---
license: apache-2.0
tags:
- generated_from_trainer
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
base_model: openai/whisper-small
metrics:
- wer
model-index:
- name: names-whisper-en-spectrogram-vanilla
  results: []
pipeline_tag: automatic-speech-recognition
language:
- en
library_name: transformers
---

<!-- 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. -->

# names-whisper-en-spectrogram-vanilla

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0402
- Ner percent: 97.9270
- Wer: 1.0079

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Ner percent | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:-----------:|:------:|
| 0.0037        | 5.0251  | 1000 | 0.0355          | 98.5904     | 0.9814 |
| 0.0006        | 10.0503 | 2000 | 0.0369          | 97.6783     | 0.9847 |
| 0.0003        | 15.0754 | 3000 | 0.0386          | 97.6783     | 0.9947 |
| 0.0002        | 20.1005 | 4000 | 0.0397          | 97.9270     | 1.0079 |
| 0.0002        | 25.1256 | 5000 | 0.0402          | 97.9270     | 1.0079 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1