transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer base_model:openai/whisper-small license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-eng
  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. -->

# whisper-small-eng

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: 0.5746
- Wer: 24.4747

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7025        | 0.03  | 100  | 0.6855          | 36.9988 |
| 0.7478        | 0.07  | 200  | 0.8034          | 35.4196 |
| 0.7516        | 0.1   | 300  | 0.7854          | 31.8551 |
| 0.7175        | 0.13  | 400  | 0.7868          | 32.9444 |
| 0.6748        | 0.17  | 500  | 0.7239          | 31.1203 |
| 0.6739        | 0.2   | 600  | 0.7045          | 29.7473 |
| 0.6262        | 0.24  | 700  | 0.6620          | 27.1239 |
| 0.585         | 0.27  | 800  | 0.6254          | 26.6147 |
| 0.5305        | 0.3   | 900  | 0.5877          | 24.6552 |
| 0.5463        | 0.34  | 1000 | 0.5746          | 24.4747 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
