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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@@ -26,8 +26,11 @@ This model is adapted from **[Wav2vec-Classroom](https://huggingface.co/aadel4/W
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This model was originally trained using the fairseq library then ported into Huggingface.
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**Use Case:**
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- Speech-to-text transcription for classroom environments.
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- Educational research and analysis of classroom discourse.
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- Low-resource ASR applications where gold-standard labels are limited.
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This model was originally trained using the fairseq library then ported into Huggingface.
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The model should be run with n-gram LM beamsearch decoding for best results. We got our best results usig [this](https://drive.google.com/drive/u/0/folders/1yAFXcbozqDUFZu-hnnzFP_8SAzDYT2JJ) 5-gram LM we trained on classroom speech text.
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**Use Case:**
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- Speech-to-text transcription for classroom environments.
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- Forced allignment of transcription with audio to provide character and word level boundaries.
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- Educational research and analysis of classroom discourse.
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- Low-resource ASR applications where gold-standard labels are limited.
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