readme: ---
  language:
  - de
  license: cc-by-4.0
  library_name: nemo
  datasets:
  - mozilla-foundation/common_voice_7_0
  - Multilingual LibriSpeech (2000 hours)
  thumbnail: null
  tags:
  - automatic-speech-recognition
  - speech
  - audio
  - CTC
  - Conformer
  - Transformer
  - NeMo
  - pytorch
  model-index:
  - name: stt_de_conformer_transducer_large
    results:
    - task:
        type: automatic-speech-recognition
      dataset:
        type: common_voice_7_0
        name: mozilla-foundation/common_voice_7_0
        config: other
        split: test
        args:
          lageangu: de
      metrics:
      - type: wer
        value: 4.93
        name: WER
  ---
  
  
  ## Model Overview
  
  <DESCRIBE IN ONE LINE THE MODEL AND ITS USE>
  
  ## 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.ASRModel.from_pretrained("iqbalc/stt_de_conformer_transducer_large")
  ```
  
  ### Transcribing using Python
  ```
  asr_model.transcribe(['filename.wav'])
  
  ```
  
  ### Transcribing many audio files
  
  ```shell
  python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="iqbalc/stt_de_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 for Automatic Speech Recognition which uses Transducer loss/decoding
  
  ## Training
  
  The NeMo toolkit was used for training the models. These models are fine-tuned with this example script and this base config.
  
  The tokenizers for these models were built using the text transcripts of the train set with this script.
  
  ### Datasets
  
  All the models in this collection are trained on a composite dataset comprising of over two thousand hours of cleaned German speech:
  
  1. MCV7.0 567 hours 
  2. MLS 1524 hours 
  3. VoxPopuli 214 hours
  
  ## Performance
  
  Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
  
  MCV7.0 test	= 4.93
  
  ## Limitations
  
  The model might perform worse for accented speech
  
  
  ## References
  [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)