**Specifying timer placement in key functions**

I'm detailing exact timer insertions around download steps, database fetch, tarfile open and processing, and segment handling, including how to avoid double counting and prepare for later output parsing to compute per-task and per-video timing summaries.


**Planning focused timing additions**

I'm identifying significant pipeline tasks missing timers (download, tar extraction, JSON parsing) to add precise timing via the existing profile system, then run tests on 1, 10, and 100 videos with multi-worker to collect detailed performance data.

**Detailing timer insertion and run configurations**

I'm mapping multiple precise timers around download, DB fetch, tar operations, segment processing, and output writing to capture comprehensive task durations, then planning runs on 1 and 10 videos single-worker and 10 and 100 videos multi-worker with language filter and unique temp dirs to avoid cache effects, ensuring the full profile-timing context for detailed analysis.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
 def resolve_r2_key(
     s3, bucket: str, r2_key: str, prefix: Optional[str]
 ) -> Optional[str]:
@@
     for candidate in candidates:
         try:
-            s3.head_object(Bucket=bucket, Key=candidate)
+            with timer("r2_head_object"):
+                s3.head_object(Bucket=bucket, Key=candidate)
             return candidate
         except Exception:
             continue
     return None
@@
 def process_segment(
     video_id: str,
     language: str,
@@
     silence_cfg: SilenceConfig,
     rng_seed: int,
 ) -> tuple[list[SegmentOutput], dict[str, int]]:
-    stats = {
-        "dropped_too_short": 0,
-        "dropped_too_long": 0,
-        "dropped_clipped": 0,
-        "chunks_emitted": 0,
-        "segments_chunked": 0,
-    }
-    outputs: list[SegmentOutput] = []
-
-    with timer("flac_decode"):
-        try:
-            audio, sr = sf.read(io.BytesIO(segment_bytes), dtype="float32")
-        except Exception:
-            return outputs, stats
-
-    if audio.ndim > 1:
-        audio = np.mean(audio, axis=1)
-
-    rng = random.Random(rng_seed)
-    windows = make_chunk_windows(audio, sr, chunk_cfg, silence_cfg, rng)
-
-    music_stats = segment_meta.get("music_stats") or {}
-    has_music = bool(music_stats.get("has_music", False))
-
-    def emit_outputs(
-        audio_slice: np.ndarray,
-        metrics: dict[str, float],
-        sample_id: str,
-        segment_index: int,
-        chunk_index: Optional[int],
-        start: Optional[float],
-        end: Optional[float],
-        original_start: Optional[float],
-        original_end: Optional[float],
-        target_srs: list[int],
-    ) -> None:
-        slice_duration = float(audio_slice.shape[0] / sr)
-        bucket = duration_bucket(slice_duration)
-        if bucket is None:
-            if slice_duration < 3.0:
-                stats["dropped_too_short"] += 1
-            else:
-                stats["dropped_too_long"] += 1
-            return
-        if metrics["clipping_fraction"] > 0.1:
-            stats["dropped_clipped"] += 1
-            return
-
-        duration_bucket_name, base_weight = bucket
-        sampling_weight = base_weight * (0.5 if has_music else 1.0)
-        quality_score = compute_quality_score(metrics, has_music)
-        effective_nyquist = metrics["effective_nyquist_hz"]
-
-        base_metadata = {
-            "id": sample_id,
-            "video_id": video_id,
-            "language": language,
-            "segment_index": segment_index,
-            "chunk_index": chunk_index,
-            "start": start,
-            "end": end,
-            "original_start": original_start,
-            "original_end": original_end,
-            "duration_sec": slice_duration,
-            "native_sr": sr,
-            "effective_nyquist_hz": effective_nyquist,
-            "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
-            "high_freq_energy_ratio": metrics["high_freq_energy_ratio"],
-            "is_truly_wideband": bool(effective_nyquist >= 12000),
-            "native_sr_bucket": classify_native_bucket(effective_nyquist),
-            "loudness_lufs": metrics["loudness_lufs"],
-            "loudness_rms_db": metrics["loudness_rms_db"],
-            "clipping_fraction": metrics["clipping_fraction"],
-            "snr_proxy": metrics["snr_proxy"],
-            "quality_score": quality_score,
-            "music_prob": float(music_stats.get("music_mean", 0.0)),
-            "music_stats": music_stats,
-            "speaker": speaker,
-            # VAD regions: Full duration placeholder; random 6s crops happen during training.
-            "vad_speech_regions": [[0.0, slice_duration]],
-            "duration_bucket": duration_bucket_name,
-            "sampling_weight": sampling_weight,
-        }
-
-        for target_sr in target_srs:
-            resampled = resample_audio(audio_slice, sr, target_sr)
-            resampled = np.clip(resampled, -1.0, 1.0)
-            audio_bytes = encode_flac(resampled, target_sr)
-            meta = dict(base_metadata)
-            meta["sample_rate"] = target_sr
-            meta["resample_target_sr"] = target_sr
-            outputs.append(SegmentOutput(target_sr, audio_bytes, meta))
-
-        if target_srs == [16000]:
-            stats["chunks_emitted"] += 1
-
-    sample_id_base = f"{video_id}__{speaker}_{index:04d}"
-    use_chunk_suffix = len(windows) > 1
-    if use_chunk_suffix:
-        stats["segments_chunked"] += 1
-
-    segment_start = segment_meta.get("start")
-    segment_end = segment_meta.get("end")
-    original_start = segment_meta.get("original_start")
-    original_end = segment_meta.get("original_end")
-
-    # Always emit 16k outputs; for long segments, emit chunked windows to boost sample count.
-    for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
-        start_idx = int(round(offset_sec * sr))
-        end_idx = int(round((offset_sec + chunk_len_sec) * sr))
-        audio_slice = audio[start_idx:end_idx]
-        if audio_slice.size == 0:
-            continue
-        slice_duration = float(audio_slice.shape[0] / sr)
-        slice_metrics = compute_audio_metrics(audio_slice, sr)
-
-        chunk_start = float(segment_start) + offset_sec if segment_start is not None else None
-        chunk_end = chunk_start + slice_duration if chunk_start is not None else None
-        chunk_original_start = (
-            float(original_start) + offset_sec if original_start is not None else None
-        )
-        chunk_original_end = (
-            chunk_original_start + slice_duration
-            if chunk_original_start is not None
-            else None
-        )
-
-        sample_id = sample_id_base
-        chunk_index = None
-        if use_chunk_suffix:
-            sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
-            chunk_index = chunk_idx
-
-        emit_outputs(
-            audio_slice,
-            slice_metrics,
-            sample_id,
-            index,
-            chunk_index,
-            chunk_start,
-            chunk_end,
-            chunk_original_start,
-            chunk_original_end,
-            [16000],
-        )
-
-    # Optional high-rate buckets: use full segment unless configured otherwise.
-    full_metrics = compute_audio_metrics(audio, sr)
-    effective_nyquist = full_metrics["effective_nyquist_hz"]
-    targets_high: list[int] = []
-    if effective_nyquist >= 8000:
-        targets_high.append(24000)
-    if effective_nyquist >= 12000:
-        targets_high.append(32000)
-    if effective_nyquist >= 16000:
-        targets_high.append(44100)
-
-    if targets_high:
-        if chunk_cfg.all_buckets and use_chunk_suffix:
-            for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
-                start_idx = int(round(offset_sec * sr))
-                end_idx = int(round((offset_sec + chunk_len_sec) * sr))
-                audio_slice = audio[start_idx:end_idx]
-                if audio_slice.size == 0:
-                    continue
-                slice_duration = float(audio_slice.shape[0] / sr)
-                slice_metrics = compute_audio_metrics(audio_slice, sr)
-                slice_effective_nyquist = slice_metrics["effective_nyquist_hz"]
-                slice_targets: list[int] = []
-                if slice_effective_nyquist >= 8000:
-                    slice_targets.append(24000)
-                if slice_effective_nyquist >= 12000:
-                    slice_targets.append(32000)
-                if slice_effective_nyquist >= 16000:
-                    slice_targets.append(44100)
-                if not slice_targets:
-                    continue
-
-                chunk_start = (
-                    float(segment_start) + offset_sec if segment_start is not None else None
-                )
-                chunk_end = (
-                    chunk_start + slice_duration if chunk_start is not None else None
-                )
-                chunk_original_start = (
-                    float(original_start) + offset_sec if original_start is not None else None
-                )
-                chunk_original_end = (
-                    chunk_original_start + slice_duration
-                    if chunk_original_start is not None
-                    else None
-                )
-
-                sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
-                emit_outputs(
-                    audio_slice,
-                    slice_metrics,
-                    sample_id,
-                    index,
-                    chunk_idx,
-                    chunk_start,
-                    chunk_end,
-                    chunk_original_start,
-                    chunk_original_end,
-                    slice_targets,
-                )
-        else:
-            emit_outputs(
-                audio,
-                full_metrics,
-                sample_id_base,
-                index,
-                None,
-                segment_start,
-                segment_end,
-                original_start,
-                original_end,
-                targets_high,
-            )
-
-    return outputs, stats
+    with timer("segment_total"):
+        stats = {
+            "dropped_too_short": 0,
+            "dropped_too_long": 0,
+            "dropped_clipped": 0,
+            "chunks_emitted": 0,
+            "segments_chunked": 0,
+        }
+        outputs: list[SegmentOutput] = []
+
+        with timer("flac_decode"):
+            try:
+                audio, sr = sf.read(io.BytesIO(segment_bytes), dtype="float32")
+            except Exception:
+                return outputs, stats
+
+        if audio.ndim > 1:
+            audio = np.mean(audio, axis=1)
+
+        rng = random.Random(rng_seed)
+        windows = make_chunk_windows(audio, sr, chunk_cfg, silence_cfg, rng)
+
+        music_stats = segment_meta.get("music_stats") or {}
+        has_music = bool(music_stats.get("has_music", False))
+
+        def emit_outputs(
+            audio_slice: np.ndarray,
+            metrics: dict[str, float],
+            sample_id: str,
+            segment_index: int,
+            chunk_index: Optional[int],
+            start: Optional[float],
+            end: Optional[float],
+            original_start: Optional[float],
+            original_end: Optional[float],
+            target_srs: list[int],
+        ) -> None:
+            slice_duration = float(audio_slice.shape[0] / sr)
+            bucket = duration_bucket(slice_duration)
+            if bucket is None:
+                if slice_duration < 3.0:
+                    stats["dropped_too_short"] += 1
+                else:
+                    stats["dropped_too_long"] += 1
+                return
+            if metrics["clipping_fraction"] > 0.1:
+                stats["dropped_clipped"] += 1
+                return
+
+            duration_bucket_name, base_weight = bucket
+            sampling_weight = base_weight * (0.5 if has_music else 1.0)
+            quality_score = compute_quality_score(metrics, has_music)
+            effective_nyquist = metrics["effective_nyquist_hz"]
+
+            base_metadata = {
+                "id": sample_id,
+                "video_id": video_id,
+                "language": language,
+                "segment_index": segment_index,
+                "chunk_index": chunk_index,
+                "start": start,
+                "end": end,
+                "original_start": original_start,
+                "original_end": original_end,
+                "duration_sec": slice_duration,
+                "native_sr": sr,
+                "effective_nyquist_hz": effective_nyquist,
+                "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
+                "high_freq_energy_ratio": metrics["high_freq_energy_ratio"],
+                "is_truly_wideband": bool(effective_nyquist >= 12000),
+                "native_sr_bucket": classify_native_bucket(effective_nyquist),
+                "loudness_lufs": metrics["loudness_lufs"],
+                "loudness_rms_db": metrics["loudness_rms_db"],
+                "clipping_fraction": metrics["clipping_fraction"],
+                "snr_proxy": metrics["snr_proxy"],
+                "quality_score": quality_score,
+                "music_prob": float(music_stats.get("music_mean", 0.0)),
+                "music_stats": music_stats,
+                "speaker": speaker,
+                # VAD regions: Full duration placeholder; random 6s crops happen during training.
+                "vad_speech_regions": [[0.0, slice_duration]],
+                "duration_bucket": duration_bucket_name,
+                "sampling_weight": sampling_weight,
+            }
+
+            for target_sr in target_srs:
+                resampled = resample_audio(audio_slice, sr, target_sr)
+                resampled = np.clip(resampled, -1.0, 1.0)
+                audio_bytes = encode_flac(resampled, target_sr)
+                meta = dict(base_metadata)
+                meta["sample_rate"] = target_sr
+                meta["resample_target_sr"] = target_sr
+                outputs.append(SegmentOutput(target_sr, audio_bytes, meta))
+
+            if target_srs == [16000]:
+                stats["chunks_emitted"] += 1
+
+        sample_id_base = f"{video_id}__{speaker}_{index:04d}"
+        use_chunk_suffix = len(windows) > 1
+        if use_chunk_suffix:
+            stats["segments_chunked"] += 1
+
+        segment_start = segment_meta.get("start")
+        segment_end = segment_meta.get("end")
+        original_start = segment_meta.get("original_start")
+        original_end = segment_meta.get("original_end")
+
+        # Always emit 16k outputs; for long segments, emit chunked windows to boost sample count.
+        for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
+            start_idx = int(round(offset_sec * sr))
+            end_idx = int(round((offset_sec + chunk_len_sec) * sr))
+            audio_slice = audio[start_idx:end_idx]
+            if audio_slice.size == 0:
+                continue
+            slice_duration = float(audio_slice.shape[0] / sr)
+            slice_metrics = compute_audio_metrics(audio_slice, sr)
+
+            chunk_start = float(segment_start) + offset_sec if segment_start is not None else None
+            chunk_end = chunk_start + slice_duration if chunk_start is not None else None
+            chunk_original_start = (
+                float(original_start) + offset_sec if original_start is not None else None
+            )
+            chunk_original_end = (
+                chunk_original_start + slice_duration
+                if chunk_original_start is not None
+                else None
+            )
+
+            sample_id = sample_id_base
+            chunk_index = None
+            if use_chunk_suffix:
+                sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
+                chunk_index = chunk_idx
+
+            emit_outputs(
+                audio_slice,
+                slice_metrics,
+                sample_id,
+                index,
+                chunk_index,
+                chunk_start,
+                chunk_end,
+                chunk_original_start,
+                chunk_original_end,
+                [16000],
+            )
+
+        # Optional high-rate buckets: use full segment unless configured otherwise.
+        full_metrics = compute_audio_metrics(audio, sr)
+        effective_nyquist = full_metrics["effective_nyquist_hz"]
+        targets_high: list[int] = []
+        if effective_nyquist >= 8000:
+            targets_high.append(24000)
+        if effective_nyquist >= 12000:
+            targets_high.append(32000)
+        if effective_nyquist >= 16000:
+            targets_high.append(44100)
+
+        if targets_high:
+            if chunk_cfg.all_buckets and use_chunk_suffix:
+                for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
+                    start_idx = int(round(offset_sec * sr))
+                    end_idx = int(round((offset_sec + chunk_len_sec) * sr))
+                    audio_slice = audio[start_idx:end_idx]
+                    if audio_slice.size == 0:
+                        continue
+                    slice_duration = float(audio_slice.shape[0] / sr)
+                    slice_metrics = compute_audio_metrics(audio_slice, sr)
+                    slice_effective_nyquist = slice_metrics["effective_nyquist_hz"]
+                    slice_targets: list[int] = []
+                    if slice_effective_nyquist >= 8000:
+                        slice_targets.append(24000)
+                    if slice_effective_nyquist >= 12000:
+                        slice_targets.append(32000)
+                    if slice_effective_nyquist >= 16000:
+                        slice_targets.append(44100)
+                    if not slice_targets:
+                        continue
+
+                    chunk_start = (
+                        float(segment_start) + offset_sec if segment_start is not None else None
+                    )
+                    chunk_end = (
+                        chunk_start + slice_duration if chunk_start is not None else None
+                    )
+                    chunk_original_start = (
+                        float(original_start) + offset_sec if original_start is not None else None
+                    )
+                    chunk_original_end = (
+                        chunk_original_start + slice_duration
+                        if chunk_original_start is not None
+                        else None
+                    )
+
+                    sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
+                    emit_outputs(
+                        audio_slice,
+                        slice_metrics,
+                        sample_id,
+                        index,
+                        chunk_idx,
+                        chunk_start,
+                        chunk_end,
+                        chunk_original_start,
+                        chunk_original_end,
+                        slice_targets,
+                    )
+            else:
+                emit_outputs(
+                    audio,
+                    full_metrics,
+                    sample_id_base,
+                    index,
+                    None,
+                    segment_start,
+                    segment_end,
+                    original_start,
+                    original_end,
+                    targets_high,
+                )
+
+        return outputs, stats
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Adding detailed timers**

I'm adding more fine-grained timers for download, tar extraction, metadata loading, and other steps to improve performance tracking.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
     def download_record(record: VideoRecord) -> Path:
-        resolved = resolve_r2_key(s3, cleaned_bucket, record.r2_tar_key, prefix)
-        if not resolved:
-            raise FileNotFoundError(f"Missing tar in R2: {record.r2_tar_key}")
-        local_path = download_dir / f"{record.youtube_id}.tar"
-        if not local_path.exists():
-            s3.download_file(cleaned_bucket, resolved, str(local_path), Config=config)
-        return local_path
+        with timer("r2_download_total"):
+            resolved = resolve_r2_key(s3, cleaned_bucket, record.r2_tar_key, prefix)
+            if not resolved:
+                raise FileNotFoundError(f"Missing tar in R2: {record.r2_tar_key}")
+            local_path = download_dir / f"{record.youtube_id}.tar"
+            if not local_path.exists():
+                s3.download_file(cleaned_bucket, resolved, str(local_path), Config=config)
+            return local_path
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto


**Adding timers around key operations**


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
-    records = fetch_video_records(
-        db_url,
-        language,
-        args.limit,
-        args.partition_count,
-        args.partition_index,
-    )
+    with timer("db_fetch_records"):
+        records = fetch_video_records(
+            db_url,
+            language,
+            args.limit,
+            args.partition_count,
+            args.partition_index,
+        )
@@
-    def handle_future(future) -> None:
+    def handle_future(future) -> None:
         is_val = future_info.pop(future, False)
         outputs, seg_stats = future.result()
@@
-            writer = writers[out.sample_rate]
-            shard_name = writer.add_sample(out.metadata["id"], out.audio_bytes, out.metadata)
+            writer = writers[out.sample_rate]
+            with timer("shard_write"):
+                shard_name = writer.add_sample(
+                    out.metadata["id"], out.audio_bytes, out.metadata
+                )
             manifest_entry = dict(out.metadata)
             manifest_entry["shard"] = f"{shard_name}.tar"
             manifest_entry["audio"] = f"{out.metadata['id']}.flac"
             line = json.dumps(manifest_entry) + "\n"
             if is_val:
                 val_manifest.write(line)
             else:
                 train_manifest.write(line)
@@
-        for video_id, tar_path in tqdm(download_paths.items(), desc="process"):
+        for video_id, tar_path in tqdm(download_paths.items(), desc="process"):
             stats["videos"] += 1
             is_val = video_id in val_ids
-            with tarfile.open(tar_path, "r") as tf:
-                meta = json.loads(tf.extractfile(f"{video_id}/metadata.json").read())
-                usable_segments = [
-                    s for s in meta.get("segments", []) if s.get("status") == "usable"
-                ]
-                # Create lookup by (speaker, start) for robust matching
-                # The filename contains 'start' timestamp (relative to intro-skipped audio),
-                # not 'original_start' (absolute time in original video)
-                segment_map = {
-                    (seg.get("speaker"), round(seg.get("start", 0.0), 2)): seg
-                    for seg in usable_segments
-                }
-
-                members = [m for m in tf.getmembers() if m.name.endswith(".flac")]
-                stats["segments_total"] += len(members)
+            with timer("tar_open"):
+                tf = tarfile.open(tar_path, "r")
+            with tf:
+                with timer("tar_meta_load"):
+                    meta = json.loads(
+                        tf.extractfile(f"{video_id}/metadata.json").read()
+                    )
+                usable_segments = [
+                    s for s in meta.get("segments", []) if s.get("status") == "usable"
+                ]
+                # Create lookup by (speaker, start) for robust matching
+                # The filename contains 'start' timestamp (relative to intro-skipped audio),
+                # not 'original_start' (absolute time in original video)
+                with timer("tar_build_segment_map"):
+                    segment_map = {
+                        (seg.get("speaker"), round(seg.get("start", 0.0), 2)): seg
+                        for seg in usable_segments
+                    }
+
+                with timer("tar_list_members"):
+                    members = [m for m in tf.getmembers() if m.name.endswith(".flac")]
+                stats["segments_total"] += len(members)
@@
-                    segment_bytes = tf.extractfile(member).read()
+                    with timer("tar_extract_segment"):
+                        segment_bytes = tf.extractfile(member).read()
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adding timers and preparing single-video test**

I added timers around fetch, segment map generation, tar open, and download records (including skipped downloads) with consistent naming. I configured a single-video test run using one worker each for download and segment, setting target shard size and directories, then prepared to run tests with profiling enabled.


python3 scripts/prepare_shards.py --language telugu --limit 1 --output-dir /tmp/xcodec_profile/one_single/out --tmp-dir /tmp/xcodec_profile/one_single/tmp --target-shard-size-mb 64 --download-workers 1 --segment-workers 1 --profile-timing


Output:
Exit code: 0
Wall time: 15.8 seconds
Output:

download:   0%|          | 0/1 [00:00<?, ?it/s]
download: 100%|██████████| 1/1 [00:00<00:00,  2.22it/s]
download: 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]

process:   0%|          | 0/1 [00:00<?, ?it/s]
process: 100%|██████████| 1/1 [00:00<00:00, 92.44it/s]

=== Timing Statistics ===
audio_metrics_basic                :     51 calls,     0.35s total,     6.79ms avg,   1.7%
audio_metrics_lufs                 :     51 calls,     0.71s total,    13.96ms avg,   3.5%
cpu_resample                       :     77 calls,     0.91s total,    11.78ms avg,   4.4%
cpu_spectral_compute               :     51 calls,     0.28s total,     5.49ms avg,   1.4%
db_fetch_records                   :      1 calls,     8.84s total,  8841.55ms avg,  43.3%
flac_decode                        :     16 calls,     0.57s total,    35.69ms avg,   2.8%
flac_encode                        :     77 calls,     2.58s total,    33.50ms avg,  12.6%
r2_download_total                  :      1 calls,     0.46s total,   457.64ms avg,   2.2%
r2_head_object                     :      1 calls,     0.14s total,   137.99ms avg,   0.7%
segment_total                      :     16 calls,     5.55s total,   347.00ms avg,  27.2%
shard_write                        :     77 calls,     0.04s total,     0.49ms avg,   0.2%
tar_build_segment_map              :      1 calls,     0.00s total,     0.04ms avg,   0.0%
tar_extract_segment                :     16 calls,     0.01s total,     0.46ms avg,   0.0%
tar_list_members                   :      1 calls,     0.00s total,     0.01ms avg,   0.0%
tar_meta_load                      :      1 calls,     0.00s total,     1.47ms avg,   0.0%
tar_open                           :      1 calls,     0.00s total,     0.59ms avg,   0.0%
TOTAL                              :                 20.43s
================================================================================
Done. Stats written to /tmp/xcodec_profile/one_single/out/stats/telugu_stats.json


**Noting slow database fetch**

The database fetch task takes 8.84s, likely from a supabase query, which is notably large. The timing data isn't wall time but can still help identify slow tasks.


python3 scripts/prepare_shards.py --language telugu --limit 10 --output-dir /tmp/xcodec_profile/ten_single/out --tmp-dir /tmp/xcodec_profile/ten_single/tmp --target-shard-size-mb 64 --download-workers 1 --segment-workers 1 --profile-timing


Output:
Exit code: 124
Wall time: 120.1 seconds
Output:
command timed out after 120089 milliseconds

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download:  10%|█         | 1/10 [00:00<00:07,  1.26it/s]
download:  20%|██        | 2/10 [00:01<00:05,  1.34it/s]
download:  30%|███       | 3/10 [00:01<00:03,  1.94it/s]
download:  40%|████      | 4/10 [00:02<00:02,  2.00it/s]
download:  50%|█████     | 5/10 [00:02<00:02,  2.32it/s]
download:  60%|██████    | 6/10 [00:03<00:02,  1.68it/s]
download:  70%|███████   | 7/10 [00:04<00:01,  1.52it/s]
download:  80%|████████  | 8/10 [00:04<00:01,  1.46it/s]
download:  90%|█████████ | 9/10 [00:05<00:00,  1.43it/s]
download: 100%|██████████| 10/10 [00:06<00:00,  1.42it/s]
download: 100%|██████████| 10/10 [00:06<00:00,  1.56it/s]

process:   0%|          | 0/10 [00:00<?, ?it/s]
process:  10%|█         | 1/10 [00:06<01:00,  6.76s/it]
process:  20%|██        | 2/10 [00:20<01:25, 10.63s/it]
process:  30%|███       | 3/10 [00:20<00:42,  6.02s/it]
process:  40%|████      | 4/10 [00:32<00:49,  8.28s/it]
process:  50%|█████     | 5/10 [00:34<00:30,  6.17s/it]
process:  60%|██████    | 6/10 [00:40<00:24,  6.01s/it]
process:  70%|███████   | 7/10 [00:48<00:20,  6.79s/it]
process:  80%|████████  | 8/10 [01:19<00:28, 14.49s/it]
process:  90%|█████████ | 9/10 [01:38<00:15, 15.84s/it]


**Checking for lingering processes before rerun**

I'm verifying if the previous Python process is still running to avoid overlapping runs before increasing the timeout and rerunning.


Output:
Exit code: 1
Wall time: 0 seconds
Output:


**Extending test timeout**

Tests timed out with no active process, so I'll rerun with a longer 5-minute timeout and consider lowering segment workers or limits to reduce execution time.


Output:
Exit code: 0
Wall time: 152.3 seconds
Output:

download:   0%|          | 0/10 [00:00<?, ?it/s]
download:  10%|█         | 1/10 [00:01<00:12,  1.39s/it]
download:  20%|██        | 2/10 [00:01<00:06,  1.25it/s]
download:  30%|███       | 3/10 [00:02<00:03,  1.84it/s]
download:  40%|████      | 4/10 [00:02<00:02,  2.10it/s]
download:  50%|█████     | 5/10 [00:04<00:04,  1.07it/s]
download:  60%|██████    | 6/10 [00:04<00:03,  1.29it/s]
download:  70%|███████   | 7/10 [00:05<00:02,  1.03it/s]
download:  80%|████████  | 8/10 [00:08<00:02,  1.49s/it]
download:  90%|█████████ | 9/10 [00:11<00:02,  2.07s/it]
download: 100%|██████████| 10/10 [00:14<00:00,  2.14s/it]
download: 100%|██████████| 10/10 [00:14<00:00,  1.42s/it]

process:   0%|          | 0/10 [00:00<?, ?it/s]
process:  10%|█         | 1/10 [00:10<01:34, 10.47s/it]
process:  20%|██        | 2/10 [00:14<00:55,  6.92s/it]
process:  30%|███       | 3/10 [00:18<00:38,  5.50s/it]
process:  40%|████      | 4/10 [00:25<00:35,  5.88s/it]
process:  50%|█████     | 5/10 [00:37<00:41,  8.38s/it]
process:  60%|██████    | 6/10 [00:38<00:23,  5.81s/it]
process:  70%|███████   | 7/10 [00:40<00:12,  4.33s/it]
process:  80%|████████  | 8/10 [01:09<00:24, 12.27s/it]
process:  90%|█████████ | 9/10 [01:40<00:18, 18.15s/it]
process: 100%|██████████| 10/10 [01:48<00:00, 15.17s/it]
process: 100%|██████████| 10/10 [01:48<00:00, 10.89s/it]

=== Timing Statistics ===
audio_metrics_basic                :   3104 calls,     9.37s total,     3.02ms avg,   3.4%
audio_metrics_lufs                 :   3104 calls,    18.52s total,     5.97ms avg,   6.6%
cpu_resample                       :   2726 calls,    16.74s total,     6.14ms avg,   6.0%
cpu_spectral_compute               :   3104 calls,    23.12s total,     7.45ms avg,   8.3%
db_fetch_records                   :      1 calls,     9.33s total,  9331.93ms avg,   3.3%
flac_decode                        :   1405 calls,    16.89s total,    12.02ms avg,   6.1%
flac_encode                        :   2726 calls,    41.01s total,    15.04ms avg,  14.7%
r2_download_total                  :     10 calls,    14.22s total,  1421.98ms avg,   5.1%
r2_head_object                     :     10 calls,     0.78s total,    77.71ms avg,   0.3%
segment_total                      :   1405 calls,   127.67s total,    90.87ms avg,  45.8%
shard_write                        :   2726 calls,     1.04s total,     0.38ms avg,   0.4%
tar_build_segment_map              :     10 calls,     0.00s total,     0.17ms avg,   0.0%
tar_extract_segment                :   1405 calls,     0.17s total,     0.12ms avg,   0.1%
tar_list_members                   :     10 calls,     0.00s total,     0.14ms avg,   0.0%
tar_meta_load                      :     10 calls,     0.11s total,    11.42ms avg,   0.0%
tar_open                           :     10 calls,     0.01s total,     0.66ms avg,   0.0%
TOTAL                              :                278.96s
================================================================================
Done. Stats written to /tmp/xcodec_profile/ten_single/out/stats/telugu_stats.json


**Configuring multi-worker analysis parameters**

Planning a 10-video multi-worker run using 8 download and 8 segment workers, testing with a segment max inflight setting left at default (128) and a timeout extended to 180,000 ms to accommodate potential run times.


python3 scripts/prepare_shards.py --language telugu --limit 10 --output-dir /tmp/xcodec_profile/ten_multi/out --tmp-dir /tmp/xcodec_profile/ten_multi/tmp --target-shard-size-mb 64 --download-workers 8 --segment-workers 8 --profile-timing


Output:
Exit code: 0
Wall time: 27.6 seconds
Output:

download:   0%|          | 0/10 [00:00<?, ?it/s]
download:  10%|█         | 1/10 [00:00<00:07,  1.21it/s]
download:  30%|███       | 3/10 [00:00<00:01,  3.74it/s]
download:  50%|█████     | 5/10 [00:01<00:00,  6.15it/s]
download:  80%|████████  | 8/10 [00:01<00:00,  9.11it/s]
download: 100%|██████████| 10/10 [00:01<00:00,  8.74it/s]
download: 100%|██████████| 10/10 [00:01<00:00,  6.53it/s]

process:   0%|          | 0/10 [00:00<?, ?it/s]
process:  40%|████      | 4/10 [00:01<00:01,  3.62it/s]
process:  50%|█████     | 5/10 [00:02<00:02,  1.71it/s]
process:  60%|██████    | 6/10 [00:03<00:02,  1.79it/s]
process:  70%|███████   | 7/10 [00:05<00:03,  1.10s/it]
process:  80%|████████  | 8/10 [00:06<00:02,  1.17s/it]
process:  90%|█████████ | 9/10 [00:10<00:01,  1.86s/it]
process: 100%|██████████| 10/10 [00:12<00:00,  1.86s/it]
process: 100%|██████████| 10/10 [00:12<00:00,  1.23s/it]

=== Timing Statistics ===
audio_metrics_basic                :   1819 calls,     8.55s total,     4.70ms avg,   3.0%
audio_metrics_lufs                 :   1819 calls,    25.29s total,    13.90ms avg,   8.9%
cpu_resample                       :   1611 calls,    11.71s total,     7.27ms avg,   4.1%
cpu_spectral_compute               :   1819 calls,    19.68s total,    10.82ms avg,   7.0%
db_fetch_records                   :      1 calls,     8.86s total,  8858.43ms avg,   3.1%
flac_decode                        :    841 calls,    16.15s total,    19.20ms avg,   5.7%
flac_encode                        :   1611 calls,    45.52s total,    28.26ms avg,  16.1%
r2_download_total                  :     10 calls,     9.67s total,   967.36ms avg,   3.4%
r2_head_object                     :     10 calls,     5.20s total,   520.22ms avg,   1.8%
segment_total                      :    841 calls,   128.64s total,   152.96ms avg,  45.5%
shard_write                        :   1611 calls,     2.70s total,     1.67ms avg,   1.0%
tar_build_segment_map              :     10 calls,     0.00s total,     0.10ms avg,   0.0%
tar_extract_segment                :    841 calls,     0.32s total,     0.38ms avg,   0.1%
tar_list_members                   :     10 calls,     0.00s total,     0.12ms avg,   0.0%
tar_meta_load                      :     10 calls,     0.34s total,    34.08ms avg,   0.1%
tar_open                           :     10 calls,     0.03s total,     3.05ms avg,   0.0%
TOTAL                              :                282.66s
================================================================================
Done. Stats written to /tmp/xcodec_profile/ten_multi/out/stats/telugu_stats.json


**Adjusting multi-worker settings**

I'm configuring 100-video multi-worker runs with 12 download and 12 segment workers to balance CPU and network load, increasing timeout to 15 minutes to accommodate processing time. This should keep the pipeline stable while handling the workload.


python3 scripts/prepare_shards.py --language telugu --limit 100 --output-dir /tmp/xcodec_profile/hundred_multi/out --tmp-dir /tmp/xcodec_profile/hundred_multi/tmp --target-shard-size-mb 64 --download-workers 12 --segment-workers 12 --profile-timing


Output:
Exit code: 0
Wall time: 318.9 seconds
Output:

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download: 100%|██████████| 100/100 [00:13<00:00,  7.41it/s]

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process: 100%|██████████| 100/100 [04:54<00:00,  2.94s/it]

=== Timing Statistics ===
audio_metrics_basic                :  35327 calls,   245.96s total,     6.96ms avg,   3.3%
audio_metrics_lufs                 :  35327 calls,   839.91s total,    23.78ms avg,  11.4%
cpu_resample                       :  29872 calls,   232.92s total,     7.80ms avg,   3.2%
cpu_spectral_compute               :  35327 calls,   491.85s total,    13.92ms avg,   6.7%
db_fetch_records                   :      1 calls,     8.82s total,  8824.36ms avg,   0.1%
flac_decode                        :  16099 calls,   481.38s total,    29.90ms avg,   6.6%
flac_encode                        :  29872 calls,  1202.81s total,    40.27ms avg,  16.4%
r2_download_total                  :    100 calls,   148.90s total,  1488.98ms avg,   2.0%
r2_head_object                     :    100 calls,    14.64s total,   146.40ms avg,   0.2%
segment_total                      :  16099 calls,  3543.43s total,   220.10ms avg,  48.3%
shard_write                        :  29872 calls,   113.99s total,     3.82ms avg,   1.6%
tar_build_segment_map              :    100 calls,     0.11s total,     1.12ms avg,   0.0%
tar_extract_segment                :  16099 calls,     9.69s total,     0.60ms avg,   0.1%
tar_list_members                   :    100 calls,     0.03s total,     0.26ms avg,   0.0%
tar_meta_load                      :    100 calls,     8.78s total,    87.78ms avg,   0.1%
tar_open                           :    100 calls,     0.40s total,     4.04ms avg,   0.0%
TOTAL                              :               7343.62s
================================================================================
Done. Stats written to /tmp/xcodec_profile/hundred_multi/out/stats/telugu_stats.json


**Interpreting timing outputs**

The totals are cumulative with nested timers, not strict wall time, but relative costs among core tasks like flac_encode and flac_decode emerge. I'll highlight flac processing time focus and approximate wall times per run using command or progress outputs.


**Summarizing profiling results and changes**

Added timing instrumentation to `scripts/prepare_shards.py` covering R2 fetch/download, tar processing, segment extraction, processing, and shard writes, then ran 1-, 10-, and 100-video runs with single/multi workers. Results show DB fetch dominates single-video runs but is negligible at scale; flac_encode is the largest time consumer across runs, followed by LUFS and spectral compute. Timer totals are cumulative across threads and nested calls, so per-call averages highlight per-segment costs (e.g., 40ms encode, 24ms LUFS, 30ms decode per call in 100-run). Wall times roughly match command durations (e.g., 5m19s for 100-video multi). Next steps include running deterministic sets or adding exclusive timing to isolate overheads, but no pipeline changes made beyond added timers.


Added timing hooks in `scripts/prepare_shards.py` around R2 lookup/download, tar open/meta/members/extract, segment processing, and shard writes, then ran 1‑video (single worker), 10‑video (single + multi), and 100‑video (multi) profiles with `--profile-timing` to quantify where time goes.

Key instrumentation added in `scripts/prepare_shards.py`: `db_fetch_records`, `r2_head_object`, `r2_download_total`, `tar_open`, `tar_meta_load`, `tar_build_segment_map`, `tar_list_members`, `tar_extract_segment`, `segment_total`, `shard_write` (plus existing decode/encode/metrics timers).

**Timing results (telugu, random sample each run; totals are cumulative across threads and nested timers, use avg per call for per‑unit cost)**

- 1 video, single worker (~15.8s wall)
  - Dominant: `db_fetch_records` 8.84s (one‑time DB overhead)
  - Segment pipeline: `segment_total` avg 347ms/segment (16 segments)
  - Hot tasks: `flac_encode` 33.5ms avg, `flac_decode` 35.7ms avg, `audio_metrics_lufs` 14.0ms avg, `cpu_resample` 11.8ms avg

- 10 videos, single worker (~152s wall)
  - Hot tasks by total time: `flac_encode` 41.01s (15.0ms avg), `cpu_spectral_compute` 23.12s (7.45ms avg), `audio_metrics_lufs` 18.52s (5.97ms avg), `flac_decode` 16.89s (12.0ms avg), `cpu_resample` 16.74s (6.14ms avg)
  - `segment_total` avg 90.9ms/segment (1,405 segments)
  - Download avg ~1.42s/video (`r2_download_total`)

- 10 videos, multi worker (8 download / 8 segment) (~27.6s wall)
  - Hot tasks: `flac_encode` 45.52s (28.3ms avg), `audio_metrics_lufs` 25.29s (13.9ms avg), `cpu_spectral_compute` 19.68s (10.8ms avg), `flac_decode` 16.15s (19.2ms avg)
  - `segment_total` avg 153ms/segment (841 segments)
  - Note: different random videos vs single‑worker run → counts differ

- 100 videos, multi worker (12 download / 12 segment) (~318.9s wall)
  - Top costs (per‑call avg):
    - `flac_encode` 1202.81s total, 40.27ms avg
    - `audio_metrics_lufs` 839.91s total, 23.78ms avg
    - `cpu_spectral_compute` 491.85s total, 13.92ms avg
    - `flac_decode` 481.38s total, 29.90ms avg
    - `cpu_resample` 232.92s total, 7.80ms avg
  - `segment_total` avg 220ms/segment (16,099 segments)
  - `tar_extract_segment` and `shard_write` are small (≈0.6ms and 3.8ms avg)

**Takeaways**
- The heavy hitters are clearly `flac_encode`, `audio_metrics_lufs`, `cpu_spectral_compute`, and `flac_decode`; `cpu_resample` is the next tier.
- R2 download is visible but not dominant at scale; DB fetch is a one‑time cost that dominates tiny runs only.
- Tar IO and shard writes are negligible compared to compute/encode.

If you want, I can run the same profiles on a fixed video list for apples‑to‑apples scaling, or sweep worker counts (e.g., 4/8/12/16) to find the best throughput before we discuss optimizations.


# Context from my IDE setup:

## Open tabs:
- prepare_shards.py: scripts/prepare_shards.py
- objective.md: objective.md
- strategy.md: strategy.md
- data_module.py: X-Codec-2.0/data_module.py
- default.yaml: X-Codec-2.0/config/dataset/default.yaml

## My request for Codex:
How about we consider to remove/eliminate the costs of flac_encode time and store either the same original formats as is in flac, or we store in wav ? any non-quality compressed format ? any other alternative to avoid re-encoding. why can't we just cut or use original segments and repack them, or use wavs directly as we are going to use them for training anyway. so, it's fine to have wav's IG. just to avoid decoding time spent. we can rather spend time uploading 2X the size. just think of this alternative and re-evaluate this strategy. either original flac, segmentation, or wav tar exporting to ensure no compression or quality loss.


**Evaluating audio encoding tradeoffs**

The current FLAC re-encoding time comes from decoding and resampling; this can be avoided by storing PCM or original FLAC frames if the sample rate matches. Since source segments come as FLAC at 48k, copying them directly avoids re-encoding only for that rate. For other rates like 16k, decoding and resampling remain necessary. An alternative is to store the native FLAC bytes in shards and perform decoding and resampling lazily during training, shifting CPU cost to training but reducing pipeline time. Storing WAV avoids FLAC decoding overhead but increases storage size and IO, though it reduces CPU load during reading. Next steps: 1) Assess training-time cost vs pipeline cost tradeoff for on-the-fly decoding; 2) Benchmark I/O and CPU impact of WAV vs FLAC storage; 3) Decide on preferred storage format balancing speed, size, and complexity.


**Analyzing FLAC encode alternatives and tradeoffs**

FLAC encoding is about 16% of total pipeline CPU time; removing it saves that portion but decoding and metrics still dominate. Avoiding re-encode requires storing original FLAC bytes without resampling or chunking, then doing any resampling on-the-fly during training—this shifts CPU cost but preserves quality and speeds up pipeline prep. Using WAV (uncompressed PCM) shards eliminates encode but increases storage and IO, though it reduces decode cost at training. Chunking and multi-sample-rate buckets require decoding and re-encoding, so skipping encode means relaxing chunking or multi-rate support, which departs from the current strategy.

Recommended next steps:
1. Prototype copying original FLAC segments into shards without re-encoding or chunking, computing metrics separately by decoding once.
2. Prototype creating WAV shards for the 16k bucket to reduce encode cost and speed training decode.
3. Benchmark FLAC encoding at minimal compression level for faster encode with still lossless output.

These tests can validate speed/storage tradeoffs without changing training pipelines now. See `scripts/prepare_shards.py` as a key location for encoding/resampling logic.


**Suggesting metric calibration and faster alternatives**

We should calibrate speed and quality using a fixed set of video IDs for consistent comparison. Metrics can be computed without resampling but still require one decode pass; optionally, LUFS computations could be skipped or replaced with faster approximations to reduce CPU. These are side considerations alongside encoding tradeoffs.


You can remove FLAC *encode* time, but you can’t eliminate *decode* time if we keep the strategy’s metrics, chunking, or resampling. In the 100‑video run, `flac_encode` was ~16% of total timed CPU, so skipping it is meaningful but it won’t erase the other big costs (`audio_metrics_lufs`, spectral compute, `flac_decode`, resample). FLAC is lossless already, so re‑encoding doesn’t hurt quality; it just costs CPU.

Here are realistic alternatives, with tradeoffs:

**Option A: Repack original FLAC bytes (no re‑encode)**
- How: read the segment bytes from the cleaned tar and write them directly into the shard tar, compute metrics by decoding once, but *do not resample or chunk*.
- Pros: eliminates `flac_encode`, preserves original quality, smallest storage.
- Cons: no 16/24/32/44k buckets, no chunking; training must resample/crop on‑the‑fly, and long‑segment “boost” is gone.
- Strategy impact: deviates from “multi‑bucket resample at sharding” in `strategy.md`.

**Option B: WAV/PCM shards (no compression)**
- How: decode (and resample if you keep 16k/24k/32k/44k), then write WAV instead of FLAC.
- Pros: removes FLAC encode cost; training decode is trivial.
- Cons: 2–4× bigger shards → more R2 upload/egress and slower I/O; still requires decode+resample during prep.
- Strategy impact: still compatible with multi‑bucket plan, just larger.

**Option C: Keep FLAC but use fastest encode (compression level 0)**
- How: same pipeline, just lower FLAC compression for faster encode.
- Pros: keeps lossless compression, smaller than WAV, faster than current.
- Cons: still encodes; speedup is limited but real.

**Option D: Skip chunking + keep original FLAC, resample at train time**
- Similar to Option A, but explicitly accepts padding inefficiency to avoid re‑encode and extra slicing.
- This aligns with random 6s crops but loses the “extra samples from long segments” benefit.

Bottom line: if your only goal is to drop `flac_encode`, Option A is the cleanest. But it shifts more work to training and gives up multi‑bucket resampled outputs. If you want to keep multi‑bucket outputs while reducing encode cost, Option B (WAV) is fastest CPU‑wise but hits storage/IO hard.

If you want, I can implement and profile one of these variants on the same fixed 10‑video and 100‑video sets. Pick one:

1) Repack original FLAC bytes (no resample, no chunk)
2) 16k‑only WAV shards (keep metrics + chunking)
3) Keep current flow but FLAC compression level 0 for faster encode


# Context from my IDE setup:

## Open tabs:
- prepare_shards.py: scripts/prepare_shards.py
- objective.md: objective.md
- strategy.md: strategy.md
- data_module.py: X-Codec-2.0/data_module.py
- default.yaml: X-Codec-2.0/config/dataset/default.yaml

## My request for Codex:
isn't option C better for us ? also, does storing in flac cause overhead during training time ? or is wav a better tradeoff for storage vs training time decode spent ? what do you think about this ? which option saves time for us now, and which saves time during training and what's the best tradeoff without quality compromise and time spent. i need fastest option for now, training time-time spent, i am okay spending extra storage for that. it just has to be quick now, and be easy for training time as well. suggest.


Summary: auto


**Recommending audio format options**

I suggest these options for balancing prep time, training speed, and storage since the user accepts extra storage but wants faster results:

1. Store raw PCM in WAV for fastest training (no decode overhead), trading larger I/O size; writing WAV uses less CPU than FLAC encoding.

2. Continue FLAC but use compression level 0 to reduce encode time 2-3x, keeping smaller sizes than WAV; decoding FLAC is more CPU-heavy during training.

WAV storage best suits quick training with acceptable storage use, while low-level FLAC is a middle ground.


**Clarifying audio format trade-offs**

Option C (FLAC level 0) speeds preprocessing with smaller size but still incurs CPU decode overhead during training. For fastest now and training time—while accepting extra storage—storing WAV shards (PCM16) at target sample rates is best, as WAV decoding is near zero CPU and simpler, though larger in size. Keeping original FLAC for archival is reasonable.

Summary:

- Current FLAC: longest prep, moderate training decode cost, best bandwidth/storage
- FLAC level 0: faster prep, still decode overhead, moderate storage
- WAV (PCM16): fastest prep and training decode, largest storage/I/O
- Repacking original FLAC skips encoding but breaks resample bucketing and doesn’t speed training

Given priorities, I recommend WAV shards for speed and simplicity, with FLAC retained as backup if storage or bandwidth become concerns.


Option C is a good compromise, but it’s not the fastest path for *both* prep and training. FLAC decode does add CPU overhead in training; WAV removes most decode cost but increases I/O and storage.

Short answer: if you’re OK with bigger shards and want fastest now + fastest training, pick WAV (PCM16) for the shard payloads.

Tradeoffs:

- FLAC (current): slower prep (encode), moderate CPU cost in training decode, best bandwidth/storage.
- FLAC level 0 (Option C): faster prep than current, still decode CPU in training, middle bandwidth/storage. Best “small change.”
- WAV/PCM16: fastest prep (no compression), fastest training decode, worst bandwidth/storage. Best if you can afford storage and have decent I/O.
- Repack original FLAC bytes: fastest prep but breaks strategy (no resample buckets/chunking) and pushes more work into training.

Given your priorities:
- Fastest now + fastest training, no quality loss, storage OK → **WAV shards (PCM16) at target SR**.
- If you want smaller shards but still faster prep → **FLAC compression level 0**.

If you want, I can implement WAV output (keeping the same metrics/bucket logic) and re‑profile 10‑video and 100‑video runs to quantify the exact speedup.


# Context from my IDE setup:

## Open tabs:
- prepare_shards.py: scripts/prepare_shards.py
- objective.md: objective.md
- strategy.md: strategy.md
- data_module.py: X-Codec-2.0/data_module.py
- default.yaml: X-Codec-2.0/config/dataset/default.yaml

## My request for Codex:
i am fine if the storage bumps 2x-4x or even higher. fk storage. i need fastest processing, realistic SR's not fake'd ones. and fastest training time, storage OK. implement -> bench -> ETA on 50k videos -> 500k videos vs workers scale.


**Planning WAV output implementation**

I'm planning to add a CLI option to output WAV (PCM16) files instead of FLAC, keeping FLAC as default for backward compatibility. This will involve adjusting encoding to write WAV bytes, updating tar filenames and manifest fields, and modifying verification and stats to handle larger WAV files while preserving shard size targets.


**Detailing WAV output integration and benchmarking plan**

I'm outlining precise script modifications to add a `--output-format` option defaulting to FLAC but supporting WAV with proper extension handling and encoding logic. I'll adjust shard writing, manifest entries, and verification for WAV, then plan benchmarks with varied worker counts and sample sizes to measure throughput and compute ETAs for large-scale runs, balancing runtime and disk usage constraints.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
-class SegmentOutput:
-    sample_rate: int
-    audio_bytes: bytes
-    metadata: dict[str, Any]
+class SegmentOutput:
+    sample_rate: int
+    audio_bytes: bytes
+    metadata: dict[str, Any]
@@
-def encode_flac(audio: np.ndarray, sr: int) -> bytes:
-    with timer("flac_encode"):
-        buf = io.BytesIO()
-        sf.write(buf, audio, sr, format="FLAC", subtype="PCM_16")
-        return buf.getvalue()
+def encode_audio(audio: np.ndarray, sr: int, output_format: str) -> bytes:
+    with timer("audio_encode"):
+        buf = io.BytesIO()
+        if output_format == "wav":
+            sf.write(buf, audio, sr, format="WAV", subtype="PCM_16")
+        else:
+            sf.write(buf, audio, sr, format="FLAC", subtype="PCM_16")
+        return buf.getvalue()
@@
 def process_segment(
     video_id: str,
     language: str,
@@
     speaker: str,
     index: int,
     chunk_cfg: ChunkConfig,
     silence_cfg: SilenceConfig,
     rng_seed: int,
+    output_format: str,
 ) -> tuple[list[SegmentOutput], dict[str, int]]:
@@
-            for target_sr in target_srs:
-                resampled = resample_audio(audio_slice, sr, target_sr)
-                resampled = np.clip(resampled, -1.0, 1.0)
-                audio_bytes = encode_flac(resampled, target_sr)
+            for target_sr in target_srs:
+                resampled = resample_audio(audio_slice, sr, target_sr)
+                resampled = np.clip(resampled, -1.0, 1.0)
+                audio_bytes = encode_audio(resampled, target_sr, output_format)
                 meta = dict(base_metadata)
                 meta["sample_rate"] = target_sr
                 meta["resample_target_sr"] = target_sr
                 outputs.append(SegmentOutput(target_sr, audio_bytes, meta))
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adding output_format argument**

I'm updating process_segment calls to include the output_format argument and integrating output format handling into the CLI and writer components.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
-class ShardWriter:
-    def __init__(self, output_dir: Path, prefix: str, target_size_bytes: int) -> None:
+class ShardWriter:
+    def __init__(
+        self, output_dir: Path, prefix: str, target_size_bytes: int, audio_ext: str
+    ) -> None:
         self.output_dir = output_dir
         self.prefix = prefix
         self.target_size_bytes = target_size_bytes
+        self.audio_ext = audio_ext
         self.shard_index = 0
         self.current_tar: Optional[tarfile.TarFile] = None
         self.current_jsonl = None
         self.current_size = 0
@@
-        arcname = f"{sample_id}.flac"
+        arcname = f"{sample_id}.{self.audio_ext}"
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Updating upload verification**


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
 def verify_uploaded_shard(
     s3,
     bucket: str,
     remote_prefix: str,
     shard_name: str,
     sample_rate: int,
+    audio_ext: str,
     tmp_dir: Path,
 ) -> dict[str, Any]:
@@
-            audio_name = f"{entry['id']}.flac"
+            audio_name = f"{entry['id']}.{audio_ext}"
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adding output-format CLI argument**

I'm integrating the output-format argument into the CLI, updating parse_args and main, and applying it consistently to writer, manifest, process_segment, and verify calls.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Prepare sharded datasets from cleaned tars.")
     parser.add_argument("--language", required=True)
@@
     parser.add_argument("--target-shard-size-mb", type=int, default=5120)
@@
     parser.add_argument(
         "--profile-timing",
         action="store_true",
         help="Enable detailed timing instrumentation for profiling.",
     )
+    parser.add_argument(
+        "--output-format",
+        choices=["flac", "wav"],
+        default="flac",
+        help="Output audio format for shards (default: flac).",
+    )
     return parser.parse_args()
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Adding audio extension handling**

I'm defining the audio file extension based on the output format and using it when creating writers and adding manifest entries, optionally considering embedding an "audio_format" metadata field but likely skipping it to avoid unnecessary changes.


Input:
*** Begin Patch
*** Update File: scripts/prepare_shards.py
@@
     if args.partition_count < 1:
         raise SystemExit("partition-count must be >= 1.")
@@
     if not 0 <= args.partition_index < args.partition_count:
         raise SystemExit("partition-index must be between 0 and partition-count - 1.")

+    output_format = args.output_format
+    audio_ext = "wav" if output_format == "wav" else "flac"
+
     partition_tag = ""
@@
     writers = {
-        16000: ShardWriter(output_dir / f"{language}_16", shard_prefix, args.target_shard_size_mb * 1024 * 1024),
-        24000: ShardWriter(output_dir / f"{language}_24", shard_prefix, args.target_shard_size_mb * 1024 * 1024),
-        32000: ShardWriter(output_dir / f"{language}_32", shard_prefix, args.target_shard_size_mb * 1024 * 1024),
-        44100: ShardWriter(output_dir / f"{language}_44", shard_prefix, args.target_shard_size_mb * 1024 * 1024),
+        16000: ShardWriter(
+            output_dir / f"{language}_16",
+            shard_prefix,
+            args.target_shard_size_mb * 1024 * 1024,
+            audio_ext,
+        ),
+        24000: ShardWriter(
+            output_dir / f"{language}_24",
+            shard_prefix,
+            args.target_shard_size_mb * 1024 * 1024,
+            audio_ext,
+        ),
+        32000: ShardWriter(
+            output_dir / f"{language}_32",
+            shard_prefix,
+            args.target_shard_size_mb * 1024 * 1024,
+            audio_ext,
+        ),
+        44100: ShardWriter(
+            output_dir / f"{language}_44",
+            shard_prefix,
+            args.target_shard_size_mb * 1024 * 1024,
+            audio_ext,
+        ),
     }
@@
-            manifest_entry["audio"] = f"{out.metadata['id']}.flac"
+            manifest_entry["audio"] = f"{out.metadata['id']}.{audio_ext}"
@@
                     future = executor.submit(
                         process_segment,
                         video_id,
                         language,
                         segment_bytes,
                         seg_meta,
                         speaker,
                         index,
                         chunk_cfg,
                         silence_cfg,
                         rng_seed,
+                        output_format,
                     )
@@
                 result = verify_uploaded_shard(
                     s3,
                     shard_bucket,
                     remote_prefix,
                     shard_name,
                     sr,
+                    audio_ext,
                     verify_dir,
                 )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/prepare_shards.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Checking calls to updated function**

I'm searching for all calls to the updated function with the new output_format parameter to update their signatures accordingly.


rg -n "process_segment" -n scripts/prepare_shards.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
555:def process_segment(
1347:                        process_segment,


**Validating pipeline with WAV output**

I'm planning to test the updated pipeline using the new `--output-format wav` option on a single video with minimal workers to confirm proper WAV writing and pipeline functionality.


/usr/bin/time -p python3 scripts/prepare_shards.py --language telugu --limit 1 --output-dir /tmp/xcodec_wav/one/out --tmp-dir /tmp/xcodec_wav/one/tmp --target-shard-size-mb 64 --download-workers 1 --segment-workers 1 --profile-timing --output-format wav


Output:
Exit code: 0
Wall time: 17.6 seconds
Output:

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download: 100%|██████████| 1/1 [00:00<00:00,  1.79it/s]
download: 100%|██████████| 1/1 [00:00<00:00,  1.79it/s]

process:   0%|          | 0/1 [00:00<?, ?it/s]
process: 100%|██████████| 1/1 [00:00<00:00, 43.64it/s]

=== Timing Statistics ===
audio_encode                       :    152 calls,     0.35s total,     2.31ms avg,   1.5%
audio_metrics_basic                :    129 calls,     0.83s total,     6.42ms avg,   3.5%
audio_metrics_lufs                 :    129 calls,     1.70s total,    13.16ms avg,   7.2%
cpu_resample                       :    152 calls,     1.47s total,     9.65ms avg,   6.3%
cpu_spectral_compute               :    129 calls,     0.80s total,     6.24ms avg,   3.4%
db_fetch_records                   :      1 calls,     9.19s total,  9186.04ms avg,  39.2%
flac_decode                        :     38 calls,     1.43s total,    37.61ms avg,   6.1%
r2_download_total                  :      1 calls,     0.57s total,   565.50ms avg,   2.4%
r2_head_object                     :      1 calls,     0.13s total,   132.79ms avg,   0.6%
segment_total                      :     38 calls,     6.84s total,   180.07ms avg,  29.2%
shard_write                        :    152 calls,     0.10s total,     0.67ms avg,   0.4%
tar_build_segment_map              :      1 calls,     0.00s total,     0.05ms avg,   0.0%
tar_extract_segment                :     38 calls,     0.02s total,     0.46ms avg,   0.1%
tar_list_members                   :      1 calls,     0.00s total,     0.01ms avg,   0.0%
tar_meta_load                      :      1 calls,     0.00s total,     2.94ms avg,   0.0%
tar_open                           :      1 calls,     0.00s total,     0.63ms avg,   0.0%
TOTAL                              :                 23.43s
================================================================================
Done. Stats written to /tmp/xcodec_wav/one/out/stats/telugu_stats.json
real 17.54
user 9.69
sys 1.91


**Benchmarking multi-video encoding**

I'm planning to benchmark with 10 videos on 8 CPU cores, targeting 64 MB shard sizes to manage output file count and size, and will measure real time and disk usage for output estimation after the run.


/usr/bin/time -p python3 scripts/prepare_shards.py --language telugu --limit 10 --output-dir /tmp/xcodec_wav/ten_multi/out --tmp-dir /tmp/xcodec_wav/ten_multi/tmp --target-shard-size-mb 64 --download-workers 8 --segment-workers 8 --profile-timing --output-format wav


Output:
Exit code: 0
Wall time: 33.5 seconds
Output:

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download: 100%|██████████| 10/10 [00:01<00:00,  7.54it/s]
download: 100%|██████████| 10/10 [00:01<00:00,  5.51it/s]

process:   0%|          | 0/10 [00:00<?, ?it/s]
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process: 100%|██████████| 10/10 [00:19<00:00,  3.89s/it]
process: 100%|██████████| 10/10 [00:19<00:00,  1.99s/it]

=== Timing Statistics ===
audio_encode                       :   2077 calls,    23.65s total,    11.39ms avg,   6.3%
audio_metrics_basic                :   3092 calls,    16.30s total,     5.27ms avg,   4.3%
audio_metrics_lufs                 :   3092 calls,    49.08s total,    15.87ms avg,  13.1%
cpu_resample                       :   2077 calls,    13.40s total,     6.45ms avg,   3.6%
cpu_spectral_compute               :   3092 calls,    35.36s total,    11.44ms avg,   9.4%
db_fetch_records                   :      1 calls,     9.14s total,  9141.02ms avg,   2.4%
flac_decode                        :   1480 calls,    31.43s total,    21.23ms avg,   8.4%
r2_download_total                  :     10 calls,    11.29s total,  1128.74ms avg,   3.0%
r2_head_object                     :     10 calls,     4.77s total,   477.03ms avg,   1.3%
segment_total                      :   1480 calls,   171.47s total,   115.86ms avg,  45.7%
shard_write                        :   2077 calls,     8.15s total,     3.92ms avg,   2.2%
tar_build_segment_map              :     10 calls,     0.00s total,     0.23ms avg,   0.0%
tar_extract_segment                :   1480 calls,     0.68s total,     0.46ms avg,   0.2%
tar_list_members                   :     10 calls,     0.00s total,     0.11ms avg,   0.0%
tar_meta_load                      :     10 calls,     0.60s total,    60.45ms avg,   0.2%
tar_open                           :     10 calls,     0.02s total,     2.46ms avg,   0.0%
TOTAL                              :                375.35s
================================================================================
Done. Stats written to /tmp/xcodec_wav/ten_multi/out/stats/telugu_stats.json
real 33.46
user 90.78
sys 12.43


Summary: auto


**Confirming full video multi setup**


/usr/bin/time -p python3 scripts/prepare_shards.py --language telugu --limit 100 --output-dir /tmp/xcodec_wav/hundred_multi/out --tmp-dir /tmp/xcodec_wav/hundred_multi/tmp --target-shard-size-mb 64 --download-workers 12 --segment-workers 12 --profile-timing --output-format wav


Output:
Exit code: 0
Wall time: 291.6 seconds
Output:

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=== Timing Statistics ===
audio_encode                       :  28679 calls,   617.29s total,    21.52ms avg,   9.2%
audio_metrics_basic                :  35879 calls,   268.01s total,     7.47ms avg,   4.0%
audio_metrics_lufs                 :  35879 calls,   979.89s total,    27.31ms avg,  14.5%
cpu_resample                       :  28679 calls,   223.94s total,     7.81ms avg,   3.3%
cpu_spectral_compute               :  35879 calls,   515.14s total,    14.36ms avg,   7.6%
db_fetch_records                   :      1 calls,     9.13s total,  9131.33ms avg,   0.1%
flac_decode                        :  16646 calls,   529.18s total,    31.79ms avg,   7.9%
r2_download_total                  :    100 calls,   157.44s total,  1574.40ms avg,   2.3%
r2_head_object                     :    100 calls,    14.40s total,   143.96ms avg,   0.2%
segment_total                      :  16646 calls,  3183.63s total,   191.26ms avg,  47.2%
shard_write                        :  28679 calls,   220.62s total,     7.69ms avg,   3.3%
tar_build_segment_map              :    100 calls,     0.03s total,     0.26ms avg,   0.0%
tar_extract_segment                :  16646 calls,    10.70s total,     0.64ms avg,   0.2%
tar_list_members                   :    100 calls,     0.02s total,     0.23ms avg,   0.0%
tar_meta_load                      :    100 calls,    10.17s total,   101.75ms avg,   0.2%
tar_open                           :    100 calls,     0.42s total,     4.17ms avg,   0.0%
TOTAL                              :               6740.01s
================================================================================
Done. Stats written to /tmp/xcodec_wav/hundred_multi/out/stats/telugu_stats.json
real 291.54
user 1152.06
sys 159.62


**Estimating ETA scaling with workers**

Calculated baseline throughput shows about 20.6 videos/min with 12 workers, projecting 1.7 days for 50k and 17 days for 500k videos. Scaling between 8 and 12 workers appears sublinear, likely due to I/O limits, so I'll estimate ETAs for 4, 8, 12, and 16 workers applying 70-80% efficiency to linear scaling.


**Refining scaling assumptions with measured runs**

Decided to base ETA table on the 12-worker 100-video run throughput, scaling linearly for 4, 8, 16 workers with efficiency factors, while noting sublinear scaling observed between 8- and 12-worker runs. Smaller runs at 4 workers may improve estimates but current data suffices for a reasonable ETA range alongside caveats.


python3 - <<'PY'
import math
baseline_videos = 100
baseline_seconds = 291.54
baseline_workers = 12
rate = baseline_videos / baseline_seconds  # videos per sec
print('baseline rate v/s', rate)

# Linear scaling assumption
for workers in [4, 8, 12, 16]:
    scaled_rate = rate * (workers / baseline_workers)
    for total in [50_000, 500_000]:
        seconds = total / scaled_rate
        hours = seconds / 3600
        days = hours / 24
        print(workers, total, hours, days)

# 8-worker measured from 10-video run
rate_8 = 10 / 33.46
print('8-worker measured v/s', rate_8)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
baseline rate v/s 0.34300610550867805
4 50000 121.475 5.061458333333333
4 500000 1214.75 50.614583333333336
8 50000 60.7375 2.5307291666666667
8 500000 607.375 25.307291666666668
12 50000 40.49166666666667 1.6871527777777777
12 500000 404.9166666666667 16.87152777777778
16 50000 30.36875 1.2653645833333333
16 500000 303.6875 12.653645833333334
8-worker measured v/s 0.29886431560071725


**Comparing baseline and measured scaling**

The measured 8-worker time is slower than linear scaling from the 12-run baseline, suggesting the baseline and 8-run sets differ in difficulty. I'll use the 12-worker baseline for consistent ETAs across worker counts while noting faster small-sample 8-worker times as potentially optimistic.


**Summarizing WAV output benchmark and ETA**

The pipeline now writes WAV (PCM16) with a new `--output-format` flag and updated timers; audio encoding time dropped from 40ms (FLAC) to 21.5ms per sample, cutting encode CPU by half but overall 100-video run improved modestly (9%) due to added I/O in shard writing. Key bottlenecks remain LUFS metrics, FLAC decoding, and spectral compute. Throughput for 100 videos at 12 workers is 20.6 videos/min. Linear-scaling ETA table shows 50k videos take 5.1, 2.5, 1.7, and 1.3 days at 4, 8, 12, and16 workers respectively, with 500k scaled by 10x. Next optimization steps include disabling LUFS or approximating spectral features. Training pipelines expect `.wav` outputs now; decode cost remains from input FLACs. Script modifications include the new output-format flag and manifest extension change.


Switched shard output to WAV (PCM16) with a new `--output-format` flag, so we avoid FLAC re‑encode while keeping the same bandwidth‑gated resampling logic from `strategy.md`. Output entries now end in `.wav` in the shard tar and manifest. Changes are in `scripts/prepare_shards.py`.

Bench results (WAV output):

- 1 video, 1/1 workers: `real 17.54s` (DB fetch dominates small runs)
- 10 videos, 8/8 workers: `real 33.46s` → ~17.9 videos/min
- 100 videos, 12/12 workers: `real 291.54s` → ~20.6 videos/min

Top per‑sample costs from 100‑video run (WAV):
- `audio_metrics_lufs` avg 27.3ms
- `flac_decode` avg 31.8ms (input still FLAC)
- `cpu_spectral_compute` avg 14.4ms
- `audio_encode` avg 21.5ms (WAV write)
- `cpu_resample` avg 7.8ms
- `shard_write` avg 7.7ms (bigger WAV payloads)

FLAC encode was ~40ms avg before; WAV encode is ~21.5ms avg now. Overall wall‑time improvement on the 100‑video run is modest because LUFS + decode + spectral are still the biggest costs, but training decode overhead will be much lower with WAV.

ETA projection (based on 100‑video, 12‑worker run; linear scaling assumption):

- 4 workers: 6.9 videos/min → 50k ≈ 121.5h (5.1d), 500k ≈ 1214.8h (50.6d)
- 8 workers: 13.7 videos/min → 50k ≈ 60.7h (2.5d), 500k ≈ 607.4h (25.3d)
- 12 workers: 20.6 videos/min → 50k ≈ 40.5h (1.7d), 500k ≈ 404.9h (16.9d)
- 16 workers: 27.4 videos/min → 50k ≈ 30.4h (1.3d), 500k ≈ 303.7h (12.7d)

Notes:
- Scaling is already showing diminishing returns; 8‑worker run (10 videos) came in at ~17.9 videos/min, so expect some variance based on video complexity and I/O.
- Input is still FLAC, so decode cost remains. To eliminate decode time too, we’d need cleaned data stored as WAV in the `1-cleaned-data` bucket.

If you want next steps, I can:
1) Add a WAV‑input fast path (no FLAC decode) by reading WAV tars if you decide to rebuild cleaned data.
2) Profile an 8/12/16 worker sweep on a fixed list of videos for tighter scaling curves.
3) Explore fast‑path metrics (approx LUFS/spectral) to cut the current top CPU costs.