---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- librispeech_asr
metrics:
- wer
model-index:
- name: Whisper-Small En-10h
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: librispeech
      type: librispeech_asr
      config: default
      split: None
      args: 'config: en, split: test-clean'
    metrics:
    - name: Wer
      type: wer
      value: 3.9809209319390937
---

<!-- 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 En-10h

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1307
- Wer: 3.9809

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 300
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.525         | 0.5556 | 100  | 0.7431          | 3.4571 |
| 0.382         | 1.1111 | 200  | 0.5645          | 3.4836 |
| 0.1704        | 1.6667 | 300  | 0.2111          | 4.0237 |
| 0.0953        | 2.2222 | 400  | 0.1527          | 4.1114 |
| 0.0904        | 2.7778 | 500  | 0.1404          | 4.0400 |
| 0.0784        | 3.3333 | 600  | 0.1355          | 4.0482 |
| 0.0793        | 3.8889 | 700  | 0.1331          | 3.9768 |
| 0.0776        | 4.4444 | 800  | 0.1318          | 3.9646 |
| 0.0629        | 5.0    | 900  | 0.1310          | 3.9830 |
| 0.0746        | 5.5556 | 1000 | 0.1307          | 3.9809 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1