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 Hu v2
    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: 15.7367
            verified: true

Whisper Tiny Hu v2

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.1930
Wer Ortho: 17.3040
Wer: 15.7367

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: 4e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 15000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.5487	0.33	1000	0.5970	55.5492	52.2206
0.3922	0.67	2000	0.4419	43.1109	39.9911
0.3242	1.0	3000	0.3662	37.2727	34.2040
0.2517	1.34	4000	0.3329	33.7890	30.8746
0.2455	1.67	5000	0.2925	30.6185	28.0196
0.1398	2.01	6000	0.2600	27.1709	24.5983
0.1421	2.34	7000	0.2491	26.1291	23.6347
0.1578	2.68	8000	0.2342	24.4761	22.0783
0.0732	3.01	9000	0.2163	22.1245	19.8547
0.0941	3.35	10000	0.2143	22.2058	19.8399
0.0936	3.68	11000	0.2094	20.5980	18.7756
0.0489	4.02	12000	0.2027	18.9630	17.2665
0.0548	4.35	13000	0.1981	18.4933	16.5491
0.0585	4.69	14000	0.1953	17.7195	15.7693
0.0356	5.02	15000	0.1930	17.3040	15.7367

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0