Instructions to use aadel4/Wav2vec_Classroom_WSP_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/Wav2vec_Classroom_WSP_FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_WSP_FT")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT") model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_WSP_FT") - Notebooks
- Google Colab
- Kaggle
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README.md
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- facebook/wav2vec2-large-robust
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- aadel4/Wav2vec_Classroom
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pipeline_tag: automatic-speech-recognition
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tags:
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- wav2vec2
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library_name: transformers
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---
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## Model Card: Wav2vec_Classroom_WSP_FT
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- facebook/wav2vec2-large-robust
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- aadel4/Wav2vec_Classroom
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pipeline_tag: automatic-speech-recognition
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library_name: transformers
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language: en
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tags:
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- audio
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- automatic-speech-recognition
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- wav2vec2
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## Model Card: Wav2vec_Classroom_WSP_FT
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