[pre-commit.ci] auto fixes from pre-commit.com hooks

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ludwig/constants.py

@@ -183,12 +183,14 @@
 CONTINUE_PROMPT = "Do you want to continue? "
 
 DEFAULT_AUDIO_TENSOR_LENGTH = 70000
-AUDIO_FEATURE_KEYS = ["type",
-                      "window_length_in_s",
-                      "window_shift_in_s",
-                      "num_fft_points",
-                      "window_type",
-                      "num_filter_bands"]
+AUDIO_FEATURE_KEYS = [
+    "type",
+    "window_length_in_s",
+    "window_shift_in_s",
+    "num_fft_points",
+    "window_type",
+    "num_filter_bands",
+]
 
 MODEL_TYPE = "model_type"
 MODEL_ECD = "ecd"

ludwig/features/audio_feature.py

@@ -15,22 +15,24 @@
 # ==============================================================================
 import logging
 import os
-from typing import Any, Dict, List, Union, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 import torchaudio
 
-from ludwig.constants import (AUDIO,
-                              BACKFILL,
-                              COLUMN,
-                              NAME,
-                              PREPROCESSING,
-                              PROC_COLUMN,
-                              SRC,
-                              TIED,
-                              TYPE,
-                              AUDIO_FEATURE_KEYS)
+from ludwig.constants import (
+    AUDIO,
+    AUDIO_FEATURE_KEYS,
+    BACKFILL,
+    COLUMN,
+    NAME,
+    PREPROCESSING,
+    PROC_COLUMN,
+    SRC,
+    TIED,
+    TYPE,
+)
 from ludwig.features.base_feature import BaseFeatureMixin
 from ludwig.features.sequence_feature import SequenceInputFeature
 from ludwig.schema.features.audio_feature import AudioInputFeatureConfig
@@ -60,8 +62,11 @@ class _AudioPreprocessing(torch.nn.Module):
 
     def __init__(self, metadata: Dict[str, Any]):
         super().__init__()
-        self.audio_feature_dict = {key: value for key, value in metadata["preprocessing"].items()
-                                   if key in AUDIO_FEATURE_KEYS and value is not None}
+        self.audio_feature_dict = {
+            key: value
+            for key, value in metadata["preprocessing"].items()
+            if key in AUDIO_FEATURE_KEYS and value is not None
+        }
         self.feature_dim = metadata["feature_dim"]
         self.max_length = metadata["max_length"]
         self.padding_value = metadata["preprocessing"]["padding_value"]
@@ -135,12 +140,14 @@ def _get_feature_dim(preprocessing_parameters, sampling_rate_in_hz):
         if feature_type == "raw":
             feature_dim = 1
         elif feature_type == "stft_phase":
-            feature_dim_symmetric = get_length_in_samp(preprocessing_parameters["window_length_in_s"],
-                                                       sampling_rate_in_hz)
+            feature_dim_symmetric = get_length_in_samp(
+                preprocessing_parameters["window_length_in_s"], sampling_rate_in_hz
+            )
             feature_dim = 2 * get_non_symmetric_length(feature_dim_symmetric)
         elif feature_type in ["stft", "group_delay"]:
-            feature_dim_symmetric = get_length_in_samp(preprocessing_parameters["window_length_in_s"],
-                                                       sampling_rate_in_hz)
+            feature_dim_symmetric = get_length_in_samp(
+                preprocessing_parameters["window_length_in_s"], sampling_rate_in_hz
+            )
             feature_dim = get_non_symmetric_length(feature_dim_symmetric)
         elif feature_type == "fbank":
             feature_dim = preprocessing_parameters["num_filter_bands"]
@@ -391,8 +398,11 @@ def add_feature_data(
 
         feature_dim = metadata[name]["feature_dim"]
         max_length = metadata[name]["max_length"]
-        audio_feature_dict = {key: value for key, value in preprocessing_parameters.items()
-                              if key in AUDIO_FEATURE_KEYS and value is not None}
+        audio_feature_dict = {
+            key: value
+            for key, value in preprocessing_parameters.items()
+            if key in AUDIO_FEATURE_KEYS and value is not None
+        }
         audio_file_length_limit_in_s = preprocessing_parameters["audio_file_length_limit_in_s"]
 
         if num_audio_utterances == 0:

ludwig/schema/preprocessing.py

@@ -560,36 +560,35 @@ class AudioPreprocessingConfig(schema_utils.BaseMarshmallowConfig):
     type: str = schema_utils.StringOptions(
         ["fbank", "group_delay", "raw", "stft", "stft_phase"],
         default="fbank",
-        description="Defines the type of audio feature to be used."
+        description="Defines the type of audio feature to be used.",
     )
 
     window_length_in_s: float = schema_utils.NonNegativeFloat(
         default=0.04,
         description="Defines the window length used for the short time Fourier transformation. This is only needed if "
-                    "the audio_feature_type is 'raw'.",
+        "the audio_feature_type is 'raw'.",
     )
 
     window_shift_in_s: float = schema_utils.NonNegativeFloat(
         default=0.02,
         description="Defines the window shift used for the short time Fourier transformation (also called "
-                    "hop_length). This is only needed if the audio_feature_type is 'raw'. "
+        "hop_length). This is only needed if the audio_feature_type is 'raw'. ",
     )
 
     num_fft_points: float = schema_utils.NonNegativeFloat(
-        default=None,
-        description="Defines the number of fft points used for the short time Fourier transformation"
+        default=None, description="Defines the number of fft points used for the short time Fourier transformation"
     )
 
     window_type: str = schema_utils.StringOptions(
         ["bartlett", "blackman", "hamming", "hann"],
         default="hamming",
-        description="Defines the type window the signal is weighted before the short time Fourier transformation."
+        description="Defines the type window the signal is weighted before the short time Fourier transformation.",
     )
 
     num_filter_bands: int = schema_utils.PositiveInteger(
         default=80,
         description="Defines the number of filters used in the filterbank. Only needed if audio_feature_type "
-                    "is 'fbank'"
+        "is 'fbank'",
     )