Output:
Chunk ID: 801551
Wall time: 3.1338 seconds
Process exited with code 1
Original token count: 4182
Output:
(EngineCore_DP0 pid=3697835) INFO 02-17 00:13:45 [parallel_state.py:1212] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://216.81.248.184:44727 backend=nccl
(EngineCore_DP0 pid=3697835) INFO 02-17 00:13:45 [parallel_state.py:1423] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946] EngineCore failed to start.
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946] Traceback (most recent call last):
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 937, in run_engine_core
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 691, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     super().__init__(
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 105, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self.model_executor = executor_class(vllm_config)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 101, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self._init_executor()
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 47, in _init_executor
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self.driver_worker.init_device()
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 326, in init_device
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self.worker.init_device()  # type: ignore
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 258, in init_device
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     from vllm.v1.worker.gpu_model_runner import (
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 153, in <module>
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     from vllm.v1.spec_decode.ngram_proposer import NgramProposer
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/spec_decode/ngram_proposer.py", line 170, in <module>
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     def batch_propose_numba(
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/decorators.py", line 221, in wrapper
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     disp = dispatcher(py_func=func, locals=locals,
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/dispatcher.py", line 772, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self.targetctx = self.targetdescr.target_context
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/registry.py", line 30, in target_context
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     return self._toplevel_target_context
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/utils.py", line 741, in __get__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     return super().__get__(*args, **kwargs)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/usr/lib/python3.10/functools.py", line 981, in __get__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     val = self.func(instance)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/registry.py", line 18, in _toplevel_target_context
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     return cpu.CPUContext(self.typing_context, self._target_name)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/cpu.py", line 42, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     super().__init__(typingctx, target)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/base.py", line 254, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self.init()
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     return func(*args, **kwargs)
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/cpu.py", line 51, in init
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     self._internal_codegen = codegen.JITCPUCodegen("numba.exec")
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/codegen.py", line 1172, in __init__
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     initialize_llvm()
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/codegen.py", line 1434, in initialize_llvm
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     ll.initialize()
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]   File "/home/ubuntu/.local/lib/python3.10/site-packages/llvmlite/binding/initfini.py", line 17, in initialize
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946]     raise RuntimeError(
(EngineCore_DP0 pid=3697835) ERROR 02-17 00:13:46 [core.py:946] RuntimeError: llvmlite.binding.initialize() is deprecated and will be removed. LLVM initialization is now handled automatically. Please remove calls to this function from your code and check for other behavioral changes that may have occurred due to LLVM updates.
(EngineCore_DP0 pid=3697835) Process EngineCore_DP0:
(EngineCore_DP0 pid=3697835) Traceback (most recent call last):
(EngineCore_DP0 pid=3697835)   File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore_DP0 pid=3697835)     self.run()
(EngineCore_DP0 pid=3697835)   File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
(EngineCore_DP0 pid=3697835)     self._target(*self._args, **self._kwargs)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 950, in run_engine_core
(EngineCore_DP0 pid=3697835)     raise e
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 937, in run_engine_core
(EngineCore_DP0 pid=3697835)     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 691, in __init__
(EngineCore_DP0 pid=3697835)     super().__init__(
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 105, in __init__
(EngineCore_DP0 pid=3697835)     self.model_executor = executor_class(vllm_config)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/executor/abstract.py", line 101, in __init__
(EngineCore_DP0 pid=3697835)     self._init_executor()
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/executor/uniproc_executor.py", line 47, in _init_executor
(EngineCore_DP0 pid=3697835)     self.driver_worker.init_device()
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/worker_base.py", line 326, in init_device
(EngineCore_DP0 pid=3697835)     self.worker.init_device()  # type: ignore
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 258, in init_device
(EngineCore_DP0 pid=3697835)     from vllm.v1.worker.gpu_model_runner import (
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 153, in <module>
(EngineCore_DP0 pid=3697835)     from vllm.v1.spec_decode.ngram_proposer import NgramProposer
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/spec_decode/ngram_proposer.py", line 170, in <module>
(EngineCore_DP0 pid=3697835)     def batch_propose_numba(
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/decorators.py", line 221, in wrapper
(EngineCore_DP0 pid=3697835)     disp = dispatcher(py_func=func, locals=locals,
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/dispatcher.py", line 772, in __init__
(EngineCore_DP0 pid=3697835)     self.targetctx = self.targetdescr.target_context
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/registry.py", line 30, in target_context
(EngineCore_DP0 pid=3697835)     return self._toplevel_target_context
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/utils.py", line 741, in __get__
(EngineCore_DP0 pid=3697835)     return super().__get__(*args, **kwargs)
(EngineCore_DP0 pid=3697835)   File "/usr/lib/python3.10/functools.py", line 981, in __get__
(EngineCore_DP0 pid=3697835)     val = self.func(instance)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/registry.py", line 18, in _toplevel_target_context
(EngineCore_DP0 pid=3697835)     return cpu.CPUContext(self.typing_context, self._target_name)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/cpu.py", line 42, in __init__
(EngineCore_DP0 pid=3697835)     super().__init__(typingctx, target)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/base.py", line 254, in __init__
(EngineCore_DP0 pid=3697835)     self.init()
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
(EngineCore_DP0 pid=3697835)     return func(*args, **kwargs)
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/cpu.py", line 51, in init
(EngineCore_DP0 pid=3697835)     self._internal_codegen = codegen.JITCPUCodegen("numba.exec")
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/codegen.py", line 1172, in __init__
(EngineCore_DP0 pid=3697835)     initialize_llvm()
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/numba/core/codegen.py", line 1434, in initialize_llvm
(EngineCore_DP0 pid=3697835)     ll.initialize()
(EngineCore_DP0 pid=3697835)   File "/home/ubuntu/.local/lib/python3.10/site-packages/llvmlite/binding/initfini.py", line 17, in initialize
(EngineCore_DP0 pid=3697835)     raise RuntimeError(
(EngineCore_DP0 pid=3697835) RuntimeError: llvmlite.binding.initialize() is deprecated and will be removed. LLVM initialization is now handled automatically. Please remove calls to this function from your code and check for other behavioral changes that may have occurred due to LLVM updates.
2026-02-17 00:13:46 | ERROR    | veena3modal.services.tts_runtime | ❌ Failed to initialize TTS runtime: Engine core initialization failed. See root cause above. Failed core proc(s): {}
Traceback (most recent call last):
  File "/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py", line 167, in initialize_runtime
    model = SparkTTSModel(
  File "/home/ubuntu/veenaModal/veena3modal/core/model_loader.py", line 128, in __init__
    self.engine = AsyncLLMEngine.from_engine_args(engine_args)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 257, in from_engine_args
    return cls(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 155, in __init__
    self.engine_core = EngineCoreClient.make_async_mp_client(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 122, in make_async_mp_client
    return AsyncMPClient(*client_args)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 819, in __init__
    super().__init__(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 479, in __init__
    with launch_core_engines(vllm_config, executor_class, log_stats) as (
  File "/usr/lib/python3.10/contextlib.py", line 142, in __exit__
    next(self.gen)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 933, in launch_core_engines
    wait_for_engine_startup(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 992, in wait_for_engine_startup
    raise RuntimeError(
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}
Traceback (most recent call last):
  File "/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py", line 167, in initialize_runtime
    model = SparkTTSModel(
  File "/home/ubuntu/veenaModal/veena3modal/core/model_loader.py", line 128, in __init__
    self.engine = AsyncLLMEngine.from_engine_args(engine_args)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 257, in from_engine_args
    return cls(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py", line 155, in __init__
    self.engine_core = EngineCoreClient.make_async_mp_client(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 122, in make_async_mp_client
    return AsyncMPClient(*client_args)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 819, in __init__
    super().__init__(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 479, in __init__
    with launch_core_engines(vllm_config, executor_class, log_stats) as (
  File "/usr/lib/python3.10/contextlib.py", line 142, in __exit__
    next(self.gen)
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 933, in launch_core_engines
    wait_for_engine_startup(
  File "/home/ubuntu/.local/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 992, in wait_for_engine_startup
    raise RuntimeError(
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/home/ubuntu/veenaModal/veena3modal/local_server.py", line 465, in <module>
    main()
  File "/home/ubuntu/veenaModal/veena3modal/local_server.py", line 426, in main
    initialize_local_runtime(
  File "/home/ubuntu/veenaModal/veena3modal/local_server.py", line 224, in initialize_local_runtime
    runtime = initialize_runtime(
  File "/home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py", line 272, in initialize_runtime
    raise RuntimeError(f"TTS runtime initialization failed: {e}") from e
RuntimeError: TTS runtime initialization failed: Engine core initialization failed. See root cause above. Failed core proc(s): {}


# 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.


python3 - <<'PY'
import llvmlite, numba
print(numba.__version__)
print(llvmlite.__version__)
print(llvmlite.binding)
PY


Output:
Chunk ID: 488aa3
Wall time: 0.3224 seconds
Process exited with code 0
Original token count: 32
Output:
0.61.2
0.46.0
<module 'llvmlite.binding' from '/home/ubuntu/.local/lib/python3.10/site-packages/llvmlite/binding/__init__.py'>


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

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

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

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

Warning: apply_patch was requested via exec_command. Use the apply_patch tool instead of exec_command.

<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>


Summary: auto


I’ll verify the current timing/stress harness state, then run a focused benchmark pass if the local server is runnable; if startup is still blocked by dependency compatibility, I’ll apply the minimal environment fix and re-run. I’ll then share the raw timing outputs and bottleneck indicators.


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


Output:
Chunk ID: 669499
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 2236
Output:
"""
FastAPI ASGI app factory for Modal deployment.

Phase 1: expose `/v1/tts/generate` and `/v1/tts/health` while reusing existing
Veena3 inference/streaming code from the Django project.

Phase 2+: extract shared code into `veena3modal/shared/` and remove Django coupling.
"""

# NOTE: Intentionally NOT using `from __future__ import annotations` here
# to ensure FastAPI recognizes Request type annotations properly

import time
import uuid
import json
from typing import Dict, Optional

# Structured logging (JSON formatted, request-scoped)
from veena3modal.shared.logging import (
    get_logger,
    set_request_context,
    clear_request_context,
    log_request_received,
    log_first_audio_emitted,
    log_request_completed,
    log_request_failed,
)

# Prometheus metrics
from veena3modal.shared.metrics import (
    record_request_received as metrics_request_received,
    record_request_completed as metrics_request_completed,
    record_request_failed as metrics_request_failed,
    record_ttfb,
    record_rtf,
    record_audio_duration,
    record_chunks_sent,
    get_metrics_text,
    get_content_type as get_metrics_content_type,
)

# Auth and rate limiting
from veena3modal.api.auth import (
    get_api_validator,
    extract_api_key,
    hash_api_key,
)
from veena3modal.api.rate_limiter import get_rate_limiter
from veena3modal.api.error_handlers import (
    ErrorCode,
    create_error_response,
    get_error_status,
    is_gpu_fault,
    handle_gpu_fault,
    FeatureFlags,
)

logger = get_logger(__name__)

# Sentence storage (fire-and-forget, non-blocking)
# Imported at module level for singleton access
_sentence_store = None

def _get_sentence_store():
    """Lazy load sentence store singleton."""
    global _sentence_store
    if _sentence_store is None:
        from veena3modal.services.sentence_store import get_sentence_store
        _sentence_store = get_sentence_store()
    return _sentence_store

def _build_perf_headers(metrics: Dict[str, object]) -> Dict[str, str]:
    """Build compact timing headers for benchmark tooling."""
    perf: Dict[str, str] = {}
    if not metrics:
        return perf

    def _pick(*keys: str):
        for key in keys:
            if key in metrics and metrics[key] is not None:
                return metrics[key]
        return None

    def _add(name: str, value: object):
        if value is None:
            return
        if isinstance(value, bool):
            perf[name] = str(value).lower()
        else:
            perf[name] = str(value)

    llm_token_total = _pick("llm_token_total", "token_total")
    llm_batch_count = _pick("llm_batch_count", "batch_count")
    generation_ms = _pick("generation_ms") or _pick("total_ms")
    llm_batch_wall_total = _pick("llm_batch_wall_ms_total", "llm_wall_ms", "total_ms")
    llm_batch_gpu_total = _pick("llm_batch_gpu_ms_total", "llm_gpu_batch_ms_total")
    llm_decode_wall_total = _pick("llm_decode_wall_ms_total")
    llm_decode_gpu_total = _pick("llm_decode_gpu_ms_total")
    llm_parse_ms = _pick("llm_parse_ms")
    decode_cpu_ms = _pick("llm_decode_cpu_ms")
    parse_ms = _pick("parse_ms")

    _add("X-LLM-Token-Total", llm_token_total)
    _add("X-LLM-Batch-Count", llm_batch_count)
    _add("X-LLM-Batch-Wall-Ms", llm_batch_wall_total)
    _add("X-LLM-Batch-GPU-Ms", llm_batch_gpu_total)
    _add("X-LLM-Batch-Wall-Ms-Min", _pick("llm_batch_wall_ms_min"))
    _add("X-LLM-Batch-Wall-Ms-Max", _pick("llm_batch_wall_ms_max"))
    _add("X-LLM-Batch-Wall-Ms-P50", _pick("llm_batch_wall_ms_p50"))
    _add("X-LLM-Batch-GPU-Ms-Min", _pick("llm_batch_gpu_ms_min"))
    _add("X-LLM-Batch-GPU-Ms-Max", _pick("llm_batch_gpu_ms_max"))
    _add("X-LLM-Batch-GPU-Ms-P50", _pick("llm_batch_gpu_ms_p50"))
    _add("X-LLM-Decode-Wall-Ms", llm_decode_wall_total)
    _add("X-LLM-Decode-GPU-Ms", llm_decode_gpu_total)
    _add("X-LLM-Decode-Wall-Ms-Min", _pick("llm_decode_wall_ms_min"))
    _add("X-LLM-Decode-Wall-Ms-Max", _pick("llm_decode_wall_ms_max"))
    _add("X-LLM-Decode-Wall-Ms-P50", _pick("llm_decode_wall_ms_p50"))
    _add("X-LLM-Decode-GPU-Ms-Min", _pick("llm_decode_gpu_ms_min"))
    _add("X-LLM-Decode-GPU-Ms-Max", _pick("llm_decode_gpu_ms_max"))
    _add("X-LLM-Decode-GPU-Ms-P50", _pick("llm_decode_gpu_ms_p50"))
    _add("X-LLM-Decode-Calls", _pick("decode_calls"))
    _add("X-LLM-Decode-CPU-Ms", decode_cpu_ms)
    _add("X-LLM-Parse-Ms", llm_parse_ms or parse_ms)
    _add("X-LLM-Parse-Avg-Ms", _pick("llm_parse_ms_avg"))
    _add("X-LLM-Tokens-Per-Batch-Min", _pick("tokens_per_batch_min"))
    _add("X-LLM-Tokens-Per-Batch-Max", _pick("tokens_per_batch_max"))
    _add("X-LLM-Tokens-Per-Batch-P50", _pick("tokens_per_batch_p50"))
    _add("X-Generation-Ms", generation_ms)
    _add("X-LLM-Generation-Ms", _pick("llm_wall_ms", generation_ms))
    _add("X-Request-Inflight", _pick("request_inflight", "active_requests"))
    _add("X-LLM-Text-Chunked", _pick("text_chunked"))
    _add("X-LLM-Chunks-Processed", _pick("chunks_processed"))
    _add("X-LLM-SR-Applied", _pick("sr_applied"))

    if llm_token_total and generation_ms:
        try:
            perf["X-LLM-Time-Per-Token-Ms"] = f"{float(generation_ms) / float(llm_token_total):.3f}"
        except Exception:
            pass

    if llm_batch_count:
        try:
            perf["X-LLM-Time-Per-Batch-Wall-Ms"] = f"{float(generation_ms or 0) / float(llm_batch_count):.3f}"
        except Exception:
            pass
        try:
            perf["X-LLM-Time-Per-Batch-GPU-Ms"] = f"{float(llm_batch_gpu_total or 0) / float(llm_batch_count):.3f}"
        except Exception:
            pass

    # Include compact JSON snapshot for downstream benchmark tooling.
    detail_keys = [
        "request_id",
        "speaker",
        "text_length",
        "llm_token_total",
        "llm_batch_count",
        "llm_batch_wall_ms_total",
        "llm_batch_gpu_ms_total",
        "llm_batch_wall_ms_min",
        "llm_batch_wall_ms_max",
        "llm_batch_wall_ms_p50",
        "llm_batch_gpu_ms_min",
        "llm_batch_gpu_ms_max",
        "llm_batch_gpu_ms_p50",
        "llm_decode_cpu_ms",
        "llm_decode_wall_ms_total",
        "llm_decode_gpu_ms_total",
        "llm_decode_wall_ms_min",
        "llm_decode_wall_ms_max",
        "llm_decode_wall_ms_p50",
        "llm_decode_gpu_ms_min",
        "llm_decode_gpu_ms_max",
        "llm_decode_gpu_ms_p50",
        "decode_calls",
        "decode_cpu_ms",
        "parse_ms",
        "llm_parse_ms",
        "llm_parse_ms_avg",
        "llm_batch_tokens",
        "tokens_per_batch_min",
        "tokens_per_batch_max",
        "tokens_per_batch_p50",
        "generation_ms",
        "audio_duration_seconds",
        "request_inflight",
        "active_requests",
        "chunks_processed",
        "text_chunked",
        "sr_applied",
    ]
    perf["X-Perf-Details"] = json.dumps(
        {k: metrics[k] for k in detail_keys if k in metrics},
        separators=(",", ":"),
    )

    return perf

# Version info (will be set dynamically after model load)
_APP_VERSION = "0.1.0"
_MODEL_VERSION: Optional[str] = None
_startup_time: Optional[float] = None

def get_model_version() -> str:
    """Return the current model version, or 'not_loaded' if not initialized."""
    return _MODEL_VERSION or "not_loaded"

def set_model_version(version: str) -> None:
    """Set the model version after loading."""
    global _MODEL_VERSION
    _MODEL_VERSION = version

def create_app():
    """
    Factory function to create FastAPI app.

    Import inside factory so local tooling can import this module without FastAPI installed.
    """
    from fastapi import FastAPI, HTTPException, Request
    from fastapi.responses import JSONResponse, Response

    global _startup_time
    _startup_time = time.time()

    app = FastAPI(
        title="Veena3 TTS (Modal)",
        version=_APP_VERSION,
        description="High-quality multilingual Text-to-Speech API with true streaming support.",
    )

    @app.get("/v1/tts/health")
    async def tts_health():
        """
        Health check endpoint.

        Returns:
            - status: "healthy" | "degraded" | "unhealthy"
            - model_loaded: whether TTS model is initialized
            - model_version: version string of loaded model
            - uptime_seconds: time since app startup
            - gpu_available: whether GPU is detected (best-effort check)
        """
        # Check model status via runtime
        from veena3modal.services import tts_runtime
        model_loaded = tts_runtime.is_initialized()

        # Use runtime's model version if available
        runtime = tts_runtime.get_runtime()
        model_version = runtime.model_version if runtime else get_model_version()

        # Check GPU availability (best-effort, non-blocking)
        gpu_available = False
        try:
            import torch
            gpu_available = torch.cuda.is_available()


rg -n "def tts_generate|_handle_streaming_request|_build_perf_headers|record_request_completed|perf" /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py


sed -n '260,520p' /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py


Output:
Chunk ID: c4437f
Wall time: 0.0507 seconds
Process exited with code 0
Original token count: 251
Output:
32:    record_request_completed as metrics_request_completed,
74:def _build_perf_headers(metrics: Dict[str, object]) -> Dict[str, str]:
76:    perf: Dict[str, str] = {}
78:        return perf
90:            perf[name] = str(value).lower()
92:            perf[name] = str(value)
139:            perf["X-LLM-Time-Per-Token-Ms"] = f"{float(generation_ms) / float(llm_token_total):.3f}"
145:            perf["X-LLM-Time-Per-Batch-Wall-Ms"] = f"{float(generation_ms or 0) / float(llm_batch_count):.3f}"
149:            perf["X-LLM-Time-Per-Batch-GPU-Ms"] = f"{float(llm_batch_gpu_total or 0) / float(llm_batch_count):.3f}"
194:    perf["X-Perf-Details"] = json.dumps(
199:    return perf
303:    async def tts_generate(request: Request):
434:            return await _handle_streaming_request(req, request_id, text, speaker, start_time)
511:            headers.update(_build_perf_headers(metrics))
633:    async def _handle_streaming_request(req, request_id: str, text: str, speaker: str, start_time: float):


Output:
Chunk ID: aa31fa
Wall time: 0.0505 seconds
Process exited with code 0
Original token count: 2597
Output:
            gpu_available = torch.cuda.is_available()
        except ImportError:
            pass  # torch not installed, skip GPU check

        # Compute uptime
        uptime = time.time() - _startup_time if _startup_time else 0

        # Determine overall status
        if model_loaded and gpu_available:
            status = "healthy"
        elif model_loaded or gpu_available:
            status = "degraded"
        else:
            status = "degraded"

        return JSONResponse(
            content={
                "status": status,
                "model_loaded": model_loaded,
                "model_version": model_version,
                "uptime_seconds": round(uptime, 2),
                "gpu_available": gpu_available,
                "app_version": _APP_VERSION,
            },
            headers={
                "X-Model-Version": model_version,
                "X-App-Version": _APP_VERSION,
            },
        )

    @app.get("/v1/tts/metrics")
    async def tts_metrics():
        """
        Prometheus metrics endpoint.

        Returns metrics in Prometheus text format for scraping.
        """
        return Response(
            content=get_metrics_text(),
            media_type=get_metrics_content_type(),
        )

    @app.post("/v1/tts/generate")
    async def tts_generate(request: Request):
        """
        Generate speech from text.

        Supports streaming (M4) and non-streaming (M3) modes.
        Currently implements non-streaming WAV generation only.
        """
        from veena3modal.api.schemas import TTSGenerateRequest, AudioFormat
        from veena3modal.services import tts_runtime

        request_id = str(uuid.uuid4())
        start_time = time.time()

        # === AUTH CHECK ===
        if FeatureFlags.is_auth_enabled():
            # Extract API key from headers
            headers_dict = dict(request.headers)
            api_key = extract_api_key(headers_dict)

            if not api_key:
                return JSONResponse(
                    status_code=401,
                    content=create_error_response(
                        code=ErrorCode.INVALID_API_KEY,
                        message="API key required. Use 'Authorization: Bearer <key>' or 'X-API-Key: <key>' header.",
                        request_id=request_id,
                    ),
                    headers={"X-Request-ID": request_id},
                )

            # Validate API key
            validator = get_api_validator()
            key_hash = hash_api_key(api_key)
            auth_result = validator.validate(key_hash)

            if not auth_result.is_valid:
                status_code = 401 if auth_result.error_code == "INVALID_API_KEY" else 403
                return JSONResponse(
                    status_code=status_code,
                    content=create_error_response(
                        code=ErrorCode(auth_result.error_code),
                        message=auth_result.error_message or "Authentication failed",
                        request_id=request_id,
                    ),
                    headers={"X-Request-ID": request_id},
                )

            # === RATE LIMIT CHECK ===
            if FeatureFlags.is_rate_limiting_enabled():
                rate_limiter = get_rate_limiter()
                allowed, remaining, reset_after = rate_limiter.check(key_hash)

                if not allowed:
                    headers = rate_limiter.get_headers(allowed, remaining, reset_after)
                    headers["X-Request-ID"] = request_id
                    return JSONResponse(
                        status_code=429,
                        content=create_error_response(
                            code=ErrorCode.RATE_LIMIT_EXCEEDED,
                            message=f"Rate limit exceeded. Retry after {int(reset_after) + 1} seconds.",
                            request_id=request_id,
                        ),
                        headers=headers,
                    )

        # Parse and validate request body
        try:
            body = await request.json()
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"Invalid JSON: {e}")

        try:
            req = TTSGenerateRequest(**body)
        except Exception as e:
            error_msg = str(e)
            # Extract validation error details
            return JSONResponse(
                status_code=400,
                content={
                    "error": {
                        "code": "VALIDATION_ERROR",
                        "message": error_msg,
                        "request_id": request_id,
                    }
                },
                headers={"X-Request-ID": request_id},
            )

        # Check if runtime is initialized
        if not tts_runtime.is_initialized():
            return JSONResponse(
                status_code=503,
                content={
                    "error": {
                        "code": "MODEL_NOT_LOADED",
                        "message": "TTS model not initialized. Please wait for warmup.",
                        "request_id": request_id,
                    }
                },
                headers={"X-Request-ID": request_id},
            )

        # Get resolved speaker
        speaker = req.get_resolved_speaker()

        # Get normalized text (apply text normalization if enabled)
        normalizer_func = None
        if req.normalize:
            try:
                from veena3modal.processing.text_normalizer import normalize_text
                normalizer_func = lambda t: normalize_text(t, verbose=req.normalize_verbose)
            except ImportError:
                logger.warning("TextNormalizer not available, skipping text normalization")

        text = req.get_normalized_text(normalizer_func=normalizer_func)

        # Set request context for structured logging
        set_request_context(request_id=request_id)

        # Log request received (lifecycle event + metrics)
        log_request_received(
            request_id=request_id,
            text_length=len(text),
            speaker=speaker,
            stream=req.stream,
            format=req.format,
        )
        metrics_request_received(speaker=speaker, stream=req.stream, format=req.format)

        # Streaming mode (M4)
        if req.stream:
            return await _handle_streaming_request(req, request_id, text, speaker, start_time)

        # Format encoding will be applied after generation (non-streaming only)
        # For streaming, only WAV is supported
        target_format = req.format

        try:
            # Get output sample rate - use_enum_values=True means req.output is already a string
            output_sr = req.output if hasattr(req, 'output') else "16khz"

            # Generate speech (non-streaming)
            if req.chunking:
                audio_bytes, metrics = await tts_runtime.generate_speech_chunked(
                    text=text,
                    speaker=speaker,
                    temperature=req.temperature,
                    top_k=req.top_k,
                    top_p=req.top_p,
                    max_tokens=req.max_tokens,
                    repetition_penalty=req.repetition_penalty,
                    seed=req.seed,
                    sample_rate=req.sample_rate,
                    output_sample_rate=output_sr,
                    request_id=request_id,
                )
            else:
                audio_bytes, metrics = await tts_runtime.generate_speech(
                    text=text,
                    speaker=speaker,
                    temperature=req.temperature,
                    top_k=req.top_k,
                    top_p=req.top_p,
                    max_tokens=req.max_tokens,
                    repetition_penalty=req.repetition_penalty,
                    seed=req.seed,
                    output_sample_rate=output_sr,
                    request_id=request_id,
                )

            if audio_bytes is None:
                return JSONResponse(
                    status_code=500,
                    content={
                        "error": {
                            "code": "GENERATION_FAILED",
                            "message": "Audio generation failed. Check logs for details.",
                            "request_id": request_id,
                        }
                    },
                    headers={"X-Request-ID": request_id},
                )

            # Calculate RTF
            total_time = time.time() - start_time
            audio_duration = metrics.get("audio_duration_seconds", 0)
            rtf = total_time / audio_duration if audio_duration > 0 else 0

            # Build response headers
            runtime = tts_runtime.get_runtime()
            model_version = runtime.model_version if runtime else get_model_version()

            # Use actual output sample rate from metrics
            output_sample_rate = metrics.get("output_sample_rate", req.sample_rate)

            headers = {
                "X-Request-ID": request_id,
                "X-Model-Version": model_version,
                "X-Format": "wav",
                "X-Sample-Rate": str(output_sample_rate),
                "X-Stream": "false",
                "X-Audio-Bytes": str(len(audio_bytes)),
                "X-Audio-Seconds": f"{audio_duration:.2f}",
                "X-TTFB-ms": str(metrics.get("ttfb_ms", 0)),
                "X-RTF": f"{rtf:.3f}",
                "X-Text-Chunked": str(metrics.get("text_chunked", False)).lower(),
                "X-SR-Applied": str(metrics.get("sr_applied", False)).lower(),
            }
            headers.update(_build_perf_headers(metrics))

            if req.seed is not None:
                headers["X-Seed"] = str(req.seed)

            # Fire-and-forget sentence storage (non-blocking, after generation)
            sentence_store = _get_sentence_store()
            sentence_store.store_fire_and_forget(
                request_id=request_id,
                text=text,


I confirmed timing header plumbing is already present in the API response path. Next I’ll validate the local benchmark script and then run end-to-end benchmarks to capture concurrency behavior and per-stage metrics.


sed -n '520,760p' /home/ubuntu/veenaModal/veena3modal/api/fastapi_app.py


Output:
Chunk ID: bea689
Wall time: 0.0524 seconds
Process exited with code 0
Original token count: 2622
Output:
                text=text,
                speaker=speaker,
                stream=False,
                format=req.format,
                temperature=req.temperature,
                top_k=req.top_k,
                top_p=req.top_p,
                max_tokens=req.max_tokens,
                repetition_penalty=req.repetition_penalty,
                seed=req.seed,
                text_chunked=metrics.get("text_chunked", False),
                ttfb_ms=metrics.get("ttfb_ms"),
                audio_duration_seconds=audio_duration,
            )

            # Log successful completion (lifecycle event + metrics)
            total_duration_ms = int(total_time * 1000)
            log_request_completed(
                request_id=request_id,
                status_code=200,
                total_duration_ms=total_duration_ms,
                audio_duration_seconds=audio_duration,
                rtf=rtf,
                chunks_sent=1,
                **metrics,
            )
            metrics_request_completed(
                status_code=200,
                duration_seconds=total_time,
                speaker=speaker,
                stream=False,
            )
            record_ttfb(ttfb_seconds=metrics.get("ttfb_ms", 0) / 1000, speaker=speaker, stream=False)
            record_rtf(rtf=rtf, speaker=speaker)
            record_audio_duration(duration_seconds=audio_duration, speaker=speaker)

            clear_request_context()

            # Apply format encoding if not WAV
            media_type = "audio/wav"
            if target_format != AudioFormat.WAV.value:
                try:
                    from veena3modal.audio.encoder import (
                        OpusEncoder, MP3Encoder, MuLawEncoder, FLACEncoder
                    )

                    # Get sample rate from output (after potential SR)
                    sample_rate = metrics.get("output_sample_rate", 16000)

                    # Strip WAV header to get PCM data
                    pcm_data = audio_bytes[44:] if len(audio_bytes) > 44 else audio_bytes

                    if target_format == AudioFormat.OPUS.value:
                        encoder = OpusEncoder(sample_rate=sample_rate)
                        audio_bytes = encoder.encode(pcm_data, sample_rate)
                        media_type = "audio/opus"
                    elif target_format == AudioFormat.MP3.value:
                        encoder = MP3Encoder(sample_rate=sample_rate)
                        audio_bytes = encoder.encode(pcm_data, sample_rate)
                        media_type = "audio/mpeg"
                    elif target_format == AudioFormat.MULAW.value:
                        encoder = MuLawEncoder()
                        audio_bytes = encoder.encode(pcm_data, sample_rate)
                        media_type = "audio/x-wav"
                    elif target_format == AudioFormat.FLAC.value:
                        encoder = FLACEncoder(sample_rate=sample_rate)
                        audio_bytes = encoder.encode(pcm_data, sample_rate)
                        media_type = "audio/flac"

                    headers["X-Audio-Bytes"] = str(len(audio_bytes))
                    logger.info(f"Encoded audio to {target_format}: {len(audio_bytes)} bytes")
                except ImportError as e:
                    logger.warning(f"Audio encoder not available: {e}")
                    # Fall back to WAV
                except Exception as e:
                    logger.error(f"Format encoding failed: {e}")
                    # Fall back to WAV

            return Response(
                content=audio_bytes,
                media_type=media_type,
                headers=headers,
            )

        except Exception as e:
            logger.exception(f"TTS generation error: {e}")

            # Log failure (lifecycle event + metrics)
            log_request_failed(
                request_id=request_id,
                status_code=500,
                error_code="INTERNAL_ERROR",
                error_message=str(e),
            )
            metrics_request_failed(
                status_code=500,
                error_code="INTERNAL_ERROR",
                speaker=speaker,
            )
            clear_request_context()

            return JSONResponse(
                status_code=500,
                content={
                    "error": {
                        "code": "INTERNAL_ERROR",
                        "message": str(e),
                        "request_id": request_id,
                    }
                },
                headers={"X-Request-ID": request_id},
            )

    async def _handle_streaming_request(req, request_id: str, text: str, speaker: str, start_time: float):
        """
        Handle streaming TTS request.

        Returns a StreamingResponse that yields WAV header + PCM chunks.
        True streaming: first bytes sent ASAP before full audio is generated.
        """
        from fastapi.responses import StreamingResponse
        from veena3modal.api.schemas import AudioFormat
        from veena3modal.services import tts_runtime

        # Check if streaming pipeline is available
        runtime = tts_runtime.get_runtime()
        if runtime is None or runtime.streaming_pipeline is None:
            return JSONResponse(
                status_code=503,
                content={
                    "error": {
                        "code": "STREAMING_UNAVAILABLE",
                        "message": "Streaming pipeline not initialized.",
                        "request_id": request_id,
                    }
                },
                headers={"X-Request-ID": request_id},
            )

        # Non-WAV formats not implemented yet (M5)
        if req.format != AudioFormat.WAV.value:
            return JSONResponse(
                status_code=501,
                content={
                    "error": {
                        "code": "FORMAT_NOT_IMPLEMENTED",
                        "message": f"Streaming format '{req.format}' not yet implemented. Use format=wav.",
                        "request_id": request_id,
                    }
                },
                headers={"X-Request-ID": request_id},
            )

        # Set request context for structured logging
        set_request_context(request_id=request_id)

        # Create async generator for streaming response
        async def audio_stream_generator():
            """Yields audio chunks as they're generated."""
            final_metrics = {}
            first_chunk_sent = False
            chunks_count = 0
            try:
                async for audio_chunk, metrics in tts_runtime.generate_speech_streaming(
                    text=text,
                    speaker=speaker,
                    temperature=req.temperature,
                    top_k=req.top_k,
                    top_p=req.top_p,
                    max_tokens=req.max_tokens,
                    repetition_penalty=req.repetition_penalty,
                    seed=req.seed,
                    enable_chunking=req.chunking,
                    request_id=request_id,
                ):
                    final_metrics = metrics
                    chunks_count += 1

                    # Fire-and-forget sentence storage after first chunk (don't block TTFB)
                    if not first_chunk_sent:
                        first_chunk_sent = True
                        ttfb_ms = metrics.get("ttfb_ms", 0)

                        # Log first audio emitted (TTFB marker)
                        log_first_audio_emitted(
                            request_id=request_id,
                            ttfb_ms=ttfb_ms,
                            chunk_size_bytes=len(audio_chunk),
                        )
                        record_ttfb(ttfb_seconds=ttfb_ms / 1000, speaker=speaker, stream=True)

                        sentence_store = _get_sentence_store()
                        sentence_store.store_fire_and_forget(
                            request_id=request_id,
                            text=text,
                            speaker=speaker,
                            stream=True,
                            format=req.format,
                            temperature=req.temperature,
                            top_k=req.top_k,
                            top_p=req.top_p,
                            max_tokens=req.max_tokens,
                            repetition_penalty=req.repetition_penalty,
                            seed=req.seed,
                            text_chunked=metrics.get("text_chunked", False),
                            ttfb_ms=ttfb_ms,
                            # audio_duration_seconds not known yet for streaming
                        )

                    yield audio_chunk

                # Stream completed - log metrics
                total_time = time.time() - start_time
                audio_duration = final_metrics.get("audio_duration_seconds", 0)
                rtf = total_time / audio_duration if audio_duration > 0 else 0

                log_request_completed(
                    request_id=request_id,
                    status_code=200,
                    total_duration_ms=int(total_time * 1000),
                    audio_duration_seconds=audio_duration,
                    rtf=rtf,
                    chunks_sent=chunks_count,
                    **final_metrics,
                )
                metrics_request_completed(
                    status_code=200,
                    duration_seconds=total_time,
                    speaker=speaker,
                    stream=True,
                )
                record_rtf(rtf=rtf, speaker=speaker)
                record_audio_duration(duration_seconds=audio_duration, speaker=speaker)
                record_chunks_sent(chunks=chunks_count, speaker=speaker)
                clear_request_context()
            except Exception as e:
                logger.exception(f"Streaming error: {e}")
                # Log streaming failure
                log_request_failed(
                    request_id=request_id,
                    status_code=500,


sed -n '1,320p' /home/ubuntu/veenaModal/scripts/stress_test_optimized_local.py


Output:
Chunk ID: b27746
Wall time: 0.0538 seconds
Process exited with code 0
Original token count: 2531
Output:
#!/usr/bin/env python3
"""Detailed local stress benchmark for optimized TTS path.

This script is focused on collecting per-request GPU/LLM timing for local tuning:
- Request wall-clock latency and server reported TTFB
- GPU timing breakdown (LLM batch wall / GPU wall, parse, decode)
- Time per token and per batch
- Concurrency visibility from in-flight counters
- End-to-end throughput and RTF with GPU utilization snapshots

Usage:
    python scripts/stress_test_optimized_local.py
    python scripts/stress_test_optimized_local.py --levels 1,5,10,20 --concurrency-scaling both
    python scripts/stress_test_optimized_local.py --text-category long --output benchmarks.json
"""

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

import httpx

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

TEST_TEXTS = {
    "short": "Hello, this is a quick test.",
    "medium": (
        "The quick brown fox jumps over the lazy dog. "
        "This is a baseline quality sentence used for benchmarking local TTS latency, "
        "TTFB, and tokenization behavior under load."
    ),
    "long": (
        "In the heart of a bustling city, where towering skyscrapers cast shadows over crowded streets, "
        "there lived a quiet linguist who believed every sentence carried a rhythm. "
        "She recorded conversations at dawn and dusk, trying to capture how meaning shifted with breath. "
        "By evening she would review them, matching sound to emotion, and every night she learned a new way to speak."
    ),
}

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

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

class GPUMonitor:
    """Background polling for quick GPU health snapshots."""

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

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

    def stop(self):
        self._running = False
        if self._thread:
            self._thread.join(timeout=3)

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

    def summary(self) -> Dict[str, Any]:
        if not self.snapshots:
            return {
                "samples": 0,
                "error": "no GPU samples captured",
            }

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

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

@dataclass
class RequestResult:
    success: bool
    status_code: int
    latency_ms: float
    ttfb_ms: float = 0.0
    server_ttfb_ms: float = 0.0
    audio_bytes: int = 0
    audio_seconds: float = 0.0
    request_id: str = ""
    error: Optional[str] = None
    speaker: str = ""
    text_length: int = 0
    stream: bool = False
    timing: Dict[str, Any] = field(default_factory=dict)
    request_inflight: Optional[int] = None

@dataclass
class LevelResult:
    level_name: str
    concurrency: int
    total_requests: int
    mode: str
    text_category: str
    chunking: bool
    results: List[RequestResult] = field(default_factory=list)
    wall_time_seconds: float = 0.0
    gpu_summary: Dict[str, Any] = field(default_factory=dict)

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

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

    @property
    def success_rate(self) -> float:
        return len(self.successes) / len(self.results) if self.results else 0.0

    @property
    def throughput_rps(self) -> float:
        return self.total_requests / self.wall_time_seconds if self.wall_time_seconds > 0 else 0.0

    @property
    def audio_duration_total(self) -> float:
        return sum(r.audio_seconds for r in self.successes)

    @property
    def effective_rtf(self) -> float:
        total_audio = self.audio_duration_total
        return self.wall_time_seconds / total_audio if total_audio > 0 else 0.0

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

    @property
    def latency_stats(self) -> Dict[str, float]:
        lat = [r.latency_ms for r in self.successes]
        if not lat:
            return {}
        return {
            "min": min(lat),
            "avg": statistics.mean(lat),
            "p50": self._percentile(lat, 50),
            "p95": self._percentile(lat, 95),
            "p99": self._percentile(lat, 99),
            "max": max(lat),
        }

    @property
    def ttfb_stats(self) -> Dict[str, float]:
        vals = [r.server_ttfb_ms for r in self.successes if r.server_ttfb_ms >= 0]
        if not vals:
            return {}
        return {
            "min": min(vals),
            "avg": statistics.mean(vals),
            "p50": self._percentile(vals, 50),
            "p95": self._percentile(vals, 95),
            "max": max(vals),
        }

    def timing_stats(self, key: str) -> Dict[str, float]:
        values: List[float] = []
        for r in self.successes:
            v = r.timing.get(key)
            if isinstance(v, (int, float)):
                values.append(float(v))
        if not values:
            return {}
        return {
            "min": min(values),
            "avg": statistics.mean(values),
            "p50": self._percentile(values, 50),
            "p95": self._percentile(values, 95),
            "p99": self._percentile(values, 99),
            "max": max(values),
        }

    def timing_rate(self, numerator_key: str, denominator_key: str) -> Tuple[float, int]:
        num = 0.0
        den = 0
        for r in self.successes:
            n = r.timing.get(numerator_key)
            d = r.timing.get(denominator_key)
            if isinstance(n, (int, float)) and isinstance(d, (int, float)) and d > 0:
                num += float(n)
                den += int(d)
        if den == 0:
            return 0.0, 0
        return num / den, den

    def observed_inflight_max(self) -> Optional[int]:
        values = [r.request_inflight for r in self.successes if isinstance(r.request_inflight, int)]
        if not values:
            return None
        return max(values)

    def error_summary(self) -> Dict[str, int]:
        errors: Dict[str, int] = {}
        for r in self.failures:
            key = r.error or f"HTTP {r.status_code}"
            errors[key[:100]] = errors.get(key[:100], 0) + 1
        return errors

def _coerce_number(raw: object) -> Optional[float]:
    if raw is None:
        return None
    v = str(raw).strip()
    if v == "":
        return None
    try:
        if "." in v:
            return float(v)
        return float(int(v))
    except ValueError:
        try:
            return float(v)
        except ValueError:
            return None

def _coerce_bool(raw: object) -> Optional[bool]:
    if raw is None:
        return None
    if isinstance(raw, bool):
        return raw
    v = str(raw).strip().lower()
    if v in {"true", "1", "yes", "y", "on"}:
        return True
    if v in {"false", "0", "no", "n", "off"}:
        return False
    return None

def _normalize_time_ms(raw: Optional[str]) -> float:
    value = _coerce_number(raw)
    if value is None:
        return 0.0
    return float(value)

def _parse_perf_headers(headers: Any) -> Dict[str, Any]:
    """Parse server timing headers into numeric metric fields."""
    metrics: Dict[str, Any] = {}

    # Explicit compact payload.
    detail_payload = headers.get("x-perf-details") if hasattr(headers, "get") else None
    if detail_payload:
        try:
            parsed = json.loads(detail_payload)


sed -n '320,680p' /home/ubuntu/veenaModal/scripts/stress_test_optimized_local.py


Output:
Chunk ID: 169a6f
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 3393
Output:
            parsed = json.loads(detail_payload)
            if isinstance(parsed, dict):
                metrics.update(parsed)
        except Exception:
            pass

    header_map = {
        "x-llm-token-total": "llm_token_total",
        "x-llm-batch-count": "llm_batch_count",
        "x-llm-batch-wall-ms": "llm_batch_wall_ms_total",
        "x-llm-batch-gpu-ms": "llm_batch_gpu_ms_total",
        "x-llm-batch-wall-ms-min": "llm_batch_wall_ms_min",
        "x-llm-batch-wall-ms-max": "llm_batch_wall_ms_max",
        "x-llm-batch-wall-ms-p50": "llm_batch_wall_ms_p50",
        "x-llm-batch-gpu-ms-min": "llm_batch_gpu_ms_min",
        "x-llm-batch-gpu-ms-max": "llm_batch_gpu_ms_max",
        "x-llm-batch-gpu-ms-p50": "llm_batch_gpu_ms_p50",
        "x-llm-decode-wall-ms": "llm_decode_wall_ms_total",
        "x-llm-decode-gpu-ms": "llm_decode_gpu_ms_total",
        "x-llm-decode-wall-ms-min": "llm_decode_wall_ms_min",
        "x-llm-decode-wall-ms-max": "llm_decode_wall_ms_max",
        "x-llm-decode-wall-ms-p50": "llm_decode_wall_ms_p50",
        "x-llm-decode-gpu-ms-min": "llm_decode_gpu_ms_min",
        "x-llm-decode-gpu-ms-max": "llm_decode_gpu_ms_max",
        "x-llm-decode-gpu-ms-p50": "llm_decode_gpu_ms_p50",
        "x-llm-decode-calls": "decode_calls",
        "x-llm-decode-cpu-ms": "llm_decode_cpu_ms",
        "x-llm-parse-ms": "llm_parse_ms",
        "x-llm-parse-avg-ms": "llm_parse_ms_avg",
        "x-llm-tokens-per-batch-min": "tokens_per_batch_min",
        "x-llm-tokens-per-batch-max": "tokens_per_batch_max",
        "x-llm-tokens-per-batch-p50": "tokens_per_batch_p50",
        "x-generation-ms": "generation_ms",
        "x-request-inflight": "request_inflight",
        "x-llm-text-chunked": "text_chunked",
        "x-llm-chunks-processed": "chunks_processed",
        "x-llm-sr-applied": "sr_applied",
        "x-llm-time-per-token-ms": "llm_time_per_token_ms",
        "x-llm-time-per-batch-wall-ms": "llm_time_per_batch_wall_ms",
        "x-llm-time-per-batch-gpu-ms": "llm_time_per_batch_gpu_ms",
    }

    bool_keys = {"text_chunked", "sr_applied"}
    int_keys = {
        "llm_token_total",
        "llm_batch_count",
        "decode_calls",
        "chunks_processed",
        "request_inflight",
    }

    if hasattr(headers, "items"):
        for raw_key, raw_val in headers.items():
            lk = str(raw_key).lower()
            target_key = header_map.get(lk)
            if target_key is None:
                continue
            if target_key in bool_keys:
                b = _coerce_bool(raw_val)
                if b is not None:
                    metrics[target_key] = b
                continue

            v = _coerce_number(raw_val)
            if v is None:
                continue
            if target_key in int_keys and float(v).is_integer():
                metrics[target_key] = int(v)
            else:
                metrics[target_key] = int(v) if float(v).is_integer() else float(v)

    if "llm_token_total" in metrics and "generation_ms" in metrics and metrics["llm_token_total"]:
        try:
            metrics["llm_time_per_token_ms"] = float(metrics["generation_ms"]) / float(metrics["llm_token_total"])
        except Exception:
            pass

    if "llm_batch_count" in metrics and "generation_ms" in metrics and metrics["llm_batch_count"]:
        try:
            metrics["llm_time_per_batch_wall_ms"] = float(metrics["generation_ms"]) / float(metrics["llm_batch_count"])
        except Exception:
            pass

    if "llm_batch_count" in metrics and "llm_batch_gpu_ms_total" in metrics and metrics["llm_batch_count"]:
        try:
            metrics["llm_time_per_batch_gpu_ms"] = float(metrics["llm_batch_gpu_ms_total"]) / float(metrics["llm_batch_count"])
        except Exception:
            pass

    return metrics

async def make_request(
    client: httpx.AsyncClient,
    base_url: str,
    text: str,
    speaker: str,
    chunking: bool,
    stream: bool = False,
    timeout: float = 120.0,
) -> RequestResult:
    url = f"{base_url}{GENERATE_PATH}"
    payload = {
        "text": text,
        "speaker": speaker,
        "stream": stream,
        "chunking": chunking,
        "format": "wav",
    }

    start = time.time()
    ttfb_time = 0.0

    try:
        if stream:
            async with client.stream(
                "POST",
                url,
                json=payload,
                headers={"Content-Type": "application/json"},
                timeout=timeout,
            ) as response:
                first = True
                total_bytes = 0
                async for chunk in response.aiter_bytes():
                    if first:
                        ttfb_time = (time.time() - start) * 1000
                        first = False
                    total_bytes += len(chunk)

                latency = (time.time() - start) * 1000
                timing = _parse_perf_headers(response.headers)

                if response.status_code == 200:
                    pcm_bytes = max(0, total_bytes - 44)
                    audio_seconds = pcm_bytes / (16000 * 2)
                    return RequestResult(
                        success=True,
                        status_code=200,
                        latency_ms=latency,
                        ttfb_ms=ttfb_time,
                        audio_bytes=total_bytes,
                        audio_seconds=audio_seconds,
                        request_id=response.headers.get("x-request-id", ""),
                        speaker=speaker,
                        text_length=len(text),
                        stream=True,
                        timing=timing,
                        request_inflight=timing.get("request_inflight"),
                        server_ttfb_ms=_normalize_time_ms(response.headers.get("x-ttfb-ms")),
                    )

                return RequestResult(
                    success=False,
                    status_code=response.status_code,
                    latency_ms=latency,
                    ttfb_ms=ttfb_time,
                    error=f"HTTP {response.status_code}",
                    speaker=speaker,
                    text_length=len(text),
                    stream=True,
                    timing=timing,
                    request_id=response.headers.get("x-request-id", ""),
                    request_inflight=timing.get("request_inflight"),
                )
        else:
            response = await client.post(
                url,
                json=payload,
                headers={"Content-Type": "application/json"},
                timeout=timeout,
            )
            latency = (time.time() - start) * 1000
            timing = _parse_perf_headers(response.headers)

            server_ttfb = _normalize_time_ms(response.headers.get("x-ttfb-ms"))
            audio_seconds = 0.0
            audio_sec_header = response.headers.get("x-audio-seconds")
            if audio_sec_header:
                parsed = _coerce_number(audio_sec_header)
                if parsed is not None:
                    audio_seconds = parsed
            elif response.status_code == 200:
                pcm_bytes = max(0, len(response.content) - 44)
                audio_seconds = pcm_bytes / (16000 * 2)

            if response.status_code == 200:
                return RequestResult(
                    success=True,
                    status_code=200,
                    latency_ms=latency,
                    ttfb_ms=latency,
                    server_ttfb_ms=server_ttfb,
                    audio_bytes=len(response.content),
                    audio_seconds=audio_seconds,
                    request_id=response.headers.get("x-request-id", ""),
                    speaker=speaker,
                    text_length=len(text),
                    stream=False,
                    timing=timing,
                    request_inflight=timing.get("request_inflight"),
                )

            return RequestResult(
                success=False,
                status_code=response.status_code,
                latency_ms=latency,
                ttfb_ms=latency,
                server_ttfb_ms=server_ttfb,
                audio_bytes=len(response.content),
                error=(response.text[:200] if response.text else f"HTTP {response.status_code}"),
                speaker=speaker,
                text_length=len(text),
                stream=False,
                timing=timing,
                request_id=response.headers.get("x-request-id", ""),
                request_inflight=timing.get("request_inflight"),
            )

    except httpx.TimeoutException:
        return RequestResult(
            success=False,
            status_code=0,
            latency_ms=(time.time() - start) * 1000,
            error="TIMEOUT",
            speaker=speaker,
            text_length=len(text),
            stream=stream,
        )
    except Exception as e:
        return RequestResult(
            success=False,
            status_code=0,
            latency_ms=(time.time() - start) * 1000,
            error=str(e)[:200],
            speaker=speaker,
            text_length=len(text),
            stream=stream,
        )

async def run_level(
    base_url: str,
    num_requests: int,
    concurrency: int,
    text_category: str,
    chunking: bool,
    stream: bool,
    gpu_monitor: Optional[GPUMonitor] = None,
) -> LevelResult:
    mode = "streaming" if stream else "non-streaming"
    level = LevelResult(
        level_name=f"{concurrency}c-{text_category}-{mode}",
        concurrency=concurrency,
        total_requests=num_requests,
        mode=mode,
        text_category=text_category,
        chunking=chunking,
    )

    semaphore = asyncio.Semaphore(concurrency)

    if text_category == "mixed":
        texts = list(TEST_TEXTS.values())
    else:
        texts = [TEST_TEXTS.get(text_category, TEST_TEXTS["short"])]

    async def limited_request(i: int, client: httpx.AsyncClient) -> RequestResult:
        async with semaphore:
            text = texts[i % len(texts)]
            speaker = SPEAKERS[i % len(SPEAKERS)]
            return await make_request(
                client=client,
                base_url=base_url,
                text=text,
                speaker=speaker,
                chunking=chunking,
                stream=stream,
            )

    if gpu_monitor:
        gpu_monitor.snapshots.clear()

    start_time = time.time()
    limits = httpx.Limits(
        max_connections=concurrency + 10,
        max_keepalive_connections=concurrency,
    )
    async with httpx.AsyncClient(limits=limits) as client:
        tasks = [limited_request(i, client) for i in range(num_requests)]
        responses = await asyncio.gather(*tasks, return_exceptions=True)

    level.wall_time_seconds = time.time() - start_time

    for item in responses:
        if isinstance(item, RequestResult):
            level.results.append(item)
        else:
            level.results.append(
                RequestResult(
                    success=False,
                    status_code=0,
                    latency_ms=0,
                    error=str(item)[:200],
                    stream=stream,
                )
            )

    if gpu_monitor:
        level.gpu_summary = gpu_monitor.summary()

    return level

def print_level_report(level: LevelResult):
    print(f"\n{'=' * 72}")
    print(f"  {level.level_name.upper()}")
    print(f"  {level.total_requests} requests, {level.concurrency} concurrent, {level.mode}, chunking={level.chunking}")
    print(f"{'=' * 72}")

    print(f"  Success:     {len(level.successes)}/{level.total_requests} ({level.success_rate:.0%})")
    print(f"  Wall time:   {level.wall_time_seconds:.2f}s")
    print(f"  Throughput:  {level.throughput_rps:.2f} req/s")
    print(f"  Audio total: {level.audio_duration_total:.2f}s")
    print(f"  Eff. RTF:    {level.effective_rtf:.3f}")

    if level.observed_inflight_max() is not None:
        print(f"  Peak inflight: {level.observed_inflight_max()}")

    latency = level.latency_stats
    if latency:
        print("\n  Latency (ms):")
        print(
            f"    min={latency['min']:.0f} avg={latency['avg']:.0f} "
            f"p50={latency['p50']:.0f} p95={latency['p95']:.0f} p99={latency['p99']:.0f} max={latency['max']:.0f}"
        )

    ttfb = level.ttfb_stats
    if ttfb and not level.mode.startswith("stream"):
        print("\n  Server TTFB (ms):")
        print(
            f"    min={ttfb['min']:.0f} avg={ttfb['avg']:.0f} "
            f"p50={ttfb['p50']:.0f} p95={ttfb['p95']:.0f} max={ttfb['max']:.0f}"
        )

    if level.mode == "non-streaming":
        llm = level.timing_stats("generation_ms")
        if llm:
            print("\n  Generation (ms):")
            print(f"    min={llm['min']:.0f} avg={llm['avg']:.0f} p50={llm['p50']:.0f} p95={llm['p95']:.0f} max={llm['max']:.0f}")
        wall = level.timing_stats("llm_batch_wall_ms_total")
        if wall:
            print("\n  LLM batch wall (ms):")
            print(f"    min={wall['min']:.0f} avg={wall['avg']:.0f} p50={wall['p50']:.0f} p95={wall['p95']:.0f} max={wall['max']:.0f}")
        gpu = level.timing_stats("llm_batch_gpu_ms_total")
        if gpu:
            print("\n  LLM batch GPU (ms):")
            print(f"    min={gpu['min']:.0f} avg={gpu['avg']:.0f} p50={gpu['p50']:.0f} p95={gpu['p95']:.0f} max={gpu['max']:.0f}")
        parse = level.timing_stats("llm_parse_ms")
        if parse:
            print("\n  Parse (ms):")


sed -n '680,980p' /home/ubuntu/veenaModal/scripts/stress_test_optimized_local.py


Output:
Chunk ID: bd3b2b
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 1774
Output:
            print("\n  Parse (ms):")
            print(f"    avg={parse['avg']:.0f} p95={parse['p95']:.0f}")
        decode = level.timing_stats("llm_decode_wall_ms_total")
        if decode:
            print("\n  Decode (ms):")
            print(f"    avg={decode['avg']:.0f} p95={decode['p95']:.0f}")

        token_ms, token_count = level.timing_rate("generation_ms", "llm_token_total")
        if token_count:
            print(f"  Token timing: avg {token_ms / token_count * 1000:.3f} us/token")

    if level.gpu_summary and "memory_mb" in level.gpu_summary:
        gpu_summary = level.gpu_summary
        mem = gpu_summary["memory_mb"]
        util = gpu_summary["gpu_util_pct"]
        temp = gpu_summary["temperature_c"]
        print("\n  GPU:")
        print(f"    Memory: {mem['avg']:.0f}MB avg, {mem['max']:.0f}MB peak / {mem['total']:.0f}MB")
        print(f"    Util:   {util['avg']:.0f}% avg, {util['max']:.0f}% peak")
        print(f"    Temp:   {temp['avg']:.0f}C avg, {temp['max']:.0f}C peak")

    errors = level.error_summary()
    if errors:
        print("\n  Errors:")
        for key, count in sorted(errors.items(), key=lambda kv: -kv[1]):
            print(f"    [{count}x] {key}")

def print_summary_table(levels: List[LevelResult]):
    print(f"\n{'=' * 110}")
    print("  STRESS BENCHMARK SUMMARY")
    print(f"{'=' * 110}")
    header = f"{'Level':<26} {'OK%':>5} {'RPS':>7} {'p50':>7} {'p95':>7} {'GPU%':>5} {'GPUmem':>8} {'RTF':>6} {'Inflight':>8}"
    print(header)
    print("-" * 110)

    for level in levels:
        lat = level.latency_stats
        p50 = f"{lat.get('p50', 0):.0f}" if lat else "---"
        p95 = f"{lat.get('p95', 0):.0f}" if lat else "---"
        gpu_util = level.gpu_summary.get("gpu_util_pct", {}).get("avg", 0)
        gpu_mem = level.gpu_summary.get("memory_mb", {}).get("max", 0)
        inflight = level.observed_inflight_max() or 0
        print(
            f"{level.level_name:<26} "
            f"{level.success_rate:>4.0%} "
            f"{level.throughput_rps:>7.2f} "
            f"{p50:>7} "
            f"{p95:>7} "
            f"{gpu_util:>4.0f}% "
            f"{gpu_mem:>7.0f}MB "
            f"{level.effective_rtf:>6.3f} "
            f"{inflight:>8d}"
        )

    print("=" * 110)

def save_results(levels: List[LevelResult], path: str):
    payload = []
    for level in levels:
        payload.append({
            "level": level.level_name,
            "concurrency": level.concurrency,
            "mode": level.mode,
            "chunking": level.chunking,
            "text_category": level.text_category,
            "success_rate": level.success_rate,
            "throughput_rps": level.throughput_rps,
            "wall_time_s": level.wall_time_seconds,
            "latency_ms": level.latency_stats,
            "ttfb_ms": level.ttfb_stats,
            "effective_rtf": level.effective_rtf,
            "audio_total_seconds": level.audio_duration_total,
            "observed_inflight_max": level.observed_inflight_max(),
            "errors": level.error_summary(),
            "gpu": level.gpu_summary,
            "timing_averages": {
                "generation_ms": level.timing_stats("generation_ms"),
                "llm_batch_wall_ms_total": level.timing_stats("llm_batch_wall_ms_total"),
                "llm_batch_gpu_ms_total": level.timing_stats("llm_batch_gpu_ms_total"),
                "llm_parse_ms": level.timing_stats("llm_parse_ms"),
                "llm_decode_wall_ms_total": level.timing_stats("llm_decode_wall_ms_total"),
            },
            "requests": [
                {
                    "request_id": r.request_id,
                    "success": r.success,
                    "status_code": r.status_code,
                    "latency_ms": r.latency_ms,
                    "ttfb_ms": r.ttfb_ms,
                    "server_ttfb_ms": r.server_ttfb_ms,
                    "audio_bytes": r.audio_bytes,
                    "audio_seconds": r.audio_seconds,
                    "request_inflight": r.request_inflight,
                    "error": r.error,
                    "timing": r.timing,
                }
                for r in level.results
            ],
        })

    with open(path, "w") as fd:
        json.dump(payload, fd, indent=2)

async def run_stress_test(args):
    base_url = args.url

    try:
        async with httpx.AsyncClient() as client:
            response = await client.get(f"{base_url}/v1/tts/health", timeout=10)
            health = response.json()
            print(f"  Health: {health.get('status')}")
            print(f"  Version: {health.get('model_version')}")
            print(f"  GPU: {health.get('gpu_available')}")
    except Exception as e:
        print(f"ERROR: server not reachable at {base_url}: {e}")
        sys.exit(1)

    levels = [int(v) for v in args.levels.split(",") if v.strip()]
    gpu_monitor = GPUMonitor(interval_seconds=0.5)
    gpu_monitor.start()

    mode_label = "streaming" if args.stream else "non-streaming"
    results: List[LevelResult] = []

    print(f"\nWarming up (1 request, non-streaming)...")
    warmup = await run_level(
        base_url=base_url,
        num_requests=1,
        concurrency=1,
        text_category=args.text,
        chunking=args.chunking,
        stream=False,
        gpu_monitor=gpu_monitor,
    )
    print(f"  warmup ok: {warmup.success_rate:.0%}")

    for conc in levels:
        num_requests = max(conc * 3, 10)
        num_requests = min(num_requests, 200)
        level = await run_level(
            base_url=base_url,
            num_requests=num_requests,
            concurrency=conc,
            text_category=args.text,
            chunking=args.chunking,
            stream=args.stream,
            gpu_monitor=gpu_monitor,
        )
        print_level_report(level)
        results.append(level)
        if conc != levels[-1]:
            print("  cooldown 3s...")
            await asyncio.sleep(3)

    gpu_monitor.stop()
    print_summary_table(results)

    if args.output:
        save_results(results, args.output)
        print(f"\nResults written to {args.output}")

def main():
    parser = argparse.ArgumentParser(description="Detailed local optimized-path TTS benchmark")
    parser.add_argument("--url", default=DEFAULT_URL, help=f"Server URL (default: {DEFAULT_URL})")
    parser.add_argument("--levels", default="1,5,10,20,50", help="Comma-separated concurrency levels")
    parser.add_argument("--text", default="short", choices=["short", "medium", "long", "mixed"], help="Input text category")
    parser.add_argument("--chunking", action="store_true", help="Enable server-side chunking", default=True)
    parser.add_argument("--no-chunking", action="store_false", dest="chunking", help="Disable chunking")
    parser.add_argument("--stream", action="store_true", help="Use streaming endpoint")
    parser.add_argument("--output", default="stress_test_optimized_local.json", help="Output JSON path")

    args = parser.parse_args()
    asyncio.run(run_stress_test(args))

if __name__ == "__main__":
    main()


rg -n "llm_batch_wall_ms_total|llm_batch_gpu_ms_total|llm_decode|llm_parse|request_inflight|tokens_per_batch" /home/ubuntu/veenaModal/veena3modal -g'*.py'