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whisper-small-sanskrit-lr-scheduler-2
Bidwill/whisper-small-sanskrit-lr-scheduler-2
whisper-small-sanskrit-lr-scheduler-2 is a automatic speech recognition model from Bidwill. 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
- Oct 10, 2023
- Updated
- Oct 12, 2023
- SHA
- 011803d7
Base models
Datasets
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 Small Sanskrit lr scheduler - Bidit Sadhukhan
This model is a fine-tuned version of openai/whisper-small on the load_data dataset. It achieves the following results on the evaluation set:
- Loss: 0.1133
- Wer: 29.2638
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: 2.55e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 11000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1184 | 0.12 | 500 | 0.1969 | 46.2909 |
| 0.0912 | 0.23 | 1000 | 0.1741 | 43.2205 |
| 0.0808 | 0.35 | 1500 | 0.1676 | 40.2062 |
| 0.0645 | 0.47 | 2000 | 0.1656 | 39.5282 |
| 0.0659 | 0.58 | 2500 | 0.1477 | 35.9704 |
| 0.0826 | 0.7 | 3000 | 0.1515 | 36.6876 |
| 0.0559 | 0.82 | 3500 | 0.1389 | 36.0769 |
| 0.0482 | 0.93 | 4000 | 0.1243 | 32.7880 |
| 0.034 | 1.05 | 4500 | 0.1352 | 34.4408 |
| 0.0275 | 1.17 | 5000 | 0.1301 | 32.0876 |
| 0.0284 | 1.28 | 5500 | 0.1249 | 30.5188 |
| 0.0268 | 1.4 | 6000 | 0.1343 | 31.7010 |
| 0.021 | 1.52 | 6500 | 0.1190 | 29.6784 |
| 0.0245 | 1.63 | 7000 | 0.1133 | 28.9444 |
| 0.0219 | 1.75 | 7500 | 0.1118 | 27.7734 |
| 0.0235 | 1.87 | 8000 | 0.1051 | 31.2808 |
| 0.0217 | 1.98 | 8500 | 0.1125 | 31.4713 |
| 0.0064 | 2.1 | 9000 | 0.1109 | 27.9751 |
| 0.0078 | 2.21 | 9500 | 0.1156 | 27.6053 |
| 0.0099 | 2.33 | 10000 | 0.1136 | 28.7483 |
| 0.01 | 2.45 | 10500 | 0.1133 | 29.2806 |
| 0.009 | 2.56 | 11000 | 0.1133 | 29.2638 |
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Created
- Oct 10, 2023
- Updated
- Oct 12, 2023