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whisper-large-v3-atco2-asr
jlvdoorn/whisper-large-v3-atco2-asr
whisper-large-v3-atco2-asr is a automatic speech recognition model from jlvdoorn. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Nov 10, 2023
- Updated
- Jan 17, 2024
- SHA
- f7a0dddf
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-atco2-asr
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7695
- Wer: 17.0374
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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: 100
- training_steps: 2800
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1388 | 3.57 | 100 | 0.5488 | 20.1957 |
| 0.0313 | 7.14 | 200 | 0.5830 | 17.5712 |
| 0.0173 | 10.71 | 300 | 0.5898 | 20.4181 |
| 0.004 | 14.29 | 400 | 0.6201 | 16.3256 |
| 0.001 | 17.86 | 500 | 0.6543 | 18.4164 |
| 0.002 | 21.43 | 600 | 0.6499 | 17.8381 |
| 0.0003 | 25.0 | 700 | 0.6724 | 17.1263 |
| 0.0002 | 28.57 | 800 | 0.6890 | 16.9929 |
| 0.0002 | 32.14 | 900 | 0.7012 | 16.8594 |
| 0.0001 | 35.71 | 1000 | 0.7104 | 16.9484 |
| 0.0001 | 39.29 | 1100 | 0.7178 | 16.9039 |
| 0.0001 | 42.86 | 1200 | 0.7241 | 17.4377 |
| 0.0001 | 46.43 | 1300 | 0.7305 | 17.3488 |
| 0.0001 | 50.0 | 1400 | 0.7358 | 17.3043 |
| 0.0001 | 53.57 | 1500 | 0.7407 | 17.3043 |
| 0.0001 | 57.14 | 1600 | 0.7451 | 17.1263 |
| 0.0001 | 60.71 | 1700 | 0.7495 | 17.2598 |
| 0.0001 | 64.29 | 1800 | 0.7529 | 17.2153 |
| 0.0001 | 67.86 | 1900 | 0.7563 | 17.2598 |
| 0.0001 | 71.43 | 2000 | 0.7593 | 17.4377 |
| 0.0001 | 75.0 | 2100 | 0.7612 | 17.3932 |
| 0.0001 | 78.57 | 2200 | 0.7632 | 17.2598 |
| 0.0 | 82.14 | 2300 | 0.7651 | 17.1263 |
| 0.0 | 85.71 | 2400 | 0.7666 | 17.0819 |
| 0.0 | 89.29 | 2500 | 0.7681 | 17.0374 |
| 0.0 | 92.86 | 2600 | 0.7686 | 17.0374 |
| 0.0 | 96.43 | 2700 | 0.7695 | 17.1263 |
| 0.0 | 100.0 | 2800 | 0.7695 | 17.0374 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Created
- Nov 10, 2023
- Updated
- Jan 17, 2024