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hindi_beekeeping_wav2vec2
TheAIchemist13/hindi_beekeeping_wav2vec2
hindi_beekeeping_wav2vec2 is a automatic speech recognition model from TheAIchemist13. 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. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2
- Access
- Public
- Created
- Oct 12, 2023
- Updated
- Oct 12, 2023
- SHA
- b0f941e9
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. -->
hindi_beekeeping_wav2vec2
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6793
- Wer: 0.4048
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: 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: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 26.5108 | 0.11 | 25 | 2.9146 | 1.1520 |
| 3.8351 | 0.22 | 50 | 2.1736 | 0.6667 |
| 2.1489 | 0.33 | 75 | 1.5539 | 0.5914 |
| 3.2097 | 0.44 | 100 | 1.9828 | 0.6840 |
| 1.5213 | 0.54 | 125 | 1.4369 | 0.5410 |
| 2.1797 | 0.65 | 150 | 1.7904 | 0.7449 |
| 1.2368 | 0.76 | 175 | 1.3543 | 0.5583 |
| 1.5755 | 0.87 | 200 | 1.5444 | 0.6712 |
| 1.1638 | 0.98 | 225 | 1.4639 | 0.6005 |
| 0.8985 | 1.09 | 250 | 1.0658 | 0.5139 |
| 0.9406 | 1.2 | 275 | 1.3181 | 0.5756 |
| 1.1215 | 1.31 | 300 | 1.0163 | 0.4680 |
| 1.3881 | 1.42 | 325 | 1.1547 | 0.5854 |
| 0.8113 | 1.53 | 350 | 0.9433 | 0.4771 |
| 0.689 | 1.63 | 375 | 1.0819 | 0.5064 |
| 0.726 | 1.74 | 400 | 1.0025 | 0.5252 |
| 0.6475 | 1.85 | 425 | 1.0670 | 0.5154 |
| 1.0345 | 1.96 | 450 | 0.9535 | 0.5019 |
| 0.8327 | 2.07 | 475 | 0.8866 | 0.4733 |
| 0.4843 | 2.18 | 500 | 0.9580 | 0.5087 |
| 0.6657 | 2.29 | 525 | 0.9019 | 0.4710 |
| 0.4843 | 2.4 | 550 | 0.8207 | 0.4665 |
| 0.6666 | 2.51 | 575 | 0.7377 | 0.4695 |
| 0.5012 | 2.61 | 600 | 0.8135 | 0.4537 |
| 0.7776 | 2.72 | 625 | 0.8131 | 0.4612 |
| 0.4538 | 2.83 | 650 | 0.8194 | 0.4590 |
| 0.5659 | 2.94 | 675 | 0.8480 | 0.4582 |
| 0.9362 | 3.05 | 700 | 0.8532 | 0.4485 |
| 0.3629 | 3.16 | 725 | 0.8447 | 0.4582 |
| 0.5201 | 3.27 | 750 | 0.7486 | 0.4409 |
| 0.3513 | 3.38 | 775 | 0.7865 | 0.4439 |
| 0.4152 | 3.49 | 800 | 0.7510 | 0.4364 |
| 0.8962 | 3.59 | 825 | 0.7758 | 0.4342 |
| 0.4558 | 3.7 | 850 | 0.7314 | 0.4296 |
| 0.3476 | 3.81 | 875 | 0.6861 | 0.4153 |
| 0.3978 | 3.92 | 900 | 0.6961 | 0.4153 |
| 0.3818 | 4.03 | 925 | 0.6960 | 0.4071 |
| 0.2847 | 4.14 | 950 | 0.7222 | 0.4048 |
| 0.3997 | 4.25 | 975 | 0.6960 | 0.4191 |
| 0.2695 | 4.36 | 1000 | 0.6894 | 0.4138 |
| 0.4229 | 4.47 | 1025 | 0.7215 | 0.4304 |
| 0.2665 | 4.58 | 1050 | 0.7096 | 0.4056 |
| 0.3158 | 4.68 | 1075 | 0.6904 | 0.4153 |
| 0.2452 | 4.79 | 1100 | 0.6777 | 0.4071 |
| 0.3034 | 4.9 | 1125 | 0.6793 | 0.4048 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- mit
- Created
- Oct 12, 2023
- Updated
- Oct 12, 2023