Downloads · 30 days
14
4% of all-time downloads
ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8
rohitp1/ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8
ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8 is a automatic speech recognition model from rohitp1. 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. --
All-time downloads
343
Public
Repo size
7.9 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin378 MB · 100%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- wavlm
- Access
- Public
- Created
- Jun 2, 2023
- Updated
- Jun 17, 2023
- SHA
- f47f3b08
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. -->
ws_w2lm_base_distill_noisy_teacher_libri_epochs_50_batch_8
This model is a fine-tuned version of rohitp1/kkkh_w2lm_base_plus_finetune_teacher_noise_libri360_50_epochs_batch_16 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0945
- Wer: 0.1041
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0562 | 2.46 | 250 | 0.0741 | 0.1135 |
| 0.0538 | 4.92 | 500 | 0.0736 | 0.1126 |
| 0.0506 | 7.38 | 750 | 0.0751 | 0.1116 |
| 0.0465 | 9.84 | 1000 | 0.0752 | 0.1099 |
| 0.0424 | 12.31 | 1250 | 0.0762 | 0.1089 |
| 0.0385 | 14.77 | 1500 | 0.0790 | 0.1078 |
| 0.0355 | 17.23 | 1750 | 0.0788 | 0.1062 |
| 0.0335 | 19.69 | 2000 | 0.0795 | 0.1053 |
| 0.0314 | 22.15 | 2250 | 0.0825 | 0.1052 |
| 0.0298 | 24.61 | 2500 | 0.0837 | 0.1055 |
| 0.0285 | 27.07 | 2750 | 0.0873 | 0.1049 |
| 0.0274 | 29.53 | 3000 | 0.0868 | 0.1043 |
| 0.0266 | 32.0 | 3250 | 0.0891 | 0.1044 |
| 0.0256 | 34.46 | 3500 | 0.0902 | 0.1044 |
| 0.0251 | 36.92 | 3750 | 0.0911 | 0.1044 |
| 0.0247 | 39.38 | 4000 | 0.0926 | 0.1042 |
| 0.0242 | 41.84 | 4250 | 0.0936 | 0.1042 |
| 0.0238 | 44.3 | 4500 | 0.0940 | 0.1042 |
| 0.0235 | 46.76 | 4750 | 0.0938 | 0.1042 |
| 0.0233 | 49.22 | 5000 | 0.0945 | 0.1041 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1
- Datasets 2.7.1
- Tokenizers 0.11.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wavlm
- Created
- Jun 2, 2023
- Updated
- Jun 17, 2023