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README.md

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+## Model Card: Wav2vec_Classroom_WSP_FT
+
+### Model Overview
+**Model Name:** Wav2vec_Classroom_WSP_FT 
+**Version:** 1.0  
+**Developed By:** Ahmed Adel Attia (University of Maryland & Stanford University)
+**Date:** 2025  
+
+**Description:**  
+NCTE-WSP-ASR is an automatic speech recognition (ASR) model trained specifically for classroom speech transcription using a weakly supervised pretraining (WSP) approach. The model first undergoes supervised pretraining on weakly transcribed classroom data (NCTE-Weak) and is then fine-tuned using a small amount of human-verified gold-standard data (NCTE-Gold). This methodology allows the model to generalize well despite the scarcity of precisely transcribed classroom speech.  
+
+This model is adapted from **[Wav2vec-Classroom](https://huggingface.co/aadel4/Wav2vec_Classroom)**, which was trained using continued pretraining (CPT) on large-scale unlabeled classroom speech data. The adaptation involves further fine-tuning to leverage weak transcriptions before final refinement on high-quality annotations.
+
+**Use Case:**  
+- Speech-to-text transcription for classroom environments.
+- Educational research and analysis of classroom discourse.
+- Low-resource ASR applications where gold-standard labels are limited.
+
+### Model Details
+**Architecture:** Wav2vec2.0-based model fine-tuned with Fairseq  
+
+**Training Data:**  
+- **NCTE-Weak:** 5000 hours of weak transcriptions from the NCTE dataset. 
+- **NCTE-Gold:** 13 hours of manually transcribed classroom recordings.
+
+**Training Strategy:**  
+1. **Weakly Supervised Pretraining (WSP):** The model is first trained using NCTE-Weak transcripts, which contain alignment errors and omissions but provide useful weak supervision.
+2. **Precise Fine-tuning:** The pretrained model is fine-tuned on NCTE-Gold, ensuring it adapts to high-quality transcriptions.
+
+### Evaluation Results
+**Word Error Rate (WER) comparison on NCTE and MPT test sets:**
+
+| Training Data | NCTE WER | MPT WER |
+|--------------|----------|---------|
+| **Baseline (TEDLIUM-trained ASR)** | 55.82 / 50.56 | 55.11 / 50.50 |
+| **NCTE-Weak only** | 36.23 / 32.30 | 50.84 / 46.09 |
+| **NCTE-Gold only** | 21.12 / 16.47 | 31.52 / 27.93 |
+| **Self-training** | 17.45 / 15.09 | 27.42 / 26.24 |
+| **NCTE-WSP-ASR (NCTE-Weak → NCTE-Gold)** | **16.54 / 13.51** | **25.07 / 23.70** |
+
+### Limitations
+- The model relies on weak supervision, and transcription quality is dependent on the balance between weak and gold-standard data.
+- Classroom noise, overlapping speech, and spontaneous interactions may still lead to recognition errors.
+- The model was trained specifically on elementary math classrooms and may not generalize well to other educational settings without further adaptation.
+
+### Usage Request
+If you use the NCTE-WSP-ASR model in your research, please acknowledge this work and refer to the original paper submitted to Interspeech 2025.
+
+For inquiries or collaborations, please contact the authors of the original paper.
+