Comments (2)
cc @Vaibhavs10
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I didn't realize that the NLL loss ignores '-100' labels in its loss computations (I thought it was an arbitrary value in padding), which explains a difference in loss values when using the pad token instead. My bad!
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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
- 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
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- [WFTE] "Num examples: Unknown" when it reached eval step on both Colab and the Python code. HOT 1
- 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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