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README.md

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+---
+language:
+- bm
+library_name: nemo
+datasets:
+- RobotsMali/an-be-kalan-bench
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- QuartzNet
+- legacy-model
+- deprecated
+- pytorch
+- Bambara
+- NeMo
+license: cc-by-4.0
+base_model: RobotsMali/stt-bm-quartznet15x5-v2
+model-index:
+- name: anbekalanNet
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: An be kalan Children's Reading Benchmark
+      type: RobotsMali/an-be-kalan-bench
+      split: test
+      args:
+        language: bm
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 40.0
+    - name: Test CER
+      type: cer
+      value: 15.0
+metrics:
+- wer
+- cer
+pipeline_tag: automatic-speech-recognition
+---
+
+# anbekalanNet (QuartzNet 15x5 char CTC Series) — [LEGACY]
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-QuartzNet-blue#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-18M-green#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-bm-orange#model-badge)](#datasets)
+
+`anbekalanNet` is the final domain-specific release of the convolutional QuartzNet framework adapted for Bambara children's reading materials. It is a fine-tuned version of [`RobotsMali/stt-bm-quartznet15x5-v2`](https://huggingface.co/RobotsMali/stt-bm-quartznet15x5-v2). Like its predecessors, the model was fine-tuned using **NVIDIA NeMo** and trained with **CTC (Connectionist Temporal Classification) Loss**.
+
+## **🚨 Obsolescence Notice**
+
+This architecture is officially retired. Field testing and benchmark evaluations demonstrate that this convolutional foundation exhibits unstable alignment paths under tight, low-resource constraints compared to hybrid attention-transducer systems.
+
+
+## NVIDIA NeMo: Installation
+
+To load or run evaluations on this legacy checkpoint, install the standard [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) package:
+
+```bash
+pip install nemo-toolkit['asr']
+
+```
+
+## How to Use This Model
+
+### Load Model with NeMo
+
+```python
+import nemo.collections.asr as nemo_asr
+
+asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="RobotsMali/anbekalanNet")
+
+```
+
+### Transcribe Audio
+
+```python
+# Downsamples or processes input natively via its internal preprocessor
+asr_model.transcribe(['sample_audio.wav'])
+
+```
+
+### Input / Output
+
+* **Input:** Accepts **16 kHz mono-channel audio (wav files)**.
+* **Output:** Generates a transcribed speech hypothesis object with a lowercase `.text` string attribute containing character-encoded text. It does not output punctuations or capitalizations.
+
+## Model Architecture
+
+QuartzNet is a convolutional ASR model consisting of **1D time-channel separable convolutions** designed to minimize parameter count while maintaining acoustic representations. This specific variant utilizes a **15x5 block structure** with roughly 18 million parameters.
+
+## Training & Fine-Tuning Configurations
+
+Four experimental setups were designed to test vocabulary limits and regularization effects. This final artifact (`anbekalanNet`) used the following strict parameters:
+
+* 
+**Optimization Window:** Regulated with an **Early Stopping mechanism** set to a **15-epoch patience window** monitored against validation metrics.
+
+* 
+**Convergence Behavior:** Due to high training-batch lexical convergence (<4% WER), validation metrics flatlined early. Operational shutdown was forced at **epoch 30** to protect the encoder from total generalization collapse.
+
+## Dataset
+
+The model was fine-tuned on the combined **Main + Duplicate** expanded subsets (**45.6 hours** total) of the [RobotsMali/an-be-kalan-bench](https://huggingface.co/datasets/RobotsMali/an-be-kalan-bench) educational children's book corpus.
+
+* 
+**Main Split (1.6h):** Pristine recordings of unique readings across 22 GAIFE books by 8 distinct speakers.
+
+
+* 
+**Duplicate Split (44h):** High-density, redundant multi-speaker tracks reading identical textual literature to introduce physical vocal variance (pitch, child vocal acoustics, and regional accents).
+
+
+
+## Performance
+
+The performance metrics below illustrate how expanding data volume rescued the QuartzNet framework from catastrophic lexical overfitting.
+
+### Overall Evaluation Metrics
+
+| Experimental Pass | Dataset Baseline Configuration | SpecAugment | Training Volume | Test WER (%) ↓ | Test CER (%) ↓ |
+| --- | --- | --- | --- | --- | --- |
+| **anbekalanNet-exp3 (this release)** | <br>**Main + Duplicate** 
+
+ | <br>**None** 
+
+ | <br>**45.6 Hours** 
+
+ | <br>**40.0%** 
+
+ | <br>**15.0%** 
+
+ |
+| *anbekalanNet-exp1* | <br>*Main Only* 
+
+ | <br>*None* 
+
+ | <br>*1.6 Hours* 
+
+ | <br>*93.0%* 
+
+ | <br>*80.0%* 
+
+ |
+| *anbekalanNet-exp2* | <br>*Main Only* 
+
+ | <br>*Active* 
+
+ | <br>*1.6 Hours* 
+
+ | <br>*64.0%* 
+
+ | <br>*23.0%* 
+
+ |
+| *anbekalanNet-exp4* | <br>*Main + Duplicate* 
+
+ | <br>*Active* 
+
+ | <br>*45.6 Hours* 
+
+ | <br>*42.0%* 
+
+ | <br>*16.0%* 
+
+ |
+
+*All results indicate greedy decoding performance without external Language Models (LMs).*
+
+## License
+
+This legacy checkpoint is archived and released under the **CC-BY-4.0** license.
+
+---
+
+**Repository & Issues:** Technical tracking for this legacy series can be referenced at [RobotsMali-AI/bambara-asr](https://github.com/RobotsMali-AI/bambara-asr/). No further architectural expansions or fine-tuning updates are planned for this model card sequence.

anbekalanNet.nemo

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