whisper-large-v3-translate-zh-v0.1-lt
This model is a fine-tuned version of openai/whisper-large-v3.
Model description
3500小时 (日语音频,中文字幕) 数据微调, 翻译模式直出中文
Usage
task='translate', language='ja'
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- dropout: 0.1
- mask_time_prob: 0.05
- mask_feature_prob: 0.2
- condition_on_previous_text_rate: 0.5
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|---|---|---|---|---|---|
| 2.1282 | 0.0739 | 1000 | 2.1852 | 1.9014 | 4.4904 |
| 1.8567 | 0.1478 | 2000 | 1.8366 | 1.7295 | 3.8716 |
| 1.6968 | 0.2217 | 3000 | 1.2615 | 1.6279 | 2.4825 |
| 1.6264 | 0.2956 | 4000 | 1.0536 | 1.5625 | 1.8101 |
| 1.5687 | 0.3695 | 5000 | 1.0932 | 1.5410 | 2.1218 |
| 1.531 | 0.4433 | 6000 | 1.5156 | 1.2533 | 2.3689 |
| 1.4875 | 0.5172 | 7000 | 1.4697 | 0.9560 | 1.5588 |
| 1.4518 | 0.5911 | 8000 | 1.4521 | 1.0170 | 1.6392 |
| 1.4472 | 0.6650 | 9000 | 1.4463 | 1.0084 | 1.6420 |
| 1.3991 | 0.7389 | 10000 | 1.4238 | 0.9266 | 1.6992 |
| 1.4266 | 0.8128 | 11000 | 1.4141 | 0.8365 | 1.3056 |
| 1.3755 | 0.8867 | 12000 | 1.4033 | 0.7904 | 1.3119 |
| 1.3833 | 0.9606 | 13000 | 1.4004 | 0.8600 | 1.3333 |
| 1.3224 | 1.0345 | 14000 | 1.3770 | 0.8243 | 1.4560 |
| 1.3295 | 1.1084 | 15000 | 1.3770 | 0.7852 | 1.4298 |
| 1.3136 | 1.1823 | 16000 | 1.3564 | 0.7176 | 1.1826 |
| 1.2832 | 1.2561 | 17000 | 1.3535 | 0.6767 | 1.1781 |
| 1.2917 | 1.3300 | 18000 | 1.3584 | 0.7255 | 1.1218 |
| 1.27 | 1.4039 | 19000 | 1.3330 | 0.6590 | 1.1242 |
| 1.2704 | 1.4778 | 20000 | 1.3379 | 0.6934 | 1.1944 |
| 1.2614 | 1.5517 | 21000 | 1.3330 | 0.6949 | 1.1820 |
| 1.2455 | 1.6256 | 22000 | 1.3350 | 0.6931 | 1.0892 |
| 1.2475 | 1.6995 | 23000 | 1.3154 | 0.6662 | 1.1576 |
| 1.2583 | 1.7734 | 24000 | 1.3164 | 0.6490 | 1.0705 |
| 1.2333 | 1.8473 | 25000 | 1.3184 | 0.6833 | 1.1480 |
| 1.2462 | 1.9212 | 26000 | 1.3125 | 0.6672 | 1.1612 |
| 1.2279 | 1.9950 | 27000 | 1.3047 | 0.6644 | 1.2179 |
| 1.1908 | 2.0689 | 28000 | 1.3047 | 0.6938 | 1.2221 |
| 1.1831 | 2.1428 | 29000 | 1.2998 | 0.6316 | 1.0717 |
| 1.1705 | 2.2167 | 30000 | 1.3018 | 0.6165 | 1.0958 |
| 1.171 | 2.2906 | 31000 | 1.3027 | 0.6109 | 1.0868 |
| 1.1567 | 2.3645 | 32000 | 1.3037 | 0.6485 | 1.1736 |
| 1.1705 | 2.4384 | 33000 | 1.2969 | 0.6078 | 1.0515 |
| 1.1819 | 2.5123 | 34000 | 1.2949 | 0.6158 | 1.0362 |
| 1.1447 | 2.5862 | 35000 | 1.2920 | 0.6365 | 1.0558 |
| 1.17 | 2.6601 | 36000 | 1.2881 | 0.6339 | 1.0868 |
| 1.1495 | 2.7340 | 37000 | 1.2949 | 0.6297 | 1.0437 |
| 1.1395 | 2.8078 | 38000 | 1.2900 | 0.6285 | 1.1221 |
| 1.15 | 2.8817 | 39000 | 1.2891 | 0.5997 | 1.0217 |
| 1.1623 | 2.9556 | 40000 | 1.2881 | 0.6085 | 1.0395 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1