Merge pull request #718 from deezer/metal_readme

Pointing warning to new Mac M1 fix with TF metal.

README.md

@@ -58,7 +58,7 @@ spleeter separate -p spleeter:2stems -o output audio_example.mp3
 
 > :warning: Note that we no longer recommend using `conda` for installing spleeter.
 
-> ⚠️ There are known issues with Apple M1 chips, mostly due to TensorFlow compatibility. Until these are fixed, you can use [this workaround](https://github.com/deezer/spleeter/issues/607#issuecomment-828352392)
+> ⚠️ There are known issues with Apple M1 chips, mostly due to TensorFlow compatibility. Until these are fixed, you can use [this workaround](https://github.com/deezer/spleeter/issues/607#issuecomment-1021669444).
 
 You should get two separated audio files (`vocals.wav` and `accompaniment.wav`) in the `output/audio_example` folder.
 

pyproject.toml

@@ -44,7 +44,7 @@ packages = [ { include = "spleeter" } ]
 include = ["LICENSE", "spleeter/resources/*.json"]
 
 [tool.poetry.dependencies]
-python = ">=3.6.1,<3.10"
+python = ">=3.6.2,<3.10"
 ffmpeg-python = "0.2.0"
 norbert = "0.2.1"
 httpx = {extras = ["http2"], version = "^0.19.0"}
@@ -62,7 +62,7 @@ llvmlite = "^0.36.0"
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.1"
 isort = "^5.7.0"
-black = "^20.8b1"
+black = "^21.7b"
 mypy = "^0.790"
 pytest-forked = "^1.3.0"
 musdb = "0.3.1"

spleeter/__init__.py

@@ -19,6 +19,6 @@ __license__ = "MIT License"
 
 
 class SpleeterError(Exception):
-    """ Custom exception for Spleeter related error. """
+    """Custom exception for Spleeter related error."""
 
     pass

spleeter/__main__.py

@@ -251,7 +251,7 @@ def evaluate(
 
 
 def entrypoint():
-    """ Application entrypoint. """
+    """Application entrypoint."""
     try:
         spleeter()
     except SpleeterError as e:

spleeter/audio/__init__.py

@@ -18,7 +18,7 @@ __license__ = "MIT License"
 
 
 class Codec(str, Enum):
-    """ Enumeration of supported audio codec. """
+    """Enumeration of supported audio codec."""
 
     WAV: str = "wav"
     MP3: str = "mp3"
@@ -29,7 +29,7 @@ class Codec(str, Enum):
 
 
 class STFTBackend(str, Enum):
-    """ Enumeration of supported STFT backend. """
+    """Enumeration of supported STFT backend."""
 
     AUTO: str = "auto"
     TENSORFLOW: str = "tensorflow"

spleeter/audio/adapter.py

@@ -28,7 +28,7 @@ __license__ = "MIT License"
 
 
 class AudioAdapter(ABC):
-    """ An abstract class for manipulating audio signal. """
+    """An abstract class for manipulating audio signal."""
 
     _DEFAULT: "AudioAdapter" = None
     """ Default audio adapter singleton instance. """

spleeter/dataset.py

@@ -129,7 +129,7 @@ def get_validation_dataset(
 
 
 class InstrumentDatasetBuilder(object):
-    """ Instrument based filter and mapper provider. """
+    """Instrument based filter and mapper provider."""
 
     def __init__(self, parent, instrument) -> None:
         """
@@ -148,7 +148,7 @@ class InstrumentDatasetBuilder(object):
         self._max_spectrogram_key = f"max_{instrument}_spectrogram"
 
     def load_waveform(self, sample):
-        """ Load waveform for given sample. """
+        """Load waveform for given sample."""
         return dict(
             sample,
             **self._parent._audio_adapter.load_tf_waveform(
@@ -161,7 +161,7 @@ class InstrumentDatasetBuilder(object):
         )
 
     def compute_spectrogram(self, sample):
-        """ Compute spectrogram of the given sample. """
+        """Compute spectrogram of the given sample."""
         return dict(
             sample,
             **{
@@ -187,7 +187,7 @@ class InstrumentDatasetBuilder(object):
         )
 
     def convert_to_uint(self, sample):
-        """ Convert given sample from float to unit. """
+        """Convert given sample from float to unit."""
         return dict(
             sample,
             **spectrogram_to_db_uint(
@@ -199,11 +199,11 @@ class InstrumentDatasetBuilder(object):
         )
 
     def filter_infinity(self, sample):
-        """ Filter infinity sample. """
+        """Filter infinity sample."""
         return tf.logical_not(tf.math.is_inf(sample[self._min_spectrogram_key]))
 
     def convert_to_float32(self, sample):
-        """ Convert given sample from unit to float. """
+        """Convert given sample from unit to float."""
         return dict(
             sample,
             **{
@@ -219,7 +219,7 @@ class InstrumentDatasetBuilder(object):
         """ """
 
         def start(sample):
-            """ mid_segment_start """
+            """mid_segment_start"""
             return tf.cast(
                 tf.maximum(
                     tf.shape(sample[self._spectrogram_key])[0] / 2
@@ -239,14 +239,14 @@ class InstrumentDatasetBuilder(object):
         )
 
     def filter_shape(self, sample):
-        """ Filter badly shaped sample. """
+        """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):
-        """ Reshape given sample. """
+        """Reshape given sample."""
         return dict(
             sample,
             **{
@@ -326,7 +326,7 @@ class DatasetBuilder(object):
             )
 
     def expand_path(self, sample):
-        """ Expands audio paths for the given sample. """
+        """Expands audio paths for the given sample."""
         return dict(
             sample,
             **{
@@ -338,15 +338,15 @@ class DatasetBuilder(object):
         )
 
     def filter_error(self, sample):
-        """ Filter errored sample. """
+        """Filter errored sample."""
         return tf.logical_not(sample["waveform_error"])
 
     def filter_waveform(self, sample):
-        """ Filter waveform from sample. """
+        """Filter waveform from sample."""
         return {k: v for k, v in sample.items() if not k == "waveform"}
 
     def harmonize_spectrogram(self, sample):
-        """ Ensure same size for vocals and mix spectrograms. """
+        """Ensure same size for vocals and mix spectrograms."""
 
         def _reduce(sample):
             return tf.reduce_min(
@@ -367,7 +367,7 @@ class DatasetBuilder(object):
         )
 
     def filter_short_segments(self, sample):
-        """ Filter out too short segment. """
+        """Filter out too short segment."""
         return tf.reduce_any(
             [
                 tf.shape(sample[f"{instrument}_spectrogram"])[0] >= self._T
@@ -376,7 +376,7 @@ class DatasetBuilder(object):
         )
 
     def random_time_crop(self, sample):
-        """ Random time crop of 11.88s. """
+        """Random time crop of 11.88s."""
         return dict(
             sample,
             **sync_apply(
@@ -393,7 +393,7 @@ class DatasetBuilder(object):
         )
 
     def random_time_stretch(self, sample):
-        """ Randomly time stretch the given sample. """
+        """Randomly time stretch the given sample."""
         return dict(
             sample,
             **sync_apply(
@@ -406,7 +406,7 @@ class DatasetBuilder(object):
         )
 
     def random_pitch_shift(self, sample):
-        """ Randomly pitch shift the given sample. """
+        """Randomly pitch shift the given sample."""
         return dict(
             sample,
             **sync_apply(
@@ -420,7 +420,7 @@ class DatasetBuilder(object):
         )
 
     def map_features(self, sample):
-        """ Select features and annotation of the given sample. """
+        """Select features and annotation of the given sample."""
         input_ = {
             f"{self._mix_name}_spectrogram": sample[f"{self._mix_name}_spectrogram"]
         }

spleeter/model/functions/blstm.py

@@ -94,5 +94,5 @@ def apply_blstm(
 def blstm(
     input_tensor: tf.Tensor, output_name: str = "output", params: Optional[Dict] = None
 ) -> tf.Tensor:
-    """ Model function applier. """
+    """Model function applier."""
     return apply(apply_blstm, input_tensor, output_name, params)

spleeter/model/functions/unet.py

@@ -193,7 +193,7 @@ def apply_unet(
 def unet(
     input_tensor: tf.Tensor, instruments: Iterable[str], params: Optional[Dict] = None
 ) -> Dict:
-    """ Model function applier. """
+    """Model function applier."""
     return apply(apply_unet, input_tensor, instruments, params)
 
 

spleeter/model/provider/github.py

@@ -51,7 +51,7 @@ def compute_file_checksum(path):
 
 
 class GithubModelProvider(ModelProvider):
-    """ A ModelProvider implementation backed on Github for remote storage. """
+    """A ModelProvider implementation backed on Github for remote storage."""
 
     DEFAULT_HOST: str = "https://github.com"
     DEFAULT_REPOSITORY: str = "deezer/spleeter"

spleeter/separator.py

@@ -53,15 +53,15 @@ class DataGenerator(object):
     """
 
     def __init__(self) -> None:
-        """ Default constructor. """
+        """Default constructor."""
         self._current_data = None
 
     def update_data(self, data) -> None:
-        """ Replace internal data. """
+        """Replace internal data."""
         self._current_data = data
 
     def __call__(self) -> Generator:
-        """ Generation process. """
+        """Generation process."""
         buffer = self._current_data
         while buffer:
             yield buffer
@@ -94,7 +94,7 @@ def create_estimator(params, MWF):
 
 
 class Separator(object):
-    """ A wrapper class for performing separation. """
+    """A wrapper class for performing separation."""
 
     def __init__(
         self,

spleeter/utils/logging.py

@@ -21,7 +21,7 @@ environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 
 class TyperLoggerHandler(logging.Handler):
-    """ A custom logger handler that use Typer echo. """
+    """A custom logger handler that use Typer echo."""
 
     def emit(self, record: logging.LogRecord) -> None:
         echo(self.format(record))