---
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ./1000
  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. -->

# ./1000

This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the 1000 SF 1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6318
- Wer Ortho: 32.5802
- Wer: 21.4926

## 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: 5e-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     |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:-------:|
| 1.1316        | 1.7699  | 100  | 0.6968          | 29.0816   | 18.8733 |
| 0.4669        | 3.5398  | 200  | 0.5156          | 27.4417   | 17.5816 |
| 0.2075        | 5.3097  | 300  | 0.5303          | 27.6968   | 16.7205 |
| 0.1163        | 7.0796  | 400  | 0.5391          | 28.6443   | 17.8687 |
| 0.0712        | 8.8496  | 500  | 0.5811          | 28.9723   | 17.5816 |
| 0.0518        | 10.6195 | 600  | 0.6104          | 31.8513   | 21.2415 |
| 0.0388        | 12.3894 | 700  | 0.6245          | 32.4344   | 21.4926 |
| 0.034         | 14.1593 | 800  | 0.6318          | 32.5802   | 21.4926 |


### Framework versions

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