---
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ./600
  results: []
---

<!-- 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. -->

# ./600

This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the 600 SF 1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6509
- Wer Ortho: 30.9402
- Wer: 20.0933

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 800
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
| 1.6008        | 2.6667  | 100  | 1.0986          | 41.0350   | 29.9605 |
| 0.8522        | 5.3333  | 200  | 0.7925          | 32.0335   | 21.0621 |
| 0.6516        | 8.0     | 300  | 0.7207          | 30.5029   | 19.9856 |
| 0.5337        | 10.6667 | 400  | 0.6885          | 30.5758   | 20.3803 |
| 0.4489        | 13.3333 | 500  | 0.6709          | 31.0496   | 20.3086 |
| 0.4003        | 16.0    | 600  | 0.6577          | 31.0496   | 20.2727 |
| 0.3588        | 18.6667 | 700  | 0.6533          | 31.0496   | 20.0933 |
| 0.3499        | 21.3333 | 800  | 0.6509          | 30.9402   | 20.0933 |


### Framework versions

- Transformers 4.44.0
- Pytorch 1.13.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1