def test_beamformer_net_bf_output(num_spk): ch = 2 inputs = torch.randn(2, 16, ch) inputs = inputs.float() ilens = torch.LongTensor([16, 12]) model = BeamformerNet( n_fft=8, hop_length=2, num_spk=num_spk, use_wpe=False, use_beamformer=True, use_noise_mask=True, ) model.eval() specs, _, masks = model(inputs, ilens) assert isinstance(masks, dict) assert "noise1" in masks assert masks["noise1"].shape == masks["spk1"].shape if num_spk > 1: assert isinstance(specs, list) assert len(specs) == num_spk for n in range(1, num_spk + 1): assert "spk{}".format(n) in masks assert masks["spk{}".format(n)].shape[-2] == ch assert specs[n - 1].shape[:-1] == masks["spk{}".format(n)][..., 0, :].shape assert specs[n - 1].shape[-1] == 2 # real and imag assert specs[n - 1].dtype == torch.float else: assert isinstance(specs, torch.Tensor) assert "spk1" in masks assert masks["spk1"].shape[-2] == ch assert specs.shape[:-1] == masks["spk1"][..., 0, :].shape assert specs.shape[-1] == 2 # real and imag assert specs.dtype == torch.float
def test_beamformer_net_wpe_output(ch, num_spk, use_dnn_mask_for_wpe): torch.random.manual_seed(0) inputs = torch.randn(2, 16, ch) if ch > 1 else torch.randn(2, 16) inputs = inputs.float() ilens = torch.LongTensor([16, 12]) model = BeamformerNet( n_fft=8, hop_length=2, num_spk=num_spk, use_wpe=True, use_dnn_mask_for_wpe=use_dnn_mask_for_wpe, taps=5, delay=3, use_beamformer=False, ) model.eval() spec, _, masks = model(inputs, ilens) assert spec.shape[0] == 2 # batch size assert spec.shape[-1] == 2 # real and imag assert spec.dtype == torch.float assert isinstance(masks, dict) if use_dnn_mask_for_wpe: assert "dereverb" in masks assert masks["dereverb"].shape == spec.shape[:-1]
def test_beamformer_net_consistency( n_fft, win_length, hop_length, num_spk, normalize_input, use_wpe, wnet_type, wlayers, wunits, wprojs, wdropout_rate, taps, delay, use_dnn_mask_for_wpe, use_beamformer, bnet_type, blayers, bunits, bprojs, badim, ref_channel, use_noise_mask, beamformer_type, bdropout_rate, ): model = BeamformerNet( n_fft=n_fft, win_length=win_length, hop_length=hop_length, num_spk=num_spk, normalize_input=normalize_input, use_wpe=use_wpe, wnet_type=wnet_type, wlayers=wlayers, wunits=wunits, wprojs=wprojs, wdropout_rate=wdropout_rate, taps=taps, delay=delay, use_dnn_mask_for_wpe=use_dnn_mask_for_wpe, use_beamformer=use_beamformer, bnet_type=bnet_type, blayers=blayers, bunits=bunits, bprojs=bprojs, badim=badim, ref_channel=ref_channel, use_noise_mask=use_noise_mask, beamformer_type=beamformer_type, bdropout_rate=bdropout_rate, ) model.eval() random_input_numpy = np.random.randn(2, 16, 2) # np.float64 random_input_torch = torch.from_numpy(random_input_numpy).float() random_input_numpy = torch.from_numpy(random_input_numpy.astype( "float32")) # np.float64-->np.float32-->torch.float32 # ensures reproducibility in the matrix inverse computation torch.random.manual_seed(0) est_speech_numpy, *_ = model(random_input_numpy, ilens=torch.LongTensor([16, 12])) torch.random.manual_seed(0) est_speech_torch, *_ = model(random_input_torch, ilens=torch.LongTensor([16, 12])) assert torch.allclose(est_speech_torch[0], est_speech_numpy[0]) assert torch.allclose(est_speech_torch[-1], est_speech_numpy[-1]) for est in est_speech_torch: assert est.dtype == torch.float