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v3-turbo-cv17-telephonic-lora

PEFT
Safetensors
Turkish
whisper
asr
lora
Turkish
Generated from Trainer
Eval Results
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v3-turbo-cv17-telephonic-lora / README.md
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metadata
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

v3-turbo-cv17-telephonic-lora

This model is a fine-tuned version of 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