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This model is a Hebrew finetune (continued training) of the OpenAI Whisper Large v3 Turbo model.
Language detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew.
Additionally, the tanslation task was not trained and also degraded. This model would not be able to translate in any reasonable capacity.
Please follow the original model card for usage details - replacing with this model name. You can also fine other weight formats ad quantizations on the ivrit ai HF page.
We created some simple example scripts using this model and weights for other inference runtimes. Find those in the "examples" folder within the training GitHub repo.
This model was trained on the following datasets:
This model was trained in two main phases:
This model is a weighted-average of the 2 lowest eval loss checkpoints (From around the end of epoch 2) from two seprate runs with the same setup. Training code can be found on the ivrit-ai Github here
The "Crowd Recital" and "Knesset" datasets contain timestamps and previous text following the Whisper expected inputs. Timestamps were used from 40% of samples from those datasets, and 50% of the previous text was used.
The "Crowd Transcribe" datasets has no timestamps or previous text and this preprocessing only included melspec feature extraction and text encoding.
Preprocessing code can be found within the training code repository.
Datasets were interleaved with 0.15:0.8:0.05 ratio (knesset:crowd-transcribe:crowd-recital).
Please refer to the ivrit-ai/hebrew-transcription-leaderboard