Downloads · 30 days
6
21% of all-time downloads
w2v-bert-punjabi_v2
cdactvm/w2v-bert-punjabi_v2
w2v-bert-punjabi_v2 is a automatic speech recognition model from cdactvm. 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
29
Public
Parameters
606M
9.7 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
- Jan 31, 2025
- Updated
- Jan 31, 2025
- SHA
- c8f90471
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-punjabi_v2
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: 0.2031
- Wer: 0.1135
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 60000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4419 | 0.2174 | 2000 | 0.3828 | 0.2268 |
| 0.3492 | 0.4348 | 4000 | 0.3401 | 0.1836 |
| 0.3205 | 0.6522 | 6000 | 0.2932 | 0.1712 |
| 0.2813 | 0.8696 | 8000 | 0.2844 | 0.1590 |
| 0.255 | 1.0870 | 10000 | 0.2562 | 0.1469 |
| 0.2451 | 1.3043 | 12000 | 0.2431 | 0.1386 |
| 0.2305 | 1.5217 | 14000 | 0.2299 | 0.1312 |
| 0.2156 | 1.7391 | 16000 | 0.2191 | 0.1274 |
| 0.2119 | 1.9565 | 18000 | 0.2269 | 0.1205 |
| 0.182 | 2.1739 | 20000 | 0.2091 | 0.1181 |
| 0.1789 | 2.3913 | 22000 | 0.1980 | 0.1136 |
| 0.1766 | 2.6087 | 24000 | 0.1945 | 0.1092 |
| 0.1657 | 2.8261 | 26000 | 0.1881 | 0.1079 |
| 0.1461 | 3.0435 | 28000 | 0.1809 | 0.1050 |
| 0.1454 | 3.2609 | 30000 | 0.1810 | 0.1029 |
| 0.1697 | 3.4783 | 32000 | 0.2085 | 0.1210 |
| 0.1763 | 3.6957 | 34000 | 0.2017 | 0.1172 |
| 0.1642 | 3.9130 | 36000 | 0.2031 | 0.1135 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Jan 31, 2025
- Updated
- Jan 31, 2025