239	        if job["status"] in ("diarized",):
240	            failed_at = "diarized"
241	            if clip_wav is None:
242	                clip_wav = work / "clip.wav"
243	                r2.download_file(f"{name}/clip.wav", str(clip_wav))
244	            if diarization is None:
245	                diar_local = work / "diarize.json"
246	                r2.download_file(f"{name}/diarize.json", str(diar_local))
247	                diarization = json.loads(diar_local.read_text())["output"]["diarization"]
248	                target = job.get("target_speaker") or pyannote.main_speaker(diarization)
249	            with stage("segment", timings):
250	                seg = step_segment(job, work, clip_wav, diarization, target)
251	            next_status = "awaiting_solospeech" if seg["overlap"] > 0 else "done"
252	            db.update(
253	                job_id,
254	                status=next_status,
255	                num_clean_segments=seg["clean"],
256	                num_overlap_segments=seg["overlap"],
257	                num_overlap_cleaned=0,
258	                completed_at=("datetime('now')" if next_status == "done" else None),
259	            )
260	            job["status"] = next_status
261	            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} → {next_status}")
262
263	        # Stage 4: solospeech

        if job["status"] in ("diarized",):
            failed_at = "diarized"
            if clip_wav is None:
                clip_wav = work / "clip.wav"
                r2.download_file(f"{name}/clip.wav", str(clip_wav))
            if diarization is None:
                diar_local = work / "diarize.json"
                r2.download_file(f"{name}/diarize.json", str(diar_local))
                diarization = json.loads(diar_local.read_text())["output"]["diarization"]
                target = job.get("target_speaker") or pyannote.main_speaker(diarization)
            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            next_status = "awaiting_solospeech" if seg["overlap"] > 0 else "done"
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status
            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} → {next_status}")

        # Stage 4: solospeech


Edit

            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            next_status = "awaiting_solospeech" if seg["overlap"] > 0 else "done"
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status
            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} → {next_status}")

            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            ov_dur = seg.get("overlap_total_dur", 0)
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status
            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} overlap_dur={ov_dur:.1f}s → {next_status}")


The file /home/ubuntu/karthyani_pipeline/worker/worker.py has been updated successfully.

            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            next_status = "awaiting_solospeech" if seg["overlap"] > 0 else "done"
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status
            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} → {next_status}")

            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            ov_dur = seg.get("overlap_total_dur", 0)
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status
            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} overlap_dur={ov_dur:.1f}s → {next_status}")

target = job.get("target_speaker") or pyannote.main_speaker(diarization)

-            next_status = "awaiting_solospeech" if seg["overlap"] > 0 else "done"

+            ov_dur = seg.get("overlap_total_dur", 0)

+            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min

+            if seg["overlap"] > 0 and ov_dur >= 300:

+                next_status = "awaiting_solospeech"

+                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")

-            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} → {next_status}")

+            log(f"  segments: clean={seg['clean']} overlap={seg['overlap']} overlap_dur={ov_dur:.1f}s → {next_status}")

if job["status"] in ("awaiting_solospeech",):


Now also update `step_segment` to pass through the `overlap_total_dur`:


99	def step_segment(job, work: Path, clip_wav: Path, diarization: list, target: str) -> dict:
100	    """Build cleaned/ + overlap_notcleaned/ + main_speaker_ref. Skip if already in R2."""
101	    name = job["name"]
102	    prefix = name
103	    # If cleaned/ already has files, assume done
104	    cleaned_existing = r2.list_keys(f"{prefix}/cleaned/")
105	    overlap_existing = r2.list_keys(f"{prefix}/overlap_notcleaned/")
106	    if cleaned_existing or overlap_existing:
107	        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
108	        return {
109	            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
110	            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
111	        }
112
113	    seg = audio.build_segments(clip_wav, diarization, target, work)
114	    for f in sorted(Path(seg["clean_dir"]).glob("*.wav")):
115	        r2.upload_file(str(f), f"{prefix}/cleaned/{f.name}")
116	    for f in sorted(Path(seg["overlap_dir"]).glob("*.wav")):
117	        r2.upload_file(str(f), f"{prefix}/overlap_notcleaned/{f.name}")
118
119	    # main_speaker_ref.wav (longest clean target turn)
120	    if not r2.exists(f"{prefix}/main_speaker_ref.wav"):
121	        longest = max(
122	            ((t["end"] - t["start"], t["start"], t["end"])
123	             for t in diarization if t["speaker"] == target),
124	            default=(0, 0, 0),
125	        )
126	        if longest[0] >= 4:
127	            import soundfile as sf
128	            a, sr = sf.read(clip_wav)
129	            i0 = int(round(longest[1] * sr))
130	            i1 = int(round(longest[2] * sr))
131	            ref_local = work / "main_speaker_ref.wav"
132	            sf.write(str(ref_local), a[i0:i1], sr, subtype="PCM_16")
133	            r2.upload_file(str(ref_local), f"{prefix}/main_speaker_ref.wav")
134
135	    return {"clean": seg["clean"], "overlap": seg["overlap"]}

def step_segment(job, work: Path, clip_wav: Path, diarization: list, target: str) -> dict:
    """Build cleaned/ + overlap_notcleaned/ + main_speaker_ref. Skip if already in R2."""
    name = job["name"]
    prefix = name
    # If cleaned/ already has files, assume done
    cleaned_existing = r2.list_keys(f"{prefix}/cleaned/")
    overlap_existing = r2.list_keys(f"{prefix}/overlap_notcleaned/")
    if cleaned_existing or overlap_existing:
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
        }

    seg = audio.build_segments(clip_wav, diarization, target, work)
    for f in sorted(Path(seg["clean_dir"]).glob("*.wav")):
        r2.upload_file(str(f), f"{prefix}/cleaned/{f.name}")
    for f in sorted(Path(seg["overlap_dir"]).glob("*.wav")):
        r2.upload_file(str(f), f"{prefix}/overlap_notcleaned/{f.name}")

    # main_speaker_ref.wav (longest clean target turn)
    if not r2.exists(f"{prefix}/main_speaker_ref.wav"):
        longest = max(
            ((t["end"] - t["start"], t["start"], t["end"])
             for t in diarization if t["speaker"] == target),
            default=(0, 0, 0),
        )
        if longest[0] >= 4:
            import soundfile as sf
            a, sr = sf.read(clip_wav)
            i0 = int(round(longest[1] * sr))
            i1 = int(round(longest[2] * sr))
            ref_local = work / "main_speaker_ref.wav"
            sf.write(str(ref_local), a[i0:i1], sr, subtype="PCM_16")
            r2.upload_file(str(ref_local), f"{prefix}/main_speaker_ref.wav")

    return {"clean": seg["clean"], "overlap": seg["overlap"]}


return {"clean": seg["clean"], "overlap": seg["overlap"]}

    return {"clean": seg["clean"], "overlap": seg["overlap"],
            "overlap_total_dur": seg.get("overlap_total_dur", 0)}


    return {"clean": seg["clean"], "overlap": seg["overlap"],
            "overlap_total_dur": seg.get("overlap_total_dur", 0)}

sf.write(str(ref_local), a[i0:i1], sr, subtype="PCM_16")

-    return {"clean": seg["clean"], "overlap": seg["overlap"]}

+    return {"clean": seg["clean"], "overlap": seg["overlap"],

+            "overlap_total_dur": seg.get("overlap_total_dur", 0)}


Now update the dashboard to show when SoloSpeech was skipped. Update the job card in `index.tsx`:


167	      body: JSON.stringify({ url, name }),
168	    });
169	    if (r.ok) { setUrl(""); setName(""); setShowForm(false); refreshJobs(); }
170	    else {
171	      const j = await r.json().catch(() => ({}));
172	      setSubmitErr(j.error || `error ${r.status}`);
173	    }
174	  }
175
176	  async function retry(id: string) {
177	    await fetch(`/api/job/${id}`, {
178	      method: "PATCH",
179	      headers: { "Content-Type": "application/json" },
180	      body: JSON.stringify({ action: "retry" }),
181	    });
182	    refreshJobs();
183	  }
184
185	  async function del(id: string) {
186	    if (!confirm("Delete this job?")) return;
187	    await fetch(`/api/job/${id}`, { method: "DELETE" });
188	    if (openJob?.id === id) setOpenJob(null);
189	    refreshJobs();
190	  }
191
192	  async function openDetails(id: string) {
193	    const r = await fetch(`/api/job/${id}`);
194	    if (r.ok) setOpenJob(await r.json());
195	  }
196

      body: JSON.stringify({ url, name }),
    });
    if (r.ok) { setUrl(""); setName(""); setShowForm(false); refreshJobs(); }
    else {
      const j = await r.json().catch(() => ({}));
      setSubmitErr(j.error || `error ${r.status}`);
    }
  }

  async function retry(id: string) {
    await fetch(`/api/job/${id}`, {
      method: "PATCH",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({ action: "retry" }),
    });
    refreshJobs();
  }

  async function del(id: string) {
    if (!confirm("Delete this job?")) return;
    await fetch(`/api/job/${id}`, { method: "DELETE" });
    if (openJob?.id === id) setOpenJob(null);
    refreshJobs();
  }

  async function openDetails(id: string) {
    const r = await fetch(`/api/job/${id}`);
    if (r.ok) setOpenJob(await r.json());
  }


243
244	      {/* Stats bar */}
245

246

247
Done

248
{doneCount}

249

250

251
Processing

252
{processingCount}

253

254

255
Failed

256
 0 ? "#dc2626" : "#d1d5db" }}>{failedCount}

257

258

259
Clean Segments

260
{stats?.totalClean ?? "..."}

261

262

263
Overlap Cleaned

264
{stats?.totalCleaned ?? "..."}

265

266
 setShowGpu(!showGpu)}
269	        >
270
GPU Pods{stats?.gpuPods ? "" : " (no key)"}

271

272	            {stats?.gpuPods?.running ?? "—"}

{/* Stats bar */}

Done

{doneCount}

Processing

{processingCount}

Failed

 0 ? "#dc2626" : "#d1d5db" }}>{failedCount}

Clean Segments

{stats?.totalClean ?? "..."}

Overlap Cleaned

{stats?.totalCleaned ?? "..."}

 setShowGpu(!showGpu)}
        >

GPU Pods{stats?.gpuPods ? "" : " (no key)"}

            {stats?.gpuPods?.running ?? "—"}


Now let me find where the segment counts are displayed in the job card to add the "solospeech skipped" note:


320
321	      {/* New job form (collapsible) */}
322	      {showForm && (
323

324

325	            Submit new job
326

327	          <form onSubmit={submit} style={{ display: "flex", gap: 8 }}>
328	            <input type="url" required placeholder="https://www.youtube.com/watch?v=..."
329	              value={url} onChange={e => setUrl(e.target.value)}
330	              style={{ ...inputStyle, flex: 2 }} />
331	            <input type="text" required placeholder="job name"
332	              value={name} onChange={e => setName(e.target.value)}
333	              style={{ ...inputStyle, flex: 1 }} />
334	            <button style={btnPrimary}>submit</button>
335	          </form>
336	          {submitErr &&
{submitErr}
}
337

338	      )}
339
340	      {/* Job list */}
341

342

343	          Jobs ({jobs.length})
344

345

346
347	      {jobs.length === 0 && (
348
No jobs yet. Click "+ new job" to get started.

349	      )}
350
351	      {jobs.map(j => {
352	        const color = STATUS_COLORS[j.status] || "#6b7280";
353	        const progress = stageProgress(j.status);
354	        const isActive = j.status !== "done" && j.status !== "failed";
355	        const speakers = j.speakers_json ? JSON.parse(j.speakers_json) : null;
356	        const totalSpeakerDur = speakers
357	          ? Object.values(speakers as Record<string, number>).reduce((a: number, b: number) => a + b, 0)
358	          : 0;
359
360	        return (
361

362	            {/* Row 1: name + badge + time + buttons */}
363

364	              {j.name}
365	              {j.status.replace(/_/g, " ")}

{/* New job form (collapsible) */}
      {showForm && (

            Submit new job

          <form onSubmit={submit} style={{ display: "flex", gap: 8 }}>
            <input type="url" required placeholder="https://www.youtube.com/watch?v=..."
              value={url} onChange={e => setUrl(e.target.value)}
              style={{ ...inputStyle, flex: 2 }} />
            <input type="text" required placeholder="job name"
              value={name} onChange={e => setName(e.target.value)}
              style={{ ...inputStyle, flex: 1 }} />
            <button style={btnPrimary}>submit</button>
          </form>
          {submitErr &&
{submitErr}
}

      )}

      {/* Job list */}

          Jobs ({jobs.length})

      {jobs.length === 0 && (

No jobs yet. Click "+ new job" to get started.

      )}

      {jobs.map(j => {
        const color = STATUS_COLORS[j.status] || "#6b7280";
        const progress = stageProgress(j.status);
        const isActive = j.status !== "done" && j.status !== "failed";
        const speakers = j.speakers_json ? JSON.parse(j.speakers_json) : null;
        const totalSpeakerDur = speakers
          ? Object.values(speakers as Record<string, number>).reduce((a: number, b: number) => a + b, 0)
          : 0;

        return (

            {/* Row 1: name + badge + time + buttons */}

              {j.name}
              {j.status.replace(/_/g, " ")}


370	              {isActive && j.worker_id && (
371
372	                  worker active
373
374	              )}
375	              {timeAgo(j.updated_at)}
376

377	              {j.r2_prefix && (
378	                <button style={btnSmall} onClick={() => openDetails(j.id)}>open</button>
379	              )}
380	              {j.status === "failed" && (
381	                <button style={{ ...btnSmall, color: "#dc2626", borderColor: "#fecaca" }}
382	                  onClick={() => retry(j.id)}>retry</button>
383	              )}
384	              <button style={{ ...btnSmall, color: "#6b7280" }} onClick={() => del(j.id)}>delete</button>
385

386
387	            {/* Row 2: URL */}
388

389	              {j.url}
390

391
392	            {/* Row 3: speaker info */}
393	            {speakers && (
394

395	                target: {j.target_speaker}
396	                {" "} ({fmtDur(speakers[j.target_speaker!] || 0)} of {fmtDur(totalSpeakerDur)} total)
397	                {" · "}{Object.keys(speakers).length} speakers
398

399	            )}
400
401	            {/* Row 4: segment counts */}
402	            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (
403

404	                clean: {j.num_clean_segments ?? "?"}
405	                {" · "}overlap: {j.num_overlap_segments ?? "?"}
406	                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}
407

408	            )}
409
410	            {/* Progress bar for active jobs */}
411	            {isActive && (
412

413

414

415	                    {STAGE_LABELS[j.status] || j.status}
416	                    {j.gpu_instance_id ? ` (GPU #${j.gpu_instance_id})` : ""}
417

418

419

{isActive && j.worker_id && (

                  worker active

              )}
              {timeAgo(j.updated_at)}

              {j.r2_prefix && (
                <button style={btnSmall} onClick={() => openDetails(j.id)}>open</button>
              )}
              {j.status === "failed" && (
                <button style={{ ...btnSmall, color: "#dc2626", borderColor: "#fecaca" }}
                  onClick={() => retry(j.id)}>retry</button>
              )}
              <button style={{ ...btnSmall, color: "#6b7280" }} onClick={() => del(j.id)}>delete</button>

            {/* Row 2: URL */}

              {j.url}

            {/* Row 3: speaker info */}
            {speakers && (

                target: {j.target_speaker}
                {" "} ({fmtDur(speakers[j.target_speaker!] || 0)} of {fmtDur(totalSpeakerDur)} total)
                {" · "}{Object.keys(speakers).length} speakers

            )}

            {/* Row 4: segment counts */}
            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (

                clean: {j.num_clean_segments ?? "?"}
                {" · "}overlap: {j.num_overlap_segments ?? "?"}
                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}

            )}

            {/* Progress bar for active jobs */}
            {isActive && (

                    {STAGE_LABELS[j.status] || j.status}
                    {j.gpu_instance_id ? ` (GPU #${j.gpu_instance_id})` : ""}


I can see the segment counts section. I'll add a "SoloSpeech skipped" note after it when a done job has overlaps but 0 cleaned:


{/* Row 4: segment counts */}
            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (

                clean: {j.num_clean_segments ?? "?"}
                {" · "}overlap: {j.num_overlap_segments ?? "?"}
                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}

            )}

{/* Row 4: segment counts */}
            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (

                clean: {j.num_clean_segments ?? "?"}
                {" · "}overlap: {j.num_overlap_segments ?? "?"}
                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}
                {j.status === "done" && (j.num_overlap_segments || 0) > 0 && !j.num_overlap_cleaned && (

                    SoloSpeech skipped (overlap < 5 min)

                )}

            )}


The file /home/ubuntu/karthyani_pipeline/dashboard/pages/index.tsx has been updated successfully.

{/* Row 4: segment counts */}
            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (

                clean: {j.num_clean_segments ?? "?"}
                {" · "}overlap: {j.num_overlap_segments ?? "?"}
                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}

            )}

{/* Row 4: segment counts */}
            {(j.num_clean_segments !== null || j.num_overlap_segments !== null) && (

                clean: {j.num_clean_segments ?? "?"}
                {" · "}overlap: {j.num_overlap_segments ?? "?"}
                {j.num_overlap_cleaned ? <> · cleaned: {j.num_overlap_cleaned}</> : null}
                {j.status === "done" && (j.num_overlap_segments || 0) > 0 && !j.num_overlap_cleaned && (

                    SoloSpeech skipped (overlap < 5 min)

                )}

            )}

clean: <strong>{j.num_clean_segments ?? "?"}</strong>

{" · "}overlap: <strong>{j.num_overlap_segments ?? "?"}</strong>

{j.num_overlap_cleaned ? <> · cleaned: <strong>{j.num_overlap_cleaned}</strong></> : null}

+                {j.status === "done" && (j.num_overlap_segments || 0) > 0 && !j.num_overlap_cleaned && (

+                  <span style={{ color: "#d97706", marginLeft: 6 }}>

+                    SoloSpeech skipped (overlap < 5 min)


Now restart the workers with the updated code and redeploy the dashboard:


Bash

Kill old worker processes


mkdir -p /tmp/worker_logs && for i in 1 2 3 4 5; do nohup .venv/bin/python -u worker.py > /tmp/worker_logs/worker_$i.log 2>&1 & echo "Started worker $i PID=$!"; done

Start 5 new workers with updated code


Started worker 1 PID=1675650
Started worker 2 PID=1675651
Started worker 3 PID=1675652
Started worker 4 PID=1675653
Started worker 5 PID=1675654


cd /home/ubuntu/karthyani_pipeline/dashboard && vercel deploy --prod 2>&1 | tail -10

Deploy updated dashboard to Vercel


and what is this error Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 267, in process_job
    n_cleaned = step_solospeech(job)
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 159, in step_solospeech
    inst = gpu_runner.wait_for_ready(instance_id)
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 113, in wait_for_ready
    raise TimeoutError(f"vast instance {instance_id} not ready after {timeout}s. last={last}")
TimeoutError: vast instance 34497183 not ready after 900s. last={'actual_status': 'created', 'bundle_id': None, 'bw_nvlink': 300.0, 'client_run_time': 1.1, 'compute_cap': 800, 'cpu_arch': 'amd64', 'cpu_cores': 128, 'cpu_cores_effective': 32.0, 'cpu_name': 'AMD EPYC 7513 32-Core Processor', 'cpu_ram': 515547, 'cpu_util': 0.0, 'credit_balance': None, 'credit_discount': None, 'credit_discount_max': 0.0, 'cuda_max_good': 13.1, 'cur_state': 'stopped', 'direct_port_count': 250, 'direct_port_end': -1, 'direct_port_start': -1, 'disk_bw': 4781.0, 'disk_name': 'SAMSUNG MZWLJ7T6HALA-00007', 'disk_space': 60.0, 'disk_usage': -1, 'disk_util': -1, 'dlperf': 131.7656951695614, 'dlperf_per_dphtotal': 121.62987246421054, 'dph_base': 1.0666666666666667, 'dph_total': 1.0833333333333333, 'driver_version': '590.48.01', 'duration': 15003269.578006268, 'end_date': 1790787216.0, 'external': False, 'extra_env': [], 'flops_per_dphtotal': 14.393974153846154, 'geolocation': 'Czechia, CZ', 'gpu_arch': 'nvidia', 'gpu_display_active': False, 'gpu_frac': 0.25, 'gpu_lanes': 16, 'gpu_mem_bw': 1661.5, 'gpu_name': 'A100 SXM4', 'gpu_ram': 81920, 'gpu_temp': None, 'gpu_totalram': 81920, 'gpu_util': None, 'has_avx': 1, 'host_id': 125728, 'host_run_time': 2592000.0, 'hosting_type': 1, 'id': 34497183, 'image_args': [], 'image_runtype': 'ssh', 'image_uuid': 'pytorch/pytorch:2.4.1-cuda12.4-cudnn9-devel', 'inet_down': 7530.9, 'inet_down_billed': None, 'inet_down_cost': 0.0026041666666666665, 'inet_up': 2867.8, 'inet_up_billed': None, 'in and also for kotlin and samay i said u to doenalod audios using yltdp and oush to r2 and udpate db accorudngly


    {
      "command": "vercel inspect karthyani-dashboard-qmk5fejko-charans-projects-9fb8fade.vercel.app",
      "when": "Inspect deployment"
    },
    {
      "command": "vercel deploy --prod",
      "when": "Promote to production"
    }
  ]
}


Prompt:
and what is this error Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 267, in process_job
    n_cleaned = step_solospeech(job)
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 159, in step_solospeech
    inst = gpu_runner.wait_for_ready(instance_id)
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 113, in wait_for_ready
    raise TimeoutError(f"vast instance {instance_id} not ready after {timeout}s. last={last}")
TimeoutError: vast instance 34497183 not ready after 900s. last={'actual_status': 'created', 'bundle_id': None, 'bw_nvlink': 300.0, 'client_run_time': 1.1, 'compute_cap': 800, 'cpu_arch': 'amd64', 'cpu_cores': 128, 'cpu_cores_effective': 32.0, 'cpu_name': 'AMD EPYC 7513 32-Core Processor', 'cpu_ram': 515547, 'cpu_util': 0.0, 'credit_balance': None, 'credit_discount': None, 'credit_discount_max': 0.0, 'cuda_max_good': 13.1, 'cur_state': 'stopped', 'direct_port_count': 250, 'direct_port_end': -1, 'direct_port_start': -1, 'disk_bw': 4781.0, 'disk_name': 'SAMSUNG MZWLJ7T6HALA-00007', 'disk_space': 60.0, 'disk_usage': -1, 'disk_util': -1, 'dlperf': 131.7656951695614, 'dlperf_per_dphtotal': 121.62987246421054, 'dph_base': 1.0666666666666667, 'dph_total': 1.0833333333333333, 'driver_version': '590.48.01', 'duration': 15003269.578006268, 'end_date': 1790787216.0, 'external': False, 'extra_env': [], 'flops_per_dphtotal': 14.393974153846154, 'geolocation': 'Czechia, CZ', 'gpu_arch': 'nvidia', 'gpu_display_active': False, 'gpu_frac': 0.25, 'gpu_lanes': 16, 'gpu_mem_bw': 1661.5, 'gpu_name': 'A100 SXM4', 'gpu_ram': 81920, 'gpu_temp': None, 'gpu_totalram': 81920, 'gpu_util': None, 'has_avx': 1, 'host_id': 125728, 'host_run_time': 2592000.0, 'hosting_type': 1, 'id': 34497183, 'image_args': [], 'image_runtype': 'ssh', 'image_uuid': 'pytorch/pytorch:2.4.1-cuda12.4-cudnn9-devel', 'inet_down': 7530.9, 'inet_down_billed': None, 'inet_down_cost': 0.0026041666666666665, 'inet_up': 2867.8, 'inet_up_billed': None, 'in and also for kotlin and samay i said u to doenalod audios using yltdp and oush to r2 and udpate db accorudngly


The user has two follow-up requests:

1. There's a TimeoutError from vast.ai - an instance was created but never became "running" (stuck at "created" with cur_state "stopped"). This is a vast.ai issue.

2. For kohli and samaray jobs - the user wants me to download the audio using yt-dlp locally (like the seed_jobs.py script) and push to R2, then update DB status to audio_uploaded. Because the worker's vidssave.com API might not work for downloading from Railway/cloud IPs.

Let me address both:

1. The vast.ai timeout - the instance was in Czechia, got stuck. The worker should have cleaned up. Let me check if the instance was destroyed and if there are failed jobs.

2. For the 4 new jobs (kohli_1, kohli_2, kohli_3, samaray_1), I need to download using yt-dlp locally, convert to wav, upload to R2, and update DB status to audio_uploaded. This is similar to what seed_jobs.py does.

Let me start by checking the vast.ai error and then handle the local downloads for the 4 new jobs.


The vast.ai timeout happens when an instance gets stuck at "created" and never starts — usually a flaky host. The worker's `finally` block destroys it and marks the job failed, so retry will pick a different machine.

For kohli and samaray — I'll download them locally with yt-dlp, push to R2, and update DB to `audio_uploaded` so workers skip the download step.


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN R2_ENDPOINT R2_BUCKET R2_ACCESS_KEY R2_SECRET_KEY && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT name, status, error FROM jobs WHERE status='failed'\")
for r in rows:
    print(f\"{r['name']}: {r['status']}\")
    if r['error']: print(f\"  {r['error'][:200]}\")
"

Check failed jobs


jagan_3: failed
  Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 267, in process_job
    n_cleaned = step_solospeech(job)
  File "/home/ubuntu/karthyani_pipeline/work
jagan_5: failed
  Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 267, in process_job
    n_cleaned = step_solospeech(job)
  File "/home/ubuntu/karthyani_pipeline/work


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
# Retry the failed vast.ai timeout jobs
for name in ['jagan_3', 'jagan_5']:
    rows = db.query('SELECT id FROM jobs WHERE name=?', [name])
    if rows:
        db.execute(\"UPDATE jobs SET status=COALESCE(failed_at_status,'pending'), error=NULL, failed_at_status=NULL, worker_id=NULL WHERE id=?\", [rows[0]['id']])
        print(f'retried {name}')
"

Retry failed vast.ai timeout jobs


retried jagan_3
retried jagan_5


Now download the 4 new jobs locally with yt-dlp:


cat > /tmp/seed_new.py << 'PYEOF'
import json, os, shutil, subprocess, sys, time, uuid
from pathlib import Path

sys.path.insert(0, "/home/ubuntu/karthyani_pipeline/worker")
for line in open("/home/ubuntu/karthyani_pipeline/worker/.env"):
    line = line.strip()
    if line and not line.startswith("#"):
        k, _, v = line.partition("=")
        os.environ.setdefault(k, v)

import db, r2

JOBS = [
    ("kohli_1", "https://www.youtube.com/watch?v=2xzIc-sY-cA"),
    ("kohli_2", "https://www.youtube.com/watch?v=1J0o3v8ptHY"),
    ("kohli_3", "https://www.youtube.com/watch?v=FoMeOqnvU3w"),
    ("samaray_1", "https://www.youtube.com/watch?v=jqUdnuxQijs"),
]

WORK = Path("/tmp/seed_new")
WORK.mkdir(exist_ok=True)
YT_DLP = "/home/ubuntu/karthyani_pipeline/worker/.venv/bin/yt-dlp"

def log(msg):
    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)

for name, url in JOBS:
    log(f"=== {name} ===")
    # Check if already in R2
    if r2.exists(f"{name}/clip.wav") and r2.find_first(f"{name}/source."):
        log(f"  already in R2, updating DB to audio_uploaded")
        rows = db.query("SELECT id, status FROM jobs WHERE name=?", [name])
        if rows and rows[0]["status"] == "pending":
            db.execute("UPDATE jobs SET status='audio_uploaded', r2_prefix=? WHERE id=?", [name, rows[0]["id"]])
            log(f"  DB updated")
        continue

    work = WORK / name
    if work.exists(): shutil.rmtree(work)
    work.mkdir()

    # Download
    log(f"  downloading...")
    t0 = time.time()
    r = subprocess.run([YT_DLP, "-f", "bestaudio", "-o", str(work / "source.%(ext)s"), "--no-progress", url],
                       capture_output=True, text=True)
    if r.returncode != 0:
        log(f"  FAILED: {r.stderr[:500]}")
        continue
    src = list(work.glob("source.*"))[0]
    log(f"  {src.name} ({src.stat().st_size/1e6:.1f} MB) in {time.time()-t0:.1f}s")

    # Convert
    clip = work / "clip.wav"
    log(f"  converting to clip.wav...")
    subprocess.run(["ffmpeg", "-y", "-i", str(src), "-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le", str(clip)],
                   capture_output=True, text=True, check=True)
    log(f"  clip.wav ({clip.stat().st_size/1e6:.1f} MB)")

    # Upload to R2
    log(f"  uploading to R2...")
    r2.upload_file(str(src), f"{name}/{src.name}")
    r2.upload_file(str(clip), f"{name}/clip.wav")
    log(f"  R2 done")

    # Update DB
    rows = db.query("SELECT id FROM jobs WHERE name=?", [name])
    if rows:
        db.execute("UPDATE jobs SET status='audio_uploaded', r2_prefix=? WHERE id=?", [name, rows[0]["id"]])
        log(f"  DB updated to audio_uploaded")

    shutil.rmtree(work, ignore_errors=True)

log("DONE")
PYEOF
python3 /tmp/seed_new.py

Download 4 new jobs with yt-dlp, push to R2, update DB


[01:24:36] === kohli_1 ===
[01:24:37]   downloading...
[01:24:39]   source.webm (21.6 MB) in 2.0s
[01:24:39]   converting to clip.wav...
[01:24:43]   clip.wav (48.8 MB)
[01:24:43]   uploading to R2...
[01:24:51]   R2 done
[01:24:53]   DB updated to audio_uploaded
[01:24:53] === kohli_2 ===
[01:24:54]   downloading...
[01:24:56]   source.webm (18.1 MB) in 2.5s
[01:24:56]   converting to clip.wav...
[01:25:00]   clip.wav (39.8 MB)
[01:25:00]   uploading to R2...
[01:25:08]   R2 done
[01:25:10]   DB updated to audio_uploaded
[01:25:10] === kohli_3 ===
[01:25:10]   downloading...
[01:25:12]   source.webm (9.7 MB) in 1.7s
[01:25:12]   converting to clip.wav...
[01:25:14]   clip.wav (24.4 MB)
[01:25:14]   uploading to R2...
[01:25:21]   R2 done
[01:25:23]   DB updated to audio_uploaded
[01:25:23] === samaray_1 ===
[01:25:23]   downloading...
[01:25:27]   source.webm (70.0 MB) in 3.2s
[01:25:27]   converting to clip.wav...
[01:25:41]   clip.wav (149.1 MB)
[01:25:41]   uploading to R2...
[01:25:50]   R2 done
[01:25:52]   DB updated to audio_uploaded
[01:25:52] DONE


All 4 done:

| Job | Source | Clip | Status |
|---|---|---|---|
| kohli_1 | 21.6 MB | 48.8 MB (~25 min) | audio_uploaded |
| kohli_2 | 18.1 MB | 39.8 MB (~20 min) | audio_uploaded |
| kohli_3 | 9.7 MB | 24.4 MB (~12 min) | audio_uploaded |
| samaray_1 | 70.0 MB | 149.1 MB (~77 min) | audio_uploaded |

Downloaded with yt-dlp locally, pushed to R2, DB updated. Workers will pick them up for diarization next. Also retried jagan_3 and jagan_5 (failed from vast.ai timeout).


do u restarted .. kill gpus also they will do only missed overlaps only ryt ?


The user is asking:
1. Did I restart the workers? (Yes, I killed the old ones and started new ones)
2. They want me to kill the vast.ai GPU instances too
3. They're confirming that the workers will only process the missed/remaining overlaps (since the pipeline is idempotent)

Let me check for running vast.ai instances and destroy them. Then confirm that yes, the pipeline is idempotent - when the workers retry jagan_3 and jagan_5, they'll skip already-uploaded segments and only process what's missing.


Yes, workers were restarted with the updated code. Let me kill the vast.ai GPU instances too — they're from the old run.


source /home/ubuntu/karthyani_pipeline/worker/.env && export VAST_API_KEY && python3 -c "
import requests, json
key = '$VAST_API_KEY'
r = requests.get(f'https://console.vast.ai/api/v0/instances/?owner=me&api_key={key}', timeout=30)
instances = r.json().get('instances', [])
print(f'{len(instances)} instances found')
for inst in instances:
    iid = inst['id']
    status = inst.get('actual_status', '?')
    label = inst.get('label', '')
    print(f'  destroying {iid} (status={status} label={label})...')
    requests.delete(f'https://console.vast.ai/api/v0/instances/{iid}/?api_key={key}', timeout=30)
    print(f'    done')
print('All destroyed')
"

Destroy all running vast.ai GPU instances


4 instances found
  destroying 34496653 (status=running label=karthyani-1775782467)...
    done
  destroying 34496728 (status=running label=karthyani-1775782599)...
    done
  destroying 34496899 (status=running label=karthyani-1775782843)...
    done
  destroying 34498241 (status=created label=karthyani-1775783979)...
    done
All destroyed


4 GPU instances destroyed. And yes — the pipeline is fully idempotent. When workers pick up those jobs again, they'll:

1. Check R2 for existing segments — skip what's already uploaded
2. Only process/upload missing overlap files
3. SoloSpeech on the GPU will also skip already-cleaned overlaps (checks R2 with `head_object` before processing each clip)

So no wasted work — they resume exactly where they left off.


update the db status accodingly with rwason saying like overlap not considering becuase the total overla audio is less than 5mins (waste of GPUz) and move status accodungly uodate code and update db also


The user wants me to:

1. Update jobs that have already been segmented - if their overlap total duration is < 5 min, mark them as done with a note explaining why SoloSpeech was skipped
2. Update the code to store this reason somewhere in the DB

Let me first check which jobs are in awaiting_solospeech status and check their overlap durations. Then I need to figure out how to store the "reason" in the DB. I could use the `error` field (but that's for errors), or I could add a new field. Actually, the simplest approach is to just use the error field with a note, or I could repurpose it.

For existing jobs in awaiting_solospeech status, I'll check their overlap total duration from the R2 file sizes, and if it's under 5 minutes, I'll mark them as done with a note about GPU waste. The simplest approach is to store this explanation in the error field since it already exists in the schema. I need to store the skip reason in the database when overlap is under 5 minutes, so I'll set the status to done and put the reason in the error field with a prefix, then update the dashboard to style these notes differently from actual errors. I need to update the worker code to store a note when skipping SoloSpeech jobs due to insufficient overlap, then backfill the existing awaiting_solospeech jobs and adjust the dashboard styling to distinguish notes from actual errors.


Let me check which jobs are in `awaiting_solospeech` and compute their overlap durations from R2, then update accordingly.


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN R2_ENDPOINT R2_BUCKET R2_ACCESS_KEY R2_SECRET_KEY && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db, r2

# Get all jobs that have overlap segments
rows = db.query(\"SELECT id, name, status, num_overlap_segments FROM jobs WHERE num_overlap_segments IS NOT NULL AND num_overlap_segments > 0\")
for row in rows:
    name = row['name']
    keys = r2.list_keys(f'{name}/overlap_notcleaned/')
    wavs = [k for k in keys if k.endswith('.wav')]
    # compute total duration from file sizes
    total_dur = 0
    for k in wavs:
        size = r2.head_object(f'{k}')
        total_dur += max(0, (size - 44) / 32000)
    print(f\"{name:15s} status={row['status']:25s} overlaps={len(wavs):3d}  dur={total_dur:.1f}s ({total_dur/60:.1f}m)\")
" 2>&1

Check overlap durations for all jobs with overlaps


Error: Exit code 1
Traceback (most recent call last):
  File "<string>", line 14, in <module>
AttributeError: module 'r2' has no attribute 'head_object'


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN R2_ENDPOINT R2_BUCKET R2_ACCESS_KEY R2_SECRET_KEY && python3 -c "
import sys, boto3; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db, os

s3 = boto3.client('s3',
    endpoint_url=os.environ['R2_ENDPOINT'],
    aws_access_key_id=os.environ['R2_ACCESS_KEY'],
    aws_secret_access_key=os.environ['R2_SECRET_KEY'],
    region_name='auto')
BUCKET = os.environ['R2_BUCKET']

def list_wavs_with_sizes(prefix):
    items = []
    paginator = s3.get_paginator('list_objects_v2')
    for page in paginator.paginate(Bucket=BUCKET, Prefix=prefix):
        for obj in page.get('Contents', []):
            if obj['Key'].endswith('.wav'):
                items.append((obj['Key'], obj['Size']))
    return items

rows = db.query(\"SELECT id, name, status, num_overlap_segments, num_overlap_cleaned FROM jobs WHERE num_overlap_segments IS NOT NULL AND num_overlap_segments > 0\")
for row in rows:
    name = row['name']
    wavs = list_wavs_with_sizes(f'{name}/overlap_notcleaned/')
    total_dur = sum(max(0, (size - 44) / 32000) for _, size in wavs)
    needs_solo = total_dur >= 300
    print(f\"{name:15s} status={row['status']:25s} overlaps={len(wavs):3d}  dur={total_dur:7.1f}s ({total_dur/60:5.1f}m)  solo={'YES' if needs_solo else 'SKIP'}\")
"

Check overlap durations using boto3


jagan_3         status=awaiting_solospeech       overlaps= 11  dur=   63.4s (  1.1m)  solo=SKIP
jagan_4         status=done                      overlaps=  4  dur=   17.6s (  0.3m)  solo=SKIP
jagan_5         status=awaiting_solospeech       overlaps=  9  dur=   25.9s (  0.4m)  solo=SKIP
rgv_1           status=awaiting_solospeech       overlaps=289  dur= 2771.4s ( 46.2m)  solo=YES
rgv_4           status=awaiting_solospeech       overlaps= 22  dur=  217.5s (  3.6m)  solo=SKIP


Only rgv_1 (46 min) needs SoloSpeech. The rest are all under 5 min. Let me update the DB:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db

skip_jobs = [
    ('jagan_3', 63.4),
    ('jagan_5', 25.9),
    ('rgv_4', 217.5),
]

for name, dur in skip_jobs:
    rows = db.query('SELECT id, status FROM jobs WHERE name=?', [name])
    if not rows: continue
    job = rows[0]
    mins = round(dur / 60, 1)
    note = f'SoloSpeech skipped: total overlap {mins}m < 5m threshold (not worth GPU cost)'
    db.execute(
        \"UPDATE jobs SET status='done', error=?, completed_at=datetime('now'), worker_id=NULL WHERE id=?\",
        [note, job['id']]
    )
    print(f'{name}: {job[\"status\"]} -> done  ({note})')
"

Update jobs with overlap < 5min to done with reason


jagan_3: awaiting_solospeech -> done  (SoloSpeech skipped: total overlap 1.1m < 5m threshold (not worth GPU cost))
jagan_5: awaiting_solospeech -> done  (SoloSpeech skipped: total overlap 0.4m < 5m threshold (not worth GPU cost))
rgv_4: awaiting_solospeech -> done  (SoloSpeech skipped: total overlap 3.6m < 5m threshold (not worth GPU cost))


Now update the worker code to also store this note when it skips SoloSpeech:


249	                target = job.get("target_speaker") or pyannote.main_speaker(diarization)
250	            with stage("segment", timings):
251	                seg = step_segment(job, work, clip_wav, diarization, target)
252	            ov_dur = seg.get("overlap_total_dur", 0)
253	            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
254	            if seg["overlap"] > 0 and ov_dur >= 300:
255	                next_status = "awaiting_solospeech"
256	            else:
257	                next_status = "done"
258	                if seg["overlap"] > 0:
259	                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
260	            db.update(
261	                job_id,
262	                status=next_status,
263	                num_clean_segments=seg["clean"],
264	                num_overlap_segments=seg["overlap"],
265	                num_overlap_cleaned=0,
266	                completed_at=("datetime('now')" if next_status == "done" else None),
267	            )
268	            job["status"] = next_status

                target = job.get("target_speaker") or pyannote.main_speaker(diarization)
            with stage("segment", timings):
                seg = step_segment(job, work, clip_wav, diarization, target)
            ov_dur = seg.get("overlap_total_dur", 0)
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )
            job["status"] = next_status


Edit

            ov_dur = seg.get("overlap_total_dur", 0)
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )

            ov_dur = seg.get("overlap_total_dur", 0)
            skip_note = None
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    skip_note = f"SoloSpeech skipped: total overlap {ov_dur/60:.1f}m < 5m threshold (not worth GPU cost)"
                    log(f"  {skip_note}")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                error=skip_note,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )


The file /home/ubuntu/karthyani_pipeline/worker/worker.py has been updated successfully.

            ov_dur = seg.get("overlap_total_dur", 0)
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )

            ov_dur = seg.get("overlap_total_dur", 0)
            skip_note = None
            # Only run SoloSpeech if overlap > 0 AND total overlap duration >= 5 min
            if seg["overlap"] > 0 and ov_dur >= 300:
                next_status = "awaiting_solospeech"
            else:
                next_status = "done"
                if seg["overlap"] > 0:
                    skip_note = f"SoloSpeech skipped: total overlap {ov_dur/60:.1f}m < 5m threshold (not worth GPU cost)"
                    log(f"  {skip_note}")
            db.update(
                job_id,
                status=next_status,
                num_clean_segments=seg["clean"],
                num_overlap_segments=seg["overlap"],
                num_overlap_cleaned=0,
                error=skip_note,
                completed_at=("datetime('now')" if next_status == "done" else None),
            )

seg = step_segment(job, work, clip_wav, diarization, target)

-                    log(f"  skipping solospeech: overlap total {ov_dur:.1f}s < 300s threshold")

+                    skip_note = f"SoloSpeech skipped: total overlap {ov_dur/60:.1f}m < 5m threshold (not worth GPU cost)"

+                    log(f"  {skip_note}")


Now update the dashboard to show these notes in a different style (info/warning rather than error red):


{/* Error */}
            {j.error && (

{j.error}

            )}

{/* Error or note */}
            {j.error && (

{j.error}

            )}


{/* Error */}
            {j.error && (

{j.error}

            )}

{/* Error or note */}
            {j.error && (

{j.error}

            )}


Now add the `noteBox` style:


const errorBox: React.CSSProperties = {
  background: "#fef2f2",
  color: "#7f1d1d",
  padding: 8,
  fontSize: 10,
  overflow: "auto",
  margin: "6px 0 0",
  borderRadius: 4,
  maxHeight: 120,
  whiteSpace: "pre-wrap",
  wordBreak: "break-all",
};

const errorBox: React.CSSProperties = {
  background: "#fef2f2",
  color: "#7f1d1d",
  padding: 8,
  fontSize: 10,
  overflow: "auto",
  margin: "6px 0 0",
  borderRadius: 4,
  maxHeight: 120,
  whiteSpace: "pre-wrap",
  wordBreak: "break-all",
};
const noteBox: React.CSSProperties = {
  background: "#fffbeb",
  color: "#92400e",
  padding: 8,
  fontSize: 11,
  margin: "6px 0 0",
  borderRadius: 4,
  whiteSpace: "pre-wrap",
};


const errorBox: React.CSSProperties = {
  background: "#fef2f2",
  color: "#7f1d1d",
  padding: 8,
  fontSize: 10,
  overflow: "auto",
  margin: "6px 0 0",
  borderRadius: 4,
  maxHeight: 120,
  whiteSpace: "pre-wrap",
  wordBreak: "break-all",
};
const noteBox: React.CSSProperties = {
  background: "#fffbeb",
  color: "#92400e",
  padding: 8,
  fontSize: 11,
  margin: "6px 0 0",
  borderRadius: 4,
  whiteSpace: "pre-wrap",
};

whiteSpace: "pre-wrap",
wordBreak: "break-all",
};
+const noteBox: React.CSSProperties = {
+  background: "#fffbeb",
+  color: "#92400e",
+  padding: 8,
+  fontSize: 11,
+  margin: "6px 0 0",
+  borderRadius: 4,
+  whiteSpace: "pre-wrap",
+};
const thStyle: React.CSSProperties = {
textAlign: "left",
padding: "4px 8px",


Restart workers and redeploy:


Bash

kill 1675650 1675651 1675652 1675653 1675654 2>/dev/null; sleep 1; cd /home/ubuntu/karthyani_pipeline/worker && mkdir -p /tmp/worker_logs && for i in 1 2 3 4 5; do nohup .venv/bin/python -u worker.py > /tmp/worker_logs/worker_$i.log 2>&1 & echo "Worker $i PID=$!"; done

Restart workers with updated code


Worker 1 PID=1682841
Worker 2 PID=1682842
Worker 3 PID=1682843
Worker 4 PID=1682844
Worker 5 PID=1682845


cd /home/ubuntu/karthyani_pipeline/dashboard && vercel deploy --prod 2>&1 | grep -E "ready|error"

Deploy dashboard


    "readyState": "READY",
  "message": "Deployment karthyani-dashboard-bwhya4fwv-charans-projects-9fb8fade.vercel.app ready.",


Done. Here's what changed:

**DB updated:**
- jagan_3 → done (overlap 1.1m < 5m)
- jagan_5 → done (overlap 0.4m < 5m)
- rgv_4 → done (overlap 3.6m < 5m)
- rgv_1 stays `awaiting_solospeech` (46.2m overlap — worth GPU cost)

**Worker code:**
- Overlap segments < 4s are now filtered out (not uploaded to R2)
- If total overlap < 5 min, status goes to `done` with a note: *"SoloSpeech skipped: total overlap Xm < 5m threshold (not worth GPU cost)"*

**Dashboard:**
- Notes show in amber/yellow box (not red error box) for done jobs
- "SoloSpeech skipped" label shown inline on segment counts


arthyani
YouTube → pyannote diarization → clean segments → SoloSpeech overlap extraction

+ new job
auto-refresh 5s
07:00:04
Done
5
Processing
16
Failed
0
Clean Segments
1902
Overlap Cleaned
4
GPU Pods
0
Active Workers
3
Jobs (21)
samaray_1
audio uploaded
16m ago
open
delete
https://www.youtube.com/watch?v=jqUdnuxQijs
Running diarization
kohli_3
audio uploaded
16m ago
open
delete
https://www.youtube.com/watch?v=FoMeOqnvU3w
Running diarization
kohli_2
audio uploaded
17m ago
open
delete
https://www.youtube.com/watch?v=1J0o3v8ptHY
Running diarization
kohli_1
audio uploaded
17m ago
open
delete
https://www.youtube.com/watch?v=2xzIc-sY-cA
Running diarization
lavanya_2
audio uploaded
44m ago
open
delete
https://www.youtube.com/watch?v=FvFoHuCxiYY
Running diarization
lavanya_9
audio uploaded
46m ago
open
delete
https://www.youtube.com/watch?v=l1XvDYY_U_k
Running diarization
lavanya_8
audio uploaded
46m ago
open
delete
https://www.youtube.com/watch?v=KDVUTrPoTY4
Running diarization
lavanya_7
audio uploaded
46m ago
open
delete
https://www.youtube.com/watch?v=R-FjC3e4c_k
Running diarization
lavanya_6
audio uploaded
47m ago
open
delete
https://www.youtube.com/watch?v=i3IQsTNpd0E
Running diarization
lavanya_5
audio uploaded
47m ago
open
delete
https://www.youtube.com/watch?v=miO-2nrSoOo
Running diarization
lavanya_4
audio uploaded
47m ago
open
delete
https://www.youtube.com/watch?v=UhBYaoubtgI
Running diarization
lavanya_3
audio uploaded
48m ago
open
delete
https://www.youtube.com/watch?v=hcxPenotMMQ
Running diarization
lavanya_1
audio uploaded
48m ago
open
delete
https://www.youtube.com/watch?v=JMuvxkosF9I
Running diarization
rgv_5
diarized
worker active
9m ago
open
delete
https://www.youtube.com/watch?v=Qk6bgmJhlfc
target: SPEAKER_01 (39m 35s of 50m 42s total) · 2 speakers
Segmenting audio
rgv_4
done
10m ago
open
delete
https://www.youtube.com/watch?v=qo0DXkF4zqA
target: SPEAKER_00 (9m 50s of 11m 21s total) · 2 speakers
clean: 25 · overlap: 22SoloSpeech skipped (overlap < 5 min)
SoloSpeech skipped: total overlap 3.6m < 5m threshold (not worth GPU cost)
rgv_1
awaiting solospeech
worker active
29m ago
open
delete
https://www.youtube.com/watch?v=CPIFWca5NaU
target: SPEAKER_04 (57m 12s of 1h 20m total) · 5 speakers
clean: 270 · overlap: 289
SoloSpeech on GPU (GPU #34496899)
jagan_5
done
11m ago
open
delete
https://www.youtube.com/watch?v=mHzdMxUvFxU
target: SPEAKER_02 (1h 25m of 1h 31m total) · 10 speakers
clean: 595 · overlap: 9SoloSpeech skipped (overlap < 5 min)
SoloSpeech skipped: total overlap 0.4m < 5m threshold (not worth GPU cost)
jagan_4
done
45m ago
open
delete
https://www.youtube.com/live/RNp5aKQSxGk
target: SPEAKER_02 (23m 58s of 24m 28s total) · 3 speakers
clean: 231 · overlap: 4 · cleaned: 4
jagan_3
done
10m ago
 see rgv1 is saying it is running gpu but on top there are 0 gpus running


That's a stale `gpu_instance_id` in the DB from the old run — I destroyed the instance but the DB wasn't cleared since the worker got killed mid-job. Let me fix it:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id, name, gpu_instance_id, worker_id FROM jobs WHERE gpu_instance_id IS NOT NULL\")
for r in rows:
    print(f\"{r['name']}: gpu_instance_id={r['gpu_instance_id']} worker_id={r['worker_id']}\")
    db.execute('UPDATE jobs SET gpu_instance_id=NULL WHERE id=?', [r['id']])
    print(f'  cleared')
"

Clear stale gpu_instance_id from DB


rgv_1: gpu_instance_id=34496899 worker_id=0321-dsm2-nvdgxa100-prxmx70052-1635287-2fd935
  cleared
rgv_4: gpu_instance_id=34498241 worker_id=None
  cleared


Also rgv_1 has a stale `worker_id` from the killed worker. Let me release it so a new worker picks it up:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id, name, status, worker_id FROM jobs WHERE worker_id IS NOT NULL\")
for r in rows:
    print(f\"{r['name']}: status={r['status']} worker={r['worker_id']}\")
# Release rgv_1's stale worker lock
rows = db.query(\"SELECT id FROM jobs WHERE name='rgv_1'\")
if rows:
    db.execute('UPDATE jobs SET worker_id=NULL WHERE id=?', [rows[0]['id']])
    print('rgv_1: worker_id cleared, will be picked up again')
"

Release stale worker lock on rgv_1


jagan_2: status=diarized worker=f621edc43a0a-1-81d7e5
rgv_1: status=awaiting_solospeech worker=0321-dsm2-nvdgxa100-prxmx70052-1635287-2fd935
rgv_5: status=diarized worker=0321-dsm2-nvdgxa100-prxmx70052-1635286-b44227
rgv_1: worker_id cleared, will be picked up again


jagan_2 and rgv_5 also have stale worker_ids from the old killed workers. Let me clear those too:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
# Clear all stale worker_ids from old PIDs (1635285-1635289)
for name in ['jagan_2', 'rgv_5']:
    rows = db.query('SELECT id FROM jobs WHERE name=?', [name])
    if rows:
        db.execute('UPDATE jobs SET worker_id=NULL WHERE id=?', [rows[0]['id']])
        print(f'{name}: worker_id cleared')
"

Clear remaining stale worker locks


jagan_2: worker_id cleared
rgv_5: worker_id cleared


Fixed. The stale `gpu_instance_id` and `worker_id` from the killed workers are all cleared now. The new workers will pick up rgv_1 (for SoloSpeech), jagan_2, and rgv_5 fresh.


remaing alsl are in running dairization is it relaly running


for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -3 /tmp/worker_logs/worker_$i.log 2>/dev/null; echo; done

Check worker logs


=== Worker 1 ===
  File "/usr/lib/python3.10/os.py", line 680, in __getitem__
    raise KeyError(key) from None
KeyError: 'TURSO_URL'

=== Worker 2 ===
  File "/usr/lib/python3.10/os.py", line 680, in __getitem__
    raise KeyError(key) from None
KeyError: 'TURSO_URL'

=== Worker 3 ===
  File "/usr/lib/python3.10/os.py", line 680, in __getitem__
    raise KeyError(key) from None
KeyError: 'TURSO_URL'

=== Worker 4 ===
  File "/usr/lib/python3.10/os.py", line 680, in __getitem__
    raise KeyError(key) from None
KeyError: 'TURSO_URL'

=== Worker 5 ===
  File "/usr/lib/python3.10/os.py", line 680, in __getitem__
    raise KeyError(key) from None
KeyError: 'TURSO_URL'


Workers crashed — env vars weren't loaded. They need to run from the worker directory with the `.env` file. Let me restart them properly:


kill 1682841 1682842 1682843 1682844 1682845 2>/dev/null; sleep 1
cd /home/ubuntu/karthyani_pipeline/worker
# Source .env into environment
set -a; source .env; set +a
mkdir -p /tmp/worker_logs
for i in 1 2 3 4 5; do
  nohup .venv/bin/python -u worker.py > /tmp/worker_logs/worker_$i.log 2>&1 &
  echo "Worker $i PID=$!"
done

Restart workers with env vars loaded


Worker 1 PID=1685458
Worker 2 PID=1685459
Worker 3 PID=1685460
Worker 4 PID=1685461
Worker 5 PID=1685462


sleep 3 && for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -5 /tmp/worker_logs/worker_$i.log 2>/dev/null; echo; done

Verify workers are running


=== Worker 1 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685458-3d6a53)

=== Worker 2 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685459-74a456)
[01:31:55] === job rgv_5 (start status=diarized) ===

=== Worker 3 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685460-0b3975)
[01:31:54] === job rgv_1 (start status=awaiting_solospeech) ===
[01:31:54]   ▶ solospeech…

=== Worker 4 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685461-af8752)

=== Worker 5 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685462-2f9cbe)
[01:31:53] === job jagan_2 (start status=diarized) ===


Workers are running now. They're picking up jobs:
- **Worker 2** → rgv_5 (segmenting)
- **Worker 3** → rgv_1 (SoloSpeech on GPU)
- **Worker 5** → jagan_2 (segmenting)

The "Running diarization" labels on the dashboard are wrong — those `audio_uploaded` jobs aren't being worked on yet because only 5 workers and 3 claimed jobs first. The label just shows what *stage is next*, not that it's actively running. The remaining 13 `audio_uploaded` jobs will get picked up as workers finish their current jobs.


sleep 10 && for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -3 /tmp/worker_logs/worker_$i.log 2>/dev/null; echo; done

Check workers after 10s


=== Worker 1 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685458-3d6a53)
[01:31:57] === job lavanya_3 (start status=audio_uploaded) ===
[01:32:00]   ▶ diarize…

=== Worker 2 ===
[01:31:59] === job rgv_5 TIMINGS: {'segment': 0.68} ===
[01:32:02] === job lavanya_4 (start status=audio_uploaded) ===
[01:32:04]   ▶ diarize…

=== Worker 3 ===
[01:31:54]   ▶ solospeech…
[01:31:55]   picked offer 33845176 (A100 SXM4 ram=81920MB $1.069/hr)
[01:31:57]   instance_id=34499207

=== Worker 4 ===
[01:31:50] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1685461-af8752)
[01:31:56] === job lavanya_1 (start status=audio_uploaded) ===
[01:31:59]   ▶ diarize…

=== Worker 5 ===
[01:31:59] === job jagan_2 TIMINGS: {'segment': 0.58} ===
[01:32:03] === job lavanya_5 (start status=audio_uploaded) ===
[01:32:05]   ▶ diarize…


All 5 workers are active now. jagan_2 and rgv_5 finished segmenting in under a second (cached). Workers are now running diarization on lavanya_1, lavanya_3, lavanya_4, lavanya_5, and rgv_1 is on SoloSpeech GPU.


y gpus are still 0 and rgv_1 showing it is in gpu


source /home/ubuntu/karthyani_pipeline/worker/.env && python3 -c "
import requests
r = requests.get(f'https://console.vast.ai/api/v0/instances/?owner=me&api_key=$VAST_API_KEY', timeout=30)
instances = r.json().get('instances', [])
print(f'{len(instances)} instances')
for i in instances:
    print(f'  id={i[\"id\"]} status={i.get(\"actual_status\")} label={i.get(\"label\")}')
"

Check vast.ai instances directly


1 instances
  id=34499207 status=created label=karthyani-1775784715


There's 1 instance but its status is `created` (not `running` yet) — that's why the dashboard shows 0 for "running" pods. The stats API filters on `actual_status === "running"`. Let me fix it to count all non-destroyed instances:


28	        { signal: AbortSignal.timeout(8000) }
29	      );
30	      if (r.ok) {
31	        const data = await r.json();
32	        const instances = data.instances || [];
33	        gpuPods = {
34	          total: instances.length,
35	          running: instances.filter((i: any) => i.actual_status === "running").length,
36	          instances: instances.map((i: any) => ({
37	            id: i.id,
38	            status: i.actual_status || i.intended_status,
39	            gpu: i.gpu_name,
40	            label: i.label,
41	            cost: i.dph_total,
42	          })),

        { signal: AbortSignal.timeout(8000) }
      );
      if (r.ok) {
        const data = await r.json();
        const instances = data.instances || [];
        gpuPods = {
          total: instances.length,
          running: instances.filter((i: any) => i.actual_status === "running").length,
          instances: instances.map((i: any) => ({
            id: i.id,
            status: i.actual_status || i.intended_status,
            gpu: i.gpu_name,
            label: i.label,
            cost: i.dph_total,
          })),


Edit

running: instances.filter((i: any) => i.actual_status === "running").length,


The file /home/ubuntu/karthyani_pipeline/dashboard/pages/api/stats.ts has been updated successfully.

import type { NextApiRequest, NextApiResponse } from "next";
import { turso } from "@/lib/turso";
import { checkAuth } from "@/lib/auth";

export default async function handler(req: NextApiRequest, res: NextApiResponse) {
  if (!checkAuth(req)) return res.status(401).json({ error: "not logged in" });

  const [countResult, segResult] = await Promise.all([
    turso.execute("SELECT status, COUNT(*) as c FROM jobs GROUP BY status"),
    turso.execute(
      "SELECT COALESCE(SUM(num_clean_segments),0) as total_clean, " +
      "COALESCE(SUM(num_overlap_segments),0) as total_overlap, " +
      "COALESCE(SUM(num_overlap_cleaned),0) as total_cleaned FROM jobs"
    ),
  ]);

  const counts: Record<string, number> = {};
  for (const r of countResult.rows as any[]) counts[r.status] = Number(r.c);
  const seg = segResult.rows[0] as any;

  // Vast.ai running instances (optional — needs VAST_API_KEY env var)
  let gpuPods: any = null;
  const vastKey = process.env.VAST_API_KEY;
  if (vastKey) {
    try {
      const r = await fetch(
        `https://console.vast.ai/api/v0/instances/?owner=me&api_key=${vastKey}`,
        { signal: AbortSignal.timeout(8000) }
      );
      if (r.ok) {
        const data = await r.json();
        const instances = data.instances || [];
        gpuPods = {
          total: instances.length,
          running: instances.filter((i: any) => i.actual_status === "running").length,
          instances: instances.map((i: any) => ({
            id: i.id,
            status: i.actual_status || i.intended_status,
            gpu: i.gpu_name,
            label: i.label,
            cost: i.dph_total,
          })),
        };
      }
    } catch {
      // vast.ai unreachable — skip
    }
  }

  // Active workers (jobs with worker_id set)
  const activeResult = await turso.execute(
    "SELECT name, status, worker_id FROM jobs WHERE worker_id IS NOT NULL"
  );

  return res.json({
    counts,
    totalClean: Number(seg.total_clean),
    totalOverlap: Number(seg.total_overlap),
    totalCleaned: Number(seg.total_cleaned),
    activeWorkers: activeResult.rows.length,
    gpuPods,
  });
}

-          running: instances.filter((i: any) => i.actual_status === "running").length,


Also rename the label to just show total active pods:


Bash

cd /home/ubuntu/karthyani_pipeline/dashboard && vercel deploy --prod 2>&1 | grep -E "ready|error"

Deploy dashboard


    "readyState": "READY",
  "message": "Deployment karthyani-dashboard-60bwjz1yz-charans-projects-9fb8fade.vercel.app ready.",


Fixed. Now GPU Pods will show **1** — it counts all active instances (created, loading, running) not just running ones. The instance is booting up and will be running shortly.


y rgv_5 is done rgv_5
done
close
Cleaned
27m 25s
79 files
Overlap (raw)
11m 12s
37 files
Overlap (cleaned)
0s
0 files
Total Usable
27m 25s
79 files
Original clip
Reference (target speaker)
diarize.json
cleaned/ (79)
27m 25s
clean_0000.wav
5s
clean_0001.wav
32s
clean_0002.wav
44s
clean_0003.wav
38s
clean_0004.wav
15s
clean_0005.wav
3s
clean_0006.wav
2s
clean_0007.wav
4s
clean_0008.wav
12s
clean_0009.wav
21s
clean_0010.wav
16s
clean_0011.wav
13s
clean_0012.wav
3s
clean_0013.wav
37s
clean_0014.wav
46s
clean_0015.wav
16s
clean_0016.wav
3s
clean_0017.wav
23s
clean_0018.wav
18s
clean_0019.wav
14s
clean_0020.wav
43s
clean_0021.wav
17s
clean_0022.wav
10s
clean_0023.wav
21s
clean_0024.wav
32s
clean_0025.wav
1m 58s
clean_0026.wav
3s
clean_0027.wav
0s
clean_0028.wav
23s
clean_0029.wav
43s
clean_0030.wav
2s
clean_0031.wav
9s
clean_0032.wav
18s
clean_0033.wav
7s
clean_0034.wav
1m 18s
clean_0035.wav
19s
clean_0036.wav
16s
clean_0037.wav
6s
clean_0038.wav
10s
clean_0039.wav
9s
clean_0040.wav
6s
clean_0041.wav
23s
clean_0042.wav
13s
clean_0043.wav
12s
clean_0044.wav
29s
clean_0045.wav
14s
clean_0046.wav
1m 7s
clean_0047.wav
41s
clean_0048.wav
0s
clean_0049.wav
10s
clean_0050.wav
5s
clean_0051.wav
28s
clean_0052.wav
0s
clean_0053.wav
45s
clean_0054.wav
16s
clean_0055.wav
1m 11s
clean_0056.wav
13s
clean_0057.wav
1m 20s
clean_0058.wav
1m 4s
clean_0059.wav
10s
clean_0060.wav
4s
clean_0061.wav
32s
clean_0062.wav
0s
clean_0063.wav
5s
clean_0064.wav
13s
clean_0065.wav
21s
clean_0066.wav
2s
clean_0067.wav
1s
clean_0068.wav
18s
clean_0069.wav
6s
clean_0070.wav
10s
clean_0071.wav
22s
clean_0072.wav
39s
clean_0073.wav
38s
clean_0074.wav
19s
clean_0075.wav
4s
clean_0076.wav
4s
clean_0077.wav
10s
clean_0078.wav
3s
overlap_notcleaned/ (37)
11m 12s
overlap_0000.wav
1m 34s
overlap_0001.wav
41s


rgv_5 has 11m 12s of overlap — that's over 5 min, so it should have gone to `awaiting_solospeech`. But it was processed by the old worker code before the update. Let me check:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id, name, status, num_overlap_segments, num_overlap_cleaned, error FROM jobs WHERE name='rgv_5'\")
r = rows[0]
print(f\"status={r['status']} overlaps={r['num_overlap_segments']} cleaned={r['num_overlap_cleaned']} error={r['error']}\")
"

Check rgv_5 status


status=done overlaps=37 cleaned=0 error=SoloSpeech skipped: total overlap 0.0m < 5m threshold (not worth GPU cost)


The issue is that `step_segment` returned `overlap_total_dur=0` because the segments were cached (already in R2). The cached path doesn't compute duration — it just returns counts. Let me look at the cached path in step_segment:

```python
if cleaned_existing or overlap_existing:
    log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
    return {
        "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
        "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
        "overlap_total_dur": seg.get("overlap_total_dur", 0),  # this line doesn't exist in cached path
    }
```

The cached path is missing the duration calculation entirely — it only returns file counts. I need to fix this by computing `overlap_total_dur` from the R2 file sizes when taking the cached path.


Found the bug. When segments are cached (already in R2), `step_segment` returns `overlap_total_dur=0` because it doesn't compute duration from the cached files. Let me fix that:


99	def step_segment(job, work: Path, clip_wav: Path, diarization: list, target: str) -> dict:
100	    """Build cleaned/ + overlap_notcleaned/ + main_speaker_ref. Skip if already in R2."""
101	    name = job["name"]
102	    prefix = name
103	    # If cleaned/ already has files, assume done
104	    cleaned_existing = r2.list_keys(f"{prefix}/cleaned/")
105	    overlap_existing = r2.list_keys(f"{prefix}/overlap_notcleaned/")
106	    if cleaned_existing or overlap_existing:
107	        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
108	        return {
109	            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
110	            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
111	        }
112
113	    seg = audio.build_segments(clip_wav, diarization, target, work)
114	    for f in sorted(Path(seg["clean_dir"]).glob("*.wav")):

def step_segment(job, work: Path, clip_wav: Path, diarization: list, target: str) -> dict:
    """Build cleaned/ + overlap_notcleaned/ + main_speaker_ref. Skip if already in R2."""
    name = job["name"]
    prefix = name
    # If cleaned/ already has files, assume done
    cleaned_existing = r2.list_keys(f"{prefix}/cleaned/")
    overlap_existing = r2.list_keys(f"{prefix}/overlap_notcleaned/")
    if cleaned_existing or overlap_existing:
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
        }

    seg = audio.build_segments(clip_wav, diarization, target, work)
    for f in sorted(Path(seg["clean_dir"]).glob("*.wav")):


The cached path returns no `overlap_total_dur`. I need to compute it from R2 file sizes:


Edit

    if cleaned_existing or overlap_existing:
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
        }

    if cleaned_existing or overlap_existing:
        ov_wavs = [k for k in overlap_existing if k.endswith(".wav")]
        # Compute total overlap duration from R2 file sizes (16kHz mono PCM16 = 32000 B/s)
        ov_total_dur = 0.0
        for k in ov_wavs:
            size = r2.get_size(k)
            if size > 44:
                ov_total_dur += (size - 44) / 32000
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(ov_wavs)} overlap_dur={ov_total_dur:.1f}s)")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len(ov_wavs),
            "overlap_total_dur": round(ov_total_dur, 2),
        }


The file /home/ubuntu/karthyani_pipeline/worker/worker.py has been updated successfully.

    if cleaned_existing or overlap_existing:
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),
        }

    if cleaned_existing or overlap_existing:
        ov_wavs = [k for k in overlap_existing if k.endswith(".wav")]
        # Compute total overlap duration from R2 file sizes (16kHz mono PCM16 = 32000 B/s)
        ov_total_dur = 0.0
        for k in ov_wavs:
            size = r2.get_size(k)
            if size > 44:
                ov_total_dur += (size - 44) / 32000
        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(ov_wavs)} overlap_dur={ov_total_dur:.1f}s)")
        return {
            "clean": len([k for k in cleaned_existing if k.endswith(".wav")]),
            "overlap": len(ov_wavs),
            "overlap_total_dur": round(ov_total_dur, 2),
        }

-        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(overlap_existing)})")

+        ov_wavs = [k for k in overlap_existing if k.endswith(".wav")]

+        # Compute total overlap duration from R2 file sizes (16kHz mono PCM16 = 32000 B/s)

+        log(f"  (cached: cleaned={len(cleaned_existing)} overlap={len(ov_wavs)} overlap_dur={ov_total_dur:.1f}s)")

-            "overlap": len([k for k in overlap_existing if k.endswith(".wav")]),

+            "overlap_total_dur": round(ov_total_dur, 2),

seg = audio.build_segments(clip_wav, diarization, target, work)


Now add the `get_size` helper to `r2.py`:


1	"""Cloudflare R2 client (boto3 S3-compatible)."""
2	import os
3	import boto3
4	from botocore.config import Config
5	from botocore.exceptions import ClientError
6
7	R2_ENDPOINT = os.environ["R2_ENDPOINT"]
8	R2_BUCKET = os.environ["R2_BUCKET"]
9	R2_ACCESS_KEY = os.environ["R2_ACCESS_KEY"]
10	R2_SECRET_KEY = os.environ["R2_SECRET_KEY"]
11
12	s3 = boto3.client(
13	    "s3",
14	    endpoint_url=R2_ENDPOINT,
15	    aws_access_key_id=R2_ACCESS_KEY,
16	    aws_secret_access_key=R2_SECRET_KEY,
17	    region_name="auto",
18	    config=Config(signature_version="s3v4", retries={"max_attempts": 5}),
19	)
20
21	def upload_file(local_path: str, key: str):
22	    s3.upload_file(local_path, R2_BUCKET, key)
23
24	def upload_bytes(data: bytes, key: str, content_type="application/octet-stream"):
25	    s3.put_object(Bucket=R2_BUCKET, Key=key, Body=data, ContentType=content_type)
26
27	def download_file(key: str, local_path: str):
28	    s3.download_file(R2_BUCKET, key, local_path)
29
30	def exists(key: str) -> bool:
31	    try:
32	        s3.head_object(Bucket=R2_BUCKET, Key=key)
33	        return True
34	    except ClientError as e:
35	        if e.response["Error"]["Code"] in ("404", "NoSuchKey", "NotFound"):
36	            return False
37	        raise
38
39	def list_keys(prefix: str):
40	    out = []
41	    token = None
42	    while True:
43	        kwargs = {"Bucket": R2_BUCKET, "Prefix": prefix}
44	        if token:
45	            kwargs["ContinuationToken"] = token
46	        r = s3.list_objects_v2(**kwargs)
47	        for obj in r.get("Contents", []):
48	            out.append(obj["Key"])
49	        if not r.get("IsTruncated"):
50	            break
51	        token = r.get("NextContinuationToken")
52	    return out
53
54	def find_first(prefix: str, suffix: str = "") -> str | None:
55	    """Return first key under prefix (optionally ending with suffix), or None."""
56	    for k in list_keys(prefix):
57	        if k.endswith(suffix):
58	            return k
59	    return None
60

"""Cloudflare R2 client (boto3 S3-compatible)."""
import os
import boto3
from botocore.config import Config
from botocore.exceptions import ClientError

R2_ENDPOINT = os.environ["R2_ENDPOINT"]
R2_BUCKET = os.environ["R2_BUCKET"]
R2_ACCESS_KEY = os.environ["R2_ACCESS_KEY"]
R2_SECRET_KEY = os.environ["R2_SECRET_KEY"]

s3 = boto3.client(
    "s3",
    endpoint_url=R2_ENDPOINT,
    aws_access_key_id=R2_ACCESS_KEY,
    aws_secret_access_key=R2_SECRET_KEY,
    region_name="auto",
    config=Config(signature_version="s3v4", retries={"max_attempts": 5}),
)

def upload_file(local_path: str, key: str):
    s3.upload_file(local_path, R2_BUCKET, key)

def upload_bytes(data: bytes, key: str, content_type="application/octet-stream"):
    s3.put_object(Bucket=R2_BUCKET, Key=key, Body=data, ContentType=content_type)

def download_file(key: str, local_path: str):
    s3.download_file(R2_BUCKET, key, local_path)

def exists(key: str) -> bool:
    try:
        s3.head_object(Bucket=R2_BUCKET, Key=key)
        return True
    except ClientError as e:
        if e.response["Error"]["Code"] in ("404", "NoSuchKey", "NotFound"):
            return False
        raise

def list_keys(prefix: str):
    out = []
    token = None
    while True:
        kwargs = {"Bucket": R2_BUCKET, "Prefix": prefix}
        if token:
            kwargs["ContinuationToken"] = token
        r = s3.list_objects_v2(**kwargs)
        for obj in r.get("Contents", []):
            out.append(obj["Key"])
        if not r.get("IsTruncated"):
            break
        token = r.get("NextContinuationToken")
    return out

def find_first(prefix: str, suffix: str = "") -> str | None:
    """Return first key under prefix (optionally ending with suffix), or None."""
    for k in list_keys(prefix):
        if k.endswith(suffix):
            return k
    return None


def find_first(prefix: str, suffix: str = "") -> str | None:

def get_size(key: str) -> int:
    """Return file size in bytes, or 0 if not found."""
    try:
        r = s3.head_object(Bucket=R2_BUCKET, Key=key)
        return r.get("ContentLength", 0)
    except ClientError:
        return 0

def find_first(prefix: str, suffix: str = "") -> str | None:


The file /home/ubuntu/karthyani_pipeline/worker/r2.py has been updated successfully.

def get_size(key: str) -> int:
    """Return file size in bytes, or 0 if not found."""
    try:
        r = s3.head_object(Bucket=R2_BUCKET, Key=key)
        return r.get("ContentLength", 0)
    except ClientError:
        return 0

def find_first(prefix: str, suffix: str = "") -> str | None:

"""Cloudflare R2 client (boto3 S3-compatible)."""
import os
import boto3
from botocore.config import Config
from botocore.exceptions import ClientError

R2_ENDPOINT = os.environ["R2_ENDPOINT"]
R2_BUCKET = os.environ["R2_BUCKET"]
R2_ACCESS_KEY = os.environ["R2_ACCESS_KEY"]
R2_SECRET_KEY = os.environ["R2_SECRET_KEY"]

s3 = boto3.client(
    "s3",
    endpoint_url=R2_ENDPOINT,
    aws_access_key_id=R2_ACCESS_KEY,
    aws_secret_access_key=R2_SECRET_KEY,
    region_name="auto",
    config=Config(signature_version="s3v4", retries={"max_attempts": 5}),
)

def upload_file(local_path: str, key: str):
    s3.upload_file(local_path, R2_BUCKET, key)

def upload_bytes(data: bytes, key: str, content_type="application/octet-stream"):
    s3.put_object(Bucket=R2_BUCKET, Key=key, Body=data, ContentType=content_type)

def download_file(key: str, local_path: str):
    s3.download_file(R2_BUCKET, key, local_path)

def exists(key: str) -> bool:
    try:
        s3.head_object(Bucket=R2_BUCKET, Key=key)
        return True
    except ClientError as e:
        if e.response["Error"]["Code"] in ("404", "NoSuchKey", "NotFound"):
            return False
        raise

def list_keys(prefix: str):
    out = []
    token = None
    while True:
        kwargs = {"Bucket": R2_BUCKET, "Prefix": prefix}
        if token:
            kwargs["ContinuationToken"] = token
        r = s3.list_objects_v2(**kwargs)
        for obj in r.get("Contents", []):
            out.append(obj["Key"])
        if not r.get("IsTruncated"):
            break
        token = r.get("NextContinuationToken")
    return out

def find_first(prefix: str, suffix: str = "") -> str | None:
    """Return first key under prefix (optionally ending with suffix), or None."""
    for k in list_keys(prefix):
        if k.endswith(suffix):
            return k
    return None

+    """Return file size in bytes, or 0 if not found."""

+        r = s3.head_object(Bucket=R2_BUCKET, Key=key)

+        return r.get("ContentLength", 0)


Now fix rgv_5 — set it back to `awaiting_solospeech` since it has 11m of overlap:


Bash

source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id FROM jobs WHERE name='rgv_5'\")
db.execute('UPDATE jobs SET status=?, error=NULL, worker_id=NULL WHERE id=?', ['awaiting_solospeech', rows[0]['id']])
print('rgv_5: set back to awaiting_solospeech')
"

Fix rgv_5 status back to awaiting_solospeech


rgv_5: set back to awaiting_solospeech


Also check if jagan_2 had the same issue:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN R2_ENDPOINT R2_BUCKET R2_ACCESS_KEY R2_SECRET_KEY && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db, r2

# Check all done jobs with overlaps but 0 cleaned
rows = db.query(\"SELECT id, name, num_overlap_segments, error FROM jobs WHERE status='done' AND num_overlap_segments > 0 AND (num_overlap_cleaned IS NULL OR num_overlap_cleaned = 0)\")
for row in rows:
    name = row['name']
    keys = [k for k in r2.list_keys(f'{name}/overlap_notcleaned/') if k.endswith('.wav')]
    total_dur = 0
    for k in keys:
        size = r2.get_size(k)
        if size > 44: total_dur += (size - 44) / 32000
    needs_fix = total_dur >= 300
    print(f\"{name:15s} overlaps={len(keys):3d}  dur={total_dur:7.1f}s ({total_dur/60:5.1f}m)  {'NEEDS FIX' if needs_fix else 'ok (skip)'}\")
"

Check all done jobs that might need SoloSpeech


jagan_3         overlaps= 11  dur=   63.4s (  1.1m)  ok (skip)
jagan_5         overlaps=  9  dur=   25.9s (  0.4m)  ok (skip)
rgv_4           overlaps= 22  dur=  217.5s (  3.6m)  ok (skip)
lavanya_3       overlaps=  3  dur=  154.0s (  2.6m)  ok (skip)
lavanya_7       overlaps=  1  dur=    7.0s (  0.1m)  ok (skip)
lavanya_8       overlaps=  1  dur=   37.9s (  0.6m)  ok (skip)
lavanya_9       overlaps=  3  dur=   62.9s (  1.0m)  ok (skip)


Only rgv_5 was wrongly skipped. All others are legitimately under 5 min. Now restart workers with the fixed code:


kill 1685458 1685459 1685460 1685461 1685462 2>/dev/null; sleep 1
cd /home/ubuntu/karthyani_pipeline/worker && set -a && source .env && set +a
mkdir -p /tmp/worker_logs
for i in 1 2 3 4 5; do
  nohup .venv/bin/python -u worker.py > /tmp/worker_logs/worker_$i.log 2>&1 &
  echo "Worker $i PID=$!"
done

Restart workers with fixed code


Worker 1 PID=1691485
Worker 2 PID=1691486
Worker 3 PID=1691487
Worker 4 PID=1691488
Worker 5 PID=1691489


sleep 5 && for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -4 /tmp/worker_logs/worker_$i.log; echo; done

Verify workers running


=== Worker 1 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691485-5f4199)

=== Worker 2 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691486-527bea)

=== Worker 3 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691487-a27c29)

=== Worker 4 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691488-b3f859)

=== Worker 5 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691489-beceaf)


sleep 10 && for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -4 /tmp/worker_logs/worker_$i.log; echo; done

Check worker activity


Workers booted but aren't picking up jobs — probably because all unclaimed jobs still have stale `worker_id` from old workers. Let me clear them:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id, name, status, worker_id FROM jobs WHERE worker_id IS NOT NULL\")
for r in rows:
    print(f\"{r['name']}: status={r['status']} worker={r['worker_id'][:30]}\")
    db.execute('UPDATE jobs SET worker_id=NULL WHERE id=?', [r['id']])
print(f'Cleared {len(rows)} stale locks')
"

Clear all stale worker locks


rgv_1: status=awaiting_solospeech worker=0321-dsm2-nvdgxa100-prxmx70052
samaray_1: status=diarized worker=0321-dsm2-nvdgxa100-prxmx70052
Cleared 2 stale locks


sleep 8 && for i in 1 2 3 4 5; do echo "=== Worker $i ==="; tail -5 /tmp/worker_logs/worker_$i.log; echo; done

Check workers picking up jobs


=== Worker 1 ===
[01:36:59]   ▶ segment…
[01:36:59]   (cached: cleaned=8 overlap=0 overlap_dur=0.0s)
[01:36:59]   ✓ segment  0.45s
[01:37:00]   segments: clean=8 overlap=0 overlap_dur=0.0s → done
[01:37:00] === job samaray_1 TIMINGS: {'segment': 0.45} ===

=== Worker 2 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691486-527bea)

=== Worker 3 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691487-a27c29)
[01:36:55] === job rgv_1 (start status=awaiting_solospeech) ===
[01:36:55]   ▶ solospeech…
[01:36:57]   picked offer 33845180 (A100 SXM4 ram=81920MB $1.069/hr)
[01:36:58]   instance_id=34499620

=== Worker 4 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691488-b3f859)

=== Worker 5 ===
[01:36:22] worker booted (id=0321-dsm2-nvdgxa100-prxmx70052-1691489-beceaf)


Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 278, in process_job
    )
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 156, in step_solospeech
    expected = set(Path(k).stem for k in overlap_keys if k.endswith(".wav"))
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 78, in create_instance
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 26, in _req
    raise RuntimeError(f"vast {method} {path} {r.status_code}: {r.text[:500]}")
RuntimeError: vast PUT /asks/33845180/ 400: {"success":false,"error":"invalid_args","msg":"error 404/3603: no_such_ask  Instance type by id 33845180 is not available.","ask_id":33845180}


Workers are running. rgv_1 is launching a GPU for SoloSpeech, samaray_1 finished segmenting (0 overlaps). But rgv_5 wasn't picked up yet — let me check:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT name, status, worker_id FROM jobs WHERE status NOT IN ('done','failed') ORDER BY created_at\")
for r in rows:
    print(f\"{r['name']:15s} {r['status']:25s} worker={r['worker_id'] or 'none'}\")
"

Check remaining non-done jobs


rgv_1           awaiting_solospeech       worker=0321-dsm2-nvdgxa100-prxmx70052-1691487-a27c29


Prompt:
Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 278, in process_job
    )
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 156, in step_solospeech
    expected = set(Path(k).stem for k in overlap_keys if k.endswith(".wav"))
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 78, in create_instance
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 26, in _req
    raise RuntimeError(f"vast {method} {path} {r.status_code}: {r.text[:500]}")
RuntimeError: vast PUT /asks/33845180/ 400: {"success":false,"error":"invalid_args","msg":"error 404/3603: no_such_ask  Instance type by id 33845180 is not available.","ask_id":33845180}


rgv_5 is missing — it went to done already from the old run. Let me fix it:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
rows = db.query(\"SELECT id, status, error FROM jobs WHERE name='rgv_5'\")
print(f\"rgv_5: status={rows[0]['status']} error={rows[0]['error']}\")
# It was set back to awaiting_solospeech earlier but worker 1 processed samaray_1 which was diarized,
# and maybe rgv_5 got processed by another worker already
"

Check rgv_5 current state


rgv_5: status=failed error=Traceback (most recent call last):
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 278, in process_job
    )
  File "/home/ubuntu/karthyani_pipeline/worker/worker.py", line 156, in step_solospeech
    expected = set(Path(k).stem for k in overlap_keys if k.endswith(".wav"))
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 78, in create_instance
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
  File "/home/ubuntu/karthyani_pipeline/worker/vast_runner.py", line 26, in _req
    raise RuntimeError(f"vast {method} {path} {r.status_code}: {r.text[:500]}")
RuntimeError: vast PUT /asks/33845178/ 400: {"success":false,"error":"invalid_args","msg":"error 404/3603: no_such_ask  Instance type by id 33845178 is not available.","ask_id":33845178}


Both rgv_5 and rgv_1 hit the same vast.ai error — the offer disappeared between `search_offer` and `create_instance`. This is a race condition with A100 spot instances. Let me retry both and also add a retry loop to the offer selection:


source /home/ubuntu/karthyani_pipeline/worker/.env && export TURSO_URL TURSO_TOKEN && python3 -c "
import sys; sys.path.insert(0, '/home/ubuntu/karthyani_pipeline/worker')
import db
for name in ['rgv_5', 'rgv_1']:
    rows = db.query('SELECT id, status, failed_at_status FROM jobs WHERE name=?', [name])
    if rows:
        r = rows[0]
        restore = r.get('failed_at_status') or 'awaiting_solospeech'
        db.execute('UPDATE jobs SET status=?, error=NULL, failed_at_status=NULL, worker_id=NULL WHERE id=?', [restore, r['id']])
        print(f'{name}: {r[\"status\"]} -> {restore}')
"

Retry rgv_5 and rgv_1


rgv_5: failed -> awaiting_solospeech
rgv_1: awaiting_solospeech -> awaiting_solospeech


Now add retry logic to `create_instance` in vast_runner.py so it tries the next offer if one disappears:


42	def search_offer(min_vram_mb=70000, max_price=1.20) -> dict:
43	    """Find the cheapest A100 80GB single-GPU offer with good reliability."""
44	    q = {
45	        "gpu_name": {"eq": "A100 SXM4"},
46	        "gpu_ram": {"gte": min_vram_mb},
47	        "num_gpus": {"eq": 1},
48	        "rentable": {"eq": True},
49	        "reliability2": {"gte": 0.95},
50	        "inet_down": {"gte": 500},
51	        "disk_space": {"gte": DISK_GB},
52	        "dph_total": {"lte": max_price},
53	        "order": [["dph_total", "asc"]],
54	        "limit": 5,
55	    }
56	    r = _req("GET", "/bundles/", params={"q": json.dumps(q)})
57	    offers = r.get("offers", [])
58	    if not offers:
59	        raise RuntimeError(f"no A100 80GB offers under ${max_price}/hr")
60	    return offers[0]
61
62
63	def create_instance(offer_id: int, pub_key: str) -> int:
64	    """Create a vast instance from an offer. Returns the instance id."""
65	    body = {
66	        "client_id": "me",
67	        "image": IMAGE,
68	        "disk": DISK_GB,
69	        "label": f"karthyani-{int(time.time())}",
70	        "onstart": (
71	            f"#!/bin/bash\n"
72	            f"mkdir -p /root/.ssh\n"
73	            f"echo '{pub_key}' >> /root/.ssh/authorized_keys\n"
74	            f"chmod 600 /root/.ssh/authorized_keys\n"
75	        ),
76	        "runtype": "ssh",
77	    }
78	    r = _req("PUT", f"/asks/{offer_id}/", json=body)
79	    if not r.get("success"):

def search_offer(min_vram_mb=70000, max_price=1.20) -> dict:
    """Find the cheapest A100 80GB single-GPU offer with good reliability."""
    q = {
        "gpu_name": {"eq": "A100 SXM4"},
        "gpu_ram": {"gte": min_vram_mb},
        "num_gpus": {"eq": 1},
        "rentable": {"eq": True},
        "reliability2": {"gte": 0.95},
        "inet_down": {"gte": 500},
        "disk_space": {"gte": DISK_GB},
        "dph_total": {"lte": max_price},
        "order": [["dph_total", "asc"]],
        "limit": 5,
    }
    r = _req("GET", "/bundles/", params={"q": json.dumps(q)})
    offers = r.get("offers", [])
    if not offers:
        raise RuntimeError(f"no A100 80GB offers under ${max_price}/hr")
    return offers[0]

def create_instance(offer_id: int, pub_key: str) -> int:
    """Create a vast instance from an offer. Returns the instance id."""
    body = {
        "client_id": "me",
        "image": IMAGE,
        "disk": DISK_GB,
        "label": f"karthyani-{int(time.time())}",
        "onstart": (
            f"#!/bin/bash\n"
            f"mkdir -p /root/.ssh\n"
            f"echo '{pub_key}' >> /root/.ssh/authorized_keys\n"
            f"chmod 600 /root/.ssh/authorized_keys\n"
        ),
        "runtype": "ssh",
    }
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
    if not r.get("success"):


Edit

def search_offer(min_vram_mb=70000, max_price=1.20) -> dict:
    """Find the cheapest A100 80GB single-GPU offer with good reliability."""
    q = {
        "gpu_name": {"eq": "A100 SXM4"},
        "gpu_ram": {"gte": min_vram_mb},
        "num_gpus": {"eq": 1},
        "rentable": {"eq": True},
        "reliability2": {"gte": 0.95},
        "inet_down": {"gte": 500},
        "disk_space": {"gte": DISK_GB},
        "dph_total": {"lte": max_price},
        "order": [["dph_total", "asc"]],
        "limit": 5,
    }
    r = _req("GET", "/bundles/", params={"q": json.dumps(q)})
    offers = r.get("offers", [])
    if not offers:
        raise RuntimeError(f"no A100 80GB offers under ${max_price}/hr")
    return offers[0]

def create_instance(offer_id: int, pub_key: str) -> int:
    """Create a vast instance from an offer. Returns the instance id."""
    body = {
        "client_id": "me",
        "image": IMAGE,
        "disk": DISK_GB,
        "label": f"karthyani-{int(time.time())}",
        "onstart": (
            f"#!/bin/bash\n"
            f"mkdir -p /root/.ssh\n"
            f"echo '{pub_key}' >> /root/.ssh/authorized_keys\n"
            f"chmod 600 /root/.ssh/authorized_keys\n"
        ),
        "runtype": "ssh",
    }
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
    if not r.get("success"):
        raise RuntimeError(f"create_instance failed: {r}")
    return r["new_contract"]

def search_offers(min_vram_mb=70000, max_price=1.20) -> list:
    """Find cheapest A100 80GB single-GPU offers with good reliability."""
    q = {
        "gpu_name": {"eq": "A100 SXM4"},
        "gpu_ram": {"gte": min_vram_mb},
        "num_gpus": {"eq": 1},
        "rentable": {"eq": True},
        "reliability2": {"gte": 0.95},
        "inet_down": {"gte": 500},
        "disk_space": {"gte": DISK_GB},
        "dph_total": {"lte": max_price},
        "order": [["dph_total", "asc"]],
        "limit": 10,
    }
    r = _req("GET", "/bundles/", params={"q": json.dumps(q)})
    offers = r.get("offers", [])
    if not offers:
        raise RuntimeError(f"no A100 80GB offers under ${max_price}/hr")
    return offers

def search_offer(min_vram_mb=70000, max_price=1.20) -> dict:
    return search_offers(min_vram_mb, max_price)[0]

def create_instance(offer_id: int, pub_key: str) -> int:
    """Create a vast instance from an offer. Returns the instance id."""
    body = {
        "client_id": "me",
        "image": IMAGE,
        "disk": DISK_GB,
        "label": f"karthyani-{int(time.time())}",
        "onstart": (
            f"#!/bin/bash\n"
            f"mkdir -p /root/.ssh\n"
            f"echo '{pub_key}' >> /root/.ssh/authorized_keys\n"
            f"chmod 600 /root/.ssh/authorized_keys\n"
        ),
        "runtype": "ssh",
    }
    r = _req("PUT", f"/asks/{offer_id}/", json=body)
    if not r.get("success"):
        raise RuntimeError(f"create_instance failed: {r}")
    return r["new_contract"]