0           | 137.93 | 4000 | 0.7793          | 0.3222    | 0.3247 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
