File size: 2,194 Bytes
46455cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
#!/usr/bin/env python3
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
To run this file, do:
cd icefall/egs/librispeech/ASR
python ./conformer_ctc3/test_model.py
"""
import torch
from train import get_params, get_ctc_model
def test_model():
params = get_params()
params.vocab_size = 500
params.blank_id = 0
params.context_size = 2
params.unk_id = 2
params.dynamic_chunk_training = False
params.short_chunk_size = 25
params.num_left_chunks = 4
params.causal_convolution = False
model = get_ctc_model(params)
num_param = sum([p.numel() for p in model.parameters()])
print(f"Number of model parameters: {num_param}")
features = torch.randn(2, 100, 80)
feature_lengths = torch.full((2,), 100)
model(x=features, x_lens=feature_lengths)
def test_model_streaming():
params = get_params()
params.vocab_size = 500
params.blank_id = 0
params.context_size = 2
params.unk_id = 2
params.dynamic_chunk_training = True
params.short_chunk_size = 25
params.num_left_chunks = 4
params.causal_convolution = True
model = get_ctc_model(params)
num_param = sum([p.numel() for p in model.parameters()])
print(f"Number of model parameters: {num_param}")
features = torch.randn(2, 100, 80)
feature_lengths = torch.full((2,), 100)
encoder_out, _ = model.encoder(x=features, x_lens=feature_lengths)
model.get_ctc_output(encoder_out)
def main():
test_model()
test_model_streaming()
if __name__ == "__main__":
main()
|