Fixed argument validation in TorchAudio filtering (#2609)

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2609

Converted argument validations in torchaudio/functional/filtering from assert based validation to the preferred if-then raise validation. Added specific error messages in all cases.

Reviewed By: mthrok

Differential Revision: D38515029

fbshipit-source-id: 6c644a042f86c6feb2bbe8bd02fdb484fe27fae9

torchaudio/functional/filtering.py

@@ -932,7 +932,8 @@ def _lfilter_core_generic_loop(input_signal_windows: Tensor, a_coeffs_flipped: T
 try:
     _lfilter_core_cpu_loop = torch.ops.torchaudio._lfilter_core_loop
 except RuntimeError as err:
-    assert str(err) == "No such operator torchaudio::_lfilter_core_loop"
+    if str(err) != "No such operator torchaudio::_lfilter_core_loop":
+        raise
     _lfilter_core_cpu_loop = _lfilter_core_generic_loop
 
 
@@ -942,14 +943,24 @@ def _lfilter_core(
     b_coeffs: Tensor,
 ) -> Tensor:
 
-    assert a_coeffs.size() == b_coeffs.size()
-    assert len(waveform.size()) == 3
-    assert waveform.device == a_coeffs.device
-    assert b_coeffs.device == a_coeffs.device
+    if a_coeffs.size() != b_coeffs.size():
+        raise ValueError(
+            "Expected coeffs to be the same size."
+            f"Found a_coeffs size: {a_coeffs.size()}, b_coeffs size: {b_coeffs.size()}"
+        )
+    if waveform.ndim != 3:
+        raise ValueError(f"Expected waveform to be 3 dimensional. Found: {waveform.ndim}")
+    if not (waveform.device == a_coeffs.device == b_coeffs.device):
+        raise ValueError(
+            "Expected waveform and coeffs to be on the same device."
+            f"Found: waveform device:{waveform.device}, a_coeffs device: {a_coeffs.device}, "
+            f"b_coeffs device: {b_coeffs.device}"
+        )
 
     n_batch, n_channel, n_sample = waveform.size()
     n_order = a_coeffs.size(1)
-    assert n_order > 0
+    if n_order <= 0:
+        raise ValueError(f"Expected n_order to be positive. Found: {n_order}")
 
     # Pad the input and create output
 
@@ -983,7 +994,8 @@ def _lfilter_core(
 try:
     _lfilter = torch.ops.torchaudio._lfilter
 except RuntimeError as err:
-    assert str(err) == "No such operator torchaudio::_lfilter"
+    if str(err) != "No such operator torchaudio::_lfilter":
+        raise
     _lfilter = _lfilter_core
 
 
@@ -1018,13 +1030,23 @@ def lfilter(waveform: Tensor, a_coeffs: Tensor, b_coeffs: Tensor, clamp: bool =
         Tensor: Waveform with dimension of either `(..., num_filters, time)` if ``a_coeffs`` and ``b_coeffs``
         are 2D Tensors, or `(..., time)` otherwise.
     """
-    assert a_coeffs.size() == b_coeffs.size()
-    assert a_coeffs.ndim <= 2
+    if a_coeffs.size() != b_coeffs.size():
+        raise ValueError(
+            "Expected coeffs to be the same size."
+            f"Found: a_coeffs size: {a_coeffs.size()}, b_coeffs size: {b_coeffs.size()}"
+        )
+    if a_coeffs.ndim > 2:
+        raise ValueError(f"Expected coeffs to have greater than 1 dimension. Found: {a_coeffs.ndim}")
 
     if a_coeffs.ndim > 1:
         if batching:
-            assert waveform.ndim > 1
-            assert waveform.shape[-2] == a_coeffs.shape[0]
+            if waveform.ndim <= 0:
+                raise ValueError("Expected waveform to have a positive number of dimensions." f"Found: {waveform.ndim}")
+            if waveform.shape[-2] != a_coeffs.shape[0]:
+                raise ValueError(
+                    "Expected number of batches in waveform and coeffs to be the same."
+                    f"Found: coeffs batches: {a_coeffs.shape[0]}, waveform batches: {waveform.shape[-2]}"
+                )
         else:
             waveform = torch.stack([waveform] * a_coeffs.shape[0], -2)
     else:
@@ -1090,7 +1112,8 @@ def _overdrive_core_loop_generic(
 try:
     _overdrive_core_loop_cpu = torch.ops.torchaudio._overdrive_core_loop
 except RuntimeError as err:
-    assert str(err) == "No such operator torchaudio::_overdrive_core_loop"
+    if str(err) != "No such operator torchaudio::_overdrive_core_loop":
+        raise
     _overdrive_core_loop_cpu = _overdrive_core_loop_generic
 
 
@@ -1377,7 +1400,11 @@ def _measure(
     boot_count: int,
 ) -> float:
 
-    assert spectrum.size()[-1] == noise_spectrum.size()[-1]
+    if spectrum.size(-1) != noise_spectrum.size(-1):
+        raise ValueError(
+            "Expected spectrum size to match noise spectrum size in final dimension."
+            f"Found: spectrum size: {spectrum.size()}, noise_spectrum size: {noise_spectrum.size()}"
+        )
 
     samplesLen_ns = samples.size()[-1]
     dft_len_ws = spectrum.size()[-1]
@@ -1495,7 +1522,7 @@ def vad(
             for when the noise level is decreasing. (Default: 0.01)
         noise_reduction_amount (float, optional) Amount of noise reduction to use in
             the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35)
-        measure_freq (float, optional) Frequency of the algorithm’s
+        measure_freq (float, optional) Frequency of the algorithm's
             processing/measurements. (Default: 20.0)
         measure_duration: (float, optional) Measurement duration.
             (Default: Twice the measurement period; i.e. with overlap.)
@@ -1565,7 +1592,11 @@ def vad(
     cepstrum_end = math.floor(sample_rate * 0.5 / hp_lifter_freq)
     cepstrum_end = min(cepstrum_end, dft_len_ws // 4)
 
-    assert cepstrum_end > cepstrum_start
+    if cepstrum_end <= cepstrum_start:
+        raise ValueError(
+            "Expected cepstrum_start to be smaller than cepstrum_end."
+            f"Found: cepstrum_start: {cepstrum_start}, cepstrum_end: {cepstrum_end}."
+        )
 
     noise_up_time_mult = math.exp(-1.0 / (noise_up_time * measure_freq))
     noise_down_time_mult = math.exp(-1.0 / (noise_down_time * measure_freq))