transcribe/chunks.py

"""Plan bounded audio uploads and stitch their overlapping word transcripts."""

import argparse
import array
import json
import math
from pathlib import Path
import sys
import wave

from subtitles import convert, normalized_words
from transcript import render_text

CONTEXT = 3.0  # Audio on each side of a seam: six seconds shared by two uploads.
ENCODING_MARGIN = 0.1  # MP3 encoder delay and frame padding at 16 kHz.
PAUSE_SEARCH = 30.0
MIN_SILENCE = 0.3
SILENCE_PEAK = 32768 * 10 ** (-35 / 20)


def pause_before(audio, earliest, latest):
    """Find the last quiet interval in a small PCM window before the limit."""
    rate = audio.getframerate()
    first, last = round(earliest * rate), round(latest * rate)
    audio.setpos(first)
    samples = array.array("h", audio.readframes(last - first))
    if sys.byteorder != "little":
        samples.byteswap()
    frame = round(0.02 * rate)
    quiet_start = None
    boundary = latest
    for offset in range(0, len(samples) + frame, frame):
        block = samples[offset:offset + frame]
        quiet = bool(block) and max(map(abs, block)) <= SILENCE_PEAK
        if quiet and quiet_start is None:
            quiet_start = offset
        elif not quiet and quiet_start is not None:
            quiet_end = min(offset, len(samples))
            if (quiet_end - quiet_start) / rate >= MIN_SILENCE:
                boundary = (first + (quiet_start + quiet_end) / 2) / rate
            quiet_start = None
    return boundary


def plan(path, max_seconds=1800):
    if not math.isfinite(max_seconds) or not 60 <= max_seconds <= 1800:
        raise ValueError("Chunk length must be between 1 and 30 minutes")
    with wave.open(str(path), "rb") as audio:
        if (audio.getnchannels(), audio.getsampwidth(), audio.getframerate()) != (1, 2, 16000):
            raise ValueError("Chunk planning requires mono 16 kHz 16-bit PCM WAV")
        duration = audio.getnframes() / audio.getframerate()
        if not duration:
            raise ValueError("Audio file contains no samples")
        chunks = []
        core_start = 0.0
        while core_start < duration:
            start = max(0.0, core_start - CONTEXT)
            if duration - start <= max_seconds - ENCODING_MARGIN:
                core_end = duration
            else:
                latest = start + max_seconds - ENCODING_MARGIN - CONTEXT
                core_end = pause_before(audio, max(core_start, latest - PAUSE_SEARCH), latest)
            end = min(duration, core_end + CONTEXT)
            chunks.append({"start": start, "end": end, "seam": core_start})
            core_start = core_end
    return chunks


def midpoint(word):
    return (word["start"] + word["end"]) / 2


def token(word):
    return "".join(c for c in word["word"].casefold() if c.isalnum())


def stitch(left, right, seam):
    """Switch transcripts at a shared word, away from either audio cut.

    Match text AND time to avoid deleting intentional repetitions. Prefer an
    anchor with a matching neighbour; accept a single shared word when speech
    is sparse. Timestamp ownership is the fallback if hypotheses disagree.
    """
    radius = CONTEXT - 0.5
    left_near = [(i, word, token(word)) for i, word in enumerate(left)
                 if abs(midpoint(word) - seam) <= radius]
    right_near = [(i, word, token(word)) for i, word in enumerate(right)
                  if abs(midpoint(word) - seam) <= radius]
    matches = {}
    for i, a, a_token in left_near:
        for j, b, b_token in right_near:
            difference = abs(midpoint(a) - midpoint(b))
            if a_token and a_token == b_token and difference <= 1.0:
                matches[i, j] = (difference, abs((midpoint(a) + midpoint(b)) / 2 - seam))
    if matches:
        def score(pair):
            i, j = pair
            run = [matches[pair][0]]
            for direction in (-1, 1):
                step = direction
                while (i + step, j + step) in matches:
                    run.append(matches[i + step, j + step][0])
                    step += direction
            # A whole matching phrase disambiguates "yes yes yes" better than
            # a single word that just happens to be closer to the seam.
            return (-len(run), round(sum(run) / len(run), 3), matches[pair][1])

        i, j = min(matches, key=score)
        return left[:i + 1] + right[j + 1:]

    if left_near or right_near:
        print(
            f"Warning: no shared words near {seam:.2f}s; joining by timestamps. "
            "Review the transcript at this boundary.", file=sys.stderr,
        )
    return ([word for word in left if midpoint(word) < seam] +
            [word for word in right if midpoint(word) >= seam])


def merge(chunks, responses):
    if len(chunks) != len(responses) or not chunks:
        raise ValueError("Expected one transcription response per chunk")
    merged = []
    for index, (chunk, response) in enumerate(zip(chunks, responses)):
        try:
            words = normalized_words(response, "Chunk stitching")
            for word in words:
                if word["end"] > chunk["end"] - chunk["start"] + 1.0:
                    raise ValueError("Word timestamp exceeds the chunk duration")
                word["start"] += chunk["start"]
                word["end"] += chunk["start"]
                if word.get("speaker") is not None:
                    # Speaker IDs belong to individual requests, not the file.
                    word["speaker"] = (index, word["speaker"])
        except ValueError as error:
            raise ValueError(f"Chunk {index + 1}: {error}") from error
        merged = stitch(merged, words, chunk["seam"]) if index else words
    return {"text": " ".join(word["word"] for word in merged), "words": merged}


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    commands = parser.add_subparsers(dest="command", required=True)
    planner = commands.add_parser("plan")
    planner.add_argument("audio")
    planner.add_argument("minutes", type=int)
    merger = commands.add_parser("merge")
    merger.add_argument("manifest", type=Path)
    merger.add_argument("--srt", action="store_true")
    args = parser.parse_args()
    try:
        if args.command == "plan":
            print(json.dumps(plan(args.audio, args.minutes * 60)))
        else:
            chunks = json.loads(args.manifest.read_text(encoding="utf-8"))
            responses = [json.loads(
                (args.manifest.parent / f"response-{i}.json").read_text(encoding="utf-8")
            ) for i in range(len(chunks))]
            response = merge(chunks, responses)
            sys.stdout.write(convert(response) if args.srt else render_text(response))
    except (ValueError, OSError, wave.Error) as error:
        parser.exit(1, f"Audio chunking failed: {error}\n")


if __name__ == "__main__":
    main()