algerian-nlp

whisper-algerian-darja-medium

Overview

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

Built on top of OpenAI's Whisper Medium (openai/whisper-medium, 769M base parameters / 833M total parameters), this model incorporates parameter-efficient LoRA adapters trained with 4-bit quantization (QLoRA) over a comprehensive sequential 3-phase curriculum covering conversational podcasts, spontaneous storytelling, and cultural narratives from the OddAdmix Algerian speech collection.

Key Highlights

Native Algerian Dialect Adaptation: Exceptional comprehension of authentic Algerian Darja vocabulary, morphology, fast colloquial speech, and code-mixed expressions.
Superior Acoustic & Language Modeling: Leveraging the 24-layer medium Whisper architecture for vastly superior contextual modeling compared to smaller model variants.
Parameter-Efficient LoRA (PEFT): Trained on 69.21M parameters (8.31% of total model weights), allowing compact adapter storage and fast inference while preserving Whisper's general acoustic features.
Low Error Rates: Achieved 0.34% WER on storytelling evaluation subsets, 0.68% WER on conversational podcast subsets, and 0.95% WER on cultural narratives.
Sequential Streaming Curriculum: Trained end-to-end for 31,661 cumulative optimization steps with dynamic zero-disk streaming on Hugging Face CDN.

Evaluation & Benchmark Results

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

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 (9,886 steps)	$1 \times 1 0 - 4$	0.68%

Cumulative Training Progress: Total training ran for 31,661 cumulative optimization steps with cosine annealing learning rate schedules and automatic best-adapter preservation per phase.

Comparison: Whisper Small vs. Whisper Medium

Table with columns: Benchmark Dataset, Whisper Small (244M) WER, Whisper Medium (769M) WER, Relative Improvement
Benchmark Dataset	Whisper Small (244M) WER	Whisper Medium (769M) WER	Relative Improvement
OddAdmix Kahwa Podcast	34.85%	0.68%	+98.0%
OddAdmix Loubna Stories	14.87%	0.34%	+97.7%
OddAdmix Rawi Stories	27.54%	0.95%	+96.5%

Architecture & Training Methodology

text

┌────────────────────────────────────────────────────────┐│                   OpenAI Whisper-Medium                ││              (4-bit NF4 Quantization Base)             ││                 (763.86M Frozen Params)                │└──────────────────────────┬─────────────────────────────┘                           │             ┌─────────────┴─────────────┐             │    LoRA Adapters (r=64)   │             │   Target: q, k, v, out,   │             │           fc1, fc2        │             │    (69.21M Trainable)     │             └─────────────┬─────────────┘                           │       ┌────────────────────┴────────────────────┐       │     Sequential Curriculum Learning      │       ├─────────────────────────────────────────┤       │ Phase 1: Kahwa Podcast (Conversational) │       │                    ▼                    │       │ Phase 2: Loubna Stories (Expressive)    │       │                    ▼                    │       │ Phase 3: Rawi Narratives (Storytelling) │       └─────────────────────────────────────────┘

Model Configuration

Base Architecture: openai/whisper-medium (Encoder-Decoder Transformer, 24 encoder layers, 24 decoder layers, 16 attention heads)
Base Model Parameters: 763,857,920
Trainable LoRA Parameters: 69,206,016 (8.3074%)
Total Parameters: 833,063,936
LoRA Hyperparameters:
- Rank ( $r$ ): 64
- Scaling factor ( $α$ ): 128
- LoRA Dropout: 0.05
- Target Modules: , , , , ,

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 bitsandbytes accelerate

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

python

import torchfrom transformers import pipeline # Initialize speech recognition pipeline with PEFT adapterpipe = pipeline(    task="automatic-speech-recognition",    model="touati-kamel/whisper-algerian-darja-medium",    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 torchimport librosafrom transformers import WhisperProcessor, WhisperForConditionalGenerationfrom peft import PeftModel device = "cuda" if torch.cuda.is_available() else "cpu"model_id = "openai/whisper-medium"adapter_id = "touati-kamel/whisper-algerian-darja-medium" # 1. Load Processorprocessor = WhisperProcessor.from_pretrained(model_id, language="arabic", task="transcribe") # 2. Load Base Model and Apply LoRA Adapterbase_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 Audioaudio, sr = librosa.load("path/to/audio.mp3", sr=16000)input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_featuresif torch.cuda.is_available():    input_features = input_features.to("cuda", dtype=torch.float16) # 4. Generate Transcriptionforced_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 Outputtranscription = 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 reimport 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-medium` (769M params)
Quantization	4-bit NF4 (`BitsAndBytesConfig`)
Hardware	1x NVIDIA Tesla T4 GPU (16 GB VRAM)
Per-Device Batch Size	4
Gradient Accumulation Steps	8 (Effective Batch Size = 32)
Mixed Precision	FP16 (`fp16=True`)

Intended Uses & Limitations

Intended Uses

High-accuracy speech-to-text transcription for Algerian podcasts, YouTube content, interviews, and media.
Algerian Darija voice assistants, customer service transcription, and interactive voice response (IVR).
Archival transcription and subtitling for Algerian cultural heritage, educational audio, and spoken narratives.

Limitations & Biases

Code-Switching: Algerian Darja frequently blends Arabic with French and Berber/Tamazight loanwords. While French tag handling was integrated during preprocessing, intense mixed French sentences may occasionally be transcribed phonetically into Arabic script.
Regional Dialectal Variations: The training data prominently covers Central (Algiers) and Western (Oran/Mostaganem) dialects. Eastern or Saharan accents with distinct phonetic nuances may exhibit slightly higher variance.
Extreme Background Noise: Best results are achieved on clear speech. Extremely noisy field recordings or heavy background music may affect transcription accuracy.

Datasets & Citations

Training Datasets

BibTeX Citation

bibtex

@misc{touati2026whisper_algerian_darja_medium,  author = {Kamel Touati},  title = {Whisper Medium 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-medium}}}

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}}

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Model Details

Model Provider

algerian-nlp

Model Tree

Base

openai/whisper-medium

Adapter

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

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Overview

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

Key Highlights

Native Algerian Dialect Adaptation: Exceptional comprehension of authentic Algerian Darja vocabulary, morphology, fast colloquial speech, and code-mixed expressions.
Superior Acoustic & Language Modeling: Leveraging the 24-layer medium Whisper architecture for vastly superior contextual modeling compared to smaller model variants.
Parameter-Efficient LoRA (PEFT): Trained on 69.21M parameters (8.31% of total model weights), allowing compact adapter storage and fast inference while preserving Whisper's general acoustic features.
Low Error Rates: Achieved 0.34% WER on storytelling evaluation subsets, 0.68% WER on conversational podcast subsets, and 0.95% WER on cultural narratives.
Sequential Streaming Curriculum: Trained end-to-end for 31,661 cumulative optimization steps with dynamic zero-disk streaming on Hugging Face CDN.

Evaluation & Benchmark Results

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

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 (9,886 steps)	$1 \times 1 0 - 4$	0.68%

Cumulative Training Progress: Total training ran for 31,661 cumulative optimization steps with cosine annealing learning rate schedules and automatic best-adapter preservation per phase.

Comparison: Whisper Small vs. Whisper Medium

Table with columns: Benchmark Dataset, Whisper Small (244M) WER, Whisper Medium (769M) WER, Relative Improvement
Benchmark Dataset	Whisper Small (244M) WER	Whisper Medium (769M) WER	Relative Improvement
OddAdmix Kahwa Podcast	34.85%	0.68%	+98.0%
OddAdmix Loubna Stories	14.87%	0.34%	+97.7%
OddAdmix Rawi Stories	27.54%	0.95%	+96.5%

Architecture & Training Methodology

text

┌────────────────────────────────────────────────────────┐│                   OpenAI Whisper-Medium                ││              (4-bit NF4 Quantization Base)             ││                 (763.86M Frozen Params)                │└──────────────────────────┬─────────────────────────────┘                           │             ┌─────────────┴─────────────┐             │    LoRA Adapters (r=64)   │             │   Target: q, k, v, out,   │             │           fc1, fc2        │             │    (69.21M Trainable)     │             └─────────────┬─────────────┘                           │       ┌────────────────────┴────────────────────┐       │     Sequential Curriculum Learning      │       ├─────────────────────────────────────────┤       │ Phase 1: Kahwa Podcast (Conversational) │       │                    ▼                    │       │ Phase 2: Loubna Stories (Expressive)    │       │                    ▼                    │       │ Phase 3: Rawi Narratives (Storytelling) │       └─────────────────────────────────────────┘

Model Configuration

Base Architecture: openai/whisper-medium (Encoder-Decoder Transformer, 24 encoder layers, 24 decoder layers, 16 attention heads)
Base Model Parameters: 763,857,920
Trainable LoRA Parameters: 69,206,016 (8.3074%)
Total Parameters: 833,063,936
LoRA Hyperparameters:
- Rank ( $r$ ): 64
- Scaling factor ( $α$ ): 128
- LoRA Dropout: 0.05
- Target Modules: , , , , ,

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 bitsandbytes accelerate

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

python

import torchfrom transformers import pipeline # Initialize speech recognition pipeline with PEFT adapterpipe = pipeline(    task="automatic-speech-recognition",    model="touati-kamel/whisper-algerian-darja-medium",    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 torchimport librosafrom transformers import WhisperProcessor, WhisperForConditionalGenerationfrom peft import PeftModel device = "cuda" if torch.cuda.is_available() else "cpu"model_id = "openai/whisper-medium"adapter_id = "touati-kamel/whisper-algerian-darja-medium" # 1. Load Processorprocessor = WhisperProcessor.from_pretrained(model_id, language="arabic", task="transcribe") # 2. Load Base Model and Apply LoRA Adapterbase_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 Audioaudio, sr = librosa.load("path/to/audio.mp3", sr=16000)input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_featuresif torch.cuda.is_available():    input_features = input_features.to("cuda", dtype=torch.float16) # 4. Generate Transcriptionforced_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 Outputtranscription = 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 reimport 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-medium` (769M params)
Quantization	4-bit NF4 (`BitsAndBytesConfig`)
Hardware	1x NVIDIA Tesla T4 GPU (16 GB VRAM)
Per-Device Batch Size	4
Gradient Accumulation Steps	8 (Effective Batch Size = 32)
Mixed Precision	FP16 (`fp16=True`)

Intended Uses & Limitations

Intended Uses

High-accuracy speech-to-text transcription for Algerian podcasts, YouTube content, interviews, and media.
Algerian Darija voice assistants, customer service transcription, and interactive voice response (IVR).
Archival transcription and subtitling for Algerian cultural heritage, educational audio, and spoken narratives.

Limitations & Biases

Code-Switching: Algerian Darja frequently blends Arabic with French and Berber/Tamazight loanwords. While French tag handling was integrated during preprocessing, intense mixed French sentences may occasionally be transcribed phonetically into Arabic script.
Regional Dialectal Variations: The training data prominently covers Central (Algiers) and Western (Oran/Mostaganem) dialects. Eastern or Saharan accents with distinct phonetic nuances may exhibit slightly higher variance.
Extreme Background Noise: Best results are achieved on clear speech. Extremely noisy field recordings or heavy background music may affect transcription accuracy.

Datasets & Citations

Training Datasets

BibTeX Citation

bibtex

@misc{touati2026whisper_algerian_darja_medium,  author = {Kamel Touati},  title = {Whisper Medium 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-medium}}}

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-medium

README

Overview

Key Highlights

Evaluation & Benchmark Results

Comparison: Whisper Small vs. Whisper Medium

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

Comparison: Whisper Small vs. Whisper Medium

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