|
|
--- |
|
|
library_name: peft |
|
|
language: |
|
|
- tr |
|
|
license: mit |
|
|
base_model: openai/whisper-large-v3-turbo |
|
|
tags: |
|
|
- asr |
|
|
- whisper |
|
|
- lora |
|
|
- Turkish |
|
|
- tr |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- dcl-ai-team/CommonVoice-17_tr_bandpass_filter_telephonic |
|
|
metrics: |
|
|
- wer |
|
|
model-index: |
|
|
- name: v3-turbo-cv17-telephonic-lora |
|
|
results: |
|
|
- task: |
|
|
type: automatic-speech-recognition |
|
|
name: Automatic Speech Recognition |
|
|
dataset: |
|
|
name: CommonVoice-17_tr_bandpass_filter_telephonic |
|
|
type: dcl-ai-team/CommonVoice-17_tr_bandpass_filter_telephonic |
|
|
metrics: |
|
|
- type: wer |
|
|
value: 14.208987174831321 |
|
|
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. --> |
|
|
|
|
|
# v3-turbo-cv17-telephonic-lora |
|
|
|
|
|
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the CommonVoice-17_tr_bandpass_filter_telephonic dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.1411 |
|
|
- Wer: 14.2090 |
|
|
|
|
|
## 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: 4 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 8 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: cosine |
|
|
- training_steps: 5000 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|
|:-------------:|:------:|:----:|:---------------:|:-------:| |
|
|
| 0.454 | 0.1138 | 500 | 0.1633 | 15.3845 | |
|
|
| 0.1463 | 0.2276 | 1000 | 0.1525 | 14.9965 | |
|
|
| 0.1393 | 0.3414 | 1500 | 0.1482 | 14.7002 | |
|
|
| 0.1344 | 0.4552 | 2000 | 0.1466 | 14.4383 | |
|
|
| 0.1305 | 0.5690 | 2500 | 0.1442 | 14.3084 | |
|
|
| 0.1235 | 0.6828 | 3000 | 0.1427 | 14.2510 | |
|
|
| 0.129 | 0.7966 | 3500 | 0.1418 | 14.2434 | |
|
|
| 0.1259 | 0.9104 | 4000 | 0.1416 | 14.1765 | |
|
|
| 0.1169 | 1.0241 | 4500 | 0.1412 | 14.2185 | |
|
|
| 0.1103 | 1.1379 | 5000 | 0.1411 | 14.2090 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.14.0 |
|
|
- Transformers 4.48.3 |
|
|
- Pytorch 2.4.1+cu121 |
|
|
- Datasets 3.1.0 |
|
|
- Tokenizers 0.21.0 |
