69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
"""
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Audio utility functions.
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"""
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import io
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import numpy as np
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import pydub
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from scipy.io import wavfile
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def audio_from_waveform(
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samples: np.ndarray, sample_rate: int, normalize: bool = False
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) -> pydub.AudioSegment:
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"""
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Convert a numpy array of samples of a waveform to an audio segment.
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"""
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# Normalize volume to fit in int16
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if normalize:
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samples *= np.iinfo(np.int16).max / np.max(np.abs(samples))
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# Transpose and convert to int16
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samples = samples.transpose(1, 0)
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samples = samples.astype(np.int16)
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# Write to the bytes of a WAV file
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wav_bytes = io.BytesIO()
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wavfile.write(wav_bytes, sample_rate, samples)
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wav_bytes.seek(0)
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# Read into pydub
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return pydub.AudioSegment.from_wav(wav_bytes)
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def apply_filters(segment: pydub.AudioSegment, compression: bool = False) -> pydub.AudioSegment:
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"""
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Apply post-processing filters to the audio segment to compress it and
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keep at a -10 dBFS level.
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"""
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# TODO(hayk): Come up with a principled strategy for these filters and experiment end-to-end.
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# TODO(hayk): Is this going to make audio unbalanced between sequential clips?
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if compression:
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segment = pydub.effects.normalize(
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segment,
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headroom=0.1,
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)
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segment = segment.apply_gain(-10 - segment.dBFS)
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# TODO(hayk): This is quite slow, ~1.7 seconds on a beefy CPU
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segment = pydub.effects.compress_dynamic_range(
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segment,
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threshold=-20.0,
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ratio=4.0,
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attack=5.0,
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release=50.0,
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)
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desired_db = -12
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segment = segment.apply_gain(desired_db - segment.dBFS)
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segment = pydub.effects.normalize(
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segment,
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headroom=0.1,
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)
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return segment
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