LEVI_whisper_large-v2

Model Card: LEVI Whisper Large-v2 Fine-Tuned Model

Model Information

• Model Name: levicu/LEVI_whisper_large-v2
• Description: This model is a fine-tuned version of the OpenAI Whisper Large-v2 model, tailored for speech recognition tasks using the LEVI v2 dataset, which consists of classroom audiovisual recording data.
• Model Architecture: openai/whisper-large-v2
• Dataset: LEVI_LoFi_v2/TRAIN (per-utterance transcript and 16k WAV audio)
  • both student and tutor speech were used
  • manifest: LEVI_LoFi_v2_TRAIN_punc+cased.csv

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)

Evaluation

• Testing Data
  • Test Data 1: LoFi Students (LEVI_LoFi_v2_TEST_punc+cased_student)
  • Test Data 2: LoFi Tutors (LEVI_LoFi_v2_TEST_punc+cased_tutor)
  • Test Data 3: HiFi Students (LEVI_orig11_HiFi_punc+cased_student)
  • Test Data 4: HiFi Tutor (LEVI_orig11_HiFi_punc+cased_tutor)
• Metric
  • Word Error Rate (WER)
• Results
  • Test Data 1: 39.9%
  • Test Data 2: 13.7%
  • Test Data 3: 42.8%
  • Test Data 4: 19.7%

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 [email protected] or [email protected].