Comments (2)
Ideally you would use the same for both. Since the KL loss is computed from the sequence of generated ids, we want the reference model in the KL loss (teacher during training) to be the same model used to generated the sequence of pseudo labels (the model during pseudo-labelling), to ensure we get the correct KL loss values.
Broadly speaking, it's always best to use the most performant model as the teacher, in order to maximise the performance of your student model. That means you should use large-v3 for both pseudo-labelling and distillation, to ensure you get the highest accuracy pseudo-labels, and thus maximise the accuracy of your student model.
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Thank you for helpful comments. Makes sense. Closing.
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Related Issues (20)
- large-v2 for english lost voice to text HOT 1
- Finetuning on which model? HOT 1
- Resuming training fails HOT 3
- [Issue] latest run_pseudo_labelling.py
- [Question] Can we distill for multiple langauges for distil-small-whisper HOT 3
- Quantize distil-whisper?
- perceptually faster inference through pre-completion inference of audio
- RuntimeError: User specified an unsupported autocast device_type 'mps'
- question about when to apply WER threshold filtering strategy with concatenated audio
- Problems in concatenate_dataset
- How to set the target language for examples in README? HOT 7
- Unable to reproduce results from the paper HOT 6
- BetterTransformer optimization / flash_attn{_2} HOT 6
- Cached English Common Voice dataset size. HOT 1
- How to use distil-whisper-large-v3-de-kd model from HF? HOT 10
- Pseudo-labelling librispeech_asr (train.360): KeyError `train-360` when not streaming. HOT 1
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- Voxpopuli text column "raw_text" HF dataset card shows empty string. HOT 1
- any executable script for running on custom data/given dataset HOT 1
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