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wavlm-libri-clean-100h-large
hrishikeshpai30/wavlm-libri-clean-100h-large
wavlm-libri-clean-100h-large is a automatic speech recognition model from hrishikeshpai30. Use it when you need speech turned into text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- wavlm
- Access
- Public
- Created
- Apr 12, 2023
- Updated
- May 15, 2023
- SHA
- f63a2d64
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. -->
wavlm-libri-clean-100h-large
This model is a fine-tuned version of microsoft/wavlm-large on the AHAZEEMI/LIBRISPEECH10H - CLEAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.0893
- Wer: 0.0655
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: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0144 | 0.42 | 300 | 0.0947 | 0.0749 |
| 0.1408 | 0.84 | 600 | 0.1347 | 0.1363 |
| 0.0396 | 1.26 | 900 | 0.1090 | 0.0935 |
| 0.0353 | 1.68 | 1200 | 0.1032 | 0.0832 |
| 0.051 | 2.1 | 1500 | 0.0969 | 0.0774 |
| 0.0254 | 2.52 | 1800 | 0.0930 | 0.0715 |
| 0.0579 | 2.94 | 2100 | 0.0894 | 0.0660 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cpu
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wavlm
- Languages
- en
- Created
- Apr 12, 2023
- Updated
- May 15, 2023