Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+---
+# Model Card: LEVI Whisper Medium Fine-Tuned Model
+
+## Model Information
+- **Model Name:** levicu/LEVI_whisper_medium
+- **Description:** This model is a fine-tuned version of the OpenAI Whisper Medium model, tailored for speech recognition tasks using the LEVI v2 dataset, which consists of classroom audiovisual recording data.
+- **Model Architecture:** openai/whisper-medium
+- **Dataset:** LEVI v2 (classroom audiovisual recording data)
+
+## Training Details
+- **Training Procedure:**
+    - LoRA Parameter Efficient Fine-tuning technique with the following parameters:
+        - r=32
+        - lora_alpha=64
+        - target_modules=["q_proj", "v_proj"]
+        - lora_dropout=0.05
+        - bias="none"
+    - INT8 quantization
+    - Trained for 6 epochs with a learning rate of 1e-4 and warmup steps of 100 without gradient accumulation.
+- **Evaluation Metrics:** Word Error Rate (WER)
+
+## Usage
+- **Usage:** The model can be used for speech recognition tasks. Inputs should be audio files, and the model outputs transcriptions.
+
+## Limitations and Ethical Considerations
+- **Limitations:** None provided.
+- **Ethical Considerations:** Consider the ethical implications of using this model, particularly in scenarios involving sensitive or private information.
+
+## License
+- **License:** Not specified.
+
+## Contact Information
+- **Contact:** For questions, feedback, or support regarding the model, please contact roso8920@colorado.edu or nich7312@colorado.edu.