README.md

metadata

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)

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: 44.1%
  • Test Data 2: 15.1%
  • Test Data 3: 44.2%
  • Test Data 4: 15.9%

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].