Initial upload of kinyarwanda-ASR model

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+Mbaza-ASR-Afrivoice-660h/Mbaza-ASR-Afrivoice-660h.nemo filter=lfs diff=lfs merge=lfs -text

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Mbaza-ASR-Afrivoice-660h/Mbaza-ASR-Afrivoice-660h.nemo

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Mbaza-ASR-Afrivoice-660h/README.md

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+---
+language:
+- rw
+license: cc-by-4.0
+library_name: nemo
+datasets:
+- DigitalUmuganda/Afrivoice_Kinyarwanda
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- Conformer
+- NeMo
+- pytorch
+
+---
+
+
+## Model Overview
+
+<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
+
+## Dependencies
+
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). 
+
+For inference just run:
+```
+pip install nemo_toolkit['all']
+``` 
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Load the model weights
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("DigitalUmuganda/Mbaza-ASR-Afrivoice-660h")
+```
+
+### Transcribing using Python
+
+```
+asr_model.transcribe(['<audio_sample>'])
+```
+
+### Transcribing many audio files
+
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="DigitalUmuganda/nemo_kin_pretrained_800h_retrained_tokenizer"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
+
+### Input
+
+This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
+
+
+