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🎨 finalizes audio package

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This commit is contained in:
Faylixe
2020-12-07 15:12:23 +01:00
parent 194a50e7cf
commit 3b72b6a0ff
3 changed files with 216 additions and 131 deletions
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16
spleeter/audio/adapter.py
View File

@@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
from importlib import import_module
from pathlib import Path
from spleeter.audio import Codec
from typing import Any, Dict, List, Union
from typing import Any, Dict, List, Optional, Union
from .. import SpleeterError
from ..types import AudioDescriptor, Signal
@@ -35,9 +35,9 @@ class AudioAdapter(ABC):
def load(
self,
audio_descriptor: AudioDescriptor,
offset: float,
duration: float,
sample_rate: float,
offset: Optional[float] = None,
duration: Optional[float] = None,
sample_rate: Optional[float] = None,
dtype: np.dtype = np.float32) -> Signal:
"""
Loads the audio file denoted by the given audio descriptor and
@@ -47,11 +47,11 @@ class AudioAdapter(ABC):
audio_descriptor (AudioDescriptor):
Describe song to load, in case of file based audio adapter,
such descriptor would be a file path.
offset (float):
offset (Optional[float]):
Start offset to load from in seconds.
duration (float):
duration (Optional[float]):
Duration to load in seconds.
sample_rate (float):
sample_rate (Optional[float]):
Sample rate to load audio with.
dtype (numpy.dtype):
(Optional) Numpy data type to use, default to `float32`.
@@ -136,7 +136,7 @@ class AudioAdapter(ABC):
data: np.ndarray,
sample_rate: float,
codec: Codec = None,
bitrate: str = None):
bitrate: str = None) -> None:
"""
Save the given audio data to the file denoted by the given path.

173
spleeter/audio/ffmpeg.py
View File

@@ -8,76 +8,92 @@
used within this library.
"""
import datetime as dt
import os
import shutil
from pathlib import Path
from typing import Dict, Optional, Union
from . import Codec
from .adapter import AudioAdapter
from .. import SpleeterError
from ..types import Signal
from ..utils.logging import get_logger
# pyright: reportMissingImports=false
# pylint: disable=import-error
import ffmpeg
import numpy as np
# pylint: enable=import-error
from .adapter import AudioAdapter
from .. import SpleeterError
from ..utils.logging import get_logger
__email__ = 'spleeter@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
def _check_ffmpeg_install():
""" Ensure FFMPEG binaries are available.
:raise SpleeterError: If ffmpeg or ffprobe is not found.
"""
for binary in ('ffmpeg', 'ffprobe'):
if shutil.which(binary) is None:
raise SpleeterError('{} binary not found'.format(binary))
def _to_ffmpeg_time(n):
""" Format number of seconds to time expected by FFMPEG.
:param n: Time in seconds to format.
:returns: Formatted time in FFMPEG format.
"""
m, s = divmod(n, 60)
h, m = divmod(m, 60)
return '%d:%02d:%09.6f' % (h, m, s)
def _to_ffmpeg_codec(codec):
ffmpeg_codecs = {
'm4a': 'aac',
'ogg': 'libvorbis',
'wma': 'wmav2',
}
return ffmpeg_codecs.get(codec) or codec
class FFMPEGProcessAudioAdapter(AudioAdapter):
""" An AudioAdapter implementation that use FFMPEG binary through
subprocess in order to perform I/O operation for audio processing.
When created, FFMPEG binary path will be checked and expended,
raising exception if not found. Such path could be infered using
FFMPEG_PATH environment variable.
"""
An AudioAdapter implementation that use FFMPEG binary through
subprocess in order to perform I/O operation for audio processing.
When created, FFMPEG binary path will be checked and expended,
raising exception if not found. Such path could be infered using
`FFMPEG_PATH` environment variable.
"""
SUPPORTED_CODECS: Dict[Codec, str] = {
Codec.M4A: 'aac',
Codec.OGG: 'libvorbis',
Codec.WMA: 'wmav2'
}
""" FFMPEG codec name mapping. """
def __init__(_) -> None:
"""
Default constructor, ensure FFMPEG binaries are available.
Raises:
SpleeterError:
If ffmpeg or ffprobe is not found.
"""
for binary in ('ffmpeg', 'ffprobe'):
if shutil.which(binary) is None:
raise SpleeterError('{} binary not found'.format(binary))
def load(
self, path, offset=None, duration=None,
sample_rate=None, dtype=np.float32):
""" Loads the audio file denoted by the given path
and returns it data as a waveform.
:param path: Path of the audio file to load data from.
:param offset: (Optional) Start offset to load from in seconds.
:param duration: (Optional) Duration to load in seconds.
:param sample_rate: (Optional) Sample rate to load audio with.
:param dtype: (Optional) Numpy data type to use, default to float32.
:returns: Loaded data a (waveform, sample_rate) tuple.
:raise SpleeterError: If any error occurs while loading audio.
_,
path: Union[Path, str],
offset: Optional[float] = None,
duration: Optional[float] = None,
sample_rate: Optional[float] = None,
dtype: np.dtype = np.float32) -> Signal:
"""
_check_ffmpeg_install()
Loads the audio file denoted by the given path
and returns it data as a waveform.
Parameters:
path (Union[Path, str]:
Path of the audio file to load data from.
offset (Optional[float]):
Start offset to load from in seconds.
duration (Optional[float]):
Duration to load in seconds.
sample_rate (Optional[float]):
Sample rate to load audio with.
dtype (numpy.dtype):
(Optional) Numpy data type to use, default to `float32`.
Returns:
Signal:
Loaded data a (waveform, sample_rate) tuple.
Raises:
SpleeterError:
If any error occurs while loading audio.
"""
if isinstance(path, Path):
path = str(path)
if not isinstance(path, str):
path = path.decode()
try:
@@ -97,9 +113,9 @@ class FFMPEGProcessAudioAdapter(AudioAdapter):
sample_rate = metadata['sample_rate']
output_kwargs = {'format': 'f32le', 'ar': sample_rate}
if duration is not None:
output_kwargs['t'] = _to_ffmpeg_time(duration)
output_kwargs['t'] = str(dt.timedelta(seconds=duration))
if offset is not None:
output_kwargs['ss'] = _to_ffmpeg_time(offset)
output_kwargs['ss'] = str(dt.timedelta(seconds=offset))
process = (
ffmpeg
.input(path)
@@ -112,29 +128,46 @@ class FFMPEGProcessAudioAdapter(AudioAdapter):
return (waveform, sample_rate)
def save(
self, path, data, sample_rate,
codec=None, bitrate=None):
""" Write waveform data to the file denoted by the given path
using FFMPEG process.
:param path: Path of the audio file to save data in.
:param data: Waveform data to write.
:param sample_rate: Sample rate to write file in.
:param codec: (Optional) Writing codec to use.
:param bitrate: (Optional) Bitrate of the written audio file.
:raise IOError: If any error occurs while using FFMPEG to write data.
self,
path: Union[Path, str],
data: np.ndarray,
sample_rate: float,
codec: Codec = None,
bitrate: str = None) -> None:
"""
_check_ffmpeg_install()
Write waveform data to the file denoted by the given path using
FFMPEG process.
Parameters:
path (Union[Path, str]):
Path like of the audio file to save data in.
data (numpy.ndarray):
Waveform data to write.
sample_rate (float):
Sample rate to write file in.
codec ():
(Optional) Writing codec to use, default to `None`.
bitrate (str):
(Optional) Bitrate of the written audio file, default to
`None`.
Raises:
IOError:
If any error occurs while using FFMPEG to write data.
"""
if isinstance(path, Path):
path = str(path)
directory = os.path.dirname(path)
if not os.path.exists(directory):
raise SpleeterError(f'output directory does not exists: {directory}')
get_logger().debug('Writing file %s', path)
raise SpleeterError(
f'output directory does not exists: {directory}')
get_logger().debug(f'Writing file {path}')
input_kwargs = {'ar': sample_rate, 'ac': data.shape[1]}
output_kwargs = {'ar': sample_rate, 'strict': '-2'}
if bitrate:
output_kwargs['audio_bitrate'] = bitrate
if codec is not None and codec != 'wav':
output_kwargs['codec'] = _to_ffmpeg_codec(codec)
output_kwargs['codec'] = self.SUPPORTED_CODECS.get(codec, codec)
process = (
ffmpeg
.input('pipe:', format='f32le', **input_kwargs)
@@ -147,4 +180,4 @@ class FFMPEGProcessAudioAdapter(AudioAdapter):
process.wait()
except IOError:
raise SpleeterError(f'FFMPEG error: {process.stderr.read()}')
get_logger().info('File %s written succesfully', path)
get_logger().info(f'File {path} written succesfully')

158
spleeter/audio/spectrogram.py
View File

@@ -1,7 +1,7 @@
#!/usr/bin/env python
# coding: utf8
""" Spectrogram specific data augmentation """
""" Spectrogram specific data augmentation. """
# pyright: reportMissingImports=false
# pylint: disable=import-error
@@ -17,25 +17,35 @@ __license__ = 'MIT License'
def compute_spectrogram_tf(
waveform,
frame_length=2048, frame_step=512,
spec_exponent=1., window_exponent=1.):
""" Compute magnitude / power spectrogram from waveform as
a n_samples x n_channels tensor.
:param waveform: Input waveform as (times x number of channels)
tensor.
:param frame_length: Length of a STFT frame to use.
:param frame_step: HOP between successive frames.
:param spec_exponent: Exponent of the spectrogram (usually 1 for
magnitude spectrogram, or 2 for power spectrogram).
:param window_exponent: Exponent applied to the Hann windowing function
(may be useful for making perfect STFT/iSTFT
reconstruction).
:returns: Computed magnitude / power spectrogram as a
(T x F x n_channels) tensor.
waveform: tf.Tensor,
frame_length: int = 2048,
frame_step: int = 512,
spec_exponent: float = 1.,
window_exponent: float = 1.) -> tf.Tensor:
"""
stft_tensor = tf.transpose(
Compute magnitude / power spectrogram from waveform as a
`n_samples x n_channels` tensor.
Parameters:
waveform (tensorflow.Tensor):
Input waveform as `(times x number of channels)` tensor.
frame_length (int):
Length of a STFT frame to use.
frame_step (int):
HOP between successive frames.
spec_exponent (float):
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).
Returns:
tensorflow.Tensor:
Computed magnitude / power spectrogram as a
`(T x F x n_channels)` tensor.
"""
stft_tensor: tf.Tensor = tf.transpose(
stft(
tf.transpose(waveform),
frame_length,
@@ -49,16 +59,25 @@ def compute_spectrogram_tf(
def time_stretch(
spectrogram,
factor=1.0,
method=tf.image.ResizeMethod.BILINEAR):
""" Time stretch a spectrogram preserving shape in tensorflow. Note that
this is an approximation in the frequency domain.
spectrogram: tf.Tensor,
factor: float = 1.0,
method: tf.image.ResizeMethod = tf.image.ResizeMethod.BILINEAR
) -> tf.Tensor:
"""
Time stretch a spectrogram preserving shape in tensorflow. Note that
this is an approximation in the frequency domain.
:param spectrogram: Input spectrogram to be time stretched as tensor.
:param factor: (Optional) Time stretch factor, must be >0, default to 1.
:param mehtod: (Optional) Interpolation method, default to BILINEAR.
:returns: Time stretched spectrogram as tensor with same shape.
Parameters:
spectrogram (tensorflow.Tensor):
Input spectrogram to be time stretched as tensor.
factor (float):
(Optional) Time stretch factor, must be > 0, default to `1`.
method (tensorflow.image.ResizeMethod):
(Optional) Interpolation method, default to `BILINEAR`.
Returns:
tensorflow.Tensor:
Time stretched spectrogram as tensor with same shape.
"""
T = tf.shape(spectrogram)[0]
T_ts = tf.cast(tf.cast(T, tf.float32) * factor, tf.int32)[0]
@@ -71,15 +90,27 @@ def time_stretch(
return tf.image.resize_image_with_crop_or_pad(ts_spec, T, F)
def random_time_stretch(spectrogram, factor_min=0.9, factor_max=1.1, **kwargs):
""" Time stretch a spectrogram preserving shape with random ratio in
tensorflow. Applies time_stretch to spectrogram with a random ratio drawn
uniformly in [factor_min, factor_max].
def random_time_stretch(
spectrogram: tf.Tensor,
factor_min: float = 0.9,
factor_max: float = 1.1,
**kwargs) -> tf.Tensor:
"""
Time stretch a spectrogram preserving shape with random ratio in
tensorflow. Applies time_stretch to spectrogram with a random ratio
drawn uniformly in `[factor_min, factor_max]`.
:param spectrogram: Input spectrogram to be time stretched as tensor.
:param factor_min: (Optional) Min time stretch factor, default to 0.9.
:param factor_max: (Optional) Max time stretch factor, default to 1.1.
:returns: Randomly time stretched spectrogram as tensor with same shape.
Parameters:
spectrogram (tensorflow.Tensor):
Input spectrogram to be time stretched as tensor.
factor_min (float):
(Optional) Min time stretch factor, default to `0.9`.
factor_max (float):
(Optional) Max time stretch factor, default to `1.1`.
Returns:
tensorflow.Tensor:
Randomly time stretched spectrogram as tensor with same shape.
"""
factor = tf.random_uniform(
shape=(1,),
@@ -88,16 +119,25 @@ def random_time_stretch(spectrogram, factor_min=0.9, factor_max=1.1, **kwargs):
def pitch_shift(
spectrogram,
semitone_shift=0.0,
method=tf.image.ResizeMethod.BILINEAR):
""" Pitch shift a spectrogram preserving shape in tensorflow. Note that
this is an approximation in the frequency domain.
spectrogram: tf.Tensor,
semitone_shift: float = 0.0,
method: tf.image.ResizeMethod = tf.image.ResizeMethod.BILINEAR
) -> tf.Tensor:
"""
Pitch shift a spectrogram preserving shape in tensorflow. Note that
this is an approximation in the frequency domain.
:param spectrogram: Input spectrogram to be pitch shifted as tensor.
:param semitone_shift: (Optional) Pitch shift in semitone, default to 0.0.
:param mehtod: (Optional) Interpolation method, default to BILINEAR.
:returns: Pitch shifted spectrogram (same shape as spectrogram).
Parameters:
spectrogram (tensorflow.Tensor):
Input spectrogram to be pitch shifted as tensor.
semitone_shift (float):
(Optional) Pitch shift in semitone, default to `0.0`.
method (tensorflow.image.ResizeMethod):
(Optional) Interpolation method, default to `BILINEAR`.
Returns:
tensorflow.Tensor:
Pitch shifted spectrogram (same shape as spectrogram).
"""
factor = 2 ** (semitone_shift / 12.)
T = tf.shape(spectrogram)[0]
@@ -112,16 +152,28 @@ def pitch_shift(
return tf.pad(ps_spec[:, :F, :], paddings, 'CONSTANT')
def random_pitch_shift(spectrogram, shift_min=-1., shift_max=1., **kwargs):
""" Pitch shift a spectrogram preserving shape with random ratio in
tensorflow. Applies pitch_shift to spectrogram with a random shift
amount (expressed in semitones) drawn uniformly in [shift_min, shift_max].
def random_pitch_shift(
spectrogram: tf.Tensor,
shift_min: float = -1.,
shift_max: float = 1.,
**kwargs) -> tf.Tensor:
"""
Pitch shift a spectrogram preserving shape with random ratio in
tensorflow. Applies pitch_shift to spectrogram with a random shift
amount (expressed in semitones) drawn uniformly in
`[shift_min, shift_max]`.
:param spectrogram: Input spectrogram to be pitch shifted as tensor.
Parameters:
spectrogram (tensorflow.Tensor):
Input spectrogram to be pitch shifted as tensor.
shift_min (float):
(Optional) Min pitch shift in semitone, default to -1.
shift_max (float):
(Optional) Max pitch shift in semitone, default to 1.
:param shift_min: (Optional) Min pitch shift in semitone, default to -1.
:param shift_max: (Optional) Max pitch shift in semitone, default to 1.
:returns: Randomly pitch shifted spectrogram (same shape as spectrogram).
Returns:
tensorflow.Tensor:
Randomly pitch shifted spectrogram (same shape as spectrogram).
"""
semitone_shift = tf.random_uniform(
shape=(1,),