Fix flanger and phaser for GPU (#702)

* Make `_generate_wave_table` device and dtype aware.
Signed-off-by: Bhargav Kathivarapu <bhargavkathivarapu31@gmail.com>

torchaudio/functional.py

@@ -1317,7 +1317,6 @@ def phaser(
     delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device)
 
     mod_buf_len = int(sample_rate / mod_speed + .5)
-    mod_buf = torch.zeros(mod_buf_len, dtype=dtype, device=device)
 
     if sinusoidal:
         wave_type = 'SINE'
@@ -1329,7 +1328,8 @@ def phaser(
                                    table_size=mod_buf_len,
                                    min=1.,
                                    max=float(delay_buf_len),
-                                   phase=math.pi / 2)
+                                   phase=math.pi / 2,
+                                   device=device)
 
     delay_pos = 0
     mod_pos = 0
@@ -1353,7 +1353,8 @@ def _generate_wave_table(
         table_size: int,
         min: float,
         max: float,
-        phase: float
+        phase: float,
+        device: torch.device
 ) -> Tensor:
     r"""A helper fucntion for phaser. Generates a table with given parameters
 
@@ -1364,18 +1365,18 @@ def _generate_wave_table(
         min (float): desired min value
         max (float): desired max value
         phase (float): desired phase
-
+        device (torch.device): Torch device on which table must be generated
     Returns:
         Tensor: A 1D tensor with wave table values
     """
 
     phase_offset = int(phase / math.pi / 2 * table_size + 0.5)
 
-    t = torch.arange(table_size).to(torch.int32)
+    t = torch.arange(table_size, device=device, dtype=torch.int32)
 
     point = (t + phase_offset) % table_size
 
-    d = torch.zeros_like(point).to(torch.float64)
+    d = torch.zeros_like(point, device=device, dtype=torch.float64)
 
     if wave_type == 'SINE':
         d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2
@@ -1487,8 +1488,6 @@ def flanger(
 
     lfo_length = int(sample_rate / speed)
 
-    lfo = torch.zeros(lfo_length, dtype=dtype, device=device)
-
     table_min = math.floor(delay_min * sample_rate + 0.5)
     table_max = delay_buf_length - 2.
 
@@ -1497,13 +1496,14 @@ def flanger(
                                table_size=lfo_length,
                                min=float(table_min),
                                max=float(table_max),
-                               phase=3 * math.pi / 2)
+                               phase=3 * math.pi / 2,
+                               device=device)
 
     output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device)
 
     delay_buf_pos = 0
     lfo_pos = 0
-    channel_idxs = torch.arange(0, n_channels)
+    channel_idxs = torch.arange(0, n_channels, device=device)
 
     for i in range(waveform.shape[-1]):