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+soloba-ctc-0.6b-v0.nemo filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- bm
+library_name: nemo
+datasets:
+- RobotsMali/kunkado
+- RobotsMali/bam-asr-early
+
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- Transducer
+- TDT
+- FastConformer
+- Conformer
+- pytorch
+- Bambara
+- NeMo
+license: cc-by-4.0
+base_model: nvidia/parakeet-ctc-0.6b
+model-index:
+- name: soloba-ctc-0.6b-v0
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: bam-asr-early
+      type: RobotsMali/bam-asr-early
+      split: test
+      args:
+        language: bm
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 35.15760898590088
+
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+---
+
+# Soloni TDT-CTC 114M Bambara
+
+<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-en-orange#model-badge)](#datasets)
+
+`soloba-ctc-0.6b-v0` is a fine tuned version of [`nvidia/parakeet-ctc-0.6b`](https://huggingface.co/nvidia/parakeet-ctc-0.6b) on [RobotsMali/kunkado](https://huggingface.co/datasets/RobotsMali/kunkado). This model cannot does produce Capitalizations but not Punctuations. The model was fine-tuned using **NVIDIA NeMo**.
+
+The model doesn't tag code swicthed expressions in its transcription since for training this model we decided to treat them as a modern variant of the Bambara Language removing all tags and markages.
+
+## **🚨 Important Note**  
+This model, along with its associated resources, is part of an **ongoing research effort**, improvements and refinements are expected in future versions. A human evaluation report of the model is coming soon. 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-ctc-0.6b-v0")
+```
+
+### 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 a string for a given speech sample and return an Hypothesis object (under nemo>=2.3)
+
+## Model Architecture
+
+This model uses a Hybrid FastConformer-TDT-CTC architecture. 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). And a Convolutional Neural Net with CTC loss, the ***Connectionist Temporal Classification*** decoder
+
+## Training
+
+The NeMo toolkit (version 2.3.0) was used for finetuning this model for **183,086 steps** over `nvidia/parakeet-ctc-0.6b` model. This version is trained with this [base config](https://github.com/diarray-hub/bambara-asr/blob/main/kunkado-training/config/soloba/soloba-ctc-v0.0.0.yaml). The full training configurations, scripts, and experimental logs are available here:
+
+🔗 [Bambara-ASR Experiments](https://github.com/diarray-hub/bambara-asr)
+
+The tokenizers for these models were built using the text transcripts of the train set with 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 and the [bam-asr-early](https://huggingface.co/datasets/RobotsMali/bam-asr-early) dataset, the semi-labelled subset, which consists of **~120 hours of automatically annotated Bambara speech data**.
+
+## Performance
+
+We report the Word Error Rate on the test set of bam-asr-early.
+
+|**Decoder (Version)**|**Tokenizer**|**Vocabulary Size**|**bam-asr-all**|
+|---------|-----------------------|-----------------|---------|---------|
+| v0 | BPE | 512            | 35.16       |
+
+
+## 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/diarray-hub/bambara-asr/issues) on github if you have any contributions 
+
+---
+

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