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GIanlucaRub
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whisper-tiny-it-5

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Italian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results
Inference Endpoints
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whisper-tiny-it-5
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---
language:
- it
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny it 5
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: it
      split: test[:10%]
      args: 'config: it, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 41.271491957848035
---

# Whisper Tiny it 5

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.760934	
- Wer: 41.271492

## Model description

This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 and the learning rate has been set to 1e-4 in the hyperparameter tuning process and it improved the performance on the evaluation set.

## Intended uses & limitations

The model is available through its [HuggingFace web app](https://huggingface.co/spaces/GIanlucaRub/whisper-it)

## Training and evaluation data

Data used for training is the initial 10% of train and validation of [Italian Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/it/train) 11.0 from Mozilla Foundation.
The dataset used for evaluation is the initial 10% of test of Italian Common Voice.


## Training procedure

After loading the pre trained model, it has been trained on the dataset.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-04
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7015        | 0.95  | 1000 | 0.9463          | 64.4689 |
| 0.3579        | 1.91  | 2000 | 0.8363          | 51.7471 |
| 0.1388        | 2.86  | 3000 | 0.7766          | 43.6425 |
| 0.0403        | 3.82  | 4000 | 0.7609          | 41.2715 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2