MaddoggProduction

whisper-small-quran-lora-everyayah

README

License: apache-2.0

Model Performance

Word Error Rate (WER): Achieved 10.89% on a randomly sampled 20% subset of the ahishamm/QURANICWhisperDataset test set.
Accuracy: The model demonstrates high precision in capturing Quranic vocabulary, standard Imla'i script nuances, and correct Tashkeel placement.

Architecture & Trade-offs

This model utilizes the standard Whisper Small architecture, featuring 12 encoder layers and 12 decoder layers (244M parameters).

Pros: It offers a great balance between speed and stability. It is significantly faster to run than the Medium or Large architectures, while its 12-layer decoder maintains much stronger contextual and grammatical stability compared to heavily pruned models.
Cons: It lacks the acoustic depth of the Large models with more layers (24, 32..), thus occasionally resulting in minor phonetic substitutions on highly similar letters. it is also prone to hallucinations when it encounters silences without proper configuration.

Training Details

The model was trained using LoRA (Low-Rank Adaptation) in a multi-stage curriculum learning process to ensure stability and precision.

Datasets

The training and evaluation process utilized a comprehensive mix of professional recitations:

Training & Validation:
- tarteel-ai/everyayah
Testing:
- ahishamm/QURANICWhisperDataset (A 20% subset was used for final WER calculation).

Methodology

Curriculum Learning: The model was trained gradually across this dataset to refine its understanding of Tajweed and Quranic sentence structures.
Data Augmentation: To ensure the model remains robust against real-world conditions (non-studio microphones, background noise, varying volumes), diverse audio augmentations including gain adjustments and spectral masking were applied during the training process.

Usage

This model is fully compatible with the Hugging Face transformers pipeline.

python
from transformers import pipeline

# Load the pipeline with automatic device mapping for multi-GPU setups
pipe = pipeline(
    "automatic-speech-recognition",
    model="MaddoggProduction/whisper-small-quran-lora-everyayah",
    device_map="auto" # auto for multiple GPUs, n: for GPU n, -1: for CPU
)

# Transcribe audio with chunking
result = pipe(
    "path_to_audio.mp3",
    chunk_length_s=30, 
    stride_length_s=3, 
    batch_size=8,
    return_timestamps=True,
    generate_kwargs={
        "task": "transcribe",
        "language": "arabic",
        #"temperature": 0.0,      # Greedy decoding, recommended for stability. Adjust as needed
        #"num_beams": 5,           # Adjust as needed, 5 for stability
    }
)

print(result["text"])

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

MaddoggProduction

Model Tree

Base

openai/whisper-small

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

Explore FriendliAI today

Get started Talk to an engineer