--- title: "balbal-v2e-zellige" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/dfb9137a-26b4-47b3-9265-b841ad06be36" updated: "2026-11-13" --- # balbal-v2e-zellige > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns Moroccan Darija speech into Arabic-script text with a timestamp for each word, on an ordinary CPU. An experimental preview for researchers and developers, under a non-commercial license. balbal-v2e-zellige is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by erayyapagci ## About ### What it is This is a speech recognition model for Moroccan Darija. It turns audio into Arabic-script text with a timestamp on every word, and it runs on a CPU. The author calls it an experimental preview, and it is the first Arabic model in the balbal line. ### Who it suits It suits researchers and developers who need Darija transcription without a GPU. A non-commercial license applies, so it fits study and prototypes. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Speech researchers | Measure Darija accuracy against larger open models | Score a test set of Moroccan recordings | | Software | Developers | Add Darija transcription to a tool that runs on a CPU | A script that transcribes a WAV file | | Media | Caption editors | Get word timestamps to line text up with audio | Draft subtitles for a Moroccan interview | If you need a commercial product or another Arabic dialect, you can skip it. ### Where to use it - Python 3.11 or 3.12 on Windows, macOS or Linux - The balbal command line, version 0.6.0 or newer - A CPU machine: the card reports about 100 times faster than real time on 8 threads - A browser: the author links a demo page for trying it Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Transcribing a recording | Researchers | Arabic-script text of the speech | | Subtitling | Caption editors | The text with a timestamp for every word | | Benchmarking | Machine learning teams | Error rates on three Moroccan Darija test sets | ### How to set it up - Python 3.11 or 3.12 - The balbal package, version 0.6.0 or newer - No GPU is needed 1. Install the balbal package with pip. 2. Run it on a WAV file. The first run downloads this model. 3. To fetch the repository files by hand, install the Hugging Face Hub tools and download them. ```bash pip install -U balbal balbal run --model zellige speech.wav # downloads this model on the first run ``` ```sh pip install -U huggingface_hub hf download erayyapagci/balbal-v2e-zellige ``` ### How to use it Run the balbal command on an audio file, or load the model from Python. Long recordings are split into 30 s pieces automatically. French and other Latin-script words are spelled with Arabic letters. ```bash balbal run --model zellige --json speech.wav # text + per-word timestamps ``` ```python import balbal model = balbal.load("balbal-v2e-zellige") print(model.transcribe("speech.wav")["text"]) ``` Ways to run it | Tool | What it does | | --- | --- | | balbal run --model zellige | Transcribes an audio file to text | | --json | Adds a timestamp for each word | | balbal.load | Loads the model inside a Python script | | transcribe | Returns the recognized text of a file | ### Example You run the balbal command with the json flag on a short Darija recording named speech.wav. It prints the recognized speech in Arabic script, and each word carries its own timestamp. ### For newcomers and experts If you are new, speech recognition means turning spoken audio into written text. The first small thing to try is installing balbal and running it on one short WAV file of Darija. If you know the field, the acoustic model is a FastConformer-CTC initialised from Sabeq-ASR. A two-layer looped refiner makes 3 passes over its output, and a product-key memory holds 193M of the 307M parameters. Decoding uses a 6-gram KenLM over SentencePiece pieces. The trade-off is accuracy: 42.85 WER on the Casablanca Morocco test set with the language model, 46.80 without it. Audio is cut into 30 s chunks → FastConformer-CTC acoustic model → Looped refiner, 3 passes → Beam search with a 6-gram KenLM → Arabic text with word timestamps ### Good to know Average WER on three Moroccan test sets | Label | Value | | --- | --- | | balbal-v2e-zellige | 44.38% | | balbal-v2e-zellige, no LM | 47.58% | | Cohere Transcribe Arabic | 53.22% | | Jais-ASR | 53.73% | | MoulSot v0.3 | 54.18% | - Preview: the author marks it experimental and asks for feedback. - Tuning: the decoder settings were chosen using a single Moroccan validation set, Casablanca Morocco. - Files: PyTorch weights are 1.2 GB and the ONNX graph is 1.25 GB. - License: cc-by-nc-sa-4.0, so it is non-commercial. - Cost to run: no API key, account or paid service is named. - Data it reaches: the first run downloads the model, and the card names no other access. - Not for: commercial use, which the license bars. ## README ````markdown # balbal-v2e-zellige Turns Moroccan Darija speech into Arabic-script text with a timestamp for each word, on an ordinary CPU. An experimental preview for researchers and developers, under a non-commercial license. Made by erayyapagci on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a speech recognition model for Moroccan Darija. It turns audio into Arabic-script text with a timestamp on every word, and it runs on a CPU. The author calls it an experimental preview, and it is the first Arabic model in the balbal line. Who it suits: It suits researchers and developers who need Darija transcription without a GPU. A non-commercial license applies, so it fits study and prototypes. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Research | Speech researchers | Measure Darija accuracy against larger open models | Score a test set of Moroccan recordings Software | Developers | Add Darija transcription to a tool that runs on a CPU | A script that transcribes a WAV file Media | Caption editors | Get word timestamps to line text up with audio | Draft subtitles for a Moroccan interview ``` If you need a commercial product or another Arabic dialect, you can skip it. Where to use it: - Python 3.11 or 3.12 on Windows, macOS or Linux - The balbal command line, version 0.6.0 or newer - A CPU machine: the card reports about 100 times faster than real time on 8 threads - A browser: the author links a demo page for trying it ```table Title: Jobs it does Use case | Who | What they get back Transcribing a recording | Researchers | Arabic-script text of the speech Subtitling | Caption editors | The text with a timestamp for every word Benchmarking | Machine learning teams | Error rates on three Moroccan Darija test sets ``` How to set it up: - Python 3.11 or 3.12 - The balbal package, version 0.6.0 or newer - No GPU is needed 1. Install the balbal package with pip. 2. Run it on a WAV file. The first run downloads this model. 3. To fetch the repository files by hand, install the Hugging Face Hub tools and download them. ```bash pip install -U balbal balbal run --model zellige speech.wav # downloads this model on the first run ``` ```sh pip install -U huggingface_hub hf download erayyapagci/balbal-v2e-zellige ``` How to use it: Run the balbal command on an audio file, or load the model from Python. Long recordings are split into 30 s pieces automatically. French and other Latin-script words are spelled with Arabic letters. ```bash balbal run --model zellige --json speech.wav # text + per-word timestamps ``` ```python import balbal model = balbal.load("balbal-v2e-zellige") print(model.transcribe("speech.wav")["text"]) ``` ```table Title: Ways to run it Tool | What it does balbal run --model zellige | Transcribes an audio file to text --json | Adds a timestamp for each word balbal.load | Loads the model inside a Python script transcribe | Returns the recognized text of a file ``` Example: You run the balbal command with the json flag on a short Darija recording named speech.wav. It prints the recognized speech in Arabic script, and each word carries its own timestamp. For newcomers and experts: If you are new, speech recognition means turning spoken audio into written text. The first small thing to try is installing balbal and running it on one short WAV file of Darija. If you know the field, the acoustic model is a FastConformer-CTC initialised from Sabeq-ASR. A two-layer looped refiner makes 3 passes over its output, and a product-key memory holds 193M of the 307M parameters. Decoding uses a 6-gram KenLM over SentencePiece pieces. The trade-off is accuracy: 42.85 WER on the Casablanca Morocco test set with the language model, 46.80 without it. ```flow Audio is cut into 30 s chunks FastConformer-CTC acoustic model Looped refiner, 3 passes Beam search with a 6-gram KenLM Arabic text with word timestamps ``` Good to know: ```chart Title: Average WER on three Moroccan test sets balbal-v2e-zellige: 44.38% balbal-v2e-zellige, no LM: 47.58% Cohere Transcribe Arabic: 53.22% Jais-ASR: 53.73% MoulSot v0.3: 54.18% ``` - Preview: the author marks it experimental and asks for feedback. - Tuning: the decoder settings were chosen using a single Moroccan validation set, Casablanca Morocco. - Files: PyTorch weights are 1.2 GB and the ONNX graph is 1.25 GB. - License: cc-by-nc-sa-4.0, so it is non-commercial. - Cost to run: no API key, account or paid service is named. - Data it reaches: the first run downloads the model, and the card names no other access. - Not for: commercial use, which the license bars. ## On Hugging Face, 13 November 2026 - Task: automatic speech recognition - Downloads in the last 30 days: 64 - Likes: 3 - License: cc-by-nc-sa-4.0 - Languages: ary, ar - Based on: MostafaAhmed98/Sabeq-ASR - Tags: onnx, speech-recognition, arabic, darija, moroccan-arabic, fastconformer, kenlm, automatic-speech-recognition, model-index ## Links - Model: https://huggingface.co/erayyapagci/balbal-v2e-zellige - Files: https://huggingface.co/erayyapagci/balbal-v2e-zellige/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download erayyapagci/balbal-v2e-zellige ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [erayyapagci/balbal-v2e-zellige on Hugging Face](https://huggingface.co/erayyapagci/balbal-v2e-zellige) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | balbal-v2e-zellige (this page) | 64 | 3 | — | cc-by-nc-sa-4.0 | 2026-11-08 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [Haas-ja-v1.0-GGUF](https://mindket.com/p/6b81478c-e1c4-4cd0-85f7-356e7044f4ed) | 5,149 | 7 | 616M | apache-2.0 | 2026-10-31 | | [Whistle](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [parakeet-ultra](https://mindket.com/p/5647b30a-a58e-4b54-b4fc-18517ea9dde6) | 51,675 | 142 | 627M | cc-by-4.0 | 2026-09-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-08 - First published: 2026-11-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open erayyapagci/balbal-v2e-zellige on Hugging Face](https://huggingface.co/erayyapagci/balbal-v2e-zellige) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/dfb9137a-26b4-47b3-9265-b841ad06be36) - [Its page on Mindket](https://mindket.com/p/dfb9137a-26b4-47b3-9265-b841ad06be36)