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---
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
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