readme: ---
  language:
  - zh
  library_name: nemo
  datasets:
  - AISHELL-2
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - Transducer
  - Conformer
  - Transformer
  - pytorch
  - NeMo
  - hf-asr-leaderboard
  license: cc-by-4.0
  model-index:
  - name: stt_zh_conformer_transducer_large
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: AISHELL-2 IOS
        type: aishell2_ios
        split: test
        args:
          language: zh
      metrics:
      - name: Test CER
        type: cer
        value: 5.3
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: AISHELL-2 Android
        type: aishell2_android
        split: test
        args:
          language: zh
      metrics:
      - name: Test CER
        type: cer
        value: 5.7
    - task:
        type: Automatic Speech Recognition
        name: automatic-speech-recognition
      dataset:
        name: AISHELL-2 Mic
        type: aishell2_mic
        split: test
        args:
          language: zh
      metrics:
      - name: Test CER
        type: cer
        value: 5.6
  ---
  
  # NVIDIA Conformer-Transducer Large (zh-ZH)
  
  <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-zh--ZH-lightgrey#model-badge)](#datasets)
  
  
  This model transcribes speech in Mandarin alphabet.
  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_zh_conformer_transducer_large")
  ```
  
  ### Transcribing using Python
  You may transcribe an audio file like this:
  
  ```
  asr_model.transcribe([PATH_TO_THE_AUDIO])
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py 
   pretrained_name="nvidia/stt_zh_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 Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-Transducer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). 
  
  ## Training
  
  The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_transducer_bpe.yaml).
  
  ### Datasets
  
  All the models in this collection are trained on AISHELL2 [4] comprising of Mandarin speech:
  
  ## Performance
  
  The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
  
  | Version | Tokenizer | Vocabulary Size | AISHELL2 Test IOS | AISHELL2 Test Android | AISHELL2 Test Mic | Train Dataset |
  |---------|-----------|-----------------|-------------------|-----------------------|-------------------|---------------|
  | 1.10.0  | Characters| 5026            | 5.3               | 5.7                    | 5.6              |     AISHELL-2 |
  
  ## Limitations
  Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
  
  ## NVIDIA Riva: Deployment
  
  [NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. 
  Additionally, Riva provides: 
  
  * World-class out-of-th