refactor test

test/torchaudio_unittest/functional/functional_impl.py

@@ -582,13 +582,17 @@ 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)
 
+    def _apply_beamforming(self, beamform_weights, specgram):
+        specgram_enhanced = np.einsum("...fc,...cft->...ft", beamform_weights.conj(), specgram)
+        return specgram_enhanced
+
     def test_apply_beamforming(self):
         channel = 4
         n_fft_bin = 10
         frame = 5
         beamform_weights = np.random.random((n_fft_bin, channel)) + np.random.random((n_fft_bin, channel)) * 1j
         specgram = np.random.random((channel, n_fft_bin, frame)) + np.random.random((channel, n_fft_bin, frame)) * 1j
-        specgram_enhanced = np.einsum("...fc,...cft->...ft", beamform_weights.conj(), specgram)
+        specgram_enhanced = self._apply_beamforming(beamform_weights, specgram)
         specgram_enhanced_audio = F.apply_beamforming(
             torch.tensor(beamform_weights, dtype=self.complex_dtype, device=self.device),
             torch.tensor(specgram, dtype=self.complex_dtype, device=self.device),