Output:
Chunk ID: cf9a89
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 4751
Output:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:80:_inflight_lock = threading.Lock()
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:81:_inflight_requests = 0
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:84:def _inflight_enter() -> int:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:86:    global _inflight_requests
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:87:    with _inflight_lock:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:88:        _inflight_requests += 1
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:89:        return _inflight_requests
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:92:def _inflight_exit() -> int:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:94:    global _inflight_requests
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:95:    with _inflight_lock:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:96:        _inflight_requests = max(0, _inflight_requests - 1)
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:97:        return _inflight_requests
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:115:def _build_perf_headers(metrics: Dict[str, Any], request_inflight: int) -> Dict[str, str]:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:127:    if request_inflight > 0:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:128:        detail_payload["request_inflight"] = request_inflight
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:133:        "x-request-inflight": "request_inflight",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:173:    if request_inflight > 0:
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:175:        metrics["request_inflight"] = request_inflight
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:210:        "llm_time_in_queue_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:467:        inflight_current = _inflight_enter()
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:502:                metrics["request_inflight"] = max(
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:503:                    int(metrics.get("request_inflight", 0) or 0),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:504:                    inflight_current,
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:545:                headers.update(_build_perf_headers(metrics, request_inflight=inflight_current))
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:606:                        "request_inflight": metrics.get("request_inflight"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:687:            _inflight_exit()
/home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:81:            tokenizer = getattr(model.engine, "tokenizer", None)
/home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:145:        results_generator = self.model.engine.generate(
/home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:293:        results_generator = self.model.engine.generate(
/home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:540:        results_generator = self.model.engine.generate(
/home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:744:        results_generator = self.model.engine.generate(
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:94:                "time_in_queue",
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:238:        results_generator = self.model.engine.generate(
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:390:            "time_in_queue": "llm_time_in_queue_ms",
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:6:This leaves ~60GB free. Running a SECOND vLLM engine instance on the same GPU doubles
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:20:    - Requires request migration between engines (complex)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:24:    - Each handles its own request queue
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:28:RECOMMENDATION: Start with Option A (duplicate engines), measure impact,
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:32:    from veena3modal.core.dual_engine import DualEngineRouter
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:36:        num_engines=2,
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:37:        gpu_memory_per_engine=0.15,
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:41:    engine = router.get_engine()  # Round-robin
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:42:    results = engine.generate(prompt, sampling_params, request_id)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:56:    """Configuration for a single vLLM engine instance."""
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:66:    Routes requests across multiple vLLM engine instances.
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:73:        - 2 engines x (1.3GB model + 10GB KV cache) = ~22.6GB
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:76:        - Each engine supports ~190 concurrent sequences
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:80:    num_engines: int = 2
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:81:    gpu_memory_per_engine: float = 0.15
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:82:    engines: list = field(default_factory=list)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:86:        """Initialize all engine instances."""
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:89:        for i in range(self.num_engines):
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:90:            logger.info(f"Initializing vLLM engine {i+1}/{self.num_engines}...")
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:91:            engine = SparkTTSModel(
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:93:                gpu_memory_utilization=self.gpu_memory_per_engine,
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:95:            self.engines.append(engine)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:98:        logger.info(f"DualEngineRouter: {self.num_engines} engines initialized")
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:100:    def get_engine(self) -> Any:
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:101:        """Get the next engine via round-robin."""
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:102:        if not self.engines:
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:103:            raise RuntimeError("No engines initialized")
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:104:        engine = self.engines[self._counter % len(self.engines)]
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:106:        return engine
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:109:        """Get tokenizer from first engine (all share the same tokenizer)."""
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:110:        if not self.engines:
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:111:            raise RuntimeError("No engines initialized")
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:112:        return self.engines[0].tokenizer
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:116:# To enable dual engine mode:
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:121:#     from veena3modal.core.dual_engine import DualEngineRouter
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:122:#     router = DualEngineRouter(model_path=model_path, num_engines=2, gpu_memory_per_engine=0.15)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:124:#     model = router.get_engine()  # Use first engine for init
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:127:#     Replace: self.model.engine.generate(...)
/home/ubuntu/veenaModal/veena3modal/core/dual_engine.py:128:#     With:    router.get_engine().engine.generate(...)
/home/ubuntu/veenaModal/veena3modal/core/bicodec_decoder.py:78:        # The multi-engine approach (Tier 3) provides more impactful gains.
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:4:Loads Spark TTS model with vLLM engine and validates speakers/emotions.
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:26:    Spark TTS Model with vLLM inference engine and BiCodec audio tokenizer.
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:44:        **engine_kwargs
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:53:            **engine_kwargs: Additional vLLM engine arguments (override defaults from VLLM_CONFIG)
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:103:        # Initialize vLLM engine with Spark TTS optimizations
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:104:        print(f"\n🔧 Initializing vLLM engine with optimizations...")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:107:        engine_kwargs_dict = VLLM_CONFIG.copy()
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:108:        engine_kwargs_dict.update({
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:118:        engine_kwargs_dict.update(engine_kwargs)
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:121:        print(f"  - dtype: {engine_kwargs_dict['dtype']}")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:122:        print(f"  - max_model_len: {engine_kwargs_dict['max_model_len']}")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:123:        print(f"  - gpu_memory_utilization: {engine_kwargs_dict['gpu_memory_utilization']}")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:124:        print(f"  - enable_prefix_caching: {engine_kwargs_dict.get('enable_prefix_caching', False)}")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:125:        print(f"  - enforce_eager (disable CUDA graphs): {engine_kwargs_dict.get('enforce_eager', True)}")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:127:        engine_args = AsyncEngineArgs(**engine_kwargs_dict)
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:128:        self.engine = AsyncLLMEngine.from_engine_args(engine_args)
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:130:        print(f"✅ vLLM engine initialized successfully!")
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:189:    def get_engine(self):
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:190:        """Get the vLLM engine."""
/home/ubuntu/veenaModal/veena3modal/core/model_loader.py:191:        return self.engine
/home/ubuntu/veenaModal/veena3modal/core/bicodec_batch_decoder.py:9:    Each streaming coroutine submits a decode request to the batch queue.
/home/ubuntu/veenaModal/veena3modal/core/bicodec_batch_decoder.py:76:        self._queue: asyncio.Queue = asyncio.Queue()
/home/ubuntu/veenaModal/veena3modal/core/bicodec_batch_decoder.py:120:        await self._queue.put(request)
/home/ubuntu/veenaModal/veena3modal/core/bicodec_batch_decoder.py:132:                    self._queue.get(),
/home/ubuntu/veenaModal/veena3modal/core/bicodec_batch_decoder.py:144:                        asyncio.wait_for(self._queue.get(), timeout=0.001),
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:2:Multi-Engine vLLM Wrapper - Transparent round-robin across N engine instances.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:4:OPTIMIZATION: A 0.5B model uses ~1.3GB weights + ~5-10GB KV cache per engine.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:5:On an 80GB A100, we can comfortably run 3 engines at ~12GB each (36GB total).
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:9:The wrapper is transparent to pipeline code: it exposes the same `.engine.generate()`
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:10:interface, but routes calls to different underlying engines via round-robin.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:13:    # Creates N engines, each with their own vLLM subprocess
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:14:    model = create_multi_engine_model(model_path, num_engines=3, gpu_memory_per_engine=0.12)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:16:    # Pipeline uses it identically to a single engine:
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:17:    model.engine.generate(prompt, sampling_params, request_id)  # routed to engine N
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:31:    Each call to generate() goes to the next engine in rotation.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:35:    def __init__(self, engines: list):
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:36:        self._engines = engines
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:38:        self._lock = threading.Lock()
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:40:    def _next_engine(self):
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:42:            engine = self._engines[self._counter % len(self._engines)]
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:44:            return engine
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:47:        """Route generate() to next engine via round-robin."""
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:48:        engine = self._next_engine()
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:49:        return engine.generate(*args, **kwargs)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:52:        """Forward any other attribute access to the first engine."""
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:53:        return getattr(self._engines[0], name)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:58:    Drop-in replacement for SparkTTSModel that wraps N engines.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:61:        .engine    -> RoundRobinEngine (dispatches generate() calls)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:62:        .tokenizer -> shared tokenizer (same across all engines)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:69:            models: List of SparkTTSModel instances (each has its own engine)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:74:        # Shared tokenizer (identical across all engines)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:80:        # Round-robin engine wrapper
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:81:        engines = [m.engine for m in models]
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:82:        self.engine = RoundRobinEngine(engines)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:90:    def get_engine(self):
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:91:        return self.engine
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:94:def create_multi_engine_model(
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:96:    num_engines: int = 2,
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:98:    gpu_memory_per_engine: float = 0.12,
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:101:    Create a multi-engine model with N vLLM instances on the same GPU.
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:103:    Memory budget per engine (A100-80GB):
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:104:        - Model weights: ~1.3GB (shared via CUDA, but each engine loads independently)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:106:        - Total per engine: ~10GB
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:107:        - 3 engines: ~30GB, leaves 50GB free
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:111:        num_engines: Number of vLLM engine instances (default: 2)
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:113:        gpu_memory_per_engine: GPU memory fraction per engine
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:120:    logger.info(f"Creating multi-engine model: {num_engines} engines x {gpu_memory_per_engine:.0%} GPU each")
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:123:    for i in range(num_engines):
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:124:        logger.info(f"  Initializing engine {i+1}/{num_engines}...")
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:128:            gpu_memory_utilization=gpu_memory_per_engine,
/home/ubuntu/veenaModal/veena3modal/core/multi_engine.py:134:    logger.info(f"Multi-engine model ready: {num_engines} engines, round-robin dispatch")
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:6:- SparkTTSModel (vLLM engine)
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:35:        The vLLM engine handles internal batching and scheduling.
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:82:    num_engines: int = 1,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:98:        num_engines: Number of vLLM engine instances (default: 1, set to 2-3 for Tier 3 optimization)
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:139:        # Load SparkTTS model with vLLM engine
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:140:        if num_engines > 1:
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:141:            # TIER 3 OPTIMIZATION: Multiple vLLM engines on same GPU
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:142:            # Each engine gets gpu_memory_utilization / num_engines to share GPU fairly.
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:143:            # E.g., 3 engines at 0.08 each = 0.24 total, ~8GB per engine, ~24GB total.
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:144:            from veena3modal.core.multi_engine import create_multi_engine_model
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:145:            per_engine_mem = gpu_memory_utilization / num_engines
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:146:            logger.info(f"Loading {num_engines} vLLM engines ({per_engine_mem:.2f} GPU mem each)...")
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:147:            model = create_multi_engine_model(
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:149:                num_engines=num_engines,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:151:                gpu_memory_per_engine=per_engine_mem,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:243:        if precompute_speaker_globals and num_engines <= 1:
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:246:        elif num_engines > 1:
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:247:            logger.info("Speaker globals cache startup precompute skipped (multi-engine mode)")
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:307:        async for request_output in runtime.model.engine.generate(


# 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.


**Checking queue time metric availability**


jq '.levels[] | select(.concurrency==96) | .requests[0].timing | keys' /tmp/stress_runtime_timeline_ext.json


Output:
Chunk ID: 01af66
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 447
Output:
[
  "audio_duration_seconds",
  "bicodec_decode_cpu_ms",
  "bicodec_decode_gpu_ms",
  "bicodec_decode_wall_ms",
  "generation_ms",
  "global_token_total",
  "llm_batch_count",
  "llm_batch_wall_ms_max",
  "llm_batch_wall_ms_min",
  "llm_batch_wall_ms_p50",
  "llm_batch_wall_ms_total",
  "llm_decode_calls",
  "llm_decode_wall_ms_max",
  "llm_decode_wall_ms_min",
  "llm_decode_wall_ms_p50",
  "llm_decode_wall_ms_total",
  "llm_generation_wall_ms",
  "llm_parse_ms",
  "llm_prefill_gpu_ms",
  "llm_prefill_wall_ms",
  "llm_prompt_token_total",
  "llm_time_per_batch_wall_ms",
  "llm_time_per_token_ms",
  "llm_token_total",
  "output_sample_rate",
  "pipeline_total_ms",
  "prompt_build_ms",
  "sampling_params_ms",
  "semantic_token_total",
  "sr_applied",
  "timeline_bicodec_done_delta_ms",
  "timeline_bicodec_done_ms",
  "timeline_bicodec_to_request_done_ms",
  "timeline_first_batch_to_llm_done_ms",
  "timeline_llm_done_delta_ms",
  "timeline_llm_done_ms",
  "timeline_llm_first_batch_delta_ms",
  "timeline_llm_first_batch_ms",
  "timeline_markers",
  "timeline_parse_done_delta_ms",
  "timeline_parse_done_ms",
  "timeline_parse_to_bicodec_done_ms",
  "timeline_post_llm_ms",
  "timeline_prompt_ready_delta_ms",
  "timeline_prompt_ready_ms",
  "timeline_request_done_delta_ms",
  "timeline_request_done_ms",
  "timeline_request_start_ms",
  "timeline_sampling_ready_delta_ms",
  "timeline_sampling_ready_ms",
  "timeline_to_first_batch_ms",
  "timeline_total_ms",
  "timeline_validation_done_delta_ms",
  "timeline_validation_done_ms",
  "timeline_wav_done_delta_ms",
  "timeline_wav_done_ms",
  "token_validation_ms",
  "tokens_generated",
  "tokens_per_batch_max",
  "tokens_per_batch_min",
  "tokens_per_batch_p50",
  "tokens_per_batch_total",
  "ttfb_ms",
  "wav_pack_ms"
]


Summary: auto


**Summarizing timeline results and next steps**


Timeline instrumentation + high-concurrency stress benchmarking are now done on the optimized path.

**Instrumentation Added**
- `veena3modal/core/pipeline.py`: per-request timeline markers (`request_start -> prompt_ready -> sampling_ready -> llm_first_batch -> llm_done -> parse_done -> validation_done -> bicodec_done -> wav_done -> request_done`) plus derived deltas.
- `veena3modal/api/fastapi_app.py`: timeline exposed in headers and `x-perf-details`.
- `scripts/stress_test_runtime_detailed.py`: timeline stats, stage-share, sample-marker output + JSON export.
- `scripts/stress_test_optimized_local.py`: same for HTTP benchmark path.

**Benchmark Artifacts**
- Runtime direct GPU path: `/tmp/stress_runtime_timeline_ext.json`
- HTTP path: `/tmp/stress_http_timeline_ext.json`
- A/B memory test (96c, 60% mem): `/tmp/stress_runtime_timeline_mem60_c96.json`

**Key Runtime Results (GPU path)**
- `c=1`: 2.36 req/s, p95 427ms, first batch 12.2ms, LLM done 411.1ms, total 424.1ms, `llm_time_per_token=3.21ms`
- `c=32`: 32.09 req/s, p95 1039ms, first batch 30.2ms, LLM done 791.8ms, total 803.6ms, `6.18ms/token`
- `c=64`: 41.68 req/s, p95 1650ms, first batch 38.2ms, LLM done 1132.1ms, total 1143.7ms, `8.84ms/token`
- `c=96`: 47.26 req/s, p95 2133ms, first batch 46.3ms, LLM done 1456.3ms, total 1467.8ms, `11.38ms/token`
- Stage share at high concurrency: LLM ~99.2%, BiCodec ~0.8%, other ~0%

**Key HTTP Results**
- `c=32`: 29.37 req/s, p95 1144ms, timeline total 810ms
- `c=64`: 36.02 req/s, p95 2043ms, timeline total 1120ms
- `c=96`: 30.65 req/s, p95 3997ms, timeline total 1128ms
- At `c=96`, client latency >> pipeline timeline (large pre-pipeline queue/overhead under load).

**A/B: Is `gpu_memory_utilization=0.25` the limiter?**
- `c=96` with 0.25 vs 0.60 memory is effectively identical:
- Throughput: `47.26` vs `47.26` req/s
- Timeline total avg: `1467.8` vs `1471.9` ms
- `llm_time_per_token`: `11.38` vs `11.41` ms
- Conclusion: memory cap is not the current bottleneck.

**Bottleneck Verdict**
- Primary bottleneck is AR LLM decode/scheduling path, not BiCodec decode.
- Peak-concurrency pain is long-tail queueing/scheduling before/around generation (especially in HTTP path).

Yes, we can do better. The most promising next optimizations are:
1. Reduce hot-path logging overhead (current INFO logs are very heavy under concurrency).
2. Cut pre-pipeline overhead and queueing in API/runtime wrappers (measure and trim non-LLM time before `timeline_request_start`).
3. Push decode throughput via engine strategy (multi-engine on A100, or scheduler tuning) since GPU memory headroom is ample.


<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>


<collaboration_mode># 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.
</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>


yes. onto next steps.


**Planning targeted performance improvements**


Next step I’ll execute is a focused optimization pass on the bottlenecks we measured: reduce hot-path logging overhead and add explicit pre-pipeline queue/overhead markers so we can separate HTTP/runtime queuing from GPU decode time in the next benchmark run.


git -C /home/ubuntu/veenaModal status --short


nl -ba /home/ubuntu/veenaModal/veena3modal/core/pipeline.py | sed -n '220,520p'


nl -ba /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py | sed -n '430,740p'


Output:
Chunk ID: bcbb30
Wall time: 0.0502 seconds
Process exited with code 0
Original token count: 172
Output:
 D CLEANUP_PLAN.md
 D PROGRESS.md
 D migration.md
 M veena3modal/api/fastapi_app.py
 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_optimized_local.py
?? scripts/stress_test_production.py
?? scripts/stress_test_runtime_detailed.py
?? stress_results_opt_local.json
?? veena3modal/__main__.py
?? veena3modal/core/bicodec_batch_decoder.py
?? veena3modal/core/dual_engine.py
?? veena3modal/core/multi_engine.py
?? veena3modal/local_server.py


Output:
Chunk ID: 56cdad
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 3789
Output:
   220	            repetition_penalty=repetition_penalty,
   221	            stop=TRAINING_STOP_TOKEN_IDS,
   222	            skip_special_tokens=False,
   223	            seed=seed,
   224	        )
   225	        perf["sampling_params_ms"] = (time.perf_counter() - t0) * 1000
   226	        _mark("sampling_ready")
   227
   228	        request_id = f"req-{uuid.uuid4().hex[:12]}"
   229	        logger.info(
   230	            "perf_non_stream_start",
   231	            extra={
   232	                "request_id": request_id,
   233	                "speaker": speaker,
   234	                "text_length": len(text),
   235	                "max_tokens": max_tokens,
   236	            },
   237	        )
   238	        results_generator = self.model.engine.generate(
   239	            prompt=prompt,
   240	            sampling_params=sampling_params,
   241	            request_id=request_id,
   242	        )
   243
   244	        final_output = None
   245	        final_request_metrics: Dict[str, float] = {}
   246
   247	        llm_batch_wall_ms: List[float] = []
   248	        llm_batch_gpu_ms: List[float] = []
   249	        llm_decode_wall_ms: List[float] = []
   250	        llm_decode_gpu_ms: List[float] = []
   251	        tokens_per_batch: List[float] = []
   252
   253	        prev_generated_count = 0
   254	        prev_gpu_cumulative_s: Optional[float] = None
   255
   256	        t_gen_start = time.perf_counter()
   257	        t_last_yield = t_gen_start
   258	        async for request_output in results_generator:
   259	            now = time.perf_counter()
   260	            batch_wall = (now - t_last_yield) * 1000
   261	            t_last_yield = now
   262
   263	            outputs = request_output.outputs or []
   264	            if not outputs:
   265	                final_output = request_output
   266	                continue
   267
   268	            output = outputs[0]
   269	            generated_count = len(output.token_ids)
   270	            new_tokens = max(0, generated_count - prev_generated_count)
   271	            prev_generated_count = generated_count
   272
   273	            raw_req_metrics = self._extract_request_metrics(getattr(request_output, "metrics", None))
   274	            if raw_req_metrics:
   275	                final_request_metrics = raw_req_metrics
   276
   277	            if new_tokens <= 0:
   278	                final_output = request_output
   279	                continue
   280
   281	            perf["llm_batch_count"] += 1
   282	            if perf["llm_batch_count"] == 1:
   283	                first_batch_timeline_ms = _mark("llm_first_batch")
   284	                logger.info(
   285	                    "perf_first_llm_batch",
   286	                    extra={
   287	                        "request_id": request_id,
   288	                        "batch_wall_ms": round(batch_wall, 3),
   289	                        "new_tokens": new_tokens,
   290	                        "timeline_ms": round(first_batch_timeline_ms, 3),
   291	                    },
   292	                )
   293	            tokens_per_batch.append(float(new_tokens))
   294	            llm_batch_wall_ms.append(float(batch_wall))
   295	            if perf["llm_batch_count"] > 1:
   296	                llm_decode_wall_ms.append(float(batch_wall))
   297
   298	            cumulative_gpu_s: Optional[float] = None
   299	            if raw_req_metrics:
   300	                if "model_execute_time" in raw_req_metrics:
   301	                    cumulative_gpu_s = raw_req_metrics["model_execute_time"]
   302	                elif "model_forward_time" in raw_req_metrics:
   303	                    cumulative_gpu_s = raw_req_metrics["model_forward_time"]
   304
   305	            if cumulative_gpu_s is not None:
   306	                if prev_gpu_cumulative_s is None:
   307	                    gpu_delta_ms = cumulative_gpu_s * 1000
   308	                elif cumulative_gpu_s >= prev_gpu_cumulative_s:
   309	                    gpu_delta_ms = (cumulative_gpu_s - prev_gpu_cumulative_s) * 1000
   310	                else:
   311	                    # Some versions report non-cumulative per-step values.
   312	                    gpu_delta_ms = cumulative_gpu_s * 1000
   313	                prev_gpu_cumulative_s = cumulative_gpu_s
   314
   315	                if gpu_delta_ms >= 0:
   316	                    llm_batch_gpu_ms.append(float(gpu_delta_ms))
   317	                    if perf["llm_batch_count"] > 1:
   318	                        llm_decode_gpu_ms.append(float(gpu_delta_ms))
   319
   320	            final_output = request_output
   321
   322	        perf["llm_generation_wall_ms"] = (time.perf_counter() - t_gen_start) * 1000
   323	        llm_done_timeline_ms = _mark("llm_done")
   324	        logger.info(
   325	            "perf_llm_generation_done",
   326	            extra={
   327	                "request_id": request_id,
   328	                "llm_generation_wall_ms": round(perf["llm_generation_wall_ms"], 3),
   329	                "llm_batch_count": perf["llm_batch_count"],
   330	                "timeline_ms": round(llm_done_timeline_ms, 3),
   331	            },
   332	        )
   333
   334	        if final_output is None:
   335	            perf["pipeline_total_ms"] = (time.perf_counter() - t_request_start) * 1000
   336	            _finalize_timeline()
   337	            return None, perf
   338
   339	        generated_text = final_output.outputs[0].text
   340	        generated_ids = final_output.outputs[0].token_ids
   341	        perf["llm_token_total"] = len(generated_ids)
   342
   343	        # Stage 3: parse BiCodec tokens from generated text
   344	        t0 = time.perf_counter()
   345	        semantic_ids, global_ids = self._extract_bicodec_tokens(generated_text)
   346	        perf["llm_parse_ms"] = (time.perf_counter() - t0) * 1000
   347	        perf["semantic_token_total"] = len(semantic_ids)
   348	        perf["global_token_total"] = len(global_ids)
   349	        parse_done_timeline_ms = _mark("parse_done")
   350	        logger.info(
   351	            "perf_token_parse_done",
   352	            extra={
   353	                "request_id": request_id,
   354	                "parse_ms": round(perf["llm_parse_ms"], 3),
   355	                "semantic_tokens": perf["semantic_token_total"],
   356	                "global_tokens": perf["global_token_total"],
   357	                "timeline_ms": round(parse_done_timeline_ms, 3),
   358	            },
   359	        )
   360
   361	        # Aggregate LLM batch stats
   362	        self._apply_stats(perf, "llm_batch_wall_ms", llm_batch_wall_ms)
   363	        self._apply_stats(perf, "llm_batch_gpu_ms", llm_batch_gpu_ms)
   364	        self._apply_stats(perf, "llm_decode_wall_ms", llm_decode_wall_ms)
   365	        self._apply_stats(perf, "llm_decode_gpu_ms", llm_decode_gpu_ms)
   366	        self._apply_stats(perf, "tokens_per_batch", tokens_per_batch)
   367
   368	        if llm_batch_wall_ms:
   369	            perf["llm_prefill_wall_ms"] = float(llm_batch_wall_ms[0])
   370	            perf["llm_prefill_gpu_ms"] = float(llm_batch_gpu_ms[0]) if llm_batch_gpu_ms else 0.0
   371	        else:
   372	            perf["llm_prefill_wall_ms"] = float(perf["llm_generation_wall_ms"])
   373	            perf["llm_prefill_gpu_ms"] = 0.0
   374
   375	        if not llm_batch_gpu_ms and final_request_metrics.get("model_execute_time") is not None:
   376	            total_gpu_ms = float(final_request_metrics["model_execute_time"]) * 1000
   377	            perf["llm_batch_gpu_ms_total"] = total_gpu_ms
   378	            if perf["llm_batch_count"] > 1:
   379	                perf["llm_decode_gpu_ms_total"] = total_gpu_ms
   380
   381	        if perf["llm_token_total"] > 0:
   382	            perf["llm_time_per_token_ms"] = float(perf["llm_generation_wall_ms"]) / float(perf["llm_token_total"])
   383	        if perf["llm_batch_count"] > 0:
   384	            perf["llm_time_per_batch_wall_ms"] = float(perf.get("llm_batch_wall_ms_total", 0.0)) / float(perf["llm_batch_count"])
   385	            if perf.get("llm_batch_gpu_ms_total", 0.0) > 0:
   386	                perf["llm_time_per_batch_gpu_ms"] = float(perf["llm_batch_gpu_ms_total"]) / float(perf["llm_batch_count"])
   387
   388	        # Pull useful request-level vLLM metrics (seconds -> ms)
   389	        sec_fields = {
   390	            "time_in_queue": "llm_time_in_queue_ms",
   391	            "scheduler_time": "llm_scheduler_ms",
   392	            "model_forward_time": "llm_model_forward_ms",
   393	            "model_execute_time": "llm_model_execute_ms",
   394	        }
   395	        for src, dst in sec_fields.items():
   396	            if src in final_request_metrics:
   397	                perf[dst] = float(final_request_metrics[src]) * 1000
   398
   399	        arrival = final_request_metrics.get("arrival_time")
   400	        first_token = final_request_metrics.get("first_token_time")
   401	        finished = final_request_metrics.get("finished_time")
   402	        if arrival is not None and first_token is not None and first_token >= arrival:
   403	            perf["llm_first_token_ms"] = (first_token - arrival) * 1000
   404	        if arrival is not None and finished is not None and finished >= arrival:
   405	            perf["llm_request_lifecycle_ms"] = (finished - arrival) * 1000
   406
   407	        if not semantic_ids or not global_ids:
   408	            perf["pipeline_total_ms"] = (time.perf_counter() - t_request_start) * 1000
   409	            _finalize_timeline()
   410	            return None, perf
   411
   412	        # Stage 4: validate BiCodec tokens
   413	        t0 = time.perf_counter()
   414	        if not self.bicodec_decoder.validate_tokens(semantic_ids, global_ids):
   415	            perf["token_validation_ms"] = (time.perf_counter() - t0) * 1000
   416	            _mark("validation_done")
   417	            perf["pipeline_total_ms"] = (time.perf_counter() - t_request_start) * 1000
   418	            _finalize_timeline()
   419	            return None, perf
   420	        perf["token_validation_ms"] = (time.perf_counter() - t0) * 1000
   421	        _mark("validation_done")
   422
   423	        # Stage 5: BiCodec decode (wall + GPU/CPU split)
   424	        decode_t0 = time.perf_counter()
   425	        decode_gpu_ms = 0.0
   426	        perf["llm_decode_calls"] = 1
   427
   428	        use_cuda_timing = (
   429	            torch.cuda.is_available()
   430	            and hasattr(self.bicodec_decoder, "device")
   431	            and getattr(self.bicodec_decoder.device, "type", "") == "cuda"
   432	        )
   433	        audio_bytes: Optional[bytes]
   434	        if use_cuda_timing:
   435	            try:
   436	                device = self.bicodec_decoder.device
   437	                start_event = torch.cuda.Event(enable_timing=True)
   438	                end_event = torch.cuda.Event(enable_timing=True)
   439	                torch.cuda.synchronize(device=device)
   440	                start_event.record()
   441	                audio_bytes = self.bicodec_decoder.decode_to_bytes(semantic_ids, global_ids)
   442	                end_event.record()
   443	                torch.cuda.synchronize(device=device)
   444	                decode_gpu_ms = float(start_event.elapsed_time(end_event))
   445	            except Exception:
   446	                audio_bytes = self.bicodec_decoder.decode_to_bytes(semantic_ids, global_ids)
   447	                decode_gpu_ms = 0.0
   448	        else:
   449	            audio_bytes = self.bicodec_decoder.decode_to_bytes(semantic_ids, global_ids)
   450
   451	        decode_wall_ms = (time.perf_counter() - decode_t0) * 1000
   452	        perf["bicodec_decode_wall_ms"] = float(decode_wall_ms)
   453	        perf["bicodec_decode_gpu_ms"] = float(max(0.0, decode_gpu_ms))
   454	        perf["bicodec_decode_cpu_ms"] = float(max(0.0, decode_wall_ms - decode_gpu_ms))
   455	        bicodec_done_timeline_ms = _mark("bicodec_done")
   456	        logger.info(
   457	            "perf_bicodec_decode_done",
   458	            extra={
   459	                "request_id": request_id,
   460	                "decode_wall_ms": round(perf["bicodec_decode_wall_ms"], 3),
   461	                "decode_gpu_ms": round(perf["bicodec_decode_gpu_ms"], 3),
   462	                "decode_cpu_ms": round(perf["bicodec_decode_cpu_ms"], 3),
   463	                "timeline_ms": round(bicodec_done_timeline_ms, 3),
   464	            },
   465	        )
   466
   467	        if audio_bytes is None:
   468	            perf["pipeline_total_ms"] = (time.perf_counter() - t_request_start) * 1000
   469	            _finalize_timeline()
   470	            return None, perf
   471
   472	        # Stage 6: WAV header
   473	        t0 = time.perf_counter()
   474	        wav_bytes = add_wav_header(audio_bytes, sample_rate=AUDIO_SAMPLE_RATE)
   475	        perf["wav_pack_ms"] = (time.perf_counter() - t0) * 1000
   476	        _mark("wav_done")
   477
   478	        perf["pipeline_total_ms"] = (time.perf_counter() - t_request_start) * 1000
   479	        _finalize_timeline()
   480	        logger.info(
   481	            "perf_non_stream_done",
   482	            extra={
   483	                "request_id": request_id,
   484	                "pipeline_total_ms": round(perf["pipeline_total_ms"], 3),
   485	                "llm_token_total": perf.get("llm_token_total"),
   486	                "timeline_total_ms": round(float(perf.get("timeline_total_ms", 0.0)), 3),
   487	            },
   488	        )
   489	        return wav_bytes, perf
   490
   491	    async def generate_speech(
   492	        self,
   493	        speaker: str,
   494	        text: str,
   495	        temperature: float = DEFAULT_TEMPERATURE,
   496	        top_k: int = DEFAULT_TOP_K,
   497	        top_p: float = DEFAULT_TOP_P,
   498	        max_tokens: int = DEFAULT_MAX_TOKENS,
   499	        repetition_penalty: float = 1.05,
   500	        seed: Optional[int] = None,
   501	    ) -> Optional[bytes]:
   502	        """
   503	        Generate speech audio (non-streaming) using Spark TTS.
   504
   505	        NOTE: This method signature changed from description-based to speaker-based
   506	        to align with Spark TTS architecture.
   507
   508	        Args:
   509	            speaker: Speaker name (one of 12 predefined speakers)
   510	            text: Text to synthesize (with optional [emotion] tags)
   511	            temperature: Sampling temperature (default: 0.8 for Spark TTS)
   512	            top_k: Top-k sampling (default: 50)
   513	            top_p: Nucleus sampling (default: 1.0)
   514	            max_tokens: Max BiCodec tokens to generate (default: 2048)
   515	            seed: Random seed for reproducibility
   516
   517	        Returns:
   518	            Audio bytes (int16 PCM WAV, 16kHz mono) or None if failed
   519	        """
   520	        wav_bytes, _ = await self.generate_speech_profiled(


Output:
Chunk ID: d188e8
Wall time: 0.0517 seconds
Process exited with code 0
Original token count: 4119
Output:
   430	                headers={"X-Request-ID": request_id},
   431	            )
   432
   433	        # Get resolved speaker
   434	        speaker = req.get_resolved_speaker()
   435
   436	        # Get normalized text (apply text normalization if enabled)
   437	        normalizer_func = None
   438	        if req.normalize:
   439	            try:
   440	                from veena3modal.processing.text_normalizer import normalize_text
   441	                normalizer_func = lambda t: normalize_text(t, verbose=req.normalize_verbose)
   442	            except ImportError:
   443	                logger.warning("TextNormalizer not available, skipping text normalization")
   444
   445	        text = req.get_normalized_text(normalizer_func=normalizer_func)
   446
   447	        # Set request context for structured logging
   448	        set_request_context(request_id=request_id)
   449
   450	        # Log request received (lifecycle event + metrics)
   451	        log_request_received(
   452	            request_id=request_id,
   453	            text_length=len(text),
   454	            speaker=speaker,
   455	            stream=req.stream,
   456	            format=req.format,
   457	        )
   458	        metrics_request_received(speaker=speaker, stream=req.stream, format=req.format)
   459
   460	        # Streaming mode (M4)
   461	        if req.stream:
   462	            return await _handle_streaming_request(req, request_id, text, speaker, start_time)
   463
   464	        # Format encoding will be applied after generation (non-streaming only)
   465	        # For streaming, only WAV is supported
   466	        target_format = req.format
   467	        inflight_current = _inflight_enter()
   468	        try:
   469	            try:
   470	                # Get output sample rate - use_enum_values=True means req.output is already a string
   471	                output_sr = req.output if hasattr(req, 'output') else "16khz"
   472
   473	                # Generate speech (non-streaming)
   474	                if req.chunking:
   475	                    audio_bytes, metrics = await tts_runtime.generate_speech_chunked(
   476	                        text=text,
   477	                        speaker=speaker,
   478	                        temperature=req.temperature,
   479	                        top_k=req.top_k,
   480	                        top_p=req.top_p,
   481	                        max_tokens=req.max_tokens,
   482	                        repetition_penalty=req.repetition_penalty,
   483	                        seed=req.seed,
   484	                        sample_rate=req.sample_rate,
   485	                        output_sample_rate=output_sr,
   486	                    )
   487	                else:
   488	                    audio_bytes, metrics = await tts_runtime.generate_speech(
   489	                        text=text,
   490	                        speaker=speaker,
   491	                        temperature=req.temperature,
   492	                        top_k=req.top_k,
   493	                        top_p=req.top_p,
   494	                        max_tokens=req.max_tokens,
   495	                        repetition_penalty=req.repetition_penalty,
   496	                        seed=req.seed,
   497	                        output_sample_rate=output_sr,
   498	                    )
   499
   500	                if metrics is None:
   501	                    metrics = {}
   502	                metrics["request_inflight"] = max(
   503	                    int(metrics.get("request_inflight", 0) or 0),
   504	                    inflight_current,
   505	                )
   506
   507	                if audio_bytes is None:
   508	                    return JSONResponse(
   509	                        status_code=500,
   510	                        content={
   511	                            "error": {
   512	                                "code": "GENERATION_FAILED",
   513	                                "message": "Audio generation failed. Check logs for details.",
   514	                                "request_id": request_id,
   515	                            }
   516	                        },
   517	                        headers={"X-Request-ID": request_id},
   518	                    )
   519
   520	                # Calculate RTF
   521	                total_time = time.time() - start_time
   522	                audio_duration = metrics.get("audio_duration_seconds", 0)
   523	                rtf = total_time / audio_duration if audio_duration > 0 else 0
   524
   525	                # Build response headers
   526	                runtime = tts_runtime.get_runtime()
   527	                model_version = runtime.model_version if runtime else get_model_version()
   528
   529	                # Use actual output sample rate from metrics
   530	                output_sample_rate = metrics.get("output_sample_rate", req.sample_rate)
   531
   532	                headers = {
   533	                    "X-Request-ID": request_id,
   534	                    "X-Model-Version": model_version,
   535	                    "X-Format": "wav",
   536	                    "X-Sample-Rate": str(output_sample_rate),
   537	                    "X-Stream": "false",
   538	                    "X-Audio-Bytes": str(len(audio_bytes)),
   539	                    "X-Audio-Seconds": f"{audio_duration:.2f}",
   540	                    "X-TTFB-ms": str(metrics.get("ttfb_ms", 0)),
   541	                    "X-RTF": f"{rtf:.3f}",
   542	                    "X-Text-Chunked": str(metrics.get("text_chunked", False)).lower(),
   543	                    "X-SR-Applied": str(metrics.get("sr_applied", False)).lower(),
   544	                }
   545	                headers.update(_build_perf_headers(metrics, request_inflight=inflight_current))
   546
   547	                if req.seed is not None:
   548	                    headers["X-Seed"] = str(req.seed)
   549
   550	                # Fire-and-forget sentence storage (non-blocking, after generation)
   551	                sentence_store = _get_sentence_store()
   552	                sentence_store.store_fire_and_forget(
   553	                    request_id=request_id,
   554	                    text=text,
   555	                    speaker=speaker,
   556	                    stream=False,
   557	                    format=req.format,
   558	                    temperature=req.temperature,
   559	                    top_k=req.top_k,
   560	                    top_p=req.top_p,
   561	                    max_tokens=req.max_tokens,
   562	                    repetition_penalty=req.repetition_penalty,
   563	                    seed=req.seed,
   564	                    text_chunked=metrics.get("text_chunked", False),
   565	                    ttfb_ms=metrics.get("ttfb_ms"),
   566	                    audio_duration_seconds=audio_duration,
   567	                )
   568
   569	                # Log successful completion (lifecycle event + metrics)
   570	                total_duration_ms = int(total_time * 1000)
   571	                log_request_completed(
   572	                    request_id=request_id,
   573	                    status_code=200,
   574	                    total_duration_ms=total_duration_ms,
   575	                    audio_duration_seconds=audio_duration,
   576	                    rtf=rtf,
   577	                    chunks_sent=1,
   578	                )
   579	                metrics_request_completed(
   580	                    status_code=200,
   581	                    duration_seconds=total_time,
   582	                    speaker=speaker,
   583	                    stream=False,
   584	                )
   585	                record_ttfb(ttfb_seconds=metrics.get("ttfb_ms", 0) / 1000, speaker=speaker, stream=False)
   586	                record_rtf(rtf=rtf, speaker=speaker)
   587	                record_audio_duration(duration_seconds=audio_duration, speaker=speaker)
   588
   589	                # High-cardinality details are kept in logs; summarize key timings.
   590	                logger.info(
   591	                    "request_perf_summary",
   592	                    extra={
   593	                        "request_id": request_id,
   594	                        "speaker": speaker,
   595	                        "generation_ms": metrics.get("generation_ms"),
   596	                        "llm_token_total": metrics.get("llm_token_total"),
   597	                        "llm_batch_count": metrics.get("llm_batch_count"),
   598	                        "llm_batch_wall_ms_total": metrics.get("llm_batch_wall_ms_total"),
   599	                        "llm_batch_gpu_ms_total": metrics.get("llm_batch_gpu_ms_total"),
   600	                        "timeline_llm_first_batch_ms": metrics.get("timeline_llm_first_batch_ms"),
   601	                        "timeline_llm_done_ms": metrics.get("timeline_llm_done_ms"),
   602	                        "timeline_bicodec_done_ms": metrics.get("timeline_bicodec_done_ms"),
   603	                        "timeline_total_ms": metrics.get("timeline_total_ms"),
   604	                        "bicodec_decode_wall_ms": metrics.get("bicodec_decode_wall_ms"),
   605	                        "bicodec_decode_gpu_ms": metrics.get("bicodec_decode_gpu_ms"),
   606	                        "request_inflight": metrics.get("request_inflight"),
   607	                    },
   608	                )
   609
   610	                clear_request_context()
   611
   612	                # Apply format encoding if not WAV
   613	                media_type = "audio/wav"
   614	                if target_format != AudioFormat.WAV.value:
   615	                    try:
   616	                        from veena3modal.audio.encoder import (
   617	                            OpusEncoder, MP3Encoder, MuLawEncoder, FLACEncoder
   618	                        )
   619
   620	                        # Get sample rate from output (after potential SR)
   621	                        sample_rate = metrics.get("output_sample_rate", 16000)
   622
   623	                        # Strip WAV header to get PCM data
   624	                        pcm_data = audio_bytes[44:] if len(audio_bytes) > 44 else audio_bytes
   625
   626	                        if target_format == AudioFormat.OPUS.value:
   627	                            encoder = OpusEncoder(sample_rate=sample_rate)
   628	                            audio_bytes = encoder.encode(pcm_data, sample_rate)
   629	                            media_type = "audio/opus"
   630	                        elif target_format == AudioFormat.MP3.value:
   631	                            encoder = MP3Encoder(sample_rate=sample_rate)
   632	                            audio_bytes = encoder.encode(pcm_data, sample_rate)
   633	                            media_type = "audio/mpeg"
   634	                        elif target_format == AudioFormat.MULAW.value:
   635	                            encoder = MuLawEncoder()
   636	                            audio_bytes = encoder.encode(pcm_data, sample_rate)
   637	                            media_type = "audio/x-wav"
   638	                        elif target_format == AudioFormat.FLAC.value:
   639	                            encoder = FLACEncoder(sample_rate=sample_rate)
   640	                            audio_bytes = encoder.encode(pcm_data, sample_rate)
   641	                            media_type = "audio/flac"
   642
   643	                        headers["X-Audio-Bytes"] = str(len(audio_bytes))
   644	                        logger.info(f"Encoded audio to {target_format}: {len(audio_bytes)} bytes")
   645	                    except ImportError as e:
   646	                        logger.warning(f"Audio encoder not available: {e}")
   647	                        # Fall back to WAV
   648	                    except Exception as e:
   649	                        logger.error(f"Format encoding failed: {e}")
   650	                        # Fall back to WAV
   651
   652	                return Response(
   653	                    content=audio_bytes,
   654	                    media_type=media_type,
   655	                    headers=headers,
   656	                )
   657
   658	            except Exception as e:
   659	                logger.exception(f"TTS generation error: {e}")
   660
   661	                # Log failure (lifecycle event + metrics)
   662	                log_request_failed(
   663	                    request_id=request_id,
   664	                    status_code=500,
   665	                    error_code="INTERNAL_ERROR",
   666	                    error_message=str(e),
   667	                )
   668	                metrics_request_failed(
   669	                    status_code=500,
   670	                    error_code="INTERNAL_ERROR",
   671	                    speaker=speaker,
   672	                )
   673	                clear_request_context()
   674
   675	                return JSONResponse(
   676	                    status_code=500,
   677	                    content={
   678	                        "error": {
   679	                            "code": "INTERNAL_ERROR",
   680	                            "message": str(e),
   681	                            "request_id": request_id,
   682	                        }
   683	                    },
   684	                    headers={"X-Request-ID": request_id},
   685	                )
   686	        finally:
   687	            _inflight_exit()
   688
   689	    async def _handle_streaming_request(req, request_id: str, text: str, speaker: str, start_time: float):
   690	        """
   691	        Handle streaming TTS request.
   692
   693	        Returns a StreamingResponse that yields WAV header + PCM chunks.
   694	        True streaming: first bytes sent ASAP before full audio is generated.
   695	        """
   696	        from fastapi.responses import StreamingResponse
   697	        from veena3modal.api.schemas import AudioFormat
   698	        from veena3modal.services import tts_runtime
   699
   700	        # Check if streaming pipeline is available
   701	        runtime = tts_runtime.get_runtime()
   702	        if runtime is None or runtime.streaming_pipeline is None:
   703	            return JSONResponse(
   704	                status_code=503,
   705	                content={
   706	                    "error": {
   707	                        "code": "STREAMING_UNAVAILABLE",
   708	                        "message": "Streaming pipeline not initialized.",
   709	                        "request_id": request_id,
   710	                    }
   711	                },
   712	                headers={"X-Request-ID": request_id},
   713	            )
   714
   715	        # Non-WAV formats not implemented yet (M5)
   716	        if req.format != AudioFormat.WAV.value:
   717	            return JSONResponse(
   718	                status_code=501,
   719	                content={
   720	                    "error": {
   721	                        "code": "FORMAT_NOT_IMPLEMENTED",
   722	                        "message": f"Streaming format '{req.format}' not yet implemented. Use format=wav.",
   723	                        "request_id": request_id,
   724	                    }
   725	                },
   726	                headers={"X-Request-ID": request_id},
   727	            )
   728
   729	        # Set request context for structured logging
   730	        set_request_context(request_id=request_id)
   731
   732	        # Create async generator for streaming response
   733	        async def audio_stream_generator():
   734	            """Yields audio chunks as they're generated."""
   735	            final_metrics = {}
   736	            first_chunk_sent = False
   737	            chunks_count = 0
   738	            try:
   739	                async for audio_chunk, metrics in tts_runtime.generate_speech_streaming(
   740	                    text=text,


**Planning performance header integration**


nl -ba /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py | sed -n '1,260p'


Output:
Chunk ID: dccff2
Wall time: 0.0515 seconds
Process exited with code 0
Original token count: 2630
Output:
     1	"""
     2	FastAPI ASGI app factory for Modal deployment.
     3
     4	Phase 1: expose `/v1/tts/generate` and `/v1/tts/health` while reusing existing
     5	Veena3 inference/streaming code from the Django project.
     6
     7	Phase 2+: extract shared code into `veena3modal/shared/` and remove Django coupling.
     8	"""
     9
    10	# NOTE: Intentionally NOT using `from __future__ import annotations` here
    11	# to ensure FastAPI recognizes Request type annotations properly
    12
    13	import time
    14	import uuid
    15	import json
    16	import threading
    17	from typing import Optional, Any, Dict
    18
    19	# Structured logging (JSON formatted, request-scoped)
    20	from veena3modal.shared.logging import (
    21	    get_logger,
    22	    set_request_context,
    23	    clear_request_context,
    24	    log_request_received,
    25	    log_first_audio_emitted,
    26	    log_request_completed,
    27	    log_request_failed,
    28	)
    29
    30	# Prometheus metrics
    31	from veena3modal.shared.metrics import (
    32	    record_request_received as metrics_request_received,
    33	    record_request_completed as metrics_request_completed,
    34	    record_request_failed as metrics_request_failed,
    35	    record_ttfb,
    36	    record_rtf,
    37	    record_audio_duration,
    38	    record_chunks_sent,
    39	    get_metrics_text,
    40	    get_content_type as get_metrics_content_type,
    41	)
    42
    43	# Auth and rate limiting
    44	from veena3modal.api.auth import (
    45	    get_api_validator,
    46	    extract_api_key,
    47	    hash_api_key,
    48	)
    49	from veena3modal.api.rate_limiter import get_rate_limiter
    50	from veena3modal.api.error_handlers import (
    51	    ErrorCode,
    52	    create_error_response,
    53	    get_error_status,
    54	    is_gpu_fault,
    55	    handle_gpu_fault,
    56	    FeatureFlags,
    57	)
    58
    59	logger = get_logger(__name__)
    60
    61	# Sentence storage (fire-and-forget, non-blocking)
    62	# Imported at module level for singleton access
    63	_sentence_store = None
    64
    65
    66	def _get_sentence_store():
    67	    """Lazy load sentence store singleton."""
    68	    global _sentence_store
    69	    if _sentence_store is None:
    70	        from veena3modal.services.sentence_store import get_sentence_store
    71	        _sentence_store = get_sentence_store()
    72	    return _sentence_store
    73
    74	# Version info (will be set dynamically after model load)
    75	_APP_VERSION = "0.1.0"
    76	_MODEL_VERSION: Optional[str] = None
    77	_startup_time: Optional[float] = None
    78
    79	# Request-level in-flight tracking (single-process scope)
    80	_inflight_lock = threading.Lock()
    81	_inflight_requests = 0
    82
    83
    84	def _inflight_enter() -> int:
    85	    """Increment in-flight counter and return current value."""
    86	    global _inflight_requests
    87	    with _inflight_lock:
    88	        _inflight_requests += 1
    89	        return _inflight_requests
    90
    91
    92	def _inflight_exit() -> int:
    93	    """Decrement in-flight counter and return current value."""
    94	    global _inflight_requests
    95	    with _inflight_lock:
    96	        _inflight_requests = max(0, _inflight_requests - 1)
    97	        return _inflight_requests
    98
    99
   100	def _serialize_header_value(value: Any) -> Optional[str]:
   101	    """Convert metric values to compact header-safe strings."""
   102	    if value is None:
   103	        return None
   104	    if isinstance(value, bool):
   105	        return "true" if value else "false"
   106	    if isinstance(value, int):
   107	        return str(value)
   108	    if isinstance(value, float):
   109	        if value.is_integer():
   110	            return str(int(value))
   111	        return f"{value:.3f}"
   112	    return str(value)
   113
   114
   115	def _build_perf_headers(metrics: Dict[str, Any], request_inflight: int) -> Dict[str, str]:
   116	    """
   117	    Build detailed timing headers consumed by local benchmark scripts.
   118
   119	    Includes a compact JSON payload (`X-Perf-Details`) and selected explicit headers
   120	    for easy parsing in shell scripts/tools.
   121	    """
   122	    perf_headers: Dict[str, str] = {}
   123	    if not isinstance(metrics, dict):
   124	        metrics = {}
   125
   126	    detail_payload: Dict[str, Any] = {}
   127	    if request_inflight > 0:
   128	        detail_payload["request_inflight"] = request_inflight
   129
   130	    # Expose selected metrics as explicit headers.
   131	    header_map = {
   132	        "x-generation-ms": "generation_ms",
   133	        "x-request-inflight": "request_inflight",
   134	        "x-timeline-first-batch-ms": "timeline_llm_first_batch_ms",
   135	        "x-timeline-llm-done-ms": "timeline_llm_done_ms",
   136	        "x-timeline-bicodec-done-ms": "timeline_bicodec_done_ms",
   137	        "x-timeline-total-ms": "timeline_total_ms",
   138	        "x-llm-token-total": "llm_token_total",
   139	        "x-llm-batch-count": "llm_batch_count",
   140	        "x-llm-batch-wall-ms": "llm_batch_wall_ms_total",
   141	        "x-llm-batch-gpu-ms": "llm_batch_gpu_ms_total",
   142	        "x-llm-batch-wall-ms-min": "llm_batch_wall_ms_min",
   143	        "x-llm-batch-wall-ms-max": "llm_batch_wall_ms_max",
   144	        "x-llm-batch-wall-ms-p50": "llm_batch_wall_ms_p50",
   145	        "x-llm-batch-gpu-ms-min": "llm_batch_gpu_ms_min",
   146	        "x-llm-batch-gpu-ms-max": "llm_batch_gpu_ms_max",
   147	        "x-llm-batch-gpu-ms-p50": "llm_batch_gpu_ms_p50",
   148	        "x-llm-decode-wall-ms": "llm_decode_wall_ms_total",
   149	        "x-llm-decode-gpu-ms": "llm_decode_gpu_ms_total",
   150	        "x-llm-decode-wall-ms-min": "llm_decode_wall_ms_min",
   151	        "x-llm-decode-wall-ms-max": "llm_decode_wall_ms_max",
   152	        "x-llm-decode-wall-ms-p50": "llm_decode_wall_ms_p50",
   153	        "x-llm-decode-gpu-ms-min": "llm_decode_gpu_ms_min",
   154	        "x-llm-decode-gpu-ms-max": "llm_decode_gpu_ms_max",
   155	        "x-llm-decode-gpu-ms-p50": "llm_decode_gpu_ms_p50",
   156	        "x-llm-decode-calls": "llm_decode_calls",
   157	        "x-llm-decode-cpu-ms": "bicodec_decode_cpu_ms",
   158	        "x-llm-parse-ms": "llm_parse_ms",
   159	        "x-llm-parse-avg-ms": "llm_parse_ms",
   160	        "x-llm-tokens-per-batch-min": "tokens_per_batch_min",
   161	        "x-llm-tokens-per-batch-max": "tokens_per_batch_max",
   162	        "x-llm-tokens-per-batch-p50": "tokens_per_batch_p50",
   163	        "x-llm-time-per-token-ms": "llm_time_per_token_ms",
   164	        "x-llm-time-per-batch-wall-ms": "llm_time_per_batch_wall_ms",
   165	        "x-llm-time-per-batch-gpu-ms": "llm_time_per_batch_gpu_ms",
   166	        "x-llm-text-chunked": "text_chunked",
   167	        "x-llm-chunks-processed": "chunks_processed",
   168	        "x-bicodec-decode-wall-ms": "bicodec_decode_wall_ms",
   169	        "x-bicodec-decode-gpu-ms": "bicodec_decode_gpu_ms",
   170	        "x-bicodec-decode-cpu-ms": "bicodec_decode_cpu_ms",
   171	    }
   172
   173	    if request_inflight > 0:
   174	        metrics = dict(metrics)
   175	        metrics["request_inflight"] = request_inflight
   176
   177	    for header_key, metric_key in header_map.items():
   178	        if metric_key not in metrics:
   179	            continue
   180	        value = metrics.get(metric_key)
   181	        serialized = _serialize_header_value(value)
   182	        if serialized is None:
   183	            continue
   184	        perf_headers[header_key] = serialized
   185	        detail_payload[metric_key] = value
   186
   187	    # Include additional timing fields (bounded set) in JSON payload only.
   188	    extra_detail_keys = (
   189	        "prompt_build_ms",
   190	        "sampling_params_ms",
   191	        "timeline_markers",
   192	        "timeline_prompt_ready_ms",
   193	        "timeline_sampling_ready_ms",
   194	        "timeline_llm_first_batch_ms",
   195	        "timeline_llm_done_ms",
   196	        "timeline_parse_done_ms",
   197	        "timeline_validation_done_ms",
   198	        "timeline_bicodec_done_ms",
   199	        "timeline_wav_done_ms",
   200	        "timeline_request_done_ms",
   201	        "timeline_to_first_batch_ms",
   202	        "timeline_first_batch_to_llm_done_ms",
   203	        "timeline_post_llm_ms",
   204	        "timeline_parse_to_bicodec_done_ms",
   205	        "timeline_bicodec_to_request_done_ms",
   206	        "timeline_total_ms",
   207	        "llm_prefill_wall_ms",
   208	        "llm_prefill_gpu_ms",
   209	        "llm_generation_wall_ms",
   210	        "llm_time_in_queue_ms",
   211	        "llm_scheduler_ms",
   212	        "llm_model_forward_ms",
   213	        "llm_model_execute_ms",
   214	        "llm_first_token_ms",
   215	        "llm_request_lifecycle_ms",
   216	        "llm_prompt_token_total",
   217	        "semantic_token_total",
   218	        "global_token_total",
   219	        "wav_pack_ms",
   220	        "pipeline_total_ms",
   221	        "token_validation_ms",
   222	    )
   223	    for key in extra_detail_keys:
   224	        if key in metrics:
   225	            detail_payload[key] = metrics[key]
   226
   227	    perf_headers["x-perf-details"] = json.dumps(
   228	        detail_payload,
   229	        separators=(",", ":"),
   230	        ensure_ascii=True,
   231	    )
   232	    return perf_headers
   233
   234
   235	def get_model_version() -> str:
   236	    """Return the current model version, or 'not_loaded' if not initialized."""
   237	    return _MODEL_VERSION or "not_loaded"
   238
   239
   240	def set_model_version(version: str) -> None:
   241	    """Set the model version after loading."""
   242	    global _MODEL_VERSION
   243	    _MODEL_VERSION = version
   244
   245
   246	def create_app():
   247	    """
   248	    Factory function to create FastAPI app.
   249
   250	    Import inside factory so local tooling can import this module without FastAPI installed.
   251	    """
   252	    from fastapi import FastAPI, HTTPException, Request
   253	    from fastapi.responses import JSONResponse, Response
   254
   255	    global _startup_time
   256	    _startup_time = time.time()
   257
   258	    app = FastAPI(
   259	        title="Veena3 TTS (Modal)",
   260	        version=_APP_VERSION,


Output:
Chunk ID: 8e1cdc
Wall time: 0.0506 seconds
Process exited with code 0
Original token count: 2291
Output:
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:395:        "ttfb_ms": 0,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:407:        audio_bytes, perf = await runtime.pipeline.generate_speech_profiled(
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:422:        metrics["ttfb_ms"] = metrics["generation_ms"]  # Non-streaming: TTFB = total time
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:557:        "ttfb_ms": 0,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:590:            audio_bytes, perf = await runtime.pipeline.generate_speech_profiled(
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:605:        metrics["ttfb_ms"] = metrics["generation_ms"]
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:690:        "ttfb_ms": 0,
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:717:                metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:765:                metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:119:    async def generate_speech_profiled(
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:153:        timeline_marks: List[Dict[str, float]] = [{"stage": "request_start", "t_ms": 0.0, "dt_ms": 0.0}]
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:155:        perf["timeline_markers"] = timeline_marks
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:156:        perf["timeline_request_start_ms"] = 0.0
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:163:            timeline_marks.append(
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:170:            perf[f"timeline_{stage}_ms"] = float(now_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:171:            perf[f"timeline_{stage}_delta_ms"] = float(dt_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:175:            if not timeline_marks or timeline_marks[-1].get("stage") != "request_done":
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:179:            perf["timeline_markers"] = list(timeline_marks)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:180:            perf["timeline_total_ms"] = float(perf.get("timeline_request_done_ms", 0.0))
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:182:            first_batch_ms = perf.get("timeline_llm_first_batch_ms")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:183:            llm_done_ms = perf.get("timeline_llm_done_ms")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:184:            parse_done_ms = perf.get("timeline_parse_done_ms")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:185:            bicodec_done_ms = perf.get("timeline_bicodec_done_ms")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:186:            request_done_ms = perf.get("timeline_request_done_ms")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:189:                perf["timeline_to_first_batch_ms"] = float(first_batch_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:191:                perf["timeline_first_batch_to_llm_done_ms"] = float(llm_done_ms - first_batch_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:193:                perf["timeline_post_llm_ms"] = float(request_done_ms - llm_done_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:195:                perf["timeline_parse_to_bicodec_done_ms"] = float(bicodec_done_ms - parse_done_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:197:                perf["timeline_bicodec_to_request_done_ms"] = float(request_done_ms - bicodec_done_ms)
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:230:            "perf_non_stream_start",
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:283:                first_batch_timeline_ms = _mark("llm_first_batch")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:290:                        "timeline_ms": round(first_batch_timeline_ms, 3),
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:323:        llm_done_timeline_ms = _mark("llm_done")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:330:                "timeline_ms": round(llm_done_timeline_ms, 3),
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:349:        parse_done_timeline_ms = _mark("parse_done")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:357:                "timeline_ms": round(parse_done_timeline_ms, 3),
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:455:        bicodec_done_timeline_ms = _mark("bicodec_done")
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:463:                "timeline_ms": round(bicodec_done_timeline_ms, 3),
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:481:            "perf_non_stream_done",
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:486:                "timeline_total_ms": round(float(perf.get("timeline_total_ms", 0.0)), 3),
/home/ubuntu/veenaModal/veena3modal/core/pipeline.py:520:        wav_bytes, _ = await self.generate_speech_profiled(
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:24:    log_request_received,
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:32:    record_request_received as metrics_request_received,
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:35:    record_ttfb,
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:134:        "x-timeline-first-batch-ms": "timeline_llm_first_batch_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:135:        "x-timeline-llm-done-ms": "timeline_llm_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:136:        "x-timeline-bicodec-done-ms": "timeline_bicodec_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:137:        "x-timeline-total-ms": "timeline_total_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:191:        "timeline_markers",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:192:        "timeline_prompt_ready_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:193:        "timeline_sampling_ready_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:194:        "timeline_llm_first_batch_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:195:        "timeline_llm_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:196:        "timeline_parse_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:197:        "timeline_validation_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:198:        "timeline_bicodec_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:199:        "timeline_wav_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:200:        "timeline_request_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:201:        "timeline_to_first_batch_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:202:        "timeline_first_batch_to_llm_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:203:        "timeline_post_llm_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:204:        "timeline_parse_to_bicodec_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:205:        "timeline_bicodec_to_request_done_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:206:        "timeline_total_ms",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:451:        log_request_received(
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:458:        metrics_request_received(speaker=speaker, stream=req.stream, format=req.format)
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:540:                    "X-TTFB-ms": str(metrics.get("ttfb_ms", 0)),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:565:                    ttfb_ms=metrics.get("ttfb_ms"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:585:                record_ttfb(ttfb_seconds=metrics.get("ttfb_ms", 0) / 1000, speaker=speaker, stream=False)
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:591:                    "request_perf_summary",
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:600:                        "timeline_llm_first_batch_ms": metrics.get("timeline_llm_first_batch_ms"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:601:                        "timeline_llm_done_ms": metrics.get("timeline_llm_done_ms"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:602:                        "timeline_bicodec_done_ms": metrics.get("timeline_bicodec_done_ms"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:603:                        "timeline_total_ms": metrics.get("timeline_total_ms"),
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:756:                        ttfb_ms = metrics.get("ttfb_ms", 0)
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:761:                            ttfb_ms=ttfb_ms,
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:764:                        record_ttfb(ttfb_seconds=ttfb_ms / 1000, speaker=speaker, stream=True)
/home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py:780:                            ttfb_ms=ttfb_ms,


**Planning to reduce logging overhead**


nl -ba /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py | sed -n '300,470p'


Output:
Chunk ID: 146a3e
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 1943
Output:
   300	        else:
   301	            status = "degraded"
   302
   303	        return JSONResponse(
   304	            content={
   305	                "status": status,
   306	                "model_loaded": model_loaded,
   307	                "model_version": model_version,
   308	                "uptime_seconds": round(uptime, 2),
   309	                "gpu_available": gpu_available,
   310	                "app_version": _APP_VERSION,
   311	            },
   312	            headers={
   313	                "X-Model-Version": model_version,
   314	                "X-App-Version": _APP_VERSION,
   315	            },
   316	        )
   317
   318	    @app.get("/v1/tts/metrics")
   319	    async def tts_metrics():
   320	        """
   321	        Prometheus metrics endpoint.
   322
   323	        Returns metrics in Prometheus text format for scraping.
   324	        """
   325	        return Response(
   326	            content=get_metrics_text(),
   327	            media_type=get_metrics_content_type(),
   328	        )
   329
   330	    @app.post("/v1/tts/generate")
   331	    async def tts_generate(request: Request):
   332	        """
   333	        Generate speech from text.
   334
   335	        Supports streaming (M4) and non-streaming (M3) modes.
   336	        Currently implements non-streaming WAV generation only.
   337	        """
   338	        from veena3modal.api.schemas import TTSGenerateRequest, AudioFormat
   339	        from veena3modal.services import tts_runtime
   340
   341	        request_id = str(uuid.uuid4())
   342	        start_time = time.time()
   343
   344	        # === AUTH CHECK ===
   345	        if FeatureFlags.is_auth_enabled():
   346	            # Extract API key from headers
   347	            headers_dict = dict(request.headers)
   348	            api_key = extract_api_key(headers_dict)
   349
   350	            if not api_key:
   351	                return JSONResponse(
   352	                    status_code=401,
   353	                    content=create_error_response(
   354	                        code=ErrorCode.INVALID_API_KEY,
   355	                        message="API key required. Use 'Authorization: Bearer <key>' or 'X-API-Key: <key>' header.",
   356	                        request_id=request_id,
   357	                    ),
   358	                    headers={"X-Request-ID": request_id},
   359	                )
   360
   361	            # Validate API key
   362	            validator = get_api_validator()
   363	            key_hash = hash_api_key(api_key)
   364	            auth_result = validator.validate(key_hash)
   365
   366	            if not auth_result.is_valid:
   367	                status_code = 401 if auth_result.error_code == "INVALID_API_KEY" else 403
   368	                return JSONResponse(
   369	                    status_code=status_code,
   370	                    content=create_error_response(
   371	                        code=ErrorCode(auth_result.error_code),
   372	                        message=auth_result.error_message or "Authentication failed",
   373	                        request_id=request_id,
   374	                    ),
   375	                    headers={"X-Request-ID": request_id},
   376	                )
   377
   378	            # === RATE LIMIT CHECK ===
   379	            if FeatureFlags.is_rate_limiting_enabled():
   380	                rate_limiter = get_rate_limiter()
   381	                allowed, remaining, reset_after = rate_limiter.check(key_hash)
   382
   383	                if not allowed:
   384	                    headers = rate_limiter.get_headers(allowed, remaining, reset_after)
   385	                    headers["X-Request-ID"] = request_id
   386	                    return JSONResponse(
   387	                        status_code=429,
   388	                        content=create_error_response(
   389	                            code=ErrorCode.RATE_LIMIT_EXCEEDED,
   390	                            message=f"Rate limit exceeded. Retry after {int(reset_after) + 1} seconds.",
   391	                            request_id=request_id,
   392	                        ),
   393	                        headers=headers,
   394	                    )
   395
   396	        # Parse and validate request body
   397	        try:
   398	            body = await request.json()
   399	        except Exception as e:
   400	            raise HTTPException(status_code=400, detail=f"Invalid JSON: {e}")
   401
   402	        try:
   403	            req = TTSGenerateRequest(**body)
   404	        except Exception as e:
   405	            error_msg = str(e)
   406	            # Extract validation error details
   407	            return JSONResponse(
   408	                status_code=400,
   409	                content={
   410	                    "error": {
   411	                        "code": "VALIDATION_ERROR",
   412	                        "message": error_msg,
   413	                        "request_id": request_id,
   414	                    }
   415	                },
   416	                headers={"X-Request-ID": request_id},
   417	            )
   418
   419	        # Check if runtime is initialized
   420	        if not tts_runtime.is_initialized():
   421	            return JSONResponse(
   422	                status_code=503,
   423	                content={
   424	                    "error": {
   425	                        "code": "MODEL_NOT_LOADED",
   426	                        "message": "TTS model not initialized. Please wait for warmup.",
   427	                        "request_id": request_id,
   428	                    }
   429	                },
   430	                headers={"X-Request-ID": request_id},
   431	            )
   432
   433	        # Get resolved speaker
   434	        speaker = req.get_resolved_speaker()
   435
   436	        # Get normalized text (apply text normalization if enabled)
   437	        normalizer_func = None
   438	        if req.normalize:
   439	            try:
   440	                from veena3modal.processing.text_normalizer import normalize_text
   441	                normalizer_func = lambda t: normalize_text(t, verbose=req.normalize_verbose)
   442	            except ImportError:
   443	                logger.warning("TextNormalizer not available, skipping text normalization")
   444
   445	        text = req.get_normalized_text(normalizer_func=normalizer_func)
   446
   447	        # Set request context for structured logging
   448	        set_request_context(request_id=request_id)
   449
   450	        # Log request received (lifecycle event + metrics)
   451	        log_request_received(
   452	            request_id=request_id,
   453	            text_length=len(text),
   454	            speaker=speaker,
   455	            stream=req.stream,
   456	            format=req.format,
   457	        )
   458	        metrics_request_received(speaker=speaker, stream=req.stream, format=req.format)
   459
   460	        # Streaming mode (M4)
   461	        if req.stream:
   462	            return await _handle_streaming_request(req, request_id, text, speaker, start_time)
   463
   464	        # Format encoding will be applied after generation (non-streaming only)
   465	        # For streaming, only WAV is supported
   466	        target_format = req.format
   467	        inflight_current = _inflight_enter()
   468	        try:
   469	            try:
   470	                # Get output sample rate - use_enum_values=True means req.output is already a string


Summary: auto


**Designing detailed request timing metrics**


nl -ba /home/ubuntu/veenaModal/veena3modal/core/pipeline.py | sed -n '1,140p'


Output:
Chunk ID: 13b2ec
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 1278
Output:
     1	"""
     2	Spark TTS Generation Pipeline
     3
     4	End-to-end pipeline for TTS generation with BiCodec audio tokenizer.
     5	Migrated from Veena3/Orpheus (SNAC) to Spark TTS (BiCodec).
     6	"""
     7
     8	import logging
     9	import re
    10	import time
    11	import uuid
    12	from typing import Optional, List, Dict, Any, Tuple
    13	from vllm import SamplingParams
    14	import torch
    15
    16	logger = logging.getLogger(__name__)
    17
    18	from veena3modal.core.constants import (
    19	    TRAINING_STOP_TOKEN_IDS,
    20	    DEFAULT_TEMPERATURE,
    21	    DEFAULT_TOP_K,
    22	    DEFAULT_TOP_P,
    23	    DEFAULT_MAX_TOKENS,
    24	    DEFAULT_SEED,
    25	    AUDIO_SAMPLE_RATE,
    26	)
    27
    28
    29	class SparkTTSPipeline:
    30	    """
    31	    End-to-end TTS pipeline for Spark TTS with BiCodec.
    32
    33	    Replaces SNAC-based token extraction with BiCodec regex-based extraction.
    34	    """
    35
    36	    def __init__(
    37	        self,
    38	        model,
    39	        prompt_builder,
    40	        bicodec_decoder,
    41	    ):
    42	        """
    43	        Initialize pipeline.
    44
    45	        Args:
    46	            model: SparkTTS Model instance
    47	            prompt_builder: IndicPromptBuilder instance (with Spark TTS format)
    48	            bicodec_decoder: BiCodecDecoder instance
    49	        """
    50	        self.model = model
    51	        self.prompt_builder = prompt_builder
    52	        self.bicodec_decoder = bicodec_decoder
    53
    54	        print(f"🚀 SparkTTSPipeline initialized")
    55
    56	    @staticmethod
    57	    def _p50(values: List[float]) -> float:
    58	        """Compute a stable p50 for small lists."""
    59	        if not values:
    60	            return 0.0
    61	        ordered = sorted(values)
    62	        return ordered[(len(ordered) - 1) // 2]
    63
    64	    @staticmethod
    65	    def _to_number(value: Any) -> Optional[float]:
    66	        """Best-effort conversion of scalar values to float."""
    67	        if value is None:
    68	            return None
    69	        if isinstance(value, (int, float)):
    70	            return float(value)
    71	        try:
    72	            return float(value)
    73	        except (TypeError, ValueError):
    74	            return None
    75
    76	    def _extract_request_metrics(self, raw_metrics: Any) -> Dict[str, float]:
    77	        """
    78	        Convert vLLM request_output.metrics into a numeric dictionary.
    79
    80	        vLLM may return a dataclass-like object or a plain dict depending on version.
    81	        """
    82	        if raw_metrics is None:
    83	            return {}
    84
    85	        numeric: Dict[str, float] = {}
    86	        if isinstance(raw_metrics, dict):
    87	            items = raw_metrics.items()
    88	        else:
    89	            names = (
    90	                "arrival_time",
    91	                "first_scheduled_time",
    92	                "first_token_time",
    93	                "finished_time",
    94	                "time_in_queue",
    95	                "scheduler_time",
    96	                "model_forward_time",
    97	                "model_execute_time",
    98	            )
    99	            items = []
   100	            for name in names:
   101	                if hasattr(raw_metrics, name):
   102	                    items.append((name, getattr(raw_metrics, name)))
   103
   104	        for key, value in items:
   105	            parsed = self._to_number(value)
   106	            if parsed is not None:
   107	                numeric[str(key)] = parsed
   108	        return numeric
   109
   110	    def _apply_stats(self, perf: Dict[str, Any], prefix: str, values: List[float]) -> None:
   111	        """Populate total/min/max/p50 stats for a list of ms samples."""
   112	        if not values:
   113	            return
   114	        perf[f"{prefix}_total"] = float(sum(values))
   115	        perf[f"{prefix}_min"] = float(min(values))
   116	        perf[f"{prefix}_max"] = float(max(values))
   117	        perf[f"{prefix}_p50"] = float(self._p50(values))
   118
   119	    async def generate_speech_profiled(
   120	        self,
   121	        speaker: str,
   122	        text: str,
   123	        temperature: float = DEFAULT_TEMPERATURE,
   124	        top_k: int = DEFAULT_TOP_K,
   125	        top_p: float = DEFAULT_TOP_P,
   126	        max_tokens: int = DEFAULT_MAX_TOKENS,
   127	        repetition_penalty: float = 1.05,
   128	        seed: Optional[int] = None,
   129	    ) -> Tuple[Optional[bytes], Dict[str, Any]]:
   130	        """
   131	        Generate speech with detailed per-request timing metrics.
   132
   133	        Returns:
   134	            (wav_bytes, perf_dict)
   135	        """
   136	        from veena3modal.audio.utils import add_wav_header
   137
   138	        perf: Dict[str, Any] = {
   139	            "llm_prompt_token_total": 0,
   140	            "llm_token_total": 0,


**Planning conditional perf logging**


rg -n "SR check|logger\.info\(|generate_speech_chunked|LongTextProcessor initialized|Applying super-resolution" /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py /home/ubuntu/veenaModal/veena3modal/processing/long_text_processor.py