oddadmix

whisper-small-arabic-dialectal-v2

README

License: apache-2.0

Results (balanced test split ~2,600 clips, `clean_text` WER/CER)

Table with columns: WER, CER
	WER	CER
This model (v2)	0.4034	0.1528

Per-dialect accuracy (v3 balanced test, `clean_text`)

WER / CER on each dialect's 200 held-out clips, easiest to hardest. Gulf/Levantine transcribe best; Maghrebi (Tunisian/Algerian/Moroccan) is hardest — consistent across every model.

Table with columns: Dialect, WER, CER, n
Dialect	WER	CER	n
Saudi/Gulf (`sa`)	0.227	0.064	200
Iraqi (`iq`)	0.311	0.091	200
Egyptian (`eg`)	0.340	0.159	200
Syrian (`sy`)	0.356	0.130	200
Bahraini (`bh`)	0.397	0.152	200
Yemeni (`ye`)	0.410	0.152	200
Palestinian (`ps`)	0.411	0.173	200
Lebanese (`lb`)	0.416	0.156	200
Libyan (`ly`)	0.416	0.162	200
Moroccan (`ma`)	0.431	0.139	200
Sudanese (`sd`)	0.462	0.186	200
Algerian (`dz`)	0.486	0.187	200
Tunisian (`tn`)	0.606	0.253	200
Overall	0.403	0.153	2600

v2 comparison — all models on the balanced dataset

Same dialect-balanced test split (~2,600 clips, oddadmix/lahgtna-v3-small), same clean_text scoring, seed 42.

Table with columns: Model, Params, WER, CER
Model	Params	WER	CER
whisper-large-v3-v2	1.54B	0.320	0.120
whisper-large-v3-turbo-v2	809M	0.332	0.125
whisper-medium-v2	769M	0.342	0.128
whisper-small-v2	244M	0.403	0.153
nemotron-3.5-asr-v2 (streaming)

whisper-large-v3-turbo-v2 is the best; Whisper fine-tunes furthest. nemotron is the streaming option. (v1 models were trained on the earlier augmentation-expanded set.)

Examples — base vs. fine-tuned (one clip per dialect)

13 held-out test clips, one per dialect. Base is openai/whisper-small untouched; fine-tuned (v2) is this model. Text is clean_text-normalized (undiacritized), matching the WER scoring. Press play to listen.

Table with columns: Dialect, Audio, Reference, Base, Fine-tuned
Dialect	Reference	Base	Fine-tuned
Egyptian (`eg`)	لا، في كافيه هنا كده جنب الدار، مع أحمد خطيبها، وزمانها جاي.	لا فك فيه هنا كده جنب الدور مع أحمد خطبها وزمنها دي	لا في كفيه هنا كده جنب الدار، مع احمد خطبها وزمنها جاي.
Lebanese (`lb`)	ما كون مأمنة حالي. ما أقدر اتحكم. امم. في عندي خوف، إيه.	ما كون مأمنة حالة ما عدر اتحكم فيه عنده خوفة	ما أكون مأمنة حالي، ما أقدر أتحكم. إم. في عندي خوف إيه.
Syrian ()

Full fine-tuning code, dependencies, and lessons are in the v1 repo (train.py, normalize.py, FINETUNE.md). This v2 uses the same recipe on the balanced dataset.

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Model Details

Model Provider

oddadmix

Model Tree

Base

openai/whisper-small

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

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README

License: apache-2.0

Results (balanced test split ~2,600 clips, `clean_text` WER/CER)

Table with columns: WER, CER
	WER	CER
This model (v2)	0.4034	0.1528

Per-dialect accuracy (v3 balanced test, `clean_text`)

WER / CER on each dialect's 200 held-out clips, easiest to hardest. Gulf/Levantine transcribe best; Maghrebi (Tunisian/Algerian/Moroccan) is hardest — consistent across every model.

Table with columns: Dialect, WER, CER, n
Dialect	WER	CER	n
Saudi/Gulf (`sa`)	0.227	0.064	200
Iraqi (`iq`)	0.311	0.091	200
Egyptian (`eg`)	0.340	0.159	200
Syrian (`sy`)	0.356	0.130	200
Bahraini (`bh`)	0.397	0.152	200
Yemeni (`ye`)	0.410	0.152	200
Palestinian (`ps`)	0.411	0.173	200
Lebanese (`lb`)	0.416	0.156	200
Libyan (`ly`)	0.416	0.162	200
Moroccan (`ma`)	0.431	0.139	200
Sudanese (`sd`)	0.462	0.186	200
Algerian (`dz`)	0.486	0.187	200
Tunisian (`tn`)	0.606	0.253	200
Overall	0.403	0.153	2600

v2 comparison — all models on the balanced dataset

Same dialect-balanced test split (~2,600 clips, oddadmix/lahgtna-v3-small), same clean_text scoring, seed 42.

Table with columns: Model, Params, WER, CER
Model	Params	WER	CER
whisper-large-v3-v2	1.54B	0.320	0.120
whisper-large-v3-turbo-v2	809M	0.332	0.125
whisper-medium-v2	769M	0.342	0.128
whisper-small-v2	244M	0.403	0.153
nemotron-3.5-asr-v2 (streaming)

whisper-large-v3-turbo-v2 is the best; Whisper fine-tunes furthest. nemotron is the streaming option. (v1 models were trained on the earlier augmentation-expanded set.)

Examples — base vs. fine-tuned (one clip per dialect)

Table with columns: Dialect, Audio, Reference, Base, Fine-tuned
Dialect	Reference	Base	Fine-tuned
Egyptian (`eg`)	لا، في كافيه هنا كده جنب الدار، مع أحمد خطيبها، وزمانها جاي.	لا فك فيه هنا كده جنب الدور مع أحمد خطبها وزمنها دي	لا في كفيه هنا كده جنب الدار، مع احمد خطبها وزمنها جاي.
Lebanese (`lb`)	ما كون مأمنة حالي. ما أقدر اتحكم. امم. في عندي خوف، إيه.	ما كون مأمنة حالة ما عدر اتحكم فيه عنده خوفة	ما أكون مأمنة حالي، ما أقدر أتحكم. إم. في عندي خوف إيه.
Syrian ()

Full fine-tuning code, dependencies, and lessons are in the v1 repo (train.py, normalize.py, FINETUNE.md). This v2 uses the same recipe on the balanced dataset.

whisper-small-arabic-dialectal-v2

README

Results (balanced test split ~2,600 clips, clean_text WER/CER)

Per-dialect accuracy (v3 balanced test, clean_text)

v2 comparison — all models on the balanced dataset

Examples — base vs. fine-tuned (one clip per dialect)

Explore FriendliAI today

README

Results (balanced test split ~2,600 clips, clean_text WER/CER)

Per-dialect accuracy (v3 balanced test, clean_text)

v2 comparison — all models on the balanced dataset

Examples — base vs. fine-tuned (one clip per dialect)

Results (balanced test split ~2,600 clips, `clean_text` WER/CER)

Per-dialect accuracy (v3 balanced test, `clean_text`)

Results (balanced test split ~2,600 clips, `clean_text` WER/CER)

Per-dialect accuracy (v3 balanced test, `clean_text`)