File size: 2,120 Bytes
f736c99 f0ac0a2 18677e5 f736c99 18677e5 f736c99 f0ac0a2 f736c99 18677e5 f736c99 f0ac0a2 f736c99 f0ac0a2 f736c99 18677e5 f736c99 18677e5 f736c99 f0ac0a2 f736c99 f0ac0a2 f736c99 |
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 |
---
base_model: openai/whisper-small
datasets:
- common_voice_17_0
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-tr
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: tr
split: test
args: tr
metrics:
- type: wer
value: 20.088563399472026
name: Wer
---
<!-- 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-small-tr
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2316
- Wer: 20.0886
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2284 | 0.3447 | 1000 | 0.2814 | 23.9819 |
| 0.1906 | 0.6894 | 2000 | 0.2606 | 22.5598 |
| 0.0945 | 1.0341 | 3000 | 0.2472 | 21.1990 |
| 0.0871 | 1.3788 | 4000 | 0.2405 | 20.6744 |
| 0.0823 | 1.7235 | 5000 | 0.2316 | 20.0886 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu124
- Datasets 3.0.0
- Tokenizers 0.19.1
|