64 lines
3.1 KiB
Python
Executable File
64 lines
3.1 KiB
Python
Executable File
#!/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])
|
|
|
|
"""
|