ivrit-ai
/

whisper-large-v3-turbo

@@ -1,7 +1,10 @@
 ---
 library_name: transformers
 datasets:
 - ivrit-ai/crowd-transcribe-v5
 language:
 - he
 metrics:
@@ -13,183 +16,67 @@ pipeline_tag: automatic-speech-recognition
 # Model Card for Model ID
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 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: apache-2.0
 datasets:
 - ivrit-ai/crowd-transcribe-v5
+- ivrit-ai/crowd-recital-whisper-training
+- ivrit-ai/knesset-plenums-whisper-training
 language:
 - he
 metrics:
 # Model Card for Model ID
+This model is a Hebrew finetune (continued training) of the OpenAI Whisper Large v3 Turbo model.
 ## Model Details
 ### Model Description
+- **Developed by:** ivrit-ai
+- **Language(s) (NLP):** Hebrew
+- **License:** Apache-2.0
+- **Finetuned from model** openai/whisper-large-v3-turbo
 ## 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-turbo#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
+- [ivrit-ai/knesset-plenums-whisper-training](https://huggingface.co/datasets/ivrit-ai/knesset-plenums-whisper-training) - A subset of a Knesset (Israeli house of representitives) plenum protocols. ~325h
 ### Training Procedure
+This model is a weighted-average of the 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](https://github.com/ivrit-ai/asr-training)
+#### Preprocessing
+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](https://github.com/ivrit-ai/asr-training).
+Datasets were interleaved with 0.15:0.8:0.05 ratio (knesset: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:** 8 x Nvidia A40 machine
+- **Duration:** ~9h run, stopped at 3 epochs
+## Evaluation
+Please refer to the [ivrit-ai/hebrew-transcription-leaderboard](https://huggingface.co/spaces/ivrit-ai/hebrew-transcription-leaderboard)