Hugging Face's logo

Hungarians
/
whisper-tiny-cv16-hu

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hungarian
whisper
hf-asr-leaderboard
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
1
whisper-tiny-cv16-hu / README.md
sarpba's picture
sarpba
Update README.md
c2cc01b
preview code
|
raw history blame contribute delete
No virus
2.89 kB
metadata
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 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: 32.2247
            verified: true

Whisper Tiny Hungarian

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16 dataset of Mozilla Foundation. It achieves the following results on the evaluation set:

  • Loss: 0.3628
  • Wer Ortho: 34.7985
  • Wer: 32.2247

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: 3.75e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • 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: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.7288 0.17 500 0.7093 59.8298 57.4443
0.5483 0.33 1000 0.5648 52.3541 49.3122
0.4647 0.5 1500 0.4912 46.1159 42.9533
0.3925 0.67 2000 0.4463 42.8674 39.9838
0.3682 0.84 2500 0.4258 41.1739 38.0487
0.3219 1.0 3000 0.3932 37.5828 34.7286
0.2638 1.17 3500 0.3909 37.8060 35.0311
0.2507 1.34 4000 0.3881 36.7856 34.1199
0.2483 1.51 4500 0.3737 35.5778 32.9881
0.2444 1.67 5000 0.3628 34.7985 32.2247

Framework versions

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