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_8_0
  - MLCommons/peoples_speech
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  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_transducer_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: 1.7
    - 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: 3.7
    - 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: 5.8
    - 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: 7.8
    - 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: 1.5
    - 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: 2.1
    - 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: 5.9
  ---
  
  # NVIDIA Conformer-Transducer Large (en-US)
  
  <style>
  img {
   display: inline;
  }
  </style>
  
  | [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--Transducer-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)
  
  
  This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
  It is a large version of Conformer-Transducer (around 120M parameters) model.  
  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-transducer) for complete architecture details.
  
  ## 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 [3], 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.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_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_model.transcribe(['2086-149220-0033.wav'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_en_conformer_transducer_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-Transducer model is an autoregressive variant of Conformer model [1] for A