---
license: apache-2.0
base_model: openai/whisper-tiny
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 Tiny Hu v3
  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: 12.7928
      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 Tiny Hu v3

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2041
- eval_wer_ortho: 13.8474
- eval_wer: 12.7928
- eval_runtime: 1002.1306
- eval_samples_per_second: 4.621
- eval_steps_per_second: 0.578
- epoch: 13.39
- step: 20000

## Model description

Hungarian language trained modell. Faster-whisper versions in int8, fp16 and ft32 floders.


## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0