Lmaana 2.4
Lmaana 2.4 is a Moroccan Darija automatic speech recognition model based
on OmniASR CTC 3B v2. It continues Lmaana 2.2 with a controlled mixed-corpus
adaptation using Dataset13 clean v4 and corrected MoulSot (Lmaana clean v1).
The selected checkpoint is mixed-adaptation step_4500. It was selected on
the separate Dataset13 and MoulSot validation sets before the held-out tests
were evaluated.
Model summary
| Field | Value |
|---|---|
| Release | Lmaana 2.4 |
| Base checkpoint | Lmaana 2.2 step_10000 |
| Architecture | OmniASR CTC 3B v2 |
| Objective | CTC full fine-tuning |
| Target language | Moroccan Darija (ary-Arab) |
| Adaptation data | 80% Dataset13, 20% corrected MoulSot |
| Adaptation schedule | 2,000 steps, selected at step 4,500 |
| Learning rate | 2e-6 |
| Tokenizer | OmniASR written tokenizer v2 |
| Decoder | Greedy CTC |
Results
All values use the same frozen splits, tokenizer, normalization and greedy CTC decoding protocol as Lmaana 2.2.
| Corpus | Split | Examples | CTC loss | UER (%) | CER (%) | WER (%) |
|---|---|---|---|---|---|---|
| Dataset13 | Validation | 6,893 | 154.0409 | 15.4345 | Not measured | 37.9410 |
| Dataset13 | Test | 6,576 | 159.4960 | 15.6460 | 16.1553 | 38.1100 |
| MoulSot | Validation | 5,187 | 65.3884 | 11.5805 | Not measured | 35.2742 |
| MoulSot | Test | 1,957 | 65.2453 | 12.6190 | 12.5567 | 39.5332 |
Improvement over Lmaana 2.2
| Held-out test | Lmaana 2.2 WER | Lmaana 2.4 WER | WER gain | CER gain |
|---|---|---|---|---|
| Dataset13 | 38.4762 | 38.1100 | 0.3662 | 0.1510 |
| MoulSot | 40.7776 | 39.5332 | 1.2444 | 1.3718 |
Training data
- Dataset13 clean v4: the primary training and evaluation corpus.
- Corrected MoulSot: stored as
omniasr_lmaana_clean_v1, with 91.68 hours of training audio. Validation and test sources are held out from its training split.
The mixed sampler targets 80% Dataset13 and 20% corrected MoulSot. MoulSot is also called Lmaana clean v1 in configuration files; these names refer to the same corrected corpus in this release.
Evaluation and selection
The mixed run started from Lmaana 2.2 and used a reinitialized optimizer. The
Dataset13 validation WER guard was 38.5618%, and the MoulSot validation target
was the Lmaana 2.2 baseline of 37.2417%. Mixed step_4500 passed both guards
with 37.9410% and 35.2742%, respectively.
Test CER is computed at corpus level after removing whitespace from references and hypotheses. UER is the tokenizer-unit error rate reported by fairseq2 and is not identical to CER.
Files
| Path | Description |
|---|---|
model/consolidated.pt | Consolidated fairseq2 checkpoint |
tokenizer/omniASR_tokenizer_written_v2.model | Character tokenizer |
config/mixed_80_20_train_fsdp.yaml | Mixed adaptation recipe |
config/test_mixed_step4500_dataset13.yaml | Dataset13 test recipe |
config/valid_mixed_step4500_lmaana_clean.yaml | MoulSot validation recipe |
config/test_mixed_step4500_lmaana_clean.yaml | MoulSot test recipe |
results/metrics_summary.json | Machine-readable release metrics |
SHA256SUMS | Checksum for the consolidated checkpoint |
Intended use and limitations
The model is intended for research and evaluation of Moroccan Darija ASR. It uses greedy decoding and may benefit from a language model or lexicon-aware decoder. Performance can vary across speakers, recording conditions, regions, code-switching patterns and writing conventions. Validate the model on the target domain before production use.
This repository contains a native fairseq2 checkpoint, not a Transformers
from_pretrained() package.