Downloads · 30 days
7
7% of all-time downloads
Whisper_large_v3_turbo_v2
aslanovaf/Whisper_large_v3_turbo_v2
Whisper_large_v3_turbo_v2 is a automatic speech recognition model from aslanovaf. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
98
Public
Parameters
809M
55 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors3.2 GB · 100%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- whisper
- Access
- Public
- Created
- Dec 4, 2024
- Updated
- Dec 5, 2024
- SHA
- 7eaf169e
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Whisper_large_v3_turbo_v2
This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6363
- Wer: 31.7384
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8106 | 1.1834 | 500 | 1.0268 | 93.4686 |
| 0.5518 | 2.3669 | 1000 | 0.8523 | 56.8544 |
| 0.4203 | 3.5503 | 1500 | 0.7787 | 52.2696 |
| 0.2934 | 4.7337 | 2000 | 0.7357 | 48.8402 |
| 0.2243 | 5.9172 | 2500 | 0.7544 | 49.3678 |
| 0.1262 | 7.1006 | 3000 | 0.7770 | 49.9682 |
| 0.1038 | 8.2840 | 3500 | 0.7445 | 43.7824 |
| 0.0791 | 9.4675 | 4000 | 0.7615 | 44.6193 |
| 0.057 | 10.6509 | 4500 | 0.7432 | 41.0079 |
| 0.0441 | 11.8343 | 5000 | 0.7307 | 40.3166 |
| 0.0313 | 13.0178 | 5500 | 0.7222 | 38.7519 |
| 0.0147 | 14.2012 | 6000 | 0.7173 | 37.2965 |
| 0.0091 | 15.3846 | 6500 | 0.6866 | 34.8949 |
| 0.0022 | 16.5680 | 7000 | 0.6540 | 33.5031 |
| 0.0025 | 17.7515 | 7500 | 0.6488 | 32.5298 |
| 0.0004 | 18.9349 | 8000 | 0.6363 | 31.7384 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- mit
- Created
- Dec 4, 2024
- Updated
- Dec 5, 2024