Automatic Speech Recognition
Transformers
PyTorch
Hebrew
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Shiry/Whisper_hebrew_medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shiry/Whisper_hebrew_medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shiry/Whisper_hebrew_medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Shiry/Whisper_hebrew_medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("Shiry/Whisper_hebrew_medium") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#3
by librarian-bot - opened
README.md
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@@ -9,12 +9,13 @@ datasets:
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- google/fleurs
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metrics:
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- wer
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model-index:
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- name: Whisper Medium Hebrew
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: google/fleurs he_il
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type: google/fleurs
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split: test
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args: he_il
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metrics:
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type: wer
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value: 34
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- google/fleurs
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metrics:
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- wer
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base_model: openai/whisper-medium
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model-index:
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- name: Whisper Medium Hebrew
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: google/fleurs he_il
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type: google/fleurs
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split: test
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args: he_il
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metrics:
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- type: wer
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value: 34
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name: Wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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