Instructions to use levicu/whisat-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use levicu/whisat-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="levicu/whisat-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("levicu/whisat-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("levicu/whisat-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
medium model upload
Browse files- README.md +1 -1
- config.json +1 -1
- generation_config.json +1 -1
README.md
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medium_model:
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- openai/whisper-medium
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pipeline_tag: automatic-speech-recognition
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base_model:
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- openai/whisper-medium
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---
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medium_model:
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- openai/whisper-medium
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pipeline_tag: automatic-speech-recognition
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base_model:
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- openai/whisper-medium
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---
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config.json
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": false,
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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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"scale_embedding": false,
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"suppress_tokens": [],
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"torch_dtype": "float32",
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"transformers_version": "4.39.3",
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"use_cache": false,
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"use_weighted_layer_sum": false,
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"vocab_size": 51865
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generation_config.json
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"transcribe": 50359,
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"translate": 50358
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},
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"transformers_version": "4.
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"use_cache": false
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}
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"transcribe": 50359,
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"translate": 50358
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},
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"transformers_version": "4.39.3",
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"use_cache": false
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}
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