Add apply_beamforming to torchaudio.functional #2232
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mthrok
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mthrok
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mthrok
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test/torchaudio_unittest/functional/torchscript_consistency_impl.py
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| @@ -582,6 +583,21 @@ def test_rnnt_loss_costs_and_gradients_random_data_with_numpy_fp32(self): | |||
| ref_costs, ref_gradients = rnnt_utils.compute_with_numpy_transducer(data=data) | |||
| self._test_costs_and_gradients(data=data, ref_costs=ref_costs, ref_gradients=ref_gradients) | |||
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| def test_apply_beamforming(self): | |||
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Tests in functional_impl.py implements test logics specific to each tested modules, so please add docstring of what is the expectation of this test.
test docstring should tell future maintainers (without context) what it is testing, so often it is the form of "(under this condition), given this input, this out comes should happen".
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Summary: This PR adds ``apply_beamforming`` method to ``torchaudio.functional``. The method employs the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum. The input arguments are the complex-valued beamforming weight Tensor and the multi-channel noisy spectrum. Pull Request resolved: pytorch#2232 Reviewed By: mthrok Differential Revision: D34474561 Pulled By: nateanl fbshipit-source-id: 8b1c3134b6e2d6e23cbedbb85022272af268b8d0
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This pull request was exported from Phabricator. Differential Revision: D34474561 |
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Summary: This PR adds ``apply_beamforming`` method to ``torchaudio.functional``. The method employs the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum. The input arguments are the complex-valued beamforming weight Tensor and the multi-channel noisy spectrum. Pull Request resolved: pytorch#2232 Reviewed By: mthrok Differential Revision: D34474561 Pulled By: nateanl fbshipit-source-id: 2910251a8f111e65375dfb50495b6a415113f06d
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This PR adds
apply_beamformingmethod totorchaudio.functional.The method employs the beamforming weight to the multi-channel noisy spectrum to obtain the single-channel enhanced spectrum.
The input arguments are the complex-valued beamforming weight Tensor and the multi-channel noisy spectrum.