51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
from __future__ import annotations
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from librespot.standard import BytesInputStream, DataInputStream, InputStream
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import logging
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import math
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class NormalizationData:
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_LOGGER: logging = logging.getLogger(__name__)
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track_gain_db: float
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track_peak: float
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album_gain_db: float
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album_peak: float
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def __init__(self, track_gain_db: float, track_peak: float,
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album_gain_db: float, album_peak: float):
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self.track_gain_db = track_gain_db
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self.track_peak = track_peak
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self.album_gain_db = album_gain_db
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self.album_peak = album_peak
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self._LOGGER.debug(
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"Loaded normalization data, track_gain: {}, track_peak: {}, album_gain: {}, album_peak: {}"
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.format(track_gain_db, track_peak, album_gain_db, album_peak))
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@staticmethod
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def read(input_stream: InputStream) -> NormalizationData:
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data_input = DataInputStream(input_stream)
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data_input.mark(16)
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skip_bytes = data_input.skip_bytes(144)
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if skip_bytes != 144:
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raise IOError()
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data = bytearray(4 * 4)
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data_input.read_fully(data)
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data_input.reset()
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buffer = BytesInputStream(data, "<")
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return NormalizationData(buffer.read_float(), buffer.read_float(),
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buffer.read_float(), buffer.read_float())
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def get_factor(self, normalisation_pregain) -> float:
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normalisation_factor = float(
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math.pow(10, (self.track_gain_db + normalisation_pregain) / 20))
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if normalisation_factor * self.track_peak > 1:
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self._LOGGER.warning(
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"Reducing normalisation factor to prevent clipping. Please add negative pregain to avoid."
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)
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normalisation_factor = 1 / self.track_peak
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return normalisation_factor
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