[black][codemod] formatting changes from black 22.3.0

Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc

examples/asr/emformer_rnnt/global_stats.py

@@ -65,7 +65,7 @@ def generate_statistics(samples):
         if idx % 100 == 0:
             logger.info(f"Processed {idx}")
 
-    return E_x, (E_x_2 - E_x ** 2) ** 0.5
+    return E_x, (E_x_2 - E_x**2) ** 0.5
 
 
 def get_dataset(args):

examples/hubert/utils/kmeans.py

@@ -120,7 +120,7 @@ class ApplyKmeans(object):
     def __init__(self, km_path, device):
         self.km_model = joblib.load(km_path)
         self.C_np = self.km_model.cluster_centers_.transpose()
-        self.Cnorm_np = (self.C_np ** 2).sum(0, keepdims=True)
+        self.Cnorm_np = (self.C_np**2).sum(0, keepdims=True)
 
         self.C = torch.from_numpy(self.C_np).to(device)
         self.Cnorm = torch.from_numpy(self.Cnorm_np).to(device)

examples/pipeline_tacotron2/train.py

@@ -206,7 +206,7 @@ def adjust_learning_rate(epoch, optimizer, learning_rate, anneal_steps, anneal_f
     if anneal_factor == 0.3:
         lr = learning_rate * ((0.1 ** (p // 2)) * (1.0 if p % 2 == 0 else 0.3))
     else:
-        lr = learning_rate * (anneal_factor ** p)
+        lr = learning_rate * (anneal_factor**p)
 
     for param_group in optimizer.param_groups:
         param_group["lr"] = lr

examples/pipeline_wavernn/datasets.py

@@ -99,7 +99,7 @@ def collate_factory(args):
         if args.loss == "crossentropy":
 
             if args.mulaw:
-                mulaw_encode = MuLawEncoding(2 ** args.n_bits)
+                mulaw_encode = MuLawEncoding(2**args.n_bits)
                 waveform = mulaw_encode(waveform)
                 target = mulaw_encode(target)
 

examples/pipeline_wavernn/main.py

@@ -332,7 +332,7 @@ def main(args):
         **loader_validation_params,
     )
 
-    n_classes = 2 ** args.n_bits if args.loss == "crossentropy" else 30
+    n_classes = 2**args.n_bits if args.loss == "crossentropy" else 30
 
     model = WaveRNN(
         upsample_scales=args.upsample_scales,

examples/pipeline_wavernn/processing.py

@@ -21,11 +21,11 @@ def normalized_waveform_to_bits(waveform: torch.Tensor, bits: int) -> torch.Tens
     r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1]"""
 
     assert abs(waveform).max() <= 1.0
-    waveform = (waveform + 1.0) * (2 ** bits - 1) / 2
-    return torch.clamp(waveform, 0, 2 ** bits - 1).int()
+    waveform = (waveform + 1.0) * (2**bits - 1) / 2
+    return torch.clamp(waveform, 0, 2**bits - 1).int()
 
 
 def bits_to_normalized_waveform(label: torch.Tensor, bits: int) -> torch.Tensor:
     r"""Transform label [0, 2 ** bits - 1] to waveform [-1, 1]"""
 
-    return 2 * label / (2 ** bits - 1.0) - 1.0
+    return 2 * label / (2**bits - 1.0) - 1.0

examples/tutorials/audio_resampling_tutorial.py

@@ -71,7 +71,7 @@ def _get_freq_ticks(sample_rate, offset, f_max):
     time, freq = [], []
     for exp in range(2, 5):
         for v in range(1, 10):
-            f = v * 10 ** exp
+            f = v * 10**exp
             if f < sample_rate // 2:
                 t = _get_inverse_log_freq(f, sample_rate, offset) / sample_rate
                 time.append(t)

test/torchaudio_unittest/transforms/transforms_test.py

@@ -16,9 +16,9 @@ class Tester(common_utils.TorchaudioTestCase):
     volume = 0.3
     waveform = torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate)
     waveform.unsqueeze_(0)  # (1, 64000)
-    waveform = (waveform * volume * 2 ** 31).long()
+    waveform = (waveform * volume * 2**31).long()
 
-    def scale(self, waveform, factor=2.0 ** 31):
+    def scale(self, waveform, factor=2.0**31):
         # scales a waveform by a factor
         if not waveform.is_floating_point():
             waveform = waveform.to(torch.get_default_dtype())

torchaudio/datasets/utils.py

@@ -126,7 +126,7 @@ def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") ->
 
     while True:
         # Read by chunk to avoid filling memory
-        chunk = file_obj.read(1024 ** 2)
+        chunk = file_obj.read(1024**2)
         if not chunk:
             break
         hash_func.update(chunk)

torchaudio/functional/filtering.py

@@ -1298,7 +1298,7 @@ def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor:
     a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y)
     b_im = b1 * math.sin(-y) + b2 * math.sin(-2 * y)
     a_im = a1 * math.sin(-y) + a2 * math.sin(-2 * y)
-    g = 1 / math.sqrt((b_re ** 2 + b_im ** 2) / (a_re ** 2 + a_im ** 2))
+    g = 1 / math.sqrt((b_re**2 + b_im**2) / (a_re**2 + a_im**2))
 
     b0 *= g
     b1 *= g

torchaudio/functional/functional.py

@@ -1150,18 +1150,18 @@ def sliding_window_cmn(
             input_part = specgram[:, window_start : window_end - window_start, :]
             cur_sum += torch.sum(input_part, 1)
             if norm_vars:
-                cur_sumsq += torch.cumsum(input_part ** 2, 1)[:, -1, :]
+                cur_sumsq += torch.cumsum(input_part**2, 1)[:, -1, :]
         else:
             if window_start > last_window_start:
                 frame_to_remove = specgram[:, last_window_start, :]
                 cur_sum -= frame_to_remove
                 if norm_vars:
-                    cur_sumsq -= frame_to_remove ** 2
+                    cur_sumsq -= frame_to_remove**2
             if window_end > last_window_end:
                 frame_to_add = specgram[:, last_window_end, :]
                 cur_sum += frame_to_add
                 if norm_vars:
-                    cur_sumsq += frame_to_add ** 2
+                    cur_sumsq += frame_to_add**2
         window_frames = window_end - window_start
         last_window_start = window_start
         last_window_end = window_end
@@ -1172,7 +1172,7 @@ def sliding_window_cmn(
             else:
                 variance = cur_sumsq
                 variance = variance / window_frames
-                variance -= (cur_sum ** 2) / (window_frames ** 2)
+                variance -= (cur_sum**2) / (window_frames**2)
                 variance = torch.pow(variance, -0.5)
                 cmn_specgram[:, t, :] *= variance
 

torchaudio/models/conv_tasnet.py

@@ -109,7 +109,7 @@ class MaskGenerator(torch.nn.Module):
         self.conv_layers = torch.nn.ModuleList([])
         for s in range(num_stacks):
             for l in range(num_layers):
-                multi = 2 ** l
+                multi = 2**l
                 self.conv_layers.append(
                     ConvBlock(
                         io_channels=num_feats,

torchaudio/models/wav2vec2/components.py

@@ -231,7 +231,7 @@ class SelfAttention(Module):
         self.dropout = torch.nn.Dropout(dropout)
         self.head_dim = head_dim
 
-        self.scaling = self.head_dim ** -0.5
+        self.scaling = self.head_dim**-0.5
 
         self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True)
         self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)

torchaudio/models/wavernn.py

@@ -396,7 +396,7 @@ class WaveRNN(nn.Module):
 
             x = torch.multinomial(posterior, 1).float()
             # Transform label [0, 2 ** n_bits - 1] to waveform [-1, 1]
-            x = 2 * x / (2 ** self.n_bits - 1.0) - 1.0
+            x = 2 * x / (2**self.n_bits - 1.0) - 1.0
 
             output.append(x)
 

torchaudio/pipelines/_tts/utils.py

@@ -185,8 +185,8 @@ def _load_phonemizer(file, dl_kwargs):
 def _unnormalize_waveform(waveform: torch.Tensor, bits: int) -> torch.Tensor:
     r"""Transform waveform [-1, 1] to label [0, 2 ** bits - 1]"""
     waveform = torch.clamp(waveform, -1, 1)
-    waveform = (waveform + 1.0) * (2 ** bits - 1) / 2
-    return torch.clamp(waveform, 0, 2 ** bits - 1).int()
+    waveform = (waveform + 1.0) * (2**bits - 1) / 2
+    return torch.clamp(waveform, 0, 2**bits - 1).int()
 
 
 def _get_taco_params(n_symbols):
@@ -219,7 +219,7 @@ def _get_taco_params(n_symbols):
 def _get_wrnn_params():
     return {
         "upsample_scales": [5, 5, 11],
-        "n_classes": 2 ** 8,  # n_bits = 8
+        "n_classes": 2**8,  # n_bits = 8
         "hop_length": 275,
         "n_res_block": 10,
         "n_rnn": 512,