Automatic Speech Recognition
PEFT
TensorBoard
Safetensors
English
wft
whisper
audio
speech
Generated from Trainer
Eval Results (legacy)
Instructions to use JacobLinCool/wft-test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JacobLinCool/wft-test-model with PEFT:
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- Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - wft | |
| - whisper | |
| - automatic-speech-recognition | |
| - audio | |
| - speech | |
| - generated_from_trainer | |
| datasets: | |
| - hf-internal-testing/librispeech_asr_dummy | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: wft-test-model | |
| results: | |
| - task: | |
| type: automatic-speech-recognition | |
| name: Automatic Speech Recognition | |
| dataset: | |
| name: hf-internal-testing/librispeech_asr_dummy | |
| type: hf-internal-testing/librispeech_asr_dummy | |
| metrics: | |
| - type: wer | |
| value: 5.905511811023622 | |
| 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. --> | |
| # wft-test-model | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the hf-internal-testing/librispeech_asr_dummy dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1185 | |
| - Wer: 5.9055 | |
| - Cer: 83.2386 | |
| - Decode Time: 0.5299 | |
| - Wer Time: 0.0047 | |
| - Cer Time: 0.0030 | |
| ## 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: 4 | |
| - 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: 50 | |
| - training_steps: 100 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Time | Wer Time | Cer Time | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:--------:|:--------:| | |
| | 2.4079 | 0.1 | 10 | 1.9885 | 312.2047 | 119.2472 | 0.5334 | 0.0169 | 0.0041 | | |
| | 1.2303 | 1.01 | 20 | 1.1646 | 258.2677 | 100.0 | 0.5213 | 1.4057 | 0.0046 | | |
| | 0.8667 | 1.11 | 30 | 0.8100 | 37.7953 | 52.3438 | 0.5008 | 0.0396 | 0.0045 | | |
| | 0.4517 | 2.02 | 40 | 0.6337 | 40.9449 | 73.7926 | 0.5137 | 0.0217 | 0.0030 | | |
| | 0.4352 | 2.12 | 50 | 0.4493 | 16.5354 | 88.1392 | 0.5203 | 0.0054 | 0.0032 | | |
| | 0.2341 | 3.03 | 60 | 0.2922 | 8.2677 | 97.5852 | 0.5434 | 0.0060 | 0.0032 | | |
| | 0.2233 | 3.13 | 70 | 0.2026 | 9.0551 | 83.5227 | 0.5359 | 0.0063 | 0.0033 | | |
| | 0.1098 | 4.04 | 80 | 0.1665 | 5.9055 | 83.9489 | 0.5316 | 0.0056 | 0.0029 | | |
| | 0.0678 | 4.14 | 90 | 0.1279 | 7.0866 | 81.1080 | 0.5388 | 0.0079 | 0.0038 | | |
| | 0.078 | 5.05 | 100 | 0.1185 | 5.9055 | 83.2386 | 0.5299 | 0.0047 | 0.0030 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.45.2 | |
| - Pytorch 2.5.0 | |
| - Datasets 3.0.2 | |
| - Tokenizers 0.20.1 |