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whisper-medium-id-augmented
Scrya/whisper-medium-id-augmented
whisper-medium-id-augmented is a automatic speech recognition model from Scrya. 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
- Dec 18, 2022
- Updated
- Dec 19, 2022
- SHA
- 14661a75
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 Medium ID - FLEURS-CV-LBV - Augmented
This model is a fine-tuned version of openai/whisper-medium on the following datasets:
It achieves the following results on the evaluation set (Common Voice 11.0):
- Loss: 0.2788
- Wer: 7.6132
- Cer: 2.3332
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training:
- mozilla-foundation/common_voice_11_0 (train+validation)
- google/fleurs (train+validation)
- indonesian-nlp/librivox-indonesia (train)
Evaluation:
- mozilla-foundation/common_voice_11_0 (test)
- google/fleurs (test)
- indonesian-nlp/librivox-indonesia (test)
Training procedure
Datasets were augmented on-the-fly using audiomentations via PitchShift, AddGaussianNoise and TimeStretch transformations at p=0.3.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.3002 | 1.9 | 1000 | 0.1659 | 8.1850 | 2.5333 |
| 0.0514 | 3.8 | 2000 | 0.1818 | 8.0559 | 2.5244 |
| 0.0145 | 5.7 | 3000 | 0.2150 | 7.8945 | 2.5281 |
| 0.0037 | 7.6 | 4000 | 0.2248 | 7.7100 | 2.3738 |
| 0.0016 | 9.51 | 5000 | 0.2402 | 7.6224 | 2.3591 |
| 0.0009 | 11.41 | 6000 | 0.2525 | 7.7654 | 2.3952 |
| 0.0005 | 13.31 | 7000 | 0.2609 | 7.5994 | 2.3487 |
| 0.0008 | 15.21 | 8000 | 0.2682 | 7.5855 | 2.3347 |
| 0.0002 | 17.11 | 9000 | 0.2756 | 7.6178 | 2.3288 |
| 0.0002 | 19.01 | 10000 | 0.2788 | 7.6132 | 2.3332 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- id
- Created
- Dec 18, 2022
- Updated
- Dec 19, 2022