readme: ---
  language:
  - ca
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_11_0
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - citrinet
  - pytorch
  - NeMo
  license: cc-by-4.0
  widget:
  - example_title: CV sample 1
    src: https://huggingface.co/projecte-aina/stt-ca-citrinet-512/tree/main/samples/common_voice_ca_34667058.wav
  model-index:
  - name: stt-ca-citrinet-512 
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Mozilla Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: ca
        split: test
        args:
          language: ca
      metrics:
      - name: Test WER
        type: wer
        value: 6.684
  ---
  
  # Aina Project's Catalan text-to-speech model
  ## Model description
  
  This model transcribes audio samples in Catalan to lowercase text without punctuation. The model was fine-tuned from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model using the [NeMo](https://github.com/NVIDIA/NeMo) toolkit. It has around 36.5M parámeters and has been trained on [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets).
  
  ## Intended uses and limitations
  
  You can use this model for Automatic Speech Recognition (ASR) in Catalan, to transcribe audio files in Catalan to plain text without punctuation.
  
  ## How to use
  ### Usage
  
  Requiered libraries:
  
  ```bash
  pip install nemo_toolkit['all']
  ```
  
  Clone the repository to download the model:
  
  ```bash
  git clone https://huggingface.co/projecte-aina/stt-ca-citrinet-512
  ```
  
  Given that `NEMO_PATH` is the path that points to the downloaded `stt-ca-citrinet-512.nemo` file, to do inference over a set of `.wav` files you should:
  
  ```python
  # Load the model
  model = nemo_asr.models.EncDecCTCModel.restore_from(NEMO_PATH)
  
  # Create a list pointing to the audio files
  paths2audio_files = ["audio_1.wav", ..., "audio_n.wav"]
  
  # Fix the batch size to whatever number suits your purpose
  batch_size = 8
  
  # Transcribe the audio files
  transcriptions = model.transcribe(paths2audio_files=paths2audio_files,
                                   batch_size=batch_size)
  # Visualize the transcriptions
  print(transcriptions)
  
  ```
  
  ## Training data
  
  This model has been trained on the training split of the Catalan version of [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets).
  
  ## Training
  ### Data preparation
  We have processed [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets) using the NeMo toolkit. We used [get_commonvoice_data.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/get_commonvoice_data.py) to process the manifests and made a subsequent data cleaning step.
  
  After cleaning the dataset and normalizing the `ñ` character to `ny`, we have used the following charset to create the final NeMo manifests for training.
  ```python
  ['c', ' ', 'ó', 'g', 'a', 'o', 'ü', 'v', 'p', 't', "'", '—', 'f', 'k', 'à', 'ï', 'í', 'ú', 'd', 'l', 'z', 'é', 'w', 'm', 'r', 'n', 'y', '-', 'u', 'i', 'h', 'ç', 'e', '·', 'q', 'è', 'ò', 'j', 'x', 's', 'b']
  ```
  
  ### Training procedure
  This model was trained starting from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model. The initial learning rate was set to 0.005 and the minimum lr for weight decay was set to 1e-7. 
  
  The model was trained for 90 steps and then continued training for another 90 steps starting from a learning rate of 1e-4.
  
  ## Evaluation
  After evaluation on the test split of Common Voice 11.0 we have obtained a WER of 6.684.
  
  ## Additional information
  
  ### Author
  Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
  
  ### Contact information
  For further information, send an email to aina@bsc.es
  
  ### Copyright
  Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 
  
  ### Licensing Information
  [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
  
  ### Funding
  This work was funded by the [Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
  
  
  ## Disclaimer
  <details>
  <summary>Click to expand</summary>
  
  The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
  
  When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
  
  In no event shall the owner and cre