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README.md

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+## Model Card: Wav2vec_Classroom_FT
+
+### Model Overview
+**Model Name:**Wav2vec_Classroom_FT  
+**Version:** 1.0  
+**Developed By:** Ahmed Adel Attia (University of Maryland and Stanford University)
+**Date:** 2025  
+
+**Description:**  
+NCTE-Baseline-ASR is an automatic speech recognition (ASR) model trained for classroom speech transcription using direct fine-tuning on a small set of human-verified gold-standard transcriptions. Unlike **NCTE-WSP-ASR**, this model does not leverage weak transcriptions for intermediate training and is solely trained on high-quality annotations.
+
+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 direct fine-tuning on a limited transcribed dataset.
+
+**Use Case:**  
+- Speech-to-text transcription for classroom environments.  
+- ASR applications requiring high precision with limited data.  
+- Benchmarking ASR performance without weakly supervised pretraining.
+
+### Model Details
+**Architecture:** Wav2vec2.0-based model fine-tuned with Fairseq  
+
+**Training Data:**  
+- **NCTE-Gold:** 13 hours of manually transcribed classroom recordings.
+
+**Training Strategy:**  
+1. **Direct Fine-tuning:** The model is fine-tuned directly on NCTE-Gold without any pretraining on weak transcripts.
+2. **Evaluation:** The model is tested on classroom ASR tasks to compare its performance with WSP-based models.
+
+### 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-Gold only (NCTE-Baseline-ASR)** | 21.12 / 16.47 | 31.52 / 27.93 |
+| **NCTE-WSP-ASR (NCTE-Weak → NCTE-Gold)** | **16.54 / 13.51** | **25.07 / 23.70** |
+
+### Limitations
+- The model is trained on a small dataset (13 hours), which limits its ability to generalize beyond classroom speech.
+- Performance is lower than **NCTE-WSP-ASR**, which benefits from weak transcripts for pretraining.
+- Background noise, overlapping speech, and speaker variations may still impact transcription quality.
+
+### Usage Request
+If you use the NCTE-Baseline-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.
+