---
base_model: openai/whisper-tiny
language:
- it
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Tiny Italian Combine 5k - Chee Li
  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 Tiny Italian Combine 5k - Chee Li

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4933
- Wer: 52.2594

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5398        | 0.0849 | 1000 | 0.6209          | 60.9740 |
| 0.4894        | 0.1699 | 2000 | 0.5541          | 56.0544 |
| 0.4558        | 0.2548 | 3000 | 0.5213          | 54.6387 |
| 0.4267        | 0.3398 | 4000 | 0.5010          | 52.4281 |
| 0.4225        | 0.4247 | 5000 | 0.4933          | 52.2594 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1