transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer hu dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
language:
- hu
widget:
- example_title: Sample 1
  src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample1.flac
- example_title: Sample 2
  src: https://huggingface.co/datasets/Hungarians/samples/resolve/main/Sample2.flac
metrics:
- wer
pipeline_tag: automatic-speech-recognition
model-index:
- name: Whisper Small Hungarian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.0 - Hungarian
      type: mozilla-foundation/common_voice_16_0
      config: hu
      split: test
      args: hu
    metrics:
    - name: Wer
      type: wer
      value: 18.8314
      verified: true

---

<!-- 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 Hungarian (training in progress)

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16 dataset of Mozilla Foundation.
It achieves the following results on the evaluation set:

Tempolary at step 11000:

- Wer: 8.4969

Unfortunatly the colab disconected, this is the end... :( maybe later continue

My own hungarian language specific compare test result (on CV11):

| Modell                             | WER           | CER             | NORMALISED WER | NORMALISED CER |
|:----------------------------------:|:-------------:|:---------------:|:--------------:|:--------------:|
| openai/whisper-tiny            	 |  112.1        | 51.33           | 108.79         | 49.64          |
| openai/whisper-base            	 |  95.87        | 42.84           | 95.68          | 41.38          |
| openai/whisper-small          	 |  53.65        | 15.89           | 49.8           | 14.63          |
| Hungarians/whisper-tiny-cv16-hu	 |  30.57	     | 8.52  	       | 27.71          | 7.86           |
| Hungarians/whisper-tiny-cv16-hu-v2     |  16.99        | 4.98            | 15.27          | 4.49           | 
| Hungarians/whisper-base-cv16-hu	 |  15.55	     | 4.07 	       | 13.68          | 3.67           |
| Hungarians/whisper-base-cv16-hu-v2     |  12.63        | 3.55            | 11.39          | 3.26           |
| Hungarians/whisper-small-cv16-hu	 |  17.86	     | 4.1 	           | 15.27          | 3.58           |
| sarpba/whisper-small-cv16-v1.5-hu|  9.94	     | 2.41	           | 8.50           | 2.14           |


## 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: 1.25e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 400
- planed training_steps: 15000
- executed steps: 11000 only (colab dc)
- mixed_precision_training: Native AMP

### Training results

| Steps | Training Loss | Validation Loss | Wer Ortho |    Wer    |
|:-----:|:-------------:|:---------------:|:---------:|:---------:|

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0