Instructions to use alphaedge-ai/whisper-large-v3-turbo-mri-32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphaedge-ai/whisper-large-v3-turbo-mri-32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alphaedge-ai/whisper-large-v3-turbo-mri-32768")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768") model = AutoModelForSpeechSeq2Seq.from_pretrained("alphaedge-ai/whisper-large-v3-turbo-mri-32768", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model card for Maori
Browse files
README.md
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| **Vocabulary size** | 51,865 tokens | 32,768 tokens | **36.82%** |
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| Metric | Original | Trimmed | Reduction |
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| **Vocabulary size** | 51,865 tokens | 32,768 tokens | **36.82%** |
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| **Model size** | 808,878,080 params | 784,432,640 params | **3.02%** |
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