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JacobLinCool
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whisper-large-v3-turbo-common_voice_16_1-zh-TW-2-lora

Automatic Speech Recognition
PEFT
TensorBoard
Safetensors
Chinese
wft
whisper
audio
speech
Generated from Trainer
Eval Results
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whisper-large-v3-turbo-common_voice_16_1-zh-TW-2-lora / README.md
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metadata
library_name: peft
language:
  - zh
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - wft
  - whisper
  - automatic-speech-recognition
  - audio
  - speech
  - generated_from_trainer
datasets:
  - JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
metrics:
  - wer
model-index:
  - name: whisper-large-v3-turbo-common_voice_16_1-zh-TW-2
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
          type: JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed
        metrics:
          - type: wer
            value: 38.545016077170416
            name: Wer

whisper-large-v3-turbo-common_voice_16_1-zh-TW-2

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the JacobLinCool/mozilla-foundation-common_voice_16_1-zh-TW-preprocessed dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2346
  • Wer: 38.5450
  • Cer: 10.8963

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: 0.0005
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • 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: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Wer Cer
No log 0 0 2.7503 76.5675 20.3917
0.9352 0.9987 377 0.2472 47.9301 13.6656
0.73 1.9980 754 0.2502 47.0056 13.5652
0.4985 2.9974 1131 0.2559 46.2018 13.7057
0.1928 3.9993 1509 0.2595 45.9606 13.0906
0.2539 4.9987 1886 0.2522 44.7950 13.1459
0.0607 5.9980 2263 0.2422 44.7548 12.5006
0.0826 6.9974 2640 0.2488 43.8907 12.4906
0.0151 7.9993 3018 0.2403 40.2331 11.4537
0.0056 8.9987 3395 0.2390 39.8312 11.5290
0.0056 9.9927 3770 0.2346 38.5450 10.8963

Framework versions

  • PEFT 0.13.2
  • Transformers 4.46.0
  • Pytorch 2.4.1+cu124
  • Datasets 3.0.2
  • Tokenizers 0.20.1