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whisper-base.en-fsc-h
gokuls/whisper-base.en-fsc-h
whisper-base.en-fsc-h is a machine learning model from gokuls. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Oct 5, 2024
- Updated
- Oct 6, 2024
- SHA
- bf3bc643
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-base.en-fsc-h
This model is a fine-tuned version of openai/whisper-base.en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0700
- Accuracy: 0.9863
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: 0.0005
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.9959 | 120 | 0.7203 | 0.7332 |
| No log | 2.0 | 241 | 0.3685 | 0.8505 |
| No log | 2.9959 | 361 | 0.2024 | 0.9293 |
| No log | 4.0 | 482 | 0.1429 | 0.9552 |
| No log | 4.9959 | 602 | 0.1143 | 0.9636 |
| No log | 6.0 | 723 | 0.1738 | 0.9565 |
| No log | 6.9959 | 843 | 0.1295 | 0.9560 |
| No log | 8.0 | 964 | 0.1407 | 0.9599 |
| 0.3435 | 8.9959 | 1084 | 0.1104 | 0.9694 |
| 0.3435 | 10.0 | 1205 | 0.1054 | 0.9718 |
| 0.3435 | 10.9959 | 1325 | 0.1042 | 0.9728 |
| 0.3435 | 12.0 | 1446 | 0.1084 | 0.9710 |
| 0.3435 | 12.9959 | 1566 | 0.0926 | 0.9789 |
| 0.3435 | 14.0 | 1687 | 0.1120 | 0.9763 |
| 0.3435 | 14.9959 | 1807 | 0.0903 | 0.9797 |
| 0.3435 | 16.0 | 1928 | 0.0957 | 0.9800 |
| 0.0255 | 16.9959 | 2048 | 0.0851 | 0.9815 |
| 0.0255 | 18.0 | 2169 | 0.0723 | 0.9844 |
| 0.0255 | 18.9959 | 2289 | 0.0784 | 0.9834 |
| 0.0255 | 20.0 | 2410 | 0.0673 | 0.9860 |
| 0.0255 | 20.9959 | 2530 | 0.0700 | 0.9863 |
| 0.0255 | 22.0 | 2651 | 0.0707 | 0.9858 |
| 0.0255 | 22.9959 | 2771 | 0.0712 | 0.9858 |
| 0.0255 | 24.0 | 2892 | 0.0715 | 0.9858 |
| 0.0015 | 24.8963 | 3000 | 0.0716 | 0.9858 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
- Type
- whisper
- License
- apache-2.0
- Created
- Oct 5, 2024
- Updated
- Oct 6, 2024