| --- |
| language: |
| - bm |
| library_name: nemo |
| license: cc-by-4.0 |
| base_model: RobotsMali/stt-bm-quartznet15x5-v2 |
| tags: |
| - automatic-speech-recognition |
| - speech |
| - audio |
| - CTC |
| - QuartzNet |
| - Bambara |
| - spoken-numbers |
| - NeMo |
| metrics: |
| - wer |
| pipeline_tag: automatic-speech-recognition |
| model-index: |
| - name: quartznum-v0 |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: MobileBAMking spoken-number test split |
| type: mobilebamking-spoken-numbers |
| split: test |
| args: |
| language: bm |
| metrics: |
| - name: Test WER |
| type: wer |
| value: 17.241379618644714 |
| --- |
| |
| # QuartzNum v0 |
|
|
| QuartzNum is an 18M-parameter QuartzNet 15x5 CTC model specialized for recognizing **Bambara spoken amounts, account numbers, and phone numbers**. It is fine-tuned from [`RobotsMali/stt-bm-quartznet15x5-v2`](https://huggingface.co/RobotsMali/stt-bm-quartznet15x5-v2) and is intended as a narrow component, not a replacement for general-purpose Bambara ASR. |
|
|
| The model is deployed in the [`mobilebamspeech`](https://github.com/RobotsMali-AI/mobilebamspeech) integration demo and the voice-first [`mobileBAMking`](https://github.com/RobotsMali-AI/mobileBAMking) proof of concept. [`NeMoOnnxSharp`](https://github.com/RobotsMali-AI/NeMoOnnxSharp) provides the ONNX Runtime/.NET inference path used on Android. |
|
|
| ## Intended Use |
|
|
| Use QuartzNum to transcribe short, isolated number expressions in the vocabulary and speaking conditions represented by the banking voice collection. Pass its text through a Bambara number parser/normalizer and validate the resulting value before application use. |
|
|
| Do not use this research checkpoint by itself to authorize transactions, identify speakers, or transcribe unrestricted speech. |
|
|
| ## Use with NVIDIA NeMo |
|
|
| ```bash |
| pip install "nemo-toolkit[asr]" |
| ``` |
|
|
| ```python |
| from nemo.collections.asr.models import EncDecCTCModel |
| |
| model = EncDecCTCModel.from_pretrained("RobotsMali/quartznum-v0") |
| model.eval() |
| predictions = model.transcribe(["spoken_number.wav"]) |
| print(predictions[0].text) |
| ``` |
|
|
| Input audio is converted by the model preprocessor to mono 16 kHz features. Output is a greedy CTC hypothesis over a lowercase character vocabulary containing the Bambara letters `ŋ`, `ɔ`, `ɛ`, and `ɲ`. |
|
|
| ## NeMo 2.5.0 Compatibility |
|
|
| This checkpoint was created with NeMo 2.5.0. Loading it with newer NeMo versions (observed with 2.7.x) can fail because the strict decoding schema expects `key_phrase_items_list`; see [NVIDIA-NeMo/Speech#15658](https://github.com/NVIDIA-NeMo/Speech/issues/15658). The following workaround was tested with Python 3.12 and allows the 2.5.0 checkpoint to load in the newer environment: |
|
|
| ```python |
| from pathlib import Path |
| |
| from nemo.collections.asr.models import ASRModel |
| from omegaconf import OmegaConf |
| |
| model_name = "RobotsMali/quartznum-v0" |
| cfg = ASRModel.from_pretrained(model_name, return_config=True) |
| OmegaConf.set_struct(cfg, False) |
| |
| for decoder in ("greedy", "beam"): |
| boosting_tree = OmegaConf.select(cfg, f"decoding.{decoder}.boosting_tree") |
| if boosting_tree is not None: |
| boosting_tree.key_phrase_items_list = None |
| |
| config_path = Path("patched_config.yaml").resolve() |
| OmegaConf.save(cfg, config_path) |
| model = ASRModel.from_pretrained( |
| model_name=model_name, |
| override_config_path=str(config_path), |
| strict=False, |
| ) |
| config_path.unlink() |
| ``` |
|
|
| ## Architecture and Training |
|
|
| QuartzNet 15x5 uses one-dimensional time-channel separable convolutions and a character CTC decoder. Training used NeMo 2.5.0, BF16 mixed precision, batches of 16, Adam at `5e-4`, and cosine annealing with 80 warm-up steps. The run allowed up to 50 epochs with early stopping patience of 15. The [training script, configuration, and archived result](https://github.com/RobotsMali-AI/bambara-asr/tree/main/quartznum) are public. |
|
|
| Training clips were derived from the 588-command MobileBAMking voice collection. For transfer commands, the preparation pipeline extracts separately recorded amount and account/phone-number audio and normalizes the labels with `bambara-normalizer`. This application-collected training set is not currently published as a standalone Hugging Face dataset. |
|
|
| ## Evaluation |
|
|
| The archived held-out evaluation reports: |
|
|
| | Split | Decoding | WER (%) ↓ | Test loss | |
| | --- | --- | ---: | ---: | |
| | MobileBAMking spoken-number test split | Greedy CTC | **17.24** | 36.29 | |
|
|
| WER measures token transcription, not end-to-end numeric-value accuracy. The split is small, narrow-domain, and drawn from the same collection workflow as training, so the figure should not be generalized to arbitrary speakers or acoustic conditions. |
|
|
| ## Limitations |
|
|
| - Long digit strings can be costly even when only one character is wrong; validate parsed values explicitly. |
| - Accuracy may degrade with noise, overlap, dialects, recording hardware, or number forms absent from training. |
| - The model does not provide confidence calibration, speaker verification, or transaction safety controls. |
| - General Bambara words and sentences are outside its intended domain. |
|
|
| ## License |
|
|
| The model is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
|
|
