---
language:
- it
license: apache-2.0
tags:
- generated_from_trainer
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-it
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 it
      type: mozilla-foundation/common_voice_11_0
      config: it
      split: test
      args: it
    metrics:
    - name: Wer
      type: wer
      value: 11.72
---

<!-- 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-it

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: 0.1919
- Wer: 11.72

## Model description

More information needed

## Intended uses & limitations

I have left this model here. BUt the "small3-it", produced later, has better performance.

## 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: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1441        | 1.68  | 1000 | 0.1912          | 0.1256 |
| 0.0653        | 3.36  | 2000 | 0.1845          | 0.1182 |
| 0.0374        | 5.03  | 3000 | 0.1919          | 0.1172 |
| 0.0238        | 6.71  | 4000 | 0.2069          | 0.1202 |
| 0.0162        | 8.39  | 5000 | 0.2184          | 0.1223 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2