File size: 3,199 Bytes
fb2a930 |
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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
library_name: transformers
language:
- fa
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper-large-v3-turbo-fa - Sadegh Karimi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: fa
split: test
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 9.627528266117483
---
<!-- 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-large-v3-turbo-fa - Sadegh Karimi
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
- Wer: 9.6275
## 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: 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: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1789 | 0.0217 | 500 | 0.2427 | 26.4099 |
| 0.2077 | 0.0435 | 1000 | 0.2296 | 27.1873 |
| 0.1928 | 0.0652 | 1500 | 0.2320 | 27.5951 |
| 0.1801 | 0.0869 | 2000 | 0.2026 | 24.0409 |
| 0.1865 | 0.1086 | 2500 | 0.1925 | 22.3742 |
| 0.1535 | 0.1304 | 3000 | 0.1872 | 22.9511 |
| 0.1463 | 0.1521 | 3500 | 0.1786 | 21.5436 |
| 0.0935 | 0.1738 | 4000 | 0.1749 | 20.5330 |
| 0.1052 | 0.1956 | 4500 | 0.1597 | 19.0314 |
| 0.091 | 0.2173 | 5000 | 0.1553 | 20.2125 |
| 0.0743 | 0.2390 | 5500 | 0.1474 | 16.9160 |
| 0.096 | 0.2607 | 6000 | 0.1352 | 15.9027 |
| 0.111 | 0.2825 | 6500 | 0.1259 | 14.9071 |
| 0.089 | 0.3042 | 7000 | 0.1179 | 14.1146 |
| 0.0813 | 0.3259 | 7500 | 0.1101 | 12.8653 |
| 0.072 | 0.3477 | 8000 | 0.1012 | 11.8138 |
| 0.0715 | 0.3694 | 8500 | 0.0948 | 10.9791 |
| 0.0683 | 0.3911 | 9000 | 0.0903 | 10.2563 |
| 0.0634 | 0.4128 | 9500 | 0.0861 | 9.6616 |
| 0.0739 | 0.4346 | 10000 | 0.0839 | 9.6275 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
|