metadata
language:
- hu
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper medium Hungarian El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: hu
split: test
metrics:
- name: Wer
type: wer
value: 18.642158316039133
Whisper medium Hungarian El Greco
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0,google/fleurs hu,hu_hu dataset. It achieves the following results on the evaluation set:
- Loss: 0.3428
- Wer: 18.6422
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: 3e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0621 | 1.05 | 1000 | 0.2690 | 20.5099 |
| 0.0174 | 2.1 | 2000 | 0.2705 | 19.2292 |
| 0.006 | 3.15 | 3000 | 0.2954 | 18.9890 |
| 0.0028 | 4.2 | 4000 | 0.3093 | 18.8023 |
| 0.0016 | 5.25 | 5000 | 0.3240 | 18.9653 |
| 0.0018 | 6.3 | 6000 | 0.3313 | 18.6451 |
| 0.0014 | 7.35 | 7000 | 0.3330 | 18.9446 |
| 0.0016 | 8.39 | 8000 | 0.3428 | 18.6422 |
| 0.0015 | 9.44 | 9000 | 0.3508 | 18.9564 |
| 0.001 | 10.49 | 10000 | 0.3569 | 18.8556 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2