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README.md

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+---
+language:
+- pt
+license: cc-by-4.0
+library_name: nemo
+datasets:
+- mozilla-foundation/common_voice_9_0
+thumbnail: null
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- QuartzNet
+- Transformer
+- NeMo
+- pytorch
+model-index:
+- name: stt_pt_quartznet15x5_ctc_small
+  results: []
+
+---
+
+
+## Model Overview
+
+This model transcribes speech in lower case Portuguese alphabet along with spaces. It is a "small" versions of QuartzNet-CTC model. 
+
+## NVIDIA NeMo: Training
+
+To train, 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.
+```
+pip install nemo_toolkit['all']
+``` 
+
+## How to Use this Model
+
+The model is available for use in the NeMo toolkit [1], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+
+### Automatically instantiate the model
+
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("dominguesm/stt_pt_quartznet15x5_ctc_small")
+```
+
+### Transcribing using Python
+First, let's get a sample
+```
+wget https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small/raw/main/audios/common_voice_pt_25555332.mp3
+```
+Then simply do:
+```
+asr_model.transcribe(['common_voice_pt_25555332.mp3'])
+```
+
+### Transcribing many audio files
+
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="dominguesm/stt_pt_quartznet15x5_ctc_small"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
+
+### Input
+
+This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
+
+### Output
+
+This model provides transcribed speech as a string for a given audio sample.
+
+## Model Architecture
+
+This model are based on the QuartzNet architecture, which is a variant of Jasper that uses 1D time-channel separable convolutional layers in its convolutional residual blocks and are therefore smaller than Jasper models.
+
+QuartzNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.
+
+## Training
+
+All training scripts will be available at: [DominguesM/stt_pt_quartznet15x5_ctc_small](https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small)
+
+**Soon more information**
+
+
+### Datasets
+
+The model was trained with a part of the Common Voices 9.0 dataset in Portuguese, totaling 26 hours of audio.
+
+* Mozilla Common Voice (v9.0)
+
+## Performance
+
+**Coming soon**
+
+## Limitations
+
+Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
+
+
+## References
+
+[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+