whisper-algerian-darja-small API & Inference Endpoint

Overview

touati-kamel/whisper-algerian-darja-small is an Automatic Speech Recognition (ASR) model specifically fine-tuned for Algerian Arabic (Darja / الدارجة الجزائرية).

Built on top of OpenAI's Whisper Small (openai/whisper-small, 244M parameters), this model incorporates parameter-efficient LoRA adapters trained with 4-bit quantization (QLoRA) over a sequential 3-phase curriculum covering conversational podcasts, spontaneous storytelling, and cultural narratives from the OddAdmix Algerian speech collection.

Key Highlights

Native Algerian Dialect Adaptation: Handles authentic Darja phonetics, vocabulary, morphology, and spontaneous conversational speech patterns.
Parameter-Efficient LoRA (PEFT): Trained on 25.95M parameters (9.70% of total model weights), allowing lightweight adapter storage and fast inference.
Significant Accuracy Gains: Achieved 14.87% WER on storytelling evaluation subsets and 27.54% WER on raw narrative benchmarks.
Sequential Streaming Curriculum: Trained end-to-end via multi-phase streaming on Hugging Face CDN without disk bottlenecking.

Evaluation & Benchmark Results

The model was evaluated iteratively across the three domain splits using Word Error Rate (WER %) and Cross-Entropy Evaluation Loss:

Table with columns: Phase, Domain / Dataset, Epochs / Steps, Learning Rate, Best WER (%), Final Eval Loss
Phase	Domain / Dataset	Epochs / Steps	Learning Rate	Best WER (%)	Final Eval Loss
Phase 1	Kahwa Podcast (`oddadmix/arabic-audio-collection-algerian-kahwa-postcast`)	2 epochs (4,942 steps)	$1 \times 1 0 - 4$	34.85%	0.521

Cumulative Training Progress: Total training ran for 15,829 cumulative optimization steps with cosine annealing learning rate schedules and automatic best-adapter preservation per phase.

Architecture & Training Methodology

markdown
┌────────────────────────────────────────────────────────┐
│                   OpenAI Whisper-Small                 │
│              (4-bit NF4 Quantization Base)             │
└──────────────────────────┬─────────────────────────────┘
                           │
             ┌─────────────┴─────────────┐
             │    LoRA Adapters (r=64)   │
             │   Target: q, k, v, out,   │
             │           fc1, fc2        │
             └─────────────┬─────────────┘
                           │
      ┌────────────────────┴────────────────────┐
      │     Sequential Curriculum Learning      │
      ├─────────────────────────────────────────┤
      │ Phase 1: Kahwa Podcast (Conversational) │
      │                    ▼                    │
      │ Phase 2: Loubna Stories (Expressive)    │
      │                    ▼                    │
      │ Phase 3: Rawi Narratives (Storytelling) │
      └─────────────────────────────────────────┘

Model Configuration

Base Architecture: openai/whisper-small (Encoder-Decoder Transformer)
Base Model Parameters: 241,734,912
Trainable LoRA Parameters: 25,952,256 (9.695%)
Total Parameters: 267,687,168
LoRA Hyperparameters:
- Rank ( $r$ ): 64
- Scaling factor ( $α$ ): 128
- LoRA Dropout: 0.05
- Target Modules: q_proj, , , , ,

Data Preprocessing & Arabic Normalization

Audio Normalization: Resampled to mono 16,000 Hz float32 arrays on-the-fly.
Text Sanitation:
- Removal of French annotations/tags: [French: ...], [FR: ...]
- Stripping non-speech markers and bracketed tokens: [...], <...>
- Arabic Diacritics (Harakat / Tashkeel) removal: \u064B to \u0652, \u0670
- Tatweel (Kashida) removal: \u0640
- Alef normalization: إ, ,

Quickstart & Inference

1. Installation

bash
pip install --upgrade transformers peft torch torchaudio soundfile librosa jiwer

2. High-Level Usage with `transformers.pipeline`

python
import torch
from transformers import pipeline

# Initialize speech recognition pipeline with PEFT adapter
pipe = pipeline(
    task="automatic-speech-recognition",
    model="touati-kamel/whisper-algerian-darja-small",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device=0 if torch.cuda.is_available() else "cpu",
    chunk_length_s=30,
)

# Transcribe an audio file (16kHz WAV or MP3)
result = pipe(
    "path/to/algerian_audio.mp3",
    generate_kwargs={"language": "arabic", "task": "transcribe"}
)

print("Transcription (Darja):", result["text"])

3. Native PyTorch + `PeftModel` Inference

python
import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "openai/whisper-small"
adapter_id = "touati-kamel/whisper-algerian-darja-small"

# 1. Load Processor
processor = WhisperProcessor.from_pretrained(model_id, language="arabic", task="transcribe")

# 2. Load Base Model and Apply LoRA Adapter
base_model = WhisperForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None,
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

# 3. Load and Preprocess Audio
audio, sr = librosa.load("path/to/audio.mp3", sr=16000)
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
if torch.cuda.is_available():
    input_features = input_features.to("cuda", dtype=torch.float16)

# 4. Generate Transcription
forced_decoder_ids = processor.get_decoder_prompt_ids(language="arabic", task="transcribe")
with torch.no_grad():
    predicted_ids = model.generate(
        input_features,
        forced_decoder_ids=forced_decoder_ids,
        max_new_tokens=225
    )

# 5. Decode Output
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print("Algerian Darja Output:", transcription)

Complete Normalization Function (Recommended for Evaluation)

To align with the training evaluation standard, use the following text normalizer:

python
import re
import string

_ARABIC_DIACRITICS = "\u064B\u064C\u064D\u064E\u064F\u0650\u0651\u0652\u0670"
_TATWEEL = "\u0640"
_PUNCT_MAP = {ord(c): None for c in string.punctuation + "\u060C\u061B\u061F\u00AB\u00BB"}

def normalize_darja_text(text: str) -> str:
    if not text:
        return ""
    # Strip diacritics & tatweel
    text = text.translate({ord(c): None for c in _ARABIC_DIACRITICS})
    text = text.replace(_TATWEEL, "")
    # Normalize Alef and Yaa
    text = text.replace("\u0625", "\u0627").replace("\u0623", "\u0627").replace("\u0622", "\u0627")
    text = text.replace("\u0649", "\u064A")
    # Remove punctuation
    text = text.translate(_PUNCT_MAP)
    # Collapse whitespace
    return " ".join(text.split()).strip()

Training Hyperparameters & Setup

Table with columns: Hyperparameter, Value
Hyperparameter	Value
Base Model	`openai/whisper-small` (244M params)
Quantization	4-bit NF4 (`BitsAndBytesConfig`)
Hardware	1x NVIDIA Tesla T4 GPU (16 GB VRAM)
Per-Device Batch Size	8
Gradient Accumulation Steps	4 (Effective Batch Size = 32)
Mixed Precision	FP16 (`fp16=True`)

Intended Uses & Limitations

Intended Uses

Speech-to-text transcription for Algerian podcasts, YouTube content, voice messages, and media.
Algerian Darija conversational assistants and voice search interfaces.
Transcription and subtitling for Algerian cultural and educational audio.

Limitations & Biases

Code-Switching: Algerian Darja frequently blends Arabic with French and Berber/Tamazight loanwords. While French tag handling was integrated during preprocessing, heavy French sentences may be transcribed phonetically into Arabic script.
Regional Dialectal Variations: The training data prominently covers Central and Western dialects. Eastern (Constantinois/Annaba) or Southern (Sahara) variants with distinct phonetic shifts may experience slight variance in accuracy.
Noisy Acoustic Environments: Performance is optimal on clean speech (podcasts, stories); background music or intense street noise may increase WER.

Datasets & Citations

Training Datasets

BibTeX Citation

bibtex
@misc{touati2026whisper_algerian_darja,
  author = {Kamel Touati},
  title = {Whisper Small Fine-Tuned for Algerian Arabic (Darja)},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/touati-kamel/whisper-algerian-darja-small}}
}

bibtex
@article{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2212.04356},
  year={2022}
}

Phase

Domain / Dataset

Epochs / Steps

Learning Rate

Best WER (%)

Final Eval Loss

Phase 1

Kahwa Podcast (oddadmix/arabic-audio-collection-algerian-kahwa-postcast)

2 epochs (4,942 steps)

1 \times 1 0 - 4

34.85%

0.521

markdown

┌────────────────────────────────────────────────────────┐
│                   OpenAI Whisper-Small                 │
│              (4-bit NF4 Quantization Base)             │
└──────────────────────────┬─────────────────────────────┘
                           │
             ┌─────────────┴─────────────┐
             │    LoRA Adapters (r=64)   │
             │   Target: q, k, v, out,   │
             │           fc1, fc2        │
             └─────────────┬─────────────┘
                           │
      ┌────────────────────┴────────────────────┐
      │     Sequential Curriculum Learning      │
      ├─────────────────────────────────────────┤
      │ Phase 1: Kahwa Podcast (Conversational) │
      │                    ▼                    │
      │ Phase 2: Loubna Stories (Expressive)    │
      │                    ▼                    │
      │ Phase 3: Rawi Narratives (Storytelling) │
      └─────────────────────────────────────────┘

python

import torch
from transformers import pipeline

# Initialize speech recognition pipeline with PEFT adapter
pipe = pipeline(
    task="automatic-speech-recognition",
    model="touati-kamel/whisper-algerian-darja-small",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device=0 if torch.cuda.is_available() else "cpu",
    chunk_length_s=30,
)

# Transcribe an audio file (16kHz WAV or MP3)
result = pipe(
    "path/to/algerian_audio.mp3",
    generate_kwargs={"language": "arabic", "task": "transcribe"}
)

print("Transcription (Darja):", result["text"])

python

import torch
import librosa
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "openai/whisper-small"
adapter_id = "touati-kamel/whisper-algerian-darja-small"

# 1. Load Processor
processor = WhisperProcessor.from_pretrained(model_id, language="arabic", task="transcribe")

# 2. Load Base Model and Apply LoRA Adapter
base_model = WhisperForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None,
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

# 3. Load and Preprocess Audio
audio, sr = librosa.load("path/to/audio.mp3", sr=16000)
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
if torch.cuda.is_available():
    input_features = input_features.to("cuda", dtype=torch.float16)

# 4. Generate Transcription
forced_decoder_ids = processor.get_decoder_prompt_ids(language="arabic", task="transcribe")
with torch.no_grad():
    predicted_ids = model.generate(
        input_features,
        forced_decoder_ids=forced_decoder_ids,
        max_new_tokens=225
    )

# 5. Decode Output
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print("Algerian Darja Output:", transcription)

python

import re
import string

_ARABIC_DIACRITICS = "\u064B\u064C\u064D\u064E\u064F\u0650\u0651\u0652\u0670"
_TATWEEL = "\u0640"
_PUNCT_MAP = {ord(c): None for c in string.punctuation + "\u060C\u061B\u061F\u00AB\u00BB"}

def normalize_darja_text(text: str) -> str:
    if not text:
        return ""
    # Strip diacritics & tatweel
    text = text.translate({ord(c): None for c in _ARABIC_DIACRITICS})
    text = text.replace(_TATWEEL, "")
    # Normalize Alef and Yaa
    text = text.replace("\u0625", "\u0627").replace("\u0623", "\u0627").replace("\u0622", "\u0627")
    text = text.replace("\u0649", "\u064A")
    # Remove punctuation
    text = text.translate(_PUNCT_MAP)
    # Collapse whitespace
    return " ".join(text.split()).strip()

Hyperparameter

Value

Base Model

openai/whisper-small (244M params)

Quantization

4-bit NF4 (BitsAndBytesConfig)

Hardware

1x NVIDIA Tesla T4 GPU (16 GB VRAM)

Per-Device Batch Size

Gradient Accumulation Steps

4 (Effective Batch Size = 32)

Mixed Precision

FP16 (fp16=True)

bibtex

@misc{touati2026whisper_algerian_darja,
  author = {Kamel Touati},
  title = {Whisper Small Fine-Tuned for Algerian Arabic (Darja)},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/touati-kamel/whisper-algerian-darja-small}}
}

bibtex

@article{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2212.04356},
  year={2022}
}

whisper-algerian-darja-small

README

Overview

Key Highlights

Evaluation & Benchmark Results

Architecture & Training Methodology

Model Configuration

Data Preprocessing & Arabic Normalization

Quickstart & Inference

1. Installation

2. High-Level Usage with `transformers.pipeline`

3. Native PyTorch + `PeftModel` Inference

Complete Normalization Function (Recommended for Evaluation)

Training Hyperparameters & Setup

Intended Uses & Limitations

Intended Uses

Limitations & Biases

Datasets & Citations

Training Datasets

BibTeX Citation

Explore FriendliAI today

README

Overview

Key Highlights

Evaluation & Benchmark Results

Architecture & Training Methodology

Model Configuration

Data Preprocessing & Arabic Normalization

Quickstart & Inference

1. Installation

2. High-Level Usage with `transformers.pipeline`

3. Native PyTorch + `PeftModel` Inference

Complete Normalization Function (Recommended for Evaluation)

Training Hyperparameters & Setup

Intended Uses & Limitations

Intended Uses

Limitations & Biases

Datasets & Citations

Training Datasets

BibTeX Citation

whisper-algerian-darja-small

README

Overview

Key Highlights

Evaluation & Benchmark Results

Architecture & Training Methodology

Model Configuration

Data Preprocessing & Arabic Normalization

Quickstart & Inference

1. Installation

2. High-Level Usage with transformers.pipeline

3. Native PyTorch + PeftModel Inference

Complete Normalization Function (Recommended for Evaluation)

Training Hyperparameters & Setup

Intended Uses & Limitations

Intended Uses

Limitations & Biases

Datasets & Citations

Training Datasets

BibTeX Citation

Explore FriendliAI today

README

Overview

Key Highlights

Evaluation & Benchmark Results

Architecture & Training Methodology

Model Configuration

Data Preprocessing & Arabic Normalization

Quickstart & Inference

1. Installation

2. High-Level Usage with transformers.pipeline

3. Native PyTorch + PeftModel Inference

Complete Normalization Function (Recommended for Evaluation)

Training Hyperparameters & Setup

Intended Uses & Limitations

Intended Uses

Limitations & Biases

Datasets & Citations

Training Datasets

BibTeX Citation

2. High-Level Usage with `transformers.pipeline`

3. Native PyTorch + `PeftModel` Inference

2. High-Level Usage with `transformers.pipeline`

3. Native PyTorch + `PeftModel` Inference