File size: 2,488 Bytes
fe62f38 68b5109 fe62f38 68b5109 fe62f38 68b5109 fe62f38 68b5109 fe62f38 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BH
results: []
---
<!-- 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. -->
# Whisper base AR - BH
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text-segments dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0231
- Wer: 0.2331
## 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: 1e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.017 | 1.3333 | 100 | 0.0233 | 0.2610 |
| 0.0148 | 2.6667 | 200 | 0.0216 | 0.2495 |
| 0.0094 | 4.0 | 300 | 0.0204 | 0.2438 |
| 0.0048 | 5.3333 | 400 | 0.0203 | 0.2409 |
| 0.0027 | 6.6667 | 500 | 0.0206 | 0.2421 |
| 0.0017 | 8.0 | 600 | 0.0213 | 0.2491 |
| 0.0007 | 9.3333 | 700 | 0.0218 | 0.2384 |
| 0.0005 | 10.6667 | 800 | 0.0221 | 0.2368 |
| 0.0003 | 12.0 | 900 | 0.0224 | 0.2372 |
| 0.0003 | 13.3333 | 1000 | 0.0226 | 0.2347 |
| 0.0003 | 14.6667 | 1100 | 0.0228 | 0.2327 |
| 0.0002 | 16.0 | 1200 | 0.0229 | 0.2335 |
| 0.0002 | 17.3333 | 1300 | 0.0230 | 0.2331 |
| 0.0002 | 18.6667 | 1400 | 0.0231 | 0.2331 |
| 0.0002 | 20.0 | 1500 | 0.0231 | 0.2335 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|