Downloads · 30 days
11
16% of all-time downloads
w2v-bert-2.0-tamil-gpu-custom_v5
Sajjo/w2v-bert-2.0-tamil-gpu-custom_v5
w2v-bert-2.0-tamil-gpu-custom_v5 is a automatic speech recognition model from Sajjo. 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
67
Public
Parameters
606M
12.1 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
- Apr 18, 2024
- Updated
- Apr 19, 2024
- SHA
- 12e23a01
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-2.0-tamil-gpu-custom.v1
This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 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: 4.43567e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.3141 | 0.25 | 300 | inf | 0.3486 |
| 0.2064 | 0.5 | 600 | inf | 0.3516 |
| 0.1763 | 0.75 | 900 | inf | 0.2858 |
| 0.1673 | 1.0 | 1200 | inf | 0.2929 |
| 0.5517 | 1.25 | 1500 | inf | 0.5617 |
| 0.7415 | 1.49 | 1800 | inf | 0.4608 |
| 0.7446 | 1.74 | 2100 | inf | 0.4608 |
| 0.7467 | 1.99 | 2400 | inf | 0.4608 |
| 0.7447 | 2.24 | 2700 | inf | 0.4608 |
| 0.7505 | 2.49 | 3000 | inf | 0.4608 |
| 0.7469 | 2.74 | 3300 | inf | 0.4608 |
| 0.7449 | 2.99 | 3600 | inf | 0.4608 |
| 0.7487 | 3.24 | 3900 | inf | 0.4608 |
| 0.7472 | 3.49 | 4200 | inf | 0.4608 |
| 0.747 | 3.74 | 4500 | inf | 0.4608 |
| 0.7462 | 3.99 | 4800 | inf | 0.4608 |
| 0.7486 | 4.23 | 5100 | inf | 0.4608 |
| 0.7503 | 4.48 | 5400 | inf | 0.4608 |
| 0.7424 | 4.73 | 5700 | inf | 0.4608 |
| 0.746 | 4.98 | 6000 | inf | 0.4608 |
| 0.7518 | 5.23 | 6300 | inf | 0.4608 |
| 0.7442 | 5.48 | 6600 | inf | 0.4608 |
| 0.7466 | 5.73 | 6900 | inf | 0.4608 |
| 0.7468 | 5.98 | 7200 | inf | 0.4608 |
| 0.7542 | 6.23 | 7500 | inf | 0.4608 |
| 0.748 | 6.48 | 7800 | inf | 0.4608 |
| 0.7453 | 6.72 | 8100 | inf | 0.4608 |
| 0.74 | 6.97 | 8400 | inf | 0.4608 |
| 1.2386 | 7.22 | 8700 | nan | 1.0 |
| 0.0 | 7.47 | 9000 | nan | 1.0 |
| 0.0 | 7.72 | 9300 | nan | 1.0 |
| 0.0 | 7.97 | 9600 | nan | 1.0 |
| 0.0 | 8.22 | 9900 | nan | 1.0 |
| 0.0 | 8.47 | 10200 | nan | 1.0 |
| 0.0 | 8.72 | 10500 | nan | 1.0 |
| 0.0 | 8.97 | 10800 | nan | 1.0 |
| 0.0 | 9.22 | 11100 | nan | 1.0 |
| 0.0 | 9.46 | 11400 | nan | 1.0 |
| 0.0 | 9.71 | 11700 | nan | 1.0 |
| 0.0 | 9.96 | 12000 | nan | 1.0 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Apr 18, 2024
- Updated
- Apr 19, 2024