Hands-On Guide: Fine-Tuning Whisper v3 for Maghrebi Dialectal Speech Recognition

Step-by-Step Engineering Tutorial: Master pre-diacritization, SpecAugment data pipeline pre-processing, and LoRA PEFT fine-tuning on OpenAI Whisper v3 for spoken Maghrebi Darija.

Step 1: Dataset Pre-processing & Normalization

Clean raw audio wave files to 16kHz mono and apply Shakkelha neural pre-diacritization to target transcriptions.

Step 2: Initialize HuggingFace LoRA Processor

from peft import LoraConfig, get_peft_model
from transformers import WhisperForConditionalGeneration

model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none"
)
model = get_peft_model(model, peft_config)

Step 3: Execute Training & Compute WER Benchmarks

Train for 10 epochs using AdamW optimizer and evaluate Word Error Rate (WER) against benchmark audio clips.