Keysat
37 lines
1.5 KiB
Python
37 lines
1.5 KiB
Python
"""Long-audio chunking (§4.1, §13.4).
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Podcasts run 1–3 h; the diarizer caps at 4 speakers/chunk and Spark 2 is a single GPU, so we cut
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long audio into ~2–3 min pieces sent SEQUENTIALLY (parallel audio → 503 FFT race). Each chunk is
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diarized independently and re-stitched across chunks by voiceprint (see speaker_stitch.py).
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Requires ffmpeg/ffprobe.
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"""
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from __future__ import annotations
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import subprocess
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from pathlib import Path
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CHUNK_SECONDS_DEFAULT = 150 # 2.5 min, within the ~2–3 min guidance
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def duration_seconds(src: str | Path) -> float:
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out = subprocess.run(
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["ffprobe", "-v", "error", "-show_entries", "format=duration",
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"-of", "default=noprint_wrappers=1:nokey=1", str(src)],
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check=True, capture_output=True, text=True,
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)
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return float(out.stdout.strip())
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def chunk_audio(src: str | Path, out_dir: str | Path, *, chunk_seconds: int = CHUNK_SECONDS_DEFAULT) -> list[Path]:
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"""Split into fixed-length WAV chunks using ffmpeg's segment muxer (no re-encode of timing).
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Returns chunk paths in order. Order matters: the queue sends them sequentially."""
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out_dir = Path(out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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pattern = str(out_dir / "chunk_%04d.wav")
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subprocess.run(
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["ffmpeg", "-y", "-i", str(src), "-f", "segment", "-segment_time", str(chunk_seconds),
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"-ar", "16000", "-ac", "1", "-reset_timestamps", "1", pattern],
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check=True, capture_output=True,
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)
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return sorted(out_dir.glob("chunk_*.wav"))
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