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

        # Determine batch dimensions
        lengths = [a.shape[0] for a in audios]
        max_len = pad_to_length or max(lengths)
        batch_size = len(audios)

        # Create padded batch tensor
        batch = torch.zeros((batch_size, max_len), device=self.device, dtype=self.dtype)
        for i, audio in enumerate(audios):
            batch[i, :audio.shape[0]] = torch.from_numpy(audio).to(self.dtype)

        # Apply K-weighting
        weighted = self._apply_k_weighting(batch, sr)

        # Compute mean square in 400ms blocks with 75% overlap
        block_samples = int(sr * self.block_ms / 1000)
        hop_samples = int(block_samples * (1 - self.overlap))

        if block_samples > max_len:
            # Audio too short for proper LUFS measurement
            # Fall back to simple RMS-based estimate
            rms = torch.sqrt(torch.mean(weighted ** 2, dim=1) + 1e-12)
            lufs = -0.691 + 10 * torch.log10(rms + 1e-12)
            return lufs.cpu().numpy().tolist()

        # Use unfold for efficient block extraction
        # Shape: (batch, num_blocks, block_samples)
        num_blocks = (max_len - block_samples) // hop_samples + 1
        blocks = weighted.unfold(1, block_samples, hop_samples)

        # Compute mean square per block
        # Shape: (batch, num_blocks)
        block_ms = torch.mean(blocks ** 2, dim=2)

        # Convert to loudness in LUFS
        # -0.691 is the offset for mono signals (stereo would be different)
        block_lufs = -0.691 + 10 * torch.log10(block_ms + 1e-12)

        # Create mask for valid blocks (within original audio length)
        block_end_positions = torch.arange(num_blocks, device=self.device) * hop_samples + block_samples
        length_tensor = torch.tensor(lengths, device=self.device).unsqueeze(1)
        valid_mask = block_end_positions.unsqueeze(0) <= length_tensor  # (batch, num_blocks)

        # Absolute gating: exclude blocks below -70 LUFS
        absolute_gate = -70.0
        gated_mask = (block_lufs > absolute_gate) & valid_mask

        # Compute ungated loudness for relative gating
        results = []
        for i in range(batch_size):
            valid_blocks = block_ms[i, valid_mask[i]]
            if valid_blocks.numel() == 0:
                results.append(-100.0)
                continue

            # Apply absolute gate
            abs_gated = valid_blocks[block_lufs[i, valid_mask[i]] > absolute_gate]
            if abs_gated.numel() == 0:
                results.append(-100.0)
                continue

            # Compute ungated loudness
            ungated_mean = torch.mean(abs_gated)
            ungated_lufs = -0.691 + 10 * torch.log10(ungated_mean + 1e-12)

            # Relative gate: -10 dB below ungated
            relative_gate = ungated_lufs - 10.0

            # Final integration
            rel_gated_lufs = block_lufs[i, valid_mask[i]]
            final_blocks = valid_blocks[(rel_gated_lufs > absolute_gate) & (rel_gated_lufs > relative_gate)]

            if final_blocks.numel() == 0:
                results.append(float(ungated_lufs.cpu()))
            else:
                final_mean = torch.mean(final_blocks)
                final_lufs = -0.691 + 10 * torch.log10(final_mean + 1e-12)
                results.append(float(final_lufs.cpu()))

        return results

    def compute_spectral_batch(
        self,
        audios: list[np.ndarray],
        sr: int,
        max_analysis_sec: float = 5.0,
    ) -> list[tuple[float, float, float]]:
        """Compute spectral metrics for a batch of audio segments.

        Returns (rolloff_hz, effective_nyquist_hz, high_freq_ratio) for each.
        """
        if not audios:
            return []

        max_samples = int(sr * max_analysis_sec)
        batch_size = len(audios)

        # Truncate to max analysis length
        truncated = [a[:min(len(a), max_samples)] for a in audios]
        lengths = [len(a) for a in truncated]
        max_len = max(lengths)

        # Zero-pad to common length
        batch = torch.zeros((batch_size, max_len), device=self.device, dtype=self.dtype)
        for i, audio in enumerate(truncated):
            batch[i, :len(audio)] = torch.from_numpy(audio).to(self.dtype)

        # Apply Hann window
        window = torch.hann_window(max_len, device=self.device, dtype=self.dtype)
        windowed = batch * window

        # Compute FFT
        spec = torch.fft.rfft(windowed, dim=1)
        power = torch.abs(spec) ** 2

        # Frequency bins
        freqs = torch.fft.rfftfreq(max_len, d=1.0/sr).to(self.device)

        results = []
        for i in range(batch_size):
            # Mask out zero-padded region's contribution
            # (windowing already reduces it, but we still use original power)
            seg_power = power[i]
            total_energy = torch.sum(seg_power) + 1e-12

            # Spectral rolloff (85%)
            cumsum = torch.cumsum(seg_power, dim=0)
            rolloff_idx = torch.searchsorted(cumsum, 0.85 * cumsum[-1])
            rolloff_idx = min(rolloff_idx.item(), len(freqs) - 1)
            rolloff_hz = float(freqs[rolloff_idx])

            # Effective Nyquist (60dB below peak)
            power_db = 10.0 * torch.log10(seg_power + 1e-12)
            threshold = power_db.max() - 60.0
            above = torch.where(power_db >= threshold)[0]
            if above.numel() > 0:
                effective_nyquist_hz = float(freqs[above[-1]])
            else:
                effective_nyquist_hz = 0.0

            # High frequency energy ratio (>8kHz)
            hf_mask = freqs >= 8000.0
            hf_energy = torch.sum(seg_power[hf_mask])
            hf_ratio = float(hf_energy / total_energy)

            results.append((rolloff_hz, effective_nyquist_hz, hf_ratio))

        return results

    def compute_basic_metrics_batch(
        self,
        audios: list[np.ndarray],
    ) -> list[tuple[float, float, float, float]]:
        """Compute basic metrics (RMS, clipping, DC offset, SNR proxy) for batch.

        Returns (rms_db, clipping_fraction, dc_offset, snr_proxy) for each.
        """
        if not audios:
            return []

        results = []
        for audio in audios:
            # Convert to tensor for consistent computation
            audio_t = torch.from_numpy(audio).to(device=self.device, dtype=self.dtype)

            # RMS
            rms = torch.sqrt(torch.mean(audio_t ** 2) + 1e-12)
            rms_db = 20.0 * torch.log10(rms + 1e-12)

            # Clipping
            clipping = torch.mean((torch.abs(audio_t) > 0.99).float())

            # DC offset
            dc_offset = torch.mean(audio_t)

            # SNR proxy
            noise_floor = torch.quantile(torch.abs(audio_t), 0.1)
            snr_proxy = 20.0 * torch.log10((rms + 1e-9) / (noise_floor + 1e-9))

            results.append((
                float(rms_db.cpu()),
                float(clipping.cpu()),
                float(dc_offset.cpu()),
                float(snr_proxy.cpu()),
            ))

        return results

    def compute_all_metrics_batch(
        self,
        audios: list[np.ndarray],
        sr: int,
    ) -> list[AudioMetrics]:
        """Compute all audio metrics for a batch of segments.

        This is the main entry point that combines LUFS, spectral, and basic metrics.
        """
        if not audios:
            return []

        # Compute all metrics in batched fashion
        lufs_values = self.compute_lufs_batch(audios, sr)
        spectral_values = self.compute_spectral_batch(audios, sr)
        basic_values = self.compute_basic_metrics_batch(audios)

        # Combine into AudioMetrics objects
        results = []
        for i in range(len(audios)):
            rolloff, eff_nyquist, hf_ratio = spectral_values[i]
            rms_db, clipping, dc_offset, snr = basic_values[i]

            results.append(AudioMetrics(
                loudness_lufs=lufs_values[i],
                loudness_rms_db=rms_db,
                clipping_fraction=clipping,
                dc_offset=dc_offset,
                snr_proxy=snr,
                spectral_rolloff_hz=rolloff,
                effective_nyquist_hz=eff_nyquist,
                high_freq_energy_ratio=hf_ratio,
            ))

        return results

# CPU fallback using pyloudnorm
def compute_metrics_cpu(audio: np.ndarray, sr: int) -> AudioMetrics:
    """Compute metrics using CPU (fallback)."""
    try:
        import pyloudnorm as pyln
        meter = pyln.Meter(sr)
        lufs = float(meter.integrated_loudness(audio.astype(np.float32)))
    except Exception:
        lufs = -100.0

    # Basic metrics
    rms = float(np.sqrt(np.mean(audio ** 2) + 1e-12))
    rms_db = 20.0 * math.log10(rms + 1e-12)
    clipping = float(np.mean(np.abs(audio) > 0.99))
    dc_offset = float(np.mean(audio))
    noise_floor = float(np.percentile(np.abs(audio), 10))
    snr_proxy = 20.0 * math.log10((rms + 1e-9) / (noise_floor + 1e-9))

    # Spectral metrics
    max_samples = min(audio.size, sr * 5)
    window = np.hanning(max_samples)
    chunk = audio[:max_samples] * window
    spec = np.fft.rfft(chunk)
    power = np.abs(spec) ** 2
    freqs = np.fft.rfftfreq(max_samples, d=1.0 / sr)

    total_energy = float(np.sum(power)) + 1e-12
    cumulative = np.cumsum(power)
    rolloff_idx = int(np.searchsorted(cumulative, 0.85 * cumulative[-1]))
    rolloff_hz = float(freqs[min(rolloff_idx, len(freqs) - 1)])

    power_db = 10.0 * np.log10(power + 1e-12)
    threshold = power_db.max() - 60.0
    above = np.where(power_db >= threshold)[0]
    eff_nyquist = float(freqs[above[-1]]) if above.size > 0 else 0.0

    hf_energy = float(np.sum(power[freqs >= 8000.0]))
    hf_ratio = hf_energy / total_energy

    return AudioMetrics(
        loudness_lufs=lufs,
        loudness_rms_db=rms_db,
        clipping_fraction=clipping,
        dc_offset=dc_offset,
        snr_proxy=snr_proxy,
        spectral_rolloff_hz=rolloff_hz,
        effective_nyquist_hz=eff_nyquist,
        high_freq_energy_ratio=hf_ratio,
    )

def benchmark_gpu_metrics():
    """Benchmark GPU vs CPU metrics computation."""
    import time

    print("=" * 60)
    print("GPU Audio Metrics Benchmark")
    print("=" * 60)

    # Generate test audio
    sr = 48000
    duration = 10.0
    num_samples = int(sr * duration)

    # Create batch of test signals
    batch_sizes = [1, 8, 32, 64, 128, 256]

    # Test audio with realistic spectral content
    t = np.linspace(0, duration, num_samples, dtype=np.float32)
    test_audio = (
        np.sin(2 * np.pi * 440 * t) * 0.3 +
        np.sin(2 * np.pi * 1000 * t) * 0.2 +
        np.sin(2 * np.pi * 5000 * t) * 0.1 +
        np.random.randn(num_samples).astype(np.float32) * 0.02
    ).astype(np.float32)

    print(f"Test audio: {duration}s at {sr}Hz")
    print()

    # CPU baseline
    print("CPU baseline (pyloudnorm):")
    start = time.perf_counter()
    for _ in range(10):
        _ = compute_metrics_cpu(test_audio, sr)
    cpu_time = (time.perf_counter() - start) / 10
    print(f"  Single segment: {cpu_time*1000:.2f}ms")
    print()

    # GPU batched
    if HAS_TORCH and torch.cuda.is_available():
        print("GPU batched:")
        computer = GPUMetricsComputer()

        # Warmup
        _ = computer.compute_all_metrics_batch([test_audio], sr)

        for batch_size in batch_sizes:
            batch = [test_audio.copy() for _ in range(batch_size)]

            start = time.perf_counter()
            results = computer.compute_all_metrics_batch(batch, sr)
            gpu_time = time.perf_counter() - start

            per_seg = gpu_time / batch_size
            speedup = cpu_time / per_seg

            print(f"  Batch {batch_size:3d}: {gpu_time*1000:7.2f}ms total, "
                  f"{per_seg*1000:6.2f}ms/seg, {speedup:5.1f}x speedup")

        # Verify accuracy
        print()
        print("Accuracy check:")
        cpu_metrics = compute_metrics_cpu(test_audio, sr)
        gpu_metrics = computer.compute_all_metrics_batch([test_audio], sr)[0]

        print(f"  LUFS:    CPU={cpu_metrics.loudness_lufs:.2f}, GPU={gpu_metrics.loudness_lufs:.2f}, "
              f"diff={abs(cpu_metrics.loudness_lufs - gpu_metrics.loudness_lufs):.2f}")
        print(f"  RMS dB:  CPU={cpu_metrics.loudness_rms_db:.2f}, GPU={gpu_metrics.loudness_rms_db:.2f}")
        print(f"  Eff Nyq: CPU={cpu_metrics.effective_nyquist_hz:.0f}, GPU={gpu_metrics.effective_nyquist_hz:.0f}")
    else:
        print("GPU not available, skipping GPU benchmark")

if __name__ == "__main__":
    benchmark_gpu_metrics()


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.


sed -n '1,260p' scripts/gpu_audio_decode.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""GPU-accelerated audio decoding using NVIDIA DALI.

DALI decodes audio directly on GPU, bypassing CPU entirely:
- Reads from disk/memory
- Decodes FLAC/WAV on GPU
- Returns GPU tensor directly

This eliminates:
1. CPU decode time (31% of original pipeline)
2. PCIe transfer of raw PCM data to GPU
"""

from __future__ import annotations

import io
import time
from typing import Optional

import numpy as np

# CPU baseline
import soundfile as sf

# DALI for GPU decode
try:
    from nvidia.dali import pipeline_def, fn
    from nvidia.dali.plugin.pytorch import feed_ndarray, DALIGenericIterator
    import nvidia.dali.types as types
    HAS_DALI = True
except ImportError:
    HAS_DALI = False

# PyTorch for tensor handling
try:
    import torch
    HAS_TORCH = True
except ImportError:
    HAS_TORCH = False

class GPUAudioDecoder:
    """GPU-accelerated audio decoder using NVIDIA DALI."""

    def __init__(self, device_id: int = 0, target_sr: Optional[int] = None):
        """
        Args:
            device_id: CUDA device ID
            target_sr: Optional target sample rate for resampling during decode
        """
        if not HAS_DALI:
            raise RuntimeError("NVIDIA DALI required. Install with: pip install nvidia-dali-cuda120")

        self.device_id = device_id
        self.target_sr = target_sr
        self._pipeline = None

    def decode_bytes(self, audio_bytes: bytes) -> tuple[np.ndarray, int]:
        """Decode audio bytes using DALI.

        Note: DALI audio decoder runs on CPU but benefits from optimized
        pipeline scheduling and prefetching. True GPU decode is for images/video.

        Args:
            audio_bytes: FLAC or WAV encoded audio bytes

        Returns:
            (audio_array, sample_rate) - audio as float32 numpy array
        """
        # DALI audio decoder is CPU-based but optimized
        @pipeline_def(batch_size=1, num_threads=4, device_id=self.device_id)
        def audio_pipeline():
            encoded = fn.external_source(name="encoded", device="cpu")
            audio, sr = fn.decoders.audio(
                encoded,
                device="cpu",  # Audio decode is CPU (GPU is for images)
                dtype=types.FLOAT,
                downmix=True,
            )
            # Move to GPU for subsequent processing
            audio_gpu = audio.gpu()
            return audio_gpu, sr

        pipe = audio_pipeline()
        pipe.build()

        # Feed the encoded bytes
        pipe.feed_input("encoded", [np.frombuffer(audio_bytes, dtype=np.uint8)])
        outputs = pipe.run()

        # Get results - audio is on GPU, sr on CPU
        audio = outputs[0].as_cpu().at(0)
        sr = int(outputs[1].as_cpu().at(0))

        return audio.flatten().astype(np.float32), sr

    def decode_batch(
        self,
        audio_bytes_list: list[bytes],
    ) -> list[tuple[np.ndarray, int]]:
        """Decode multiple audio files in batch.

        DALI provides optimized parallel decoding with prefetching.

        Args:
            audio_bytes_list: List of encoded audio bytes

        Returns:
            List of (audio_array, sample_rate) tuples
        """
        if not audio_bytes_list:
            return []

        batch_size = len(audio_bytes_list)

        @pipeline_def(batch_size=batch_size, num_threads=8, device_id=self.device_id, prefetch_queue_depth=2)
        def batch_audio_pipeline():
            encoded = fn.external_source(name="encoded", device="cpu")
            audio, sr = fn.decoders.audio(
                encoded,
                device="cpu",
                dtype=types.FLOAT,
                downmix=True,
            )
            audio_gpu = audio.gpu()
            return audio_gpu, sr

        pipe = batch_audio_pipeline()
        pipe.build()

        # Convert bytes to numpy arrays for DALI
        encoded_arrays = [np.frombuffer(b, dtype=np.uint8) for b in audio_bytes_list]
        pipe.feed_input("encoded", encoded_arrays)

        outputs = pipe.run()
        audio_batch = outputs[0].as_cpu()
        sr_batch = outputs[1].as_cpu()

        results = []
        for i in range(batch_size):
            audio = audio_batch.at(i).flatten().astype(np.float32)
            sr = int(sr_batch.at(i))
            results.append((audio, sr))

        return results

def decode_cpu(audio_bytes: bytes) -> tuple[np.ndarray, int]:
    """CPU baseline using soundfile."""
    audio, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32")
    if audio.ndim > 1:
        audio = np.mean(audio, axis=1)
    return audio.astype(np.float32), sr

def benchmark_decode():
    """Compare CPU vs GPU decode performance."""
    print("=" * 60)
    print("Audio Decode Benchmark: CPU vs GPU (DALI)")
    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)

    test_audio = (
        np.sin(2 * np.pi * 440 * t) * 0.3 +
        np.sin(2 * np.pi * 1000 * t) * 0.2 +
        np.random.randn(num_samples).astype(np.float32) * 0.02
    ).astype(np.float32)

    # Encode to FLAC
    buf = io.BytesIO()
    sf.write(buf, test_audio, sr, format="FLAC")
    flac_bytes = buf.getvalue()

    print(f"Test: {duration}s audio at {sr}Hz, FLAC size: {len(flac_bytes)/1024:.1f}KB")
    print()

    # CPU baseline
    print("CPU (soundfile):")
    times = []
    for _ in range(10):
        start = time.perf_counter()
        audio, decoded_sr = decode_cpu(flac_bytes)
        times.append(time.perf_counter() - start)
    cpu_time = np.mean(times) * 1000
    print(f"  Single decode: {cpu_time:.2f}ms")

    # Batch CPU
    batch_sizes = [1, 8, 32, 64]
    print(f"  Batch decode:")
    for bs in batch_sizes:
        batch = [flac_bytes] * bs
        start = time.perf_counter()
        for b in batch:
            _ = decode_cpu(b)
        elapsed = (time.perf_counter() - start) * 1000
        print(f"    Batch {bs:3d}: {elapsed:.1f}ms total, {elapsed/bs:.2f}ms/seg")

    # GPU (DALI)
    if HAS_DALI:
        print()
        print("GPU (DALI):")
        decoder = GPUAudioDecoder()

        # Warmup
        _ = decoder.decode_bytes(flac_bytes)

        # Single decode
        times = []
        for _ in range(10):
            start = time.perf_counter()
            audio, decoded_sr = decoder.decode_bytes(flac_bytes)
            times.append(time.perf_counter() - start)
        gpu_time = np.mean(times) * 1000
        speedup = cpu_time / gpu_time
        print(f"  Single decode: {gpu_time:.2f}ms ({speedup:.1f}x speedup)")

        # Batch GPU
        print(f"  Batch decode:")
        for bs in batch_sizes:
            batch = [flac_bytes] * bs
            start = time.perf_counter()
            results = decoder.decode_batch(batch)
            elapsed = (time.perf_counter() - start) * 1000
            per_seg = elapsed / bs
            speedup = cpu_time / per_seg
            print(f"    Batch {bs:3d}: {elapsed:.1f}ms total, {per_seg:.2f}ms/seg ({speedup:.1f}x)")

        # Verify correctness
        print()
        print("Correctness check:")
        cpu_audio, cpu_sr = decode_cpu(flac_bytes)
        gpu_audio, gpu_sr = decoder.decode_bytes(flac_bytes)
        print(f"  SR match: {cpu_sr == gpu_sr}")
        print(f"  Length: CPU={len(cpu_audio)}, GPU={len(gpu_audio)}")
        if len(cpu_audio) == len(gpu_audio):
            diff = np.abs(cpu_audio - gpu_audio).max()
            print(f"  Max diff: {diff:.6f}")
    else:
        print("\nDALI not available, skipping GPU benchmark")

if __name__ == "__main__":
    benchmark_decode()


Summary: auto


sed -n '1,260p' scripts/optimized_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""Optimized audio processing pipeline with GPU-accelerated metrics.

This module implements a high-performance pipeline with:
1. Multiprocess FLAC decoding (bypasses GIL)
2. GPU-batched metrics computation (LUFS, spectral analysis)
3. GPU-batched resampling
4. Multiprocess WAV encoding

Key architecture:
- Producer pool: Parallel decode from tar files
- GPU batch processor: Metrics + resample in batches
- Consumer pool: Parallel encode to output format

Expected performance: 80-150 seg/s (3-5x over current 26 seg/s)
"""

from __future__ import annotations

import io
import json
import math
import multiprocessing as mp
import os
import queue
import random
import tarfile
import threading
import time
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional

import numpy as np
import soundfile as sf
from scipy import signal

# GPU metrics
try:
    from gpu_audio_metrics import GPUMetricsComputer, AudioMetrics
    HAS_GPU_METRICS = True
except ImportError:
    HAS_GPU_METRICS = False
    GPUMetricsComputer = None
    AudioMetrics = None

# DALI for optimized batch decode
try:
    from gpu_audio_decode import GPUAudioDecoder
    HAS_DALI = True
except ImportError:
    HAS_DALI = False
    GPUAudioDecoder = None

# GPU resampling
try:
    import torch
    import torchaudio
    HAS_TORCH = True
except ImportError:
    HAS_TORCH = False
    torch = None
    torchaudio = None

# Import chunking logic from prepare_shards
try:
    from prepare_shards import (
        ChunkConfig,
        SilenceConfig,
        make_chunk_windows,
        duration_bucket,
        classify_native_bucket,
    )
except ImportError:
    # Minimal fallback
    @dataclass(frozen=True)
    class ChunkConfig:
        enabled: bool = True
        min_sec: float = 6.0
        max_sec: float = 10.0
        threshold_sec: float = 12.0
        all_buckets: bool = False

    @dataclass(frozen=True)
    class SilenceConfig:
        enabled: bool = True
        frame_sec: float = 0.05
        hop_sec: float = 0.01
        search_sec: float = 0.5
        percentile: float = 20.0
        db_drop: float = 20.0

@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:
    """Segment after FLAC decoding."""
    input: SegmentInput
    audio: np.ndarray
    sr: int

@dataclass
class ProcessedChunk:
    """Chunk after metrics computation and chunking."""
    video_id: str
    language: str
    sample_id: str
    audio: np.ndarray
    sr: int
    metrics: dict[str, float]
    metadata: dict[str, Any]
    target_srs: list[int]
    is_val: bool

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

@dataclass
class PipelineStats:
    """Statistics for pipeline performance tracking."""
    segments_decoded: int = 0
    segments_processed: int = 0
    chunks_emitted: int = 0
    outputs_generated: int = 0
    dropped_too_short: int = 0
    dropped_too_long: int = 0
    dropped_clipped: int = 0
    decode_time_ms: float = 0.0
    metrics_time_ms: float = 0.0
    resample_time_ms: float = 0.0
    encode_time_ms: float = 0.0

def decode_segment(segment_bytes: bytes) -> Optional[tuple[np.ndarray, int]]:
    """Decode FLAC bytes to numpy array. Runs in worker process."""
    try:
        audio, sr = sf.read(io.BytesIO(segment_bytes), dtype="float32")
        if audio.ndim > 1:
            audio = np.mean(audio, axis=1)
        return audio.astype(np.float32), sr
    except Exception:
        return None

def encode_audio_worker(audio: np.ndarray, sr: int, output_format: str) -> bytes:
    """Encode audio to WAV/FLAC bytes. Runs in worker process."""
    buf = io.BytesIO()
    if output_format == "wav":
        sf.write(buf, audio, sr, format="WAV", subtype="PCM_16")
    else:
        sf.write(buf, audio, sr, format="FLAC", subtype="PCM_16")
    return buf.getvalue()

class GPUBatchProcessor:
    """GPU-accelerated batch processor for metrics and resampling."""

    def __init__(self, device: str = "cuda"):
        self.use_gpu = HAS_TORCH and torch.cuda.is_available()
        self.device = torch.device(device if self.use_gpu else "cpu")

        # GPU metrics computer
        if HAS_GPU_METRICS and self.use_gpu:
            self.metrics_computer = GPUMetricsComputer(device=device)
        else:
            self.metrics_computer = None

        # Resampler cache
        self._resamplers: dict[tuple[int, int], Any] = {}

        if self.use_gpu:
            print(f"GPUBatchProcessor: Using {torch.cuda.get_device_name()}")
            print(f"  GPU metrics: {self.metrics_computer is not None}")
        else:
            print("GPUBatchProcessor: Using CPU (GPU not available)")

    def compute_metrics_batch(
        self,
        audios: list[np.ndarray],
        sr: int,
    ) -> list[dict[str, float]]:
        """Compute audio metrics for a batch of segments."""
        if not audios:
            return []

        if self.metrics_computer is not None:
            # GPU path
            metrics_list = self.metrics_computer.compute_all_metrics_batch(audios, sr)
            return [
                {
                    "loudness_lufs": m.loudness_lufs,
                    "loudness_rms_db": m.loudness_rms_db,
                    "clipping_fraction": m.clipping_fraction,
                    "dc_offset": m.dc_offset,
                    "snr_proxy": m.snr_proxy,
                    "spectral_rolloff_hz": m.spectral_rolloff_hz,
                    "effective_nyquist_hz": m.effective_nyquist_hz,
                    "high_freq_energy_ratio": m.high_freq_energy_ratio,
                }
                for m in metrics_list
            ]
        else:
            # CPU fallback
            from prepare_shards import compute_audio_metrics
            return [compute_audio_metrics(a, sr) for a in audios]

    def resample_batch(
        self,
        audios: list[np.ndarray],
        src_sr: int,
        dst_sr: int,
    ) -> list[np.ndarray]:
        """Resample a batch of audio to target sample rate."""
        if not audios or src_sr == dst_sr:
            return audios

        if self.use_gpu:
            # GPU path with batching
            resampler = self._get_resampler(src_sr, dst_sr)

            # Pad to max length
            lengths = [a.shape[0] for a in audios]
            max_len = max(lengths)

            batch = np.zeros((len(audios), max_len), dtype=np.float32)
            for i, a in enumerate(audios):
                batch[i, :a.shape[0]] = a

            batch_t = torch.from_numpy(batch).to(self.device)
            with torch.no_grad():
                resampled_t = resampler(batch_t)
            resampled = resampled_t.cpu().numpy()

            # Trim to actual lengths
            ratio = dst_sr / src_sr
            results = []
            for i, orig_len in enumerate(lengths):
                new_len = int(orig_len * ratio)
                results.append(resampled[i, :new_len].astype(np.float32))
            return results


sed -n '260,720p' scripts/optimized_pipeline.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            return results
        else:
            # CPU fallback
            results = []
            gcd = math.gcd(src_sr, dst_sr)
            up = dst_sr // gcd
            down = src_sr // gcd
            for audio in audios:
                resampled = signal.resample_poly(audio, up, down)
                results.append(resampled.astype(np.float32))
            return results

    def _get_resampler(self, src_sr: int, dst_sr: int):
        """Get or create resampler for the rate pair."""
        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]

class OptimizedPipeline:
    """High-performance audio processing pipeline.

    Uses multiprocessing for I/O-bound operations (decode/encode) and
    GPU batching for compute-bound operations (metrics/resample).
    Optionally uses DALI for optimized batch decode (5x faster).
    """

    def __init__(
        self,
        decode_workers: int = 8,
        encode_workers: int = 8,
        gpu_batch_size: int = 64,
        output_format: str = "wav",
        chunk_cfg: Optional[ChunkConfig] = None,
        silence_cfg: Optional[SilenceConfig] = None,
        use_multiprocess: bool = True,
        use_dali: bool = True,
    ):
        self.decode_workers = decode_workers
        self.encode_workers = encode_workers
        self.gpu_batch_size = gpu_batch_size
        self.output_format = output_format
        self.use_multiprocess = use_multiprocess
        self.use_dali = use_dali and HAS_DALI

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

        # GPU processor
        self.gpu_processor = GPUBatchProcessor()

        # DALI decoder for batch decode (5x speedup)
        self.dali_decoder = GPUAudioDecoder() if self.use_dali else None
        if self.use_dali:
            print(f"Using DALI for batch decode (5x speedup)")

        # Statistics
        self.stats = PipelineStats()

        # Worker pools (created on demand)
        self._decode_pool = None
        self._encode_pool = None

    def _get_decode_pool(self):
        """Get or create decode worker pool."""
        if self._decode_pool is None:
            if self.use_multiprocess:
                self._decode_pool = ProcessPoolExecutor(max_workers=self.decode_workers)
            else:
                self._decode_pool = ThreadPoolExecutor(max_workers=self.decode_workers)
        return self._decode_pool

    def _get_encode_pool(self):
        """Get or create encode worker pool."""
        if self._encode_pool is None:
            if self.use_multiprocess:
                self._encode_pool = ProcessPoolExecutor(max_workers=self.encode_workers)
            else:
                self._encode_pool = ThreadPoolExecutor(max_workers=self.encode_workers)
        return self._encode_pool

    def process_batch(
        self,
        segments: list[SegmentInput],
    ) -> list[OutputSegment]:
        """Process a batch of segments through the full pipeline.

        Pipeline stages:
        1. Parallel decode (multiprocess)
        2. Chunking (sequential, per-segment)
        3. Batched metrics computation (GPU)
        4. Batched resampling (GPU)
        5. Parallel encode (multiprocess)
        """
        if not segments:
            return []

        # Stage 1: Parallel decode
        start = time.perf_counter()
        decoded = self._decode_batch(segments)
        self.stats.decode_time_ms += (time.perf_counter() - start) * 1000
        self.stats.segments_decoded += len(decoded)

        if not decoded:
            return []

        # Stage 2: Chunking + collect audio slices
        chunks = self._process_segments_to_chunks(decoded)
        self.stats.segments_processed += len(decoded)
        self.stats.chunks_emitted += len(chunks)

        if not chunks:
            return []

        # Stage 3: Compute metrics for all chunks (GPU batched)
        start = time.perf_counter()
        self._compute_metrics_for_chunks(chunks)
        self.stats.metrics_time_ms += (time.perf_counter() - start) * 1000

        # Filter by quality
        valid_chunks = [c for c in chunks if c.metrics["clipping_fraction"] <= 0.1]
        dropped = len(chunks) - len(valid_chunks)
        self.stats.dropped_clipped += dropped

        # Stage 4: Resample and encode (GPU resample + parallel encode)
        start = time.perf_counter()
        outputs = self._resample_and_encode(valid_chunks)
        self.stats.resample_time_ms += (time.perf_counter() - start) * 1000
        self.stats.outputs_generated += len(outputs)

        return outputs

    def _decode_batch(
        self,
        segments: list[SegmentInput],
    ) -> list[DecodedSegment]:
        """Decode segments - uses DALI batch decode if available (5x faster)."""
        if self.use_dali and self.dali_decoder is not None:
            # DALI batch decode path (5x faster)
            try:
                audio_bytes_list = [seg.segment_bytes for seg in segments]
                results = self.dali_decoder.decode_batch(audio_bytes_list)

                decoded = []
                for seg, (audio, sr) in zip(segments, results):
                    if audio is not None:
                        decoded.append(DecodedSegment(input=seg, audio=audio, sr=sr))
                return decoded
            except Exception as e:
                print(f"DALI decode failed, falling back to CPU: {e}")
                # Fall through to CPU path

        # CPU fallback with multiprocess/threading
        pool = self._get_decode_pool()

        # Submit decode jobs
        futures = {
            pool.submit(decode_segment, seg.segment_bytes): seg
            for seg in segments
        }

        decoded = []
        for future in as_completed(futures):
            seg = futures[future]
            result = future.result()
            if result is not None:
                audio, sr = result
                decoded.append(DecodedSegment(input=seg, audio=audio, sr=sr))

        return decoded

    def _process_segments_to_chunks(
        self,
        decoded: list[DecodedSegment],
    ) -> list[ProcessedChunk]:
        """Apply chunking logic and create ProcessedChunk objects."""
        chunks = []

        for dec in decoded:
            seg = dec.input
            audio = dec.audio
            sr = dec.sr

            # Get chunk windows
            rng = random.Random(seg.rng_seed)
            windows = make_chunk_windows(audio, sr, self.chunk_cfg, self.silence_cfg, rng)

            music_stats = seg.segment_meta.get("music_stats") or {}
            has_music = bool(music_stats.get("has_music", False))

            sample_id_base = f"{seg.video_id}__{seg.speaker}_{seg.index:04d}"
            use_chunk_suffix = len(windows) > 1

            segment_start = seg.segment_meta.get("start")
            original_start = seg.segment_meta.get("original_start")

            # Process 16k windows
            for chunk_idx, (offset_sec, chunk_len_sec) in enumerate(windows):
                start_idx = int(round(offset_sec * sr))
                end_idx = int(round((offset_sec + chunk_len_sec) * sr))
                audio_slice = audio[start_idx:end_idx]

                if audio_slice.size == 0:
                    continue

                slice_duration = float(audio_slice.shape[0] / sr)
                bucket = duration_bucket(slice_duration)
                if bucket is None:
                    if slice_duration < 3.0:
                        self.stats.dropped_too_short += 1
                    else:
                        self.stats.dropped_too_long += 1
                    continue

                duration_bucket_name, base_weight = bucket
                sampling_weight = base_weight * (0.5 if has_music else 1.0)

                chunk_start = float(segment_start) + offset_sec if segment_start is not None else None
                chunk_end = chunk_start + slice_duration if chunk_start is not None else None
                chunk_original_start = float(original_start) + offset_sec if original_start is not None else None
                chunk_original_end = chunk_original_start + slice_duration if chunk_original_start is not None else None

                sample_id = sample_id_base
                chunk_index = None
                if use_chunk_suffix:
                    sample_id = f"{sample_id_base}_c{chunk_idx:02d}"
                    chunk_index = chunk_idx

                metadata = {
                    "id": sample_id,
                    "video_id": seg.video_id,
                    "language": seg.language,
                    "segment_index": seg.index,
                    "chunk_index": chunk_index,
                    "start": chunk_start,
                    "end": chunk_end,
                    "original_start": chunk_original_start,
                    "original_end": chunk_original_end,
                    "duration_sec": slice_duration,
                    "native_sr": sr,
                    "speaker": seg.speaker,
                    "music_prob": float(music_stats.get("music_mean", 0.0)),
                    "music_stats": music_stats,
                    "vad_speech_regions": [[0.0, slice_duration]],
                    "duration_bucket": duration_bucket_name,
                    "sampling_weight": sampling_weight,
                }

                chunks.append(ProcessedChunk(
                    video_id=seg.video_id,
                    language=seg.language,
                    sample_id=sample_id,
                    audio=audio_slice,
                    sr=sr,
                    metrics={},  # Computed in next stage
                    metadata=metadata,
                    target_srs=[16000],  # Start with 16k
                    is_val=seg.is_val,
                ))

        return chunks

    def _compute_metrics_for_chunks(
        self,
        chunks: list[ProcessedChunk],
    ) -> None:
        """Compute metrics for all chunks using GPU batching."""
        if not chunks:
            return

        # Group by sample rate for batched processing
        by_sr: dict[int, list[ProcessedChunk]] = {}
        for chunk in chunks:
            by_sr.setdefault(chunk.sr, []).append(chunk)

        for sr, sr_chunks in by_sr.items():
            # Process in GPU batch sizes
            for i in range(0, len(sr_chunks), self.gpu_batch_size):
                batch = sr_chunks[i:i + self.gpu_batch_size]
                audios = [c.audio for c in batch]

                metrics_list = self.gpu_processor.compute_metrics_batch(audios, sr)

                for chunk, metrics in zip(batch, metrics_list):
                    chunk.metrics = metrics

                    # Update metadata with metrics
                    eff_nyquist = metrics["effective_nyquist_hz"]
                    chunk.metadata.update({
                        "loudness_lufs": metrics["loudness_lufs"],
                        "loudness_rms_db": metrics["loudness_rms_db"],
                        "clipping_fraction": metrics["clipping_fraction"],
                        "dc_offset": metrics["dc_offset"],
                        "snr_proxy": metrics["snr_proxy"],
                        "spectral_rolloff_hz": metrics["spectral_rolloff_hz"],
                        "effective_nyquist_hz": eff_nyquist,
                        "high_freq_energy_ratio": metrics["high_freq_energy_ratio"],
                        "is_truly_wideband": bool(eff_nyquist >= 12000),
                        "native_sr_bucket": classify_native_bucket(eff_nyquist),
                    })

                    # Compute quality score
                    has_music = chunk.metadata.get("music_prob", 0) > 0.5
                    quality_score = self._compute_quality_score(metrics, has_music)
                    chunk.metadata["quality_score"] = quality_score

                    # Determine target sample rates based on bandwidth
                    targets = [16000]
                    if eff_nyquist >= 8000:
                        targets.append(24000)
                    if eff_nyquist >= 12000:
                        targets.append(32000)
                    if eff_nyquist >= 16000:
                        targets.append(44100)
                    chunk.target_srs = targets

    def _compute_quality_score(self, metrics: dict, has_music: bool) -> float:
        """Compute quality score from metrics."""
        def normalize(value, min_val, max_val):
            if max_val <= min_val:
                return 0.0
            return max(0.0, min(1.0, (value - min_val) / (max_val - min_val)))

        score = (
            0.3 * (1.0 - metrics["clipping_fraction"])
            + 0.2 * normalize(metrics["snr_proxy"], 10, 40)
            + 0.2 * (1.0 if not has_music else 0.5)
            + 0.2 * normalize(metrics["loudness_lufs"], -40, -14)
            + 0.1 * (1.0 if abs(metrics["dc_offset"]) < 0.01 else 0.5)
        )
        return max(0.0, min(1.0, score))

    def _resample_and_encode(
        self,
        chunks: list[ProcessedChunk],
    ) -> list[OutputSegment]:
        """Resample chunks to target rates and encode to output format."""
        if not chunks:
            return []

        outputs = []
        encode_pool = self._get_encode_pool()
        encode_futures = []

        # Group chunks by (source_sr, target_sr) for batched resampling
        resample_groups: dict[tuple[int, int], list[tuple[ProcessedChunk, int]]] = {}

        for chunk in chunks:
            for target_sr in chunk.target_srs:
                key = (chunk.sr, target_sr)
                resample_groups.setdefault(key, []).append((chunk, target_sr))

        # Process each resample group
        for (src_sr, dst_sr), group in resample_groups.items():
            # Batch resample
            audios = [c.audio for c, _ in group]
            resampled = self.gpu_processor.resample_batch(audios, src_sr, dst_sr)

            # Submit encode jobs
            for (chunk, target_sr), audio in zip(group, resampled):
                audio = np.clip(audio, -1.0, 1.0)

                # Create metadata for this output
                meta = dict(chunk.metadata)
                meta["sample_rate"] = target_sr
                meta["resample_target_sr"] = target_sr

                # Submit encode task
                future = encode_pool.submit(
                    encode_audio_worker, audio, target_sr, self.output_format
                )
                encode_futures.append((future, meta, chunk.is_val, target_sr))

        # Collect encoded outputs
        for future, meta, is_val, sr in encode_futures:
            try:
                audio_bytes = future.result()
                outputs.append(OutputSegment(
                    sample_rate=sr,
                    audio_bytes=audio_bytes,
                    metadata=meta,
                    is_val=is_val,
                ))
            except Exception as e:
                print(f"Encode error: {e}")

        return outputs

    def close(self):
        """Shutdown worker pools."""
        if self._decode_pool is not None:
            self._decode_pool.shutdown(wait=True)
            self._decode_pool = None
        if self._encode_pool is not None:
            self._encode_pool.shutdown(wait=True)
            self._encode_pool = None

    def print_stats(self):
        """Print pipeline statistics."""
        total_time = (
            self.stats.decode_time_ms +
            self.stats.metrics_time_ms +
            self.stats.resample_time_ms +
            self.stats.encode_time_ms
        )

        print("\n=== Pipeline Statistics ===")
        print(f"Segments decoded:   {self.stats.segments_decoded}")
        print(f"Segments processed: {self.stats.segments_processed}")
        print(f"Chunks emitted:     {self.stats.chunks_emitted}")
        print(f"Outputs generated:  {self.stats.outputs_generated}")
        print(f"Dropped (short):    {self.stats.dropped_too_short}")
        print(f"Dropped (long):     {self.stats.dropped_too_long}")
        print(f"Dropped (clipped):  {self.stats.dropped_clipped}")
        print()
        print(f"Timing breakdown:")
        if total_time > 0:
            print(f"  Decode:   {self.stats.decode_time_ms:8.1f}ms ({100*self.stats.decode_time_ms/total_time:5.1f}%)")
            print(f"  Metrics:  {self.stats.metrics_time_ms:8.1f}ms ({100*self.stats.metrics_time_ms/total_time:5.1f}%)")
            print(f"  Resample: {self.stats.resample_time_ms:8.1f}ms ({100*self.stats.resample_time_ms/total_time:5.1f}%)")
            print(f"  Encode:   {self.stats.encode_time_ms:8.1f}ms ({100*self.stats.encode_time_ms/total_time:5.1f}%)")
            print(f"  Total:    {total_time:8.1f}ms")

        if self.stats.segments_decoded > 0 and total_time > 0:
            throughput = self.stats.segments_decoded / (total_time / 1000)
            print(f"\nThroughput: {throughput:.1f} segments/sec")

if __name__ == "__main__":
    print("Optimized Audio Processing Pipeline")
    print("=" * 50)
    print(f"GPU Metrics: {HAS_GPU_METRICS}")
    print(f"DALI Decode: {HAS_DALI}")
    print(f"PyTorch/CUDA: {HAS_TORCH}")
    print()
    print("Usage: Import OptimizedPipeline and call process_batch()")
    print("See prepare_shards.py for integration example.")


**Planning benchmarking with real R2 data**

Planning to create a benchmark script that fetches real videos from R2, processes them with the new optimized pipeline class (which isn't integrated with prepare_shards), and computes throughput. Also considering an audit script to run end-to-end reliability tests on several videos, comparing outputs against the default prepare_shards pipeline for validation.


**Designing detailed benchmark and audit script**

Planning a new script that downloads specified numbers of real videos from R2, runs them through the optimized pipeline with configurable batching and resource options, measures both download and processing time separately, and optionally performs a thorough audit by writing and verifying shards for 10 videos. It will compare results against the CPU baseline pipeline for counts and metrics differences, and compute throughput-based ETA estimates for large-scale processing with horizontal scaling. The script will reuse existing prepare_shards utilities for fetching and writing and handle optional DALI usage.


python3 - <<'PY'
import importlib.util
for mod in ['nvidia.dali','torch','torchaudio']:
    print(mod, 'ok' if importlib.util.find_spec(mod) else 'missing')
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
nvidia.dali ok
torch ok
torchaudio ok


**Checking GPU availability next**


python3 - <<'PY'
import torch
print('cuda', torch.cuda.is_available())
if torch.cuda.is_available():
    print(torch.cuda.get_device_name())
PY


Output:
Exit code: 0
Wall time: 1.9 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]
cuda True
NVIDIA A100-SXM4-80GB


**Planning script dependencies**

I'm identifying key utility functions from prepare_shards to reuse, including environment loading, R2 client setup, record fetching, filename parsing, and segment seeding, to build the new script. Next, I'll inspect make_segment_seed and related functions in prepare_shards.


rg -n "def parse_segment_filename|def make_segment_seed" -n scripts/prepare_shards.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
523:def make_segment_seed(base_seed: int, video_id: str, speaker: str, index: int) -> int:
528:def parse_segment_filename(filename: str) -> Optional[tuple[str, float]]:


sed -n '500,560p' scripts/prepare_shards.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            search_low = offset + cfg.min_sec
            search_high = min(offset + cfg.max_sec, latest_cut)
        boundary = find_silence_boundary(
            times,
            rms,
            search_low,
            search_high,
            threshold_rms,
            median_db,
            silence_cfg.db_drop,
        )
        if boundary is None:
            if not windows:
                return [(0.0, duration_sec)]
            windows.append((offset, duration_sec - offset))
            return windows
        windows.append((offset, boundary - offset))
        offset = boundary

    windows.append((offset, duration_sec - offset))
    return windows

def make_segment_seed(base_seed: int, video_id: str, speaker: str, index: int) -> int:
    key = f"{video_id}-{speaker}-{index}-{base_seed}"
    return zlib.adler32(key.encode("utf-8")) & 0xFFFFFFFF

def parse_segment_filename(filename: str) -> Optional[tuple[str, float]]:
    """Parse segment filename to extract speaker and start time.

    Filename format: segments/SPEAKER_XX_NNNN_start-end.flac
    Example: segments/SPEAKER_00_0007_35.10-37.49.flac

    Returns (speaker, start_time) for matching against metadata.
    """
    base = filename.split("/")[-1]
    if not base.endswith(".flac"):
        return None

    # Remove .flac extension
    base = base[:-5]

    # Try to extract time range (last part after final underscore)
    parts = base.split("_")
    if len(parts) < 3:
        return None

    # Speaker is everything except last two parts (index and time range)
    speaker = "_".join(parts[:-2])

    # Time range is the last part: "35.10-37.49"
    time_range = parts[-1]
    if "-" not in time_range:
        return None

    try:
        start_str = time_range.split("-")[0]
        start_time = float(start_str)
    except (ValueError, IndexError):
        return None


**Planning segmented benchmark and audit modes**

I'm designing a new script with benchmark and audit modes: benchmark will process segments and gather stats without saving outputs, while audit will decode outputs in memory to validate audio integrity and optionally write shards to verify writing correctness.


**Planning batch processing with pipeline output writing**

I'm planning to batch segments across videos for GPU efficiency, process them with the optimized pipeline which always encodes outputs, and count outputs without storing audio bytes to control memory. For audit, I'll reuse ShardWriter from prepare_shards to write outputs and manifests, enabling correctness checks without full integration into prepare_shards, balancing thorough testing with implementation scope.


rg -n "class ShardWriter" -n scripts/prepare_shards.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
801:class ShardWriter:


sed -n '780,900p' scripts/prepare_shards.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                        chunk_original_start,
                        chunk_original_end,
                        slice_targets,
                    )
            else:
                emit_outputs(
                    audio,
                    full_metrics,
                    sample_id_base,
                    index,
                    None,
                    segment_start,
                    segment_end,
                    original_start,
                    original_end,
                    targets_high,
                )

        return outputs, stats

class ShardWriter:
    def __init__(
        self, output_dir: Path, prefix: str, target_size_bytes: int, audio_ext: str
    ) -> None:
        self.output_dir = output_dir
        self.prefix = prefix
        self.target_size_bytes = target_size_bytes
        self.audio_ext = audio_ext
        self.shard_index = 0
        self.current_tar: Optional[tarfile.TarFile] = None
        self.current_jsonl = None
        self.current_size = 0
        self.current_count = 0
        self.current_name = ""
        self.output_dir.mkdir(parents=True, exist_ok=True)

    def _open_new_shard(self) -> None:
        if self.current_tar:
            self.close()
        shard_name = f"{self.prefix}-{self.shard_index:06d}"
        tar_path = self.output_dir / f"{shard_name}.tar"
        jsonl_path = self.output_dir / f"{shard_name}.jsonl"
        self.current_tar = tarfile.open(tar_path, "w")
        self.current_jsonl = jsonl_path.open("w", encoding="utf-8")
        self.current_size = 0
        self.current_count = 0
        self.current_name = shard_name
        self.shard_index += 1

    def add_sample(self, sample_id: str, audio_bytes: bytes, metadata: dict[str, Any]) -> str:
        if self.current_tar is None:
            self._open_new_shard()
        if (
            self.current_count > 0
            and self.current_size + len(audio_bytes) > self.target_size_bytes
        ):
            self._open_new_shard()

        assert self.current_tar is not None
        assert self.current_jsonl is not None

        arcname = f"{sample_id}.{self.audio_ext}"
        info = tarfile.TarInfo(name=arcname)
        info.size = len(audio_bytes)
        info.mtime = int(time.time())
        self.current_tar.addfile(info, io.BytesIO(audio_bytes))
        self.current_jsonl.write(json.dumps(metadata) + "\n")

        self.current_size += len(audio_bytes)
        self.current_count += 1
        return self.current_name

    def close(self) -> None:
        if self.current_tar:
            self.current_tar.close()
        if self.current_jsonl:
            self.current_jsonl.close()
        self.current_tar = None
        self.current_jsonl = None

def create_prefix_placeholders(
    s3,
    bucket: str,
    languages: list[str],
    rates: list[int],
    extra_prefixes: list[str],
    subprefix: Optional[str],
) -> None:
    prefixes = []
    for lang in languages:
        for rate in rates:
            base = f"{lang}_{rate}"
            prefixes.append(base)
            if subprefix:
                prefixes.append(f"{base}/{subprefix.strip('/')}")
    prefixes.extend(extra_prefixes)
    if subprefix:
        prefixes.extend([f"{p}/{subprefix.strip('/')}" for p in extra_prefixes])

    for prefix in prefixes:
        key = f"{prefix.strip('/')}/.keep"
        try:
            s3.put_object(Bucket=bucket, Key=key, Body=b"")
        except Exception:
            continue

def upload_directory(
    s3,
    bucket: str,
    local_dir: Path,
    remote_prefix: str,
    max_concurrency: int,
) -> list[str]:
    uploaded = []
    config = TransferConfig(
        multipart_threshold=8 * 1024 * 1024,
        multipart_chunksize=64 * 1024 * 1024,
        max_concurrency=max_concurrency,


**Planning detailed audit implementation**

I'm designing an audit to read and verify output shard files with tar and jsonl, track per-sample and per-video counts, and validate audio correctness and metadata. I'll also compare GPU pipeline results to CPU baseline counts for consistency checks.


**Designing combined benchmark and audit script**

I'm outlining a single script supporting benchmark and audit modes with detailed CLI options, segment loading from R2 tarballs, batch processing via the optimized pipeline, shard writing with validation, and optional CPU baseline comparisons for 10 videos. I'm planning thorough output verification including audio decoding, metadata checks, and shard read-back consistency.


**Planning thorough output consistency checks**

I'm finalizing audit details to verify each jsonl entry matches an audio file in the tar, decode outputs for validation, and implement CPU baseline processing to compare output counts and loudness metrics keyed by segment IDs and sample rates. This supports detailed discrepancy analysis between optimized and CPU pipelines for 10 videos.


Input:
*** Begin Patch
*** Add File: scripts/benchmark_optimized_pipeline.py
+#!/usr/bin/env python3
+"""Benchmark and audit the optimized pipeline on real R2 data."""
+
+from __future__ import annotations
+
+import argparse
+import io
+import json
+import time
+import tarfile
+from collections import Counter
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterable, Optional
+
+import soundfile as sf
+from boto3.s3.transfer import TransferConfig
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from optimized_pipeline import OptimizedPipeline, SegmentInput
+from prepare_shards import (
+    ChunkConfig,
+    SilenceConfig,
+    ShardWriter,
+    fetch_video_records,
+    load_env,
+    make_r2_client,
+    make_segment_seed,
+    parse_segment_filename,
+    resolve_r2_key,
+    process_segment,
+)
+
+
+@dataclass(frozen=True)
+class BenchmarkStats:
+    videos: int
+    segments: int
+    outputs: int
+    elapsed_sec: float
+    download_sec: float
+
+    @property
+    def segments_per_sec(self) -> float:
+        return self.segments / self.elapsed_sec if self.elapsed_sec > 0 else 0.0
+
+    @property
+    def videos_per_min(self) -> float:
+        return self.videos / (self.elapsed_sec / 60.0) if self.elapsed_sec > 0 else 0.0
+
+
+def download_tars(
+    records: list,
+    download_dir: Path,
+    s3,
+    bucket: str,
+    prefix: Optional[str],
+    workers: int,
+) -> dict[str, Path]:
+    download_dir.mkdir(parents=True, exist_ok=True)
+    config = TransferConfig(
+        multipart_threshold=8 * 1024 * 1024,
+        multipart_chunksize=64 * 1024 * 1024,
+        max_concurrency=max(4, workers),
+        use_threads=True,
+    )
+
+    def download_one(record) -> tuple[str, Optional[Path]]:
+        resolved = resolve_r2_key(s3, bucket, record.r2_tar_key, prefix)
+        if not resolved:
+            return record.youtube_id, None
+        local_path = download_dir / f"{record.youtube_id}.tar"
+        if not local_path.exists():
+            s3.download_file(bucket, resolved, str(local_path), Config=config)
+        return record.youtube_id, local_path
+
+    downloaded: dict[str, Path] = {}
+    with ThreadPoolExecutor(max_workers=workers) as executor:
+        futures = [executor.submit(download_one, record) for record in records]
+        for future in as_completed(futures):
+            vid, path = future.result()
+            if path is not None:
+                downloaded[vid] = path
+    return downloaded
+
+
+def iter_segments_from_tar(
+    tar_path: Path,
+    video_id: str,
+    language: str,
+    seed: int,
+) -> Iterable[SegmentInput]:
+    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 = {
+            (seg.get("speaker"), round(seg.get("start", 0.0), 2)): seg
+            for seg in usable_segments
+        }
+        members = [m for m in tf.getmembers() if m.name.endswith(".flac")]
+
+        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]
+
+            segment_bytes = tf.extractfile(member).read()
+            filename_parts = member.name.split("_")
+            try:
+                index = int(filename_parts[-2])
+            except (ValueError, IndexError):
+                index = 0
+            rng_seed = make_segment_seed(seed, video_id, speaker, index)
+
+            yield SegmentInput(
+                video_id=video_id,
+                language=language,
+                segment_bytes=segment_bytes,
+                segment_meta=seg_meta,
+                speaker=speaker,
+                index=index,
+                is_val=False,
+                rng_seed=rng_seed,
+            )
+
+
+def run_benchmark(
+    download_paths: dict[str, Path],
+    language: str,
+    seed: int,
+    pipeline_batch_size: int,
+    pipeline: OptimizedPipeline,
+) -> BenchmarkStats:
+    segments_total = 0
+    outputs_total = 0
+    batch: list[SegmentInput] = []
+
+    start = time.perf_counter()
+    for video_id, tar_path in download_paths.items():
+        for segment in iter_segments_from_tar(tar_path, video_id, language, seed):
+            segments_total += 1
+            batch.append(segment)
+            if len(batch) >= pipeline_batch_size:
+                outputs = pipeline.process_batch(batch)
+                outputs_total += len(outputs)
+                batch = []
+
+    if batch:
+        outputs = pipeline.process_batch(batch)
+        outputs_total += len(outputs)
+
+    elapsed = time.perf_counter() - start
+    return BenchmarkStats(
+        videos=len(download_paths),
+        segments=segments_total,
+        outputs=outputs_total,
+        elapsed_sec=elapsed,
+        download_sec=0.0,
+    )
+
+
+def validate_audio_bytes(audio_bytes: bytes, expected_sr: int, expected_duration: float) -> tuple[bool, str]:
+    try:
+        audio, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32")
+    except Exception as exc:
+        return False, f"decode_error: {exc}"
+    if sr != expected_sr:
+        return False, f"sr_mismatch: {sr} != {expected_sr}"
+    if audio.size == 0:
+        return False, "empty_audio"
+    if not (audio == audio).all():
+        return False, "nan_audio"
+    duration = audio.shape[0] / sr
+    if abs(duration - expected_duration) > 0.05:
+        return False, f"duration_mismatch: {duration:.3f}s != {expected_duration:.3f}s"
+    if audio.max() > 1.01 or audio.min() < -1.01:
+        return False, "amplitude_out_of_range"
+    return True, "ok"
+
+
+def audit_pipeline(
+    download_paths: dict[str, Path],
+    language: str,
+    seed: int,
+    pipeline_batch_size: int,
+    pipeline: OptimizedPipeline,
+    output_dir: Path,
+    compare_cpu: bool,
+) -> dict[str, Any]:
+    output_dir.mkdir(parents=True, exist_ok=True)
+    audio_ext = "wav" if pipeline.output_format == "wav" else "flac"
+
+    writers = {
+        16000: ShardWriter(output_dir / f"{language}_16", "shard", 512 * 1024 * 1024, audio_ext),
+        24000: ShardWriter(output_dir / f"{language}_24", "shard", 512 * 1024 * 1024, audio_ext),
+        32000: ShardWriter(output_dir / f"{language}_32", "shard", 512 * 1024 * 1024, audio_ext),
+        44100: ShardWriter(output_dir / f"{language}_44", "shard", 512 * 1024 * 1024, audio_ext),
+    }
+    manifest_path = output_dir / f"{language}_audit_manifest.jsonl"
+    manifest = manifest_path.open("w", encoding="utf-8")
+
+    batch: list[SegmentInput] = []
+    outputs_total = 0
+    segments_total = 0
+    invalid_audio = []
+    output_counts = Counter()
+    meta_missing = Counter()
+    opt_metadata = {}
+
+    for video_id, tar_path in download_paths.items():
+        for segment in iter_segments_from_tar(tar_path, video_id, language, seed):
+            segments_total += 1
+            batch.append(segment)
+            if len(batch) >= pipeline_batch_size:
+                outputs = pipeline.process_batch(batch)
+                outputs_total += len(outputs)
+                _handle_outputs(outputs, writers, manifest, audio_ext, output_counts,
+                                meta_missing, invalid_audio, opt_metadata)
+                batch = []
+
+    if batch:
+        outputs = pipeline.process_batch(batch)
+        outputs_total += len(outputs)
+        _handle_outputs(outputs, writers, manifest, audio_ext, output_counts,
+                        meta_missing, invalid_audio, opt_metadata)
+
+    for writer in writers.values():
+        writer.close()
+    manifest.close()
+
+    shard_validation = validate_shards(output_dir, audio_ext)
+
+    cpu_comparison = None
+    if compare_cpu:
+        cpu_comparison = compare_with_cpu(download_paths, language, seed, pipeline.output_format)
+
+    return {
+        "segments_total": segments_total,
+        "outputs_total": outputs_total,
+        "output_counts": dict(output_counts),
+        "meta_missing": dict(meta_missing),
+        "invalid_audio": invalid_audio[:20],
+        "invalid_audio_count": len(invalid_audio),
+        "pipeline_stats": pipeline.stats,
+        "shard_validation": shard_validation,
+        "cpu_comparison": cpu_comparison,
+    }
+
+
+def _handle_outputs(
+    outputs,
+    writers,
+    manifest,
+    audio_ext: str,
+    output_counts: Counter,
+    meta_missing: Counter,
+    invalid_audio: list,
+    opt_metadata: dict,
+) -> None:
+    for out in outputs:
+        meta = dict(out.metadata)
+        required_keys = [
+            "id", "video_id", "language", "duration_sec", "native_sr",
+            "effective_nyquist_hz", "loudness_lufs", "quality_score",
+            "native_sr_bucket", "sample_rate", "resample_target_sr",
+        ]
+        for key in required_keys:
+            if key not in meta:
+                meta_missing[key] += 1
+
+        valid, reason = validate_audio_bytes(
+            out.audio_bytes,
+            meta.get("sample_rate", out.sample_rate),
+            float(meta.get("duration_sec", 0.0)),
+        )
+        if not valid:
+            invalid_audio.append({"id": meta.get("id"), "reason": reason})
+
+        output_counts[out.sample_rate] += 1
+        shard_name = writers[out.sample_rate].add_sample(meta["id"], out.audio_bytes, meta)
+        meta["shard"] = f"{shard_name}.tar"
+        meta["audio"] = f"{meta['id']}.{audio_ext}"
+        manifest.write(json.dumps(meta) + "\n")
+
+        opt_metadata[(meta["id"], out.sample_rate)] = meta
+
+
+def validate_shards(output_dir: Path, audio_ext: str) -> dict[str, Any]:
+    issues = []
+    total_entries = 0
+    total_audio_files = 0
+    for jsonl_path in output_dir.rglob("*.jsonl"):
+        if jsonl_path.name.endswith("_audit_manifest.jsonl"):
+            continue
+        tar_path = jsonl_path.with_suffix(".tar")
+        if not tar_path.exists():
+            issues.append(f"missing_tar:{tar_path}")
+            continue
+        with jsonl_path.open("r", encoding="utf-8") as fh:
+            lines = [json.loads(line) for line in fh if line.strip()]
+        total_entries += len(lines)
+        with tarfile.open(tar_path, "r") as tf:
+            members = [m for m in tf.getmembers() if m.name.endswith(f".{audio_ext}")]
+            total_audio_files += len(members)
+            member_names = {m.name for m in members}
+            for entry in lines:
+                expected = f"{entry['id']}.{audio_ext}"
+                if expected not in member_names:
+                    issues.append(f"missing_audio:{tar_path.name}:{expected}")
+    return {
+        "total_entries": total_entries,
+        "total_audio_files": total_audio_files,
+        "issues_sample": issues[:20],
+        "issue_count": len(issues),
+    }
+
+
+def compare_with_cpu(
+    download_paths: dict[str, Path],
+    language: str,
+    seed: int,
+    output_format: str,
+) -> dict[str, Any]:
+    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_counts = Counter()
+    cpu_outputs = {}
+    cpu_stats = Counter()
+
+    for video_id, tar_path in download_paths.items():
+        for segment in iter_segments_from_tar(tar_path, video_id, language, seed):
+            outputs, seg_stats = process_segment(
+                segment.video_id,
+                segment.language,
+                segment.segment_bytes,
+                segment.segment_meta,
+                segment.speaker,
+                segment.index,
+                chunk_cfg,
+                silence_cfg,
+                segment.rng_seed,
+                output_format,
+            )
+            cpu_stats.update(seg_stats)
+            for out in outputs:
+                cpu_counts[out.sample_rate] += 1
+                key = (out.metadata["id"], out.sample_rate)
+                cpu_outputs[key] = out.metadata
+
+    return {
+        "cpu_counts": dict(cpu_counts),
+        "cpu_stats": dict(cpu_stats),
+        "cpu_output_keys": len(cpu_outputs),
+    }
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Benchmark optimized pipeline on real data.")
+    parser.add_argument("--language", default="hindi")
+    parser.add_argument("--limit-videos", type=int, default=100)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--download-dir", type=Path, default=Path("/tmp/optimized_pipeline/downloads"))
+    parser.add_argument("--download-workers", type=int, default=8)
+    parser.add_argument("--pipeline-batch-size", type=int, default=256)
+    parser.add_argument("--decode-workers", type=int, default=8)
+    parser.add_argument("--encode-workers", type=int, default=8)
+    parser.add_argument("--gpu-batch-size", type=int, default=128)
+    parser.add_argument("--output-format", choices=["wav", "flac"], default="wav")
+    parser.add_argument("--no-dali", action="store_true")
+    parser.add_argument("--no-multiprocess", action="store_true")
+    parser.add_argument("--mode", choices=["benchmark", "audit"], default="benchmark")
+    parser.add_argument("--audit-output-dir", type=Path, default=Path("/tmp/optimized_pipeline/audit"))
+    parser.add_argument("--compare-cpu", action="store_true")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    env = load_env(Path(".env"))
+    db_url = env.get("DATABASE_URL")
+    cleaned_bucket = env.get("R2_ClEANED_BUCKET") or env.get("R2_CLEANED_BUCKET")
+    prefix = env.get("R2_PREFIX")
+    if not db_url or not cleaned_bucket:
+        raise SystemExit("DATABASE_URL or R2_CLEANED_BUCKET missing in .env")
+
+    records = fetch_video_records(db_url, args.language, args.limit_videos)
+    if not records:
+        raise SystemExit("No records fetched from DB.")
+
+    s3 = make_r2_client(env)
+    start_download = time.perf_counter()
+    download_paths = download_tars(
+        records,
+        args.download_dir,
+        s3,
+        cleaned_bucket,
+        prefix,
+        args.download_workers,
+    )
+    download_sec = time.perf_counter() - start_download
+
+    if not download_paths:
+        raise SystemExit("No tar files downloaded.")
+
+    pipeline = OptimizedPipeline(
+        decode_workers=args.decode_workers,
+        encode_workers=args.encode_workers,
+        gpu_batch_size=args.gpu_batch_size,
+        output_format=args.output_format,
+        use_multiprocess=not args.no_multiprocess,
+        use_dali=not args.no_dali,
+    )
+
+    if args.mode == "benchmark":
+        stats = run_benchmark(
+            download_paths,
+            args.language,
+            args.seed,
+            args.pipeline_batch_size,
+            pipeline,
+        )
+        stats = BenchmarkStats(
+            videos=stats.videos,
+            segments=stats.segments,
+            outputs=stats.outputs,
+            elapsed_sec=stats.elapsed_sec,
+            download_sec=download_sec,
+        )
+        print("\nBenchmark complete")
+        print(f"  videos: {stats.videos}")
+        print(f"  segments: {stats.segments}")
+        print(f"  outputs: {stats.outputs}")
+        print(f"  download time: {stats.download_sec:.2f}s")
+        print(f"  processing time: {stats.elapsed_sec:.2f}s")
+        print(f"  segments/sec: {stats.segments_per_sec:.2f}")
+        print(f"  videos/min: {stats.videos_per_min:.2f}")
+        pipeline.print_stats()
+    else:
+        audit = audit_pipeline(
+            download_paths,
+            args.language,
+            args.seed,
+            args.pipeline_batch_size,
+            pipeline,
+            args.audit_output_dir,
+            args.compare_cpu,
+        )
+        print("\nAudit complete")
+        print(f"  segments: {audit['segments_total']}")
+        print(f"  outputs: {audit['outputs_total']}")
+        print(f"  output counts: {audit['output_counts']}")
+        print(f"  invalid audio count: {audit['invalid_audio_count']}")
+        print(f"  missing metadata keys: {audit['meta_missing']}")
+        print(f"  shard validation issues: {audit['shard_validation']['issue_count']}")
+        if audit["cpu_comparison"] is not None:
+            print(f"  CPU counts: {audit['cpu_comparison']['cpu_counts']}")
+        pipeline.print_stats()
+
+    pipeline.close()
+
+
+if __name__ == "__main__":
+    main()
*** End Patch


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


Summary: auto


**Verifying function usage and cleaning unused variables**

I confirmed that imported functions are used with correct parameters and identified an unused metadata dictionary in the audit code that can be removed to keep the code clean without affecting behavior.


**Reviewing shard validation and preparing benchmark run**

I confirmed shard validation logic handles tar exclusions and shard metadata correctly and aligned audio file extensions; next, I’m planning a benchmark run on 100 videos with optimized batch and worker settings to test the full pipeline and audit.


python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 100 --pipeline-batch-size 256 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12


Output:
Exit code: 0
Wall time: 360.2 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]
GPUMetricsComputer: Using NVIDIA A100-SXM4-80GB
GPUBatchProcessor: Using NVIDIA A100-SXM4-80GB
  GPU metrics: True
Using DALI for batch decode (5x speedup)

Benchmark complete
  videos: 100
  segments: 15878
  outputs: 36959
  download time: 13.81s
  processing time: 328.94s
  segments/sec: 48.27
  videos/min: 18.24

=== Pipeline Statistics ===
Segments decoded:   15878
Segments processed: 15878
Chunks emitted:     12511
Outputs generated:  36959
Dropped (short):    6446
Dropped (long):     20
Dropped (clipped):  0

Timing breakdown:
  Decode:    18876.2ms (  6.0%)
  Metrics:  148987.8ms ( 47.6%)
  Resample: 145108.0ms ( 46.4%)
  Encode:        0.0ms (  0.0%)
  Total:    312972.1ms

Throughput: 50.7 segments/sec


**Noticing encode time missing**

Encode time isn't tracked separately and may be included within resample time, causing potential misreporting; preparing to process 500 videos with a longer timeout accordingly.


python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 256 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12


Output:
Exit code: 0
Wall time: 1738.1 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]
GPUMetricsComputer: Using NVIDIA A100-SXM4-80GB
GPUBatchProcessor: Using NVIDIA A100-SXM4-80GB
  GPU metrics: True
Using DALI for batch decode (5x speedup)

Benchmark complete
  videos: 500
  segments: 97588
  outputs: 208594
  download time: 68.52s
  processing time: 1655.40s
  segments/sec: 58.95
  videos/min: 18.12

=== Pipeline Statistics ===
Segments decoded:   97588
Segments processed: 97588
Chunks emitted:     69077
Outputs generated:  208594
Dropped (short):    43137
Dropped (long):     84
Dropped (clipped):  24

Timing breakdown:
  Decode:    91412.2ms (  5.8%)
  Metrics:  801496.5ms ( 50.7%)
  Resample: 686418.1ms ( 43.5%)
  Encode:        0.0ms (  0.0%)
  Total:    1579326.9ms

Throughput: 61.8 segments/sec


**Preparing audit run for 10 videos**

I'm setting the script to audit mode with compare-cpu, limiting to 10 videos, setting the output directory, and configuring the pipeline batch size to 128 before running.


python3 scripts/benchmark_optimized_pipeline.py --mode audit --language hindi --limit-videos 10 --pipeline-batch-size 128 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12 --audit-output-dir /tmp/optimized_pipeline/audit_10 --compare-cpu


Output:
Exit code: 0
Wall time: 123.9 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]
GPUMetricsComputer: Using NVIDIA A100-SXM4-80GB
GPUBatchProcessor: Using NVIDIA A100-SXM4-80GB
  GPU metrics: True
Using DALI for batch decode (5x speedup)

Audit complete
  segments: 1410
  outputs: 2446
  output counts: {16000: 1050, 24000: 969, 32000: 369, 44100: 58}
  invalid audio count: 0
  missing metadata keys: {}
  shard validation issues: 0
  CPU counts: {16000: 1050, 24000: 685, 32000: 239, 44100: 32}

=== Pipeline Statistics ===
Segments decoded:   1410
Segments processed: 1410
Chunks emitted:     1050
Outputs generated:  2446
Dropped (short):    650
Dropped (long):     3
Dropped (clipped):  0

Timing breakdown:
  Decode:     2054.2ms (  7.7%)
  Metrics:   15735.5ms ( 58.9%)
  Resample:   8920.5ms ( 33.4%)
  Encode:        0.0ms (  0.0%)
  Total:     26710.3ms

Throughput: 52.8 segments/sec


**Planning deeper output difference analysis**

Audit shows GPU-optimized outputs differ notably from CPU, possibly due to spectral metric discrepancies, so I plan to enhance the audit script to compute and summarize deltas in key metrics like effective Nyquist frequency for clearer comparison.


**Designing script enhancements for output and metadata diffing**

I'm planning to modify the audit script to capture and return optimized output metadata alongside CPU metadata, enabling set-based key comparisons and metric diffs like loudness and effective Nyquist across common outputs for clearer discrepancy analysis.


Input:
*** Begin Patch
*** Update File: scripts/benchmark_optimized_pipeline.py
@@
-    opt_metadata = {}
+    opt_metadata: dict[tuple[str, int], dict[str, Any]] = {}
@@
-    cpu_comparison = None
-    if compare_cpu:
-        cpu_comparison = compare_with_cpu(download_paths, language, seed, pipeline.output_format)
+    cpu_comparison = None
+    if compare_cpu:
+        cpu_comparison = compare_with_cpu(download_paths, language, seed, pipeline.output_format)
+        opt_keys = set(opt_metadata.keys())
+        cpu_keys = set(cpu_comparison["cpu_metadata"].keys())
+        missing_in_opt = cpu_keys - opt_keys
+        extra_in_opt = opt_keys - cpu_keys
+        common = opt_keys & cpu_keys
+
+        diff_stats = {}
+        if common:
+            lufs_diffs = []
+            nyq_diffs = []
+            for key in common:
+                opt_meta = opt_metadata[key]
+                cpu_meta = cpu_comparison["cpu_metadata"][key]
+                if "loudness_lufs" in opt_meta and "loudness_lufs" in cpu_meta:
+                    lufs_diffs.append(abs(opt_meta["loudness_lufs"] - cpu_meta["loudness_lufs"]))
+                if "effective_nyquist_hz" in opt_meta and "effective_nyquist_hz" in cpu_meta:
+                    nyq_diffs.append(abs(opt_meta["effective_nyquist_hz"] - cpu_meta["effective_nyquist_hz"]))
+            if lufs_diffs:
+                diff_stats["lufs_mean_abs"] = sum(lufs_diffs) / len(lufs_diffs)
+                diff_stats["lufs_p95_abs"] = sorted(lufs_diffs)[int(0.95 * len(lufs_diffs)) - 1]
+            if nyq_diffs:
+                diff_stats["nyquist_mean_abs_hz"] = sum(nyq_diffs) / len(nyq_diffs)
+                diff_stats["nyquist_p95_abs_hz"] = sorted(nyq_diffs)[int(0.95 * len(nyq_diffs)) - 1]
+
+        cpu_comparison["missing_in_opt"] = len(missing_in_opt)
+        cpu_comparison["extra_in_opt"] = len(extra_in_opt)
+        cpu_comparison["common_keys"] = len(common)
+        cpu_comparison["diff_stats"] = diff_stats

     return {
@@
-        "cpu_comparison": cpu_comparison,
+        "cpu_comparison": cpu_comparison,
     }
@@
-    cpu_outputs = {}
+    cpu_outputs: dict[tuple[str, int], dict[str, Any]] = {}
@@
-    return {
+    return {
         "cpu_counts": dict(cpu_counts),
         "cpu_stats": dict(cpu_stats),
-        "cpu_output_keys": len(cpu_outputs),
+        "cpu_output_keys": len(cpu_outputs),
+        "cpu_metadata": cpu_outputs,
     }
*** End Patch


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


**Rerunning audit with sample videos**

Planning to rerun the audit on 10 videos, expecting skipped downloads for existing files, and to keep outputs organized, possibly using a new output directory.


python3 scripts/benchmark_optimized_pipeline.py --mode audit --language hindi --limit-videos 10 --pipeline-batch-size 128 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12 --audit-output-dir /tmp/optimized_pipeline/audit_10b --compare-cpu


Output:
Exit code: 0
Wall time: 130.4 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]
GPUMetricsComputer: Using NVIDIA A100-SXM4-80GB
GPUBatchProcessor: Using NVIDIA A100-SXM4-80GB
  GPU metrics: True
Using DALI for batch decode (5x speedup)

Audit complete
  segments: 2039
  outputs: 3385
  output counts: {16000: 1030, 24000: 1028, 32000: 970, 44100: 357}
  invalid audio count: 0
  missing metadata keys: {}
  shard validation issues: 0
  CPU counts: {16000: 1030, 24000: 919, 32000: 864, 44100: 312}

=== Pipeline Statistics ===
Segments decoded:   2039
Segments processed: 2039
Chunks emitted:     1030
Outputs generated:  3385
Dropped (short):    1116
Dropped (long):     0
Dropped (clipped):  0

Timing breakdown:
  Decode:     1976.0ms (  7.9%)
  Metrics:   14591.6ms ( 58.2%)
  Resample:   8506.0ms ( 33.9%)
  Encode:        0.0ms (  0.0%)
  Total:     25073.7ms

Throughput: 81.3 segments/sec


**Planning audit script patch**

I'm preparing to modify the audit script to print diff summary stats alongside CPU counts when CPU comparison data is present, enabling direct access to needed delta information.


Input:
*** Begin Patch
*** Update File: scripts/benchmark_optimized_pipeline.py
@@
         print(f"  shard validation issues: {audit['shard_validation']['issue_count']}")
         if audit["cpu_comparison"] is not None:
-            print(f"  CPU counts: {audit['cpu_comparison']['cpu_counts']}")
+            comparison = audit["cpu_comparison"]
+            print(f"  CPU counts: {comparison['cpu_counts']}")
+            print(
+                "  CPU vs optimized keys: "
+                f"missing_in_opt={comparison['missing_in_opt']}, "
+                f"extra_in_opt={comparison['extra_in_opt']}, "
+                f"common={comparison['common_keys']}"
+            )
+            if comparison["diff_stats"]:
+                print(f"  Metric diffs: {comparison['diff_stats']}")
         pipeline.print_stats()
*** End Patch


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


**Running audit with new output directory**


python3 scripts/benchmark_optimized_pipeline.py --mode audit --language hindi --limit-videos 10 --pipeline-batch-size 128 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12 --audit-output-dir /tmp/optimized_pipeline/audit_10c --compare-cpu


Output:
Exit code: 0
Wall time: 196.4 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]
GPUMetricsComputer: Using NVIDIA A100-SXM4-80GB
GPUBatchProcessor: Using NVIDIA A100-SXM4-80GB
  GPU metrics: True
Using DALI for batch decode (5x speedup)

Audit complete
  segments: 2122
  outputs: 4988
  output counts: {16000: 1632, 24000: 1632, 32000: 1503, 44100: 221}
  invalid audio count: 0
  missing metadata keys: {}
  shard validation issues: 0
  CPU counts: {16000: 1632, 24000: 1277, 32000: 1179, 44100: 163}
  CPU vs optimized keys: missing_in_opt=431, extra_in_opt=1168, common=3820
  Metric diffs: {'lufs_mean_abs': 0.05816970466181517, 'lufs_p95_abs': 0.14563651258330879, 'nyquist_mean_abs_hz': 140.33697908523743, 'nyquist_p95_abs_hz': 857.537059170294}

=== Pipeline Statistics ===
Segments decoded:   2122
Segments processed: 2122
Chunks emitted:     1632
Outputs generated:  4988
Dropped (short):    838
Dropped (long):     3
Dropped (clipped):  0

Timing breakdown:
  Decode:     3102.1ms (  7.0%)
  Metrics:   23585.5ms ( 53.5%)
  Resample:  17400.5ms ( 39.5%)
  Encode:        0.0ms (  0.0%)
  Total:     44088.2ms

Throughput: 48.1 segments/sec


**Analyzing metric discrepancies between CPU and GPU**

The GPU outputs more segments classified as higher sample rates due to effective Nyquist threshold differences, causing extra and missing keys versus CPU that could impact reliability. I'll propose aligning GPU spectral metrics to CPU or adding tolerance for gating decisions while noting the audio data and metadata remain consistent. I'll also plan scaling estimates using 500-run throughput to reduce variance.


**Computing precise scaling estimates**

I'm calculating processing-only time for 500k videos using 18.12 videos/min throughput from the 500-run benchmark, yielding about 19.2 days single-node, 3.8 days on 5 machines, and 1.9 days on 10 machines. I'll note download time is parallel and minimal and include segment throughput and pipeline improvements in the summary.


python3 - <<'PY'
videos_per_min = 18.12
videos = 500_000
minutes = videos / videos_per_min
hours = minutes / 60
print('minutes', minutes)
print('hours', hours)
print('days', hours/24)

for machines in [5,10]:
    rate = videos_per_min * machines
    minutes = videos / rate
    hours = minutes / 60
    print(machines, 'machines: minutes', minutes, 'hours', hours, 'days', hours/24)
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
minutes 27593.81898454746
hours 459.89698307579096
days 19.162374294824623
5 machines: minutes 5518.763796909492 hours 91.97939661515821 days 3.8324748589649253
10 machines: minutes 2759.381898454746 hours 45.989698307579104 days 1.9162374294824627


Summary: auto


**Summarizing performance and quality metrics**

The analysis includes detailed stats for 100-run and 500-run scales, showing improved segments/sec stability at 500 runs and pipeline drop counts. It notes that GPU output counts differ from CPU due to spectral metric permissiveness, and audio validation found no issues. Metric differences are small but highlight potential thresholding effects, all flagged for review.


**Detailing timing stats and variability notes**

I'm noting per-run pipeline decode/metrics/resample times while encode timing remains untracked, with metrics/resample still dominant. I'll mention large 500-run time (27.6 min) as manageable given dataset size, and highlight output count variation due to random video selection. The audit script’s NaN check is effective, and output counts per sample rate and audit output location will be included. Finally, I'll prepare to summarize code changes, including the parallel pipeline update, pending git status confirmation.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
## optimisations
 M scripts/__pycache__/prepare_shards.cpython-310.pyc
?? optimisations.md
?? scripts/__pycache__/fast_audio_pipeline_v2.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_decode.cpython-310.pyc
?? scripts/__pycache__/gpu_audio_metrics.cpython-310.pyc
?? scripts/__pycache__/optimized_pipeline.cpython-310.pyc
?? scripts/benchmark_optimized_pipeline.py
?? scripts/gpu_audio_decode.py
?? scripts/gpu_audio_metrics.py
?? scripts/optimized_pipeline.py


**Summarizing test run context and next-step suggestions**

I noted that test results come from variable samples due to random video selection and highlighted CPU vs. GPU metric differences, proposing to report throughput from the longer run as baseline. I'll mention these nuances and suggest next steps for stricter metric alignment without removing untracked pycache files.


**Detailing benchmark and audit results with scaling estimates**

Added a real-data benchmark and audit harness for the optimized pipeline in `scripts/benchmark_optimized_pipeline.py` and ran it on R2 Hindi data to measure throughput and validate output quality. Results include 100- and 500-video runs, a 10-video audit comparing CPU vs. optimized outputs, plus scaling and ETA estimates for large-scale processing on multiple GPU nodes.

**Bench Results (Hindi, real R2, DALI+GPU metrics)**
- 100 videos: 15,878 segments, 36,959 outputs; 5.48 min processing; 18.24 videos/min; 48.27 seg/s; 13.8s download time
- 500 videos: 97,588 segments, 208,594 outputs; 27.59 min processing; 18.12 videos/min; 58.95 seg/s; 68.5s download time

**Audit (10 videos)**
- Segments: 2,122; outputs: 4,988
- Audio valid with no metadata or shard issues
- CPU comparison: fewer 24k/32k/44k outputs on CPU, optimized pipeline emits 737 more outputs total due to spectral gating differences
- Metric differences: mean absolute LUFS diff 0.058, p95 0.146; Nyquist mean abs 140 Hz, p95 858 Hz
- Audit outputs stored in `/tmp/optimized_pipeline/audit_10c`, downloads in `/tmp/optimized_pipeline/downloads`

**Scaling and ETA Estimates**
- Single A100 GPU node (18.12 vids/min): 19.16 days for 500k videos (processing only)
- 5 nodes: 3.83 days
- 10 nodes: 1.92 days
- Throughput improved 2.4× vs. previous baseline (7.56 vids/min)
- Estimates assume same instance (A100 80GB) and no network or storage bottlenecks

**Observations and Notes**
- Pipeline encode time not measured (stats show 0 because timing not tracked)
- Optimized pipeline uses DALI decode on CPU (small overhead) and GPU for metrics/resampling
- Output count differences from CPU due to GPU spectral metric gating thresholds; may require tuning for parity
- Benchmark and audit use random video selection; results may vary on different runs

**Next Steps Suggestions**
1. Tune GPU spectral metric parameters or gating thresholds to match CPU outputs if strict output parity is needed
2. Integrate `OptimizedPipeline` into `scripts/prepare_shards.py` with CLI flag for production use
3. Run extended audits across multiple languages to confirm gating and output consistency

**Tests and Commands Run**
- `python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --num-videos 100`
- `python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --num-videos 500`
- `python3 scripts/benchmark_optimized_pipeline.py --mode audit --num-videos 10 --compare-cpu`

This harness facilitates reproducible benchmarking and auditing of the optimized pipeline and supports scaling decisions for large-scale video processing.


Added a real‑data benchmark/audit harness for the new `OptimizedPipeline` and used it to pull Hindi tars from R2, measure throughput at 100/500 videos, and run an end‑to‑end 10‑video reliability audit (audio decode/metadata/shard consistency + CPU comparison). The harness lives at `scripts/benchmark_optimized_pipeline.py` so you can repeat or tune runs without touching the main pipeline.

**Bench Results (real R2, DALI + GPU metrics, Hindi)**
- 100 videos: 15,878 segments; 36,959 outputs; 13.8s download; 328.9s processing; 48.27 seg/s; 18.24 videos/min
- 500 videos: 97,588 segments; 208,594 outputs; 68.5s download; 1,655.4s processing; 58.95 seg/s; 18.12 videos/min
- Drop rates (500 run): 43,137 short, 84 long, 24 clipped; metrics+resample dominate runtime (encode time isn’t tracked in the pipeline stats yet)

**ETA for 500k (from 500‑video run)**
- 1× A100 node @ 18.12 videos/min: 19.16 days
- 5 machines (linear): 3.83 days
- 10 machines (linear): 1.92 days
These assume similar hardware/throughput and that R2 I/O is not a bottleneck (download time was ~1 min for 500 videos).

**10‑Video End‑to‑End Audit (real R2, Hindi)**
- Outputs: 4,988 total; by SR: 16k=1,632; 24k=1,632; 32k=1,503; 44.1k=221
- Audio validation: 0 invalid decodes; duration/SR match OK; metadata completeness OK
- Shard validation: 0 missing tar/jsonl entries; shards read back clean from `/tmp/optimized_pipeline/audit_10c`
- CPU comparison (same inputs): 16k counts match; optimized emits more 24k/32k/44.1k
  - CPU vs optimized keys: missing_in_opt=431, extra_in_opt=1168, common=3820
  - Metric diffs (mean abs): LUFS 0.058 (p95 0.146), effective‑Nyquist 140 Hz (p95 858 Hz)
  - Interpretation: GPU spectral metrics are close numerically, but the Nyquist thresholding crosses the 8k/12k/16k gates more often, yielding extra high‑rate outputs vs CPU.

**Risk/Decision Point**
- If strict output parity with the CPU pipeline is required, we should tighten GPU spectral gating (windowing/threshold) or use CPU spectral values for bucket gating while keeping GPU LUFS. If the extra high‑rate outputs are acceptable, the audit looks clean and I’d proceed.

**Commands run**
- `python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 100 --pipeline-batch-size 256 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12`
- `python3 scripts/benchmark_optimized_pipeline.py --mode benchmark --language hindi --limit-videos 500 --pipeline-batch-size 256 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12`
- `python3 scripts/benchmark_optimized_pipeline.py --mode audit --language hindi --limit-videos 10 --pipeline-batch-size 128 --gpu-batch-size 256 --decode-workers 12 --encode-workers 12 --audit-output-dir /tmp/optimized_pipeline/audit_10c --compare-cpu`

If you want, I can:
1) Align GPU spectral gating to match CPU counts and re‑run the audit.
2) Wire `OptimizedPipeline` into `scripts/prepare_shards.py` as a production flag.
3) Run a multi‑language audit sweep to confirm the SR‑bucket behavior across the whole corpus.


# Context from my IDE setup:

## Open tabs:
- optimisations.md: optimisations.md
- Optimisation.md: Optimisation.md
- prepare_shards.py: scripts/prepare_shards.py
- fast_audio_pipeline_v2.py: scripts/fast_audio_pipeline_v2.py
- benchmark_gpu.py: scripts/benchmark_gpu.py

## My request for Codex:
can you debug why is the mismatch occuring and also, can we go higher with workers, or batches, or anything ? or a certain mechanism, to keep both resources busy all the time ? main task is to debug why the mismatch is happening and making sure that GPU pipeline is absolutely fucking accurate and precise. matching with CPU is not the whole point, but making sure GPU ones are accurate is the point. CPU calculations might not be precise too right ? or is it because of precision calculations ? think and debug and findout. and also see if we can go higher in-terms of batching/utilisation to make it even faster. i was assuming the pipleine went higher becayse the other agent mentioned we had 13x speedup with respect to GPU metrics and 5x speedup with DALI. but these ETAs seeems to be normal what you mentioned on 5machines and 10. what is the real bottleneck now ? and how can we fix it to go brrrr insanely quick and fast.


**Analyzing GPU vs CPU spectral metric differences**

I’m evaluating performance and accuracy discrepancies between GPU and CPU spectral metrics, focusing on differences in windowing, padding, and precision. The GPU pipeline applies a fixed-length Hann window with zero-padding across batches, while CPU uses variable-length windows per sample, causing potential metric mismatches and smearing effects.