Instructions to use alphaedge-ai/whisper-tiny-hat-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-tiny-hat-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-tiny-hat-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-tiny-hat-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-tiny-hat-32768") - Notebooks
- Google Colab
- Kaggle
Update model card for Haitian Creole
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README.md
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# whisper-tiny-hat-32768
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This model is a **19.42%
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This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
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## Model Statistics
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# whisper-tiny-hat-32768
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This model is a **19.42% smaller** version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) optimized for **Haitian Creole** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming) method.
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This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
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## Model Statistics
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