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576 lines
24 KiB
576 lines
24 KiB
"""Contains the audio segment class."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import io
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import soundfile
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import scikits.samplerate
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from scipy import signal
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import random
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class AudioSegment(object):
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"""Monaural audio segment abstraction.
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:param samples: Audio samples [num_samples x num_channels].
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:type samples: ndarray.float32
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:param sample_rate: Audio sample rate.
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:type sample_rate: int
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:raises TypeError: If the sample data type is not float or int.
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"""
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def __init__(self, samples, sample_rate):
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"""Create audio segment from samples.
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Samples are convert float32 internally, with int scaled to [-1, 1].
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"""
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self._samples = self._convert_samples_to_float32(samples)
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self._sample_rate = sample_rate
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if self._samples.ndim >= 2:
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self._samples = np.mean(self._samples, 1)
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def __eq__(self, other):
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"""Return whether two objects are equal."""
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if type(other) is not type(self):
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return False
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if self._sample_rate != other._sample_rate:
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return False
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if self._samples.shape != other._samples.shape:
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return False
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if np.any(self.samples != other._samples):
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return False
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return True
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def __ne__(self, other):
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"""Return whether two objects are unequal."""
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return not self.__eq__(other)
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def __str__(self):
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"""Return human-readable representation of segment."""
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return ("%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, "
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"rms=%.2fdB" % (type(self), self.num_samples, self.sample_rate,
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self.duration, self.rms_db))
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@classmethod
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def from_file(cls, file):
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"""Create audio segment from audio file.
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:param filepath: Filepath or file object to audio file.
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:type filepath: basestring|file
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:return: Audio segment instance.
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:rtype: AudioSegment
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"""
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samples, sample_rate = soundfile.read(file, dtype='float32')
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return cls(samples, sample_rate)
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@classmethod
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def from_bytes(cls, bytes):
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"""Create audio segment from a byte string containing audio samples.
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:param bytes: Byte string containing audio samples.
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:type bytes: str
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:return: Audio segment instance.
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:rtype: AudioSegment
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"""
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samples, sample_rate = soundfile.read(
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io.BytesIO(bytes), dtype='float32')
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return cls(samples, sample_rate)
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@classmethod
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def concatenate(cls, *segments):
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"""Concatenate an arbitrary number of audio segments together.
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:param *segments: Input audio segments to be concatenated.
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:type *segments: tuple of AudioSegment
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:return: Audio segment instance as concatenating results.
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:rtype: AudioSegment
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:raises ValueError: If the number of segments is zero, or if the
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sample_rate of any two segment does not match.
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:raises TypeError: If every item in segments is not AudioSegment
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instance.
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"""
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# Perform basic sanity-checks.
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if len(segments) == 0:
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raise ValueError("No audio segments are given to concatenate.")
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sample_rate = segments[0]._sample_rate
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for seg in segments:
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if sample_rate != seg._sample_rate:
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raise ValueError("Can't concatenate segments with "
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"different sample rates")
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if type(seg) is not cls:
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raise TypeError("Only audio segments of the same type "
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"instance can be concatenated.")
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samples = np.concatenate([seg.samples for seg in segments])
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return cls(samples, sample_rate)
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def to_wav_file(self, filepath, dtype='float32'):
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"""Save audio segment to disk as wav file.
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:param filepath: WAV filepath or file object to save the
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audio segment.
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:type filepath: basestring|file
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:param dtype: Subtype for audio file. Options: 'int16', 'int32',
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'float32', 'float64'. Default is 'float32'.
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:type dtype: str
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:raises TypeError: If dtype is not supported.
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"""
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samples = self._convert_samples_from_float32(self._samples, dtype)
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subtype_map = {
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'int16': 'PCM_16',
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'int32': 'PCM_32',
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'float32': 'FLOAT',
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'float64': 'DOUBLE'
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}
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soundfile.write(
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filepath,
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samples,
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self._sample_rate,
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format='WAV',
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subtype=subtype_map[dtype])
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@classmethod
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def slice_from_file(cls, file, start=None, end=None):
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"""Loads a small section of an audio without having to load
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the entire file into the memory which can be incredibly wasteful.
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:param file: Input audio filepath or file object.
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:type file: basestring|file
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:param start: Start time in seconds. If start is negative, it wraps
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around from the end. If not provided, this function
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reads from the very beginning.
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:type start: float
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:param end: End time in seconds. If end is negative, it wraps around
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from the end. If not provided, the default behvaior is
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to read to the end of the file.
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:type end: float
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:return: AudioSegment instance of the specified slice of the input
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audio file.
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:rtype: AudioSegment
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:raise ValueError: If start or end is incorrectly set, e.g. out of
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bounds in time.
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"""
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sndfile = soundfile.SoundFile(file)
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sample_rate = sndfile.samplerate
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duration = float(len(sndfile)) / sample_rate
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start = 0. if start is None else start
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end = 0. if end is None else end
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if start < 0.0:
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start += duration
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if end < 0.0:
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end += duration
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if start < 0.0:
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raise ValueError("The slice start position (%f s) is out of "
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"bounds." % start)
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if end < 0.0:
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raise ValueError("The slice end position (%f s) is out of bounds." %
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end)
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if start > end:
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raise ValueError("The slice start position (%f s) is later than "
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"the slice end position (%f s)." % (start, end))
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if end > duration:
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raise ValueError("The slice end position (%f s) is out of bounds "
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"(> %f s)" % (end, duration))
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start_frame = int(start * sample_rate)
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end_frame = int(end * sample_rate)
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sndfile.seek(start_frame)
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data = sndfile.read(frames=end_frame - start_frame, dtype='float32')
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return cls(data, sample_rate)
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@classmethod
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def make_silence(cls, duration, sample_rate):
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"""Creates a silent audio segment of the given duration and
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sample rate.
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:param duration: Length of silence in seconds.
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:type duration: float
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:param sample_rate: Sample rate.
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:type sample_rate: float
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:return: Silent AudioSegment instance of the given duration.
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:rtype: AudioSegment
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"""
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samples = np.zeros(int(duration * sample_rate))
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return cls(samples, sample_rate)
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def superimposed(self, other):
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"""Add samples from another segment to those of this segment
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(sample-wise addition, not segment concatenation).
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:param other: Segment containing samples to be added in.
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:type other: AudioSegments
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:raise TypeError: If type of two segments don't match.
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:raise ValueError: If the sample_rate of two segments not equal, or if
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the length of segments don't match.
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"""
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if type(self) != type(other):
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raise TypeError("Cannot add segments of different types: %s "
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"and %s." % (type(self), type(other)))
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if self._sample_rate != other._sample_rate:
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raise ValueError("Sample rates must match to add segments.")
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if len(self._samples) != len(other._samples):
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raise ValueError("Segment lengths must match to add segments.")
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self._samples += other._samples
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def to_bytes(self, dtype='float32'):
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"""Create a byte string containing the audio content.
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:param dtype: Data type for export samples. Options: 'int16','int32',
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'float32', 'float64'. Default is 'float32'.
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:type dtype: str
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:return: Byte string containing audio content.
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:rtype: str
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"""
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samples = self._convert_samples_from_float32(self._samples, dtype)
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return samples.tostring()
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def apply_gain(self, gain):
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"""Apply gain in decibels to samples.
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Note that this is an in-place transformation.
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:param gain: Gain in decibels to apply to samples.
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:type gain: float
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"""
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self._samples *= 10.**(gain / 20.)
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def change_speed(self, speed_rate):
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"""Change the audio speed by linear interpolation.
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Note that this is an in-place transformation.
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:param speed_rate: Rate of speed change:
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speed_rate > 1.0, speed up the audio;
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speed_rate = 1.0, unchanged;
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speed_rate < 1.0, slow down the audio;
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speed_rate <= 0.0, not allowed, raise ValueError.
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:type speed_rate: float
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:raises ValueError: If speed_rate <= 0.0.
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"""
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if speed_rate <= 0:
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raise ValueError("speed_rate should be greater than zero.")
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old_length = self._samples.shape[0]
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new_length = int(old_length / speed_rate)
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old_indices = np.arange(old_length)
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new_indices = np.linspace(start=0, stop=old_length, num=new_length)
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self._samples = np.interp(new_indices, old_indices, self._samples)
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def normalize(self, target_db=-20, max_gain_db=300.0):
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"""Normalize audio to be of the desired RMS value in decibels.
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Note that this is an in-place transformation.
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:param target_db: Target RMS value in decibels. This value should be
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less than 0.0 as 0.0 is full-scale audio.
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:type target_db: float
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:param max_gain_db: Max amount of gain in dB that can be applied for
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normalization. This is to prevent nans when
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attempting to normalize a signal consisting of
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all zeros.
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:type max_gain_db: float
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:raises ValueError: If the required gain to normalize the segment to
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the target_db value exceeds max_gain_db.
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"""
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gain = target_db - self.rms_db
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if gain > max_gain_db:
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raise ValueError(
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"Unable to normalize segment to %f dB because the "
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"the probable gain have exceeds max_gain_db (%f dB)" %
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(target_db, max_gain_db))
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self.apply_gain(min(max_gain_db, target_db - self.rms_db))
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def normalize_online_bayesian(self,
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target_db,
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prior_db,
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prior_samples,
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startup_delay=0.0):
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"""Normalize audio using a production-compatible online/causal
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algorithm. This uses an exponential likelihood and gamma prior to
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make online estimates of the RMS even when there are very few samples.
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Note that this is an in-place transformation.
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:param target_db: Target RMS value in decibels.
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:type target_bd: float
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:param prior_db: Prior RMS estimate in decibels.
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:type prior_db: float
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:param prior_samples: Prior strength in number of samples.
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:type prior_samples: float
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:param startup_delay: Default 0.0s. If provided, this function will
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accrue statistics for the first startup_delay
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seconds before applying online normalization.
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:type startup_delay: float
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"""
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# Estimate total RMS online.
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startup_sample_idx = min(self.num_samples - 1,
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int(self.sample_rate * startup_delay))
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prior_mean_squared = 10.**(prior_db / 10.)
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prior_sum_of_squares = prior_mean_squared * prior_samples
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cumsum_of_squares = np.cumsum(self.samples**2)
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sample_count = np.arange(len(self.num_samples)) + 1
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if startup_sample_idx > 0:
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cumsum_of_squares[:startup_sample_idx] = \
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cumsum_of_squares[startup_sample_idx]
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sample_count[:startup_sample_idx] = \
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sample_count[startup_sample_idx]
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mean_squared_estimate = ((cumsum_of_squares + prior_sum_of_squares) /
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(sample_count + prior_samples))
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rms_estimate_db = 10 * np.log10(mean_squared_estimate)
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# Compute required time-varying gain.
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gain_db = target_db - rms_estimate_db
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self.apply_gain(gain_db)
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def resample(self, target_sample_rate, quality='sinc_medium'):
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"""Resample the audio to a target sample rate.
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Note that this is an in-place transformation.
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:param target_sample_rate: Target sample rate.
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:type target_sample_rate: int
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:param quality: One of {'sinc_fastest', 'sinc_medium', 'sinc_best'}.
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Sets resampling speed/quality tradeoff.
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See http://www.mega-nerd.com/SRC/api_misc.html#Converters
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:type quality: str
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"""
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resample_ratio = target_sample_rate / self._sample_rate
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self._samples = scikits.samplerate.resample(
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self._samples, r=resample_ratio, type=quality)
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self._sample_rate = target_sample_rate
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def pad_silence(self, duration, sides='both'):
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"""Pad this audio sample with a period of silence.
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Note that this is an in-place transformation.
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:param duration: Length of silence in seconds to pad.
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:type duration: float
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:param sides: Position for padding:
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'beginning' - adds silence in the beginning;
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'end' - adds silence in the end;
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'both' - adds silence in both the beginning and the end.
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:type sides: str
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:raises ValueError: If sides is not supported.
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"""
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if duration == 0.0:
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return self
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cls = type(self)
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silence = self.make_silence(duration, self._sample_rate)
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if sides == "beginning":
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padded = cls.concatenate(silence, self)
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elif sides == "end":
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padded = cls.concatenate(self, silence)
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elif sides == "both":
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padded = cls.concatenate(silence, self, silence)
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else:
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raise ValueError("Unknown value for the kwarg %s" % sides)
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self._samples = padded._samples
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def subsegment(self, start_sec=None, end_sec=None):
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"""Return new AudioSegment containing audio between given boundaries.
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:param start_sec: Beginning of subsegment in seconds.
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:type start_sec: float
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:param end_sec: End of subsegment in seconds.
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:type end_sec: float
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"""
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start_sec = 0.0 if start_sec is None else start_sec
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end_sec = self.duration if end_sec is None else end_sec
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if start_sec < 0.0:
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start_sec = self.duration + start_sec
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if end_sec < 0.0:
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end_sec = self.duration + end_sec
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start_sample = int(round(start_sec * self._sample_rate))
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end_sample = int(round(end_sec * self._sample_rate))
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self._samples = self._samples[start_sample:end_sample]
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def random_subsegment(self, subsegment_length, rng=None):
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"""Return a random subsegment of a specified length in seconds.
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:param subsegment_length: Subsegment length in seconds.
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:type subsegment_length: float
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:param rng: Random number generator state.
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:type rng: random.Random
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:raises ValueError: If the length of subsegment greater than
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origineal segemnt.
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"""
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rng = random.Random() if rng is None else rng
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if subsegment_length > self.duration:
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raise ValueError("Length of subsegment must not be greater "
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"than original segment.")
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start_time = rng.uniform(0.0, self.duration - subsegment_length)
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self.subsegment(start_time, start_time + subsegment_length)
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def convolve(self, impulse_segment, allow_resample=False):
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"""Convolve this audio segment with the given impulse_segment.
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Note that this is an in-place transformation.
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:param impulse_segment: Impulse response segments.
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:type impulse_segment: AudioSegment
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:param allow_resample: Indicates whether resampling is allowed when
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the impulse_segment has a different sample
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rate from this signal.
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:type allow_resample: bool
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:raises ValueError: If the sample rate is not match between two
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audio segments when resample is not allowed.
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"""
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if allow_resample and self.sample_rate != impulse_segment.sample_rate:
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impulse_segment = impulse_segment.resample(self.sample_rate)
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if self.sample_rate != impulse_segment.sample_rate:
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raise ValueError("Impulse segment's sample rate (%d Hz) is not"
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"equal to base signal sample rate (%d Hz)." %
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(impulse_segment.sample_rate, self.sample_rate))
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samples = signal.fftconvolve(self.samples, impulse_segment.samples,
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"full")
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self._samples = samples
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def convolve_and_normalize(self, impulse_segment, allow_resample=False):
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"""Convolve and normalize the resulting audio segment so that it
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has the same average power as the input signal.
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:param impulse_segment: Impulse response segments.
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:type impulse_segment: AudioSegment
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:param allow_resample: Indicates whether resampling is allowed when
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the impulse_segment has a different sample
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rate from this signal.
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:type allow_resample: bool
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"""
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target_db = self.rms_db
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self.convolve(impulse_segment, allow_resample=allow_resample)
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self.normalize(target_db)
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def add_noise(self,
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noise,
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snr_dB,
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allow_downsampling=False,
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max_gain_db=300.0,
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rng=None):
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"""Adds the given noise segment at a specific signal-to-noise ratio.
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If the noise segment is longer than this segment, a random subsegment
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of matching length is sampled from it and used instead.
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:param noise: Noise signal to add.
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:type noise: AudioSegment
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:param snr_dB: Signal-to-Noise Ratio, in decibels.
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:type snr_dB: float
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:param allow_downsampling: Whether to allow the noise signal to be
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downsampled to match the base signal sample
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rate.
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:type allow_downsampling: bool
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:param max_gain_db: Maximum amount of gain to apply to noise signal
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before adding it in. This is to prevent attempting
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to apply infinite gain to a zero signal.
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:type max_gain_db: float
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:param rng: Random number generator state.
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:type rng: None|random.Random
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:raises ValueError: If the sample rate does not match between the two
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audio segments when downsampling is not allowed, or
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if the duration of noise segments is shorter than
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original audio segments.
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"""
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rng = random.Random() if rng is None else rng
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if allow_downsampling and noise.sample_rate > self.sample_rate:
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noise = noise.resample(self.sample_rate)
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if noise.sample_rate != self.sample_rate:
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raise ValueError("Noise sample rate (%d Hz) is not equal to base "
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"signal sample rate (%d Hz)." % (noise.sample_rate,
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self.sample_rate))
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if noise.duration < self.duration:
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raise ValueError("Noise signal (%f sec) must be at least as long as"
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" base signal (%f sec)." %
|
|
(noise.duration, self.duration))
|
|
noise_gain_db = min(self.rms_db - noise.rms_db - snr_dB, max_gain_db)
|
|
noise.random_subsegment(self.duration, rng=rng)
|
|
noise.apply_gain(noise_gain_db)
|
|
self.superimposed(noise)
|
|
|
|
@property
|
|
def samples(self):
|
|
"""Return audio samples.
|
|
|
|
:return: Audio samples.
|
|
:rtype: ndarray
|
|
"""
|
|
return self._samples.copy()
|
|
|
|
@property
|
|
def sample_rate(self):
|
|
"""Return audio sample rate.
|
|
|
|
:return: Audio sample rate.
|
|
:rtype: int
|
|
"""
|
|
return self._sample_rate
|
|
|
|
@property
|
|
def num_samples(self):
|
|
"""Return number of samples.
|
|
|
|
:return: Number of samples.
|
|
:rtype: int
|
|
"""
|
|
return self._samples.shape[0]
|
|
|
|
@property
|
|
def duration(self):
|
|
"""Return audio duration.
|
|
|
|
:return: Audio duration in seconds.
|
|
:rtype: float
|
|
"""
|
|
return self._samples.shape[0] / float(self._sample_rate)
|
|
|
|
@property
|
|
def rms_db(self):
|
|
"""Return root mean square energy of the audio in decibels.
|
|
|
|
:return: Root mean square energy in decibels.
|
|
:rtype: float
|
|
"""
|
|
# square root => multiply by 10 instead of 20 for dBs
|
|
mean_square = np.mean(self._samples**2)
|
|
return 10 * np.log10(mean_square)
|
|
|
|
def _convert_samples_to_float32(self, samples):
|
|
"""Convert sample type to float32.
|
|
|
|
Audio sample type is usually integer or float-point.
|
|
Integers will be scaled to [-1, 1] in float32.
|
|
"""
|
|
float32_samples = samples.astype('float32')
|
|
if samples.dtype in np.sctypes['int']:
|
|
bits = np.iinfo(samples.dtype).bits
|
|
float32_samples *= (1. / 2**(bits - 1))
|
|
elif samples.dtype in np.sctypes['float']:
|
|
pass
|
|
else:
|
|
raise TypeError("Unsupported sample type: %s." % samples.dtype)
|
|
return float32_samples
|
|
|
|
def _convert_samples_from_float32(self, samples, dtype):
|
|
"""Convert sample type from float32 to dtype.
|
|
|
|
Audio sample type is usually integer or float-point. For integer
|
|
type, float32 will be rescaled from [-1, 1] to the maximum range
|
|
supported by the integer type.
|
|
|
|
This is for writing a audio file.
|
|
"""
|
|
dtype = np.dtype(dtype)
|
|
output_samples = samples.copy()
|
|
if dtype in np.sctypes['int']:
|
|
bits = np.iinfo(dtype).bits
|
|
output_samples *= (2**(bits - 1) / 1.)
|
|
min_val = np.iinfo(dtype).min
|
|
max_val = np.iinfo(dtype).max
|
|
output_samples[output_samples > max_val] = max_val
|
|
output_samples[output_samples < min_val] = min_val
|
|
elif samples.dtype in np.sctypes['float']:
|
|
min_val = np.finfo(dtype).min
|
|
max_val = np.finfo(dtype).max
|
|
output_samples[output_samples > max_val] = max_val
|
|
output_samples[output_samples < min_val] = min_val
|
|
else:
|
|
raise TypeError("Unsupported sample type: %s." % samples.dtype)
|
|
return output_samples.astype(dtype)
|