Apply arc lint to pytorch audio (#2096)

Summary:
Pull Request resolved: https://github.com/pytorch/audio/pull/2096

run: `arc lint --apply-patches --paths-cmd 'hg files -I "./**/*.py"'`

Reviewed By: mthrok

Differential Revision: D33297351

fbshipit-source-id: 7bf5956edf0717c5ca90219f72414ff4eeaf5aa8

examples/source_separation/eval.py

 from argparse import ArgumentParser
 from pathlib import Path
 
-from lightning_train import _get_model, _get_dataloader, sisdri_metric
 import mir_eval
 import torch
+from lightning_train import _get_model, _get_dataloader, sisdri_metric
 
 
 def _eval(model, data_loader, device):
@@ -19,12 +19,9 @@ def _eval(model, data_loader, device):
             mix = mix.repeat(1, src.shape[1], 1).cpu().detach().numpy()
             sdr, sir, sar, _ = mir_eval.separation.bss_eval_sources(src[0], est[0])
             sdr_mix, sir_mix, sar_mix, _ = mir_eval.separation.bss_eval_sources(src[0], mix[0])
-            results += torch.tensor([
-                sdr.mean() - sdr_mix.mean(),
-                sisdri,
-                sir.mean() - sir_mix.mean(),
-                sar.mean() - sar_mix.mean()
-            ])
+            results += torch.tensor(
+                [sdr.mean() - sdr_mix.mean(), sisdri, sir.mean() - sir_mix.mean(), sar.mean() - sar_mix.mean()]
+            )
     results /= len(data_loader)
     print("SDR improvement: ", results[0].item())
     print("Si-SDR improvement: ", results[1].item())
@@ -63,28 +60,20 @@ def cli_main():
         help="Sample rate of audio files in the given dataset. (default: 8000)",
     )
     parser.add_argument(
-        "--exp-dir",
-        default=Path("./exp"),
-        type=Path,
-        help="The directory to save checkpoints and logs."
-    )
-    parser.add_argument(
-        "--gpu-device",
-        default=-1,
-        type=int,
-        help="The gpu device for model inference. (default: -1)"
+        "--exp-dir", default=Path("./exp"), type=Path, help="The directory to save checkpoints and logs."
     )
+    parser.add_argument("--gpu-device", default=-1, type=int, help="The gpu device for model inference. (default: -1)")
 
     args = parser.parse_args()
 
     model = _get_model(num_sources=2)
-    state_dict = torch.load(args.exp_dir / 'best_model.pth')
+    state_dict = torch.load(args.exp_dir / "best_model.pth")
     model.load_state_dict(state_dict)
 
     if args.gpu_device != -1:
-        device = torch.device('cuda:' + str(args.gpu_device))
+        device = torch.device("cuda:" + str(args.gpu_device))
     else:
-        device = torch.device('cpu')
+        device = torch.device("cpu")
 
     model = model.to(device)
 

examples/source_separation/lightning_train.py

 #!/usr/bin/env python3
 
 # pyre-strict
-from pathlib import Path
 from argparse import ArgumentParser
+from pathlib import Path
 from typing import (
     Any,
     Callable,
@@ -34,10 +34,7 @@ class Batch(TypedDict):
 
 
 def sisdri_metric(
-    estimate: torch.Tensor,
-    reference: torch.Tensor,
-    mix: torch.Tensor,
-    mask: torch.Tensor
+    estimate: torch.Tensor, reference: torch.Tensor, mix: torch.Tensor, mask: torch.Tensor
 ) -> torch.Tensor:
     """Compute the improvement of scale-invariant SDR. (SI-SDRi).
 
@@ -100,11 +97,7 @@ def sdri_metric(
     return sdri.mean().item()
 
 
-def si_sdr_loss(
-    estimate: torch.Tensor,
-    reference: torch.Tensor,
-    mask: torch.Tensor
-) -> torch.Tensor:
+def si_sdr_loss(estimate: torch.Tensor, reference: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
     """Compute the Si-SDR loss.
 
     Args:
@@ -181,22 +174,16 @@ class ConvTasNetModule(LightningModule):
         """
         return self.model(x)
 
-    def training_step(
-        self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any
-    ) -> Dict[str, Any]:
+    def training_step(self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any) -> Dict[str, Any]:
         return self._step(batch, batch_idx, "train")
 
-    def validation_step(
-        self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any
-    ) -> Dict[str, Any]:
+    def validation_step(self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any) -> Dict[str, Any]:
         """
         Operates on a single batch of data from the validation set.
         """
         return self._step(batch, batch_idx, "val")
 
-    def test_step(
-        self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any
-    ) -> Optional[Dict[str, Any]]:
+    def test_step(self, batch: Batch, batch_idx: int, *args: Any, **kwargs: Any) -> Optional[Dict[str, Any]]:
         """
         Operates on a single batch of data from the test set.
         """
@@ -222,11 +209,7 @@ class ConvTasNetModule(LightningModule):
         lr_scheduler = self.lr_scheduler
         if not lr_scheduler:
             return self.optim
-        epoch_schedulers = {
-            'scheduler': lr_scheduler,
-            'monitor': 'Losses/val_loss',
-            'interval': 'epoch'
-        }
+        epoch_schedulers = {"scheduler": lr_scheduler, "monitor": "Losses/val_loss", "interval": "epoch"}
         return [self.optim], [epoch_schedulers]
 
     def _compute_metrics(
@@ -305,11 +288,9 @@ def _get_dataloader(
     train_dataset, valid_dataset, eval_dataset = dataset_utils.get_dataset(
         dataset_type, root_dir, num_speakers, sample_rate, librimix_task, librimix_tr_split
     )
-    train_collate_fn = dataset_utils.get_collate_fn(
-        dataset_type, mode='train', sample_rate=sample_rate, duration=3
-    )
+    train_collate_fn = dataset_utils.get_collate_fn(dataset_type, mode="train", sample_rate=sample_rate, duration=3)
 
-    test_collate_fn = dataset_utils.get_collate_fn(dataset_type, mode='test', sample_rate=sample_rate)
+    test_collate_fn = dataset_utils.get_collate_fn(dataset_type, mode="test", sample_rate=sample_rate)
 
     train_loader = DataLoader(
         train_dataset,
@@ -367,10 +348,7 @@ def cli_main():
         help="Sample rate of audio files in the given dataset. (default: 8000)",
     )
     parser.add_argument(
-        "--exp-dir",
-        default=Path("./exp"),
-        type=Path,
-        help="The directory to save checkpoints and logs."
+        "--exp-dir", default=Path("./exp"), type=Path, help="The directory to save checkpoints and logs."
     )
     parser.add_argument(
         "--epochs",
@@ -409,9 +387,7 @@ def cli_main():
     model = _get_model(num_sources=args.num_speakers)
 
     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-        optimizer, mode="min", factor=0.5, patience=5
-    )
+    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=5)
     train_loader, valid_loader, eval_loader = _get_dataloader(
         args.dataset,
         args.root_dir,
@@ -438,12 +414,7 @@ def cli_main():
     )
     checkpoint_dir = args.exp_dir / "checkpoints"
     checkpoint = ModelCheckpoint(
-        checkpoint_dir,
-        monitor="Losses/val_loss",
-        mode="min",
-        save_top_k=5,
-        save_weights_only=True,
-        verbose=True
+        checkpoint_dir, monitor="Losses/val_loss", mode="min", save_top_k=5, save_weights_only=True, verbose=True
     )
     callbacks = [
         checkpoint,

examples/source_separation/train.py

@@ -15,13 +15,12 @@ number of training subprocesses (as operaiton mode 2). You can reduce the number
 When launching the script as a worker process of a distributed training, you need to configure
 the coordination of the workers.
 """
-import sys
-import logging
 import argparse
+import logging
 import subprocess
+import sys
 
 import torch
-
 from utils import dist_utils
 
 _LG = dist_utils.getLogger(__name__)
@@ -88,19 +87,13 @@ def _parse_args(args=None):
         type=int,
         help="Set random seed value. (default: None)",
     )
-    parser.add_argument(
-        "rest", nargs=argparse.REMAINDER, help="Model-specific arguments."
-    )
+    parser.add_argument("rest", nargs=argparse.REMAINDER, help="Model-specific arguments.")
     namespace = parser.parse_args(args)
     if namespace.worker_id is None:
         if namespace.device_id is not None:
-            raise ValueError(
-                "`--device-id` cannot be provided when runing as master process."
-            )
+            raise ValueError("`--device-id` cannot be provided when runing as master process.")
         if namespace.num_workers > max_world_size:
-            raise ValueError(
-                "--num-workers ({num_workers}) cannot exceed {device_count}."
-            )
+            raise ValueError("--num-workers ({num_workers}) cannot exceed {device_count}.")
     if namespace.rest[:1] == ["--"]:
         namespace.rest = namespace.rest[1:]
     return namespace
@@ -120,7 +113,7 @@ def _main(cli_args):
             world_size=args.num_workers,
             rank=args.worker_id,
             local_rank=args.device_id,
-            backend='nccl' if torch.cuda.is_available() else 'gloo',
+            backend="nccl" if torch.cuda.is_available() else "gloo",
             init_method=args.sync_protocol,
         )
         if args.random_seed is not None:
@@ -137,12 +130,7 @@ def _run_training_subprocesses(num_workers, original_args):
     for i in range(num_workers):
         worker_arg = ["--worker-id", f"{i}", "--num-workers", f"{num_workers}"]
         device_arg = ["--device-id", f"{i}"] if torch.cuda.is_available() else []
-        command = (
-            [sys.executable, "-u", sys.argv[0]]
-            + worker_arg
-            + device_arg
-            + original_args
-        )
+        command = [sys.executable, "-u", sys.argv[0]] + worker_arg + device_arg + original_args
         _LG.info("Launching worker %s: `%s`", i, " ".join(command))
         worker = subprocess.Popen(command)
         workers.append(worker)
@@ -163,9 +151,7 @@ def _run_training(args):
 
 def _init_logger(rank=None, debug=False):
     worker_fmt = "[master]" if rank is None else f"[worker {rank:2d}]"
-    message_fmt = (
-        "%(levelname)5s: %(funcName)10s: %(message)s" if debug else "%(message)s"
-    )
+    message_fmt = "%(levelname)5s: %(funcName)10s: %(message)s" if debug else "%(message)s"
     logging.basicConfig(
         level=logging.DEBUG if debug else logging.INFO,
         format=f"%(asctime)s: {worker_fmt} {message_fmt}",

examples/source_separation/utils/__init__.py

@@ -4,4 +4,4 @@ from . import (
     metrics,
 )
 
-__all__ = ['dataset', 'dist_utils', 'metrics']
+__all__ = ["dataset", "dist_utils", "metrics"]

examples/source_separation/utils/dataset/__init__.py

 from . import utils, wsj0mix
 
-__all__ = ['utils', 'wsj0mix']
+__all__ = ["utils", "wsj0mix"]

examples/source_separation/utils/dataset/utils.py

-from typing import List
-from functools import partial
 from collections import namedtuple
+from functools import partial
+from typing import List
 
-from torchaudio.datasets import LibriMix
 import torch
+from torchaudio.datasets import LibriMix
 
 from . import wsj0mix
 
@@ -30,8 +30,8 @@ def _fix_num_frames(sample: wsj0mix.SampleType, target_num_frames: int, sample_r
     src = torch.cat(sample[2], 0)  # [num_sources, time]
 
     num_channels, num_frames = src.shape
-    num_seconds = torch.div(num_frames, sample_rate, rounding_mode='floor')
-    target_seconds = torch.div(target_num_frames, sample_rate, rounding_mode='floor')
+    num_seconds = torch.div(num_frames, sample_rate, rounding_mode="floor")
+    target_seconds = torch.div(target_num_frames, sample_rate, rounding_mode="floor")
     if num_frames >= target_num_frames:
         if random_start and num_frames > target_num_frames:
             start_frame = torch.randint(num_seconds - target_seconds + 1, [1]) * sample_rate
@@ -81,7 +81,7 @@ def collate_fn_wsj0mix_test(samples: List[wsj0mix.SampleType], sample_rate):
 def get_collate_fn(dataset_type, mode, sample_rate=None, duration=4):
     assert mode in ["train", "test"]
     if dataset_type in ["wsj0mix", "librimix"]:
-        if mode == 'train':
+        if mode == "train":
             if sample_rate is None:
                 raise ValueError("sample_rate is not given.")
             return partial(collate_fn_wsj0mix_train, sample_rate=sample_rate, duration=duration)

examples/source_separation/utils/dataset/wsj0mix.py

@@ -2,9 +2,8 @@ from pathlib import Path
 from typing import Union, Tuple, List
 
 import torch
-from torch.utils.data import Dataset
-
 import torchaudio
+from torch.utils.data import Dataset
 
 SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
 
@@ -21,6 +20,7 @@ class WSJ0Mix(Dataset):
             different sample rate, raises ``ValueError``.
         audio_ext (str, optional): The extension of audio files to find. (default: ".wav")
     """
+
     def __init__(
         self,
         root: Union[str, Path],
@@ -51,9 +51,7 @@ class WSJ0Mix(Dataset):
         for i, dir_ in enumerate(self.src_dirs):
             src = self._load_audio(str(dir_ / filename))
             if mixed.shape != src.shape:
-                raise ValueError(
-                    f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}"
-                )
+                raise ValueError(f"Different waveform shapes. mixed: {mixed.shape}, src[{i}]: {src.shape}")
             srcs.append(src)
         return self.sample_rate, mixed, srcs
 

examples/source_separation/utils/dist_utils.py

-import os
 import csv
-import types
 import logging
+import os
+import types
 
 import torch
 import torch.distributed as dist
@@ -22,9 +22,7 @@ def getLogger(name):
 _LG = getLogger(__name__)
 
 
-def setup_distributed(
-    world_size, rank, local_rank, backend="nccl", init_method="env://"
-):
+def setup_distributed(world_size, rank, local_rank, backend="nccl", init_method="env://"):
     """Perform env setup and initialization for distributed training"""
     if init_method == "env://":
         _set_env_vars(world_size, rank, local_rank)

examples/source_separation/utils/metrics.py

 import math
-from typing import Optional
 from itertools import permutations
+from typing import Optional
 
 import torch
 
 
 def sdr(
-        estimate: torch.Tensor,
-        reference: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        epsilon: float = 1e-8
+    estimate: torch.Tensor, reference: torch.Tensor, mask: Optional[torch.Tensor] = None, epsilon: float = 1e-8
 ) -> torch.Tensor:
     """Computes source-to-distortion ratio.
 
@@ -86,11 +83,11 @@ class PIT(torch.nn.Module):
         self.utility_func = utility_func
 
     def forward(
-            self,
-            estimate: torch.Tensor,
-            reference: torch.Tensor,
-            mask: Optional[torch.Tensor] = None,
-            epsilon: float = 1e-8
+        self,
+        estimate: torch.Tensor,
+        reference: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        epsilon: float = 1e-8,
     ) -> torch.Tensor:
         """Compute utterance-level PIT Loss
 
@@ -112,9 +109,7 @@ class PIT(torch.nn.Module):
         batch_size, num_speakers = reference.shape[:2]
         num_permute = math.factorial(num_speakers)
 
-        util_mat = torch.zeros(
-            batch_size, num_permute, dtype=estimate.dtype, device=estimate.device
-        )
+        util_mat = torch.zeros(batch_size, num_permute, dtype=estimate.dtype, device=estimate.device)
         for i, idx in enumerate(permutations(range(num_speakers))):
             util = self.utility_func(estimate, reference[:, idx, :], mask=mask, epsilon=epsilon)
             util_mat[:, i] = util.mean(dim=1)  # take the average over speaker dimension
@@ -125,10 +120,8 @@ _sdr_pit = PIT(utility_func=sdr)
 
 
 def sdr_pit(
-        estimate: torch.Tensor,
-        reference: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        epsilon: float = 1e-8):
+    estimate: torch.Tensor, reference: torch.Tensor, mask: Optional[torch.Tensor] = None, epsilon: float = 1e-8
+):
     """Computes scale-invariant source-to-distortion ratio.
 
     1. adjust both estimate and reference to have 0-mean
@@ -164,11 +157,11 @@ def sdr_pit(
 
 
 def sdri(
-        estimate: torch.Tensor,
-        reference: torch.Tensor,
-        mix: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-        epsilon: float = 1e-8,
+    estimate: torch.Tensor,
+    reference: torch.Tensor,
+    mix: torch.Tensor,
+    mask: Optional[torch.Tensor] = None,
+    epsilon: float = 1e-8,
 ) -> torch.Tensor:
     """Compute the improvement of SDR (SDRi).
 

examples/test/test_interactive_asr.py

@@ -93,9 +93,7 @@ class ASRTest(unittest.TestCase):
 
     def test_transcribe_file(self):
         task, generator, models, sp, tgt_dict = setup_asr(self.args, self.logger)
-        _, transcription = transcribe_file(
-            self.args, task, generator, models, sp, tgt_dict
-        )
+        _, transcription = transcribe_file(self.args, task, generator, models, sp, tgt_dict)
 
         expected_transcription = [["THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG"]]
         self.assertEqual(transcription, expected_transcription, msg=str(transcription))

examples/tutorials/audio_data_augmentation_tutorial.py

@@ -32,9 +32,9 @@ print(torchaudio.__version__)
 
 import math
 import os
-import requests
 
 import matplotlib.pyplot as plt
+import requests
 from IPython.display import Audio, display
 
 
@@ -164,7 +164,7 @@ def get_rir_sample(*, resample=None, processed=False):
     rir_raw, sample_rate = _get_sample(SAMPLE_RIR_PATH, resample=resample)
     if not processed:
         return rir_raw, sample_rate
-    rir = rir_raw[:, int(sample_rate * 1.01): int(sample_rate * 1.3)]
+    rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
     rir = rir / torch.norm(rir, p=2)
     rir = torch.flip(rir, [1])
     return rir, sample_rate
@@ -225,9 +225,7 @@ effects = [
 ]
 
 # Apply effects
-waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(
-    waveform1, sample_rate1, effects
-)
+waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(waveform1, sample_rate1, effects)
 
 print_stats(waveform1, sample_rate=sample_rate1, src="Original")
 print_stats(waveform2, sample_rate=sample_rate2, src="Effects Applied")
@@ -291,7 +289,7 @@ play_audio(rir_raw, sample_rate)
 # the signal power, then flip along the time axis.
 #
 
-rir = rir_raw[:, int(sample_rate * 1.01): int(sample_rate * 1.3)]
+rir = rir_raw[:, int(sample_rate * 1.01) : int(sample_rate * 1.3)]
 rir = rir / torch.norm(rir, p=2)
 rir = torch.flip(rir, [1])
 

examples/tutorials/audio_feature_augmentation_tutorial.py

@@ -33,10 +33,10 @@ print(torchaudio.__version__)
 # -------------------------------------------------------------------------------
 
 import os
-import requests
 
 import librosa
 import matplotlib.pyplot as plt
+import requests
 
 
 _SAMPLE_DIR = "_assets"
@@ -125,17 +125,13 @@ stretch = T.TimeStretch()
 
 rate = 1.2
 spec_ = stretch(spec, rate)
-plot_spectrogram(
-    torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect="equal", xmax=304
-)
+plot_spectrogram(torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect="equal", xmax=304)
 
 plot_spectrogram(torch.abs(spec[0]), title="Original", aspect="equal", xmax=304)
 
 rate = 0.9
 spec_ = stretch(spec, rate)
-plot_spectrogram(
-    torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect="equal", xmax=304
-)
+plot_spectrogram(torch.abs(spec_[0]), title=f"Stretched x{rate}", aspect="equal", xmax=304)
 
 ######################################################################
 # TimeMasking

examples/tutorials/audio_feature_extractions_tutorial.py

@@ -51,10 +51,10 @@ print(torchaudio.__version__)
 # -------------------------------------------------------------------------------
 
 import os
-import requests
 
 import librosa
 import matplotlib.pyplot as plt
+import requests
 from IPython.display import Audio, display
 
 
@@ -199,9 +199,7 @@ def plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc):
 
     axis2 = axis.twinx()
     time_axis = torch.linspace(0, end_time, nfcc.shape[1])
-    ln2 = axis2.plot(
-        time_axis, nfcc[0], linewidth=2, label="NFCC", color="blue", linestyle="--"
-    )
+    ln2 = axis2.plot(time_axis, nfcc[0], linewidth=2, label="NFCC", color="blue", linestyle="--")
 
     lns = ln1 + ln2
     labels = [l.get_label() for l in lns]

examples/tutorials/audio_io_tutorial.py

@@ -32,13 +32,13 @@ print(torchaudio.__version__)
 
 import io
 import os
-import requests
 import tarfile
 
 import boto3
+import matplotlib.pyplot as plt
+import requests
 from botocore import UNSIGNED
 from botocore.config import Config
-import matplotlib.pyplot as plt
 from IPython.display import Audio, display
 
 
@@ -348,14 +348,12 @@ frame_offset, num_frames = 16000, 16000  # Fetch and decode the 1 - 2 seconds
 print("Fetching all the data...")
 with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
     waveform1, sample_rate1 = torchaudio.load(response.raw)
-    waveform1 = waveform1[:, frame_offset: frame_offset + num_frames]
+    waveform1 = waveform1[:, frame_offset : frame_offset + num_frames]
     print(f" - Fetched {response.raw.tell()} bytes")
 
 print("Fetching until the requested frames are available...")
 with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
-    waveform2, sample_rate2 = torchaudio.load(
-        response.raw, frame_offset=frame_offset, num_frames=num_frames
-    )
+    waveform2, sample_rate2 = torchaudio.load(response.raw, frame_offset=frame_offset, num_frames=num_frames)
     print(f" - Fetched {response.raw.tell()} bytes")
 
 print("Checking the resulting waveform ... ", end="")

examples/tutorials/audio_resampling_tutorial.py

@@ -38,8 +38,8 @@ import time
 
 import librosa
 import matplotlib.pyplot as plt
-from IPython.display import Audio, display
 import pandas as pd
+from IPython.display import Audio, display
 
 
 DEFAULT_OFFSET = 201
@@ -56,9 +56,7 @@ def _get_log_freq(sample_rate, max_sweep_rate, offset):
 
     """
     start, stop = math.log(offset), math.log(offset + max_sweep_rate // 2)
-    return (
-        torch.exp(torch.linspace(start, stop, sample_rate, dtype=torch.double)) - offset
-    )
+    return torch.exp(torch.linspace(start, stop, sample_rate, dtype=torch.double)) - offset
 
 
 def _get_inverse_log_freq(freq, sample_rate, offset):
@@ -192,9 +190,7 @@ def benchmark_resample(
         waveform_np = waveform.squeeze().numpy()
         begin = time.time()
         for _ in range(iters):
-            librosa.resample(
-                waveform_np, sample_rate, resample_rate, res_type=librosa_type
-            )
+            librosa.resample(waveform_np, sample_rate, resample_rate, res_type=librosa_type)
         elapsed = time.time() - begin
         return elapsed / iters
 
@@ -264,14 +260,10 @@ play_audio(waveform, sample_rate)
 sample_rate = 48000
 resample_rate = 32000
 
-resampled_waveform = F.resample(
-    waveform, sample_rate, resample_rate, lowpass_filter_width=6
-)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=6)
 plot_sweep(resampled_waveform, resample_rate, title="lowpass_filter_width=6")
 
-resampled_waveform = F.resample(
-    waveform, sample_rate, resample_rate, lowpass_filter_width=128
-)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=128)
 plot_sweep(resampled_waveform, resample_rate, title="lowpass_filter_width=128")
 
 
@@ -315,14 +307,10 @@ plot_sweep(resampled_waveform, resample_rate, title="rolloff=0.8")
 sample_rate = 48000
 resample_rate = 32000
 
-resampled_waveform = F.resample(
-    waveform, sample_rate, resample_rate, resampling_method="sinc_interpolation"
-)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="sinc_interpolation")
 plot_sweep(resampled_waveform, resample_rate, title="Hann Window Default")
 
-resampled_waveform = F.resample(
-    waveform, sample_rate, resample_rate, resampling_method="kaiser_window"
-)
+resampled_waveform = F.resample(waveform, sample_rate, resample_rate, resampling_method="kaiser_window")
 plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Default")
 
 
@@ -351,13 +339,9 @@ resampled_waveform = F.resample(
 plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Best (torchaudio)")
 
 librosa_resampled_waveform = torch.from_numpy(
-    librosa.resample(
-        waveform.squeeze().numpy(), sample_rate, resample_rate, res_type="kaiser_best"
-    )
+    librosa.resample(waveform.squeeze().numpy(), sample_rate, resample_rate, res_type="kaiser_best")
 ).unsqueeze(0)
-plot_sweep(
-    librosa_resampled_waveform, resample_rate, title="Kaiser Window Best (librosa)"
-)
+plot_sweep(librosa_resampled_waveform, resample_rate, title="Kaiser Window Best (librosa)")
 
 mse = torch.square(resampled_waveform - librosa_resampled_waveform).mean().item()
 print("torchaudio and librosa kaiser best MSE:", mse)
@@ -372,18 +356,12 @@ resampled_waveform = F.resample(
     resampling_method="kaiser_window",
     beta=8.555504641634386,
 )
-plot_specgram(
-    resampled_waveform, resample_rate, title="Kaiser Window Fast (torchaudio)"
-)
+plot_specgram(resampled_waveform, resample_rate, title="Kaiser Window Fast (torchaudio)")
 
 librosa_resampled_waveform = torch.from_numpy(
-    librosa.resample(
-        waveform.squeeze().numpy(), sample_rate, resample_rate, res_type="kaiser_fast"
-    )
+    librosa.resample(waveform.squeeze().numpy(), sample_rate, resample_rate, res_type="kaiser_fast")
 ).unsqueeze(0)
-plot_sweep(
-    librosa_resampled_waveform, resample_rate, title="Kaiser Window Fast (librosa)"
-)
+plot_sweep(librosa_resampled_waveform, resample_rate, title="Kaiser Window Fast (librosa)")
 
 mse = torch.square(resampled_waveform - librosa_resampled_waveform).mean().item()
 print("torchaudio and librosa kaiser fast MSE:", mse)
@@ -426,29 +404,19 @@ for label in configs:
     waveform = get_sine_sweep(sample_rate)
 
     # sinc 64 zero-crossings
-    f_time = benchmark_resample(
-        "functional", waveform, sample_rate, resample_rate, lowpass_filter_width=64
-    )
-    t_time = benchmark_resample(
-        "transforms", waveform, sample_rate, resample_rate, lowpass_filter_width=64
-    )
+    f_time = benchmark_resample("functional", waveform, sample_rate, resample_rate, lowpass_filter_width=64)
+    t_time = benchmark_resample("transforms", waveform, sample_rate, resample_rate, lowpass_filter_width=64)
     times.append([None, 1000 * f_time, 1000 * t_time])
     rows.append("sinc (width 64)")
 
     # sinc 6 zero-crossings
-    f_time = benchmark_resample(
-        "functional", waveform, sample_rate, resample_rate, lowpass_filter_width=16
-    )
-    t_time = benchmark_resample(
-        "transforms", waveform, sample_rate, resample_rate, lowpass_filter_width=16
-    )
+    f_time = benchmark_resample("functional", waveform, sample_rate, resample_rate, lowpass_filter_width=16)
+    t_time = benchmark_resample("transforms", waveform, sample_rate, resample_rate, lowpass_filter_width=16)
     times.append([None, 1000 * f_time, 1000 * t_time])
     rows.append("sinc (width 16)")
 
     # kaiser best
-    lib_time = benchmark_resample(
-        "librosa", waveform, sample_rate, resample_rate, librosa_type="kaiser_best"
-    )
+    lib_time = benchmark_resample("librosa", waveform, sample_rate, resample_rate, librosa_type="kaiser_best")
     f_time = benchmark_resample(
         "functional",
         waveform,
@@ -473,9 +441,7 @@ for label in configs:
     rows.append("kaiser_best")
 
     # kaiser fast
-    lib_time = benchmark_resample(
-        "librosa", waveform, sample_rate, resample_rate, librosa_type="kaiser_fast"
-    )
+    lib_time = benchmark_resample("librosa", waveform, sample_rate, resample_rate, librosa_type="kaiser_fast")
     f_time = benchmark_resample(
         "functional",
         waveform,
@@ -499,8 +465,6 @@ for label in configs:
     times.append([1000 * lib_time, 1000 * f_time, 1000 * t_time])
     rows.append("kaiser_fast")
 
-    df = pd.DataFrame(
-        times, columns=["librosa", "functional", "transforms"], index=rows
-    )
+    df = pd.DataFrame(times, columns=["librosa", "functional", "transforms"], index=rows)
     df.columns = pd.MultiIndex.from_product([[f"{label} time (ms)"], df.columns])
     display(df.round(2))

examples/tutorials/forced_alignment_tutorial.py

@@ -40,12 +40,12 @@ Recognition <https://arxiv.org/abs/2007.09127>`__.
 import os
 from dataclasses import dataclass
 
-import torch
-import torchaudio
-import requests
+import IPython
 import matplotlib
 import matplotlib.pyplot as plt
-import IPython
+import requests
+import torch
+import torchaudio
 
 matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
@@ -325,7 +325,7 @@ def plot_trellis_with_segments(trellis, segments, transcript):
     trellis_with_path = trellis.clone()
     for i, seg in enumerate(segments):
         if seg.label != "|":
-            trellis_with_path[seg.start + 1: seg.end + 1, i + 1] = float("nan")
+            trellis_with_path[seg.start + 1 : seg.end + 1, i + 1] = float("nan")
 
     fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))
     ax1.set_title("Path, label and probability for each label")
@@ -383,12 +383,8 @@ def merge_words(segments, separator="|"):
             if i1 != i2:
                 segs = segments[i1:i2]
                 word = "".join([seg.label for seg in segs])
-                score = sum(seg.score * seg.length for seg in segs) / sum(
-                    seg.length for seg in segs
-                )
-                words.append(
-                    Segment(word, segments[i1].start, segments[i2 - 1].end, score)
-                )
+                score = sum(seg.score * seg.length for seg in segs) / sum(seg.length for seg in segs)
+                words.append(Segment(word, segments[i1].start, segments[i2 - 1].end, score))
             i1 = i2 + 1
             i2 = i1
         else:
@@ -408,7 +404,7 @@ def plot_alignments(trellis, segments, word_segments, waveform):
     trellis_with_path = trellis.clone()
     for i, seg in enumerate(segments):
         if seg.label != "|":
-            trellis_with_path[seg.start + 1: seg.end + 1, i + 1] = float("nan")
+            trellis_with_path[seg.start + 1 : seg.end + 1, i + 1] = float("nan")
 
     fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))
 
@@ -464,9 +460,7 @@ def display_segment(i):
     x1 = int(ratio * word.end)
     filename = f"_assets/{i}_{word.label}.wav"
     torchaudio.save(filename, waveform[:, x0:x1], bundle.sample_rate)
-    print(
-        f"{word.label} ({word.score:.2f}): {x0 / bundle.sample_rate:.3f} - {x1 / bundle.sample_rate:.3f} sec"
-    )
+    print(f"{word.label} ({word.score:.2f}): {x0 / bundle.sample_rate:.3f} - {x1 / bundle.sample_rate:.3f} sec")
     return IPython.display.Audio(filename)
 
 

examples/tutorials/mvdr_tutorial.py

@@ -44,10 +44,11 @@ MVDR with torchaudio
 #
 
 import os
+
+import IPython.display as ipd
 import requests
 import torch
 import torchaudio
-import IPython.display as ipd
 
 torch.random.manual_seed(0)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -167,9 +168,7 @@ for solution in ["ref_channel", "stv_evd", "stv_power"]:
 
 results_single = {}
 for solution in ["ref_channel", "stv_evd", "stv_power"]:
-    mvdr = torchaudio.transforms.MVDR(
-        ref_channel=0, solution=solution, multi_mask=False
-    )
+    mvdr = torchaudio.transforms.MVDR(ref_channel=0, solution=solution, multi_mask=False)
     stft_est = mvdr(spec_mix, irm_speech[0], irm_noise[0])
     est = istft(stft_est, length=mix.shape[-1])
     results_single[solution] = est
@@ -211,9 +210,7 @@ def si_sdr(estimate, reference, epsilon=1e-8):
 #
 
 for solution in results_single:
-    print(
-        solution + ": ", si_sdr(results_single[solution][None, ...], reverb_clean[0:1])
-    )
+    print(solution + ": ", si_sdr(results_single[solution][None, ...], reverb_clean[0:1]))
 
 ######################################################################
 # Multi-channel mask results
@@ -221,9 +218,7 @@ for solution in results_single:
 #
 
 for solution in results_multi:
-    print(
-        solution + ": ", si_sdr(results_multi[solution][None, ...], reverb_clean[0:1])
-    )
+    print(solution + ": ", si_sdr(results_multi[solution][None, ...], reverb_clean[0:1]))
 
 ######################################################################
 # Original audio

examples/tutorials/speech_recognition_pipeline_tutorial.py

@@ -41,12 +41,12 @@ pre-trained models from wav2vec 2.0
 
 import os
 
-import torch
-import torchaudio
-import requests
+import IPython
 import matplotlib
 import matplotlib.pyplot as plt
-import IPython
+import requests
+import torch
+import torchaudio
 
 matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 

examples/tutorials/tacotron2_pipeline_tutorial.py

@@ -7,6 +7,10 @@ Text-to-Speech with Tacotron2
 
 """
 
+import IPython
+import matplotlib
+import matplotlib.pyplot as plt
+
 ######################################################################
 # Overview
 # --------
@@ -58,10 +62,6 @@ Text-to-Speech with Tacotron2
 
 import torch
 import torchaudio
-import matplotlib
-import matplotlib.pyplot as plt
-
-import IPython
 
 matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
@@ -271,9 +271,7 @@ fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9))
 ax1.imshow(spec[0].cpu().detach())
 ax2.plot(waveforms[0].cpu().detach())
 
-torchaudio.save(
-    "_assets/output_wavernn.wav", waveforms[0:1].cpu(), sample_rate=vocoder.sample_rate
-)
+torchaudio.save("_assets/output_wavernn.wav", waveforms[0:1].cpu(), sample_rate=vocoder.sample_rate)
 IPython.display.Audio("_assets/output_wavernn.wav")
 
 
@@ -332,9 +330,7 @@ checkpoint = torch.hub.load_state_dict_from_url(
     progress=False,
     map_location=device,
 )
-state_dict = {
-    key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()
-}
+state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()}
 
 waveglow.load_state_dict(state_dict)
 waveglow = waveglow.remove_weightnorm(waveglow)