Converting a PyTorch QuartzNet Model

Danger

The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Python tutorials.

NeMo project provides the QuartzNet model.

Downloading the Pre-trained QuartzNet Model

To download the pre-trained model, refer to the NeMo Speech Models Catalog. Here are the instructions on how to obtain QuartzNet in ONNX format.

1. Install the NeMo toolkit, using the instructions.

2. Run the following code:

   import nemo
   import nemo.collections.asr as nemo_asr
   
   quartznet = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="QuartzNet15x5Base-En")
   # Export QuartzNet model to ONNX format
   quartznet.decoder.export('decoder_qn.onnx')
   quartznet.encoder.export('encoder_qn.onnx')
   quartznet.export('qn.onnx')
   

   This code produces 3 ONNX model files: encoder_qn.onnx, decoder_qn.onnx, qn.onnx. They are decoder, encoder, and a combined decoder(encoder(x)) models, respectively.

Converting an ONNX QuartzNet model to IR

If using a combined model:

mo --input_model <MODEL_DIR>/qt.onnx --input_shape [B,64,X]

If using separate models:

mo --input_model <MODEL_DIR>/encoder_qt.onnx --input_shape [B,64,X]
mo --input_model <MODEL_DIR>/decoder_qt.onnx --input_shape [B,1024,Y]

Where shape is determined by the audio file Mel-Spectrogram length: B - batch dimension, X - dimension based on the input length, Y - determined by encoder output, usually X / 2.