Whisper Tiny Hu
This model is a fine-tuned version of openai/whisper-base on the sarpba/big_audio_data_hun dataset 40% 475000 lines from database (475h voice). It achieves the following results on the evaluation set:
- Loss: 0.8324
- Wer Ortho: 40.6270
- Wer: 36.4265
Quanted model tests on google/flerus
Model | WER | CER | Normalized_WER | Normalized_CER | Database | Split | Runtime |
---|---|---|---|---|---|---|---|
int8_bfloat16 | 43.86 | 14.33 | 39.4 | 14.33 | google/fleurs | test | 126.79 |
bfloat16 | 43.39 | 14.1 | 38.96 | 14.11 | google/fleurs | test | 119.79 |
int8_float32 | 41.7 | 12.82 | 36.95 | 12.85 | google/fleurs | test | 134.13 |
int8 | 41.69 | 12.84 | 36.96 | 12.86 | google/fleurs | test | 136.07 |
int8_float16 | 41.66 | 12.97 | 36.96 | 13 | google/fleurs | test | 126.24 |
float16 | 41.52 | 12.9 | 36.79 | 12.95 | google/fleurs | test | 117.99 |
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: 7e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
---|---|---|---|---|---|
0.3295 | 0.2693 | 1000 | 0.9198 | 56.3713 | 52.8136 |
0.2606 | 0.5385 | 2000 | 0.8563 | 51.6035 | 47.1599 |
0.2154 | 0.8078 | 3000 | 0.8362 | 48.4217 | 45.0160 |
0.1679 | 1.0770 | 4000 | 0.8385 | 46.1071 | 42.3727 |
0.1612 | 1.3463 | 5000 | 0.8393 | 45.8802 | 41.9313 |
0.1527 | 1.6155 | 6000 | 0.8277 | 43.4648 | 39.1404 |
0.1491 | 1.8848 | 7000 | 0.8253 | 44.1254 | 39.7092 |
0.1146 | 2.1540 | 8000 | 0.8385 | 42.2041 | 37.9032 |
0.1095 | 2.4233 | 9000 | 0.8387 | 41.7541 | 37.3534 |
0.109 | 2.6925 | 10000 | 0.8351 | 41.3986 | 37.1251 |
0.1039 | 2.9618 | 11000 | 0.8324 | 40.6270 | 36.4265 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Base model
openai/whisper-base