readme: ---
  language:
  - it
  library_name: nemo
  datasets:
  - facebook/voxpopuli
  - facebook/multilingual_librispeech
  - mozilla-foundation/common_voice_11_0
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  - Riva
  license: cc-by-4.0
  model-index:
  - name: stt_it_conformer_ctc_large
    results:
    - task:
        type: Automatic Speech Recognition
        name: speech-recognition
      dataset:
        name: common-voice-11-0
        type: mozilla-foundation/common_voice_11_0
        config: it
        split: dev
        args:
          language: it
      metrics:
      - name: Dev WER
        type: wer
        value: 5.38
    - task:
        type: Automatic Speech Recognition
        name: speech-recognition
      dataset:
        name: common-voice-11-0
        type: mozilla-foundation/common_voice_11_0
        config: it
        split: test
        args:
          language: it
      metrics:
      - name: Test WER
        type: wer
        value: 5.92
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech
        type: facebook/multilingual_librispeech
        config: italian
        split: dev
        args:
          language: it
      metrics:
      - name: Dev WER
        type: wer
        value: 13.16
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech
        type: facebook/multilingual_librispeech
        config: italian
        split: test
        args:
          language: it
      metrics:
      - name: Test WER
        type: wer
        value: 10.62
    - task:
        type: Automatic Speech Recognition
        name: speech-recognition
      dataset:
        name: VoxPopuli
        type: facebook/voxpopuli
        config: it
        split: dev
        args:
          language: it
      metrics:
      - name: Dev WER
        type: wer
        value: 13.43
    - task:
        type: Automatic Speech Recognition
        name: speech-recognition
      dataset:
        name: VoxPopuli
        type: facebook/voxpopuli
        config: it
        split: test
        args:
          language: it
      metrics:
      - name: Test WER
        type: wer
        value: 16.75
  ---
  # NVIDIA Conformer-CTC Large (it)
  
  <style>
  img {
   display: inline;
  }
  </style>
  
  | [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--CTC-lightgrey#model-badge)](#model-architecture)
  | [![Model size](https://img.shields.io/badge/Params-120M-lightgrey#model-badge)](#model-architecture)
  | [![Language](https://img.shields.io/badge/Language-it-lightgrey#model-badge)](#datasets)
  | [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) |
  
  
  This model transcribes speech in lowercase Italian alphabet including spaces, and was trained on a composite dataset comprising of 487 hours of Italian speech. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters.
  See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
  It is also compatible with NVIDIA Riva for [production-grade server deployments](#deployment-with-nvidia-riva). 
  
  
  ## Usage
  
  The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
  
  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']
  ```
  
  ### Automatically instantiate the model
  
  ```python
  import nemo.collections.asr as nemo_asr
  asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_it_conformer_ctc_large")
  ```
  
  ### Transcribing using Python
  
  Simply do:
  
  ```
  asr_model.transcribe(['sample.wav'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_it_conformer_ctc_large" 
   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
  
  Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc). 
  
  ## Training
  
  The NeMo toolkit [3] was used for training these models for over several hundred epochs. These models are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py