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https://github.com/ksyasuda/SubMiner.git
synced 2026-09-12 05:16:19 -07:00
fix(mining): ignore stale captions and improve waveform contrast
- Use active parsed cue text and timings for overlay mining - Normalize waveform levels against the clip's noise floor
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@@ -58,16 +58,44 @@ test('speech waveform seeks cached windows by source timestamps', () => {
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assert.equal(args.includes('-map'), false);
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});
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test('waveform peaks are normalized without flattening quieter sections', () => {
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test('waveform levels rise with loudness and top out at the reference level', () => {
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const peaks = computeWaveformPeaks(pcm([0, 1_000, -2_000, 4_000, -8_000, 16_000]), 3);
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assert.equal(peaks.length, 3);
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assert.ok((peaks[0] ?? 0) > 0);
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assert.ok((peaks[0] ?? 0) < (peaks[1] ?? 0));
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assert.equal(peaks[0], 0);
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assert.ok((peaks[1] ?? 0) > 0);
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assert.ok((peaks[1] ?? 0) < (peaks[2] ?? 0));
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assert.equal(peaks[2], 1);
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});
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test('waveform flattens steady background noise and keeps speech bursts tall', () => {
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// 20 slices of steady noise at a fixed level with an 18 dB louder "speech" burst in the middle.
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const noise = 1_000;
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const samples: number[] = [];
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for (let slice = 0; slice < 20; slice += 1) {
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const level = slice >= 8 && slice < 12 ? noise * 8 : noise;
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for (let sample = 0; sample < 50; sample += 1) {
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samples.push(sample % 2 === 0 ? level : -level);
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}
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}
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const peaks = computeWaveformPeaks(pcm(samples), 20);
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for (const [index, peak] of peaks.entries()) {
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if (index >= 8 && index < 12) assert.equal(peak, 1);
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else assert.equal(peak, 0);
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}
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});
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test('waveform stays flat when the whole range is a single steady level', () => {
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const peaks = computeWaveformPeaks(
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pcm(Array.from({ length: 400 }, (_, i) => (i % 2 ? 900 : -900))),
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40,
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);
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assert.ok(peaks.every((peak) => peak === 0));
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});
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test('speech waveform uses a mono downmix when the source has no center activity', async () => {
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const calls: string[][] = [];
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const peaks = await generateSpeechWaveform(
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@@ -5,7 +5,13 @@ const WAVEFORM_SAMPLE_RATE = 8_000;
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const WAVEFORM_POINT_COUNT = 480;
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const WAVEFORM_TIMEOUT_MS = 15_000;
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const MAX_WAVEFORM_BYTES = 16 * 1024 * 1024;
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const SPEECH_FILTER = 'highpass=f=120,lowpass=f=4000';
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// Keep the band where speech intelligibility lives; bass, drums, and hum sit below it.
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const SPEECH_FILTER = 'highpass=f=250,lowpass=f=3500';
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const NOISE_FLOOR_PERCENTILE = 0.2;
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const REFERENCE_PERCENTILE = 0.95;
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const NOISE_GATE_DB = 3;
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const MIN_DISPLAY_RANGE_DB = 12;
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const SILENCE_DB = -100;
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const CENTER_CHANNEL_FILTER = `pan=mono|c0=FC,${SPEECH_FILTER}`;
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const DOWNMIX_FILTER = `aformat=channel_layouts=mono,${SPEECH_FILTER}`;
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@@ -111,11 +117,22 @@ function runFfmpeg(args: string[]): Promise<Buffer> {
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});
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}
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function percentile(sortedValues: number[], fraction: number): number {
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const index = Math.min(sortedValues.length - 1, Math.floor(sortedValues.length * fraction));
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return sortedValues[index] ?? SILENCE_DB;
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}
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/**
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* Turns mono PCM into 0..1 display heights. Each point is the RMS level of its slice in
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* dB, measured against the clip's own noise floor (a low percentile of the slices), so
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* constant background noise draws flat and sustained speech stands out. Peak sampling
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* would instead follow music transients and lift the floor to nearly speech height.
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*/
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export function computeWaveformPeaks(pcm: Buffer, pointCount = WAVEFORM_POINT_COUNT): number[] {
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const sampleCount = Math.floor(pcm.byteLength / 2);
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if (sampleCount === 0 || pointCount <= 0) return [];
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const resolvedPointCount = Math.min(pointCount, sampleCount);
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const peaks = Array.from({ length: resolvedPointCount }, () => 0);
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const levelsDb = Array.from({ length: resolvedPointCount }, () => SILENCE_DB);
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for (let point = 0; point < resolvedPointCount; point += 1) {
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const sampleStart = Math.floor((point * sampleCount) / resolvedPointCount);
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@@ -123,18 +140,25 @@ export function computeWaveformPeaks(pcm: Buffer, pointCount = WAVEFORM_POINT_CO
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sampleStart + 1,
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Math.floor(((point + 1) * sampleCount) / resolvedPointCount),
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);
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let peak = 0;
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let energy = 0;
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for (let sample = sampleStart; sample < sampleEnd; sample += 1) {
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peak = Math.max(peak, Math.abs(pcm.readInt16LE(sample * 2)) / 32_768);
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const value = pcm.readInt16LE(sample * 2) / 32_768;
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energy += value * value;
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}
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peaks[point] = peak;
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const rms = Math.sqrt(energy / (sampleEnd - sampleStart));
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levelsDb[point] = rms > 0 ? Math.max(SILENCE_DB, 20 * Math.log10(rms)) : SILENCE_DB;
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}
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const sortedPeaks = [...peaks].sort((left, right) => left - right);
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const referenceIndex = Math.min(sortedPeaks.length - 1, Math.floor(sortedPeaks.length * 0.95));
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const referencePeak = Math.max(sortedPeaks[referenceIndex] ?? 0, 0.01);
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return peaks.map(
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(peak) => Math.round(Math.sqrt(Math.min(1, peak / referencePeak)) * 1_000) / 1_000,
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const sortedLevels = [...levelsDb].sort((left, right) => left - right);
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const floorDb = percentile(sortedLevels, NOISE_FLOOR_PERCENTILE) + NOISE_GATE_DB;
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const referenceDb = Math.max(
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percentile(sortedLevels, REFERENCE_PERCENTILE),
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floorDb + MIN_DISPLAY_RANGE_DB,
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);
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return levelsDb.map(
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(levelDb) =>
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Math.round(Math.min(1, Math.max(0, (levelDb - floorDb) / (referenceDb - floorDb))) * 1_000) /
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1_000,
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);
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}
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