Instructions to use alakxender/whisper-large-v3-cv17-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alakxender/whisper-large-v3-cv17-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="alakxender/whisper-large-v3-cv17-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("alakxender/whisper-large-v3-cv17-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("alakxender/whisper-large-v3-cv17-dv") - Notebooks
- Google Colab
- Kaggle
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Parent(s): 75e1df2
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README.md
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model-index:
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- name: Whisper Large v3 DV - Alakxender
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results: []
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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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- Transformers 4.41.0.dev0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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model-index:
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- name: Whisper Large v3 DV - Alakxender
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results: []
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pipeline_tag: automatic-speech-recognition
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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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- Transformers 4.41.0.dev0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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