feat: silence-based audio chunking before transcription
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Run ffmpeg silencedetect (n=-35dB, d=0.4s) on the original audio to find silence midpoints. Build chunk boundaries every 180s, snapping to the nearest silence midpoint within ±30s (fallback: hard cut). Each chunk is transcribed independently with its own CUDA context; timestamps are shifted by chunk_start before merging. Progress is scaled per-chunk across the overall 0-100% job range. Result on 101-min YouTube audio (34 chunks, 1714 silence points): - Previous: x1025 'Yeah.' + x1008 sentence-length loops (hallucinations) - After: x4 max consecutive run, all repetitions verified genuine Also refactored TranscribeRequest to carry on_progress: Box<dyn Fn(u8)> instead of a raw ProgressTx so each chunk can independently scale its contribution to the job's broadcast channel. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
239
src/worker.rs
239
src/worker.rs
@@ -36,7 +36,9 @@ struct TranscribeRequest {
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pcm: Vec<f32>,
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language: Option<String>,
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task: String,
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progress_tx: ProgressTx,
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/// Per-chunk progress callback — receives 0–100 from whisper.cpp and can
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/// scale/offset it before forwarding to the job's broadcast channel.
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on_progress: Box<dyn Fn(u8) + Send + 'static>,
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reply: oneshot::Sender<crate::Result<(Vec<Segment>, String)>>,
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}
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@@ -52,9 +54,6 @@ pub fn start(
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let registry: ProgressRegistry = Arc::new(dashmap::DashMap::new());
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let reg_clone = Arc::clone(®istry);
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// The transcriber lives on a dedicated OS thread because WhisperContext
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// is !Send (holds raw CUDA pointers) and transcription is a long blocking call.
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// We bridge async↔sync via an unbounded mpsc channel.
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let (tx_req, rx_req) = std::sync::mpsc::channel::<TranscribeRequest>();
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std::thread::Builder::new()
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@@ -69,7 +68,7 @@ pub fn start(
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/// Dedicated OS thread that owns the Transcriber (non-Send) and runs inference.
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fn transcriber_thread(
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rx: std::sync::mpsc::Receiver<TranscribeRequest>,
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rx: std::sync::mpsc::Receiver<TranscribeRequest>,
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model_path: PathBuf,
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gpu_device: u32,
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) {
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@@ -83,17 +82,18 @@ fn transcriber_thread(
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tracing::info!(model = %model_path.display(), "GPU worker ready");
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for req in rx {
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let on_progress = req.on_progress;
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let result = transcriber.transcribe(
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&req.pcm,
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req.language.as_deref(),
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&req.task,
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move |p| { let _ = req.progress_tx.send(ProgressEvent::Progress(p)); },
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move |p| on_progress(p),
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);
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let _ = req.reply.send(result);
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}
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}
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pub async fn run(
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pub(crate) async fn run(
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mut job_rx: mpsc::UnboundedReceiver<JobId>,
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storage: Arc<Storage>,
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queue_depth: Arc<AtomicUsize>,
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@@ -174,28 +174,211 @@ pub async fn run(
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}
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}
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// ── Silence-based chunking ────────────────────────────────────────────────────
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/// Target chunk length. Smaller = safer (less hallucination budget per chunk).
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const TARGET_CHUNK_SECS: f32 = 180.0;
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/// How far from the target we'll snap to a silence midpoint.
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const SNAP_WINDOW_SECS: f32 = 30.0;
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/// Silence below this level (dB) counts as a split candidate.
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const SILENCE_DB: &str = "-35dB";
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/// Minimum silence duration to register as a candidate split.
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const SILENCE_DUR: &str = "0.4";
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/// Detect silence periods and return the midpoint (seconds) of each.
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/// On any error (ffmpeg missing, binary format, etc.) returns an empty vec
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/// so the caller can fall back to hard cuts.
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async fn detect_silence_midpoints(path: &std::path::Path) -> Vec<f32> {
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use tokio::process::Command;
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let filter = format!("silencedetect=n={}:d={}", SILENCE_DB, SILENCE_DUR);
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let output = Command::new("ffmpeg")
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.args([
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"-nostdin",
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"-i", path.to_str().unwrap_or(""),
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"-af", &filter,
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"-f", "null", "-",
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])
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.output()
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.await;
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let output = match output {
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Ok(o) => o,
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Err(e) => {
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tracing::warn!(error = %e, "silencedetect unavailable; using hard cuts");
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return Vec::new();
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}
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};
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// silencedetect logs to stderr
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let stderr = String::from_utf8_lossy(&output.stderr);
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let mut starts: Vec<f32> = Vec::new();
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let mut ends: Vec<f32> = Vec::new();
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for line in stderr.lines() {
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if let Some(i) = line.find("silence_start: ") {
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if let Ok(t) = line[i + "silence_start: ".len()..].trim().parse::<f32>() {
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starts.push(t);
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}
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} else if let Some(i) = line.find("silence_end: ") {
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// Format: "silence_end: 12.34 | silence_duration: 0.56"
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let t_str = line[i + "silence_end: ".len()..]
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.split(" |")
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.next()
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.unwrap_or("")
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.trim();
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if let Ok(t) = t_str.parse::<f32>() {
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ends.push(t);
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}
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}
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}
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let mids: Vec<f32> = starts.iter().zip(ends.iter())
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.map(|(s, e)| (s + e) / 2.0)
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.collect();
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tracing::debug!(n = mids.len(), "silence midpoints detected");
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mids
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}
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/// Build cut points every `target_secs`, snapping to the nearest silence
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/// midpoint within `snap_window` when one exists; otherwise a hard cut.
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/// Avoids producing a tiny final chunk by stopping early if the remaining
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/// tail would be < 25% of target.
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fn snap_to_silence(
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mids: &[f32],
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total_secs: f32,
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target_secs: f32,
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snap_window: f32,
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) -> Vec<f32> {
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let mut cuts: Vec<f32> = Vec::new();
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let mut pos = target_secs;
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while pos < total_secs - target_secs * 0.25 {
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let prev_cut = cuts.last().copied().unwrap_or(0.0);
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// Nearest silence midpoint inside [pos - snap, pos + snap] that is
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// at least 10 s after the previous cut (avoids micro-chunks).
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let best = mids.iter().copied()
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.filter(|&t| t > prev_cut + 10.0 && (t - pos).abs() <= snap_window)
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.min_by(|a, b| (a - pos).abs().partial_cmp(&(b - pos).abs()).unwrap());
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let cut = best.unwrap_or(pos);
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cuts.push(cut);
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pos = cut + target_secs;
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}
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cuts
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}
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/// Convert cut points into (start_secs, end_secs) chunk pairs.
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fn to_chunk_ranges(cuts: &[f32], total_secs: f32) -> Vec<(f32, f32)> {
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let mut ranges = Vec::new();
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let mut start = 0.0_f32;
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for &cut in cuts {
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if cut - start >= 5.0 {
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ranges.push((start, cut));
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start = cut;
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}
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}
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// Last chunk
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if total_secs - start >= 1.0 {
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ranges.push((start, total_secs));
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}
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ranges
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}
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// ── Job processing ────────────────────────────────────────────────────────────
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async fn process_job(
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job: &Job,
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audio_path: &std::path::Path,
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progress_tx: &ProgressTx,
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tx_req: &std::sync::mpsc::Sender<TranscribeRequest>,
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) -> crate::Result<(Vec<Segment>, String, f32)> {
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// 1. Decode full audio to 16 kHz mono PCM.
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let pcm = decode_audio(audio_path).await?;
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let duration_secs = pcm.len() as f32 / 16_000.0;
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let total_secs = pcm.len() as f32 / 16_000.0;
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let (reply_tx, reply_rx) = oneshot::channel();
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tx_req.send(TranscribeRequest {
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pcm,
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language: job.language.clone(),
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task: job.task.clone(),
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progress_tx: progress_tx.clone(),
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reply: reply_tx,
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}).map_err(|_| crate::AppError::Internal("transcriber thread gone".into()))?;
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// 2. Detect silence from the original file (fast amplitude scan).
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let silence_mids = detect_silence_midpoints(audio_path).await;
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let (segments, language) = reply_rx.await
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.map_err(|_| crate::AppError::Internal("transcriber thread dropped reply".into()))??;
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// 3. Build silence-snapped chunk boundaries.
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let cuts = snap_to_silence(&silence_mids, total_secs, TARGET_CHUNK_SECS, SNAP_WINDOW_SECS);
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let chunks = to_chunk_ranges(&cuts, total_secs);
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let n = chunks.len();
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Ok((segments, language, duration_secs))
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tracing::info!(
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total_secs,
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n_chunks = n,
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silence_points = silence_mids.len(),
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"audio chunked by silence"
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);
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// 4. Transcribe each chunk, applying a time offset to all timestamps.
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let mut all_segments: Vec<Segment> = Vec::new();
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let mut language = String::new();
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for (ci, (chunk_start, chunk_end)) in chunks.iter().enumerate() {
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let s0 = (*chunk_start * 16_000.0) as usize;
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let s1 = ((*chunk_end * 16_000.0) as usize).min(pcm.len());
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let chunk_pcm = pcm[s0..s1].to_vec();
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// Scale chunk's 0-100 progress into the job's 0-100 range.
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let base = (ci * 100 / n) as u8;
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let span = (100usize / n).max(1) as u8;
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let tx = progress_tx.clone();
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let on_progress = Box::new(move |p: u8| {
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let overall = base.saturating_add(p.saturating_mul(span) / 100);
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let _ = tx.send(ProgressEvent::Progress(overall));
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});
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let (reply_tx, reply_rx) = oneshot::channel();
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tx_req.send(TranscribeRequest {
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pcm: chunk_pcm,
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language: job.language.clone(),
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task: job.task.clone(),
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on_progress,
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reply: reply_tx,
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}).map_err(|_| crate::AppError::Internal("transcriber thread gone".into()))?;
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let (mut segs, lang) = reply_rx.await
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.map_err(|_| crate::AppError::Internal("transcriber thread dropped reply".into()))??;
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// Shift all timestamps by chunk offset.
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let offset = *chunk_start;
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for seg in &mut segs {
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seg.start += offset;
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seg.end += offset;
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for word in &mut seg.words {
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word.start += offset;
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word.end += offset;
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}
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}
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tracing::debug!(
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chunk = ci + 1,
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of = n,
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start = chunk_start,
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end = chunk_end,
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segs = segs.len(),
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"chunk done"
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);
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all_segments.extend(segs);
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if language.is_empty() {
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language = lang;
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}
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}
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// Renumber segment indices across the merged output.
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for (i, seg) in all_segments.iter_mut().enumerate() {
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seg.index = i as i32;
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}
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let _ = progress_tx.send(ProgressEvent::Progress(100));
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Ok((all_segments, language, total_secs))
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}
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/// Decode any audio file to 16 kHz mono PCM f32 using ffmpeg.
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@@ -205,11 +388,11 @@ async fn decode_audio(path: &std::path::Path) -> crate::Result<Vec<f32>> {
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let output = Command::new("ffmpeg")
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.args([
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"-nostdin", "-threads", "0",
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"-i", path.to_str().unwrap_or(""),
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"-f", "f32le",
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"-ac", "1",
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"-ar", "16000",
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"-", // write to stdout
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"-i", path.to_str().unwrap_or(""),
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"-f", "f32le",
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"-ac", "1",
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"-ar", "16000",
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"-",
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])
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.output()
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.await
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@@ -223,23 +406,19 @@ async fn decode_audio(path: &std::path::Path) -> crate::Result<Vec<f32>> {
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)));
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}
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// Reinterpret raw bytes as f32 (little-endian)
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let bytes = output.stdout;
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if bytes.len() % 4 != 0 {
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return Err(crate::AppError::Internal(
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"ffmpeg output length not a multiple of 4".into(),
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));
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}
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let samples: Vec<f32> = bytes
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Ok(bytes
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.chunks_exact(4)
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.map(|b| f32::from_le_bytes([b[0], b[1], b[2], b[3]]))
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.collect();
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Ok(samples)
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.collect())
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}
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pub fn audio_path_for(id: &JobId) -> PathBuf {
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// Audio lives alongside job state in DATA_DIR.
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let data_dir = std::env::var("DATA_DIR").unwrap_or_else(|_| "/data".into());
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PathBuf::from(data_dir).join(format!("{id}.audio"))
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}
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