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README.md

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+---
+language:
+- bm
+library_name: nemo
+datasets:
+- RobotsMali/bam-asr-all
+
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- QuartzNet
+- pytorch
+- Bambara
+- NeMo
+license: cc-by-4.0
+base_model: stt_fr_quartznet15x5
+model-index:
+- name: stt-bm-quartznet15x5
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: bam-asr-all
+      type: RobotsMali/bam-asr-all
+      split: test
+      args:
+        language: bm
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 46.5
+
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+---
+
+# QuartzNet 15x5 CTC Bambara
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-QuartzNet-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-19M-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-bm-lightgrey#model-badge)](#datasets)
+
+`stt-bm-quartznet15x5` is a fine-tuned version of NVIDIA’s [`stt_fr_quartznet15x5`](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_fr_quartznet15x5) optimized for **Bambara ASR**. This model cannot write **Punctuations and Capitalizations**, it utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the training set of bam-asr-all dataset.
+
+The model was fine-tuned using **NVIDIA NeMo** and is trained with **CTC (Connectionist Temporal Classification) Loss**.
+
+## NVIDIA NeMo: Training
+
+To fine-tune or use the model, install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend installing it after setting up the latest PyTorch version.
+
+```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/stt-bm-quartznet15x5")
+```
+
+### Transcribe Audio
+```python
+# Assuming you have a test audio file named sample_audio.wav
+asr_model.transcribe(['sample_audio.wav'])
+```
+
+### Input
+
+This model accepts **16 kHz mono-channel audio (wav files)** as input.
+
+### Output
+
+This model provides transcribed speech as a string for a given speech sample.
+
+## Model Architecture
+
+QuartzNet is a convolutional architecture, which consists of **1D time-channel separable convolutions** optimized for speech recognition. More information on QuartzNet can be found here: [QuartzNet Model](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/asr/models.html#quartznet).
+
+## Training
+
+The NeMo toolkit was used to fine-tune this model for **25939 steps** over the `stt_fr_quartznet15x5` model. This model is trained with this [base config](https://github.com/diarray-hub/bambara-asr/blob/main/configs/quartznet-20m-config-v2.yaml). The full training configurations, scripts, and experimental logs are available here:
+
+🔗 [Bambara-ASR Experiments](https://github.com/diarray-hub/bambara-asr)
+
+## Dataset
+This model was fine-tuned on the [bam-asr-all](https://huggingface.co/datasets/RobotsMali/bam-asr-all) dataset, which consists of **37 hours of transcribed Bambara speech data**. The dataset is primarily derived from **Jeli-ASR dataset** (~87%).
+
+## Performance
+
+The performance of Automatic Speech Recognition models is measured using **Word Error Rate (WER%)**.
+
+|**Version**|**Tokenizer**|**Vocabulary Size**|**bam-asr-all (test set)**|
+|---------|-----------------------|-----------------|---------|
+| V2  | Character-wise | 45            | 46.5         |
+
+These are **greedy WER numbers without external LM**.
+
+## License
+This model is released under the **CC-BY-4.0** license. By using this model, you agree to the terms of the license.
+
+---
+
+More details are available in the **Experimental Technical Report**:
+📄 [Draft Technical Report - Weights & Biases](https://wandb.ai/yacoudiarra-wl/bam-asr-nemo-training/reports/Draft-Technical-Report-V1--VmlldzoxMTIyOTMzOA).
+
+Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/diarray-hub/bambara-asr/issues) on GitHub if you have any contributions.
+
+---

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