Last formatting.

spleeter/__main__.py

@@ -160,7 +160,7 @@ def separate(
 EVALUATION_AUDIO_DIRECTORY: str = "audio"
 
 
-def _compile_metrics(metrics_output_directory) -> Dict:
+def _compile_metrics(metrics_output_directory: str) -> Dict:
     """
     Compiles metrics from given directory and returns results as dict.
 

spleeter/audio/adapter.py

@@ -31,7 +31,7 @@ class AudioAdapter(ABC):
     """An abstract class for manipulating audio signal."""
 
     _DEFAULT: Optional["AudioAdapter"] = None
-    """ Default audio adapter singleton instance. """
+    """Default audio adapter singleton instance."""
 
     @abstractmethod
     def load(
@@ -51,13 +51,13 @@ def load(
                 Describe song to load, in case of file based audio adapter,
                 such descriptor would be a file path.
             offset (Optional[float]):
-                Start offset to load from in seconds.
+                (Optional) Start offset to load from in seconds.
             duration (Optional[float]):
-                Duration to load in seconds.
+                (Optional) Duration to load in seconds.
             sample_rate (Optional[float]):
-                Sample rate to load audio with.
+                (Optional) Sample rate to load audio with.
             dtype (bytes):
-                (Optional)data type to use, default to `b'float32'`.
+                (Optional) Data type to use, default to `b'float32'`.
 
         Returns:
             Signal:
@@ -78,17 +78,17 @@ def load_waveform(
         Load the audio and convert it to a tensorflow waveform.
 
         Parameters:
-            audio_descriptor ():
+            audio_descriptor (Any):
                 Describe song to load, in case of file based audio adapter,
                 such descriptor would be a file path.
             offset (float):
-                Start offset to load from in seconds.
+                (Optional) Start offset to load from in seconds.
             duration (float):
-                Duration to load in seconds.
+                (Optional) Duration to load in seconds.
             sample_rate (float):
-                Sample rate to load audio with.
+                (Optional) Sample rate to load audio with.
             dtype (bytes):
-                (Optional)data type to use, default to `b'float32'`.
+                (Optional) Data type to use, default to `b'float32'`.
             waveform_name (str):
                 (Optional) Name of the key in output dict, default to
                 `'waveform'`.
@@ -146,11 +146,11 @@ def save(
         Parameters:
             path (Union[Path, str]):
                 Path like of the audio file to save data in.
-            data (numpy.ndarray):
+            data (np.ndarray):
                 Waveform data to write.
             sample_rate (float):
                 Sample rate to write file in.
-            codec ():
+            codec (Codec):
                 (Optional) Writing codec to use, default to `None`.
             bitrate (str):
                 (Optional) Bitrate of the written audio file, default to

spleeter/audio/ffmpeg.py

@@ -2,10 +2,10 @@
 # coding: utf8
 
 """
-    This module provides an AudioAdapter implementation based on FFMPEG
-    process. Such implementation is POSIXish and depends on nothing except
-    standard Python libraries. Thus this implementation is the default one
-    used within this library.
+This module provides an AudioAdapter implementation based on FFMPEG
+process. Such implementation is POSIXish and depends on nothing except
+standard Python libraries. Thus this implementation is the default one
+used within this library.
 """
 
 import datetime as dt
@@ -77,13 +77,13 @@ def load(
             path (Union[Path, str]:
                 Path of the audio file to load data from.
             offset (Optional[float]):
-                Start offset to load from in seconds.
+                (Optional) Start offset to load from in seconds.
             duration (Optional[float]):
-                Duration to load in seconds.
+                (Optional) Duration to load in seconds.
             sample_rate (Optional[float]):
-                Sample rate to load audio with.
+                (Optional) Sample rate to load audio with.
             dtype (bytes):
-                (Optional)data type to use, default to `b'float32'`.
+                (Optional) Data type to use, default to `b'float32'`.
 
         Returns:
             Signal:

spleeter/audio/spectrogram.py

@@ -30,15 +30,15 @@ def compute_spectrogram_tf(
         waveform (tf.Tensor):
             Input waveform as `(times x number of channels)` tensor.
         frame_length (int):
-            Length of a STFT frame to use.
+            (Optional) Length of a STFT frame to use.
         frame_step (int):
-            HOP between successive frames.
+            (Optional) HOP between successive frames.
         spec_exponent (float):
-            Exponent of the spectrogram (usually 1 for magnitude
-            spectrogram, or 2 for power spectrogram).
+            (Optional) Exponent of the spectrogram (usually 1 for
+            magnitude spectrogram, or 2 for power spectrogram).
         window_exponent (float):
-            Exponent applied to the Hann windowing function (may be
-            useful for making perfect STFT/iSTFT reconstruction).
+            (Optional) Exponent applied to the Hann windowing function
+            (may be useful for making perfect STFT/iSTFT reconstruction).
 
     Returns:
         tf.Tensor:

spleeter/dataset.py

@@ -2,23 +2,23 @@
 # coding: utf8
 
 """
-    Module for building data preprocessing pipeline using the tensorflow
-    data API. Data preprocessing such as audio loading, spectrogram
-    computation, cropping, feature caching or data augmentation is done
-    using a tensorflow dataset object that output a tuple (input_, output)
-    where:
-
-    -   input is a dictionary with a single key that contains the (batched)
-        mix spectrogram of audio samples
-    -   output is a dictionary of spectrogram of the isolated tracks
-        (ground truth)
+Module for building data preprocessing pipeline using the tensorflow
+data API. Data preprocessing such as audio loading, spectrogram
+computation, cropping, feature caching or data augmentation is done
+using a tensorflow dataset object that output a tuple (input_, output)
+where:
+
+-   input is a dictionary with a single key that contains the (batched)
+    mix spectrogram of audio samples
+-   output is a dictionary of spectrogram of the isolated tracks
+    (ground truth)
 """
 
 import os
 import time
 from os.path import exists
 from os.path import sep as SEPARATOR
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple
 
 # pyright: reportMissingImports=false
 # pylint: disable=import-error
@@ -131,14 +131,14 @@ def get_validation_dataset(
 class InstrumentDatasetBuilder(object):
     """Instrument based filter and mapper provider."""
 
-    def __init__(self, parent, instrument) -> None:
+    def __init__(self, parent: Any, instrument: Any) -> None:
         """
         Default constructor.
 
         Parameters:
-            parent:
+            parent (Any):
                 Parent dataset builder.
-            instrument:
+            instrument (Any):
                 Target instrument.
         """
         self._parent = parent
@@ -147,7 +147,7 @@ def __init__(self, parent, instrument) -> None:
         self._min_spectrogram_key = f"min_{instrument}_spectrogram"
         self._max_spectrogram_key = f"max_{instrument}_spectrogram"
 
-    def load_waveform(self, sample):
+    def load_waveform(self, sample: Dict) -> Dict:
         """Load waveform for given sample."""
         return dict(
             sample,
@@ -160,7 +160,7 @@ def load_waveform(self, sample):
             ),
         )
 
-    def compute_spectrogram(self, sample):
+    def compute_spectrogram(self, sample: Dict) -> Dict:
         """Compute spectrogram of the given sample."""
         return dict(
             sample,
@@ -175,7 +175,7 @@ def compute_spectrogram(self, sample):
             },
         )
 
-    def filter_frequencies(self, sample):
+    def filter_frequencies(self, sample: Dict) -> Dict:
         return dict(
             sample,
             **{
@@ -185,7 +185,7 @@ def filter_frequencies(self, sample):
             },
         )
 
-    def convert_to_uint(self, sample):
+    def convert_to_uint(self, sample: Dict) -> Dict:
         """Convert given sample from float to unit."""
         return dict(
             sample,
@@ -197,11 +197,11 @@ def convert_to_uint(self, sample):
             ),
         )
 
-    def filter_infinity(self, sample):
+    def filter_infinity(self, sample: Dict) -> tf.Tensor:
         """Filter infinity sample."""
         return tf.logical_not(tf.math.is_inf(sample[self._min_spectrogram_key]))
 
-    def convert_to_float32(self, sample):
+    def convert_to_float32(self, sample: Dict) -> Dict:
         """Convert given sample from unit to float."""
         return dict(
             sample,
@@ -214,7 +214,7 @@ def convert_to_float32(self, sample):
             },
         )
 
-    def time_crop(self, sample):
+    def time_crop(self, sample: Dict) -> Dict:
         def start(sample):
             """mid_segment_start"""
             return tf.cast(
@@ -235,14 +235,14 @@ def start(sample):
             },
         )
 
-    def filter_shape(self, sample):
+    def filter_shape(self, sample: Dict) -> bool:
         """Filter badly shaped sample."""
         return check_tensor_shape(
             sample[self._spectrogram_key],
             (self._parent._T, self._parent._F, self._parent._n_channels),
         )
 
-    def reshape_spectrogram(self, sample):
+    def reshape_spectrogram(self, sample: Dict) -> Dict:
         """Reshape given sample."""
         return dict(
             sample,
@@ -272,17 +272,6 @@ def __init__(
     ) -> None:
         """
         Default constructor.
-
-        NOTE: Probably need for AudioAdapter.
-
-        Parameters:
-            audio_params (Dict):
-                Audio parameters to use.
-            audio_adapter (AudioAdapter):
-                Audio adapter to use.
-            audio_path (str):
-            random_seed (int):
-            chunk_duration (float):
         """
         # Length of segment in frames (if fs=22050 and
         # frame_step=512, then T=512 corresponds to 11.89s)
@@ -321,7 +310,7 @@ def check_parameters_compatibility(self):
                 "(for instance reducing T or frame_step or increasing chunk duration)."
             )
 
-    def expand_path(self, sample):
+    def expand_path(self, sample: Dict) -> Dict:
         """Expands audio paths for the given sample."""
         return dict(
             sample,
@@ -333,15 +322,15 @@ def expand_path(self, sample):
             },
         )
 
-    def filter_error(self, sample):
+    def filter_error(self, sample: Dict) -> tf.Tensor:
         """Filter errored sample."""
         return tf.logical_not(sample["waveform_error"])
 
-    def filter_waveform(self, sample):
+    def filter_waveform(self, sample: Dict) -> Dict:
         """Filter waveform from sample."""
         return {k: v for k, v in sample.items() if not k == "waveform"}
 
-    def harmonize_spectrogram(self, sample):
+    def harmonize_spectrogram(self, sample: Dict) -> Dict:
         """Ensure same size for vocals and mix spectrograms."""
 
         def _reduce(sample):
@@ -362,7 +351,7 @@ def _reduce(sample):
             },
         )
 
-    def filter_short_segments(self, sample):
+    def filter_short_segments(self, sample: Dict) -> tf.Tensor:
         """Filter out too short segment."""
         return tf.reduce_any(
             [
@@ -371,7 +360,7 @@ def filter_short_segments(self, sample):
             ]
         )
 
-    def random_time_crop(self, sample):
+    def random_time_crop(self, sample: Dict) -> Dict:
         """Random time crop of 11.88s."""
         return dict(
             sample,
@@ -388,7 +377,7 @@ def random_time_crop(self, sample):
             ),
         )
 
-    def random_time_stretch(self, sample):
+    def random_time_stretch(self, sample: Dict) -> Dict:
         """Randomly time stretch the given sample."""
         return dict(
             sample,
@@ -401,7 +390,7 @@ def random_time_stretch(self, sample):
             ),
         )
 
-    def random_pitch_shift(self, sample):
+    def random_pitch_shift(self, sample: Dict) -> Dict:
         """Randomly pitch shift the given sample."""
         return dict(
             sample,
@@ -415,7 +404,7 @@ def random_pitch_shift(self, sample):
             ),
         )
 
-    def map_features(self, sample):
+    def map_features(self, sample: Dict) -> Tuple[Dict, Dict]:
         """Select features and annotation of the given sample."""
         input_ = {
             f"{self._mix_name}_spectrogram": sample[f"{self._mix_name}_spectrogram"]