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62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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from nemo.collections.asr.models import ASRModel
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class TestASRSubsamplingConvChunking:
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@pytest.mark.with_downloads()
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@pytest.mark.unit
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def test_forward(self):
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asr_model = ASRModel.from_pretrained("stt_en_fastconformer_ctc_large")
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asr_model = asr_model.eval()
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asr_model.preprocessor.featurizer.dither = 0.0
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asr_model.preprocessor.featurizer.pad_to = 0
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len = 512
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input_signal_batch1 = torch.randn(size=(1, len), device=asr_model.device)
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length_batch1 = torch.randint(low=321, high=500, size=[1], device=asr_model.device)
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input_signal_batch4 = torch.randn(size=(4, len), device=asr_model.device)
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length_batch4 = torch.randint(low=321, high=500, size=[4], device=asr_model.device)
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with torch.inference_mode():
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# regular inference
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logprobs_batch1_nosplit, _, _ = asr_model.forward(
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input_signal=input_signal_batch1, input_signal_length=length_batch1
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)
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logprobs_batch4_nosplit, _, _ = asr_model.forward(
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input_signal=input_signal_batch4, input_signal_length=length_batch4
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)
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# force chunking to 2
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asr_model.change_subsampling_conv_chunking_factor(subsampling_conv_chunking_factor=2)
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# chunked inference by channels as batch is 1
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logprobs_batch1_split, _, _ = asr_model.forward(
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input_signal=input_signal_batch1, input_signal_length=length_batch1
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)
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# chunked inference by batch as it is 4 [> 1]
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logprobs_batch4_split, _, _ = asr_model.forward(
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input_signal=input_signal_batch4, input_signal_length=length_batch4
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)
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diff = torch.mean(torch.abs(logprobs_batch1_split - logprobs_batch1_nosplit))
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assert diff <= 0.2
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diff = torch.mean(torch.abs(logprobs_batch4_split - logprobs_batch4_nosplit))
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assert diff <= 0.2
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