Downloads · 30 days
26
19% of all-time downloads
w2v-bert-cv-grain-lg_both
sulaimank/w2v-bert-cv-grain-lg_both
w2v-bert-cv-grain-lg_both is a automatic speech recognition model from sulaimank. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
134
Public
Parameters
606M
26.7 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.4 GB · 100%
At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Nov 1, 2024
- Updated
- Nov 2, 2024
- SHA
- fb0b9e58
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. -->
w2v-bert-cv-grain-lg_both
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 16.2243
- Wer: 1.0
- Cer: 1.0
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: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.4609 | 1.0 | 5406 | 0.1400 | 0.1423 | 0.0296 |
| 0.2829 | 2.0 | 10812 | 0.1133 | 0.0968 | 0.0213 |
| 0.2369 | 3.0 | 16218 | 0.1033 | 0.0883 | 0.0193 |
| 0.2106 | 4.0 | 21624 | 0.0848 | 0.0681 | 0.0162 |
| 0.197 | 5.0 | 27030 | 0.0871 | 0.0681 | 0.0159 |
| 0.2459 | 6.0 | 32436 | 0.1335 | 0.1022 | 0.0203 |
| 0.3563 | 7.0 | 37842 | 0.1809 | 0.1254 | 0.0267 |
| 0.6033 | 8.0 | 43248 | 0.5575 | 0.7032 | 0.1768 |
| 4.656 | 9.0 | 48654 | 16.9063 | 0.9980 | 0.9837 |
| 10.5595 | 10.0 | 54060 | 12.4706 | 1.0 | 1.0 |
| 17.1148 | 11.0 | 59466 | 16.2280 | 1.0 | 1.0 |
| 17.4223 | 12.0 | 64872 | 16.2273 | 1.0 | 1.0 |
| 17.4172 | 13.0 | 70278 | 16.2222 | 1.0 | 1.0 |
| 17.4159 | 14.0 | 75684 | 16.2243 | 1.0 | 1.0 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Nov 1, 2024
- Updated
- Nov 2, 2024