readme: ---
  language:
  - uk
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_10_0
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Citrinet
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  - Riva
  model-index:
  - name: stt_uk_citrinet_1024_gamma_0_25
    results:
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 10.0
        config: uk
        split: test
        type: mozilla-foundation/common_voice_10_0
      metrics:
      - name: Test WER
        type: wer
        value: 5.02
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 10.0
        config: uk
        split: dev
        type: mozilla-foundation/common_voice_10_0
      metrics:
      - name: Test WER
        type: wer
        value: 4.65
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 9.0
        config: uk
        split: test
        type: mozilla-foundation/common_voice_9_0
      metrics:
      - name: Test WER
        type: wer
        value: 3.75
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 9.0
        config: uk
        split: dev
        type: mozilla-foundation/common_voice_9_0
      metrics:
      - name: Test WER
        type: wer
        value: 4.88
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 8.0
        config: uk
        split: test
        type: mozilla-foundation/common_voice_8_0
      metrics:
      - name: Test WER
        type: wer
        value: 3.52
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
    - dataset:
        args:
          language: uk
        name: Mozilla Common Voice 8.0
        config: uk
        split: dev
        type: mozilla-foundation/common_voice_8_0
      metrics:
      - name: Test WER
        type: wer
        value: 5.02
      task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
  license: cc-by-4.0
  ---
  # NVIDIA Streaming Citrinet 1024 (uk)
  
  <style>
  img {
   display: inline;
  }
  </style>
  
  | [![Model architecture](https://img.shields.io/badge/Model_Arch-Citrinet--CTC-lightgrey#model-badge)](#model-architecture)
  | [![Model size](https://img.shields.io/badge/Params-141M-lightgrey#model-badge)](#model-architecture)
  | [![Language](https://img.shields.io/badge/Language-uk-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 Ukrainian alphabet including spaces and apostrophes, and is trained on 69 hours of Ukrainian speech data.
  It is a non-autoregressive "large" variant of Streaming Citrinet, with around 141 million parameters. Model is fine-tuned from pre-trained Russian Citrinet-1024 model on Ukrainian speech data using Cross-Language Transfer Learning [4] approach.
  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 the 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.EncDecCTCModel.from_pretrained("nvidia/stt_uk_citrinet_1024_gamma_0_25")
  ```
  
  ### Transcribing using Python
  First, let's get a sample.
  
  Then simply do:
  ```
  asr_model.transcribe(['<Path of audio file(s)>'])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_uk_citrinet_1024_gamma_0_25" 
   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
  
  Streaming Citrinet-1024 model is a non-autoregressive, streaming variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet).
  
  ## Tra