Comments (2)
Hey @silvacarl2! Sorry for the late reply here! The best option would be to fine-tune on the closest scenario to what you expect at inference time. If you expect the model to transcribe the entire spoken name, then you should go with that.
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excellent. we now have 14M samples to use for fine tuning. yes i think entire spoken name is the way to go.
Any pointers to the latest best techniques to fine tuning whisper for a use case like this?
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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
- Padding conflict in loss computation HOT 2
- Whisper finetune HOT 1
- 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
- Increasing WER & Validation Loss During Whisper Fine-Tuning HOT 1
- Poor Real-Time Performance of Whisper Models Fine-Tuned on Synthetic Data HOT 1
- Colab runtime crash HOT 2
- 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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