readme: ---
  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:
    - task:
        type: automatic-speech-recognition
      dataset:
        type: common_voice
        name: Common Voice Portuguese
        config: clean
        split: test
        args:
          language: pt
      metrics:
      - type: wer
        value: 49.17
        name: Test WER
      - type: cer
        value: 18.59
        name: Test CER
  ---
  
  
  ## 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)
  
  
  ### 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
  
  | Metric | Score |
  | ------- | ----- |
  | WER     | 49%   |
  | CER     | 18%   |
  
  The metrics were obtained using the following code:
  
  **Attention**: The steps below must be performed after downloading the dataset (Mozilla Commom Voices 9.0 PT) and following the steps of pre-processing the audio data and `manifest` files contained in the file [`notebooks/Finetuning CTC model Portuguese.ipynb`](https://github.com/DominguesM/stt_pt_quartznet15x5_ctc_small)
  
  ```bash
  $ wget -P scripts/ "https://raw.githubusercontent.com/NVIDIA/NeMo/v1.9.0/examples/asr/speech_to_text_eval.py"
  
  $ wget -P scripts/ "https://raw.githubusercontent.com/NVIDIA/NeMo/v1.9.0/examples/asr/transcribe_speech.py"
  
  $ python scripts/speech_to_text_eval.py \
      pretrained_name="dominguesm/stt_pt_quartznet15x5_ctc_small" \
      dataset_manifest="manifests/pt/commonvoice_test_manifest_processed.json" \
      output_filename="./evaluation_transcripts.json" \
      batch_size=32 \
      amp=true \
      use_cer=false
  ```
  
  ## 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.
  
  ## Citation
  
  If you use our work, please cite:
  
  ```cite
  @misc{domingues2022quartznet15x15-small-portuguese,
    title={Fine-tuned {Quartznet}-15x5 CTC small model for speech recognition in {P}ortuguese},
    author={Domingues, Maicon},
    howpublished={\url{https://huggingface.co/dominguesm/stt_pt_quartznet15x5_ctc_small}},
    year={2022}
  }
  ```
  
  ## References
  
  [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
  
  