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---
library_name: transformers
language:
- fa
license: apache-2.0
base_model: SadeghK/whisper-base
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_20_0
metrics:
- wer
model-index:
- name: whisper-base-fa - Sadegh Karimi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 20.0
type: mozilla-foundation/common_voice_20_0
args: 'config: fa, split: train, test'
metrics:
- name: Wer
type: wer
value: 10.37120429344725
---
<!-- 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-fa - Sadegh Karimi
This model is a fine-tuned version of [SadeghK/whisper-base](https://huggingface.co/SadeghK/whisper-base) on the Common Voice 20.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0813
- Wer: 10.3712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Use convert-to-ggml.ipynb to convert to ggml
To run faster with whisper.cpp, use convert-to-ggml.ipynb to convert model.
Model is already converted and saved as "ggml-base-fa.bin"
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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: 50000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1234 | 0.0493 | 1000 | 0.1698 | 21.8312 |
| 0.1303 | 0.0986 | 2000 | 0.1663 | 22.9153 |
| 0.1241 | 0.1479 | 3000 | 0.1623 | 20.8843 |
| 0.1223 | 0.1972 | 4000 | 0.1616 | 20.7470 |
| 0.1281 | 0.2465 | 5000 | 0.1522 | 19.3606 |
| 0.1111 | 0.2958 | 6000 | 0.1483 | 20.0901 |
| 0.1097 | 0.3451 | 7000 | 0.1452 | 19.0445 |
| 0.1439 | 0.3944 | 8000 | 0.1367 | 18.0251 |
| 0.1053 | 0.4437 | 9000 | 0.1347 | 17.5902 |
| 0.1248 | 0.4930 | 10000 | 0.1281 | 16.9486 |
| 0.1081 | 0.5423 | 11000 | 0.1252 | 15.9200 |
| 0.1062 | 0.5916 | 12000 | 0.1222 | 15.8167 |
| 0.1139 | 0.6409 | 13000 | 0.1181 | 15.6038 |
| 0.1011 | 0.6902 | 14000 | 0.1145 | 15.0918 |
| 0.098 | 0.7395 | 15000 | 0.1141 | 15.0194 |
| 0.1176 | 0.7888 | 16000 | 0.1091 | 14.1048 |
| 0.0933 | 0.8381 | 17000 | 0.1067 | 13.9028 |
| 0.0981 | 0.8874 | 18000 | 0.1042 | 13.6391 |
| 0.0909 | 0.9367 | 19000 | 0.1012 | 13.2119 |
| 0.0714 | 0.9860 | 20000 | 0.1001 | 13.1826 |
| 0.0491 | 1.0353 | 21000 | 0.0985 | 12.9251 |
| 0.059 | 1.0846 | 22000 | 0.0966 | 12.6799 |
| 0.0492 | 1.1339 | 23000 | 0.0959 | 12.4501 |
| 0.0625 | 1.1832 | 24000 | 0.0943 | 12.5241 |
| 0.0429 | 1.2325 | 25000 | 0.0946 | 12.4424 |
| 0.0403 | 1.2818 | 26000 | 0.0931 | 12.1370 |
| 0.0474 | 1.3311 | 27000 | 0.0921 | 11.7330 |
| 0.0484 | 1.3804 | 28000 | 0.0910 | 11.5710 |
| 0.0585 | 1.4297 | 29000 | 0.0896 | 11.7067 |
| 0.0431 | 1.4790 | 30000 | 0.0890 | 11.3875 |
| 0.045 | 1.5283 | 31000 | 0.0875 | 11.2842 |
| 0.0494 | 1.5776 | 32000 | 0.0862 | 11.5433 |
| 0.0448 | 1.6269 | 33000 | 0.0854 | 11.0282 |
| 0.0508 | 1.6762 | 34000 | 0.0849 | 11.0498 |
| 0.0432 | 1.7255 | 35000 | 0.0837 | 10.7583 |
| 0.0356 | 1.7748 | 36000 | 0.0826 | 10.8339 |
| 0.0353 | 1.8241 | 37000 | 0.0819 | 10.5300 |
| 0.043 | 1.8734 | 38000 | 0.0815 | 10.4838 |
| 0.0434 | 1.9227 | 39000 | 0.0812 | 10.5038 |
| 0.0382 | 1.9720 | 40000 | 0.0809 | 10.4684 |
| 0.0342 | 2.0213 | 41000 | 0.0833 | 10.4853 |
| 0.0249 | 2.0706 | 42000 | 0.0841 | 10.7783 |
| 0.0237 | 2.1199 | 43000 | 0.0835 | 10.5100 |
| 0.0282 | 2.1692 | 44000 | 0.0835 | 10.5563 |
| 0.0277 | 2.2185 | 45000 | 0.0830 | 10.7151 |
| 0.0328 | 2.2678 | 46000 | 0.0824 | 10.3959 |
| 0.0268 | 2.3171 | 47000 | 0.0822 | 10.4560 |
| 0.0395 | 2.3664 | 48000 | 0.0817 | 10.3311 |
| 0.0298 | 2.4157 | 49000 | 0.0815 | 10.4128 |
| 0.029 | 2.4650 | 50000 | 0.0813 | 10.3712 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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