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README.md

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+---
+language:
+- bm
+library_name: nemo
+datasets:
+- RobotsMali/kunkado
+
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- Transducer
+- FastConformer
+- Conformer
+- pytorch
+- Bambara
+- NeMo
+license: cc-by-4.0
+base_model: RobotsMali/soloba-tdt-0.6b-v0.5
+model-index:
+- name: soloba-tdt-0.6b-v1.5
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Kunkado
+      type: RobotsMali/kunkado
+      split: test
+      args:
+        language: bm
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 39.7866505648225
+    - name: Test CER
+      type: cer
+      value: 23.216155838453484
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Nyana Eval
+      type: RobotsMali/nyana-eval
+      split: test
+      args:
+        language: bm
+    metrics:
+    - name: Test WER
+      type: wer
+      value: XX.XXX
+    - name: Test CER
+      type: cer
+      value: YY.YYY
+
+metrics:
+- wer
+- cer
+pipeline_tag: automatic-speech-recognition
+---
+
+# Soloba-TDT-600M Series
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--CTC-blue#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-0.6B-green#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-bm-orange#model-badge)](#datasets)
+
+`soloba-tdt-0.6b-v1.5` is a fine tuned version of [`RobotsMali/soloba-tdt-0.6b-v0.5`](https://huggingface.co/RobotsMali/soloba-ctc-0.6b-v2) on RobotsMali/kunkado. This model does not consistently produce Capitalizations and Punctuations and it cannot produce acoustic event tags like those found in Kunkado its transcriptions. It was fine-tuned using **NVIDIA NeMo**.
+
+## **🚨 Important Note**  
+This model, along with its associated resources, is part of an **ongoing research effort**, improvements and refinements are expected in future versions. Users should be aware that:  
+
+- **The model may not generalize very well accross all speaking conditions and dialects.**  
+- **Community feedback is welcome, and contributions are encouraged to refine the model further.** 
+
+## NVIDIA NeMo: Training
+
+To fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
+
+```bash
+pip install nemo-toolkit['asr']
+``` 
+
+## How to Use This Model
+
+Note that this model has been released for research purposes primarily.
+
+### Load Model with NeMo
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained(model_name="RobotsMali/soloba-tdt-0.6b-v1.5")
+```
+
+### Transcribe Audio
+```python
+model.eval()
+# Assuming you have a test audio file named sample_audio.wav
+asr_model.transcribe(['sample_audio.wav'])
+```
+
+### Input
+
+This model accepts any **mono-channel audio (wav files)** as input and resamples them to *16 kHz sample rate* before performing the forward pass
+
+### Output
+
+This model provides transcribed speech as an hypothesis object with a text attribute containing the transcription string for a given speech sample. (nemo>=2.3)
+
+## Model Architecture
+
+This model uses a FastConformer Ecoder and an autoregressive Token-and-Duration Transducer decoder, a variant of RNN-T that predicts jointly learn to predict a token and its duration. FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).
+
+
+## Training
+
+The NeMo toolkit was used for finetuning this model for **40,000 steps** over `RobotsMali/soloba-tdt-0.6b-v0.5` model with bacth_size 32. The finetuning codes and configurations can be found at [RobotsMali-AI/bambara-asr](https://github.com/RobotsMali-AI/bambara-asr/).
+
+The tokenizer for this model was trained on the text transcripts of the train set of RobotsMali/kunkado using this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
+
+## Dataset
+This model was fine-tuned on the [kunkado](https://huggingface.co/datasets/RobotsMali/kunkado) dataset, the human-reviewed subset, which consists of **~40 hours of transcribed Bambara speech data**. The text was normalized with the [bambara-normalizer](https://pypi.org/project/bambara-normalizer/) prior to training, normalizing numbers, removing punctuations and removings tags.
+
+
+## Performance
+
+We report the Word Error Rate (WER) and Character Error Rate (CER) for this model:
+
+| Benchmark | Decoding | WER (%) &darr; | CER (%) &darr; |
+|---------------|----------|-----------------|-----------------|
+| Kunkado | CTC | 39.78 | 23.21 |
+| Nyana Eval    | CTC | XX.XX | YY.YY |
+
+## License
+This model is released under the **CC-BY-4.0** license. By using this model, you agree to the terms of the license.
+
+---
+
+Feel free to open a discussion on Hugging Face or [file an issue](https://github.com/RobotsMali-AI/bambara-asr/issues) on GitHub for help or contributions.

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