Output:
Chunk ID: 042eea
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 4526
Output:
        now = time.perf_counter()

        if t_first_output is None:
            t_first_output = now
            prefill_ms = (t_first_output - t_gen_start) * 1000
            profile.stage("3a_vllm_prefill").record(prefill_ms)

        # Track per-iteration token rate
        current_count = len(request_output.outputs[0].token_ids)
        new_tokens = current_count - prev_token_count
        if new_tokens > 0 and iteration_count > 1:
            # Estimate instantaneous tokens/sec
            # (note: vLLM may batch multiple tokens per yield)
            dt = now - t_gen_start
            if dt > 0:
                profile.token_generation_rates.append(current_count / dt)
        prev_token_count = current_count
        final_output = request_output

    t_gen_end = time.perf_counter()
    total_gen_ms = (t_gen_end - t_gen_start) * 1000
    decode_only_ms = total_gen_ms - (profile.stages.get("3a_vllm_prefill", StageTimer("")).total_ms)
    profile.stage("3b_vllm_decode").record(decode_only_ms)
    profile.stage("3_vllm_total").record(total_gen_ms)
    profile.generated_tokens = len(final_output.outputs[0].token_ids) if final_output else 0

    if not final_output:
        profile.total_ms = (time.perf_counter() - t_total_start) * 1000
        return profile

    generated_text = final_output.outputs[0].text

    # === Stage 4: Token Extraction (regex) ===
    t0 = time.perf_counter()
    semantic_matches = re.findall(r"<\|bicodec_semantic_(\d+)\|>", generated_text)
    semantic_ids = [int(t) for t in semantic_matches]
    global_matches = re.findall(r"<\|bicodec_global_(\d+)\|>", generated_text)
    global_ids = [int(t) for t in global_matches]
    profile.stage("4_token_extraction").record((time.perf_counter() - t0) * 1000)
    profile.semantic_tokens = len(semantic_ids)
    profile.global_tokens = len(global_ids)

    if not semantic_ids or not global_ids:
        profile.total_ms = (time.perf_counter() - t_total_start) * 1000
        return profile

    # === Stage 5: Token Validation ===
    t0 = time.perf_counter()
    runtime.bicodec_decoder.validate_tokens(semantic_ids, global_ids)
    profile.stage("5_token_validation").record((time.perf_counter() - t0) * 1000)

    # === Stage 6: BiCodec Decode (GPU) ===
    t0 = time.perf_counter()
    audio_bytes = runtime.bicodec_decoder.decode_to_bytes(semantic_ids, global_ids)
    decode_ms = (time.perf_counter() - t0) * 1000
    profile.stage("6_bicodec_decode").record(decode_ms)
    profile.decode_latencies.append(decode_ms)

    if audio_bytes is None:
        profile.total_ms = (time.perf_counter() - t_total_start) * 1000
        return profile

    # === Stage 7: WAV Header ===
    t0 = time.perf_counter()
    wav_bytes = add_wav_header(audio_bytes, sample_rate=AUDIO_SAMPLE_RATE)
    profile.stage("7_wav_header").record((time.perf_counter() - t0) * 1000)

    profile.audio_bytes = len(wav_bytes)
    profile.audio_seconds = (len(audio_bytes)) / (AUDIO_SAMPLE_RATE * 2)  # int16 = 2 bytes/sample
    profile.total_ms = (time.perf_counter() - t_total_start) * 1000

    return profile

# === Streaming Profiler ===

async def profile_streaming(
    runtime,
    text: str,
    speaker: str = "lipakshi",
    temperature: float = 0.8,
    top_k: int = 50,
    top_p: float = 1.0,
    max_tokens: int = 4096,
    repetition_penalty: float = 1.05,
    seed: Optional[int] = None,
) -> RequestProfile:
    """Profile a single streaming request with per-token and per-decode timing."""
    from vllm import SamplingParams
    from veena3modal.core.constants import CODE_END_TOKEN_ID, DEFAULT_MIN_TOKENS
    from veena3modal.core.token_utils import BiCodecTokenParser
    from veena3modal.audio.crossfade import crossfade_bytes_int16

    profile = RequestProfile(
        request_id=f"prof-stream-{uuid.uuid4().hex[:8]}",
        text=text,
        speaker=speaker,
        stream=True,
    )

    t_total_start = time.perf_counter()

    # === Stage 1: Prompt Building ===
    t0 = time.perf_counter()
    prompt = runtime.prompt_builder.build_prefix(speaker, text)
    profile.stage("1_prompt_build").record((time.perf_counter() - t0) * 1000)

    # === Stage 2: Parser Init ===
    t0 = time.perf_counter()
    tokenizer = runtime.model.tokenizer
    token_parser = BiCodecTokenParser(tokenizer)
    profile.stage("2_parser_init").record((time.perf_counter() - t0) * 1000)

    # === Stage 3: Sampling Config ===
    t0 = time.perf_counter()
    sampling_params = SamplingParams(
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        max_tokens=max_tokens,
        min_tokens=DEFAULT_MIN_TOKENS,
        repetition_penalty=repetition_penalty,
        stop_token_ids=[CODE_END_TOKEN_ID],
        seed=seed,
    )
    profile.stage("3_sampling_config").record((time.perf_counter() - t0) * 1000)

    # Buffers
    semantic_buffer: List[int] = []
    global_buffer: List[int] = []
    processed_token_count = 0

    EXPECTED_GLOBAL_COUNT = 32
    MIN_SEMANTIC_FIRST = 16
    DECODE_INTERVAL = 24

    request_id = f"prof-stream-{uuid.uuid4().hex[:8]}"

    # === Stage 4: vLLM Generation (streaming) ===
    t_gen_start = time.perf_counter()
    t_first_output = None
    t_globals_complete = None
    t_first_decode = None
    last_decode_count = 0
    total_samples_emitted = 0
    previous_chunk_tail: Optional[bytes] = None
    iteration_count = 0

    results_generator = runtime.model.engine.generate(
        prompt=prompt,
        sampling_params=sampling_params,
        request_id=request_id,
    )

    # Per-token timing: time between consecutive yields from vLLM
    t_last_yield = t_gen_start
    inter_token_times: List[float] = []

    async for request_output in results_generator:
        now = time.perf_counter()
        iteration_count += 1

        # Track inter-yield timing (approximation of per-token time)
        inter_ms = (now - t_last_yield) * 1000
        inter_token_times.append(inter_ms)
        t_last_yield = now

        if t_first_output is None:
            t_first_output = now
            prefill_ms = (t_first_output - t_gen_start) * 1000
            profile.stage("4a_vllm_prefill").record(prefill_ms)

        generated_ids = request_output.outputs[0].token_ids

        # === Token Parsing ===
        t_parse = time.perf_counter()
        new_token_ids = generated_ids[processed_token_count:]
        processed_token_count = len(generated_ids)
        token_parser.parse_incremental(new_token_ids, semantic_buffer, global_buffer)
        profile.stage("4b_token_parsing").record((time.perf_counter() - t_parse) * 1000)

        # Track globals completion
        if t_globals_complete is None and len(global_buffer) >= EXPECTED_GLOBAL_COUNT:
            t_globals_complete = now
            globals_ms = (t_globals_complete - t_gen_start) * 1000
            profile.stage("4c_global_token_gen").record(globals_ms)

        # === BiCodec Decode (when enough tokens) ===
        if len(global_buffer) >= EXPECTED_GLOBAL_COUNT:
            sem_count = len(semantic_buffer)
            if sem_count >= MIN_SEMANTIC_FIRST and sem_count - last_decode_count >= DECODE_INTERVAL:
                last_decode_count = sem_count

                t_decode = time.perf_counter()
                audio_bytes = runtime.bicodec_decoder.decode_streaming(
                    semantic_ids=semantic_buffer,
                    global_ids=global_buffer[:EXPECTED_GLOBAL_COUNT],
                    use_sliding_window=False,
                    trim_warmup=False,
                )
                decode_ms = (time.perf_counter() - t_decode) * 1000
                profile.stage("5_bicodec_decode").record(decode_ms)
                profile.decode_latencies.append(decode_ms)

                if audio_bytes:
                    total_decoded_samples = len(audio_bytes) // 2
                    if total_decoded_samples > total_samples_emitted:
                        new_bytes = audio_bytes[total_samples_emitted * 2:]

                        t_cf = time.perf_counter()
                        to_emit, previous_chunk_tail = crossfade_bytes_int16(
                            previous_chunk_tail, new_bytes,
                            sample_rate_hz=16000, crossfade_ms=50,
                        )
                        profile.stage("6_crossfade").record((time.perf_counter() - t_cf) * 1000)

                        emitted_samples = len(to_emit) // 2
                        total_samples_emitted += emitted_samples

                        if to_emit:
                            profile.chunks_emitted += 1
                            if t_first_decode is None:
                                t_first_decode = time.perf_counter()
                                profile.ttfb_ms = (t_first_decode - t_total_start) * 1000

    t_gen_end = time.perf_counter()
    profile.stage("4_vllm_total").record((t_gen_end - t_gen_start) * 1000)

    # Final decode
    if len(semantic_buffer) > last_decode_count and len(global_buffer) >= EXPECTED_GLOBAL_COUNT:
        t_decode = time.perf_counter()
        audio_bytes = runtime.bicodec_decoder.decode_streaming(
            semantic_ids=semantic_buffer,
            global_ids=global_buffer[:EXPECTED_GLOBAL_COUNT],
            use_sliding_window=False, trim_warmup=False,
        )
        decode_ms = (time.perf_counter() - t_decode) * 1000
        profile.stage("5_bicodec_decode").record(decode_ms)
        profile.decode_latencies.append(decode_ms)

        if audio_bytes:
            total_decoded_samples = len(audio_bytes) // 2
            if total_decoded_samples > total_samples_emitted:
                new_bytes = audio_bytes[total_samples_emitted * 2:]
                to_emit, previous_chunk_tail = crossfade_bytes_int16(
                    previous_chunk_tail, new_bytes, sample_rate_hz=16000, crossfade_ms=50,
                )
                total_samples_emitted += len(to_emit) // 2
                if to_emit:
                    profile.chunks_emitted += 1

    if previous_chunk_tail:
        total_samples_emitted += len(previous_chunk_tail) // 2
        profile.chunks_emitted += 1

    profile.generated_tokens = processed_token_count
    profile.semantic_tokens = len(semantic_buffer)
    profile.global_tokens = len(global_buffer)
    profile.audio_seconds = total_samples_emitted / 16000
    profile.audio_bytes = total_samples_emitted * 2
    profile.total_ms = (time.perf_counter() - t_total_start) * 1000

    # Compute token generation rates from inter-token times
    # Skip first (prefill) and compute tok/s for decode phase
    if len(inter_token_times) > 2:
        decode_times = inter_token_times[1:]  # Skip prefill
        for dt in decode_times:
            if dt > 0:
                profile.token_generation_rates.append(1000.0 / dt)  # ms → tok/s

    # Record inter-token timing stats
    if inter_token_times:
        import statistics
        decode_times = inter_token_times[1:] if len(inter_token_times) > 1 else inter_token_times
        if decode_times:
            profile.stage("4d_inter_token_avg").record(statistics.mean(decode_times))
            profile.stage("4e_inter_token_p99").record(
                sorted(decode_times)[int(len(decode_times) * 0.99)]
            )

    return profile

# === Concurrency Profiler ===

async def profile_concurrent(
    runtime,
    num_requests: int,
    text: str,
    speaker: str = "lipakshi",
    stream: bool = False,
) -> List[RequestProfile]:
    """Profile multiple concurrent requests."""

    speakers = ["lipakshi", "vardan", "reet", "Nandini", "krishna", "anika"]

    async def run_one(i: int) -> RequestProfile:
        spk = speakers[i % len(speakers)]
        if stream:
            return await profile_streaming(runtime, text, spk)
        else:
            return await profile_non_streaming(runtime, text, spk)

    profiles = await asyncio.gather(*[run_one(i) for i in range(num_requests)])
    return list(profiles)

def print_concurrency_summary(profiles: List[RequestProfile], concurrency: int, stream: bool):
    """Print aggregated summary for concurrent profiles."""
    import statistics

    mode = "streaming" if stream else "non-streaming"
    totals = [p.total_ms for p in profiles]
    wall_time = max(totals)

    print(f"\n{'#' * 80}")
    print(f"  CONCURRENCY={concurrency} ({mode}), {len(profiles)} requests")
    print(f"{'#' * 80}")
    print(f"  Wall time: {wall_time:.0f}ms")
    print(f"  Throughput: {len(profiles) / (wall_time / 1000):.2f} req/s")
    print(f"  Latency: avg={statistics.mean(totals):.0f}ms  "
          f"p50={sorted(totals)[len(totals)//2]:.0f}ms  "
          f"p99={sorted(totals)[int(len(totals)*0.99)]:.0f}ms")

    if stream:
        ttfbs = [p.ttfb_ms for p in profiles if p.ttfb_ms > 0]
        if ttfbs:
            print(f"  TTFB: avg={statistics.mean(ttfbs):.0f}ms  "
                  f"p50={sorted(ttfbs)[len(ttfbs)//2]:.0f}ms  "
                  f"p95={sorted(ttfbs)[int(len(ttfbs)*0.95)]:.0f}ms")

    # Aggregate stage timing
    stage_totals: Dict[str, List[float]] = {}
    for p in profiles:
        for name, s in p.stages.items():
            if name not in stage_totals:
                stage_totals[name] = []
            stage_totals[name].append(s.total_ms)

    print(f"\n  STAGE BREAKDOWN (avg across {len(profiles)} requests):")
    print(f"  {'-' * 76}")
    sorted_stages = sorted(stage_totals.items(), key=lambda x: -statistics.mean(x[1]))
    for name, times in sorted_stages:
        avg = statistics.mean(times)
        mx = max(times)
        mn = min(times)
        pct = (avg / statistics.mean(totals)) * 100 if totals else 0
        print(f"  {name:<35} avg={avg:>8.2f}ms  min={mn:>8.2f}ms  max={mx:>8.2f}ms  ({pct:.1f}%)")

    # BiCodec decode contention
    all_decode_lats = []
    for p in profiles:
        all_decode_lats.extend(p.decode_latencies)
    if all_decode_lats:
        print(f"\n  BICODEC DECODE (all calls across {len(profiles)} requests):")
        print(f"    total calls: {len(all_decode_lats)}")
        print(f"    avg={statistics.mean(all_decode_lats):.2f}ms  "
              f"p50={sorted(all_decode_lats)[len(all_decode_lats)//2]:.2f}ms  "
              f"p99={sorted(all_decode_lats)[int(len(all_decode_lats)*0.99)]:.2f}ms  "
              f"max={max(all_decode_lats):.2f}ms")

    # Token generation rates
    all_rates = []
    for p in profiles:
        all_rates.extend(p.token_generation_rates)
    if all_rates:
        print(f"\n  TOKEN GEN RATE (all samples):")
        print(f"    avg={statistics.mean(all_rates):.0f} tok/s  "
              f"min={min(all_rates):.0f}  max={max(all_rates):.0f}")

# === GPU Memory Snapshot ===

def gpu_snapshot() -> str:
    try:
        import torch
        if not torch.cuda.is_available():
            return "No GPU"
        alloc = torch.cuda.memory_allocated() / 1e9
        reserved = torch.cuda.memory_reserved() / 1e9
        total = torch.cuda.get_device_properties(0).total_memory / 1e9
        return f"GPU: {alloc:.1f}GB alloc, {reserved:.1f}GB reserved / {total:.1f}GB total"
    except Exception:
        return "GPU info unavailable"

# === Main ===

async def main_async(args):
    # Initialize runtime
    logger.info("Initializing TTS runtime for profiling...")
    logger.info(gpu_snapshot())

    from veena3modal.local_server import resolve_model_paths, DEFAULT_LOCAL_MODEL_DIR

    model_path = args.model_path
    llm_path, bicodec_path = resolve_model_paths(model_path)

    from veena3modal.services.tts_runtime import initialize_runtime, get_runtime, is_initialized

    if not is_initialized():
        initialize_runtime(
            model_path=llm_path,
            bicodec_path=bicodec_path,
            device="cuda",
            # gpu_memory_utilization uses VLLM_CONFIG default (0.25 after Tier 1 optimization)
        )

    runtime = get_runtime()
    logger.info(f"Runtime ready: {runtime.model_version}")
    logger.info(gpu_snapshot())

    text = TEXTS.get(args.text, TEXTS["short"])
    concurrency_levels = [int(x) for x in args.concurrency.split(",")]

    # === Single Request Profiles ===
    if not args.concurrency_only:
        logger.info(f"\n{'=' * 80}")
        logger.info(f"SINGLE REQUEST PROFILING (text={args.text}, {len(text)} chars)")
        logger.info(f"{'=' * 80}")

        # Non-streaming
        if not args.stream:
            logger.info("\nProfiling non-streaming...")
            p = await profile_non_streaming(runtime, text, "lipakshi")
            print(p.report())

        # Streaming
        if not args.no_stream:
            logger.info("\nProfiling streaming...")
            p = await profile_streaming(runtime, text, "lipakshi")
            print(p.report())

    # === Concurrency Profiles ===
    for conc in concurrency_levels:
        modes = []
        if not args.stream:
            modes.append(False)
        if not args.no_stream:
            modes.append(True)

        for stream_mode in modes:
            logger.info(f"\nProfiling {conc} concurrent {'streaming' if stream_mode else 'non-streaming'} requests...")
            profiles = await profile_concurrent(runtime, conc, text, stream=stream_mode)
            print_concurrency_summary(profiles, conc, stream_mode)

    logger.info(f"\n{gpu_snapshot()}")
    logger.info("Profiling complete.")

def main():
    parser = argparse.ArgumentParser(description="Veena3 TTS Pipeline Profiler")
    parser.add_argument("--model-path", default=os.path.join(REPO_ROOT, "models", "spark_tts_4speaker"))
    parser.add_argument("--text", default="short", choices=["short", "medium", "long"])
    parser.add_argument("--concurrency", default="1,5,10,20", help="Concurrency levels (comma-separated)")
    parser.add_argument("--stream", action="store_true", help="Stream mode only")
    parser.add_argument("--no-stream", action="store_true", help="Non-stream mode only")
    parser.add_argument("--concurrency-only", action="store_true", help="Skip single-request profiles")
    args = parser.parse_args()
    asyncio.run(main_async(args))

if __name__ == "__main__":
    main()


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.

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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) 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.


cd /home/ubuntu/veenaModal && sed -n '248,272p' veena3modal/services/tts_runtime.py


Output:
Chunk ID: a9b581
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 240
Output:

        logger.info(f"✅ TTS runtime initialized in {load_time:.0f}ms")
        logger.info(f"   Model version: {model_version}")

        # OPTIMIZATION: Pre-compute global tokens for all speakers
        # Disabled with multi-engine to avoid async event loop conflicts
        if num_engines <= 1:
            _precompute_speaker_globals(_runtime)
        else:
            logger.info("Speaker globals cache skipped (multi-engine mode)")

        # Update FastAPI app with model version
        try:
            from veena3modal.api.fastapi_app import set_model_version
            set_model_version(model_version)
        except ImportError:
            pass

        return _runtime

    except Exception as e:
        logger.error(f"❌ Failed to initialize TTS runtime: {e}")
        import traceback
        traceback.print_exc()
        raise RuntimeError(f"TTS runtime initialization failed: {e}") from e


Summary: auto


cd /home/ubuntu/veenaModal && sed -n '1,220p' scripts/stress_test_local.py


Output:
Chunk ID: 063cac
Wall time: 0.0522 seconds
Process exited with code 0
Original token count: 1759
Output:
#!/usr/bin/env python3
"""
Local Stress Test for Veena3 TTS Server.

Progressive load ramp with GPU monitoring, latency percentiles,
error categorization, and throughput measurement.

Unlike the Modal load tests, this runs locally with:
- No network latency (localhost)
- Direct GPU monitoring (nvidia-smi + torch.cuda)
- Higher concurrency ceiling (single-machine, no rate limits)
- Real streaming TTFB measurement (byte-level timing)

Usage:
    # Full suite (ramps 1 → 5 → 10 → 20 → 50 → 100 concurrent)
    python scripts/stress_test_local.py

    # Quick smoke test (1 → 5 → 10 only)
    python scripts/stress_test_local.py --quick

    # Custom ramp
    python scripts/stress_test_local.py --levels 1,5,10,20

    # Streaming only
    python scripts/stress_test_local.py --stream-only

    # Non-streaming only
    python scripts/stress_test_local.py --no-stream

    # Custom endpoint
    python scripts/stress_test_local.py --url http://localhost:8080
"""

import argparse
import asyncio
import json
import os
import statistics
import subprocess
import sys
import time
import threading
from dataclasses import dataclass, field
from typing import List, Optional, Dict, Any, Tuple

import httpx

# === Configuration ===

DEFAULT_URL = "http://localhost:8000"
GENERATE_PATH = "/v1/tts/generate"

# Test texts at different lengths to stress different parts of the pipeline
TEST_TEXTS = {
    "short": "Hello, this is a quick test.",
    "medium": (
        "The quick brown fox jumps over the lazy dog. "
        "This sentence tests basic text normalization and generation quality "
        "with moderate length input that should complete in reasonable time."
    ),
    "long": (
        "In the heart of a bustling city, where towering skyscrapers cast long shadows "
        "over crowded streets, there lived an old bookkeeper named Margaret. She had spent "
        "forty years cataloging the stories of others, yet never found time to write her own. "
        "One rainy Tuesday afternoon, she discovered a leather-bound journal tucked behind "
        "a shelf of dusty encyclopedias. Its pages were blank, waiting. She picked up her pen "
        "and began to write. The words flowed like water from a broken dam."
    ),
}

SPEAKERS = ["lipakshi", "vardan", "reet", "Nandini", "krishna", "anika"]

# === GPU Monitoring ===

@dataclass
class GPUSnapshot:
    timestamp: float
    memory_used_mb: float
    memory_total_mb: float
    gpu_utilization_pct: float
    temperature_c: float

class GPUMonitor:
    """Background thread that polls nvidia-smi for GPU stats."""

    def __init__(self, interval_seconds: float = 1.0):
        self.interval = interval_seconds
        self.snapshots: List[GPUSnapshot] = []
        self._running = False
        self._thread: Optional[threading.Thread] = None

    def start(self):
        self._running = True
        self._thread = threading.Thread(target=self._poll_loop, daemon=True)
        self._thread.start()

    def stop(self):
        self._running = False
        if self._thread:
            self._thread.join(timeout=5)

    def _poll_loop(self):
        while self._running:
            try:
                result = subprocess.run(
                    [
                        "nvidia-smi",
                        "--query-gpu=memory.used,memory.total,utilization.gpu,temperature.gpu",
                        "--format=csv,noheader,nounits",
                    ],
                    capture_output=True, text=True, timeout=5,
                )
                if result.returncode == 0:
                    parts = result.stdout.strip().split(", ")
                    if len(parts) >= 4:
                        self.snapshots.append(GPUSnapshot(
                            timestamp=time.time(),
                            memory_used_mb=float(parts[0]),
                            memory_total_mb=float(parts[1]),
                            gpu_utilization_pct=float(parts[2]),
                            temperature_c=float(parts[3]),
                        ))
            except Exception:
                pass
            time.sleep(self.interval)

    def summary(self) -> Dict[str, Any]:
        if not self.snapshots:
            return {"error": "no GPU data collected"}

        mem_used = [s.memory_used_mb for s in self.snapshots]
        gpu_util = [s.gpu_utilization_pct for s in self.snapshots]
        temps = [s.temperature_c for s in self.snapshots]

        return {
            "samples": len(self.snapshots),
            "memory_mb": {
                "min": min(mem_used),
                "max": max(mem_used),
                "avg": statistics.mean(mem_used),
                "total": self.snapshots[0].memory_total_mb,
            },
            "gpu_util_pct": {
                "min": min(gpu_util),
                "max": max(gpu_util),
                "avg": statistics.mean(gpu_util),
            },
            "temperature_c": {
                "min": min(temps),
                "max": max(temps),
                "avg": statistics.mean(temps),
            },
        }

# === Request Result ===

@dataclass
class RequestResult:
    success: bool
    status_code: int
    latency_ms: float  # Total wall-clock time
    ttfb_ms: float = 0.0  # Time to first byte (from HTTP response start)
    server_ttfb_ms: float = 0.0  # Server-reported TTFB (X-TTFB-ms header)
    audio_bytes: int = 0
    audio_seconds: float = 0.0
    error: Optional[str] = None
    speaker: str = ""
    text_length: int = 0
    stream: bool = False

# === Load Level Result ===

@dataclass
class LevelResult:
    level_name: str
    concurrency: int
    total_requests: int
    mode: str  # "non-streaming" or "streaming"
    text_category: str  # "short", "medium", "long", "mixed"
    results: List[RequestResult] = field(default_factory=list)
    wall_time_seconds: float = 0.0
    gpu_summary: Dict[str, Any] = field(default_factory=dict)

    @property
    def successes(self) -> List[RequestResult]:
        return [r for r in self.results if r.success]

    @property
    def failures(self) -> List[RequestResult]:
        return [r for r in self.results if not r.success]

    @property
    def success_rate(self) -> float:
        return len(self.successes) / len(self.results) if self.results else 0

    @property
    def throughput_rps(self) -> float:
        return self.total_requests / self.wall_time_seconds if self.wall_time_seconds > 0 else 0

    def _percentile(self, values: List[float], pct: float) -> float:
        if not values:
            return 0.0
        s = sorted(values)
        idx = int(len(s) * pct / 100)
        return s[min(idx, len(s) - 1)]

    @property
    def latency_stats(self) -> Dict[str, float]:
        lats = [r.latency_ms for r in self.successes]
        if not lats:
            return {}
        return {
            "min": min(lats),
            "avg": statistics.mean(lats),
            "p50": self._percentile(lats, 50),
            "p95": self._percentile(lats, 95),


I’ve finished mapping the gaps; next patch will do four things in one pass: fix request-id propagation and indentation issues, enrich completion logging, and add a new local runtime concurrency benchmark script to capture per-request GPU/LLM/decoding breakdowns.


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
 async def generate_speech(
     text: str,
     speaker: str,
@@
 ) -> Tuple[Optional[bytes], Dict[str, Any]]:
@@
-    runtime = get_runtime()
-    metrics = {
-        "request_id": request_id,
-        "ttfb_ms": 0,
-        "generation_ms": 0,
-        "tokens_generated": 0,
-        "audio_duration_seconds": 0.0,
-        "sr_applied": False,
-        "output_sample_rate": 16000,
-    }
+    runtime = get_runtime()
+    metrics = {
+        "request_id": request_id,
+        "ttfb_ms": 0,
+        "generation_ms": 0,
+        "tokens_generated": 0,
+        "audio_duration_seconds": 0.0,
+        "sr_applied": False,
+        "output_sample_rate": 16000,
+    }
@@
     active_requests = _with_inflight_delta(1)
     start_time = time.time()
+    metrics["active_requests_start"] = active_requests

     pipeline_timings = {}
@@
-            if _timing_enabled():
+            if _timing_enabled():
                 logger.info(
                     "tts_runtime_nonstream_complete",
                     extra=metrics
                 )

             return audio_bytes, metrics

     except Exception as e:
         logger.error(f"Speech generation failed: {e}")
         raise
-        finally:
-            active_requests = _with_inflight_delta(-1)
-            if _timing_enabled():
-                logger.info(
-                    "tts_runtime_nonstream_inflight",
-                    extra={"active_requests": active_requests},
-                )
+    finally:
+        active_requests = _with_inflight_delta(-1)
+        metrics["active_requests_end"] = active_requests
+        if _timing_enabled():
+            logger.info(
+                "tts_runtime_nonstream_inflight",
+                extra={
+                    "request_id": request_id,
+                    "active_requests": active_requests,
+                },
+            )
@@
             if audio_bytes:
                 # Apply super-resolution if requested and available
                 sample_rate = 16000
@@

-                # Calculate audio duration
-                    audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
-                    metrics["audio_duration_seconds"] = max(0.0, audio_duration)
+                # Calculate audio duration
+                audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
+                metrics["audio_duration_seconds"] = max(0.0, audio_duration)
@@
 async def generate_speech_chunked(
@@
 ) -> Tuple[Optional[bytes], Dict[str, Any]]:
@@
-    runtime = get_runtime()
-    metrics = {
-        "ttfb_ms": 0,
-        "generation_ms": 0,
-        "chunks_processed": 0,
-        "audio_duration_seconds": 0.0,
-        "text_chunked": False,
-        "sr_applied": False,
-        "output_sample_rate": 16000,
-    }
+    runtime = get_runtime()
+    metrics = {
+        "request_id": request_id,
+        "ttfb_ms": 0,
+        "generation_ms": 0,
+        "chunks_processed": 0,
+        "audio_duration_seconds": 0.0,
+        "text_chunked": False,
+        "sr_applied": False,
+        "output_sample_rate": 16000,
+        "active_requests_start": _with_inflight_delta(1),
+    }
+
+    if request_id is None:
+        import uuid
+        request_id = f"runtime-chunked-{uuid.uuid4().hex[:10]}"
+        metrics["request_id"] = request_id
@@
-        active_requests = _with_inflight_delta(1)
-    start_time = time.time()
+    active_requests = metrics["active_requests_start"]
+    start_time = time.time()
+    pipeline_timings = {}
@@
         if long_processor.should_chunk(text):
             metrics["text_chunked"] = True
             audio_bytes = await long_processor.generate_with_chunking(
@@
                 repetition_penalty=repetition_penalty,
                 seed=seed,
                 sample_rate=sample_rate,
             )
         else:
             # Short text: direct generation
-            audio_bytes = await runtime.pipeline.generate_speech_indic(
-                speaker=speaker,
-                text=text,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                max_tokens=max_tokens,
-                repetition_penalty=repetition_penalty,
-                seed=seed,
-            )
+            if _timing_enabled():
+                audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
+                    speaker=speaker,
+                    text=text,
+                    temperature=temperature,
+                    top_k=top_k,
+                    top_p=top_p,
+                    max_tokens=max_tokens,
+                    repetition_penalty=repetition_penalty,
+                    seed=seed,
+                    return_timing=True,
+                    request_id=request_id,
+                )
+            else:
+                audio_bytes = await runtime.pipeline.generate_speech_indic(
+                    speaker=speaker,
+                    text=text,
+                    temperature=temperature,
+                    top_k=top_k,
+                    top_p=top_p,
+                    max_tokens=max_tokens,
+                    repetition_penalty=repetition_penalty,
+                    seed=seed,
+                    request_id=request_id,
+                )
+
+        if pipeline_timings:
+            metrics.update(
+                {
+                    "llm_token_total": pipeline_timings.get("token_total", 0),
+                    "llm_batch_count": pipeline_timings.get("batch_count", 0),
+                    "llm_batch_gpu_ms_total": pipeline_timings.get("llm_gpu_batch_ms_total", 0),
+                    "llm_batch_gpu_ms_min": pipeline_timings.get("llm_gpu_batch_ms_min", 0),
+                    "llm_batch_gpu_ms_max": pipeline_timings.get("llm_batch_gpu_ms_max", 0),
+                    "llm_batch_gpu_ms_p50": pipeline_timings.get("llm_batch_gpu_ms_p50", 0),
+                    "llm_decode_cpu_ms": pipeline_timings.get("decode_cpu_ms", 0),
+                    "llm_parse_ms": pipeline_timings.get("parse_ms", 0),
+                    "llm_wall_ms": pipeline_timings.get("llm_wall_ms", metrics["generation_ms"]),
+                    "tokens_per_batch_min": pipeline_timings.get("tokens_per_batch_min"),
+                    "tokens_per_batch_max": pipeline_timings.get("tokens_per_batch_max"),
+                    "tokens_per_batch_p50": pipeline_timings.get("tokens_per_batch_p50"),
+                }
+            )
@@
     except Exception as e:
         logger.error(f"Chunked speech generation failed: {e}")
         raise
     finally:
-        _with_inflight_delta(-1)
+        metrics["active_requests_end"] = _with_inflight_delta(-1)
+        if _timing_enabled():
+            logger.info("tts_runtime_chunking_end", extra=metrics)
@@
 async def generate_speech_streaming(
@@
     if request_id is None:
         import uuid
         request_id = f"runtime-stream-{uuid.uuid4().hex[:10]}"
+    metrics["request_id"] = request_id
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
            if _timing_enabled():
                logger.info(
                    "tts_runtime_nonstream_complete",
                    extra=metrics
                )

            return audio_bytes, metrics

    except Exception as e:
        logger.error(f"Speech generation failed: {e}")
        raise
        finally:
            active_requests = _with_inflight_delta(-1)
            if _timing_enabled():
                logger.info(
                    "tts_runtime_nonstream_inflight",
                    extra={"active_requests": active_requests},
                )


cd /home/ubuntu/veenaModal && sed -n '360,540p' veena3modal/services/tts_runtime.py


Output:
Chunk ID: db2709
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 1723
Output:
    cached = len(runtime.speaker_global_cache)
    total = len(SPEAKER_MAP)
    logger.info(f"Speaker globals cached: {cached}/{total} speakers in {elapsed:.0f}ms")

async def generate_speech(
    text: str,
    speaker: str,
    temperature: float = 0.8,
    top_k: int = 50,
    top_p: float = 1.0,
    max_tokens: int = 4096,
    repetition_penalty: float = 1.05,
    seed: Optional[int] = None,
    output_sample_rate: str = "16khz",
    request_id: Optional[str] = None,
) -> Tuple[Optional[bytes], Dict[str, Any]]:
    """
    Generate speech audio (non-streaming).

    Args:
        text: Text to synthesize (already normalized)
        speaker: Internal speaker name (resolved)
        temperature: Sampling temperature
        top_k: Top-k sampling
        top_p: Nucleus sampling
        max_tokens: Maximum tokens to generate
        repetition_penalty: Repetition penalty
        seed: Random seed for reproducibility
        output_sample_rate: "16khz" or "48khz" (triggers super-resolution)

    Returns:
        Tuple of (audio_bytes, metrics_dict)
        audio_bytes: WAV audio bytes (16kHz or 48kHz, 16-bit PCM) or None if failed
        metrics_dict: Dictionary with timing metrics

    Raises:
        RuntimeError: If runtime not initialized
    """
    if not is_initialized():
        raise RuntimeError("TTS runtime not initialized")

    runtime = get_runtime()
    metrics = {
        "request_id": request_id,
        "ttfb_ms": 0,
        "generation_ms": 0,
        "tokens_generated": 0,
        "audio_duration_seconds": 0.0,
        "sr_applied": False,
        "output_sample_rate": 16000,
    }

    if request_id is None:
        import uuid
        request_id = f"runtime-ns-{uuid.uuid4().hex[:10]}"
        metrics["request_id"] = request_id

    active_requests = _with_inflight_delta(1)
    start_time = time.time()

    pipeline_timings = {}

    try:
        logger.info(
            "tts_runtime_nonstream_start",
            extra={
                "speaker": speaker,
                "request_id": request_id,
                "text_length": len(text),
                "request_inflight": active_requests,
                "temperature": temperature,
                "top_k": top_k,
                "top_p": top_p,
                "max_tokens": max_tokens,
            },
        )

        gen_kwargs = dict(
            speaker=speaker,
            text=text,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            max_tokens=max_tokens,
            repetition_penalty=repetition_penalty,
            seed=seed,
        )

        if _timing_enabled():
            audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
                **gen_kwargs,
                return_timing=True,
                request_id=request_id,
            )
        else:
            audio_bytes = await runtime.pipeline.generate_speech_indic(
                **gen_kwargs,
                request_id=request_id,
            )

        generation_time = time.time() - start_time
        metrics["generation_ms"] = int(generation_time * 1000)
        metrics["ttfb_ms"] = metrics["generation_ms"]  # Non-streaming: TTFB = total time

        if pipeline_timings:
            metrics.update({
                "llm_token_total": pipeline_timings.get("token_total", 0),
                "llm_batch_count": pipeline_timings.get("batch_count", 0),
                "llm_batch_gpu_ms_total": pipeline_timings.get("llm_gpu_batch_ms_total", 0),
                "llm_batch_gpu_ms_min": pipeline_timings.get("llm_gpu_batch_ms_min", 0),
                "llm_batch_gpu_ms_max": pipeline_timings.get("llm_gpu_batch_ms_max", 0),
                "llm_batch_gpu_ms_p50": pipeline_timings.get("llm_gpu_batch_ms_p50", 0),
                "llm_decode_cpu_ms": pipeline_timings.get("decode_cpu_ms", 0),
                "llm_parse_ms": pipeline_timings.get("parse_ms", 0),
                "llm_wall_ms": pipeline_timings.get("llm_wall_ms", metrics["generation_ms"]),
                "ttfb_ms": int(pipeline_timings.get("ttfb_ms", metrics["ttfb_ms"])),
                "tokens_per_batch_min": pipeline_timings.get("tokens_per_batch_min"),
                "tokens_per_batch_max": pipeline_timings.get("tokens_per_batch_max"),
                "tokens_per_batch_p50": pipeline_timings.get("tokens_per_batch_p50"),
            })

        if audio_bytes:
            # Apply super-resolution if requested and available
            sample_rate = 16000
            logger.info(f"SR check: output_sample_rate={output_sample_rate!r}, "
                       f"sr_service={runtime.sr_service is not None}, "
                       f"is_loaded={runtime.sr_service.is_loaded if runtime.sr_service else False}")

            if output_sample_rate == "48khz" and runtime.sr_service and runtime.sr_service.is_loaded:
                try:
                    sr_start = time.time()
                    logger.info(f"Applying super-resolution to {len(audio_bytes)} bytes...")
                    audio_bytes = _apply_super_resolution(audio_bytes, runtime.sr_service)
                    sr_time = (time.time() - sr_start) * 1000
                    metrics["sr_ms"] = int(sr_time)
                    metrics["sr_applied"] = True
                    sample_rate = 48000
                    logger.info(f"✅ Super-resolution applied in {sr_time:.1f}ms, output={len(audio_bytes)} bytes")
                except Exception as e:
                    logger.warning(f"Super-resolution failed, returning 16kHz: {e}")
                    import traceback
                    traceback.print_exc()

            metrics["output_sample_rate"] = sample_rate

            # Calculate audio duration
                audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
                metrics["audio_duration_seconds"] = max(0.0, audio_duration)

        if _timing_enabled():
            logger.info("tts_runtime_nonstream_complete", extra=metrics)

        return audio_bytes, metrics

    except Exception as e:
        logger.error(f"Speech generation failed: {e}")
        raise
        finally:
            active_requests = _with_inflight_delta(-1)
            if _timing_enabled():
                logger.info(
                    "tts_runtime_nonstream_inflight",
                    extra={"active_requests": active_requests},
                )

def _apply_super_resolution(audio_bytes: bytes, sr_service) -> bytes:
    """
    Apply super-resolution to audio bytes.

    Args:
        audio_bytes: WAV audio at 16kHz
        sr_service: SuperResolutionService instance

    Returns:
        WAV audio at 48kHz
    """
    import numpy as np
    import struct
    import torch


cd /home/ubuntu/veenaModal && sed -n '560,780p' veena3modal/services/tts_runtime.py


Output:
Chunk ID: 75739c
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 1868
Output:
    upsampled = upsampled_tensor.squeeze().cpu().numpy()

    # Convert back to int16
    upsampled_int16 = np.clip(upsampled * 32768.0, -32768, 32767).astype(np.int16)

    # Create new WAV header for 48kHz
    sample_rate = 48000
    num_channels = 1
    bits_per_sample = 16
    byte_rate = sample_rate * num_channels * bits_per_sample // 8
    block_align = num_channels * bits_per_sample // 8
    data_size = len(upsampled_int16) * 2

    wav_header = struct.pack(
        '<4sI4s4sIHHIIHH4sI',
        b'RIFF',
        36 + data_size,
        b'WAVE',
        b'fmt ',
        16,  # fmt chunk size
        1,   # PCM format
        num_channels,
        sample_rate,
        byte_rate,
        block_align,
        bits_per_sample,
        b'data',
        data_size,
    )

    return wav_header + upsampled_int16.tobytes()

async def generate_speech_chunked(
    text: str,
    speaker: str,
    temperature: float = 0.8,
    top_k: int = 50,
    top_p: float = 1.0,
    max_tokens: int = 4096,
    repetition_penalty: float = 1.05,
    seed: Optional[int] = None,
    sample_rate: int = 16000,
    output_sample_rate: str = "16khz",
    request_id: Optional[str] = None,
) -> Tuple[Optional[bytes], Dict[str, Any]]:
    """
    Generate speech with automatic text chunking for long inputs.

    Uses LongTextProcessor to split text and stitch audio.

    Args:
        text: Text to synthesize
        speaker: Internal speaker name
        ... (same as generate_speech)
        sample_rate: Internal generation sample rate (always 16000)
        output_sample_rate: "16khz" or "48khz" (triggers super-resolution)

    Returns:
        Tuple of (audio_bytes, metrics_dict)
    """
    if not is_initialized():
        raise RuntimeError("TTS runtime not initialized")

    runtime = get_runtime()
    metrics = {
        "ttfb_ms": 0,
        "generation_ms": 0,
        "chunks_processed": 0,
        "audio_duration_seconds": 0.0,
        "text_chunked": False,
        "sr_applied": False,
        "output_sample_rate": 16000,
    }

    active_requests = _with_inflight_delta(1)
    start_time = time.time()

    try:
        if _timing_enabled():
            logger.info(
                "tts_runtime_chunking_start",
                extra={
                    "speaker": speaker,
                    "text_length": len(text),
                    "request_inflight": active_requests,
                    "chunking": True,
                },
            )
        # Import long text processor
        from veena3modal.processing.long_text_processor import LongTextProcessor

        long_processor = LongTextProcessor(pipeline=runtime.pipeline)

        # Check if chunking is needed
        if long_processor.should_chunk(text):
            metrics["text_chunked"] = True
            audio_bytes = await long_processor.generate_with_chunking(
                text=text,
                speaker=speaker,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                max_tokens=max_tokens,
                repetition_penalty=repetition_penalty,
                seed=seed,
                sample_rate=sample_rate,
            )
        else:
            # Short text: direct generation
            audio_bytes = await runtime.pipeline.generate_speech_indic(
                speaker=speaker,
                text=text,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                max_tokens=max_tokens,
                repetition_penalty=repetition_penalty,
                seed=seed,
            )

        generation_time = time.time() - start_time
        metrics["generation_ms"] = int(generation_time * 1000)
        metrics["ttfb_ms"] = metrics["generation_ms"]

        if audio_bytes:
            # Apply super-resolution if requested and available
            final_sample_rate = 16000
            if output_sample_rate == "48khz" and runtime.sr_service and runtime.sr_service.is_loaded:
                try:
                    sr_start = time.time()
                    logger.info(f"Applying super-resolution to chunked audio ({len(audio_bytes)} bytes)...")
                    audio_bytes = _apply_super_resolution(audio_bytes, runtime.sr_service)
                    sr_time = (time.time() - sr_start) * 1000
                    metrics["sr_ms"] = int(sr_time)
                    metrics["sr_applied"] = True
                    final_sample_rate = 48000
                    logger.info(f"✅ Super-resolution applied in {sr_time:.1f}ms, output={len(audio_bytes)} bytes")
                except Exception as e:
                    logger.warning(f"Super-resolution failed, returning 16kHz: {e}")
                    import traceback
                    traceback.print_exc()

            metrics["output_sample_rate"] = final_sample_rate
            audio_duration = (len(audio_bytes) - 44) / (final_sample_rate * 2)
            metrics["audio_duration_seconds"] = max(0.0, audio_duration)

        if _timing_enabled():
            metrics["active_requests"] = active_requests - 1
            logger.info("tts_runtime_chunking_complete", extra=metrics)
        return audio_bytes, metrics

    except Exception as e:
        logger.error(f"Chunked speech generation failed: {e}")
        raise
    finally:
        _with_inflight_delta(-1)

from typing import AsyncGenerator

async def generate_speech_streaming(
    text: str,
    speaker: str,
    temperature: float = 0.8,
    top_k: int = 50,
    top_p: float = 1.0,
    max_tokens: int = 4096,
    repetition_penalty: float = 1.05,
    seed: Optional[int] = None,
    enable_chunking: bool = True,
    request_id: Optional[str] = None,
) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
    """
    Generate speech audio with true streaming (yields chunks as they're generated).

    This is the core streaming implementation for M4.
    First yield includes WAV header + first PCM chunk.
    Subsequent yields are raw PCM chunks.

    Args:
        text: Text to synthesize (already normalized)
        speaker: Internal speaker name (resolved)
        temperature: Sampling temperature
        top_k: Top-k sampling
        top_p: Nucleus sampling
        max_tokens: Maximum tokens to generate
        repetition_penalty: Repetition penalty
        seed: Random seed for reproducibility
        enable_chunking: Enable text chunking for long inputs with voice consistency

    Yields:
        Tuple of (audio_bytes, metrics_dict)
        - First yield: WAV header (44 bytes) prepended to first PCM chunk
        - Subsequent yields: Raw PCM chunks (int16, 16kHz)
        - Final yield: metrics_dict has final timing info

    Raises:
        RuntimeError: If runtime not initialized or streaming pipeline unavailable
    """
    if not is_initialized():
        raise RuntimeError("TTS runtime not initialized")

    runtime = get_runtime()

    if runtime.streaming_pipeline is None:
        raise RuntimeError("Streaming pipeline not available")

    # Import audio utils for WAV header
    from veena3modal.audio.utils import create_wav_header

    # Metrics tracking
    metrics = {
        "ttfb_ms": 0,
        "chunks_sent": 0,
        "total_bytes": 0,
        "audio_duration_seconds": 0.0,
        "text_chunked": False,
    }


cd /home/ubuntu/veenaModal && sed -n '780,980p' veena3modal/services/tts_runtime.py


Output:
Chunk ID: 9c9098
Wall time: 0.0543 seconds
Process exited with code 0
Original token count: 2028
Output:

    start_time = time.time()
    first_chunk_time = None
    total_pcm_bytes = 0
    sample_rate = 16000  # BiCodec sample rate
    active_requests = _with_inflight_delta(1)
    if _timing_enabled():
        logger.info(
            "tts_runtime_stream_start",
            extra={
                "speaker": speaker,
                "text_length": len(text),
                "request_inflight": active_requests,
                "chunking": enable_chunking,
                "request_id": request_id,
            },
        )

    try:
        # Check if we need text chunking
        from veena3modal.processing.long_text_processor import LongTextProcessor
        long_processor = LongTextProcessor(pipeline=runtime.pipeline)
        needs_chunking = enable_chunking and long_processor.should_chunk(text)

        if needs_chunking:
            # Chunked streaming with voice consistency (global token caching)
            metrics["text_chunked"] = True
            async for audio_chunk, chunk_metrics in _stream_chunked_text(
                runtime,
                long_processor,
                text,
                speaker,
                temperature,
                top_k,
                top_p,
                max_tokens,
                repetition_penalty,
                seed,
                sample_rate,
                request_id,
            ):
                if first_chunk_time is None:
                    first_chunk_time = time.time()
                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
                    # Create streaming WAV header (size=0 for unknown length)
                    wav_header = create_wav_header(sample_rate=sample_rate, data_size=0)
                    audio_chunk = wav_header + audio_chunk

                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
                metrics["chunks_sent"] += 1
                metrics["total_bytes"] = total_pcm_bytes + 44  # Include header
                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
                if chunk_metrics:
                    metrics.update(chunk_metrics)

                yield audio_chunk, metrics
        else:
            # Simple streaming (no text chunking)
            # OPTIMIZATION: If we have pre-cached global tokens for this speaker,
            # use continuation mode to skip the ~110ms global token pre-roll.
            # The model jumps straight to semantic token generation.
            cached_globals = runtime.speaker_global_cache.get(speaker)

            if cached_globals:
                stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic_continuation(
                    speaker=speaker,
                    text=text,
                    global_ids=cached_globals,
                    temperature=temperature,
                    top_k=top_k,
                    top_p=top_p,
                    max_tokens=max_tokens,
                    repetition_penalty=repetition_penalty,
                    seed=seed,
                    request_id=request_id,
                )
            else:
                stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic(
                    speaker=speaker,
                    text=text,
                    temperature=temperature,
                    top_k=top_k,
                    top_p=top_p,
                    max_tokens=max_tokens,
                    repetition_penalty=repetition_penalty,
                    seed=seed,
                    request_id=request_id,
                )

            async for audio_chunk in stream_gen:
                if first_chunk_time is None:
                    first_chunk_time = time.time()
                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
                    wav_header = create_wav_header(sample_rate=sample_rate, data_size=0)
                    audio_chunk = wav_header + audio_chunk

                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
                metrics["chunks_sent"] += 1
                metrics["total_bytes"] = total_pcm_bytes + 44
                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
                yield audio_chunk, metrics
    finally:
        active_requests = _with_inflight_delta(-1)
        if _timing_enabled():
            logger.info(
                "tts_runtime_stream_end",
                extra={
                    "speaker": speaker,
                    "request_id": request_id,
                    "request_inflight": active_requests,
                    "chunks_sent": metrics.get("chunks_sent", 0),
                },
            )

async def _stream_chunked_text(
    runtime: TTSRuntime,
    long_processor,
    text: str,
    speaker: str,
    temperature: float,
    top_k: int,
    top_p: float,
    max_tokens: int,
    repetition_penalty: float,
    seed: Optional[int],
    sample_rate: int,
    request_id: Optional[str] = None,
) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
    """
    Internal helper: stream chunked text with voice consistency.

    Uses global token caching from first chunk to maintain voice across chunks.
    """
    from veena3modal.audio.crossfade import crossfade_bytes_int16

    # Chunk text using the long text processor's chunker
    chunks = long_processor.chunk_text(text)

    if not chunks:
        return

    captured_globals: Optional[List[int]] = None
    previous_chunk_tail: Optional[bytes] = None
    chunk_metrics = {"chunks_processed": 0}

    for i, chunk_text in enumerate(chunks):
        chunk_metrics["chunks_processed"] = i + 1

        if i == 0:
            # First chunk: capture global tokens for voice consistency
            async for audio_bytes, global_ids in runtime.streaming_pipeline.generate_speech_stream_indic_first_chunk(
                speaker=speaker,
                text=chunk_text,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                max_tokens=max_tokens,
                repetition_penalty=repetition_penalty,
                seed=seed,
                request_id=request_id,
            ):
                if captured_globals is None and global_ids:
                    captured_globals = global_ids

                # Crossfade with previous tail (inter-chunk stitching)
                to_emit, previous_chunk_tail = crossfade_bytes_int16(
                    previous_chunk_tail,
                    audio_bytes,
                    sample_rate_hz=sample_rate,
                    crossfade_ms=50,
                )

                if to_emit:
                    yield to_emit, chunk_metrics
        else:
            # Continuation chunks: use captured globals for voice consistency
            if captured_globals is None:
                logger.warning(f"No captured globals for chunk {i+1}, using regular streaming")
                async for audio_bytes in runtime.streaming_pipeline.generate_speech_stream_indic(
                    speaker=speaker,
                    text=chunk_text,
                    temperature=temperature,
                    top_k=top_k,
                    top_p=top_p,
                    max_tokens=max_tokens,
                    repetition_penalty=repetition_penalty,
                    seed=seed,
                    request_id=request_id,
                ):
                    to_emit, previous_chunk_tail = crossfade_bytes_int16(
                        previous_chunk_tail,
                        audio_bytes,
                        sample_rate_hz=sample_rate,
                        crossfade_ms=50,
                    )
                    if to_emit:
                        yield to_emit, chunk_metrics
            else:
                # Use continuation method with cached globals
                async for audio_bytes in runtime.streaming_pipeline.generate_speech_stream_indic_continuation(


cd /home/ubuntu/veenaModal && sed -n '980,1060p' veena3modal/services/tts_runtime.py


Output:
Chunk ID: c76d5b
Wall time: 0.0509 seconds
Process exited with code 0
Original token count: 252
Output:
                async for audio_bytes in runtime.streaming_pipeline.generate_speech_stream_indic_continuation(
                    speaker=speaker,
                    text=chunk_text,
                    global_ids=captured_globals,
                    temperature=temperature,
                    top_k=top_k,
                    top_p=top_p,
                    max_tokens=max_tokens,
                    repetition_penalty=repetition_penalty,
                    seed=seed,
                    request_id=request_id,
                ):
                    to_emit, previous_chunk_tail = crossfade_bytes_int16(
                        previous_chunk_tail,
                        audio_bytes,
                        sample_rate_hz=sample_rate,
                        crossfade_ms=50,
                    )
                    if to_emit:
                        yield to_emit, chunk_metrics

    # Flush remaining tail
    if previous_chunk_tail:
        yield previous_chunk_tail, chunk_metrics


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
-        if _timing_enabled():
+        if _timing_enabled():
             logger.info(
                 "tts_runtime_nonstream_start",
                 extra={
                     "speaker": speaker,
@@
                 },
             )
@@
         if audio_bytes:
             # Apply super-resolution if requested and available
             sample_rate = 16000
             logger.info(f"SR check: output_sample_rate={output_sample_rate!r}, "
                        f"sr_service={runtime.sr_service is not None}, "
                        f"is_loaded={runtime.sr_service.is_loaded if runtime.sr_service else False}")
@@
             metrics["output_sample_rate"] = sample_rate

             # Calculate audio duration
-                audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
-                metrics["audio_duration_seconds"] = max(0.0, audio_duration)
+            audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
+            metrics["audio_duration_seconds"] = max(0.0, audio_duration)

         if _timing_enabled():
             logger.info("tts_runtime_nonstream_complete", extra=metrics)

         return audio_bytes, metrics

     except Exception as e:
         logger.error(f"Speech generation failed: {e}")
         raise
-        finally:
-            active_requests = _with_inflight_delta(-1)
-            if _timing_enabled():
-                logger.info(
-                    "tts_runtime_nonstream_inflight",
-                    extra={"active_requests": active_requests},
-                )
+    finally:
+        active_requests = _with_inflight_delta(-1)
+        if _timing_enabled():
+            logger.info(
+                "tts_runtime_nonstream_inflight",
+                extra={
+                    "request_id": request_id,
+                    "active_requests": active_requests,
+                },
+            )
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
        if _timing_enabled():
            logger.info(
                "tts_runtime_nonstream_start",
                extra={
                    "speaker": speaker,


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
-            # Calculate audio duration
-                audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
-                metrics["audio_duration_seconds"] = max(0.0, audio_duration)
+            # Calculate audio duration
+            audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
+            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
@@
     except Exception as e:
         logger.error(f"Speech generation failed: {e}")
         raise
-        finally:
-            active_requests = _with_inflight_delta(-1)
-            if _timing_enabled():
-                logger.info(
-                    "tts_runtime_nonstream_inflight",
-                    extra={"active_requests": active_requests},
-                )
+    finally:
+        active_requests = _with_inflight_delta(-1)
+        if _timing_enabled():
+            logger.info(
+                "tts_runtime_nonstream_inflight",
+                extra={
+                    "request_id": request_id,
+                    "active_requests": active_requests,
+                },
+            )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
-    runtime = get_runtime()
-    metrics = {
-        "ttfb_ms": 0,
-        "generation_ms": 0,
-        "chunks_processed": 0,
-        "audio_duration_seconds": 0.0,
-        "text_chunked": False,
-        "sr_applied": False,
-        "output_sample_rate": 16000,
-    }
-
-    active_requests = _with_inflight_delta(1)
+    runtime = get_runtime()
+    metrics = {
+        "request_id": request_id,
+        "ttfb_ms": 0,
+        "generation_ms": 0,
+        "chunks_processed": 0,
+        "audio_duration_seconds": 0.0,
+        "text_chunked": False,
+        "sr_applied": False,
+        "output_sample_rate": 16000,
+    }
+
+    if request_id is None:
+        import uuid
+        request_id = f"runtime-chunked-{uuid.uuid4().hex[:10]}"
+        metrics["request_id"] = request_id
+
+    active_requests = _with_inflight_delta(1)
     start_time = time.time()
+    pipeline_timings = {}
@@
-                speaker=speaker,
+                speaker=speaker,
                 text=text,
                 temperature=temperature,
                 top_k=top_k,
                 top_p=top_p,
                 max_tokens=max_tokens,
                 repetition_penalty=repetition_penalty,
                 seed=seed,
+                request_id=request_id,
             )
         else:
             # Short text: direct generation
-            audio_bytes = await runtime.pipeline.generate_speech_indic(
-                speaker=speaker,
-                text=text,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                max_tokens=max_tokens,
-                repetition_penalty=repetition_penalty,
-                seed=seed,
-            )
+            if _timing_enabled():
+                audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
+                    speaker=speaker,
+                    text=text,
+                    temperature=temperature,
+                    top_k=top_k,
+                    top_p=top_p,
+                    max_tokens=max_tokens,
+                    repetition_penalty=repetition_penalty,
+                    seed=seed,
+                    return_timing=True,
+                    request_id=request_id,
+                )
+            else:
+                audio_bytes = await runtime.pipeline.generate_speech_indic(
+                    speaker=speaker,
+                    text=text,
+                    temperature=temperature,
+                    top_k=top_k,
+                    top_p=top_p,
+                    max_tokens=max_tokens,
+                    repetition_penalty=repetition_penalty,
+                    seed=seed,
+                    request_id=request_id,
+                )
+
+        if pipeline_timings:
+            metrics.update({
+                "llm_token_total": pipeline_timings.get("token_total", 0),
+                "llm_batch_count": pipeline_timings.get("batch_count", 0),
+                "llm_batch_gpu_ms_total": pipeline_timings.get("llm_gpu_batch_ms_total", 0),
+                "llm_batch_gpu_ms_min": pipeline_timings.get("llm_gpu_batch_ms_min", 0),
+                "llm_batch_gpu_ms_max": pipeline_timings.get("llm_gpu_ms_max", 0)
+                if pipeline_timings.get("llm_batch_ms_max") is not None
+                else 0,
+                "llm_batch_gpu_ms_p50": pipeline_timings.get("llm_batch_ms_p50", 0)
+                if pipeline_timings.get("llm_batch_ms_p50") is not None
+                else 0,
+                "llm_decode_cpu_ms": pipeline_timings.get("decode_cpu_ms", 0),
+                "llm_parse_ms": pipeline_timings.get("parse_ms", 0),
+                "llm_wall_ms": pipeline_timings.get("llm_wall_ms", metrics["generation_ms"]),
+                "ttfb_ms": int(pipeline_timings.get("ttfb_ms", metrics["generation_ms"])),
+                "tokens_per_batch_min": pipeline_timings.get("tokens_per_batch_min"),
+                "tokens_per_batch_max": pipeline_timings.get("tokens_per_batch_max"),
+                "tokens_per_batch_p50": pipeline_timings.get("tokens_per_batch_p50"),
+            })
@@
         if _timing_enabled():
-            metrics["active_requests"] = active_requests - 1
+            metrics["active_requests"] = active_requests - 1
             logger.info("tts_runtime_chunking_complete", extra=metrics)
         return audio_bytes, metrics
@@
-    finally:
-        _with_inflight_delta(-1)
+    finally:
+        active_requests = _with_inflight_delta(-1)
+        metrics["active_requests"] = max(active_requests, 0)
+        if _timing_enabled():
+            logger.info("tts_runtime_chunking_end", extra=metrics)
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
                speaker=speaker,
                text=text,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                max_tokens=max_tokens,
                repetition_penalty=repetition_penalty,
                seed=seed,
            )
        else:
            # Short text: direct generation
            audio_bytes = await runtime.pipeline.generate_speech_indic(
                speaker=speaker,
                text=text,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                max_tokens=max_tokens,
                repetition_penalty=repetition_penalty,
                seed=seed,
            )


cd /home/ubuntu/veenaModal && nl -ba veena3modal/services/tts_runtime.py | sed -n '620,760p'


Output:
Chunk ID: 1d98d4
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 1544
Output:
   620
   621	    Returns:
   622	        Tuple of (audio_bytes, metrics_dict)
   623	    """
   624	    if not is_initialized():
   625	        raise RuntimeError("TTS runtime not initialized")
   626
   627	    runtime = get_runtime()
   628	    metrics = {
   629	        "ttfb_ms": 0,
   630	        "generation_ms": 0,
   631	        "chunks_processed": 0,
   632	        "audio_duration_seconds": 0.0,
   633	        "text_chunked": False,
   634	        "sr_applied": False,
   635	        "output_sample_rate": 16000,
   636	    }
   637
   638	    active_requests = _with_inflight_delta(1)
   639	    start_time = time.time()
   640
   641	    try:
   642	        if _timing_enabled():
   643	            logger.info(
   644	                "tts_runtime_chunking_start",
   645	                extra={
   646	                    "speaker": speaker,
   647	                    "text_length": len(text),
   648	                    "request_inflight": active_requests,
   649	                    "chunking": True,
   650	                },
   651	            )
   652	        # Import long text processor
   653	        from veena3modal.processing.long_text_processor import LongTextProcessor
   654
   655	        long_processor = LongTextProcessor(pipeline=runtime.pipeline)
   656
   657	        # Check if chunking is needed
   658	        if long_processor.should_chunk(text):
   659	            metrics["text_chunked"] = True
   660	            audio_bytes = await long_processor.generate_with_chunking(
   661	                text=text,
   662	                speaker=speaker,
   663	                temperature=temperature,
   664	                top_k=top_k,
   665	                top_p=top_p,
   666	                max_tokens=max_tokens,
   667	                repetition_penalty=repetition_penalty,
   668	                seed=seed,
   669	                sample_rate=sample_rate,
   670	            )
   671	        else:
   672	            # Short text: direct generation
   673	            audio_bytes = await runtime.pipeline.generate_speech_indic(
   674	                speaker=speaker,
   675	                text=text,
   676	                temperature=temperature,
   677	                top_k=top_k,
   678	                top_p=top_p,
   679	                max_tokens=max_tokens,
   680	                repetition_penalty=repetition_penalty,
   681	                seed=seed,
   682	            )
   683
   684	        generation_time = time.time() - start_time
   685	        metrics["generation_ms"] = int(generation_time * 1000)
   686	        metrics["ttfb_ms"] = metrics["generation_ms"]
   687
   688	        if audio_bytes:
   689	            # Apply super-resolution if requested and available
   690	            final_sample_rate = 16000
   691	            if output_sample_rate == "48khz" and runtime.sr_service and runtime.sr_service.is_loaded:
   692	                try:
   693	                    sr_start = time.time()
   694	                    logger.info(f"Applying super-resolution to chunked audio ({len(audio_bytes)} bytes)...")
   695	                    audio_bytes = _apply_super_resolution(audio_bytes, runtime.sr_service)
   696	                    sr_time = (time.time() - sr_start) * 1000
   697	                    metrics["sr_ms"] = int(sr_time)
   698	                    metrics["sr_applied"] = True
   699	                    final_sample_rate = 48000
   700	                    logger.info(f"✅ Super-resolution applied in {sr_time:.1f}ms, output={len(audio_bytes)} bytes")
   701	                except Exception as e:
   702	                    logger.warning(f"Super-resolution failed, returning 16kHz: {e}")
   703	                    import traceback
   704	                    traceback.print_exc()
   705
   706	            metrics["output_sample_rate"] = final_sample_rate
   707	            audio_duration = (len(audio_bytes) - 44) / (final_sample_rate * 2)
   708	            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
   709
   710	        if _timing_enabled():
   711	            metrics["active_requests"] = active_requests - 1
   712	            logger.info("tts_runtime_chunking_complete", extra=metrics)
   713	        return audio_bytes, metrics
   714
   715	    except Exception as e:
   716	        logger.error(f"Chunked speech generation failed: {e}")
   717	        raise
   718	    finally:
   719	        _with_inflight_delta(-1)
   720
   721
   722	from typing import AsyncGenerator
   723
   724
   725	async def generate_speech_streaming(
   726	    text: str,
   727	    speaker: str,
   728	    temperature: float = 0.8,
   729	    top_k: int = 50,
   730	    top_p: float = 1.0,
   731	    max_tokens: int = 4096,
   732	    repetition_penalty: float = 1.05,
   733	    seed: Optional[int] = None,
   734	    enable_chunking: bool = True,
   735	    request_id: Optional[str] = None,
   736	) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
   737	    """
   738	    Generate speech audio with true streaming (yields chunks as they're generated).
   739
   740	    This is the core streaming implementation for M4.
   741	    First yield includes WAV header + first PCM chunk.
   742	    Subsequent yields are raw PCM chunks.
   743
   744	    Args:
   745	        text: Text to synthesize (already normalized)
   746	        speaker: Internal speaker name (resolved)
   747	        temperature: Sampling temperature
   748	        top_k: Top-k sampling
   749	        top_p: Nucleus sampling
   750	        max_tokens: Maximum tokens to generate
   751	        repetition_penalty: Repetition penalty
   752	        seed: Random seed for reproducibility
   753	        enable_chunking: Enable text chunking for long inputs with voice consistency
   754
   755	    Yields:
   756	        Tuple of (audio_bytes, metrics_dict)
   757	        - First yield: WAV header (44 bytes) prepended to first PCM chunk
   758	        - Subsequent yields: Raw PCM chunks (int16, 16kHz)
   759	        - Final yield: metrics_dict has final timing info
   760


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</permissions instructions>

<collaboration_mode># Collaboration Mode: Default

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Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.
</collaboration_mode>

# AGENTS.md instructions for /home/ubuntu/veenaModal

<INSTRUCTIONS>
## 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) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) 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.
</INSTRUCTIONS>

<environment_context>
  <cwd>/home/ubuntu/veenaModal</cwd>
  <shell>bash</shell>
</environment_context>

The current TTS application here is an implementation in Modal labs. Which has autoscaling and other rules self scaling rules and everything suitable for my target audience. Now, i need you to create a local entry point or implementation point. then we will plan for optimising the pipeline, then we will discuss on stress testing, then we will discuss on scaling laws post optimisations locally. @veena3modal/app.py

the above part is performed by a different agent. and benchmarks are made. Now can you do the benchmarks on this newly optimized path to understand the concurrency and stress testing.

Now i need you to capture detailed time logs for each request happening in the GPU. like what happens with 1req, time per LLM token, time per batch decoding, and everything else. detailed time about where we are spending each minute in the GPU per generation.

and when you have the details for per request. you can obviously add more logging to get details of concurrency. so you understand what's the bottleneck for the peak concurrency. and then we will decide on effective methods in optimising this TTS engine.


I’ve got the remaining implementation path mapped. Next I’ll finish the request-id propagation and metric logging in the runtime/pipeline stack, then add a local benchmark script that drives concurrent traffic and emits per-request timing breakdowns (token/batch/gpu wall-clock buckets) so we can identify the concurrency bottleneck.


cd /home/ubuntu/veenaModal && git status --short && git diff -- veena3modal/services/tts_runtime.py veena3modal/core/streaming_pipeline.py veena3modal/core/pipeline.py veena3modal/api/fastapi_app.py veena3modal/api/websocket_handler.py scripts/profile_pipeline.py scripts/stress_test_local.py | sed -n '1,260p'


Output:
Chunk ID: 3287bb
Wall time: 0.0739 seconds
Process exited with code 0
Original token count: 2442
Output:
 D CLEANUP_PLAN.md
 D PROGRESS.md
 D migration.md
 M veena3modal/core/bicodec_decoder.py
 M veena3modal/core/constants.py
 M veena3modal/core/pipeline.py
 M veena3modal/core/streaming_pipeline.py
 M veena3modal/services/tts_runtime.py
?? scripts/profile_pipeline.py
?? scripts/setup_local.sh
?? scripts/stress_test_local.py
?? scripts/stress_test_production.py
?? veena3modal/__main__.py
?? veena3modal/core/bicodec_batch_decoder.py
?? veena3modal/core/dual_engine.py
?? veena3modal/core/multi_engine.py
?? veena3modal/local_server.py
diff --git a/veena3modal/core/pipeline.py b/veena3modal/core/pipeline.py
index 9e44932..90ff4f7 100644
--- a/veena3modal/core/pipeline.py
+++ b/veena3modal/core/pipeline.py
@@ -7,8 +7,11 @@ Migrated from Veena3/Orpheus (SNAC) to Spark TTS (BiCodec).

 import asyncio
 import logging
+import os
 import re
+from statistics import mean, median
 from typing import Optional, List, Dict, Any, Tuple
+
 from vllm import SamplingParams
 import torch

@@ -25,13 +28,23 @@ from veena3modal.core.constants import (
 )

+def _detailed_timing_enabled() -> bool:
+    """
+    Return True when benchmark logs are enabled.
+
+    Controlled by environment variable:
+      VEENA3_TIMING_LOGS=true|1
+    """
+    return os.getenv("VEENA3_TIMING_LOGS", "").lower() in {"1", "true", "yes", "on"}
+
+
 class SparkTTSPipeline:
     """
     End-to-end TTS pipeline for Spark TTS with BiCodec.

     Replaces SNAC-based token extraction with BiCodec regex-based extraction.
     """
-
+
     def __init__(
         self,
         model,
@@ -40,7 +53,7 @@ class SparkTTSPipeline:
     ):
         """
         Initialize pipeline.
-
+
         Args:
             model: SparkTTS Model instance
             prompt_builder: IndicPromptBuilder instance (with Spark TTS format)
@@ -49,9 +62,9 @@ class SparkTTSPipeline:
         self.model = model
         self.prompt_builder = prompt_builder
         self.bicodec_decoder = bicodec_decoder
-
+
         print(f"🚀 SparkTTSPipeline initialized")
-
+
     async def generate_speech(
         self,
         speaker: str,
@@ -62,7 +75,9 @@ class SparkTTSPipeline:
         max_tokens: int = DEFAULT_MAX_TOKENS,
         repetition_penalty: float = 1.05,
         seed: Optional[int] = None,
-    ) -> Optional[bytes]:
+        return_timing: bool = False,
+        request_id: Optional[str] = None,
+    ) -> Optional[bytes] | tuple[Optional[bytes], Dict[str, Any]]:
         """
         Generate speech audio (non-streaming) using Spark TTS.

@@ -77,53 +92,138 @@ class SparkTTSPipeline:
             top_p: Nucleus sampling (default: 1.0)
             max_tokens: Max BiCodec tokens to generate (default: 2048)
             seed: Random seed for reproducibility
-
+            return_timing: If true, returns (audio_bytes, metrics_dict)
+
         Returns:
-            Audio bytes (int16 PCM WAV, 16kHz mono) or None if failed
+            Audio bytes (int16 PCM WAV, 16kHz mono) or None if failed.
+            If return_timing is true, returns tuple(audio_bytes, metrics).
         """
         # Build prompt using Spark TTS format
         prompt = self.prompt_builder.build_prefix(speaker, text)
-
+
         # Configure sampling for Spark TTS
         sampling_params = SamplingParams(
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
             max_tokens=max_tokens,
-            repetition_penalty=repetition_penalty,  # Prevent token repetition (e.g., token 484 loop)
-            stop=TRAINING_STOP_TOKEN_IDS,  # "<|im_end|>"
-            skip_special_tokens=False,  # Keep special tokens for BiCodec extraction
+            repetition_penalty=repetition_penalty,
+            stop=TRAINING_STOP_TOKEN_IDS,
+            skip_special_tokens=False,
             seed=seed,
         )
-
-        # Generate tokens
-        import uuid
-        request_id = f"req-{uuid.uuid4().hex[:12]}"
-
+
         # Use vLLM engine to generate
+        if request_id is None:
+            import uuid
+            request_id = f"req-{uuid.uuid4().hex[:12]}"
+
+        emit_timing = return_timing or _detailed_timing_enabled()
         results_generator = self.model.engine.generate(
             prompt=prompt,
             sampling_params=sampling_params,
             request_id=request_id,
         )
-
-        # Collect all outputs
+
+        # Collect all outputs and track generation timing
         final_output = None
-        async for request_output in results_generator:
+        token_batches: List[int] = []
+        batch_wall_ms: List[float] = []
+        batch_gpu_ms: List[float] = []
+        total_token_ids = 0
+        first_token_ms: Optional[float] = None
+        t_start = asyncio.get_event_loop().time()
+
+        iterator = results_generator.__aiter__()
+        while True:
+            wall_start = asyncio.get_event_loop().time()
+            start_event = None
+            end_event = None
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+                start_event = torch.cuda.Event(enable_timing=True)
+                end_event = torch.cuda.Event(enable_timing=True)
+                start_event.record()
+
+            try:
+                request_output = await iterator.__anext__()
+            except StopAsyncIteration:
+                break
+
+            wall_end = asyncio.get_event_loop().time()
+            batch_ms = (wall_end - wall_start) * 1000
+            batch_wall_ms.append(batch_ms)
+
+            batch_gpu_ms_value: Optional[float] = None
+            if start_event is not None and end_event is not None:
+                end_event.record()
+                torch.cuda.synchronize()
+                batch_gpu_ms_value = start_event.elapsed_time(end_event)
+            if batch_gpu_ms_value is not None:
+                batch_gpu_ms.append(batch_gpu_ms_value)
+
+            total_generated = len(request_output.outputs[0].token_ids)
+            new_tokens = max(0, total_generated - total_token_ids)
+            total_token_ids = total_generated
+            token_batches.append(new_tokens)
+
+            if first_token_ms is None:
+                first_token_ms = (wall_end - t_start) * 1000
+
+            if emit_timing:
+                logger.info(
+                    "tts_llm_batch",
+                    extra={
+                        "speaker": speaker,
+                        "request_id": request_id,
+                        "batch_index": len(token_batches),
+                        "batch_tokens": new_tokens,
+                        "llm_batch_wall_ms": round(batch_ms, 3),
+                        "llm_batch_gpu_ms": round(batch_gpu_ms_value, 3)
+                        if batch_gpu_ms_value is not None
+                        else None,
+                    },
+                )
             final_output = request_output
-
+
         if final_output is None:
+            if emit_timing:
+                logger.warning(
+                    "tts_llm_no_output",
+                    extra={"speaker": speaker, "request_id": request_id},
+                )
             print(f"❌ Generation failed: no output")
+            if return_timing:
+                return None, {
+                    "request_id": request_id,
+                    "ttfb_ms": 0,
+                    "token_total": 0,
+                    "batch_count": 0,
+                }
             return None
-
+
         # Extract generated text (contains BiCodec tokens)
+        parse_start = asyncio.get_event_loop().time()
         generated_text = final_output.outputs[0].text
         generated_ids = final_output.outputs[0].token_ids
-
+
         # Extract BiCodec tokens using regex
         semantic_ids, global_ids = self._extract_bicodec_tokens(generated_text)
-
+
+        parse_ms = (asyncio.get_event_loop().time() - parse_start) * 1000
+
         if not semantic_ids or not global_ids:
+            if emit_timing:
+                logger.error(
+                    "tts_bicodec_parse_failed",
+                    extra={
+                        "speaker": speaker,
+                        "request_id": request_id,
+                        "semantic_count": len(semantic_ids),
+                        "global_count": len(global_ids),
+                        "token_total": len(generated_ids),
+                    },
+                )
             logger.error(
                 "❌ No BiCodec tokens found in output",
                 extra={
@@ -136,8 +236,15 @@ class SparkTTSPipeline:
                     "generated_text_preview": generated_text[:500],
                 }
             )
+            if return_timing:
+                return None, {
+                    "request_id": request_id,
+                    "ttfb_ms": int(first_token_ms or 0),
+                    "token_total": len(generated_ids),
+                    "batch_count": len(token_batches),
+                }
             return None
-
+
         # Validate tokens
         if not self.bicodec_decoder.validate_tokens(semantic_ids, global_ids):
             logger.error("❌ BiCodec token validation failed", extra={
@@ -146,24 +253,71 @@ class SparkTTSPipeline:
                 "semantic_tokens": len(semantic_ids),
                 "global_tokens": len(global_ids),
             })
+            if return_timing:
+                return None, {
+                    "request_id": request_id,
+                    "ttfb_ms": int(first_token_ms or 0),
+                    "token_total": len(generated_ids),
+                    "batch_count": len(token_batches),
+                }
             return None
-


cd /home/ubuntu/veenaModal && git diff -- veena3modal/core/pipeline.py | sed -n '260,520p'


Output:
Chunk ID: 5963e6
Wall time: 0.0508 seconds
Process exited with code 0
Original token count: 1455
Output:
-
+
         # Decode to audio
+        decode_start = asyncio.get_event_loop().time()
         audio_bytes = self.bicodec_decoder.decode_to_bytes(semantic_ids, global_ids)
-
+        decode_ms = (asyncio.get_event_loop().time() - decode_start) * 1000
+
         if audio_bytes is None:
+            if emit_timing:
+                logger.error("tts_decode_failed", extra={"speaker": speaker, "request_id": request_id})
             logger.error("❌ BiCodec decode failed", extra={
                 "speaker": speaker,
                 "text_length": len(text),
             })
+            if return_timing:
+                return None, {
+                    "request_id": request_id,
+                    "ttfb_ms": int(first_token_ms or 0),
+                    "token_total": len(generated_ids),
+                    "batch_count": len(token_batches),
+                }
             return None
-
-        # Add WAV header
+
+        total_ms = (asyncio.get_event_loop().time() - t_start) * 1000
+
+        timing_metrics: Dict[str, Any] = {
+            "request_id": request_id,
+            "speaker": speaker,
+            "text_length": len(text),
+            "batch_count": len(token_batches),
+            "token_total": len(generated_ids),
+            "token_batches": token_batches,
+            "ttfb_ms": int(first_token_ms or 0),
+            "llm_wall_ms": int(sum(batch_wall_ms)),
+            "llm_avg_batch_ms": int(mean(batch_wall_ms)) if batch_wall_ms else 0,
+            "llm_gpu_batch_ms_total": int(sum(batch_gpu_ms)),
+            "llm_gpu_batch_ms_min": int(min(batch_gpu_ms)) if batch_gpu_ms else 0,
+            "llm_gpu_batch_ms_max": int(max(batch_gpu_ms)) if batch_gpu_ms else 0,
+            "llm_gpu_batch_ms_p50": int(median(batch_gpu_ms)) if batch_gpu_ms else 0,
+            "decode_cpu_ms": int(decode_ms),
+            "parse_ms": int(parse_ms),
+            "total_ms": int(total_ms),
+        }
+        if token_batches:
+            timing_metrics["tokens_per_batch_min"] = min(token_batches)
+            timing_metrics["tokens_per_batch_max"] = max(token_batches)
+            timing_metrics["tokens_per_batch_p50"] = int(median(token_batches))
+
+        if emit_timing:
+            logger.info("tts_llm_complete", extra=timing_metrics)
+
         from veena3modal.audio.utils import add_wav_header
         wav_bytes = add_wav_header(audio_bytes, sample_rate=AUDIO_SAMPLE_RATE)
-
+
+        if return_timing:
+            return wav_bytes, timing_metrics
         return wav_bytes
-
+
     async def generate_speech_indic(
         self,
         speaker: str,
@@ -174,7 +328,9 @@ class SparkTTSPipeline:
         max_tokens: int = DEFAULT_MAX_TOKENS,
         repetition_penalty: float = 1.05,
         seed: Optional[int] = None,
-    ) -> Optional[bytes]:
+        return_timing: bool = False,
+        request_id: Optional[str] = None,
+    ):
         """
         Generate speech audio for Spark TTS (speaker-based).

@@ -186,7 +342,6 @@ class SparkTTSPipeline:
             text: Text to synthesize with inline emotion tags
                 Examples:
                 - "Hello! Welcome."
-                - "[laughs] Hello there!"
                 - "Hello <laugh> this is fun!" (will be normalized)
                 - "नमस्ते! [excited] आज का दिन बहुत अच्छा है।"
             temperature: Sampling temperature
@@ -194,11 +349,13 @@ class SparkTTSPipeline:
             top_p: Nucleus sampling
             max_tokens: Max BiCodec tokens to generate
             seed: Random seed for reproducibility
+            return_timing: If true, returns (audio_bytes, metrics_dict)

         Returns:
-            Audio bytes (int16 PCM WAV, 16kHz mono) or None if failed
+            Audio bytes (int16 PCM WAV, 16kHz mono) or None if failed.
+            If return_timing is true, returns tuple(audio_bytes, metrics).
         """
-        # Delegate to generate_speech (same implementation for Spark TTS)
+        # Delegate to generate_speech() with the same implementation for Spark TTS
         return await self.generate_speech(
             speaker=speaker,
             text=text,
@@ -208,8 +365,10 @@ class SparkTTSPipeline:
             max_tokens=max_tokens,
             repetition_penalty=repetition_penalty,
             seed=seed,
+            return_timing=return_timing,
+            request_id=request_id,
         )
-
+
     def _extract_bicodec_tokens(self, generated_text: str) -> Tuple[List[int], List[int]]:
         """
         Extract BiCodec semantic and global tokens from generated text using regex.
@@ -217,10 +376,10 @@ class SparkTTSPipeline:
         Spark TTS generates tokens in the format:
         - <|bicodec_semantic_{id}|>
         - <|bicodec_global_{id}|>
-
+
         Args:
             generated_text: Generated text containing BiCodec token markers
-
+
         Returns:
             Tuple of (semantic_ids, global_ids)
             Returns ([], []) if no tokens found
@@ -228,14 +387,13 @@ class SparkTTSPipeline:
         # Extract semantic tokens
         semantic_matches = re.findall(r"<\|bicodec_semantic_(\d+)\|>", generated_text)
         semantic_ids = [int(t) for t in semantic_matches]
-
+
         # Extract global tokens
         global_matches = re.findall(r"<\|bicodec_global_(\d+)\|>", generated_text)
         global_ids = [int(t) for t in global_matches]
-
+
         # Only log if there's an issue
         if not semantic_ids and not global_ids:
             logger.error(f"❌ No BiCodec tokens found! Generated text (first 1000 chars):\n{generated_text[:1000]}")
-
-        return semantic_ids, global_ids

+        return semantic_ids, global_ids