Fix style checks in examples/tutorials (#2006)

examples/tutorials/audio_data_augmentation_tutorial.py

@@ -22,16 +22,17 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
-#@markdown
-#@markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/), which is licensed under Creative Commos BY 4.0.
-
-#-------------------------------------------------------------------------------
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
+# @markdown
+# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
+# @markdown which is licensed under Creative Commos BY 4.0.
+
+# -------------------------------------------------------------------------------
 # Preparation of data and helper functions.
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 
 import math
 import os
@@ -46,17 +47,18 @@ _SAMPLE_DIR = "_assets"
 SAMPLE_WAV_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.wav"
 SAMPLE_WAV_PATH = os.path.join(_SAMPLE_DIR, "steam.wav")
 
-SAMPLE_RIR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav"
+SAMPLE_RIR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/room-response/rm1/impulse/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo.wav"  # noqa: E501
 SAMPLE_RIR_PATH = os.path.join(_SAMPLE_DIR, "rir.wav")
 
-SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
 SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")
 
-SAMPLE_NOISE_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/distractors/rm1/babb/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav"
+SAMPLE_NOISE_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/distant-16k/distractors/rm1/babb/Lab41-SRI-VOiCES-rm1-babb-mc01-stu-clo.wav"  # noqa: E501
 SAMPLE_NOISE_PATH = os.path.join(_SAMPLE_DIR, "bg.wav")
 
 os.makedirs(_SAMPLE_DIR, exist_ok=True)
 
+
 def _fetch_data():
     uri = [
         (SAMPLE_WAV_URL, SAMPLE_WAV_PATH),
@@ -65,28 +67,33 @@ def _fetch_data():
         (SAMPLE_NOISE_URL, SAMPLE_NOISE_PATH),
     ]
     for url, path in uri:
-    with open(path, 'wb') as file_:
+        with open(path, "wb") as file_:
             file_.write(requests.get(url).content)
 
+
 _fetch_data()
 
+
 def _get_sample(path, resample=None):
-  effects = [
-    ["remix", "1"]
-  ]
+    effects = [["remix", "1"]]
     if resample:
-    effects.extend([
+        effects.extend(
+            [
                 ["lowpass", f"{resample // 2}"],
-      ["rate", f'{resample}'],
-    ])
+                ["rate", f"{resample}"],
+            ]
+        )
     return torchaudio.sox_effects.apply_effects_file(path, effects=effects)
 
+
 def get_sample(*, resample=None):
     return _get_sample(SAMPLE_WAV_PATH, resample=resample)
 
+
 def get_speech_sample(*, resample=None):
     return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample)
 
+
 def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None):
     waveform = waveform.numpy()
 
@@ -100,7 +107,7 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
         axes[c].plot(time_axis, waveform[c], linewidth=1)
         axes[c].grid(True)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
         if ylim:
@@ -108,6 +115,7 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def print_stats(waveform, sample_rate=None, src=None):
     if src:
         print("-" * 10)
@@ -125,11 +133,11 @@ def print_stats(waveform, sample_rate=None, src=None):
     print(waveform)
     print()
 
+
 def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     waveform = waveform.numpy()
 
     num_channels, num_frames = waveform.shape
-  time_axis = torch.arange(0, num_frames) / sample_rate
 
     figure, axes = plt.subplots(num_channels, 1)
     if num_channels == 1:
@@ -137,12 +145,13 @@ def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     for c in range(num_channels):
         axes[c].specgram(waveform[c], Fs=sample_rate)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def play_audio(waveform, sample_rate):
     waveform = waveform.numpy()
 
@@ -154,15 +163,17 @@ def play_audio(waveform, sample_rate):
     else:
         raise ValueError("Waveform with more than 2 channels are not supported.")
 
+
 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
 
+
 def get_noise_sample(*, resample=None):
     return _get_sample(SAMPLE_NOISE_PATH, resample=resample)
 
@@ -218,10 +229,11 @@ effects = [
 
 # Apply effects
 waveform2, sample_rate2 = torchaudio.sox_effects.apply_effects_tensor(
-    waveform1, sample_rate1, effects)
+    waveform1, sample_rate1, effects
+)
 
-plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-.1, 3.2))
-plot_waveform(waveform2, sample_rate2, title="Effects Applied", xlim=(-.1, 3.2))
+plot_waveform(waveform1, sample_rate1, title="Original", xlim=(-0.1, 3.2))
+plot_waveform(waveform2, sample_rate2, title="Effects Applied", xlim=(-0.1, 3.2))
 print_stats(waveform1, sample_rate=sample_rate1, src="Original")
 print_stats(waveform2, sample_rate=sample_rate2, src="Effects Applied")
 
@@ -268,7 +280,7 @@ 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])
 
@@ -281,7 +293,7 @@ plot_waveform(rir, sample_rate, title="Room Impulse Response", ylim=None)
 
 speech, _ = get_speech_sample(resample=sample_rate)
 
-speech_ = torch.nn.functional.pad(speech, (rir.shape[1]-1, 0))
+speech_ = torch.nn.functional.pad(speech, (rir.shape[1] - 1, 0))
 augmented = torch.nn.functional.conv1d(speech_[None, ...], rir[None, ...])[0]
 
 plot_waveform(speech, sample_rate, title="Original", ylim=None)
@@ -312,7 +324,7 @@ play_audio(augmented, sample_rate)
 sample_rate = 8000
 speech, _ = get_speech_sample(resample=sample_rate)
 noise, _ = get_noise_sample(resample=sample_rate)
-noise = noise[:, :speech.shape[1]]
+noise = noise[:, : speech.shape[1]]
 
 plot_waveform(noise, sample_rate, title="Background noise")
 plot_specgram(noise, sample_rate, title="Background noise")
@@ -346,7 +358,7 @@ plot_specgram(waveform, sample_rate, title="Original")
 play_audio(waveform, sample_rate)
 
 configs = [
-    ({"format": "wav", "encoding": 'ULAW', "bits_per_sample": 8}, "8 bit mu-law"),
+    ({"format": "wav", "encoding": "ULAW", "bits_per_sample": 8}, "8 bit mu-law"),
     ({"format": "gsm"}, "GSM-FR"),
     ({"format": "mp3", "compression": -9}, "MP3"),
     ({"format": "vorbis", "compression": -1}, "Vorbis"),
@@ -373,7 +385,7 @@ play_audio(speech, sample_rate)
 
 # Apply RIR
 rir, _ = get_rir_sample(resample=sample_rate, processed=True)
-speech_ = torch.nn.functional.pad(speech, (rir.shape[1]-1, 0))
+speech_ = torch.nn.functional.pad(speech, (rir.shape[1] - 1, 0))
 speech = torch.nn.functional.conv1d(speech_[None, ...], rir[None, ...])[0]
 
 plot_specgram(speech, sample_rate, title="RIR Applied")
@@ -384,7 +396,7 @@ play_audio(speech, sample_rate)
 # the noise contains the acoustic feature of the environment. Therefore, we add
 # the noise after RIR application.
 noise, _ = get_noise_sample(resample=sample_rate)
-noise = noise[:, :speech.shape[1]]
+noise = noise[:, : speech.shape[1]]
 
 snr_db = 8
 scale = math.exp(snr_db / 10) * noise.norm(p=2) / speech.norm(p=2)
@@ -399,7 +411,14 @@ speech, sample_rate = torchaudio.sox_effects.apply_effects_tensor(
     sample_rate,
     effects=[
         ["lowpass", "4000"],
-      ["compand", "0.02,0.05", "-60,-60,-30,-10,-20,-8,-5,-8,-2,-8", "-8", "-7", "0.05"],
+        [
+            "compand",
+            "0.02,0.05",
+            "-60,-60,-30,-10,-20,-8,-5,-8,-2,-8",
+            "-8",
+            "-7",
+            "0.05",
+        ],
         ["rate", "8000"],
     ],
 )

examples/tutorials/audio_datasets_tutorial.py

@@ -23,14 +23,14 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
 
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 # Preparation of data and helper functions.
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 import multiprocessing
 import os
 
@@ -42,19 +42,21 @@ _SAMPLE_DIR = "_assets"
 YESNO_DATASET_PATH = os.path.join(_SAMPLE_DIR, "yes_no")
 os.makedirs(YESNO_DATASET_PATH, exist_ok=True)
 
+
 def _download_yesno():
     if os.path.exists(os.path.join(YESNO_DATASET_PATH, "waves_yesno.tar.gz")):
         return
     torchaudio.datasets.YESNO(root=YESNO_DATASET_PATH, download=True)
 
+
 YESNO_DOWNLOAD_PROCESS = multiprocessing.Process(target=_download_yesno)
 YESNO_DOWNLOAD_PROCESS.start()
 
+
 def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     waveform = waveform.numpy()
 
     num_channels, num_frames = waveform.shape
-  time_axis = torch.arange(0, num_frames) / sample_rate
 
     figure, axes = plt.subplots(num_channels, 1)
     if num_channels == 1:
@@ -62,12 +64,13 @@ def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     for c in range(num_channels):
         axes[c].specgram(waveform[c], Fs=sample_rate)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def play_audio(waveform, sample_rate):
     waveform = waveform.numpy()
 
@@ -79,10 +82,12 @@ def play_audio(waveform, sample_rate):
     else:
         raise ValueError("Waveform with more than 2 channels are not supported.")
 
+
 ######################################################################
 # Here, we show how to use the ``YESNO`` dataset.
 #
 
+
 YESNO_DOWNLOAD_PROCESS.join()
 
 dataset = torchaudio.datasets.YESNO(YESNO_DATASET_PATH, download=True)

examples/tutorials/audio_feature_augmentation_tutorial.py

@@ -20,16 +20,17 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
-#@markdown
-#@markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/), which is licensed under Creative Commos BY 4.0.
-
-#-------------------------------------------------------------------------------
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
+# @markdown
+# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
+# @markdown which is licensed under Creative Commos BY 4.0.
+
+# -------------------------------------------------------------------------------
 # Preparation of data and helper functions.
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 
 import os
 import requests
@@ -40,40 +41,45 @@ import matplotlib.pyplot as plt
 
 _SAMPLE_DIR = "_assets"
 
-SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
 SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")
 
 os.makedirs(_SAMPLE_DIR, exist_ok=True)
 
+
 def _fetch_data():
     uri = [
         (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH),
     ]
     for url, path in uri:
-    with open(path, 'wb') as file_:
+        with open(path, "wb") as file_:
             file_.write(requests.get(url).content)
 
+
 _fetch_data()
 
+
 def _get_sample(path, resample=None):
-  effects = [
-    ["remix", "1"]
-  ]
+    effects = [["remix", "1"]]
     if resample:
-    effects.extend([
+        effects.extend(
+            [
                 ["lowpass", f"{resample // 2}"],
-      ["rate", f'{resample}'],
-    ])
+                ["rate", f"{resample}"],
+            ]
+        )
     return torchaudio.sox_effects.apply_effects_file(path, effects=effects)
 
+
 def get_speech_sample(*, resample=None):
     return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample)
 
+
 def get_spectrogram(
-    n_fft = 400,
-    win_len = None,
-    hop_len = None,
-    power = 2.0,
+    n_fft=400,
+    win_len=None,
+    hop_len=None,
+    power=2.0,
 ):
     waveform, _ = get_speech_sample()
     spectrogram = T.Spectrogram(
@@ -86,17 +92,19 @@ def get_spectrogram(
     )
     return spectrogram(waveform)
 
-def plot_spectrogram(spec, title=None, ylabel='freq_bin', aspect='auto', xmax=None):
+
+def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
     fig, axs = plt.subplots(1, 1)
-  axs.set_title(title or 'Spectrogram (db)')
+    axs.set_title(title or "Spectrogram (db)")
     axs.set_ylabel(ylabel)
-  axs.set_xlabel('frame')
-  im = axs.imshow(librosa.power_to_db(spec), origin='lower', aspect=aspect)
+    axs.set_xlabel("frame")
+    im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
     if xmax:
         axs.set_xlim((0, xmax))
     fig.colorbar(im, ax=axs)
     plt.show(block=False)
 
+
 ######################################################################
 # SpecAugment
 # -----------
@@ -111,18 +119,23 @@ def plot_spectrogram(spec, title=None, ylabel='freq_bin', aspect='auto', xmax=No
 # ~~~~~~~~~~~
 #
 
+
 spec = get_spectrogram(power=None)
 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)
+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

@@ -38,16 +38,17 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
-#@markdown
-#@markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/), which is licensed under Creative Commos BY 4.0.
-
-#-------------------------------------------------------------------------------
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
+# @markdown
+# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
+# @markdown which is licensed under Creative Commos BY 4.0.
+
+# -------------------------------------------------------------------------------
 # Preparation of data and helper functions.
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 
 import os
 import requests
@@ -59,7 +60,7 @@ from IPython.display import Audio, display
 
 _SAMPLE_DIR = "_assets"
 
-SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
 SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")
 
 os.makedirs(_SAMPLE_DIR, exist_ok=True)
@@ -70,25 +71,29 @@ def _fetch_data():
         (SAMPLE_WAV_SPEECH_URL, SAMPLE_WAV_SPEECH_PATH),
     ]
     for url, path in uri:
-    with open(path, 'wb') as file_:
+        with open(path, "wb") as file_:
             file_.write(requests.get(url).content)
 
+
 _fetch_data()
 
+
 def _get_sample(path, resample=None):
-  effects = [
-    ["remix", "1"]
-  ]
+    effects = [["remix", "1"]]
     if resample:
-    effects.extend([
+        effects.extend(
+            [
                 ["lowpass", f"{resample // 2}"],
-      ["rate", f'{resample}'],
-    ])
+                ["rate", f"{resample}"],
+            ]
+        )
     return torchaudio.sox_effects.apply_effects_file(path, effects=effects)
 
+
 def get_speech_sample(*, resample=None):
     return _get_sample(SAMPLE_WAV_SPEECH_PATH, resample=resample)
 
+
 def print_stats(waveform, sample_rate=None, src=None):
     if src:
         print("-" * 10)
@@ -106,17 +111,19 @@ def print_stats(waveform, sample_rate=None, src=None):
     print(waveform)
     print()
 
-def plot_spectrogram(spec, title=None, ylabel='freq_bin', aspect='auto', xmax=None):
+
+def plot_spectrogram(spec, title=None, ylabel="freq_bin", aspect="auto", xmax=None):
     fig, axs = plt.subplots(1, 1)
-  axs.set_title(title or 'Spectrogram (db)')
+    axs.set_title(title or "Spectrogram (db)")
     axs.set_ylabel(ylabel)
-  axs.set_xlabel('frame')
-  im = axs.imshow(librosa.power_to_db(spec), origin='lower', aspect=aspect)
+    axs.set_xlabel("frame")
+    im = axs.imshow(librosa.power_to_db(spec), origin="lower", aspect=aspect)
     if xmax:
         axs.set_xlim((0, xmax))
     fig.colorbar(im, ax=axs)
     plt.show(block=False)
 
+
 def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None):
     waveform = waveform.numpy()
 
@@ -130,7 +137,7 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
         axes[c].plot(time_axis, waveform[c], linewidth=1)
         axes[c].grid(True)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
         if ylim:
@@ -138,6 +145,7 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def play_audio(waveform, sample_rate):
     waveform = waveform.numpy()
 
@@ -149,14 +157,16 @@ def play_audio(waveform, sample_rate):
     else:
         raise ValueError("Waveform with more than 2 channels are not supported.")
 
+
 def plot_mel_fbank(fbank, title=None):
     fig, axs = plt.subplots(1, 1)
-  axs.set_title(title or 'Filter bank')
-  axs.imshow(fbank, aspect='auto')
-  axs.set_ylabel('frequency bin')
-  axs.set_xlabel('mel bin')
+    axs.set_title(title or "Filter bank")
+    axs.imshow(fbank, aspect="auto")
+    axs.set_ylabel("frequency bin")
+    axs.set_xlabel("mel bin")
     plt.show(block=False)
 
+
 def plot_pitch(waveform, sample_rate, pitch):
     figure, axis = plt.subplots(1, 1)
     axis.set_title("Pitch Feature")
@@ -164,16 +174,16 @@ def plot_pitch(waveform, sample_rate, pitch):
 
     end_time = waveform.shape[1] / sample_rate
     time_axis = torch.linspace(0, end_time, waveform.shape[1])
-  axis.plot(time_axis, waveform[0], linewidth=1, color='gray', alpha=0.3)
+    axis.plot(time_axis, waveform[0], linewidth=1, color="gray", alpha=0.3)
 
     axis2 = axis.twinx()
     time_axis = torch.linspace(0, end_time, pitch.shape[1])
-  ln2 = axis2.plot(
-      time_axis, pitch[0], linewidth=2, label='Pitch', color='green')
+    axis2.plot(time_axis, pitch[0], linewidth=2, label="Pitch", color="green")
 
     axis2.legend(loc=0)
     plt.show(block=False)
 
+
 def plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc):
     figure, axis = plt.subplots(1, 1)
     axis.set_title("Kaldi Pitch Feature")
@@ -181,22 +191,24 @@ def plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc):
 
     end_time = waveform.shape[1] / sample_rate
     time_axis = torch.linspace(0, end_time, waveform.shape[1])
-  axis.plot(time_axis, waveform[0], linewidth=1, color='gray', alpha=0.3)
+    axis.plot(time_axis, waveform[0], linewidth=1, color="gray", alpha=0.3)
 
     time_axis = torch.linspace(0, end_time, pitch.shape[1])
-  ln1 = axis.plot(time_axis, pitch[0], linewidth=2, label='Pitch', color='green')
+    ln1 = axis.plot(time_axis, pitch[0], linewidth=2, label="Pitch", color="green")
     axis.set_ylim((-1.3, 1.3))
 
     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='--')
+        time_axis, nfcc[0], linewidth=2, label="NFCC", color="blue", linestyle="--"
+    )
 
     lns = ln1 + ln2
     labels = [l.get_label() for l in lns]
     axis.legend(lns, labels, loc=0)
     plt.show(block=False)
 
+
 ######################################################################
 # Spectrogram
 # -----------
@@ -206,7 +218,6 @@ def plot_kaldi_pitch(waveform, sample_rate, pitch, nfcc):
 #
 
 
-
 waveform, sample_rate = get_speech_sample()
 
 n_fft = 1024
@@ -226,7 +237,7 @@ spectrogram = T.Spectrogram(
 spec = spectrogram(waveform)
 
 print_stats(spec)
-plot_spectrogram(spec[0], title='torchaudio')
+plot_spectrogram(spec[0], title="torchaudio")
 
 ######################################################################
 # GriffinLim
@@ -280,10 +291,10 @@ sample_rate = 6000
 mel_filters = F.melscale_fbanks(
     int(n_fft // 2 + 1),
     n_mels=n_mels,
-    f_min=0.,
-    f_max=sample_rate/2.,
+    f_min=0.0,
+    f_max=sample_rate / 2.0,
     sample_rate=sample_rate,
-    norm='slaney'
+    norm="slaney",
 )
 plot_mel_fbank(mel_filters, "Mel Filter Bank - torchaudio")
 
@@ -300,16 +311,16 @@ mel_filters_librosa = librosa.filters.mel(
     sample_rate,
     n_fft,
     n_mels=n_mels,
-    fmin=0.,
-    fmax=sample_rate/2.,
-    norm='slaney',
+    fmin=0.0,
+    fmax=sample_rate / 2.0,
+    norm="slaney",
     htk=True,
 ).T
 
 plot_mel_fbank(mel_filters_librosa, "Mel Filter Bank - librosa")
 
 mse = torch.square(mel_filters - mel_filters_librosa).mean().item()
-print('Mean Square Difference: ', mse)
+print("Mean Square Difference: ", mse)
 
 ######################################################################
 # MelSpectrogram
@@ -336,15 +347,14 @@ mel_spectrogram = T.MelSpectrogram(
     center=True,
     pad_mode="reflect",
     power=2.0,
-    norm='slaney',
+    norm="slaney",
     onesided=True,
     n_mels=n_mels,
     mel_scale="htk",
 )
 
 melspec = mel_spectrogram(waveform)
-plot_spectrogram(
-    melspec[0], title="MelSpectrogram - torchaudio", ylabel='mel freq')
+plot_spectrogram(melspec[0], title="MelSpectrogram - torchaudio", ylabel="mel freq")
 
 ######################################################################
 # Comparison against librosa
@@ -365,14 +375,13 @@ melspec_librosa = librosa.feature.melspectrogram(
     pad_mode="reflect",
     power=2.0,
     n_mels=n_mels,
-    norm='slaney',
+    norm="slaney",
     htk=True,
 )
-plot_spectrogram(
-    melspec_librosa, title="MelSpectrogram - librosa", ylabel='mel freq')
+plot_spectrogram(melspec_librosa, title="MelSpectrogram - librosa", ylabel="mel freq")
 
 mse = torch.square(melspec - melspec_librosa).mean().item()
-print('Mean Square Difference: ', mse)
+print("Mean Square Difference: ", mse)
 
 ######################################################################
 # MFCC
@@ -391,11 +400,11 @@ mfcc_transform = T.MFCC(
     sample_rate=sample_rate,
     n_mfcc=n_mfcc,
     melkwargs={
-      'n_fft': n_fft,
-      'n_mels': n_mels,
-      'hop_length': hop_length,
-      'mel_scale': 'htk',
-    }
+        "n_fft": n_fft,
+        "n_mels": n_mels,
+        "hop_length": hop_length,
+        "mel_scale": "htk",
+    },
 )
 
 mfcc = mfcc_transform(waveform)
@@ -409,18 +418,27 @@ plot_spectrogram(mfcc[0])
 
 
 melspec = librosa.feature.melspectrogram(
-  y=waveform.numpy()[0], sr=sample_rate, n_fft=n_fft,
-  win_length=win_length, hop_length=hop_length,
-  n_mels=n_mels, htk=True, norm=None)
+    y=waveform.numpy()[0],
+    sr=sample_rate,
+    n_fft=n_fft,
+    win_length=win_length,
+    hop_length=hop_length,
+    n_mels=n_mels,
+    htk=True,
+    norm=None,
+)
 
 mfcc_librosa = librosa.feature.mfcc(
     S=librosa.core.spectrum.power_to_db(melspec),
-  n_mfcc=n_mfcc, dct_type=2, norm='ortho')
+    n_mfcc=n_mfcc,
+    dct_type=2,
+    norm="ortho",
+)
 
 plot_spectrogram(mfcc_librosa)
 
 mse = torch.square(mfcc - mfcc_librosa).mean().item()
-print('Mean Square Difference: ', mse)
+print("Mean Square Difference: ", mse)
 
 ######################################################################
 # Pitch

examples/tutorials/audio_io_tutorial.py

@@ -21,12 +21,13 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
-#@markdown
-#@markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/), which is licensed under Creative Commos BY 4.0.
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
+# @markdown
+# @markdown In this tutorial, we will use a speech data from [VOiCES dataset](https://iqtlabs.github.io/voices/),
+# @markdown which is licensed under Creative Commos BY 4.0.
 
 
 import io
@@ -51,7 +52,7 @@ SAMPLE_MP3_PATH = os.path.join(_SAMPLE_DIR, "steam.mp3")
 SAMPLE_GSM_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/steam-train-whistle-daniel_simon.gsm"
 SAMPLE_GSM_PATH = os.path.join(_SAMPLE_DIR, "steam.gsm")
 
-SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SAMPLE_WAV_SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
 SAMPLE_WAV_SPEECH_PATH = os.path.join(_SAMPLE_DIR, "speech.wav")
 
 SAMPLE_TAR_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit.tar.gz"
@@ -61,6 +62,7 @@ SAMPLE_TAR_ITEM = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp
 S3_BUCKET = "pytorch-tutorial-assets"
 S3_KEY = "VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
 
+
 def _fetch_data():
     os.makedirs(_SAMPLE_DIR, exist_ok=True)
     uri = [
@@ -71,11 +73,13 @@ def _fetch_data():
         (SAMPLE_TAR_URL, SAMPLE_TAR_PATH),
     ]
     for url, path in uri:
-    with open(path, 'wb') as file_:
+        with open(path, "wb") as file_:
             file_.write(requests.get(url).content)
 
+
 _fetch_data()
 
+
 def print_stats(waveform, sample_rate=None, src=None):
     if src:
         print("-" * 10)
@@ -93,6 +97,7 @@ def print_stats(waveform, sample_rate=None, src=None):
     print(waveform)
     print()
 
+
 def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None):
     waveform = waveform.numpy()
 
@@ -106,7 +111,7 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
         axes[c].plot(time_axis, waveform[c], linewidth=1)
         axes[c].grid(True)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
         if ylim:
@@ -114,11 +119,11 @@ def plot_waveform(waveform, sample_rate, title="Waveform", xlim=None, ylim=None)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     waveform = waveform.numpy()
 
     num_channels, num_frames = waveform.shape
-  time_axis = torch.arange(0, num_frames) / sample_rate
 
     figure, axes = plt.subplots(num_channels, 1)
     if num_channels == 1:
@@ -126,12 +131,13 @@ def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     for c in range(num_channels):
         axes[c].specgram(waveform[c], Fs=sample_rate)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def play_audio(waveform, sample_rate):
     waveform = waveform.numpy()
 
@@ -143,20 +149,23 @@ def play_audio(waveform, sample_rate):
     else:
         raise ValueError("Waveform with more than 2 channels are not supported.")
 
+
 def _get_sample(path, resample=None):
-  effects = [
-    ["remix", "1"]
-  ]
+    effects = [["remix", "1"]]
     if resample:
-    effects.extend([
+        effects.extend(
+            [
                 ["lowpass", f"{resample // 2}"],
-      ["rate", f'{resample}'],
-    ])
+                ["rate", f"{resample}"],
+            ]
+        )
     return torchaudio.sox_effects.apply_effects_file(path, effects=effects)
 
+
 def get_sample(*, resample=None):
     return _get_sample(SAMPLE_WAV_PATH, resample=resample)
 
+
 def inspect_file(path):
     print("-" * 10)
     print("Source:", path)
@@ -164,14 +173,16 @@ def inspect_file(path):
     print(f" - File size: {os.path.getsize(path)} bytes")
     print(f" - {torchaudio.info(path)}")
 
+
 ######################################################################
-# Quering audio metadata
-# ----------------------
+# Querying audio metadata
+# -----------------------
 #
 # Function ``torchaudio.info`` fetches audio metadata. You can provide
 # a path-like object or file-like object.
 #
 
+
 metadata = torchaudio.info(SAMPLE_WAV_PATH)
 print(metadata)
 
@@ -295,15 +306,15 @@ with requests.get(SAMPLE_WAV_SPEECH_URL, stream=True) as response:
 plot_specgram(waveform, sample_rate, title="HTTP datasource")
 
 # Load audio from tar file
-with tarfile.open(SAMPLE_TAR_PATH, mode='r') as tarfile_:
+with tarfile.open(SAMPLE_TAR_PATH, mode="r") as tarfile_:
     fileobj = tarfile_.extractfile(SAMPLE_TAR_ITEM)
     waveform, sample_rate = torchaudio.load(fileobj)
 plot_specgram(waveform, sample_rate, title="TAR file")
 
 # Load audio from S3
-client = boto3.client('s3', config=Config(signature_version=UNSIGNED))
+client = boto3.client("s3", config=Config(signature_version=UNSIGNED))
 response = client.get_object(Bucket=S3_BUCKET, Key=S3_KEY)
-waveform, sample_rate = torchaudio.load(response['Body'])
+waveform, sample_rate = torchaudio.load(response["Body"])
 plot_specgram(waveform, sample_rate, title="From S3")
 
 
@@ -337,13 +348,14 @@ 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)
+        response.raw, frame_offset=frame_offset, num_frames=num_frames
+    )
     print(f" - Fetched {response.raw.tell()} bytes")
 
 print("Checking the resulting waveform ... ", end="")
@@ -389,9 +401,7 @@ inspect_file(path)
 # Save as 16-bit signed integer Linear PCM
 # The resulting file occupies half the storage but loses precision
 path = f"{_SAMPLE_DIR}/save_example_PCM_S16.wav"
-torchaudio.save(
-    path, waveform, sample_rate,
-    encoding="PCM_S", bits_per_sample=16)
+torchaudio.save(path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
 inspect_file(path)
 
 
@@ -435,4 +445,3 @@ torchaudio.save(buffer_, waveform, sample_rate, format="wav")
 
 buffer_.seek(0)
 print(buffer_.read(16))
-

examples/tutorials/audio_resampling_tutorial.py

@@ -24,14 +24,14 @@ print(torchaudio.__version__)
 # --------------------------------------------------------
 #
 
-#@title Prepare data and utility functions. {display-mode: "form"}
-#@markdown
-#@markdown You do not need to look into this cell.
-#@markdown Just execute once and you are good to go.
+# @title Prepare data and utility functions. {display-mode: "form"}
+# @markdown
+# @markdown You do not need to look into this cell.
+# @markdown Just execute once and you are good to go.
 
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 # Preparation of data and helper functions.
-#-------------------------------------------------------------------------------
+# -------------------------------------------------------------------------------
 
 import math
 import time
@@ -46,7 +46,7 @@ DEFAULT_OFFSET = 201
 SWEEP_MAX_SAMPLE_RATE = 48000
 DEFAULT_LOWPASS_FILTER_WIDTH = 6
 DEFAULT_ROLLOFF = 0.99
-DEFAULT_RESAMPLING_METHOD = 'sinc_interpolation'
+DEFAULT_RESAMPLING_METHOD = "sinc_interpolation"
 
 
 def _get_log_freq(sample_rate, max_sweep_rate, offset):
@@ -55,15 +55,18 @@ def _get_log_freq(sample_rate, max_sweep_rate, offset):
     offset is used to avoid negative infinity `log(offset + x)`.
 
     """
-  half = sample_rate // 2
     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):
     """Find the time where the given frequency is given by _get_log_freq"""
     half = sample_rate // 2
     return sample_rate * (math.log(1 + freq / offset) / math.log(1 + half / offset))
 
+
 def _get_freq_ticks(sample_rate, offset, f_max):
     # Given the original sample rate used for generating the sweep,
     # find the x-axis value where the log-scale major frequency values fall in
@@ -80,6 +83,7 @@ def _get_freq_ticks(sample_rate, offset, f_max):
     freq.append(f_max)
     return time, freq
 
+
 def get_sine_sweep(sample_rate, offset=DEFAULT_OFFSET):
     max_sweep_rate = sample_rate
     freq = _get_log_freq(sample_rate, max_sweep_rate, offset)
@@ -88,9 +92,16 @@ def get_sine_sweep(sample_rate, offset=DEFAULT_OFFSET):
     signal = torch.sin(cummulative).unsqueeze(dim=0)
     return signal
 
-def plot_sweep(waveform, sample_rate, title, max_sweep_rate=SWEEP_MAX_SAMPLE_RATE, offset=DEFAULT_OFFSET):
+
+def plot_sweep(
+    waveform,
+    sample_rate,
+    title,
+    max_sweep_rate=SWEEP_MAX_SAMPLE_RATE,
+    offset=DEFAULT_OFFSET,
+):
     x_ticks = [100, 500, 1000, 5000, 10000, 20000, max_sweep_rate // 2]
-  y_ticks = [1000, 5000, 10000, 20000, sample_rate//2]
+    y_ticks = [1000, 5000, 10000, 20000, sample_rate // 2]
 
     time, freq = _get_freq_ticks(max_sweep_rate, offset, sample_rate // 2)
     freq_x = [f if f in x_ticks and f <= max_sweep_rate // 2 else None for f in freq]
@@ -100,13 +111,14 @@ def plot_sweep(waveform, sample_rate, title, max_sweep_rate=SWEEP_MAX_SAMPLE_RAT
     axis.specgram(waveform[0].numpy(), Fs=sample_rate)
     plt.xticks(time, freq_x)
     plt.yticks(freq_y, freq_y)
-  axis.set_xlabel('Original Signal Frequency (Hz, log scale)')
-  axis.set_ylabel('Waveform Frequency (Hz)')
+    axis.set_xlabel("Original Signal Frequency (Hz, log scale)")
+    axis.set_ylabel("Waveform Frequency (Hz)")
     axis.xaxis.grid(True, alpha=0.67)
     axis.yaxis.grid(True, alpha=0.67)
-  figure.suptitle(f'{title} (sample rate: {sample_rate} Hz)')
+    figure.suptitle(f"{title} (sample rate: {sample_rate} Hz)")
     plt.show(block=True)
 
+
 def play_audio(waveform, sample_rate):
     waveform = waveform.numpy()
 
@@ -118,11 +130,11 @@ def play_audio(waveform, sample_rate):
     else:
         raise ValueError("Waveform with more than 2 channels are not supported.")
 
+
 def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     waveform = waveform.numpy()
 
     num_channels, num_frames = waveform.shape
-  time_axis = torch.arange(0, num_frames) / sample_rate
 
     figure, axes = plt.subplots(num_channels, 1)
     if num_channels == 1:
@@ -130,12 +142,13 @@ def plot_specgram(waveform, sample_rate, title="Spectrogram", xlim=None):
     for c in range(num_channels):
         axes[c].specgram(waveform[c], Fs=sample_rate)
         if num_channels > 1:
-      axes[c].set_ylabel(f'Channel {c+1}')
+            axes[c].set_ylabel(f"Channel {c+1}")
         if xlim:
             axes[c].set_xlim(xlim)
     figure.suptitle(title)
     plt.show(block=False)
 
+
 def benchmark_resample(
     method,
     waveform,
@@ -146,18 +159,30 @@ def benchmark_resample(
     resampling_method=DEFAULT_RESAMPLING_METHOD,
     beta=None,
     librosa_type=None,
-    iters=5
+    iters=5,
 ):
     if method == "functional":
         begin = time.time()
         for _ in range(iters):
-      F.resample(waveform, sample_rate, resample_rate, lowpass_filter_width=lowpass_filter_width,
-                 rolloff=rolloff, resampling_method=resampling_method)
+            F.resample(
+                waveform,
+                sample_rate,
+                resample_rate,
+                lowpass_filter_width=lowpass_filter_width,
+                rolloff=rolloff,
+                resampling_method=resampling_method,
+            )
         elapsed = time.time() - begin
         return elapsed / iters
     elif method == "transforms":
-    resampler = T.Resample(sample_rate, resample_rate, lowpass_filter_width=lowpass_filter_width,
-                           rolloff=rolloff, resampling_method=resampling_method, dtype=waveform.dtype)
+        resampler = T.Resample(
+            sample_rate,
+            resample_rate,
+            lowpass_filter_width=lowpass_filter_width,
+            rolloff=rolloff,
+            resampling_method=resampling_method,
+            dtype=waveform.dtype,
+        )
         begin = time.time()
         for _ in range(iters):
             resampler(waveform)
@@ -167,10 +192,13 @@ 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
 
+
 ######################################################################
 # To resample an audio waveform from one freqeuncy to another, you can use
 # ``transforms.Resample`` or ``functional.resample``.
@@ -202,6 +230,7 @@ def benchmark_resample(
 # plotted waveform, and color intensity the amplitude.
 #
 
+
 sample_rate = 48000
 resample_rate = 32000
 
@@ -235,10 +264,14 @@ 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")
 
 
@@ -282,10 +315,14 @@ 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")
 
 
@@ -301,7 +338,7 @@ plot_sweep(resampled_waveform, resample_rate, title="Kaiser Window Default")
 sample_rate = 48000
 resample_rate = 32000
 
-### kaiser_best
+# kaiser_best
 resampled_waveform = F.resample(
     waveform,
     sample_rate,
@@ -309,18 +346,23 @@ resampled_waveform = F.resample(
     lowpass_filter_width=64,
     rolloff=0.9475937167399596,
     resampling_method="kaiser_window",
-    beta=14.769656459379492
+    beta=14.769656459379492,
 )
 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')).unsqueeze(0)
-plot_sweep(librosa_resampled_waveform, resample_rate, title="Kaiser Window Best (librosa)")
+    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)"
+)
 
 mse = torch.square(resampled_waveform - librosa_resampled_waveform).mean().item()
 print("torchaudio and librosa kaiser best MSE:", mse)
 
-### kaiser_fast
+# kaiser_fast
 resampled_waveform = F.resample(
     waveform,
     sample_rate,
@@ -328,13 +370,20 @@ resampled_waveform = F.resample(
     lowpass_filter_width=16,
     rolloff=0.85,
     resampling_method="kaiser_window",
-    beta=8.555504641634386
+    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')).unsqueeze(0)
-plot_sweep(librosa_resampled_waveform, resample_rate, title="Kaiser Window Fast (librosa)")
+    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)"
+)
 
 mse = torch.square(resampled_waveform - librosa_resampled_waveform).mean().item()
 print("torchaudio and librosa kaiser fast MSE:", mse)
@@ -377,19 +426,29 @@ 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(f"sinc (width 64)")
+    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(f"sinc (width 16)")
+    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,
@@ -398,7 +457,8 @@ for label in configs:
         lowpass_filter_width=64,
         rolloff=0.9475937167399596,
         resampling_method="kaiser_window",
-      beta=14.769656459379492)
+        beta=14.769656459379492,
+    )
     t_time = benchmark_resample(
         "transforms",
         waveform,
@@ -407,12 +467,15 @@ for label in configs:
         lowpass_filter_width=64,
         rolloff=0.9475937167399596,
         resampling_method="kaiser_window",
-      beta=14.769656459379492)
+        beta=14.769656459379492,
+    )
     times.append([1000 * lib_time, 1000 * f_time, 1000 * t_time])
-  rows.append(f"kaiser_best")
+    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,
@@ -421,7 +484,8 @@ for label in configs:
         lowpass_filter_width=16,
         rolloff=0.85,
         resampling_method="kaiser_window",
-      beta=8.555504641634386)
+        beta=8.555504641634386,
+    )
     t_time = benchmark_resample(
         "transforms",
         waveform,
@@ -430,12 +494,13 @@ for label in configs:
         lowpass_filter_width=16,
         rolloff=0.85,
         resampling_method="kaiser_window",
-      beta=8.555504641634386)
+        beta=8.555504641634386,
+    )
     times.append([1000 * lib_time, 1000 * f_time, 1000 * t_time])
-  rows.append(f"kaiser_fast")
+    rows.append("kaiser_fast")
 
-  df = pd.DataFrame(times,
-                    columns=["librosa", "functional", "transforms"],
-                    index=rows)
-  df.columns = pd.MultiIndex.from_product([[f"{label} time (ms)"],df.columns])
+    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

@@ -47,21 +47,21 @@ import matplotlib
 import matplotlib.pyplot as plt
 import IPython
 
-matplotlib.rcParams['figure.figsize'] = [16.0, 4.8]
+matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
 torch.random.manual_seed(0)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 print(torch.__version__)
 print(torchaudio.__version__)
 print(device)
 
-SPEECH_URL = 'https://download.pytorch.org/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav'
-SPEECH_FILE = '_assets/speech.wav'
+SPEECH_URL = "https://download.pytorch.org/torchaudio/tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SPEECH_FILE = "_assets/speech.wav"
 
 if not os.path.exists(SPEECH_FILE):
-  os.makedirs('_assets', exist_ok=True)
-  with open(SPEECH_FILE, 'wb') as file:
+    os.makedirs("_assets", exist_ok=True)
+    with open(SPEECH_FILE, "wb") as file:
         file.write(requests.get(SPEECH_URL).content)
 
 ######################################################################
@@ -142,12 +142,13 @@ plt.show()
 # [`distill.pub <https://distill.pub/2017/ctc/>`__])
 #
 
-transcript = 'I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT'
+transcript = "I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT"
 dictionary = {c: i for i, c in enumerate(labels)}
 
 tokens = [dictionary[c] for c in transcript]
 print(list(zip(transcript, tokens)))
 
+
 def get_trellis(emission, tokens, blank_id=0):
     num_frame = emission.size(0)
     num_tokens = len(tokens)
@@ -155,10 +156,10 @@ def get_trellis(emission, tokens, blank_id=0):
     # Trellis has extra diemsions for both time axis and tokens.
     # The extra dim for tokens represents <SoS> (start-of-sentence)
     # The extra dim for time axis is for simplification of the code.
-  trellis = torch.full((num_frame+1, num_tokens+1), -float('inf'))
+    trellis = torch.full((num_frame + 1, num_tokens + 1), -float("inf"))
     trellis[:, 0] = 0
     for t in range(num_frame):
-    trellis[t+1, 1:] = torch.maximum(
+        trellis[t + 1, 1:] = torch.maximum(
             # Score for staying at the same token
             trellis[t, 1:] + emission[t, blank_id],
             # Score for changing to the next token
@@ -166,12 +167,13 @@ def get_trellis(emission, tokens, blank_id=0):
         )
     return trellis
 
+
 trellis = get_trellis(emission, tokens)
 
 ################################################################################
 # Visualization
 ################################################################################
-plt.imshow(trellis[1:, 1:].T, origin='lower')
+plt.imshow(trellis[1:, 1:].T, origin="lower")
 plt.annotate("- Inf", (trellis.size(1) / 5, trellis.size(1) / 1.5))
 plt.colorbar()
 plt.show()
@@ -203,6 +205,7 @@ plt.show()
 # emission matrix.
 #
 
+
 @dataclass
 class Point:
     token_index: int
@@ -214,9 +217,9 @@ def backtrack(trellis, emission, tokens, blank_id=0):
     # Note:
     # j and t are indices for trellis, which has extra dimensions
     # for time and tokens at the beginning.
-  # When refering to time frame index `T` in trellis,
+    # When referring to time frame index `T` in trellis,
     # the corresponding index in emission is `T-1`.
-  # Similarly, when refering to token index `J` in trellis,
+    # Similarly, when referring to token index `J` in trellis,
     # the corresponding index in transcript is `J-1`.
     j = trellis.size(1) - 1
     t_start = torch.argmax(trellis[:, j]).item()
@@ -227,14 +230,14 @@ def backtrack(trellis, emission, tokens, blank_id=0):
         # Note (again):
         # `emission[J-1]` is the emission at time frame `J` of trellis dimension.
         # Score for token staying the same from time frame J-1 to T.
-    stayed = trellis[t-1, j] + emission[t-1, blank_id]
+        stayed = trellis[t - 1, j] + emission[t - 1, blank_id]
         # Score for token changing from C-1 at T-1 to J at T.
-    changed = trellis[t-1, j-1] + emission[t-1, tokens[j-1]]
+        changed = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
 
         # 2. Store the path with frame-wise probability.
-    prob = emission[t-1, tokens[j-1] if changed > stayed else 0].exp().item()
+        prob = emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
         # Return token index and time index in non-trellis coordinate.
-    path.append(Point(j-1, t-1, prob))
+        path.append(Point(j - 1, t - 1, prob))
 
         # 3. Update the token
         if changed > stayed:
@@ -242,21 +245,24 @@ def backtrack(trellis, emission, tokens, blank_id=0):
             if j == 0:
                 break
     else:
-    raise ValueError('Failed to align')
+        raise ValueError("Failed to align")
     return path[::-1]
 
+
 path = backtrack(trellis, emission, tokens)
 print(path)
 
+
 ################################################################################
 # Visualization
 ################################################################################
 def plot_trellis_with_path(trellis, path):
     # To plot trellis with path, we take advantage of 'nan' value
     trellis_with_path = trellis.clone()
-  for i, p in enumerate(path):
-    trellis_with_path[p.time_index, p.token_index] = float('nan')
-  plt.imshow(trellis_with_path[1:, 1:].T, origin='lower')
+    for _, p in enumerate(path):
+        trellis_with_path[p.time_index, p.token_index] = float("nan")
+    plt.imshow(trellis_with_path[1:, 1:].T, origin="lower")
+
 
 plot_trellis_with_path(trellis, path)
 plt.title("The path found by backtracking")
@@ -270,6 +276,7 @@ plt.show()
 # probability for the merged segments.
 #
 
+
 # Merge the labels
 @dataclass
 class Segment:
@@ -285,6 +292,7 @@ class Segment:
     def length(self):
         return self.end - self.start
 
+
 def merge_repeats(path):
     i1, i2 = 0, 0
     segments = []
@@ -292,14 +300,23 @@ def merge_repeats(path):
         while i2 < len(path) and path[i1].token_index == path[i2].token_index:
             i2 += 1
         score = sum(path[k].score for k in range(i1, i2)) / (i2 - i1)
-    segments.append(Segment(transcript[path[i1].token_index], path[i1].time_index, path[i2-1].time_index + 1, score))
+        segments.append(
+            Segment(
+                transcript[path[i1].token_index],
+                path[i1].time_index,
+                path[i2 - 1].time_index + 1,
+                score,
+            )
+        )
         i1 = i2
     return segments
 
+
 segments = merge_repeats(path)
 for seg in segments:
     print(seg)
 
+
 ################################################################################
 # Visualization
 ################################################################################
@@ -307,41 +324,42 @@ def plot_trellis_with_segments(trellis, segments, transcript):
     # To plot trellis with path, we take advantage of 'nan' value
     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')
+        if seg.label != "|":
+            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")
-  ax1.imshow(trellis_with_path.T, origin='lower')
+    ax1.imshow(trellis_with_path.T, origin="lower")
     ax1.set_xticks([])
 
     for i, seg in enumerate(segments):
-    if seg.label != '|':
-      ax1.annotate(seg.label, (seg.start + .7, i + 0.3), weight='bold')
-      ax1.annotate(f'{seg.score:.2f}', (seg.start - .3, i + 4.3))
+        if seg.label != "|":
+            ax1.annotate(seg.label, (seg.start + 0.7, i + 0.3), weight="bold")
+            ax1.annotate(f"{seg.score:.2f}", (seg.start - 0.3, i + 4.3))
 
     ax2.set_title("Label probability with and without repetation")
     xs, hs, ws = [], [], []
     for seg in segments:
-    if seg.label != '|':
-      xs.append((seg.end + seg.start) / 2 + .4)
+        if seg.label != "|":
+            xs.append((seg.end + seg.start) / 2 + 0.4)
             hs.append(seg.score)
             ws.append(seg.end - seg.start)
-      ax2.annotate(seg.label, (seg.start + .8, -0.07), weight='bold')
-  ax2.bar(xs, hs, width=ws, color='gray', alpha=0.5, edgecolor='black')
+            ax2.annotate(seg.label, (seg.start + 0.8, -0.07), weight="bold")
+    ax2.bar(xs, hs, width=ws, color="gray", alpha=0.5, edgecolor="black")
 
     xs, hs = [], []
     for p in path:
         label = transcript[p.token_index]
-    if label != '|':
+        if label != "|":
             xs.append(p.time_index + 1)
             hs.append(p.score)
 
     ax2.bar(xs, hs, width=0.5, alpha=0.5)
-  ax2.axhline(0, color='black')
+    ax2.axhline(0, color="black")
     ax2.set_xlim(ax1.get_xlim())
     ax2.set_ylim(-0.1, 1.1)
 
+
 plot_trellis_with_segments(trellis, segments, transcript)
 plt.tight_layout()
 plt.show()
@@ -357,38 +375,44 @@ plt.show()
 #
 
 # Merge words
-def merge_words(segments, separator='|'):
+def merge_words(segments, separator="|"):
     words = []
     i1, i2 = 0, 0
     while i1 < len(segments):
         if i2 >= len(segments) or segments[i2].label == 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))
+                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)
+                )
             i1 = i2 + 1
             i2 = i1
         else:
             i2 += 1
     return words
 
+
 word_segments = merge_words(segments)
 for word in word_segments:
     print(word)
 
+
 ################################################################################
 # Visualization
 ################################################################################
 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')
+        if seg.label != "|":
+            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.imshow(trellis_with_path[1:, 1:].T, origin='lower')
+    ax1.imshow(trellis_with_path[1:, 1:].T, origin="lower")
     ax1.set_xticks([])
     ax1.set_yticks([])
 
@@ -397,9 +421,9 @@ def plot_alignments(trellis, segments, word_segments, waveform):
         ax1.axvline(word.end - 0.5)
 
     for i, seg in enumerate(segments):
-    if seg.label != '|':
+        if seg.label != "|":
             ax1.annotate(seg.label, (seg.start, i + 0.3))
-      ax1.annotate(f'{seg.score:.2f}', (seg.start , i + 4), fontsize=8)
+            ax1.annotate(f"{seg.score:.2f}", (seg.start, i + 4), fontsize=8)
 
     # The original waveform
     ratio = waveform.size(0) / (trellis.size(0) - 1)
@@ -407,22 +431,29 @@ def plot_alignments(trellis, segments, word_segments, waveform):
     for word in word_segments:
         x0 = ratio * word.start
         x1 = ratio * word.end
-    ax2.axvspan(x0, x1, alpha=0.1, color='red')
-    ax2.annotate(f'{word.score:.2f}', (x0, 0.8))
+        ax2.axvspan(x0, x1, alpha=0.1, color="red")
+        ax2.annotate(f"{word.score:.2f}", (x0, 0.8))
 
     for seg in segments:
-    if seg.label != '|':
+        if seg.label != "|":
             ax2.annotate(seg.label, (seg.start * ratio, 0.9))
     xticks = ax2.get_xticks()
     plt.xticks(xticks, xticks / bundle.sample_rate)
-  ax2.set_xlabel('time [second]')
+    ax2.set_xlabel("time [second]")
     ax2.set_yticks([])
     ax2.set_ylim(-1.0, 1.0)
     ax2.set_xlim(0, waveform.size(-1))
 
-plot_alignments(trellis, segments, word_segments, waveform[0],)
+
+plot_alignments(
+    trellis,
+    segments,
+    word_segments,
+    waveform[0],
+)
 plt.show()
 
+
 # A trick to embed the resulting audio to the generated file.
 # `IPython.display.Audio` has to be the last call in a cell,
 # and there should be only one call par cell.
@@ -433,9 +464,12 @@ 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

@@ -40,7 +40,7 @@ MVDR with torchaudio
 # which was generated with;
 #
 # - ``SSB07200001.wav`` from `AISHELL-3 <https://www.openslr.org/93/>`__ (Apache License v.2.0)
-# - ``noise-sound-bible-0038.wav`` from `MUSAN <http://www.openslr.org/17/>`__ (Attribution 4.0 International — CC BY 4.0)
+# - ``noise-sound-bible-0038.wav`` from `MUSAN <http://www.openslr.org/17/>`__ (Attribution 4.0 International — CC BY 4.0)  # noqa: E501
 #
 
 import os
@@ -50,24 +50,24 @@ import torchaudio
 import IPython.display as ipd
 
 torch.random.manual_seed(0)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 print(torch.__version__)
 print(torchaudio.__version__)
 print(device)
 
 filenames = [
-    'mix.wav',
-    'reverb_clean.wav',
-    'clean.wav',
+    "mix.wav",
+    "reverb_clean.wav",
+    "clean.wav",
 ]
-base_url = 'https://download.pytorch.org/torchaudio/tutorial-assets/mvdr'
+base_url = "https://download.pytorch.org/torchaudio/tutorial-assets/mvdr"
 
 for filename in filenames:
-    os.makedirs('_assets', exist_ok=True)
+    os.makedirs("_assets", exist_ok=True)
     if not os.path.exists(filename):
-        with open(f'_assets/{filename}', 'wb') as file:
-            file.write(requests.get(f'{base_url}/{filename}').content)
+        with open(f"_assets/{filename}", "wb") as file:
+            file.write(requests.get(f"{base_url}/{filename}").content)
 
 ######################################################################
 # Generate the Ideal Ratio Mask (IRM)
@@ -79,9 +79,9 @@ for filename in filenames:
 # ~~~~~~~~~~~~~~~~~~
 #
 
-mix, sr = torchaudio.load('_assets/mix.wav')
-reverb_clean, sr2 = torchaudio.load('_assets/reverb_clean.wav')
-clean, sr3 = torchaudio.load('_assets/clean.wav')
+mix, sr = torchaudio.load("_assets/mix.wav")
+reverb_clean, sr2 = torchaudio.load("_assets/reverb_clean.wav")
+clean, sr3 = torchaudio.load("_assets/clean.wav")
 assert sr == sr2
 
 noise = mix - reverb_clean
@@ -125,8 +125,7 @@ spec_noise = stft(noise)
 #
 
 
-def get_irms(spec_clean, spec_noise, spec_mix):
-    mag_mix = spec_mix.abs() ** 2
+def get_irms(spec_clean, spec_noise):
     mag_clean = spec_clean.abs() ** 2
     mag_noise = spec_noise.abs() ** 2
     irm_speech = mag_clean / (mag_clean + mag_noise)
@@ -134,12 +133,13 @@ def get_irms(spec_clean, spec_noise, spec_mix):
 
     return irm_speech, irm_noise
 
+
 ######################################################################
 # .. note::
 #    We use reverberant clean speech as the target here,
 #    you can also set it to dry clean speech.
 
-irm_speech, irm_noise = get_irms(spec_reverb_clean, spec_noise, spec_mix)
+irm_speech, irm_noise = get_irms(spec_reverb_clean, spec_noise)
 
 ######################################################################
 # Apply MVDR
@@ -152,7 +152,7 @@ irm_speech, irm_noise = get_irms(spec_reverb_clean, spec_noise, spec_mix)
 #
 
 results_multi = {}
-for solution in ['ref_channel', 'stv_evd', 'stv_power']:
+for solution in ["ref_channel", "stv_evd", "stv_power"]:
     mvdr = torchaudio.transforms.MVDR(ref_channel=0, solution=solution, multi_mask=True)
     stft_est = mvdr(spec_mix, irm_speech, irm_noise)
     est = istft(stft_est, length=mix.shape[-1])
@@ -166,8 +166,10 @@ for solution in ['ref_channel', 'stv_evd', 'stv_power']:
 # The channel selection may depend on the design of the microphone array
 
 results_single = {}
-for solution in ['ref_channel', 'stv_evd', 'stv_power']:
-    mvdr = torchaudio.transforms.MVDR(ref_channel=0, solution=solution, multi_mask=False)
+for solution in ["ref_channel", "stv_evd", "stv_power"]:
+    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
@@ -177,6 +179,7 @@ for solution in ['ref_channel', 'stv_evd', 'stv_power']:
 # ~~~~~~~~~~~~~~~~~~~~~
 #
 
+
 def si_sdr(estimate, reference, epsilon=1e-8):
     estimate = estimate - estimate.mean()
     reference = reference - reference.mean()
@@ -196,6 +199,7 @@ def si_sdr(estimate, reference, epsilon=1e-8):
     sisdr = 10 * torch.log10(reference_pow) - 10 * torch.log10(error_pow)
     return sisdr.item()
 
+
 ######################################################################
 # Results
 # -------
@@ -207,7 +211,9 @@ 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
@@ -215,7 +221,9 @@ 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
@@ -253,39 +261,39 @@ ipd.Audio(clean[0], rate=16000)
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_multi['ref_channel'], rate=16000)
+ipd.Audio(results_multi["ref_channel"], rate=16000)
 
 ######################################################################
 # Multi-channel mask, stv_evd solution
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_multi['stv_evd'], rate=16000)
+ipd.Audio(results_multi["stv_evd"], rate=16000)
 
 ######################################################################
 # Multi-channel mask, stv_power solution
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_multi['stv_power'], rate=16000)
+ipd.Audio(results_multi["stv_power"], rate=16000)
 
 ######################################################################
 # Single-channel mask, ref_channel solution
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_single['ref_channel'], rate=16000)
+ipd.Audio(results_single["ref_channel"], rate=16000)
 
 ######################################################################
 # Single-channel mask, stv_evd solution
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_single['stv_evd'], rate=16000)
+ipd.Audio(results_single["stv_evd"], rate=16000)
 
 ######################################################################
 # Single-channel mask, stv_power solution
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
-ipd.Audio(results_single['stv_power'], rate=16000)
+ipd.Audio(results_single["stv_power"], rate=16000)

examples/tutorials/speech_recognition_pipeline_tutorial.py

@@ -48,21 +48,21 @@ import matplotlib
 import matplotlib.pyplot as plt
 import IPython
 
-matplotlib.rcParams['figure.figsize'] = [16.0, 4.8]
+matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
 torch.random.manual_seed(0)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 print(torch.__version__)
 print(torchaudio.__version__)
 print(device)
 
-SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"
+SPEECH_URL = "https://pytorch-tutorial-assets.s3.amazonaws.com/VOiCES_devkit/source-16k/train/sp0307/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav"  # noqa: E501
 SPEECH_FILE = "_assets/speech.wav"
 
 if not os.path.exists(SPEECH_FILE):
-  os.makedirs('_assets', exist_ok=True)
-  with open(SPEECH_FILE, 'wb') as file:
+    os.makedirs("_assets", exist_ok=True)
+    with open(SPEECH_FILE, "wb") as file:
         file.write(requests.get(SPEECH_URL).content)
 
 
@@ -241,6 +241,7 @@ print("Class labels:", bundle.get_labels())
 # We start by defining greedy decoding algorithm.
 #
 
+
 class GreedyCTCDecoder(torch.nn.Module):
     def __init__(self, labels, blank=0):
         super().__init__()
@@ -258,7 +259,7 @@ class GreedyCTCDecoder(torch.nn.Module):
         indices = torch.argmax(emission, dim=-1)  # [num_seq,]
         indices = torch.unique_consecutive(indices, dim=-1)
         indices = [i for i in indices if i != self.blank]
-    return ''.join([self.labels[i] for i in indices])
+        return "".join([self.labels[i] for i in indices])
 
 
 ######################################################################

examples/tutorials/tacotron2_pipeline_tutorial.py

@@ -63,7 +63,7 @@ import matplotlib.pyplot as plt
 
 import IPython
 
-matplotlib.rcParams['figure.figsize'] = [16.0, 4.8]
+matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]
 
 torch.random.manual_seed(0)
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -99,14 +99,16 @@ print(device)
 # that are not in the table are ignored.
 #
 
-symbols = '_-!\'(),.:;? abcdefghijklmnopqrstuvwxyz'
+symbols = "_-!'(),.:;? abcdefghijklmnopqrstuvwxyz"
 look_up = {s: i for i, s in enumerate(symbols)}
 symbols = set(symbols)
 
+
 def text_to_sequence(text):
     text = text.lower()
     return [look_up[s] for s in text if s in symbols]
 
+
 text = "Hello world! Text to speech!"
 print(text_to_sequence(text))
 
@@ -136,7 +138,7 @@ print(lengths)
 # The intermediate representation can be retrieved as follow.
 #
 
-print([processor.tokens[i] for i in processed[0, :lengths[0]]])
+print([processor.tokens[i] for i in processed[0, : lengths[0]]])
 
 
 ######################################################################
@@ -179,7 +181,7 @@ print(lengths)
 # The intermediate representation looks like the following.
 #
 
-print([processor.tokens[i] for i in processed[0, :lengths[0]]])
+print([processor.tokens[i] for i in processed[0, : lengths[0]]])
 
 
 ######################################################################
@@ -269,7 +271,9 @@ 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")
 
 
@@ -298,7 +302,11 @@ 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_griffinlim.wav", waveforms[0:1].cpu(), sample_rate=vocoder.sample_rate)
+torchaudio.save(
+    "_assets/output_griffinlim.wav",
+    waveforms[0:1].cpu(),
+    sample_rate=vocoder.sample_rate,
+)
 IPython.display.Audio("_assets/output_griffinlim.wav")
 
 
@@ -313,9 +321,20 @@ IPython.display.Audio("_assets/output_griffinlim.wav")
 
 # Workaround to load model mapped on GPU
 # https://stackoverflow.com/a/61840832
-waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32', pretrained=False)
-checkpoint = torch.hub.load_state_dict_from_url('https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth', progress=False, map_location=device)
-state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()}
+waveglow = torch.hub.load(
+    "NVIDIA/DeepLearningExamples:torchhub",
+    "nvidia_waveglow",
+    model_math="fp32",
+    pretrained=False,
+)
+checkpoint = torch.hub.load_state_dict_from_url(
+    "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth",  # noqa: E501
+    progress=False,
+    map_location=device,
+)
+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)