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Export NeMo FastConformer Hybrid Transducer Large Streaming to ONNX (#844)

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Fangjun Kuang
2024-05-08 19:07:49 +08:00
committed by GitHub
parent a9f936e92b
commit 68b25abf27
9 changed files with 611 additions and 1 deletions
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scripts/nemo/fast-conformer-hybrid-transducer-ctc/show-onnx-transudcer.py Executable file
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#!/usr/bin/env python3
# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
import onnxruntime
def show(filename):
session_opts = onnxruntime.SessionOptions()
session_opts.log_severity_level = 3
sess = onnxruntime.InferenceSession(filename, session_opts)
for i in sess.get_inputs():
print(i)
print("-----")
for i in sess.get_outputs():
print(i)
def main():
print("=========encoder==========")
show("./encoder.onnx")
print("=========decoder==========")
show("./decoder.onnx")
print("=========joiner==========")
show("./joiner.onnx")
if __name__ == "__main__":
main()
"""
=========encoder==========
NodeArg(name='audio_signal', type='tensor(float)', shape=['audio_signal_dynamic_axes_1', 80, 'audio_signal_dynamic_axes_2'])
NodeArg(name='length', type='tensor(int64)', shape=['length_dynamic_axes_1'])
NodeArg(name='cache_last_channel', type='tensor(float)', shape=['cache_last_channel_dynamic_axes_1', 17, 'cache_last_channel_dynamic_axes_2', 512])
NodeArg(name='cache_last_time', type='tensor(float)', shape=['cache_last_time_dynamic_axes_1', 17, 512, 'cache_last_time_dynamic_axes_2'])
NodeArg(name='cache_last_channel_len', type='tensor(int64)', shape=['cache_last_channel_len_dynamic_axes_1'])
-----
NodeArg(name='outputs', type='tensor(float)', shape=['outputs_dynamic_axes_1', 512, 'outputs_dynamic_axes_2'])
NodeArg(name='encoded_lengths', type='tensor(int64)', shape=['encoded_lengths_dynamic_axes_1'])
NodeArg(name='cache_last_channel_next', type='tensor(float)', shape=['cache_last_channel_next_dynamic_axes_1', 17, 'cache_last_channel_next_dynamic_axes_2', 512])
NodeArg(name='cache_last_time_next', type='tensor(float)', shape=['cache_last_time_next_dynamic_axes_1', 17, 512, 'cache_last_time_next_dynamic_axes_2'])
NodeArg(name='cache_last_channel_next_len', type='tensor(int64)', shape=['cache_last_channel_next_len_dynamic_axes_1'])
=========decoder==========
NodeArg(name='targets', type='tensor(int32)', shape=['targets_dynamic_axes_1', 'targets_dynamic_axes_2'])
NodeArg(name='target_length', type='tensor(int32)', shape=['target_length_dynamic_axes_1'])
NodeArg(name='states.1', type='tensor(float)', shape=[1, 'states.1_dim_1', 640])
NodeArg(name='onnx::LSTM_3', type='tensor(float)', shape=[1, 1, 640])
-----
NodeArg(name='outputs', type='tensor(float)', shape=['outputs_dynamic_axes_1', 640, 'outputs_dynamic_axes_2'])
NodeArg(name='prednet_lengths', type='tensor(int32)', shape=['prednet_lengths_dynamic_axes_1'])
NodeArg(name='states', type='tensor(float)', shape=[1, 'states_dynamic_axes_1', 640])
NodeArg(name='74', type='tensor(float)', shape=[1, 'LSTM74_dim_1', 640])
=========joiner==========
NodeArg(name='encoder_outputs', type='tensor(float)', shape=['encoder_outputs_dynamic_axes_1', 512, 'encoder_outputs_dynamic_axes_2'])
NodeArg(name='decoder_outputs', type='tensor(float)', shape=['decoder_outputs_dynamic_axes_1', 640, 'decoder_outputs_dynamic_axes_2'])
-----
NodeArg(name='outputs', type='tensor(float)', shape=['outputs_dynamic_axes_1', 'outputs_dynamic_axes_2', 'outputs_dynamic_axes_3', 1025])
"""