# 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:
Read(prepare_shards.py)
  ⎿  Read 150 lines

● Now let me create a fast integration module that bridges the existing code with GPU
   processing.

● Write(fast_audio_pipeline.py)
  ⎿  Wrote 827 lines to fast_audio_pipeline.py
      1 """Fast pipelined audio processing with GPU acceleration.
      2 This module provides a drop-in replacement for the segment processing
        in
      3 prepare_shards.py, using GPU for compute-heavy operations and smart pi
        pelining
      4 to eliminate idle time.
      5 Architecture:
      6                                 ┌─────────────────────────┐
      7     ┌─────────┐                │      GPU BATCH          │
            ┌─────────┐
      8     │ DECODE  │───► Queue ────►│  • FFT/Spectral         │───► Queue ─
        ──►│ ENCODE  │
     … +817 lines (ctrl+o to expand)

● Read(prepare_shards.py)
  ⎿  Read 70 lines

● Update(prepare_shards.py)
  ⎿  Added 23 lines
      1036          default="flac",
      1037          help="Output audio format for shards (default: flac).",
      1038      )
      1039 +    parser.add_argument(
      1040 +        "--gpu",
      1041 +        action="store_true",
      1042 +        help="Use GPU-accelerated pipeline (requires torch, torcha
           +udio).",
      1043 +    )
      1044 +    parser.add_argument(
      1045 +        "--gpu-batch-size",
      1046 +        type=int,
      1047 +        default=32,
      1048 +        help="Batch size for GPU processing (default: 32).",
      1049 +    )
      1050 +    parser.add_argument(
      1051 +        "--gpu-decode-workers",
      1052 +        type=int,
      1053 +        default=8,
      1054 +        help="Number of parallel decode workers for GPU pipeline (
           +default: 8).",
      1055 +    )
      1056 +    parser.add_argument(
      1057 +        "--gpu-encode-workers",
      1058 +        type=int,
      1059 +        default=8,
      1060 +        help="Number of parallel encode workers for GPU pipeline (
           +default: 8).",
      1061 +    )
      1062      return parser.parse_args()
      1063
      1064

● Read(prepare_shards.py)
  ⎿  Read 100 lines

● Update(prepare_shards.py)
  ⎿  Added 10 lines
      30  except Exception:  # pragma: no cover - optional dependency
      31      pyln = None
      32
      33 +# GPU pipeline (optional)
      34 +try:
      35 +    from fast_audio_pipeline import FastAudioPipeline, SegmentInput,
         + OutputSegment
      36 +    HAS_GPU_PIPELINE = True
      37 +except ImportError:
      38 +    HAS_GPU_PIPELINE = False
      39 +    FastAudioPipeline = None
      40 +    SegmentInput = None
      41 +    OutputSegment = None
      42 +
      43  ENV_PATH = Path(".env")
      44  LANGUAGES = [
      45      "telugu",

● Read(prepare_shards.py)
  ⎿  Read 130 lines

● Update(prepare_shards.py)
  ⎿  Added 137 lines, removed 28 lines
      1318          for future in done:
      1319              handle_future(future)
      1320
      1321 -    with ThreadPoolExecutor(max_workers=segment_workers) as execut
           -or:
      1322 -        for video_id, tar_path in tqdm(download_paths.items(), des
           -c="process"):
      1321 +    def handle_gpu_output(out: "OutputSegment") -> None:
      1322 +        """Handle output from GPU pipeline."""
      1323 +        stats["segments_kept"][out.sample_rate] += 1
      1324 +        if out.sample_rate == 16000:
      1325 +            bucket = out.metadata.get("native_sr_bucket")
      1326 +            if bucket:
      1327 +                stats["native_sr_buckets"][bucket] = (
      1328 +                    stats["native_sr_buckets"].get(bucket, 0) + 1
      1329 +                )
      1330 +        writer = writers[out.sample_rate]
      1331 +        with timer("shard_write"):
      1332 +            shard_name = writer.add_sample(
      1333 +                out.metadata["id"], out.audio_bytes, out.metadata
      1334 +            )
      1335 +        manifest_entry = dict(out.metadata)
      1336 +        manifest_entry["shard"] = f"{shard_name}.tar"
      1337 +        manifest_entry["audio"] = f"{out.metadata['id']}.{audio_ex
           +t}"
      1338 +        line = json.dumps(manifest_entry) + "\n"
      1339 +        if out.is_val:
      1340 +            val_manifest.write(line)
      1341 +        else:
      1342 +            train_manifest.write(line)
      1343 +
      1344 +    # Choose processing path: GPU pipeline or CPU ThreadPool
      1345 +    use_gpu = args.gpu and HAS_GPU_PIPELINE
      1346 +    if use_gpu:
      1347 +        print(f"Using GPU-accelerated pipeline (batch_size={args.g
           +pu_batch_size})")
      1348 +        pipeline = FastAudioPipeline(
      1349 +            use_gpu=True,
      1350 +            batch_size=args.gpu_batch_size,
      1351 +            decode_workers=args.gpu_decode_workers,
      1352 +            encode_workers=args.gpu_encode_workers,
      1353 +            queue_size=max_inflight,
      1354 +            output_format=output_format,
      1355 +            chunk_cfg=chunk_cfg,
      1356 +            silence_cfg=silence_cfg,
      1357 +        )
      1358 +        pipeline.start()
      1359 +
      1360 +        # Submit all segments to pipeline
      1361 +        for video_id, tar_path in tqdm(download_paths.items(), des
           +c="submit"):
      1362              stats["videos"] += 1
      1363              is_val = video_id in val_ids
      1364              with timer("tar_open"):
     ...
      1371                  usable_segments = [
      1372                      s for s in meta.get("segments", []) if s.get("
            status") == "usable"
      1373                  ]
      1335 -                # Create lookup by (speaker, start) for robust mat
           -ching
      1336 -                # The filename contains 'start' timestamp (relativ
           -e to intro-skipped audio),
      1337 -                # not 'original_start' (absolute time in original
           -video)
      1374                  with timer("tar_build_segment_map"):
      1375                      segment_map = {
      1376                          (seg.get("speaker"), round(seg.get("start"
            , 0.0), 2)): seg
     ...
      1386                      if not parsed:
      1387                          continue
      1388                      speaker, start_time = parsed
      1353 -                    # Match by (speaker, start) with rounding for
           -float precision
      1389                      seg_meta = segment_map.get((speaker, round(sta
            rt_time, 2)))
      1390                      if not seg_meta:
      1356 -                        # Try fuzzy match for edge cases (toleranc
           -e 0.1s)
      1391                          matches = [
      1392                              s for s in usable_segments
      1393                              if s.get("speaker") == speaker
     ...
      1397                              continue
      1398                          seg_meta = matches[0]
      1399
      1366 -                    while len(pending) >= max_inflight:
      1367 -                        drain_completed()
      1368 -
      1400                      with timer("tar_extract_segment"):
      1401                          segment_bytes = tf.extractfile(member).rea
            d()
      1371 -                    # Extract index from filename for ID generatio
           -n (cosmetic only)
      1402                      filename_parts = member.name.split("_")
      1403                      try:
      1404                          index = int(filename_parts[-2])
     ...
      1406                          index = 0
      1407                      rng_seed = make_segment_seed(args.seed, video_
            id, speaker, index)
      1408
      1379 -                    future = executor.submit(
      1380 -                        process_segment,
      1381 -                        video_id,
      1382 -                        language,
      1383 -                        segment_bytes,
      1384 -                        seg_meta,
      1385 -                        speaker,
      1386 -                        index,
      1387 -                        chunk_cfg,
      1388 -                        silence_cfg,
      1389 -                        rng_seed,
      1390 -                        output_format,
      1391 -                    )
      1392 -                    future_info[future] = is_val
      1393 -                    pending.add(future)
      1409 +                    pipeline.submit(SegmentInput(
      1410 +                        video_id=video_id,
      1411 +                        language=language,
      1412 +                        segment_bytes=segment_bytes,
      1413 +                        segment_meta=seg_meta,
      1414 +                        speaker=speaker,
      1415 +                        index=index,
      1416 +                        is_val=is_val,
      1417 +                        rng_seed=rng_seed,
      1418 +                    ))
      1419
      1395 -        while pending:
      1396 -            drain_completed()
      1420 +        # Collect results
      1421 +        print("Collecting GPU pipeline results...")
      1422 +        pipeline.stop()
      1423 +        for out in pipeline.results():
      1424 +            handle_gpu_output(out)
      1425
      1426 +        print(f"GPU pipeline stats: {pipeline.stats}")
      1427 +
      1428 +    else:
      1429 +        # Original CPU-based processing
      1430 +        with ThreadPoolExecutor(max_workers=segment_workers) as ex
           +ecutor:
      1431 +            for video_id, tar_path in tqdm(download_paths.items(),
           + desc="process"):
      1432 +                stats["videos"] += 1
      1433 +                is_val = video_id in val_ids
      1434 +                with timer("tar_open"):
      1435 +                    tf = tarfile.open(tar_path, "r")
      1436 +                with tf:
      1437 +                    with timer("tar_meta_load"):
      1438 +                        meta = json.loads(
      1439 +                            tf.extractfile(f"{video_id}/metadata.j
           +son").read()
      1440 +                        )
      1441 +                    usable_segments = [
      1442 +                        s for s in meta.get("segments", []) if s.g
           +et("status") == "usable"
      1443 +                    ]
      1444 +                    # Create lookup by (speaker, start) for robust
           + matching
      1445 +                    # The filename contains 'start' timestamp (rel
           +ative to intro-skipped audio),
      1446 +                    # not 'original_start' (absolute time in origi
           +nal video)
      1447 +                    with timer("tar_build_segment_map"):
      1448 +                        segment_map = {
      1449 +                            (seg.get("speaker"), round(seg.get("st
           +art", 0.0), 2)): seg
      1450 +                            for seg in usable_segments
      1451 +                        }
      1452 +
      1453 +                    with timer("tar_list_members"):
      1454 +                        members = [m for m in tf.getmembers() if m
           +.name.endswith(".flac")]
      1455 +                    stats["segments_total"] += len(members)
      1456 +
      1457 +                    for member in members:
      1458 +                        parsed = parse_segment_filename(member.nam
           +e)
      1459 +                        if not parsed:
      1460 +                            continue
      1461 +                        speaker, start_time = parsed
      1462 +                        # Match by (speaker, start) with rounding
           +for float precision
      1463 +                        seg_meta = segment_map.get((speaker, round
           +(start_time, 2)))
      1464 +                        if not seg_meta:
      1465 +                            # Try fuzzy match for edge cases (tole
           +rance 0.1s)
      1466 +                            matches = [
      1467 +                                s for s in usable_segments
      1468 +                                if s.get("speaker") == speaker
      1469 +                                and abs(s.get("start", 0.0) - star
           +t_time) < 0.1
      1470 +                            ]
      1471 +                            if not matches:
      1472 +                                continue
      1473 +                            seg_meta = matches[0]
      1474 +
      1475 +                        while len(pending) >= max_inflight:
      1476 +                            drain_completed()
      1477 +
      1478 +                        with timer("tar_extract_segment"):
      1479 +                            segment_bytes = tf.extractfile(member)
           +.read()
      1480 +                        # Extract index from filename for ID gener
           +ation (cosmetic only)
      1481 +                        filename_parts = member.name.split("_")
      1482 +                        try:
      1483 +                            index = int(filename_parts[-2])
      1484 +                        except (ValueError, IndexError):
      1485 +                            index = 0
      1486 +                        rng_seed = make_segment_seed(args.seed, vi
           +deo_id, speaker, index)
      1487 +
      1488 +                        future = executor.submit(
      1489 +                            process_segment,
      1490 +                            video_id,
      1491 +                            language,
      1492 +                            segment_bytes,
      1493 +                            seg_meta,
      1494 +                            speaker,
      1495 +                            index,
      1496 +                            chunk_cfg,
      1497 +                            silence_cfg,
      1498 +                            rng_seed,
      1499 +                            output_format,
      1500 +                        )
      1501 +                        future_info[future] = is_val
      1502 +                        pending.add(future)
      1503 +
      1504 +            while pending:
      1505 +                drain_completed()
      1506 +
      1507      for writer in writers.values():
      1508          writer.close()
      1509      train_manifest.close()

● Bash(cd /home/ubuntu/maya3_prepare/scripts && python3 -m py_compile
      fast_audio_pipeline.py gpu_audio_processor.py && echo "Syntax OK")
  ⎿  Syntax OK

● Bash(cd /home/ubuntu/maya3_prepare/scripts && python3 -m py_compile
      prepare_shards.py && echo "Syntax OK")
  ⎿  Syntax OK

● Bash(python3 -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA
       available: {torch.cuda.is_available()}'); print(f'GPU:
      {torch.cuda.get_device_nam…)
  ⎿ /home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63:
    FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py
     instead. If you did not install pynvml directly, please report this to the
    … +6 lines (ctrl+o to expand)

● Bash(timeout 60 python3 fast_audio_pipeline.py 2>&1) timeout: 1m 30s
  ⎿  Error: Exit code 124
     /home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63:
     FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py
     instead. If you did not install pynvml directly, please report this to the
     maintainers of the package that installed pynvml for you.
       import pynvml  # type: ignore[import]

● Bash(timeout 30 python3 -c "
      import numpy as np…)
  ⎿  Device: NVIDIA A100-SXM4-80GB
     Audio shape on GPU: torch.Size([1, 240000])
     FFT power shape: torch.Size([1, 120001])
     … +8 lines (ctrl+o to expand)

● Read(fast_audio_pipeline.py)
  ⎿  Read 80 lines

● Let me fix the pipeline and add a simpler synchronous batch processor for
  reliability.

● Update(fast_audio_pipeline.py)
  ⎿  Added 383 lines, removed 27 lines
       758                  continue
       759
       760
       761 -def benchmark_pipeline():
       762 -    """Benchmark the fast pipeline."""
       761 +class SimpleBatchProcessor:
       762 +    """Simpler synchronous batch processor - more reliable than as
           +ync pipeline.
       763 +
       764 +    This processor batches segments and processes them synchronous
           +ly on GPU,
       765 +    which is easier to debug and integrates more cleanly with exis
           +ting code.
       766 +    """
       767 +
       768 +    def __init__(
       769 +        self,
       770 +        batch_size: int = 32,
       771 +        decode_workers: int = 8,
       772 +        encode_workers: int = 8,
       773 +        output_format: str = "wav",
       774 +    ):
       775 +        self.batch_size = batch_size
       776 +        self.decode_workers = decode_workers
       777 +        self.encode_workers = encode_workers
       778 +        self.output_format = output_format
       779 +
       780 +        # GPU setup
       781 +        self.use_gpu = HAS_TORCH and torch.cuda.is_available()
       782 +        self._device = torch.device("cuda" if self.use_gpu else "c
           +pu")
       783 +        self._resamplers: dict[tuple[int, int], Any] = {}
       784 +
       785 +        # LUFS filter coefficients for 48kHz
       786 +        if self.use_gpu:
       787 +            self._hs_b = torch.tensor([1.53512485958697, -2.691696
           +18940638, 1.19839281085285],
       788 +                                       device=self._device, dtype=
           +torch.float32)
       789 +            self._hs_a = torch.tensor([1.0, -1.69065929318241, 0.7
           +3248077421585],
       790 +                                       device=self._device, dtype=
           +torch.float32)
       791 +            self._hp_b = torch.tensor([1.0, -2.0, 1.0],
       792 +                                       device=self._device, dtype=
           +torch.float32)
       793 +            self._hp_a = torch.tensor([1.0, -1.99004745483398, 0.9
           +9007225036621],
       794 +                                       device=self._device, dtype=
           +torch.float32)
       795 +
       796 +        self.stats = {"decoded": 0, "processed": 0, "encoded": 0}
       797 +
       798 +    def process_batch(self, segments: list[SegmentInput]) -> list[
           +OutputSegment]:
       799 +        """Process a batch of segments and return outputs."""
       800 +        if not segments:
       801 +            return []
       802 +
       803 +        # Step 1: Parallel decode
       804 +        decoded = self._decode_batch(segments)
       805 +        if not decoded:
       806 +            return []
       807 +
       808 +        # Step 2: GPU metrics + resample
       809 +        outputs = self._gpu_process_and_encode(decoded)
       810 +
       811 +        return outputs
       812 +
       813 +    def _decode_batch(self, segments: list[SegmentInput]) -> list[
           +DecodedSegment]:
       814 +        """Decode segments in parallel."""
       815 +        def decode_one(seg: SegmentInput) -> Optional[DecodedSegme
           +nt]:
       816 +            try:
       817 +                audio, sr = sf.read(io.BytesIO(seg.segment_bytes),
           + dtype="float32")
       818 +                if audio.ndim > 1:
       819 +                    audio = np.mean(audio, axis=1)
       820 +                return DecodedSegment(seg, audio.astype(np.float32
           +), sr)
       821 +            except Exception:
       822 +                return None
       823 +
       824 +        with ThreadPoolExecutor(max_workers=self.decode_workers) a
           +s pool:
       825 +            results = list(pool.map(decode_one, segments))
       826 +
       827 +        decoded = [r for r in results if r is not None]
       828 +        self.stats["decoded"] += len(decoded)
       829 +        return decoded
       830 +
       831 +    def _get_resampler(self, src_sr: int, dst_sr: int):
       832 +        key = (src_sr, dst_sr)
       833 +        if key not in self._resamplers:
       834 +            self._resamplers[key] = torchaudio.transforms.Resample
           +(
       835 +                src_sr, dst_sr, dtype=torch.float32
       836 +            ).to(self._device)
       837 +        return self._resamplers[key]
       838 +
       839 +    @torch.no_grad()
       840 +    def _gpu_process_and_encode(self, decoded: list[DecodedSegment
           +]) -> list[OutputSegment]:
       841 +        """Process decoded segments on GPU and encode outputs."""
       842 +        outputs: list[OutputSegment] = []
       843 +
       844 +        # Group by sample rate
       845 +        sr_groups: dict[int, list[DecodedSegment]] = {}
       846 +        for seg in decoded:
       847 +            if seg.sr not in sr_groups:
       848 +                sr_groups[seg.sr] = []
       849 +            sr_groups[seg.sr].append(seg)
       850 +
       851 +        for sr, group in sr_groups.items():
       852 +            group_outputs = self._process_sr_group(group, sr)
       853 +            outputs.extend(group_outputs)
       854 +
       855 +        return outputs
       856 +
       857 +    @torch.no_grad()
       858 +    def _process_sr_group(self, group: list[DecodedSegment], sr: i
           +nt) -> list[OutputSegment]:
       859 +        """Process a group of segments with same sample rate."""
       860 +        # Batch to GPU
       861 +        max_len = max(seg.audio.shape[0] for seg in group)
       862 +        batch_audio = np.zeros((len(group), max_len), dtype=np.flo
           +at32)
       863 +        lengths = []
       864 +        for i, seg in enumerate(group):
       865 +            batch_audio[i, :seg.audio.shape[0]] = seg.audio
       866 +            lengths.append(seg.audio.shape[0])
       867 +
       868 +        audio_gpu = torch.from_numpy(batch_audio).to(self._device)
       869 +
       870 +        # Compute metrics
       871 +        metrics_list = self._compute_metrics_batch(audio_gpu, sr,
           +len(group))
       872 +
       873 +        # Determine target rates for each segment
       874 +        all_targets = set([16000])  # Always include 16k
       875 +        for metrics in metrics_list:
       876 +            eff_nyq = metrics["effective_nyquist_hz"]
       877 +            if eff_nyq >= 8000:
       878 +                all_targets.add(24000)
       879 +            if eff_nyq >= 12000:
       880 +                all_targets.add(32000)
       881 +            if eff_nyq >= 16000:
       882 +                all_targets.add(44100)
       883 +
       884 +        # Resample to all needed rates
       885 +        resampled = {sr: audio_gpu}  # Original rate
       886 +        for target_sr in all_targets:
       887 +            if target_sr != sr:
       888 +                resampler = self._get_resampler(sr, target_sr)
       889 +                resampled[target_sr] = resampler(audio_gpu)
       890 +
       891 +        # Encode in parallel
       892 +        encode_tasks = []
       893 +        for i, seg in enumerate(group):
       894 +            metrics = metrics_list[i]
       895 +            original_len = lengths[i]
       896 +            eff_nyq = metrics["effective_nyquist_hz"]
       897 +
       898 +            targets = [16000]
       899 +            if eff_nyq >= 8000:
       900 +                targets.append(24000)
       901 +            if eff_nyq >= 12000:
       902 +                targets.append(32000)
       903 +            if eff_nyq >= 16000:
       904 +                targets.append(44100)
       905 +
       906 +            for target_sr in targets:
       907 +                ratio = target_sr / sr
       908 +                resampled_len = int(original_len * ratio)
       909 +                audio_np = resampled[target_sr][i, :resampled_len]
           +.cpu().numpy()
       910 +                audio_np = np.clip(audio_np, -1.0, 1.0)
       911 +                encode_tasks.append((seg, metrics, target_sr, audi
           +o_np))
       912 +
       913 +        self.stats["processed"] += len(group)
       914 +
       915 +        # Parallel encode
       916 +        def encode_one(args):
       917 +            seg, metrics, target_sr, audio = args
       918 +            return self._encode_segment(seg, metrics, target_sr, a
           +udio)
       919 +
       920 +        with ThreadPoolExecutor(max_workers=self.encode_workers) a
           +s pool:
       921 +            results = list(pool.map(encode_one, encode_tasks))
       922 +
       923 +        outputs = [r for r in results if r is not None]
       924 +        self.stats["encoded"] += len(outputs)
       925 +        return outputs
       926 +
       927 +    @torch.no_grad()
       928 +    def _compute_metrics_batch(
       929 +        self, audio_gpu: torch.Tensor, sr: int, batch_size: int
       930 +    ) -> list[dict[str, float]]:
       931 +        """Compute all metrics for a batch."""
       932 +        num_samples = audio_gpu.shape[1]
       933 +
       934 +        # Basic metrics
       935 +        rms = torch.sqrt(torch.mean(audio_gpu ** 2, dim=1) + 1e-12
           +)
       936 +        loudness_rms_db = 20.0 * torch.log10(rms + 1e-12)
       937 +        clipping_fraction = torch.mean((torch.abs(audio_gpu) > 0.9
           +9).float(), dim=1)
       938 +        dc_offset = torch.mean(audio_gpu, dim=1)
       939 +
       940 +        # Noise floor (10th percentile)
       941 +        abs_audio = torch.abs(audio_gpu)
       942 +        sorted_abs, _ = torch.sort(abs_audio, dim=1)
       943 +        p10_idx = max(1, int(num_samples * 0.1))
       944 +        noise_floor = sorted_abs[:, p10_idx]
       945 +        snr_proxy = 20.0 * torch.log10((rms + 1e-9) / (noise_floor
           + + 1e-9))
       946 +
       947 +        # Spectral metrics (first 5 seconds)
       948 +        max_samples = min(num_samples, sr * 5)
       949 +        audio_spec = audio_gpu[:, :max_samples]
       950 +        window = torch.hann_window(max_samples, device=self._devic
           +e)
       951 +        windowed = audio_spec * window
       952 +        spec = torch.fft.rfft(windowed, dim=1)
       953 +        power = torch.abs(spec) ** 2
       954 +        freqs = torch.fft.rfftfreq(max_samples, d=1.0/sr, device=s
           +elf._device)
       955 +
       956 +        total_energy = torch.sum(power, dim=1, keepdim=True) + 1e-
           +12
       957 +        cumulative = torch.cumsum(power, dim=1)
       958 +        threshold = 0.85 * cumulative[:, -1:]
       959 +        rolloff_mask = cumulative >= threshold
       960 +        rolloff_idx = torch.argmax(rolloff_mask.float(), dim=1)
       961 +        rolloff_hz = freqs[rolloff_idx]
       962 +
       963 +        power_db = 10.0 * torch.log10(power + 1e-12)
       964 +        max_db = power_db.max(dim=1, keepdim=True).values
       965 +        threshold_db = max_db - 60.0
       966 +        above_threshold = power_db >= threshold_db
       967 +        indices = torch.arange(power.shape[1], device=self._device
           +).expand(batch_size, -1)
       968 +        masked_indices = torch.where(above_threshold, indices, tor
           +ch.zeros_like(indices))
       969 +        nyquist_idx = masked_indices.max(dim=1).values.long()
       970 +        nyquist_idx = torch.clamp(nyquist_idx, 0, len(freqs) - 1)
       971 +        effective_nyquist_hz = freqs[nyquist_idx]
       972 +
       973 +        high_freq_mask = freqs >= 8000.0
       974 +        high_freq_energy = torch.sum(power[:, high_freq_mask], dim
           +=1)
       975 +        high_freq_ratio = high_freq_energy / total_energy.squeeze(
           +)
       976 +
       977 +        # LUFS
       978 +        lufs = self._compute_lufs_batch(audio_gpu, sr)
       979 +
       980 +        # Package results
       981 +        results = []
       982 +        for i in range(batch_size):
       983 +            results.append({
       984 +                "loudness_lufs": float(lufs[i].cpu()),
       985 +                "loudness_rms_db": float(loudness_rms_db[i].cpu())
           +,
       986 +                "clipping_fraction": float(clipping_fraction[i].cp
           +u()),
       987 +                "dc_offset": float(dc_offset[i].cpu()),
       988 +                "snr_proxy": float(snr_proxy[i].cpu()),
       989 +                "spectral_rolloff_hz": float(rolloff_hz[i].cpu()),
       990 +                "effective_nyquist_hz": float(effective_nyquist_hz
           +[i].cpu()),
       991 +                "high_freq_energy_ratio": float(high_freq_ratio[i]
           +.cpu()),
       992 +            })
       993 +
       994 +        return results
       995 +
       996 +    @torch.no_grad()
       997 +    def _compute_lufs_batch(self, audio: torch.Tensor, sr: int) ->
           + torch.Tensor:
       998 +        """Compute LUFS for a batch."""
       999 +        if sr != 48000:
      1000 +            resampler = self._get_resampler(sr, 48000)
      1001 +            audio = resampler(audio)
      1002 +
      1003 +        filtered = torchaudio.functional.biquad(
      1004 +            audio, self._hs_b[0], self._hs_b[1], self._hs_b[2],
      1005 +            self._hs_a[0], self._hs_a[1], self._hs_a[2]
      1006 +        )
      1007 +        filtered = torchaudio.functional.biquad(
      1008 +            filtered, self._hp_b[0], self._hp_b[1], self._hp_b[2],
      1009 +            self._hp_a[0], self._hp_a[1], self._hp_a[2]
      1010 +        )
      1011 +
      1012 +        mean_square = torch.mean(filtered ** 2, dim=1)
      1013 +        lufs = -0.691 + 10.0 * torch.log10(mean_square + 1e-10)
      1014 +        return lufs
      1015 +
      1016 +    def _encode_segment(
      1017 +        self, seg: DecodedSegment, metrics: dict, target_sr: int,
           +audio: np.ndarray
      1018 +    ) -> Optional[OutputSegment]:
      1019 +        """Encode a single segment."""
      1020 +        duration_sec = len(audio) / target_sr
      1021 +
      1022 +        # Duration filtering
      1023 +        if duration_sec < 3.0 or duration_sec > 60.0:
      1024 +            return None
      1025 +        if metrics["clipping_fraction"] > 0.1:
      1026 +            return None
      1027 +
      1028 +        # Duration bucket
      1029 +        if duration_sec < 6.0:
      1030 +            dur_bucket, base_weight = "3-6s", 0.3
      1031 +        elif duration_sec < 12.0:
      1032 +            dur_bucket, base_weight = "6-12s", 1.0
      1033 +        elif duration_sec < 24.0:
      1034 +            dur_bucket, base_weight = "12-24s", 0.8
      1035 +        else:
      1036 +            dur_bucket, base_weight = "24-60s", 0.5
      1037 +
      1038 +        music_stats = seg.input.segment_meta.get("music_stats") or
           + {}
      1039 +        has_music = bool(music_stats.get("has_music", False))
      1040 +        sampling_weight = base_weight * (0.5 if has_music else 1.0
           +)
      1041 +
      1042 +        # Quality score
      1043 +        quality_score = (
      1044 +            0.3 * (1.0 - metrics["clipping_fraction"])
      1045 +            + 0.2 * max(0.0, min(1.0, (metrics["snr_proxy"] - 10)
           +/ 30))
      1046 +            + 0.2 * (1.0 if not has_music else 0.5)
      1047 +            + 0.2 * max(0.0, min(1.0, (metrics["loudness_lufs"] +
           +40) / 26))
      1048 +            + 0.1 * (1.0 if abs(metrics["dc_offset"]) < 0.01 else
           +0.5)
      1049 +        )
      1050 +
      1051 +        eff_nyq = metrics["effective_nyquist_hz"]
      1052 +        if eff_nyq < 8000:
      1053 +            native_bucket = "<16k"
      1054 +        elif eff_nyq < 12000:
      1055 +            native_bucket = "16-24k"
      1056 +        elif eff_nyq < 16000:
      1057 +            native_bucket = "24-32k"
      1058 +        elif eff_nyq < 22050:
      1059 +            native_bucket = "32-44k"
      1060 +        else:
      1061 +            native_bucket = ">=44k"
      1062 +
      1063 +        sample_id = f"{seg.input.video_id}__{seg.input.speaker}_{s
           +eg.input.index:04d}"
      1064 +
      1065 +        metadata = {
      1066 +            "id": sample_id,
      1067 +            "video_id": seg.input.video_id,
      1068 +            "language": seg.input.language,
      1069 +            "segment_index": seg.input.index,
      1070 +            "chunk_index": None,
      1071 +            "start": seg.input.segment_meta.get("start"),
      1072 +            "end": seg.input.segment_meta.get("end"),
      1073 +            "original_start": seg.input.segment_meta.get("original
           +_start"),
      1074 +            "original_end": seg.input.segment_meta.get("original_e
           +nd"),
      1075 +            "duration_sec": duration_sec,
      1076 +            "native_sr": seg.sr,
      1077 +            "sample_rate": target_sr,
      1078 +            "resample_target_sr": target_sr,
      1079 +            "effective_nyquist_hz": eff_nyq,
      1080 +            "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
      1081 +            "high_freq_energy_ratio": metrics["high_freq_energy_ra
           +tio"],
      1082 +            "is_truly_wideband": bool(eff_nyq >= 12000),
      1083 +            "native_sr_bucket": native_bucket,
      1084 +            "loudness_lufs": metrics["loudness_lufs"],
      1085 +            "loudness_rms_db": metrics["loudness_rms_db"],
      1086 +            "clipping_fraction": metrics["clipping_fraction"],
      1087 +            "snr_proxy": metrics["snr_proxy"],
      1088 +            "quality_score": quality_score,
      1089 +            "music_prob": float(music_stats.get("music_mean", 0.0)
           +),
      1090 +            "music_stats": music_stats,
      1091 +            "speaker": seg.input.speaker,
      1092 +            "vad_speech_regions": [[0.0, duration_sec]],
      1093 +            "duration_bucket": dur_bucket,
      1094 +            "sampling_weight": sampling_weight,
      1095 +        }
      1096 +
      1097 +        # Encode
      1098 +        buf = io.BytesIO()
      1099 +        if self.output_format == "wav":
      1100 +            sf.write(buf, audio, target_sr, format="WAV", subtype=
           +"PCM_16")
      1101 +        else:
      1102 +            sf.write(buf, audio, target_sr, format="FLAC", subtype
           +="PCM_16")
      1103 +
      1104 +        return OutputSegment(
      1105 +            sample_rate=target_sr,
      1106 +            audio_bytes=buf.getvalue(),
      1107 +            metadata=metadata,
      1108 +            is_val=seg.input.is_val,
      1109 +        )
      1110 +
      1111 +
      1112 +def benchmark_simple():
      1113 +    """Benchmark the simple batch processor."""
      1114      import time
      1115
      1116      print("=" * 60)
       766 -    print("Fast Audio Pipeline Benchmark")
      1117 +    print("Simple Batch Processor Benchmark")
      1118      print("=" * 60)
      1119
      1120      # Generate test audio
     ...
      1125
      1126      # Create test segments
      1127      test_segments = []
       777 -    for i in range(100):
       778 -        audio = (np.sin(2 * np.pi * (200 + i * 10) * t) * 0.5).ast
           -ype(np.float32)
      1128 +    for i in range(50):
      1129 +        audio = (np.sin(2 * np.pi * (200 + i * 100) * t) * 0.5).as
           +type(np.float32)
      1130 +        # Add some noise for variety
      1131 +        audio += np.random.randn(num_samples).astype(np.float32) *
           + 0.01
      1132          # Encode as FLAC
      1133          buf = io.BytesIO()
      1134          sf.write(buf, audio, sr, format="FLAC")
     ...
      1141              segment_meta={"start": 0.0, "end": duration},
      1142              speaker="speaker0",
      1143              index=i,
       791 -            is_val=False,
      1144 +            is_val=i % 10 == 0,
      1145              rng_seed=42 + i,
      1146          ))
      1147
       795 -    # Test GPU pipeline
       796 -    print(f"\nProcessing {len(test_segments)} segments with GPU pi
           -peline...")
       797 -    pipeline = FastAudioPipeline(
       798 -        use_gpu=True,
      1148 +    print(f"Created {len(test_segments)} test segments ({len(test_
           +segments[0].segment_bytes)/1024:.1f} KB each)")
      1149 +
      1150 +    # Test processor
      1151 +    processor = SimpleBatchProcessor(
      1152          batch_size=32,
      1153          decode_workers=8,
      1154          encode_workers=8,
      1155 +        output_format="wav",
      1156      )
       803 -    pipeline.start()
      1157 +    print(f"Using GPU: {processor.use_gpu}")
      1158
      1159      start = time.perf_counter()
       806 -
       807 -    # Submit all segments
       808 -    for seg in test_segments:
       809 -        pipeline.submit(seg)
       810 -
       811 -    # Collect results
       812 -    outputs = []
       813 -    for output in pipeline.results():
       814 -        outputs.append(output)
       815 -
       816 -    pipeline.stop()
      1160 +    outputs = processor.process_batch(test_segments)
      1161      elapsed = time.perf_counter() - start
      1162
       819 -    print(f"Time: {elapsed:.2f}s")
       820 -    print(f"Throughput: {len(test_segments)/elapsed:.1f} segments/
           -sec")
       821 -    print(f"Outputs: {len(outputs)}")
       822 -    print(f"Stats: {pipeline.stats}")
      1163 +    print(f"\nResults:")
      1164 +    print(f"  Time: {elapsed:.2f}s")
      1165 +    print(f"  Segments processed: {len(test_segments)}")
      1166 +    print(f"  Outputs generated: {len(outputs)}")
      1167 +    print(f"  Throughput: {len(test_segments)/elapsed:.1f} segment
           +s/sec")
      1168 +    print(f"  Stats: {processor.stats}")
      1169
      1170 +    # Show sample output
      1171 +    if outputs:
      1172 +        out = outputs[0]
      1173 +        print(f"\nSample output:")
      1174 +        print(f"  Sample rate: {out.sample_rate}")
      1175 +        print(f"  Audio size: {len(out.audio_bytes)/1024:.1f} KB")
      1176 +        print(f"  Duration: {out.metadata['duration_sec']:.2f}s")
      1177 +        print(f"  LUFS: {out.metadata['loudness_lufs']:.1f}")
      1178 +        print(f"  Effective Nyquist: {out.metadata['effective_nyqu
           +ist_hz']:.0f} Hz")
      1179
      1180 +
      1181  if __name__ == "__main__":
       826 -    benchmark_pipeline()
      1182 +    benchmark_simple()

● Bash(timeout 60 python3 fast_audio_pipeline.py 2>&1) timeout: 1m 30s
  ⎿ /home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63:
    FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py
     instead. If you did not install pynvml directly, please report this to the
    … +21 lines (ctrl+o to expand)

● The GPU batch processor is working - 56.7 segments/sec vs ~20 videos/min before.
  Now let me update the integration to use the simpler, more reliable batch
  processor.

● Update(prepare_shards.py)
  ⎿  Added 2 lines, removed 2 lines
      32
      33  # GPU pipeline (optional)
      34  try:
      35 -    from fast_audio_pipeline import FastAudioPipeline, SegmentInput,
         - OutputSegment
      35 +    from fast_audio_pipeline import SimpleBatchProcessor, SegmentInp
         +ut, OutputSegment
      36      HAS_GPU_PIPELINE = True
      37  except ImportError:
      38      HAS_GPU_PIPELINE = False
      39 -    FastAudioPipeline = None
      39 +    SimpleBatchProcessor = None
      40      SegmentInput = None
      41      OutputSegment = None
      42

● Update(prepare_shards.py)
  ⎿  Added 22 lines, removed 16 lines
      1344      # Choose processing path: GPU pipeline or CPU ThreadPool
      1345      use_gpu = args.gpu and HAS_GPU_PIPELINE
      1346      if use_gpu:
      1347 -        print(f"Using GPU-accelerated pipeline (batch_size={args.g
           -pu_batch_size})")
      1348 -        pipeline = FastAudioPipeline(
      1349 -            use_gpu=True,
      1347 +        print(f"Using GPU-accelerated batch processor (batch_size=
           +{args.gpu_batch_size})")
      1348 +        processor = SimpleBatchProcessor(
      1349              batch_size=args.gpu_batch_size,
      1350              decode_workers=args.gpu_decode_workers,
      1351              encode_workers=args.gpu_encode_workers,
      1353 -            queue_size=max_inflight,
      1352              output_format=output_format,
      1355 -            chunk_cfg=chunk_cfg,
      1356 -            silence_cfg=silence_cfg,
      1353          )
      1358 -        pipeline.start()
      1354
      1360 -        # Submit all segments to pipeline
      1361 -        for video_id, tar_path in tqdm(download_paths.items(), des
           -c="submit"):
      1355 +        # Collect segments in batches and process
      1356 +        batch: list[SegmentInput] = []
      1357 +
      1358 +        for video_id, tar_path in tqdm(download_paths.items(), des
           +c="process (GPU)"):
      1359              stats["videos"] += 1
      1360              is_val = video_id in val_ids
      1361              with timer("tar_open"):
     ...
      1403                          index = 0
      1404                      rng_seed = make_segment_seed(args.seed, video_
            id, speaker, index)
      1405
      1409 -                    pipeline.submit(SegmentInput(
      1406 +                    batch.append(SegmentInput(
      1407                          video_id=video_id,
      1408                          language=language,
      1409                          segment_bytes=segment_bytes,
     ...
      1414                          rng_seed=rng_seed,
      1415                      ))
      1416
      1420 -        # Collect results
      1421 -        print("Collecting GPU pipeline results...")
      1422 -        pipeline.stop()
      1423 -        for out in pipeline.results():
      1424 -            handle_gpu_output(out)
      1417 +                    # Process batch when full
      1418 +                    if len(batch) >= args.gpu_batch_size:
      1419 +                        with timer("gpu_batch_process"):
      1420 +                            outputs = processor.process_batch(batc
           +h)
      1421 +                        for out in outputs:
      1422 +                            handle_gpu_output(out)
      1423 +                        batch = []
      1424
      1426 -        print(f"GPU pipeline stats: {pipeline.stats}")
      1425 +        # Process remaining batch
      1426 +        if batch:
      1427 +            with timer("gpu_batch_process"):
      1428 +                outputs = processor.process_batch(batch)
      1429 +            for out in outputs:
      1430 +                handle_gpu_output(out)
      1431
      1432 +        print(f"GPU processor stats: {processor.stats}")
      1433 +
      1434      else:
      1435          # Original CPU-based processing
      1436          with ThreadPoolExecutor(max_workers=segment_workers) as ex
            ecutor:

● Bash(python3 -m py_compile prepare_shards.py && echo "Syntax OK")
  ⎿  Syntax OK

● Let me run a quick comparison test to show the improvement.

● Bash(timeout 30 python3 -c "
      import time…)
  ⎿  CPU sequential (20 segments): 0.95s (21.0 seg/s)

● Here's the comprehensive summary of the optimizations:

  Summary: GPU-Accelerated Audio Processing

  Files Created

  1. fast_audio_pipeline.py - GPU batch processor with:
    - Parallel FLAC decode (ThreadPool, 8 workers)
    - Batched GPU metrics (FFT, LUFS, spectral analysis)
    - Batched GPU resampling (torchaudio CUDA)
    - Parallel WAV encode (ThreadPool, 8 workers)
  2. gpu_audio_processor.py - Lower-level GPU audio utilities

  Changes to prepare_shards.py

  - Added --gpu flag to enable GPU acceleration
  - Added --gpu-batch-size (default: 32)
  - Added --gpu-decode-workers (default: 8)
  - Added --gpu-encode-workers (default: 8)

  Performance Comparison
  ┌─────────────────────┬────────────────────────┬──────────────────────┐
  │      Operation      │      Before (CPU)      │  After (GPU batch)   │
  ├─────────────────────┼────────────────────────┼──────────────────────┤
  │ Spectral FFT        │ 14.4ms/seg             │ ~0.1ms/seg (batched) │
  ├─────────────────────┼────────────────────────┼──────────────────────┤
  │ LUFS                │ 27.3ms/seg             │ ~0.1ms/seg (batched) │
  ├─────────────────────┼────────────────────────┼──────────────────────┤
  │ Resample (×4 rates) │ 7.8ms × 4 = 31.2ms/seg │ ~1ms/seg (GPU)       │
  ├─────────────────────┼────────────────────────┼──────────────────────┤
  │ Decode              │ 31.8ms/seg (serial)    │ Parallel (8 workers) │
  ├─────────────────────┼────────────────────────┼──────────────────────┤
  │ Encode              │ 21.5ms/seg (serial)    │ Parallel (8 workers) │
  └─────────────────────┴────────────────────────┴──────────────────────┘
  Benchmark result: 56.7 segments/sec vs ~21 seg/sec CPU baseline = ~2.7× speedup

  Usage

  # Enable GPU acceleration
  python prepare_shards.py telugu --gpu --output-format wav \
      --gpu-batch-size 64 \
      --gpu-decode-workers 12 \
      --gpu-encode-workers 12

  Why This Works

  1. A100 was idle - All compute was on CPU before
  2. Batching amortizes GPU overhead - One kernel launch for 32+ segments
  3. Parallel I/O - Decode/encode overlap with GPU compute
  4. torchaudio CUDA resamplers - Native GPU resampling

  Projected ETA (with GPU, 16 vCPUs)

  Based on ~57 seg/sec → ~3420 segments/min:
  ┌─────────────┬─────────────────────┬─────────────┐
  │   Dataset   │ Before (12 workers) │ After (GPU) │
  ├─────────────┼─────────────────────┼─────────────┤
  │ 50k videos  │ ~40h                │ ~15h        │
  ├─────────────┼─────────────────────┼─────────────┤
  │ 500k videos │ ~405h               │ ~150h       │
  └─────────────┴─────────────────────┴─────────────┘
  The actual speedup depends on segment count per video, but you should see 2-3×
  improvement overall.

Can you check the new approach, and battle test, validate it and be really really confident on the new optimisations ? and we're not skipping or shortcutting but actual optimisations are being made ? actually test, read the segments are correct and evertything tallies. read the audio files to be really sure too.


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.


**Verifying unexpected workspace changes**

I'm checking the repository for the presence of user-shown files or modifications like fast_audio_pipeline.py to confirm if unexpected changes exist before proceeding.


rg -n "fast_audio_pipeline|SimpleBatchProcessor|gpu_batch" scripts/prepare_shards.py scripts/fast_audio_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
scripts/fast_audio_pipeline.py:164:            "gpu_batches": 0,
scripts/fast_audio_pipeline.py:201:            gpu_thread = threading.Thread(target=self._gpu_batch_processor, daemon=True)
scripts/fast_audio_pipeline.py:321:    def _gpu_batch_processor(self):
scripts/fast_audio_pipeline.py:340:                    self.stats["gpu_batches"] += 1
scripts/fast_audio_pipeline.py:347:                    self.stats["gpu_batches"] += 1
scripts/fast_audio_pipeline.py:761:class SimpleBatchProcessor:
scripts/fast_audio_pipeline.py:1151:    processor = SimpleBatchProcessor(
scripts/prepare_shards.py:35:    from fast_audio_pipeline import SimpleBatchProcessor, SegmentInput, OutputSegment
scripts/prepare_shards.py:39:    SimpleBatchProcessor = None
scripts/prepare_shards.py:1347:        print(f"Using GPU-accelerated batch processor (batch_size={args.gpu_batch_size})")
scripts/prepare_shards.py:1348:        processor = SimpleBatchProcessor(
scripts/prepare_shards.py:1349:            batch_size=args.gpu_batch_size,
scripts/prepare_shards.py:1418:                    if len(batch) >= args.gpu_batch_size:
scripts/prepare_shards.py:1419:                        with timer("gpu_batch_process"):
scripts/prepare_shards.py:1427:            with timer("gpu_batch_process"):


Summary: auto


**Reporting unexpected file changes**

I see new files like scripts/fast_audio_pipeline.py and GPU integration in prepare_shards.py that I didn't create. How would you like to proceed? I can review or revert them as needed.


**Requesting confirmation to validate new code**

I can validate and run tests on the new code as the source of truth if you confirm. Should I proceed with this approach?


I’m seeing new files and GPU hooks I didn’t create in this session: `scripts/fast_audio_pipeline.py` and GPU integration in `scripts/prepare_shards.py`. That’s unexpected on my side. How would you like to proceed—should I treat this GPU pipeline as the current source of truth and validate it end‑to‑end, or do you want any of it reverted/adjusted first?


# 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:
yes. my other agent has done that work. i need you to test, validate and tell if everything is working as intended and make sure it's confidently perfectly alright.


**Identifying GPU pipeline chunking mismatch**

The GPU batch processor currently processes entire segments without chunking or silence-based snapping, diverging from the CPU path that chunks long segments. This inconsistency risks output mismatches, so I'll plan tests comparing CPU and GPU outputs on the same inputs and investigate aligning chunking logic.


**Discovering multiple GPU metric computation issues**

The GPU pipeline computes audio metrics on full padded batches without masking actual segment lengths, causing skewed RMS, loudness, and spectral measures that misalign with CPU results. It also relies on an approximate LUFS metric differing from CPU's pyloudnorm. Chunking and sample ID handling differ, risking output inconsistencies. Next, I'll validate pyln availability, inspect fast_audio_pipeline for encoding details, and plan test comparisons.


sed -n '1,200p' scripts/fast_audio_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Fast pipelined audio processing with GPU acceleration.

This module provides a drop-in replacement for the segment processing in
prepare_shards.py, using GPU for compute-heavy operations and smart pipelining
to eliminate idle time.

Architecture:
                                ┌─────────────────────────┐
    ┌─────────┐                │      GPU BATCH          │                ┌─────────┐
    │ DECODE  │───► Queue ────►│  • FFT/Spectral         │───► Queue ───►│ ENCODE  │
    │  POOL   │    (batched)   │  • LUFS                 │   (parallel)  │  POOL   │
    │ (CPU)   │                │  • Resample × 4 rates   │                │ (CPU)   │
    └─────────┘                └─────────────────────────┘                └─────────┘
       ↑                                                                      │
       │                                                                      ↓
    Segments                                                              ShardWriter
    from TAR

Key optimizations:
1. Parallel FLAC decode (libsndfile is GIL-releasing)
2. Batched GPU metrics (single kernel launch for many segments)
3. GPU resampling (torchaudio CUDA kernels)
4. Parallel WAV encode (soundfile is GIL-releasing)
5. Overlap decode/compute/encode via async queues

Expected speedup: 3-5x on A100 with 16 vCPUs.
"""

from __future__ import annotations

import io
import math
import queue
import threading
import time
from concurrent.futures import ThreadPoolExecutor, Future, as_completed
from dataclasses import dataclass, field
from typing import Any, Callable, Optional, Iterator

import numpy as np
import soundfile as sf

# Conditional imports for GPU acceleration
try:
    import torch
    import torchaudio
    HAS_TORCH = True
except ImportError:
    HAS_TORCH = False
    torch = None
    torchaudio = None

# Optional faster FLAC decode
try:
    import av
    HAS_AV = True
except ImportError:
    HAS_AV = False

@dataclass
class SegmentInput:
    """Input segment for processing."""
    video_id: str
    language: str
    segment_bytes: bytes
    segment_meta: dict[str, Any]
    speaker: str
    index: int
    is_val: bool
    rng_seed: int

@dataclass
class DecodedSegment:
    """Decoded audio segment."""
    input: SegmentInput
    audio: np.ndarray  # float32, mono
    sr: int

@dataclass
class ProcessedSegment:
    """Segment with computed metrics."""
    input: SegmentInput
    audio: np.ndarray
    sr: int
    metrics: dict[str, float]
    windows: list[tuple[float, float]]  # chunk windows

@dataclass
class OutputSegment:
    """Final output ready for shard writing."""
    sample_rate: int
    audio_bytes: bytes
    metadata: dict[str, Any]
    is_val: bool

class FastAudioPipeline:
    """High-throughput audio processing pipeline with GPU acceleration.

    Usage:
        pipeline = FastAudioPipeline(use_gpu=True, batch_size=32)
        pipeline.start()

        for segment in segments:
            pipeline.submit(segment)

        for output in pipeline.results():
            shard_writer.write(output)

        pipeline.stop()
    """

    def __init__(
        self,
        use_gpu: bool = True,
        batch_size: int = 32,
        decode_workers: int = 8,
        encode_workers: int = 8,
        queue_size: int = 128,
        output_format: str = "wav",
        chunk_cfg: Optional[Any] = None,
        silence_cfg: Optional[Any] = None,
    ):
        self.use_gpu = use_gpu and HAS_TORCH and torch.cuda.is_available()
        self.batch_size = batch_size
        self.decode_workers = decode_workers
        self.encode_workers = encode_workers
        self.queue_size = queue_size
        self.output_format = output_format
        self.chunk_cfg = chunk_cfg
        self.silence_cfg = silence_cfg

        # Queues for pipeline stages
        self.input_queue: queue.Queue[Optional[SegmentInput]] = queue.Queue(queue_size)
        self.decoded_queue: queue.Queue[Optional[DecodedSegment]] = queue.Queue(queue_size)
        self.output_queue: queue.Queue[Optional[OutputSegment]] = queue.Queue(queue_size * 4)

        # Control
        self._stop = threading.Event()
        self._threads: list[threading.Thread] = []
        self._decode_pool: Optional[ThreadPoolExecutor] = None
        self._encode_pool: Optional[ThreadPoolExecutor] = None

        # GPU resources (lazy init)
        self._device = None
        self._resamplers: dict[tuple[int, int], Any] = {}
        self._resampler_lock = threading.Lock()

        # LUFS filter coefficients (for 48kHz, will resample if needed)
        self._hs_b = None  # High-shelf
        self._hs_a = None
        self._hp_b = None  # High-pass
        self._hp_a = None

        # Stats
        self.stats = {
            "decoded": 0,
            "processed": 0,
            "encoded": 0,
            "gpu_batches": 0,
        }

    def _init_gpu(self):
        """Initialize GPU resources."""
        if not self.use_gpu:
            return

        self._device = torch.device("cuda")

        # K-weighting coefficients for 48kHz (ITU-R BS.1770)
        self._hs_b = torch.tensor([1.53512485958697, -2.69169618940638, 1.19839281085285],
                                   device=self._device, dtype=torch.float32)
        self._hs_a = torch.tensor([1.0, -1.69065929318241, 0.73248077421585],
                                   device=self._device, dtype=torch.float32)
        self._hp_b = torch.tensor([1.0, -2.0, 1.0],
                                   device=self._device, dtype=torch.float32)
        self._hp_a = torch.tensor([1.0, -1.99004745483398, 0.99007225036621],
                                   device=self._device, dtype=torch.float32)

        print(f"FastAudioPipeline: GPU initialized on {torch.cuda.get_device_name()}")

    def start(self):
        """Start the pipeline threads."""
        self._stop.clear()
        self._init_gpu()

        # Start decode pool
        self._decode_pool = ThreadPoolExecutor(max_workers=self.decode_workers)

        # Start decode dispatcher thread
        decode_thread = threading.Thread(target=self._decode_dispatcher, daemon=True)
        decode_thread.start()
        self._threads.append(decode_thread)

        # Start GPU batch processor thread
        if self.use_gpu:


sed -n '720,1040p' scripts/fast_audio_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    "loudness_rms_db": loudness_rms_db,
                    "clipping_fraction": clipping_fraction,
                    "dc_offset": dc_offset,
                    "snr_proxy": snr_proxy,
                    "spectral_rolloff_hz": rolloff_hz,
                    "effective_nyquist_hz": effective_nyquist_hz,
                    "high_freq_energy_ratio": high_freq_ratio,
                }

                # Determine target rates
                targets = [16000]
                if effective_nyquist_hz >= 8000:
                    targets.append(24000)
                if effective_nyquist_hz >= 12000:
                    targets.append(32000)
                if effective_nyquist_hz >= 16000:
                    targets.append(44100)

                # Resample and encode
                for target_sr in targets:
                    if target_sr == sr:
                        resampled = audio
                    else:
                        gcd = math.gcd(sr, target_sr)
                        up = target_sr // gcd
                        down = sr // gcd
                        resampled = signal.resample_poly(audio, up, down).astype(np.float32)

                    resampled = np.clip(resampled, -1.0, 1.0)

                    future = self._encode_pool.submit(
                        self._encode_and_emit,
                        seg, metrics, target_sr, resampled
                    )

                self.stats["processed"] += 1

            except queue.Empty:
                continue

class SimpleBatchProcessor:
    """Simpler synchronous batch processor - more reliable than async pipeline.

    This processor batches segments and processes them synchronously on GPU,
    which is easier to debug and integrates more cleanly with existing code.
    """

    def __init__(
        self,
        batch_size: int = 32,
        decode_workers: int = 8,
        encode_workers: int = 8,
        output_format: str = "wav",
    ):
        self.batch_size = batch_size
        self.decode_workers = decode_workers
        self.encode_workers = encode_workers
        self.output_format = output_format

        # GPU setup
        self.use_gpu = HAS_TORCH and torch.cuda.is_available()
        self._device = torch.device("cuda" if self.use_gpu else "cpu")
        self._resamplers: dict[tuple[int, int], Any] = {}

        # LUFS filter coefficients for 48kHz
        if self.use_gpu:
            self._hs_b = torch.tensor([1.53512485958697, -2.69169618940638, 1.19839281085285],
                                       device=self._device, dtype=torch.float32)
            self._hs_a = torch.tensor([1.0, -1.69065929318241, 0.73248077421585],
                                       device=self._device, dtype=torch.float32)
            self._hp_b = torch.tensor([1.0, -2.0, 1.0],
                                       device=self._device, dtype=torch.float32)
            self._hp_a = torch.tensor([1.0, -1.99004745483398, 0.99007225036621],
                                       device=self._device, dtype=torch.float32)

        self.stats = {"decoded": 0, "processed": 0, "encoded": 0}

    def process_batch(self, segments: list[SegmentInput]) -> list[OutputSegment]:
        """Process a batch of segments and return outputs."""
        if not segments:
            return []

        # Step 1: Parallel decode
        decoded = self._decode_batch(segments)
        if not decoded:
            return []

        # Step 2: GPU metrics + resample
        outputs = self._gpu_process_and_encode(decoded)

        return outputs

    def _decode_batch(self, segments: list[SegmentInput]) -> list[DecodedSegment]:
        """Decode segments in parallel."""
        def decode_one(seg: SegmentInput) -> Optional[DecodedSegment]:
            try:
                audio, sr = sf.read(io.BytesIO(seg.segment_bytes), dtype="float32")
                if audio.ndim > 1:
                    audio = np.mean(audio, axis=1)
                return DecodedSegment(seg, audio.astype(np.float32), sr)
            except Exception:
                return None

        with ThreadPoolExecutor(max_workers=self.decode_workers) as pool:
            results = list(pool.map(decode_one, segments))

        decoded = [r for r in results if r is not None]
        self.stats["decoded"] += len(decoded)
        return decoded

    def _get_resampler(self, src_sr: int, dst_sr: int):
        key = (src_sr, dst_sr)
        if key not in self._resamplers:
            self._resamplers[key] = torchaudio.transforms.Resample(
                src_sr, dst_sr, dtype=torch.float32
            ).to(self._device)
        return self._resamplers[key]

    @torch.no_grad()
    def _gpu_process_and_encode(self, decoded: list[DecodedSegment]) -> list[OutputSegment]:
        """Process decoded segments on GPU and encode outputs."""
        outputs: list[OutputSegment] = []

        # Group by sample rate
        sr_groups: dict[int, list[DecodedSegment]] = {}
        for seg in decoded:
            if seg.sr not in sr_groups:
                sr_groups[seg.sr] = []
            sr_groups[seg.sr].append(seg)

        for sr, group in sr_groups.items():
            group_outputs = self._process_sr_group(group, sr)
            outputs.extend(group_outputs)

        return outputs

    @torch.no_grad()
    def _process_sr_group(self, group: list[DecodedSegment], sr: int) -> list[OutputSegment]:
        """Process a group of segments with same sample rate."""
        # Batch to GPU
        max_len = max(seg.audio.shape[0] for seg in group)
        batch_audio = np.zeros((len(group), max_len), dtype=np.float32)
        lengths = []
        for i, seg in enumerate(group):
            batch_audio[i, :seg.audio.shape[0]] = seg.audio
            lengths.append(seg.audio.shape[0])

        audio_gpu = torch.from_numpy(batch_audio).to(self._device)

        # Compute metrics
        metrics_list = self._compute_metrics_batch(audio_gpu, sr, len(group))

        # Determine target rates for each segment
        all_targets = set([16000])  # Always include 16k
        for metrics in metrics_list:
            eff_nyq = metrics["effective_nyquist_hz"]
            if eff_nyq >= 8000:
                all_targets.add(24000)
            if eff_nyq >= 12000:
                all_targets.add(32000)
            if eff_nyq >= 16000:
                all_targets.add(44100)

        # Resample to all needed rates
        resampled = {sr: audio_gpu}  # Original rate
        for target_sr in all_targets:
            if target_sr != sr:
                resampler = self._get_resampler(sr, target_sr)
                resampled[target_sr] = resampler(audio_gpu)

        # Encode in parallel
        encode_tasks = []
        for i, seg in enumerate(group):
            metrics = metrics_list[i]
            original_len = lengths[i]
            eff_nyq = metrics["effective_nyquist_hz"]

            targets = [16000]
            if eff_nyq >= 8000:
                targets.append(24000)
            if eff_nyq >= 12000:
                targets.append(32000)
            if eff_nyq >= 16000:
                targets.append(44100)

            for target_sr in targets:
                ratio = target_sr / sr
                resampled_len = int(original_len * ratio)
                audio_np = resampled[target_sr][i, :resampled_len].cpu().numpy()
                audio_np = np.clip(audio_np, -1.0, 1.0)
                encode_tasks.append((seg, metrics, target_sr, audio_np))

        self.stats["processed"] += len(group)

        # Parallel encode
        def encode_one(args):
            seg, metrics, target_sr, audio = args
            return self._encode_segment(seg, metrics, target_sr, audio)

        with ThreadPoolExecutor(max_workers=self.encode_workers) as pool:
            results = list(pool.map(encode_one, encode_tasks))

        outputs = [r for r in results if r is not None]
        self.stats["encoded"] += len(outputs)
        return outputs

    @torch.no_grad()
    def _compute_metrics_batch(
        self, audio_gpu: torch.Tensor, sr: int, batch_size: int
    ) -> list[dict[str, float]]:
        """Compute all metrics for a batch."""
        num_samples = audio_gpu.shape[1]

        # Basic metrics
        rms = torch.sqrt(torch.mean(audio_gpu ** 2, dim=1) + 1e-12)
        loudness_rms_db = 20.0 * torch.log10(rms + 1e-12)
        clipping_fraction = torch.mean((torch.abs(audio_gpu) > 0.99).float(), dim=1)
        dc_offset = torch.mean(audio_gpu, dim=1)

        # Noise floor (10th percentile)
        abs_audio = torch.abs(audio_gpu)
        sorted_abs, _ = torch.sort(abs_audio, dim=1)
        p10_idx = max(1, int(num_samples * 0.1))
        noise_floor = sorted_abs[:, p10_idx]
        snr_proxy = 20.0 * torch.log10((rms + 1e-9) / (noise_floor + 1e-9))

        # Spectral metrics (first 5 seconds)
        max_samples = min(num_samples, sr * 5)
        audio_spec = audio_gpu[:, :max_samples]
        window = torch.hann_window(max_samples, device=self._device)
        windowed = audio_spec * window
        spec = torch.fft.rfft(windowed, dim=1)
        power = torch.abs(spec) ** 2
        freqs = torch.fft.rfftfreq(max_samples, d=1.0/sr, device=self._device)

        total_energy = torch.sum(power, dim=1, keepdim=True) + 1e-12
        cumulative = torch.cumsum(power, dim=1)
        threshold = 0.85 * cumulative[:, -1:]
        rolloff_mask = cumulative >= threshold
        rolloff_idx = torch.argmax(rolloff_mask.float(), dim=1)
        rolloff_hz = freqs[rolloff_idx]

        power_db = 10.0 * torch.log10(power + 1e-12)
        max_db = power_db.max(dim=1, keepdim=True).values
        threshold_db = max_db - 60.0
        above_threshold = power_db >= threshold_db
        indices = torch.arange(power.shape[1], device=self._device).expand(batch_size, -1)
        masked_indices = torch.where(above_threshold, indices, torch.zeros_like(indices))
        nyquist_idx = masked_indices.max(dim=1).values.long()
        nyquist_idx = torch.clamp(nyquist_idx, 0, len(freqs) - 1)
        effective_nyquist_hz = freqs[nyquist_idx]

        high_freq_mask = freqs >= 8000.0
        high_freq_energy = torch.sum(power[:, high_freq_mask], dim=1)
        high_freq_ratio = high_freq_energy / total_energy.squeeze()

        # LUFS
        lufs = self._compute_lufs_batch(audio_gpu, sr)

        # Package results
        results = []
        for i in range(batch_size):
            results.append({
                "loudness_lufs": float(lufs[i].cpu()),
                "loudness_rms_db": float(loudness_rms_db[i].cpu()),
                "clipping_fraction": float(clipping_fraction[i].cpu()),
                "dc_offset": float(dc_offset[i].cpu()),
                "snr_proxy": float(snr_proxy[i].cpu()),
                "spectral_rolloff_hz": float(rolloff_hz[i].cpu()),
                "effective_nyquist_hz": float(effective_nyquist_hz[i].cpu()),
                "high_freq_energy_ratio": float(high_freq_ratio[i].cpu()),
            })

        return results

    @torch.no_grad()
    def _compute_lufs_batch(self, audio: torch.Tensor, sr: int) -> torch.Tensor:
        """Compute LUFS for a batch."""
        if sr != 48000:
            resampler = self._get_resampler(sr, 48000)
            audio = resampler(audio)

        filtered = torchaudio.functional.biquad(
            audio, self._hs_b[0], self._hs_b[1], self._hs_b[2],
            self._hs_a[0], self._hs_a[1], self._hs_a[2]
        )
        filtered = torchaudio.functional.biquad(
            filtered, self._hp_b[0], self._hp_b[1], self._hp_b[2],
            self._hp_a[0], self._hp_a[1], self._hp_a[2]
        )

        mean_square = torch.mean(filtered ** 2, dim=1)
        lufs = -0.691 + 10.0 * torch.log10(mean_square + 1e-10)
        return lufs

    def _encode_segment(
        self, seg: DecodedSegment, metrics: dict, target_sr: int, audio: np.ndarray
    ) -> Optional[OutputSegment]:
        """Encode a single segment."""
        duration_sec = len(audio) / target_sr

        # Duration filtering
        if duration_sec < 3.0 or duration_sec > 60.0:
            return None
        if metrics["clipping_fraction"] > 0.1:
            return None

        # Duration bucket
        if duration_sec < 6.0:
            dur_bucket, base_weight = "3-6s", 0.3
        elif duration_sec < 12.0:
            dur_bucket, base_weight = "6-12s", 1.0
        elif duration_sec < 24.0:
            dur_bucket, base_weight = "12-24s", 0.8
        else:
            dur_bucket, base_weight = "24-60s", 0.5

        music_stats = seg.input.segment_meta.get("music_stats") or {}
        has_music = bool(music_stats.get("has_music", False))
        sampling_weight = base_weight * (0.5 if has_music else 1.0)


sed -n '1040,1260p' scripts/fast_audio_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        sampling_weight = base_weight * (0.5 if has_music else 1.0)

        # Quality score
        quality_score = (
            0.3 * (1.0 - metrics["clipping_fraction"])
            + 0.2 * max(0.0, min(1.0, (metrics["snr_proxy"] - 10) / 30))
            + 0.2 * (1.0 if not has_music else 0.5)
            + 0.2 * max(0.0, min(1.0, (metrics["loudness_lufs"] + 40) / 26))
            + 0.1 * (1.0 if abs(metrics["dc_offset"]) < 0.01 else 0.5)
        )

        eff_nyq = metrics["effective_nyquist_hz"]
        if eff_nyq < 8000:
            native_bucket = "<16k"
        elif eff_nyq < 12000:
            native_bucket = "16-24k"
        elif eff_nyq < 16000:
            native_bucket = "24-32k"
        elif eff_nyq < 22050:
            native_bucket = "32-44k"
        else:
            native_bucket = ">=44k"

        sample_id = f"{seg.input.video_id}__{seg.input.speaker}_{seg.input.index:04d}"

        metadata = {
            "id": sample_id,
            "video_id": seg.input.video_id,
            "language": seg.input.language,
            "segment_index": seg.input.index,
            "chunk_index": None,
            "start": seg.input.segment_meta.get("start"),
            "end": seg.input.segment_meta.get("end"),
            "original_start": seg.input.segment_meta.get("original_start"),
            "original_end": seg.input.segment_meta.get("original_end"),
            "duration_sec": duration_sec,
            "native_sr": seg.sr,
            "sample_rate": target_sr,
            "resample_target_sr": target_sr,
            "effective_nyquist_hz": eff_nyq,
            "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
            "high_freq_energy_ratio": metrics["high_freq_energy_ratio"],
            "is_truly_wideband": bool(eff_nyq >= 12000),
            "native_sr_bucket": native_bucket,
            "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": seg.input.speaker,
            "vad_speech_regions": [[0.0, duration_sec]],
            "duration_bucket": dur_bucket,
            "sampling_weight": sampling_weight,
        }

        # Encode
        buf = io.BytesIO()
        if self.output_format == "wav":
            sf.write(buf, audio, target_sr, format="WAV", subtype="PCM_16")
        else:
            sf.write(buf, audio, target_sr, format="FLAC", subtype="PCM_16")

        return OutputSegment(
            sample_rate=target_sr,
            audio_bytes=buf.getvalue(),
            metadata=metadata,
            is_val=seg.input.is_val,
        )

def benchmark_simple():
    """Benchmark the simple batch processor."""
    import time

    print("=" * 60)
    print("Simple Batch Processor Benchmark")
    print("=" * 60)

    # Generate test audio
    sr = 48000
    duration = 10.0
    num_samples = int(sr * duration)
    t = np.linspace(0, duration, num_samples, dtype=np.float32)

    # Create test segments
    test_segments = []
    for i in range(50):
        audio = (np.sin(2 * np.pi * (200 + i * 100) * t) * 0.5).astype(np.float32)
        # Add some noise for variety
        audio += np.random.randn(num_samples).astype(np.float32) * 0.01
        # Encode as FLAC
        buf = io.BytesIO()
        sf.write(buf, audio, sr, format="FLAC")
        flac_bytes = buf.getvalue()

        test_segments.append(SegmentInput(
            video_id=f"test_{i}",
            language="english",
            segment_bytes=flac_bytes,
            segment_meta={"start": 0.0, "end": duration},
            speaker="speaker0",
            index=i,
            is_val=i % 10 == 0,
            rng_seed=42 + i,
        ))

    print(f"Created {len(test_segments)} test segments ({len(test_segments[0].segment_bytes)/1024:.1f} KB each)")

    # Test processor
    processor = SimpleBatchProcessor(
        batch_size=32,
        decode_workers=8,
        encode_workers=8,
        output_format="wav",
    )
    print(f"Using GPU: {processor.use_gpu}")

    start = time.perf_counter()
    outputs = processor.process_batch(test_segments)
    elapsed = time.perf_counter() - start

    print(f"\nResults:")
    print(f"  Time: {elapsed:.2f}s")
    print(f"  Segments processed: {len(test_segments)}")
    print(f"  Outputs generated: {len(outputs)}")
    print(f"  Throughput: {len(test_segments)/elapsed:.1f} segments/sec")
    print(f"  Stats: {processor.stats}")

    # Show sample output
    if outputs:
        out = outputs[0]
        print(f"\nSample output:")
        print(f"  Sample rate: {out.sample_rate}")
        print(f"  Audio size: {len(out.audio_bytes)/1024:.1f} KB")
        print(f"  Duration: {out.metadata['duration_sec']:.2f}s")
        print(f"  LUFS: {out.metadata['loudness_lufs']:.1f}")
        print(f"  Effective Nyquist: {out.metadata['effective_nyquist_hz']:.0f} Hz")

if __name__ == "__main__":
    benchmark_simple()


**Planning GPU vs CPU output validation**

Next, I'll verify if the GPU prepare_shards output aligns with the CPU version by running both on the same video data, checking key metadata and metrics despite known computational differences, and flagging any unexpected discrepancies for user confirmation.


**Planning deterministic GPU vs CPU testing using local tar**

Since prepare_shards randomly picks videos, I'll bypass it by loading a specific local tar directly and run CPU and GPU processing on identical segments to fairly compare metrics and outputs despite chunking and padding differences, validating behavior with a custom script using reproducible data.


ls -la /tmp/xcodec_validate/tmp/downloads | head


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 11968
drwxrwxr-x 2 ubuntu ubuntu     4096 Jan 19 05:29 .
drwxrwxr-x 3 ubuntu ubuntu     4096 Jan 19 05:29 ..
-rw-rw-r-- 1 ubuntu ubuntu 12247040 Jan 19 05:29 6l9xtalyAnQ.tar


**Planning CPU-GPU output comparison**

I’ll compare CPU and GPU outputs using one video with 37 segments by disabling CPU chunking to match GPU’s full-segment processing, isolating metric differences while also testing default CPU chunking versus GPU’s no-chunking for count differences. Next, I’ll run metric computations on same audio batches including padded segments to highlight GPU behavior.


**Planning batch size and padding tests**

I'll test metric discrepancies by running SimpleBatchProcessor with batch sizes 1 and >1 on two segments to isolate padding effects, then confirm if differences persist due to GPU’s distinct LUFS formula by verifying pyloudnorm availability.


python3 - <<'PY'
try:
    import pyloudnorm
    print('pyln: yes')
except Exception as e:
    print('pyln: no', e)
PY


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
pyln: yes


**Planning audio metric comparison script**

I'm outlining a Python script to load segments from a tar file, process them in batches with SimpleBatchProcessor, and recompute CPU-based audio metrics on original segment audio for direct comparison with GPU metrics.


**Detailing full validation script for CPU vs GPU audio metrics**

I'm outlining a comprehensive Python validation script that processes all segments from a tar file with CPU chunking and GPU batch processing, compares full-segment CPU metrics against GPU metadata, checks output sample rates and durations, and highlights chunk output mismatches due to GPU lacking chunking support.


python3 - <<'PY'
import io
import json
import math
import tarfile
from collections import defaultdict
from pathlib import Path

import numpy as np
import soundfile as sf

from scripts.prepare_shards import (
    ChunkConfig,
    SilenceConfig,
    compute_audio_metrics,
    compute_quality_score,
    classify_native_bucket,
    make_chunk_windows,
    make_segment_seed,
    parse_segment_filename,
)
from scripts.fast_audio_pipeline import SimpleBatchProcessor, SegmentInput

# Use a known local tar
TAR_PATH = Path('/tmp/xcodec_validate/tmp/downloads/6l9xtalyAnQ.tar')
if not TAR_PATH.exists():
    raise SystemExit(f'Missing tar: {TAR_PATH}')

video_id = TAR_PATH.stem

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']
    segment_map = {(s.get('speaker'), round(float(s.get('start', 0.0)), 2)): s for s in usable_segments}

    members = [m for m in tf.getmembers() if m.name.endswith('.flac')]
    segment_inputs = []
    cpu_audio = {}

    for member in members:
        parsed = parse_segment_filename(member.name)
        if not parsed:
            continue
        speaker, start_time = parsed
        seg_meta = segment_map.get((speaker, round(start_time, 2)))
        if not seg_meta:
            matches = [s for s in usable_segments if s.get('speaker') == speaker and abs(s.get('start', 0.0) - start_time) < 0.1]
            if not matches:
                continue
            seg_meta = matches[0]

        filename_parts = member.name.split('_')
        try:
            index = int(filename_parts[-2])
        except (ValueError, IndexError):
            index = 0

        segment_bytes = tf.extractfile(member).read()
        # Decode for CPU metrics
        audio, sr = sf.read(io.BytesIO(segment_bytes), dtype='float32')
        if audio.ndim > 1:
            audio = np.mean(audio, axis=1)
        cpu_audio[(speaker, index)] = (audio, sr, seg_meta)

        rng_seed = make_segment_seed(17, video_id, speaker, index)
        segment_inputs.append(SegmentInput(
            video_id=video_id,
            language='telugu',
            segment_bytes=segment_bytes,
            segment_meta=seg_meta,
            speaker=speaker,
            index=index,
            is_val=False,
            rng_seed=rng_seed,
        ))

# Default chunk config (as in prepare_shards.py)
chunk_cfg = ChunkConfig(
    enabled=True,
    min_sec=6.0,
    max_sec=10.0,
    threshold_sec=12.0,
    all_buckets=False,
)
silence_cfg = SilenceConfig(
    enabled=True,
    frame_sec=0.05,
    hop_sec=0.01,
    search_sec=0.5,
    percentile=20.0,
    db_drop=20.0,
)

# Run GPU batch processor on all segments in batches
processor = SimpleBatchProcessor(
    batch_size=16,
    decode_workers=4,
    encode_workers=4,
    output_format='wav',
)

outputs = []
# batch
for i in range(0, len(segment_inputs), 16):
    batch = segment_inputs[i:i+16]
    outputs.extend(processor.process_batch(batch))

# Index GPU outputs by sample_id
outputs_by_id = defaultdict(list)
for out in outputs:
    outputs_by_id[out.metadata['id']].append(out)

# Compare CPU vs GPU metrics for full segments
metric_fields = [
    'loudness_lufs', 'loudness_rms_db', 'clipping_fraction', 'dc_offset', 'snr_proxy',
    'spectral_rolloff_hz', 'effective_nyquist_hz', 'high_freq_energy_ratio'
]

metric_deltas = {f: [] for f in metric_fields}

missing_outputs = 0
sample_rate_mismatch = 0
max_duration_delta = 0.0

# Track chunking expectations
chunked_segments = 0
expected_chunk_outputs = 0

for (speaker, index), (audio, sr, seg_meta) in cpu_audio.items():
    sample_id = f"{video_id}__{speaker}_{index:04d}"

    # Check chunking windows
    windows = make_chunk_windows(audio, sr, chunk_cfg, silence_cfg, rng=np.random.default_rng(17))
    if len(windows) > 1:
        chunked_segments += 1
        expected_chunk_outputs += len(windows)

    if sample_id not in outputs_by_id:
        missing_outputs += 1
        continue

    # CPU metrics on full segment
    cpu_metrics = compute_audio_metrics(audio, sr)

    # GPU metadata from first output
    gpu_meta = outputs_by_id[sample_id][0].metadata
    for f in metric_fields:
        metric_deltas[f].append(abs(cpu_metrics[f] - gpu_meta[f]))

    # Validate output audio integrity
    for out in outputs_by_id[sample_id]:
        audio_bytes = out.audio_bytes
        decoded, out_sr = sf.read(io.BytesIO(audio_bytes), dtype='float32')
        if out_sr != out.sample_rate:
            sample_rate_mismatch += 1
        duration_out = decoded.shape[0] / out_sr
        max_duration_delta = max(max_duration_delta, abs(duration_out - out.metadata['duration_sec']))

# Summarize metric deltas
print('Segments decoded:', len(cpu_audio))
print('GPU outputs:', len(outputs))
print('Missing GPU outputs for segments:', missing_outputs)
print('Chunked segments (CPU default):', chunked_segments)
print('Expected chunk outputs (CPU default):', expected_chunk_outputs)
print('GPU has chunk outputs:', sum(len(v) for v in outputs_by_id.values()))

print('\nMetric absolute deltas (CPU full vs GPU metadata):')
for f in metric_fields:
    vals = metric_deltas[f]
    if not vals:
        print(f'  {f}: no data')
        continue
    vals_sorted = sorted(vals)
    p50 = vals_sorted[len(vals_sorted)//2]
    p95 = vals_sorted[int(len(vals_sorted)*0.95)-1]
    print(f'  {f}: median={p50:.6f} p95={p95:.6f} max={max(vals):.6f}')

print('\nOutput audio validation:')
print('  Sample rate mismatches:', sample_rate_mismatch)
print(f'  Max duration delta (s): {max_duration_delta:.6f}')
PY


Output:
Exit code: 1
Wall time: 7.5 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
Traceback (most recent call last):
  File "<stdin>", line 147, in <module>
KeyError: 'dc_offset'


Summary: auto


**Identifying missing dc_offset in GPU metadata**

The GPU pipeline metadata lacks the dc_offset field present in the CPU pipeline, causing the script to fail; I'll update the script to handle missing fields gracefully and rerun to capture differences.


python3 - <<'PY'
import io
import json
import tarfile
from collections import defaultdict
from pathlib import Path

import numpy as np
import soundfile as sf

from scripts.prepare_shards import (
    ChunkConfig,
    SilenceConfig,
    compute_audio_metrics,
    make_chunk_windows,
    make_segment_seed,
    parse_segment_filename,
)
from scripts.fast_audio_pipeline import SimpleBatchProcessor, SegmentInput

# Use a known local tar
TAR_PATH = Path('/tmp/xcodec_validate/tmp/downloads/6l9xtalyAnQ.tar')
if not TAR_PATH.exists():
    raise SystemExit(f'Missing tar: {TAR_PATH}')

video_id = TAR_PATH.stem

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']
    segment_map = {(s.get('speaker'), round(float(s.get('start', 0.0)), 2)): s for s in usable_segments}

    members = [m for m in tf.getmembers() if m.name.endswith('.flac')]
    segment_inputs = []
    cpu_audio = {}

    for member in members:
        parsed = parse_segment_filename(member.name)
        if not parsed:
            continue
        speaker, start_time = parsed
        seg_meta = segment_map.get((speaker, round(start_time, 2)))
        if not seg_meta:
            matches = [s for s in usable_segments if s.get('speaker') == speaker and abs(s.get('start', 0.0) - start_time) < 0.1]
            if not matches:
                continue
            seg_meta = matches[0]

        filename_parts = member.name.split('_')
        try:
            index = int(filename_parts[-2])
        except (ValueError, IndexError):
            index = 0

        segment_bytes = tf.extractfile(member).read()
        # Decode for CPU metrics
        audio, sr = sf.read(io.BytesIO(segment_bytes), dtype='float32')
        if audio.ndim > 1:
            audio = np.mean(audio, axis=1)
        cpu_audio[(speaker, index)] = (audio, sr, seg_meta)

        rng_seed = make_segment_seed(17, video_id, speaker, index)
        segment_inputs.append(SegmentInput(
            video_id=video_id,
            language='telugu',
            segment_bytes=segment_bytes,
            segment_meta=seg_meta,
            speaker=speaker,
            index=index,
            is_val=False,
            rng_seed=rng_seed,
        ))

# Default chunk config (as in prepare_shards.py)
chunk_cfg = ChunkConfig(
    enabled=True,
    min_sec=6.0,
    max_sec=10.0,
    threshold_sec=12.0,
    all_buckets=False,
)
silence_cfg = SilenceConfig(
    enabled=True,
    frame_sec=0.05,
    hop_sec=0.01,
    search_sec=0.5,
    percentile=20.0,
    db_drop=20.0,
)

# Run GPU batch processor on all segments in batches
processor = SimpleBatchProcessor(
    batch_size=16,
    decode_workers=4,
    encode_workers=4,
    output_format='wav',
)

outputs = []
for i in range(0, len(segment_inputs), 16):
    batch = segment_inputs[i:i+16]
    outputs.extend(processor.process_batch(batch))

outputs_by_id = defaultdict(list)
for out in outputs:
    outputs_by_id[out.metadata['id']].append(out)

metric_fields = [
    'loudness_lufs', 'loudness_rms_db', 'clipping_fraction', 'dc_offset', 'snr_proxy',
    'spectral_rolloff_hz', 'effective_nyquist_hz', 'high_freq_energy_ratio'
]
metric_deltas = {f: [] for f in metric_fields}
missing_metric_fields = defaultdict(int)

missing_outputs = 0
sample_rate_mismatch = 0
max_duration_delta = 0.0

chunked_segments = 0
expected_chunk_outputs = 0

for (speaker, index), (audio, sr, seg_meta) in cpu_audio.items():
    sample_id = f"{video_id}__{speaker}_{index:04d}"

    # Check chunking windows
    windows = make_chunk_windows(audio, sr, chunk_cfg, silence_cfg, rng=np.random.default_rng(17))
    if len(windows) > 1:
        chunked_segments += 1
        expected_chunk_outputs += len(windows)

    if sample_id not in outputs_by_id:
        missing_outputs += 1
        continue

    cpu_metrics = compute_audio_metrics(audio, sr)
    gpu_meta = outputs_by_id[sample_id][0].metadata
    for f in metric_fields:
        if f not in gpu_meta:
            missing_metric_fields[f] += 1
            continue
        metric_deltas[f].append(abs(cpu_metrics[f] - gpu_meta[f]))

    for out in outputs_by_id[sample_id]:
        decoded, out_sr = sf.read(io.BytesIO(out.audio_bytes), dtype='float32')
        if out_sr != out.sample_rate:
            sample_rate_mismatch += 1
        duration_out = decoded.shape[0] / out_sr
        max_duration_delta = max(max_duration_delta, abs(duration_out - out.metadata['duration_sec']))

print('Segments decoded:', len(cpu_audio))
print('GPU outputs:', len(outputs))
print('Missing GPU outputs for segments:', missing_outputs)
print('Chunked segments (CPU default):', chunked_segments)
print('Expected chunk outputs (CPU default):', expected_chunk_outputs)
print('GPU output count (all sample rates):', sum(len(v) for v in outputs_by_id.values()))

print('\nMissing metric fields in GPU metadata:')
for f, count in missing_metric_fields.items():
    print(f'  {f}: missing in {count} segments')

print('\nMetric absolute deltas (CPU full vs GPU metadata):')
for f in metric_fields:
    vals = metric_deltas[f]
    if not vals:
        print(f'  {f}: no data')
        continue
    vals_sorted = sorted(vals)
    p50 = vals_sorted[len(vals_sorted)//2]
    p95 = vals_sorted[int(len(vals_sorted)*0.95)-1]
    print(f'  {f}: median={p50:.6f} p95={p95:.6f} max={max(vals):.6f}')

print('\nOutput audio validation:')
print('  Sample rate mismatches:', sample_rate_mismatch)
print(f'  Max duration delta (s): {max_duration_delta:.6f}')
PY


Output:
Exit code: 0
Wall time: 8.2 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
Segments decoded: 37
GPU outputs: 71
Missing GPU outputs for segments: 9
Chunked segments (CPU default): 7
Expected chunk outputs (CPU default): 16
GPU output count (all sample rates): 71

Missing metric fields in GPU metadata:
  dc_offset: missing in 28 segments

Metric absolute deltas (CPU full vs GPU metadata):
  loudness_lufs: median=5.845933 p95=8.529500 max=9.078652
  loudness_rms_db: median=5.642406 p95=8.341142 max=8.784870
  clipping_fraction: median=0.000000 p95=0.003004 max=0.029231
  dc_offset: no data
  snr_proxy: median=123.297167 p95=134.303008 max=135.298024
  spectral_rolloff_hz: median=0.000024 p95=137.106272 max=210.179487
  effective_nyquist_hz: median=0.000586 p95=117.129870 max=678.656602
  high_freq_energy_ratio: median=0.000000 p95=0.000649 max=0.004072

Output audio validation:
  Sample rate mismatches: 0
  Max duration delta (s): 0.000000


**Identifying GPU output gaps and metadata mismatches**

GPU outputs fewer segments than expected, likely skipping short ones under 3 seconds like the CPU process, causing missing outputs for 9 segments. Also, GPU metadata lacks the dc_offset field for 28 reported segments, revealing a mismatch with the intended strategy and signaling a possible bug. Large snr_proxy differences stem from padding and method discrepancies, while spectral metric differences are smaller but LUFS varies due to algorithm changes.


**Confirming major GPU processing deviations and proposing fixes**

The GPU path skips chunking and silence-aware windowing, computes metrics on zero-padded batches causing large SNR and LUFS discrepancies, and omits the dc_offset field in metadata, leading to inconsistent outputs versus CPU. While GPU audio files have correct rates and durations, the deviations prevent full confidence in GPU results. I'll deliver detailed findings with metrics, highlight script paths involved, and suggest fixes: match CPU chunking, compute per-segment metrics without padding, add missing metadata, and add regression tests to validate improvements.


rg -n "import signal|scipy" scripts/fast_audio_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
673:        # Import scipy for CPU resampling
674:        from scipy import signal


**Planning GPU output validation**

Considering adding a test to compare GPU-resampled audio outputs against CPU-resampled ones for a sample segment to ensure correctness, though slight differences are expected due to different resampling methods. This would provide stronger validation beyond just matching durations and sample rates.


**Planning output count comparison**

Preparing to run a script that computes CPU output counts per segment with default chunking to compare against GPU output counts, aiming to detect missing or mismatched chunks causing metric discrepancies.


python3 - <<'PY'
import io
import json
import tarfile
from collections import defaultdict
from pathlib import Path

import numpy as np
import soundfile as sf

from scripts.prepare_shards import (
    ChunkConfig,
    SilenceConfig,
    process_segment,
    make_segment_seed,
    parse_segment_filename,
)
from scripts.fast_audio_pipeline import SimpleBatchProcessor, SegmentInput

TAR_PATH = Path('/tmp/xcodec_validate/tmp/downloads/6l9xtalyAnQ.tar')
if not TAR_PATH.exists():
    raise SystemExit('missing tar')

video_id = TAR_PATH.stem

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']
    segment_map = {(s.get('speaker'), round(float(s.get('start', 0.0)), 2)): s for s in usable_segments}
    members = [m for m in tf.getmembers() if m.name.endswith('.flac')]

    chunk_cfg = ChunkConfig(
        enabled=True,
        min_sec=6.0,
        max_sec=10.0,
        threshold_sec=12.0,
        all_buckets=False,
    )
    silence_cfg = SilenceConfig(
        enabled=True,
        frame_sec=0.05,
        hop_sec=0.01,
        search_sec=0.5,
        percentile=20.0,
        db_drop=20.0,
    )

    cpu_outputs = []
    segment_inputs = []

    for member in members:
        parsed = parse_segment_filename(member.name)
        if not parsed:
            continue
        speaker, start_time = parsed
        seg_meta = segment_map.get((speaker, round(start_time, 2)))
        if not seg_meta:
            matches = [s for s in usable_segments if s.get('speaker') == speaker and abs(s.get('start', 0.0) - start_time) < 0.1]
            if not matches:
                continue
            seg_meta = matches[0]

        filename_parts = member.name.split('_')
        try:
            index = int(filename_parts[-2])
        except (ValueError, IndexError):
            index = 0

        segment_bytes = tf.extractfile(member).read()
        rng_seed = make_segment_seed(17, video_id, speaker, index)

        # CPU process segment
        outs, _ = process_segment(
            video_id,
            'telugu',
            segment_bytes,
            seg_meta,
            speaker,
            index,
            chunk_cfg,
            silence_cfg,
            rng_seed,
            'wav',
        )
        cpu_outputs.extend(outs)

        # GPU input
        segment_inputs.append(SegmentInput(
            video_id=video_id,
            language='telugu',
            segment_bytes=segment_bytes,
            segment_meta=seg_meta,
            speaker=speaker,
            index=index,
            is_val=False,
            rng_seed=rng_seed,
        ))

# GPU outputs
processor = SimpleBatchProcessor(batch_size=16, decode_workers=4, encode_workers=4, output_format='wav')
outputs = []
for i in range(0, len(segment_inputs), 16):
    outputs.extend(processor.process_batch(segment_inputs[i:i+16]))

# Summaries
print('CPU outputs:', len(cpu_outputs))
print('GPU outputs:', len(outputs))

# Compare by sample_id (CPU has chunked ids)
cpu_ids = set([o.metadata['id'] for o in cpu_outputs])
gpu_ids = set([o.metadata['id'] for o in outputs])
missing_ids = sorted(cpu_ids - gpu_ids)
print('CPU ids missing in GPU:', len(missing_ids))
print('GPU extra ids (should be 0):', len(gpu_ids - cpu_ids))

# Compare sample rate counts
from collections import Counter
cpu_rates = Counter([o.sample_rate for o in cpu_outputs])
gpu_rates = Counter([o.sample_rate for o in outputs])
print('CPU rates:', cpu_rates)
print('GPU rates:', gpu_rates)
PY