---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- librispeech
metrics:
- wer
model-index:
- name: Whisper Tiny English - Francesco Bonzi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: LibriSpeech ASR
      type: librispeech
      config: clean
      split: None
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 6.599969567863664
---

<!-- 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 Tiny English - Francesco Bonzi

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the LibriSpeech ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1858
- Wer: 6.6000

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1884        | 0.56  | 1000 | 0.2044          | 7.2257 |
| 0.1119        | 1.12  | 2000 | 0.1911          | 6.8510 |
| 0.1203        | 1.68  | 3000 | 0.1873          | 6.6038 |
| 0.0832        | 2.24  | 4000 | 0.1858          | 6.6000 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.15.2