Comments (1)
Hey @monk1337! Awesome that you reduce the WER by over half in just 1k training steps! The increasing WER after 1k steps looks like it could well be a case of over-fitting. You could combat this by:
- Introducing regularisation through dropout and activation dropout (set these config attributes to either 0.1 or 0.2 to activate dropout. In my experience, a small amount of dropout helps for small datasets. Going above a dropout of 0.2 is too severe and hurts performance)
- Using a larger dataset (in practice this may not be feasible, but it is one valid solution to reduce over fitting)
If you have the script you used to train the model and pushed the checkpoint to the Hugging Face Hub, I'd be happy to advise further!
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Related Issues (20)
- How to use the resulting whisper checkpoints when finetuning HOT 9
- Fine-tuned Whisper models perform worse than OpenAI HOT 8
- Super large number of epoch HOT 1
- Using finetuned whisper checkpoints for inference HOT 1
- Whisper parameters HOT 1
- Add Keras Dreambooth notebook to this repo
- Lambda platform doesn't support Tensorflow-Gpu HOT 1
- Visualize ControlNet results using Weights & Biases Tables HOT 11
- WhisperPositionalEmbedding HOT 1
- this is more question than an issue HOT 2
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- common_voice map error in the notebook of fine-tune-whisper-non-streaming HOT 1
- huggan.pytorch.lightweight_gan.lightweight_gan.LightweightGAN _from_pretrained requires use_auth_token but this is not passed by the from_pretrained method inherited from ModelHubMixin HOT 5
- Poor Real-Time Performance of Whisper Models Fine-Tuned on Synthetic Data HOT 1
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- How to prepare audio dataset for whisper fine-tuning with timestamps?
- The fine tuning script run_speech_recognition_seq2seq_streaming.py use interleave_datasets which will truncate the train split HOT 2
- Using rename_column and remove_column method for a IterableDataset object leads to its feature property become None --- in the Whisper Fine-Tuning Event HOT 1
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