YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)

Model Card: Wav2vec-Classroom

Model Overview

Model Name: Wav2vec-Classroom
Version: 1.0
Developed By: Ahmed Adel Attia (University of Maryland & Stanford University)
Date: 2025

Description:
Wav2vec-Classroom is an automatic speech recognition (ASR) model designed for robust performance in classroom environments. The model is adapted from Wav2vec2.0 using Continued Pretraining (CPT) on large-scale unlabeled classroom audio data, followed by fine-tuning on a small set of transcribed classroom recordings. This approach enhances the model’s ability to handle classroom noise, overlapping speech, and diverse microphone setups.

Use Case:

  • Speech-to-text transcription for classroom recordings.
  • Automatic feedback generation for educational AI tools.
  • ASR research in low-resource, noisy environments.

Model Details

Architecture: Wav2vec2.0-based self-supervised model, fine-tuned with Fairseq

Training Data:

  • Unlabeled Classroom Audio (NCTE dataset): 5235 hours of classroom recordings used for self-supervised CPT.
  • NCTE-Gold: 5.15 hours of human-verified classroom transcriptions for supervised fine-tuning.

Training Strategy:

  1. Continued Pretraining (CPT): The model is initialized with a pre-trained Wav2vec2.0 checkpoint and further pre-trained on 5235 hours of unlabeled classroom speech data. This step allows the model to learn domain-specific acoustic representations.
  2. Supervised Fine-tuning: The CPT-pretrained model is then fine-tuned using the NCTE-Gold dataset for better alignment with transcriptions.

Evaluation Results

Word Error Rate (WER) comparison on NCTE and MPT test sets:

Training Data NCTE WER MPT WER
Pretraining from Scratch (W2V-SCR) 30.25 / 38.59 51.39 / 38.59
Wav2vec2.0-LV60K (No CPT) 30.39 / 33.56 39.11 / 37.82
Wav2vec2.0-Robust (No CPT) 27.99 / 31.49 35.07 / 36.36
Wav2vec2.0-Robust (CPT) 17.71 / 26.50 25.04 / 30.97

Limitations

  • The model is optimized for classroom speech and may not generalize well to other domains.
  • Background noise, overlapping speech, and speaker variations may still impact performance.
  • The amount of labeled training data remains limited, which may affect ASR accuracy in extreme cases.

Usage Request

If you use the Wav2vec-Classroom model in your research, please acknowledge this work and cite the following paper:

CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments
Ahmed Adel Attia, Dorottya Demszky, Tolulopé Ògúnrẹ̀mí, Jing Liu, Carol Espy-Wilson
ICASSP 2025

@article{attia2024cpt_wav2vec,
  title={CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments},
  author={Ahmed Adel Attia and Dorottya Demszky and Tolulopé Ògúnrẹ̀mí and Jing Liu and Carol Espy-Wilson},
  journal={ICASSP 2025},
  year={2024}
}
Downloads last month
7
Safetensors
Model size
317M params
Tensor type
F32
·
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.

Model tree for aadel4/Wav2vec_Classroom

Finetunes
2 models