ivrit-ai
/

whisper-large-v3

@@ -1,199 +1,78 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- ivrit-ai/crowd-transcribe-v5
+- ivrit-ai/crowd-recital-whisper-training
+language:
+- he
+metrics:
+- wer
+base_model:
+- openai/whisper-large-v3
 ---
 # Model Card for Model ID
+This model is a Hebrew finetune (continued training) of the OpenAI Whisper Large v3 model.
 ## Model Details
 ### Model Description
+- **Developed by:** ivrit-ai
+- **Language(s) (NLP):** Hebrew
+- **License:** Apache-2.0
+- **Finetuned from model** openai/whisper-large-v3
 ## Bias, Risks, and Limitations
+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.
 ## How to Get Started with the Model
+Please follow the original [model card](https://huggingface.co/openai/whisper-large-v3#usage) for usage details - replacing with this model name.
+You can also fine other weight formats ad quantizations on the [ivrit ai](https://huggingface.co/ivrit-ai) HF page.
 ## Training Details
 ### Training Data
+This model was trained on the following datasets:
+- [ivrit-ai/crowd-transcribe-v5](https://huggingface.co/datasets/ivrit-ai/crowd-transcribe-v5) - Publicly accessible audio sources have beem crowd-transcribed segment-by-segment - ~300h
+- [ivrit-ai/crowd-recital-whisper-training](https://huggingface.co/datasets/ivrit-ai/crowd-recital-whisper-training) - Crowd-sourced recording of Wikipedia atricle snippets. ~50h
 ### Training Procedure
+This model is an average of the lowest eval lass from two seprate runs with the same setup.
+#### Preprocessing
+The "Crowd Recital" dataset contains timestamps and previous text following the Whisper expected inputs.
+Timestamps were used across all 50h from this 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](https://github.com/ivrit-ai/asr-training).
+Datasets were interleaved with 0.95:0.05 ratio (crowd-transcribe:crowd-recital).
+#### Training Hyperparameters
+- **Training regime:** bf16 mixed precision with sdpa
+- **Learning Rate:** 1e-5, Linear decay, 800 steps warmup for 3 epochs
+- **Batch Size:** 32
+#### Training Hardward / Duration
+- **GPU Type:** Single Nvidia L40S machine
+- **Duration:** 24h run, stopped at 2 epochs
+## Evaluation
+Please refer to the [ivrit-ai/hebrew-transcription-leaderboard](https://huggingface.co/spaces/ivrit-ai/hebrew-transcription-leaderboard)