readme: ---
  language:
  - en
  library_name: nemo
  datasets:
  - librispeech_asr
  - fisher_corpus
  - Switchboard-1
  - WSJ-0
  - WSJ-1
  - National-Singapore-Corpus-Part-1
  - National-Singapore-Corpus-Part-6
  - vctk
  - VoxPopuli-(EN)
  - Europarl-ASR-(EN)
  - Multilingual-LibriSpeech-(2000-hours)
  - mozilla-foundation/common_voice_7_0
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  - Riva
  license: cc-by-4.0
  widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
  model-index:
  - name: stt_en_conformer_ctc_large
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (clean)
        type: librispeech_asr
        config: clean
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 2.2
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: LibriSpeech (other)
        type: librispeech_asr
        config: other
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 4.3
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Multilingual LibriSpeech
        type: facebook/multilingual_librispeech
        config: english
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 7.2
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Mozilla Common Voice 7.0
        type: mozilla-foundation/common_voice_7_0
        config: en
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 8.0
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Mozilla Common Voice 8.0
        type: mozilla-foundation/common_voice_8_0
        config: en
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 9.48
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Wall Street Journal 92
        type: wsj_0
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 2.0
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: Wall Street Journal 93
        type: wsj_1
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 2.9
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: National Singapore Corpus
        type: nsc_part_1
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 7.0
  ---
  
  # NVIDIA Conformer-CTC Large (en-US)
  
  <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-en--US-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 English alphabet including spaces and apostrophes, and is trained on several thousand hours of English speech data.
  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_en_conformer_ctc_large")
  ```
  
  ### Transcribing using Python
  First, let's get a sample
  ```
  wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
  ```
  Then simply do:
  ```
  asr_