148 lines
4.1 KiB
Python
Executable File
148 lines
4.1 KiB
Python
Executable File
#!/usr/bin/python3
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import argparse
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import hashlib
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import logging
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import os
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import pytiger.logging.config
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from tinytag import TinyTag
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from pydub import AudioSegment
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from model import Tracks, session
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# "Constants"
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ROOT = "/home/kae/music"
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# Instantiate logging
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pytiger.logging.config.basic_config(stderr=False, level=logging.INFO)
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log = logging.getLogger(__name__)
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log.info("Starting")
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def main():
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"Main loop"
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# Parse command line
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p = argparse.ArgumentParser()
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p.add_argument('-u', '--update',
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action="store_true", dest="update",
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default=True, help="Update database")
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args = p.parse_args()
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# Run as required
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if args.update:
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log.info("Updating database")
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update_db()
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log.info("Finished")
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def get_audio_segment(path):
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try:
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if path.endswith('.mp3'):
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return AudioSegment.from_mp3(path)
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elif path.endswith('.flac'):
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return AudioSegment.from_file(path, "flac")
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except AttributeError:
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return None
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def leading_silence(audio_segment, silence_threshold=-50.0,
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chunk_size=10):
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"""
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Returns the millisecond/index that the leading silence ends.
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audio_segment - the segment to find silence in
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silence_threshold - the upper bound for how quiet is silent in dFBS
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chunk_size - chunk size for interating over the segment in ms
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https://github.com/jiaaro/pydub/blob/master/pydub/silence.py
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"""
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trim_ms = 0 # ms
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assert chunk_size > 0 # to avoid infinite loop
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while (
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audio_segment[trim_ms:trim_ms + chunk_size].dBFS <
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silence_threshold and trim_ms < len(audio_segment)):
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trim_ms += chunk_size
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# if there is no end it should return the length of the segment
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return min(trim_ms, len(audio_segment))
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def fade_point(audio_segment, fade_threshold=-20.0, chunk_size=10):
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"""
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Returns the millisecond/index of the point where the fade is down to
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fade_threshold and doesn't get louder again.
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audio_segment - the sdlg_search_database_uiegment to find silence in
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fade_threshold - the upper bound for how quiet is silent in dFBS
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chunk_size - chunk size for interating over the segment in ms
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"""
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assert chunk_size > 0 # to avoid infinite loop
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segment_length = audio_segment.duration_seconds * 1000 # ms
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trim_ms = segment_length - chunk_size
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while (
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audio_segment[trim_ms:trim_ms + chunk_size].dBFS < fade_threshold
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and trim_ms > 0):
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trim_ms -= chunk_size
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# if there is no trailing silence, return lenght of track (it's less
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# the chunk_size, but for chunk_size = 10ms, this may be ignored)
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return int(trim_ms)
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def trailing_silence(audio_segment, silence_threshold=-50.0,
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chunk_size=10):
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return fade_point(audio_segment, silence_threshold, chunk_size)
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def update_db():
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"""
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Repopulate database
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TODO: remove missing files
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"""
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count = 0
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for root, dirs, files in os.walk(ROOT):
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for f in files:
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count += 1
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path = os.path.join(root, f)
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ext = os.path.splitext(f)[1]
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if ext in [".flac", ".mp3"]:
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track = Tracks.get_or_create(os.path.relpath(path, ROOT))
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tag = TinyTag.get(path)
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audio = get_audio_segment(path)
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track.title = tag.title
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track.artist = tag.artist
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track.duration = int(tag.duration * 1000)
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track.start_gap = leading_silence(audio)
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track.fade_at = fade_point(audio)
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track.silence_at = trailing_silence(audio)
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track.mtime = os.path.getmtime(path)
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session.commit()
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elif ext not in [".jpg"]:
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print(f"Unrecognised file type: {path}")
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print(f"{count} files processed")
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def md5(path):
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"https://stackoverflow.nl9om/questions/3431825/"
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"generating-an-md5-checksum-of-a-file"
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hash_md5 = hashlib.md5()
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with open(path, "rb") as f:
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for chunk in iter(lambda: f.read(4096), b""):
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hash_md5.update(chunk)
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return hash_md5.hexdigest()
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if __name__ == '__main__':
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main()
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