Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py
@@
         while True:
             wall_start = time.time()
             parse_start = time.time()
@@
             parse_wall_ms_list.append(parse_wall_ms)
             batch_token_counts.append(len(new_token_ids))
             batch_wall_ms.append((time.time() - t_iter) * 1000 if t_iter else 0.0)
+            if emit_timing:
+                logger.info(
+                    "tts_stream_llm_batch",
+                    extra={
+                        "speaker": speaker,
+                        "request_id": request_id,
+                        "batch_index": len(batch_wall_ms),
+                        "batch_tokens": len(new_token_ids),
+                        "llm_batch_wall_ms": int((time.time() - t_iter) * 1000 if t_iter else 0.0),
+                        "llm_batch_gpu_ms": int(batch_gpu_ms[-1]) if batch_gpu_ms else None,
+                    },
+                )
@@
                                 if to_emit:
                                     samples_emitted_in_this_chunk = len(to_emit) // 2
                                     total_samples_emitted_to_user += samples_emitted_in_this_chunk

                                 if to_emit:
                                     total_audio_chunks += 1
                                     if t_first_chunk_decoded is None:
                                         t_first_chunk_decoded = t_decode_end

                                     if emit_timing:
-                                    logger.info(
-                                        "tts_stream_decode_batch",
-                                        extra={
-                                            "speaker": speaker,
-                                            "request_id": request_id,
-                                            "batch_index": decode_calls,
-                                            "decode_wall_ms": int((t_decode_end - t_decode_start) * 1000),
-                                            "decode_gpu_ms": int(decode_gpu_ms_this)
-                                            if decode_gpu_ms_this is not None else None,
-                                            "semantic_count": len(semantic_buffer),
-                                        },
-                                    )
+                                        logger.info(
+                                            "tts_stream_decode_batch",
+                                            extra={
+                                                "speaker": speaker,
+                                                "request_id": request_id,
+                                                "batch_index": decode_calls,
+                                                "decode_wall_ms": int((t_decode_end - t_decode_start) * 1000),
+                                                "decode_gpu_ms": int(decode_gpu_ms_this)
+                                                if decode_gpu_ms_this is not None else None,
+                                                "semantic_count": len(semantic_buffer),
+                                            },
+                                        )

                                     yield to_emit
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py:
        while True:
            wall_start = time.time()
            parse_start = time.time()


# 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.


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py
@@
-            parse_wall_ms_list.append(parse_wall_ms)
-            batch_token_counts.append(len(new_token_ids))
-            batch_wall_ms.append((time.time() - t_iter) * 1000 if t_iter else 0.0)
+            parse_wall_ms_list.append(parse_wall_ms)
+            batch_token_counts.append(len(new_token_ids))
+            batch_wall_ms_value = (time.time() - t_iter) * 1000 if t_iter else 0.0
+            batch_wall_ms.append(batch_wall_ms_value)
+            if emit_timing:
+                logger.info(
+                    "tts_stream_llm_batch",
+                    extra={
+                        "speaker": speaker,
+                        "request_id": request_id,
+                        "batch_index": len(batch_wall_ms),
+                        "batch_tokens": len(new_token_ids),
+                        "llm_batch_wall_ms": int(batch_wall_ms_value),
+                        "llm_batch_gpu_ms": int(batch_gpu_ms[-1]) if batch_gpu_ms else None,
+                    },
+                )
*** End Patch


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


Summary: auto


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py
@@
-                        samples_emitted_in_this_chunk = len(to_emit) // 2
-                        total_samples_emitted_to_user += samples_emitted_in_this_chunk
-
-                                if to_emit:
-                                    total_audio_chunks += 1
-                                    if t_first_chunk_decoded is None:
-                                        t_first_chunk_decoded = t_decode_end
-
-                                    if emit_timing:
-                                    logger.info(
-                                        "tts_stream_decode_batch",
-                                        extra={
-                                            "speaker": speaker,
-                                            "request_id": request_id,
-                                            "batch_index": decode_calls,
-                                            "decode_wall_ms": int((t_decode_end - t_decode_start) * 1000),
-                                            "decode_gpu_ms": int(decode_gpu_ms_this)
-                                            if decode_gpu_ms_this is not None else None,
-                                            "semantic_count": len(semantic_buffer),
-                                        },
-                                    )
-
-                                    yield to_emit
+                        samples_emitted_in_this_chunk = len(to_emit) // 2
+                        total_samples_emitted_to_user += samples_emitted_in_this_chunk
+
+                        if to_emit:
+                            total_audio_chunks += 1
+                            if t_first_chunk_decoded is None:
+                                t_first_chunk_decoded = t_decode_end
+
+                            if emit_timing:
+                                logger.info(
+                                    "tts_stream_decode_batch",
+                                    extra={
+                                        "speaker": speaker,
+                                        "request_id": request_id,
+                                        "batch_index": decode_calls,
+                                        "decode_wall_ms": int((t_decode_end - t_decode_start) * 1000),
+                                        "decode_gpu_ms": int(decode_gpu_ms_this)
+                                        if decode_gpu_ms_this is not None else None,
+                                        "semantic_count": len(semantic_buffer),
+                                    },
+                                )
+
+                            yield to_emit
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py
@@
-                        if to_emit:
-                            total_audio_chunks += 1
-                            yield (to_emit, captured_globals)
+                        if to_emit:
+                            total_audio_chunks += 1
+                            yield (to_emit, captured_globals)
@@
-	                    if to_emit:
-	                        total_audio_chunks += 1
-	                        yield to_emit
+                        if to_emit:
+                            total_audio_chunks += 1
+                            yield to_emit
*** End Patch


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


nl -ba veena3modal/core/streaming_pipeline.py | sed -n '440,500p'


Output:
Chunk ID: f9498b
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 1025
Output:
   440	                            decode_gpu_ms += decode_gpu_ms_this
   441	                            decode_gpu_samples.append(decode_gpu_ms_this)
   442	                            batch_gpu_ms.append(decode_gpu_ms_this)
   443
   444	                        if audio_bytes:
   445	                            total_samples_decoded = len(audio_bytes) // 2
   446
   447	                            if len(semantic_buffer) <= WINDOW_SIZE:
   448	                                # Early phase: extract new samples from full decode
   449	                                if total_samples_decoded > total_samples_emitted_to_user:
   450	                                    new_bytes_start = total_samples_emitted_to_user * 2
   451	                                    new_audio_bytes = audio_bytes[new_bytes_start:]
   452	                                else:
   453	                                    new_audio_bytes = b""
   454	                            else:
   455	                                # Windowed phase: take only the tail portion of the window decode
   456	                                # The last DECODE_INTERVAL tokens' worth of audio is the new content
   457	                                # Each token produces ~320 samples at 16kHz/50TPS
   458	                                new_samples_approx = DECODE_INTERVAL * 320
   459	                                if total_samples_decoded > new_samples_approx:
   460	                                    new_bytes_start = (total_samples_decoded - new_samples_approx) * 2
   461	                                else:
   462	                                    new_bytes_start = 0
   463	                                new_audio_bytes = audio_bytes[new_bytes_start:]
   464
   465	                            if new_audio_bytes:
   466	                                to_emit, previous_chunk_tail = crossfade_bytes_int16(
   467	                                    previous_chunk_tail,
   468	                                    new_audio_bytes,
   469	                                    sample_rate_hz=16000,
   470	                                    crossfade_ms=CROSSFADE_MS,
   471	                                )
   472
   473	                        samples_emitted_in_this_chunk = len(to_emit) // 2
   474	                        total_samples_emitted_to_user += samples_emitted_in_this_chunk
   475
   476	                        if to_emit:
   477	                            total_audio_chunks += 1
   478	                            if t_first_chunk_decoded is None:
   479	                                t_first_chunk_decoded = t_decode_end
   480
   481	                            if emit_timing:
   482	                                logger.info(
   483	                                    "tts_stream_decode_batch",
   484	                                    extra={
   485	                                        "speaker": speaker,
   486	                                        "request_id": request_id,
   487	                                        "batch_index": decode_calls,
   488	                                        "decode_wall_ms": int((t_decode_end - t_decode_start) * 1000),
   489	                                        "decode_gpu_ms": int(decode_gpu_ms_this)
   490	                                        if decode_gpu_ms_this is not None else None,
   491	                                        "semantic_count": len(semantic_buffer),
   492	                                    },
   493	                                )
   494
   495	                            yield to_emit
   496
   497	        # Final chunk: decode any remaining semantic tokens
   498	        # Use the same 32 global tokens (they get pooled)
   499	        if len(semantic_buffer) > last_decode_count and len(global_buffer) >= EXPECTED_GLOBAL_COUNT:
   500	            audio_bytes = self.snac_decoder.decode_streaming(


nl -ba veena3modal/core/streaming_pipeline.py | sed -n '940,1010p'


Output:
Chunk ID: bae6a5
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 913
Output:
   940	                        )
   941
   942	                    if audio_bytes:
   943	                        total_samples_decoded = len(audio_bytes) // 2
   944
   945	                        if total_samples_decoded > total_samples_emitted_to_user:
   946	                            new_bytes_start = total_samples_emitted_to_user * 2
   947	                            new_audio_bytes = audio_bytes[new_bytes_start:]
   948
   949	                            to_emit, previous_chunk_tail = crossfade_bytes_int16(
   950	                                previous_chunk_tail,
   951	                                new_audio_bytes,
   952	                                sample_rate_hz=16000,
   953	                                crossfade_ms=CROSSFADE_MS,
   954	                            )
   955
   956	                            samples_emitted_in_this_chunk = len(to_emit) // 2
   957	                            total_samples_emitted_to_user += samples_emitted_in_this_chunk
   958
   959	                        if to_emit:
   960	                            total_audio_chunks += 1
   961	                            yield to_emit
   962
   963	        # Final chunk processing
   964	        if len(semantic_buffer) > last_decode_count:
   965	            audio_bytes = self.snac_decoder.decode_streaming(
   966	                semantic_ids=semantic_buffer,
   967	                global_ids=global_ids,  # Use captured globals
   968	                use_sliding_window=False,
   969	                trim_warmup=False
   970	            )
   971	            if audio_bytes:
   972	                total_samples_decoded = len(audio_bytes) // 2
   973
   974	                if total_samples_decoded > total_samples_emitted_to_user:
   975	                    new_bytes_start = total_samples_emitted_to_user * 2
   976	                    new_audio_bytes = audio_bytes[new_bytes_start:]
   977
   978	                    to_emit, previous_chunk_tail = crossfade_bytes_int16(
   979	                        previous_chunk_tail,
   980	                        new_audio_bytes,
   981	                        sample_rate_hz=16000,
   982	                        crossfade_ms=CROSSFADE_MS,
   983	                    )
   984
   985	                    samples_emitted_in_final = len(to_emit) // 2
   986	                    total_samples_emitted_to_user += samples_emitted_in_final
   987
   988	                        if to_emit:
   989	                            total_audio_chunks += 1
   990	                            yield to_emit
   991
   992	        # Flush remaining tail
   993	        if previous_chunk_tail:
   994	            total_audio_chunks += 1
   995	            tail_samples = len(previous_chunk_tail) // 2
   996	            total_samples_emitted_to_user += tail_samples
   997	            yield previous_chunk_tail
   998
   999	        t_end = time.time()
  1000	        request_metrics.update(
  1001	            {
  1002	                "request_id": request_id,
  1003	                "speaker": speaker,
  1004	                "audio_duration_seconds": total_samples_emitted_to_user / 16000,
  1005	                "audio_chunks": total_audio_chunks,
  1006	                "semantic_tokens": len(semantic_buffer),
  1007	                "global_tokens": len(global_ids),
  1008	                "generation_ms": int((t_end - t_start) * 1000),
  1009	            }
  1010	        )


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/core/streaming_pipeline.py
@@
-                    samples_emitted_in_final = len(to_emit) // 2
-                    total_samples_emitted_to_user += samples_emitted_in_final
-
-                        if to_emit:
-                            total_audio_chunks += 1
-                            yield to_emit
+                    samples_emitted_in_final = len(to_emit) // 2
+                    total_samples_emitted_to_user += samples_emitted_in_final
+
+                    if to_emit:
+                        total_audio_chunks += 1
+                        yield to_emit
*** End Patch


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


I’ve started implementing the requested timing wiring and also fixed broken indentation blocks in the streaming pipeline that would otherwise break runtime logs. Next I’m finishing runtime/API propagation plus the benchmark script.


rg -n "if to_emit:\s*$" veena3modal/core/streaming_pipeline.py | head -n 40


Output:
Chunk ID: 3ed84b
Wall time: 0.0509 seconds
Process exited with code 0
Original token count: 60
Output:
476:                        if to_emit:
524:                    if to_emit:
743:                                if to_emit:
773:                        if to_emit:
959:                        if to_emit:
988:                    if to_emit:


nl -ba veena3modal/services/tts_runtime.py | sed -n '340,980p'


Output:
Chunk ID: 43f772
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 7225
Output:
   340	                else:
   341	                    logger.warning(f"  Failed to capture globals for {speaker_name}")
   342	            except Exception as e:
   343	                logger.warning(f"  Error caching globals for {speaker_name}: {e}")
   344
   345	    # Run async warmup in event loop
   346	    try:
   347	        loop = asyncio.get_event_loop()
   348	        if loop.is_running():
   349	            # Already in async context (unlikely at startup, but handle it)
   350	            import concurrent.futures
   351	            with concurrent.futures.ThreadPoolExecutor() as pool:
   352	                pool.submit(lambda: asyncio.run(_run_all())).result()
   353	        else:
   354	            loop.run_until_complete(_run_all())
   355	    except RuntimeError:
   356	        # No event loop exists yet
   357	        asyncio.run(_run_all())
   358
   359	    elapsed = (time.time() - t_start) * 1000
   360	    cached = len(runtime.speaker_global_cache)
   361	    total = len(SPEAKER_MAP)
   362	    logger.info(f"Speaker globals cached: {cached}/{total} speakers in {elapsed:.0f}ms")
   363
   364
   365	async def generate_speech(
   366	    text: str,
   367	    speaker: str,
   368	    temperature: float = 0.8,
   369	    top_k: int = 50,
   370	    top_p: float = 1.0,
   371	    max_tokens: int = 4096,
   372	    repetition_penalty: float = 1.05,
   373	    seed: Optional[int] = None,
   374	    output_sample_rate: str = "16khz",
   375	    request_id: Optional[str] = None,
   376	) -> Tuple[Optional[bytes], Dict[str, Any]]:
   377	    """
   378	    Generate speech audio (non-streaming).
   379
   380	    Args:
   381	        text: Text to synthesize (already normalized)
   382	        speaker: Internal speaker name (resolved)
   383	        temperature: Sampling temperature
   384	        top_k: Top-k sampling
   385	        top_p: Nucleus sampling
   386	        max_tokens: Maximum tokens to generate
   387	        repetition_penalty: Repetition penalty
   388	        seed: Random seed for reproducibility
   389	        output_sample_rate: "16khz" or "48khz" (triggers super-resolution)
   390
   391	    Returns:
   392	        Tuple of (audio_bytes, metrics_dict)
   393	        audio_bytes: WAV audio bytes (16kHz or 48kHz, 16-bit PCM) or None if failed
   394	        metrics_dict: Dictionary with timing metrics
   395
   396	    Raises:
   397	        RuntimeError: If runtime not initialized
   398	    """
   399	    if not is_initialized():
   400	        raise RuntimeError("TTS runtime not initialized")
   401
   402	    runtime = get_runtime()
   403	    metrics = {
   404	        "request_id": request_id,
   405	        "ttfb_ms": 0,
   406	        "generation_ms": 0,
   407	        "tokens_generated": 0,
   408	        "audio_duration_seconds": 0.0,
   409	        "sr_applied": False,
   410	        "output_sample_rate": 16000,
   411	    }
   412
   413	    if request_id is None:
   414	        import uuid
   415	        request_id = f"runtime-ns-{uuid.uuid4().hex[:10]}"
   416	        metrics["request_id"] = request_id
   417
   418	    active_requests = _with_inflight_delta(1)
   419	    start_time = time.time()
   420
   421	    pipeline_timings = {}
   422
   423	    try:
   424	        logger.info(
   425	            "tts_runtime_nonstream_start",
   426	            extra={
   427	                "speaker": speaker,
   428	                "request_id": request_id,
   429	                "text_length": len(text),
   430	                "request_inflight": active_requests,
   431	                "temperature": temperature,
   432	                "top_k": top_k,
   433	                "top_p": top_p,
   434	                "max_tokens": max_tokens,
   435	            },
   436	        )
   437
   438	        gen_kwargs = dict(
   439	            speaker=speaker,
   440	            text=text,
   441	            temperature=temperature,
   442	            top_k=top_k,
   443	            top_p=top_p,
   444	            max_tokens=max_tokens,
   445	            repetition_penalty=repetition_penalty,
   446	            seed=seed,
   447	        )
   448
   449	        if _timing_enabled():
   450	            audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
   451	                **gen_kwargs,
   452	                return_timing=True,
   453	                request_id=request_id,
   454	            )
   455	        else:
   456	            audio_bytes = await runtime.pipeline.generate_speech_indic(
   457	                **gen_kwargs,
   458	                request_id=request_id,
   459	            )
   460
   461	        generation_time = time.time() - start_time
   462	        metrics["generation_ms"] = int(generation_time * 1000)
   463	        metrics["ttfb_ms"] = metrics["generation_ms"]  # Non-streaming: TTFB = total time
   464
   465	        if pipeline_timings:
   466	            metrics.update({
   467	                "llm_token_total": pipeline_timings.get("token_total", 0),
   468	                "llm_batch_count": pipeline_timings.get("batch_count", 0),
   469	                "llm_batch_gpu_ms_total": pipeline_timings.get("llm_gpu_batch_ms_total", 0),
   470	                "llm_batch_gpu_ms_min": pipeline_timings.get("llm_gpu_batch_ms_min", 0),
   471	                "llm_batch_gpu_ms_max": pipeline_timings.get("llm_gpu_batch_ms_max", 0),
   472	                "llm_batch_gpu_ms_p50": pipeline_timings.get("llm_gpu_batch_ms_p50", 0),
   473	                "llm_decode_cpu_ms": pipeline_timings.get("decode_cpu_ms", 0),
   474	                "llm_parse_ms": pipeline_timings.get("parse_ms", 0),
   475	                "llm_wall_ms": pipeline_timings.get("llm_wall_ms", metrics["generation_ms"]),
   476	                "ttfb_ms": int(pipeline_timings.get("ttfb_ms", metrics["ttfb_ms"])),
   477	                "tokens_per_batch_min": pipeline_timings.get("tokens_per_batch_min"),
   478	                "tokens_per_batch_max": pipeline_timings.get("tokens_per_batch_max"),
   479	                "tokens_per_batch_p50": pipeline_timings.get("tokens_per_batch_p50"),
   480	            })
   481
   482	        if audio_bytes:
   483	            # Apply super-resolution if requested and available
   484	            sample_rate = 16000
   485	            logger.info(f"SR check: output_sample_rate={output_sample_rate!r}, "
   486	                       f"sr_service={runtime.sr_service is not None}, "
   487	                       f"is_loaded={runtime.sr_service.is_loaded if runtime.sr_service else False}")
   488
   489	            if output_sample_rate == "48khz" and runtime.sr_service and runtime.sr_service.is_loaded:
   490	                try:
   491	                    sr_start = time.time()
   492	                    logger.info(f"Applying super-resolution to {len(audio_bytes)} bytes...")
   493	                    audio_bytes = _apply_super_resolution(audio_bytes, runtime.sr_service)
   494	                    sr_time = (time.time() - sr_start) * 1000
   495	                    metrics["sr_ms"] = int(sr_time)
   496	                    metrics["sr_applied"] = True
   497	                    sample_rate = 48000
   498	                    logger.info(f"✅ Super-resolution applied in {sr_time:.1f}ms, output={len(audio_bytes)} bytes")
   499	                except Exception as e:
   500	                    logger.warning(f"Super-resolution failed, returning 16kHz: {e}")
   501	                    import traceback
   502	                    traceback.print_exc()
   503
   504	            metrics["output_sample_rate"] = sample_rate
   505
   506	            # Calculate audio duration
   507	            audio_duration = (len(audio_bytes) - 44) / (sample_rate * 2)  # -44 for WAV header
   508	            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
   509
   510	        if _timing_enabled():
   511	            logger.info("tts_runtime_nonstream_complete", extra=metrics)
   512
   513	        return audio_bytes, metrics
   514
   515	    except Exception as e:
   516	        logger.error(f"Speech generation failed: {e}")
   517	        raise
   518	    finally:
   519	        active_requests = _with_inflight_delta(-1)
   520	        if _timing_enabled():
   521	            logger.info(
   522	                "tts_runtime_nonstream_inflight",
   523	                extra={
   524	                    "request_id": request_id,
   525	                    "active_requests": active_requests,
   526	                },
   527	            )
   528
   529
   530	def _apply_super_resolution(audio_bytes: bytes, sr_service) -> bytes:
   531	    """
   532	    Apply super-resolution to audio bytes.
   533
   534	    Args:
   535	        audio_bytes: WAV audio at 16kHz
   536	        sr_service: SuperResolutionService instance
   537
   538	    Returns:
   539	        WAV audio at 48kHz
   540	    """
   541	    import numpy as np
   542	    import struct
   543	    import torch
   544
   545	    # Parse WAV header and extract PCM data
   546	    if len(audio_bytes) < 44:
   547	        raise ValueError("Invalid WAV data")
   548
   549	    # Extract PCM data (skip 44-byte WAV header)
   550	    pcm_data = np.frombuffer(audio_bytes[44:], dtype=np.int16)
   551
   552	    # Convert to float32 for SR model
   553	    audio_float = pcm_data.astype(np.float32) / 32768.0
   554
   555	    # Convert numpy to torch tensor for process_chunk
   556	    audio_tensor = torch.from_numpy(audio_float)
   557
   558	    # Apply super-resolution using process_chunk (16kHz -> 48kHz)
   559	    # process_chunk expects [batch, samples] or [samples] and returns [batch, samples]
   560	    upsampled_tensor = sr_service.process_chunk(audio_tensor)
   561
   562	    # Convert back to numpy, squeeze batch dim if present
   563	    upsampled = upsampled_tensor.squeeze().cpu().numpy()
   564
   565	    # Convert back to int16
   566	    upsampled_int16 = np.clip(upsampled * 32768.0, -32768, 32767).astype(np.int16)
   567
   568	    # Create new WAV header for 48kHz
   569	    sample_rate = 48000
   570	    num_channels = 1
   571	    bits_per_sample = 16
   572	    byte_rate = sample_rate * num_channels * bits_per_sample // 8
   573	    block_align = num_channels * bits_per_sample // 8
   574	    data_size = len(upsampled_int16) * 2
   575
   576	    wav_header = struct.pack(
   577	        '<4sI4s4sIHHIIHH4sI',
   578	        b'RIFF',
   579	        36 + data_size,
   580	        b'WAVE',
   581	        b'fmt ',
   582	        16,  # fmt chunk size
   583	        1,   # PCM format
   584	        num_channels,
   585	        sample_rate,
   586	        byte_rate,
   587	        block_align,
   588	        bits_per_sample,
   589	        b'data',
   590	        data_size,
   591	    )
   592
   593	    return wav_header + upsampled_int16.tobytes()
   594
   595
   596	async def generate_speech_chunked(
   597	    text: str,
   598	    speaker: str,
   599	    temperature: float = 0.8,
   600	    top_k: int = 50,
   601	    top_p: float = 1.0,
   602	    max_tokens: int = 4096,
   603	    repetition_penalty: float = 1.05,
   604	    seed: Optional[int] = None,
   605	    sample_rate: int = 16000,
   606	    output_sample_rate: str = "16khz",
   607	    request_id: Optional[str] = None,
   608	) -> Tuple[Optional[bytes], Dict[str, Any]]:
   609	    """
   610	    Generate speech with automatic text chunking for long inputs.
   611
   612	    Uses LongTextProcessor to split text and stitch audio.
   613
   614	    Args:
   615	        text: Text to synthesize
   616	        speaker: Internal speaker name
   617	        ... (same as generate_speech)
   618	        sample_rate: Internal generation sample rate (always 16000)
   619	        output_sample_rate: "16khz" or "48khz" (triggers super-resolution)
   620
   621	    Returns:
   622	        Tuple of (audio_bytes, metrics_dict)
   623	    """
   624	    if not is_initialized():
   625	        raise RuntimeError("TTS runtime not initialized")
   626
   627	    runtime = get_runtime()
   628	    if request_id is None:
   629	        import uuid
   630	        request_id = f"runtime-chunk-{uuid.uuid4().hex[:10]}"
   631	    metrics = {
   632	        "request_id": request_id,
   633	        "ttfb_ms": 0,
   634	        "generation_ms": 0,
   635	        "chunks_processed": 0,
   636	        "audio_duration_seconds": 0.0,
   637	        "text_chunked": False,
   638	        "sr_applied": False,
   639	        "output_sample_rate": 16000,
   640	    }
   641
   642	    active_requests = _with_inflight_delta(1)
   643	    start_time = time.time()
   644
   645	    try:
   646	        if _timing_enabled():
   647	            logger.info(
   648	                "tts_runtime_chunking_start",
   649	                extra={
   650	                    "speaker": speaker,
   651	                    "text_length": len(text),
   652	                    "request_inflight": active_requests,
   653	                    "chunking": True,
   654	                },
   655	            )
   656	        # Import long text processor
   657	        from veena3modal.processing.long_text_processor import LongTextProcessor
   658
   659	        long_processor = LongTextProcessor(pipeline=runtime.pipeline)
   660
   661	        # Check if chunking is needed
   662	        if long_processor.should_chunk(text):
   663	            metrics["text_chunked"] = True
   664	            if _timing_enabled():
   665	                chunk_result = await long_processor.generate_with_chunking(
   666	                    text=text,
   667	                    speaker=speaker,
   668	                    temperature=temperature,
   669	                    top_k=top_k,
   670	                    top_p=top_p,
   671	                    max_tokens=max_tokens,
   672	                    repetition_penalty=repetition_penalty,
   673	                    seed=seed,
   674	                    sample_rate=sample_rate,
   675	                    request_id=request_id,
   676	                    return_timing=True,
   677	                )
   678	                if isinstance(chunk_result, tuple):
   679	                    audio_bytes, chunk_timing = chunk_result
   680	                else:
   681	                    audio_bytes = chunk_result
   682	                    chunk_timing = {}
   683	            else:
   684	                audio_bytes = await long_processor.generate_with_chunking(
   685	                    text=text,
   686	                    speaker=speaker,
   687	                    temperature=temperature,
   688	                    top_k=top_k,
   689	                    top_p=top_p,
   690	                    max_tokens=max_tokens,
   691	                    repetition_penalty=repetition_penalty,
   692	                    seed=seed,
   693	                    sample_rate=sample_rate,
   694	                    request_id=request_id,
   695	                )
   696	                chunk_timing = {}
   697	        else:
   698	            # Short text: direct generation
   699	            gen_kwargs = dict(
   700	                speaker=speaker,
   701	                text=text,
   702	                temperature=temperature,
   703	                top_k=top_k,
   704	                top_p=top_p,
   705	                max_tokens=max_tokens,
   706	                repetition_penalty=repetition_penalty,
   707	                seed=seed,
   708	                request_id=request_id,
   709	            )
   710	            if _timing_enabled():
   711	                audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
   712	                    **gen_kwargs,
   713	                    return_timing=True,
   714	                )
   715	                chunk_timing = pipeline_timings
   716	            else:
   717	                audio_bytes = await runtime.pipeline.generate_speech_indic(
   718	                    **gen_kwargs,
   719	                )
   720	                chunk_timing = {}
   721
   722	        generation_time = time.time() - start_time
   723	        metrics["generation_ms"] = int(generation_time * 1000)
   724	        metrics["ttfb_ms"] = metrics["generation_ms"]
   725
   726	        if chunk_timing:
   727	            metrics.update({
   728	                "llm_token_total": chunk_timing.get("token_total", 0),
   729	                "llm_batch_count": chunk_timing.get("batch_count", 0),
   730	                "llm_batch_gpu_ms_total": chunk_timing.get("llm_gpu_batch_ms_total", 0),
   731	                "llm_batch_wall_ms_total": chunk_timing.get("llm_wall_ms", metrics["generation_ms"]),
   732	                "llm_parse_ms": chunk_timing.get("parse_ms", 0),
   733	                "llm_decode_cpu_ms": chunk_timing.get("decode_cpu_ms", 0),
   734	                "llm_batch_wall_ms_min": chunk_timing.get("llm_batch_wall_ms_min", 0),
   735	                "llm_batch_wall_ms_max": chunk_timing.get("llm_batch_wall_ms_max", 0),
   736	                "llm_batch_gpu_ms_min": chunk_timing.get("llm_batch_gpu_ms_min", 0),
   737	                "llm_batch_gpu_ms_max": chunk_timing.get("llm_batch_gpu_ms_max", 0),
   738	                "llm_batch_gpu_ms_p50": chunk_timing.get("llm_batch_gpu_ms_p50", 0),
   739	                "tokens_per_batch_min": chunk_timing.get("tokens_per_batch_min"),
   740	                "tokens_per_batch_max": chunk_timing.get("tokens_per_batch_max"),
   741	                "tokens_per_batch_p50": chunk_timing.get("tokens_per_batch_p50"),
   742	                "llm_batch_tokens": chunk_timing.get("token_batches", []),
   743	            })
   744	            if metrics["text_chunked"]:
   745	                metrics["chunks_processed"] = chunk_timing.get("chunks_processed", 0)
   746
   747	        if audio_bytes:
   748	            # Apply super-resolution if requested and available
   749	            final_sample_rate = 16000
   750	            if output_sample_rate == "48khz" and runtime.sr_service and runtime.sr_service.is_loaded:
   751	                try:
   752	                    sr_start = time.time()
   753	                    logger.info(f"Applying super-resolution to chunked audio ({len(audio_bytes)} bytes)...")
   754	                    audio_bytes = _apply_super_resolution(audio_bytes, runtime.sr_service)
   755	                    sr_time = (time.time() - sr_start) * 1000
   756	                    metrics["sr_ms"] = int(sr_time)
   757	                    metrics["sr_applied"] = True
   758	                    final_sample_rate = 48000
   759	                    logger.info(f"✅ Super-resolution applied in {sr_time:.1f}ms, output={len(audio_bytes)} bytes")
   760	                except Exception as e:
   761	                    logger.warning(f"Super-resolution failed, returning 16kHz: {e}")
   762	                    import traceback
   763	                    traceback.print_exc()
   764
   765	            metrics["output_sample_rate"] = final_sample_rate
   766	            audio_duration = (len(audio_bytes) - 44) / (final_sample_rate * 2)
   767	            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
   768
   769	        if _timing_enabled():
   770	            metrics["active_requests"] = active_requests - 1
   771	            metrics["request_inflight"] = active_requests - 1
   772	            logger.info("tts_runtime_chunking_complete", extra=metrics)
   773	        return audio_bytes, metrics
   774
   775	    except Exception as e:
   776	        logger.error(f"Chunked speech generation failed: {e}")
   777	        raise
   778	    finally:
   779	        _with_inflight_delta(-1)
   780
   781
   782	from typing import AsyncGenerator
   783
   784
   785	async def generate_speech_streaming(
   786	    text: str,
   787	    speaker: str,
   788	    temperature: float = 0.8,
   789	    top_k: int = 50,
   790	    top_p: float = 1.0,
   791	    max_tokens: int = 4096,
   792	    repetition_penalty: float = 1.05,
   793	    seed: Optional[int] = None,
   794	    enable_chunking: bool = True,
   795	    request_id: Optional[str] = None,
   796	) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
   797	    """
   798	    Generate speech audio with true streaming (yields chunks as they're generated).
   799
   800	    This is the core streaming implementation for M4.
   801	    First yield includes WAV header + first PCM chunk.
   802	    Subsequent yields are raw PCM chunks.
   803
   804	    Args:
   805	        text: Text to synthesize (already normalized)
   806	        speaker: Internal speaker name (resolved)
   807	        temperature: Sampling temperature
   808	        top_k: Top-k sampling
   809	        top_p: Nucleus sampling
   810	        max_tokens: Maximum tokens to generate
   811	        repetition_penalty: Repetition penalty
   812	        seed: Random seed for reproducibility
   813	        enable_chunking: Enable text chunking for long inputs with voice consistency
   814
   815	    Yields:
   816	        Tuple of (audio_bytes, metrics_dict)
   817	        - First yield: WAV header (44 bytes) prepended to first PCM chunk
   818	        - Subsequent yields: Raw PCM chunks (int16, 16kHz)
   819	        - Final yield: metrics_dict has final timing info
   820
   821	    Raises:
   822	        RuntimeError: If runtime not initialized or streaming pipeline unavailable
   823	    """
   824	    if not is_initialized():
   825	        raise RuntimeError("TTS runtime not initialized")
   826
   827	    runtime = get_runtime()
   828
   829	    if runtime.streaming_pipeline is None:
   830	        raise RuntimeError("Streaming pipeline not available")
   831
   832	    # Import audio utils for WAV header
   833	    from veena3modal.audio.utils import create_wav_header
   834
   835	    # Metrics tracking
   836	    metrics = {
   837	        "ttfb_ms": 0,
   838	        "chunks_sent": 0,
   839	        "total_bytes": 0,
   840	        "audio_duration_seconds": 0.0,
   841	        "text_chunked": False,
   842	    }
   843
   844	    start_time = time.time()
   845	    first_chunk_time = None
   846	    total_pcm_bytes = 0
   847	    sample_rate = 16000  # BiCodec sample rate
   848	    active_requests = _with_inflight_delta(1)
   849	    if _timing_enabled():
   850	        logger.info(
   851	            "tts_runtime_stream_start",
   852	            extra={
   853	                "speaker": speaker,
   854	                "text_length": len(text),
   855	                "request_inflight": active_requests,
   856	                "chunking": enable_chunking,
   857	                "request_id": request_id,
   858	            },
   859	        )
   860
   861	    try:
   862	        # Check if we need text chunking
   863	        from veena3modal.processing.long_text_processor import LongTextProcessor
   864	        long_processor = LongTextProcessor(pipeline=runtime.pipeline)
   865	        needs_chunking = enable_chunking and long_processor.should_chunk(text)
   866
   867	        if needs_chunking:
   868	            # Chunked streaming with voice consistency (global token caching)
   869	            metrics["text_chunked"] = True
   870	            async for audio_chunk, chunk_metrics in _stream_chunked_text(
   871	                runtime,
   872	                long_processor,
   873	                text,
   874	                speaker,
   875	                temperature,
   876	                top_k,
   877	                top_p,
   878	                max_tokens,
   879	                repetition_penalty,
   880	                seed,
   881	                sample_rate,
   882	                request_id,
   883	            ):
   884	                if first_chunk_time is None:
   885	                    first_chunk_time = time.time()
   886	                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
   887	                    # Create streaming WAV header (size=0 for unknown length)
   888	                    wav_header = create_wav_header(sample_rate=sample_rate, data_size=0)
   889	                    audio_chunk = wav_header + audio_chunk
   890
   891	                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
   892	                metrics["chunks_sent"] += 1
   893	                metrics["total_bytes"] = total_pcm_bytes + 44  # Include header
   894	                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
   895	                if chunk_metrics:
   896	                    metrics.update(chunk_metrics)
   897
   898	                yield audio_chunk, metrics
   899	        else:
   900	            # Simple streaming (no text chunking)
   901	            # OPTIMIZATION: If we have pre-cached global tokens for this speaker,
   902	            # use continuation mode to skip the ~110ms global token pre-roll.
   903	            # The model jumps straight to semantic token generation.
   904	            cached_globals = runtime.speaker_global_cache.get(speaker)
   905
   906	            if cached_globals:
   907	                stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic_continuation(
   908	                    speaker=speaker,
   909	                    text=text,
   910	                    global_ids=cached_globals,
   911	                    temperature=temperature,
   912	                    top_k=top_k,
   913	                    top_p=top_p,
   914	                    max_tokens=max_tokens,
   915	                    repetition_penalty=repetition_penalty,
   916	                    seed=seed,
   917	                    request_id=request_id,
   918	                )
   919	            else:
   920	                stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic(
   921	                    speaker=speaker,
   922	                    text=text,
   923	                    temperature=temperature,
   924	                    top_k=top_k,
   925	                    top_p=top_p,
   926	                    max_tokens=max_tokens,
   927	                    repetition_penalty=repetition_penalty,
   928	                    seed=seed,
   929	                    request_id=request_id,
   930	                )
   931
   932	            async for audio_chunk in stream_gen:
   933	                if first_chunk_time is None:
   934	                    first_chunk_time = time.time()
   935	                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
   936	                    wav_header = create_wav_header(sample_rate=sample_rate, data_size=0)
   937	                    audio_chunk = wav_header + audio_chunk
   938
   939	                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
   940	                metrics["chunks_sent"] += 1
   941	                metrics["total_bytes"] = total_pcm_bytes + 44
   942	                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
   943	                yield audio_chunk, metrics
   944	    finally:
   945	        active_requests = _with_inflight_delta(-1)
   946	        if _timing_enabled():
   947	            logger.info(
   948	                "tts_runtime_stream_end",
   949	                extra={
   950	                    "speaker": speaker,
   951	                    "request_id": request_id,
   952	                    "request_inflight": active_requests,
   953	                    "chunks_sent": metrics.get("chunks_sent", 0),
   954	                },
   955	            )
   956
   957
   958	async def _stream_chunked_text(
   959	    runtime: TTSRuntime,
   960	    long_processor,
   961	    text: str,
   962	    speaker: str,
   963	    temperature: float,
   964	    top_k: int,
   965	    top_p: float,
   966	    max_tokens: int,
   967	    repetition_penalty: float,
   968	    seed: Optional[int],
   969	    sample_rate: int,
   970	    request_id: Optional[str] = None,
   971	) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
   972	    """
   973	    Internal helper: stream chunked text with voice consistency.
   974
   975	    Uses global token caching from first chunk to maintain voice across chunks.
   976	    """
   977	    from veena3modal.audio.crossfade import crossfade_bytes_int16
   978
   979	    # Chunk text using the long text processor's chunker
   980	    chunks = long_processor.chunk_text(text)


nl -ba veena3modal/services/tts_runtime.py | sed -n '980,1210p'


Output:
Chunk ID: 809055
Wall time: 0.0517 seconds
Process exited with code 0
Original token count: 1046
Output:
   980	    chunks = long_processor.chunk_text(text)
   981
   982	    if not chunks:
   983	        return
   984
   985	    captured_globals: Optional[List[int]] = None
   986	    previous_chunk_tail: Optional[bytes] = None
   987	    chunk_metrics = {"chunks_processed": 0}
   988
   989	    for i, chunk_text in enumerate(chunks):
   990	        chunk_metrics["chunks_processed"] = i + 1
   991
   992	        if i == 0:
   993	            # First chunk: capture global tokens for voice consistency
   994	            async for audio_bytes, global_ids in runtime.streaming_pipeline.generate_speech_stream_indic_first_chunk(
   995	                speaker=speaker,
   996	                text=chunk_text,
   997	                temperature=temperature,
   998	                top_k=top_k,
   999	                top_p=top_p,
  1000	                max_tokens=max_tokens,
  1001	                repetition_penalty=repetition_penalty,
  1002	                seed=seed,
  1003	                request_id=request_id,
  1004	            ):
  1005	                if captured_globals is None and global_ids:
  1006	                    captured_globals = global_ids
  1007
  1008	                # Crossfade with previous tail (inter-chunk stitching)
  1009	                to_emit, previous_chunk_tail = crossfade_bytes_int16(
  1010	                    previous_chunk_tail,
  1011	                    audio_bytes,
  1012	                    sample_rate_hz=sample_rate,
  1013	                    crossfade_ms=50,
  1014	                )
  1015
  1016	                if to_emit:
  1017	                    yield to_emit, chunk_metrics
  1018	        else:
  1019	            # Continuation chunks: use captured globals for voice consistency
  1020	            if captured_globals is None:
  1021	                logger.warning(f"No captured globals for chunk {i+1}, using regular streaming")
  1022	                async for audio_bytes in runtime.streaming_pipeline.generate_speech_stream_indic(
  1023	                    speaker=speaker,
  1024	                    text=chunk_text,
  1025	                    temperature=temperature,
  1026	                    top_k=top_k,
  1027	                    top_p=top_p,
  1028	                    max_tokens=max_tokens,
  1029	                    repetition_penalty=repetition_penalty,
  1030	                    seed=seed,
  1031	                    request_id=request_id,
  1032	                ):
  1033	                    to_emit, previous_chunk_tail = crossfade_bytes_int16(
  1034	                        previous_chunk_tail,
  1035	                        audio_bytes,
  1036	                        sample_rate_hz=sample_rate,
  1037	                        crossfade_ms=50,
  1038	                    )
  1039	                    if to_emit:
  1040	                        yield to_emit, chunk_metrics
  1041	            else:
  1042	                # Use continuation method with cached globals
  1043	                async for audio_bytes in runtime.streaming_pipeline.generate_speech_stream_indic_continuation(
  1044	                    speaker=speaker,
  1045	                    text=chunk_text,
  1046	                    global_ids=captured_globals,
  1047	                    temperature=temperature,
  1048	                    top_k=top_k,
  1049	                    top_p=top_p,
  1050	                    max_tokens=max_tokens,
  1051	                    repetition_penalty=repetition_penalty,
  1052	                    seed=seed,
  1053	                    request_id=request_id,
  1054	                ):
  1055	                    to_emit, previous_chunk_tail = crossfade_bytes_int16(
  1056	                        previous_chunk_tail,
  1057	                        audio_bytes,
  1058	                        sample_rate_hz=sample_rate,
  1059	                        crossfade_ms=50,
  1060	                    )
  1061	                    if to_emit:
  1062	                        yield to_emit, chunk_metrics
  1063
  1064	    # Flush remaining tail
  1065	    if previous_chunk_tail:
  1066	        yield previous_chunk_tail, chunk_metrics


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
     if not is_initialized():
         raise RuntimeError("TTS runtime not initialized")

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

     # Metrics tracking
     metrics = {
+        "request_id": request_id,
+        "speaker": speaker,
         "ttfb_ms": 0,
         "chunks_sent": 0,
         "total_bytes": 0,
         "audio_duration_seconds": 0.0,
         "text_chunked": False,
+        "text_length": len(text),
     }
+    pipeline_metrics: Dict[str, Any] = {
+        "request_id": request_id,
+        "speaker": speaker,
+        "text_length": len(text),
+    }
@@
-        if needs_chunking:
+        if needs_chunking:
             # Chunked streaming with voice consistency (global token caching)
             metrics["text_chunked"] = True
             async for audio_chunk, chunk_metrics in _stream_chunked_text(
                 runtime,
                 long_processor,
@@
                 repetition_penalty,
                 seed,
                 sample_rate,
                 request_id,
+                request_metrics=pipeline_metrics,
             ):
                 if first_chunk_time is None:
                     first_chunk_time = time.time()
                     metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
@@
                 metrics["chunks_sent"] += 1
                 metrics["total_bytes"] = total_pcm_bytes + 44  # Include header
                 metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
                 if chunk_metrics:
                     metrics.update(chunk_metrics)
@@
         else:
             # Simple streaming (no text chunking)
@@
                 stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic_continuation(
                     speaker=speaker,
                     text=text,
                     global_ids=captured_globals,
                     temperature=temperature,
@@
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=pipeline_metrics,
                 )
             else:
                 stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic(
                     speaker=speaker,
@@
                     top_k=top_k,
                     top_p=top_p,
                     max_tokens=max_tokens,
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=pipeline_metrics,
                 )

             async for audio_chunk in stream_gen:
                 if first_chunk_time is None:
                     first_chunk_time = time.time()
@@
                 total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
                 metrics["chunks_sent"] += 1
                 metrics["total_bytes"] = total_pcm_bytes + 44
                 metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
                 yield audio_chunk, metrics
     finally:
         active_requests = _with_inflight_delta(-1)
+        metrics.update(pipeline_metrics)
+        if not metrics.get("generation_ms"):
+            metrics["generation_ms"] = int((time.time() - start_time) * 1000)
         if _timing_enabled():
             logger.info(
                 "tts_runtime_stream_end",
                 extra={
                     "speaker": speaker,
                     "request_id": request_id,
                     "request_inflight": active_requests,
                     "chunks_sent": metrics.get("chunks_sent", 0),
                 },
             )
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
    if not is_initialized():
        raise RuntimeError("TTS runtime not initialized")

    runtime = get_runtime()

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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
     if not is_initialized():
         raise RuntimeError("TTS runtime not initialized")

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

     if runtime.streaming_pipeline is None:
         raise RuntimeError("Streaming pipeline not available")
@@
-    metrics = {
-        "ttfb_ms": 0,
-        "chunks_sent": 0,
-        "total_bytes": 0,
-        "audio_duration_seconds": 0.0,
-        "text_chunked": False,
-    }
+    metrics = {
+        "request_id": request_id,
+        "speaker": speaker,
+        "ttfb_ms": 0,
+        "chunks_sent": 0,
+        "total_bytes": 0,
+        "audio_duration_seconds": 0.0,
+        "text_chunked": False,
+        "text_length": len(text),
+    }
+    pipeline_metrics: Dict[str, Any] = {
+        "request_id": request_id,
+        "speaker": speaker,
+        "text_length": len(text),
+        "text_chunked": False,
+    }
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                 top_k,
                 top_p,
                 max_tokens,
                 repetition_penalty,
                 seed,
                 sample_rate,
                 request_id,
+                request_metrics=pipeline_metrics,
             ):
                 if first_chunk_time is None:
                     first_chunk_time = time.time()
                     metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                     temperature=temperature,
                     top_k=top_k,
                     top_p=top_p,
                     max_tokens=max_tokens,
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=pipeline_metrics,
                 )
             else:
                 stream_gen = runtime.streaming_pipeline.generate_speech_stream_indic(
                     speaker=speaker,
@@
                     top_k=top_k,
                     top_p=top_p,
                     max_tokens=max_tokens,
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=pipeline_metrics,
                 )
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
     finally:
         active_requests = _with_inflight_delta(-1)
+        metrics.update(pipeline_metrics)
+        metrics["generation_ms"] = int((time.time() - start_time) * 1000)
+        metrics["text_chunked"] = bool(metrics.get("text_chunked", False))
         if _timing_enabled():
             logger.info(
                 "tts_runtime_stream_end",
                 extra={
                     "speaker": speaker,
                     "request_id": request_id,
                     "request_inflight": active_requests,
                     "chunks_sent": metrics.get("chunks_sent", 0),
+                    "generation_ms": metrics["generation_ms"],
                 },
             )
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
 async def _stream_chunked_text(
     runtime: TTSRuntime,
@@
     repetition_penalty: float,
     seed: Optional[int],
     sample_rate: int,
     request_id: Optional[str] = None,
+    request_metrics: Optional[Dict[str, Any]] = None,
 ) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
@@
-    chunk_metrics = {"chunks_processed": 0}
+    chunk_metrics = request_metrics if request_metrics is not None else {"request_id": request_id}
+    if "request_id" not in chunk_metrics:
+        chunk_metrics["request_id"] = request_id
+    if "chunks_processed" not in chunk_metrics:
+        chunk_metrics["chunks_processed"] = 0
@@
                 repetition_penalty=repetition_penalty,
                 seed=seed,
                 request_id=request_id,
+                request_metrics=chunk_metrics,
             ):
                 if captured_globals is None and global_ids:
                     captured_globals = global_ids
@@
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=chunk_metrics,
                 ):
@@
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=chunk_metrics,
                 ):
                     to_emit, previous_chunk_tail = crossfade_bytes_int16(
@@
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=chunk_metrics,
                 ):
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
                    repetition_penalty=repetition_penalty,
                    seed=seed,
                    request_id=request_id,
                ):


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
 async def _stream_chunked_text(
@@
     repetition_penalty: float,
     seed: Optional[int],
     sample_rate: int,
     request_id: Optional[str] = None,
+    request_metrics: Optional[Dict[str, Any]] = None,
 ) -> AsyncGenerator[Tuple[bytes, Dict[str, Any]], None]:
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
-    chunk_metrics = {"chunks_processed": 0}
+    chunk_metrics = request_metrics if request_metrics is not None else {"request_id": request_id}
+    if "request_id" not in chunk_metrics:
+        chunk_metrics["request_id"] = request_id
+    if "chunks_processed" not in chunk_metrics:
+        chunk_metrics["chunks_processed"] = 0
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                 top_p=top_p,
                 max_tokens=max_tokens,
                 repetition_penalty=repetition_penalty,
                 seed=seed,
                 request_id=request_id,
+                request_metrics=chunk_metrics,
             ):
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                     top_p=top_p,
                     max_tokens=max_tokens,
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=chunk_metrics,
                 ):
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                     top_p=top_p,
                     max_tokens=max_tokens,
                     repetition_penalty=repetition_penalty,
                     seed=seed,
                     request_id=request_id,
+                    request_metrics=chunk_metrics,
                 ):
*** End Patch


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


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                 "speaker": speaker,
+                "request_id": request_id,
                 "text_length": len(text),
                 "request_inflight": active_requests,
                 "chunking": True,
                 "chunking": True,
                 },
             )
@@
                 audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
                     **gen_kwargs,
                     return_timing=True,
+                    request_id=request_id,
                 )
                 chunk_timing = pipeline_timings
             else:
                 audio_bytes = await runtime.pipeline.generate_speech_indic(
                     **gen_kwargs,
+                    request_id=request_id,
                 )
                 chunk_timing = {}
*** End Patch


Output:
apply_patch verification failed: Failed to find expected lines in /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py:
                "speaker": speaker,
                "text_length": len(text),
                "request_inflight": active_requests,
                "chunking": True,
                "chunking": True,
                },
            )


nl -ba veena3modal/services/tts_runtime.py | sed -n '638,718p'


Output:
Chunk ID: 1635bf
Wall time: 0.0516 seconds
Process exited with code 0
Original token count: 888
Output:
   638	        "sr_applied": False,
   639	        "output_sample_rate": 16000,
   640	    }
   641
   642	    active_requests = _with_inflight_delta(1)
   643	    start_time = time.time()
   644
   645	    try:
   646	        if _timing_enabled():
   647	            logger.info(
   648	                "tts_runtime_chunking_start",
   649	                extra={
   650	                    "speaker": speaker,
   651	                    "text_length": len(text),
   652	                    "request_inflight": active_requests,
   653	                    "chunking": True,
   654	                },
   655	            )
   656	        # Import long text processor
   657	        from veena3modal.processing.long_text_processor import LongTextProcessor
   658
   659	        long_processor = LongTextProcessor(pipeline=runtime.pipeline)
   660
   661	        # Check if chunking is needed
   662	        if long_processor.should_chunk(text):
   663	            metrics["text_chunked"] = True
   664	            if _timing_enabled():
   665	                chunk_result = await long_processor.generate_with_chunking(
   666	                    text=text,
   667	                    speaker=speaker,
   668	                    temperature=temperature,
   669	                    top_k=top_k,
   670	                    top_p=top_p,
   671	                    max_tokens=max_tokens,
   672	                    repetition_penalty=repetition_penalty,
   673	                    seed=seed,
   674	                    sample_rate=sample_rate,
   675	                    request_id=request_id,
   676	                    return_timing=True,
   677	                )
   678	                if isinstance(chunk_result, tuple):
   679	                    audio_bytes, chunk_timing = chunk_result
   680	                else:
   681	                    audio_bytes = chunk_result
   682	                    chunk_timing = {}
   683	            else:
   684	                audio_bytes = await long_processor.generate_with_chunking(
   685	                    text=text,
   686	                    speaker=speaker,
   687	                    temperature=temperature,
   688	                    top_k=top_k,
   689	                    top_p=top_p,
   690	                    max_tokens=max_tokens,
   691	                    repetition_penalty=repetition_penalty,
   692	                    seed=seed,
   693	                    sample_rate=sample_rate,
   694	                    request_id=request_id,
   695	                )
   696	                chunk_timing = {}
   697	        else:
   698	            # Short text: direct generation
   699	            gen_kwargs = dict(
   700	                speaker=speaker,
   701	                text=text,
   702	                temperature=temperature,
   703	                top_k=top_k,
   704	                top_p=top_p,
   705	                max_tokens=max_tokens,
   706	                repetition_penalty=repetition_penalty,
   707	                seed=seed,
   708	                request_id=request_id,
   709	            )
   710	            if _timing_enabled():
   711	                audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
   712	                    **gen_kwargs,
   713	                    return_timing=True,
   714	                )
   715	                chunk_timing = pipeline_timings
   716	            else:
   717	                audio_bytes = await runtime.pipeline.generate_speech_indic(
   718	                    **gen_kwargs,


Input:
*** Begin Patch
*** Update File: /home/ubuntu/veenaModal/veena3modal/services/tts_runtime.py
@@
                 extra={
                     "speaker": speaker,
+                    "request_id": request_id,
                     "text_length": len(text),
                     "request_inflight": active_requests,
                     "chunking": True,
                 },
             )
@@
                 audio_bytes, pipeline_timings = await runtime.pipeline.generate_speech_indic(
                     **gen_kwargs,
                     return_timing=True,
+                    request_id=request_id,
                 )
                 chunk_timing = pipeline_timings
             else:
                 audio_bytes = await runtime.pipeline.generate_speech_indic(
                     **gen_kwargs,
+                    request_id=request_id,
                 )
*** End Patch


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


<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>

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

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

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

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


I’ll finish this by wiring request-level timing through the runtime + API paths, then extend/add the local stress benchmark script to capture per-request GPU/LLM/decode concurrency metrics. I’ll inspect only the touched files needed for that and then apply a single patch batch.


rg --line-number "def generate_speech_chunked|def generate_speech_streaming|def generate_speech\(|return_timing|request_id|metrics|tts_runtime_chunking_start" veena3modal/services/tts_runtime.py


rg --line-number "def generate_with_chunking|return_timing|request_id|pipeline_timings|timings" veena3modal/processing/long_text_processor.py


rg --line-number "generate_speech_streaming\(|request_id|metrics" veena3modal/api/websocket_handler.py


Output:
Chunk ID: 9787fe
Wall time: 0.0507 seconds
Process exited with code 0
Original token count: 1596
Output:
312:        request_id = f"warmup-{speaker_name}-{int(time.time() * 1000)}"
318:            request_id=request_id,
365:async def generate_speech(
375:    request_id: Optional[str] = None,
392:        Tuple of (audio_bytes, metrics_dict)
394:        metrics_dict: Dictionary with timing metrics
403:    metrics = {
404:        "request_id": request_id,
413:    if request_id is None:
415:        request_id = f"runtime-ns-{uuid.uuid4().hex[:10]}"
416:        metrics["request_id"] = request_id
428:                "request_id": request_id,
452:                return_timing=True,
453:                request_id=request_id,
458:                request_id=request_id,
462:        metrics["generation_ms"] = int(generation_time * 1000)
463:        metrics["ttfb_ms"] = metrics["generation_ms"]  # Non-streaming: TTFB = total time
466:            metrics.update({
475:                "llm_wall_ms": pipeline_timings.get("llm_wall_ms", metrics["generation_ms"]),
476:                "ttfb_ms": int(pipeline_timings.get("ttfb_ms", metrics["ttfb_ms"])),
495:                    metrics["sr_ms"] = int(sr_time)
496:                    metrics["sr_applied"] = True
504:            metrics["output_sample_rate"] = sample_rate
508:            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
511:            logger.info("tts_runtime_nonstream_complete", extra=metrics)
513:        return audio_bytes, metrics
524:                    "request_id": request_id,
596:async def generate_speech_chunked(
607:    request_id: Optional[str] = None,
622:        Tuple of (audio_bytes, metrics_dict)
628:    if request_id is None:
630:        request_id = f"runtime-chunk-{uuid.uuid4().hex[:10]}"
631:    metrics = {
632:        "request_id": request_id,
648:                "tts_runtime_chunking_start",
651:                    "request_id": request_id,
664:            metrics["text_chunked"] = True
676:                    request_id=request_id,
677:                    return_timing=True,
695:                    request_id=request_id,
709:                request_id=request_id,
714:                    return_timing=True,
715:                    request_id=request_id,
721:                    request_id=request_id,
726:        metrics["generation_ms"] = int(generation_time * 1000)
727:        metrics["ttfb_ms"] = metrics["generation_ms"]
730:            metrics.update({
734:                "llm_batch_wall_ms_total": chunk_timing.get("llm_wall_ms", metrics["generation_ms"]),
747:            if metrics["text_chunked"]:
748:                metrics["chunks_processed"] = chunk_timing.get("chunks_processed", 0)
759:                    metrics["sr_ms"] = int(sr_time)
760:                    metrics["sr_applied"] = True
768:            metrics["output_sample_rate"] = final_sample_rate
770:            metrics["audio_duration_seconds"] = max(0.0, audio_duration)
773:            metrics["active_requests"] = active_requests - 1
774:            metrics["request_inflight"] = active_requests - 1
775:            logger.info("tts_runtime_chunking_complete", extra=metrics)
776:        return audio_bytes, metrics
788:async def generate_speech_streaming(
798:    request_id: Optional[str] = None,
819:        Tuple of (audio_bytes, metrics_dict)
822:        - Final yield: metrics_dict has final timing info
832:    if request_id is None:
834:        request_id = f"runtime-stream-{uuid.uuid4().hex[:10]}"
843:    metrics = {
844:        "request_id": request_id,
853:    pipeline_metrics: Dict[str, Any] = {
854:        "request_id": request_id,
873:                "request_id": request_id,
885:            metrics["text_chunked"] = True
886:            async for audio_chunk, chunk_metrics in _stream_chunked_text(
898:                request_id,
899:                request_metrics=pipeline_metrics,
903:                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
908:                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
909:                metrics["chunks_sent"] += 1
910:                metrics["total_bytes"] = total_pcm_bytes + 44  # Include header
911:                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
912:                if chunk_metrics:
913:                    metrics.update(chunk_metrics)
915:                yield audio_chunk, metrics
934:                    request_id=request_id,
935:                    request_metrics=pipeline_metrics,
947:                    request_id=request_id,
948:                    request_metrics=pipeline_metrics,
954:                    metrics["ttfb_ms"] = int((first_chunk_time - start_time) * 1000)
958:                total_pcm_bytes += len(audio_chunk) - (44 if metrics["chunks_sent"] == 0 else 0)
959:                metrics["chunks_sent"] += 1
960:                metrics["total_bytes"] = total_pcm_bytes + 44
961:                metrics["audio_duration_seconds"] = total_pcm_bytes / (sample_rate * 2)
962:                yield audio_chunk, metrics
965:        metrics.update(pipeline_metrics)
966:        metrics["generation_ms"] = int((time.time() - start_time) * 1000)
967:        metrics["text_chunked"] = bool(metrics.get("text_chunked", False))
973:                    "request_id": request_id,
975:                    "chunks_sent": metrics.get("chunks_sent", 0),
976:                    "generation_ms": metrics["generation_ms"],
993:    request_id: Optional[str] = None,
994:    request_metrics: Optional[Dict[str, Any]] = None,
1011:    chunk_metrics = request_metrics if request_metrics is not None else {"request_id": request_id}
1012:    if "request_id" not in chunk_metrics:
1013:        chunk_metrics["request_id"] = request_id
1014:    if "chunks_processed" not in chunk_metrics:
1015:        chunk_metrics["chunks_processed"] = 0
1018:        chunk_metrics["chunks_processed"] = i + 1
1031:                request_id=request_id,
1032:                request_metrics=chunk_metrics,
1046:                    yield to_emit, chunk_metrics
1060:                    request_id=request_id,
1061:                    request_metrics=chunk_metrics,
1070:                        yield to_emit, chunk_metrics
1083:                    request_id=request_id,
1084:                    request_metrics=chunk_metrics,
1093:                        yield to_emit, chunk_metrics
1097:        yield previous_chunk_tail, chunk_metrics


Output:
Chunk ID: 651399
Wall time: 0.0506 seconds
Process exited with code 0
Original token count: 24
Output:
141:    async def generate_with_chunking(
332:    async def generate_with_chunking_streaming(


Output:
Chunk ID: 8e3c7a
Wall time: 0.0506 seconds
Process exited with code 0
Original token count: 1074
Output:
28:# Prometheus metrics
29:from veena3modal.shared.metrics import (
30:    record_request_received as metrics_request_received,
31:    record_request_completed as metrics_request_completed,
32:    record_request_failed as metrics_request_failed,
37:    get_metrics_text,
38:    get_content_type as get_metrics_content_type,
161:    @app.get("/v1/tts/metrics")
162:    async def tts_metrics():
164:        Prometheus metrics endpoint.
166:        Returns metrics in Prometheus text format for scraping.
169:            content=get_metrics_text(),
170:            media_type=get_metrics_content_type(),
184:        request_id = str(uuid.uuid4())
199:                        request_id=request_id,
201:                    headers={"X-Request-ID": request_id},
216:                        request_id=request_id,
218:                    headers={"X-Request-ID": request_id},
228:                    headers["X-Request-ID"] = request_id
234:                            request_id=request_id,
256:                        "request_id": request_id,
259:                headers={"X-Request-ID": request_id},
270:                        "request_id": request_id,
273:                headers={"X-Request-ID": request_id},
291:        set_request_context(request_id=request_id)
293:        # Log request received (lifecycle event + metrics)
295:            request_id=request_id,
301:        metrics_request_received(speaker=speaker, stream=req.stream, format=req.format)
305:            return await _handle_streaming_request(req, request_id, text, speaker, start_time)
317:                audio_bytes, metrics = await tts_runtime.generate_speech_chunked(
330:                audio_bytes, metrics = await tts_runtime.generate_speech(
349:                            "request_id": request_id,
352:                    headers={"X-Request-ID": request_id},
357:            audio_duration = metrics.get("audio_duration_seconds", 0)
364:            # Use actual output sample rate from metrics
365:            output_sample_rate = metrics.get("output_sample_rate", req.sample_rate)
368:                "X-Request-ID": request_id,
375:                "X-TTFB-ms": str(metrics.get("ttfb_ms", 0)),
377:                "X-Text-Chunked": str(metrics.get("text_chunked", False)).lower(),
378:                "X-SR-Applied": str(metrics.get("sr_applied", False)).lower(),
387:                request_id=request_id,
398:                text_chunked=metrics.get("text_chunked", False),
399:                ttfb_ms=metrics.get("ttfb_ms"),
403:            # Log successful completion (lifecycle event + metrics)
406:                request_id=request_id,
413:            metrics_request_completed(
419:            record_ttfb(ttfb_seconds=metrics.get("ttfb_ms", 0) / 1000, speaker=speaker, stream=False)
434:                    sample_rate = metrics.get("output_sample_rate", 16000)
474:            # Log failure (lifecycle event + metrics)
476:                request_id=request_id,
481:            metrics_request_failed(
494:                        "request_id": request_id,
497:                headers={"X-Request-ID": request_id},
500:    async def _handle_streaming_request(req, request_id: str, text: str, speaker: str, start_time: float):
520:                        "request_id": request_id,
523:                headers={"X-Request-ID": request_id},
534:                        "request_id": request_id,
537:                headers={"X-Request-ID": request_id},
541:        set_request_context(request_id=request_id)
546:            final_metrics = {}
550:                async for audio_chunk, metrics in tts_runtime.generate_speech_streaming(
561:                    final_metrics = metrics
567:                        ttfb_ms = metrics.get("ttfb_ms", 0)
571:                            request_id=request_id,
579:                            request_id=request_id,
590:                            text_chunked=metrics.get("text_chunked", False),
597:                # Stream completed - log metrics
599:                audio_duration = final_metrics.get("audio_duration_seconds", 0)
603:                    request_id=request_id,
610:                metrics_request_completed(
624:                    request_id=request_id,
629:                metrics_request_failed(
645:            "X-Request-ID": request_id,


Output:
Chunk ID: cea30b
Wall time: 0.0510 seconds
Process exited with code 0
Original token count: 501
Output:
13:4. Server sends JSON: {"event": "complete", "metrics": {...}}
38:from veena3modal.shared.metrics import (
39:    record_request_received as metrics_request_received,
40:    record_request_completed as metrics_request_completed,
41:    record_request_failed as metrics_request_failed,
112:def create_error_message(code: str, message: str, request_id: str) -> str:
119:            "request_id": request_id,
125:    request_id: str,
132:        "request_id": request_id,
154:    request_id: str,
155:    metrics: Dict[str, Any],
160:        "request_id": request_id,
161:        "metrics": metrics,
181:    request_id = str(uuid.uuid4())
182:    set_request_context(request_id=request_id)
186:    logger.info(f"WebSocket connection accepted: {request_id}")
202:                    request_id=request_id,
215:                        request_id=request_id,
232:                request_id=request_id,
240:                request_id=request_id,
260:                request_id=request_id,
270:            request_id=request_id,
276:        metrics_request_received(speaker=speaker, stream=True, format="wav")
283:                request_id=request_id,
293:            request_id=request_id,
303:        final_metrics = {}
318:                            logger.info(f"Client requested cancel: {request_id}")
342:            async for audio_chunk, metrics in tts_runtime.generate_speech_streaming(
354:                    logger.info(f"Generation cancelled: {request_id}")
357:                final_metrics = metrics
366:                        request_id=request_id,
392:        audio_duration = final_metrics.get("audio_duration_seconds", 0)
395:        completion_metrics = {
396:            "request_id": request_id,
407:            request_id=request_id,
408:            metrics=completion_metrics,
414:                request_id=request_id,
421:            metrics_request_completed(
437:            request_id=request_id,
442:        metrics_request_failed(
451:                request_id=request_id,


Output:
Chunk ID: 8193ff
Wall time: 0.0508 seconds
Process exited with code 0
Original token count: 189
Output:
analyze_missing_words.py
asr_validation_openai.py
asr_with_openai_final.py
comprehensive_validation.py
create_visual_map.py
detailed_word_analysis.py
elevenlabs_asr_analysis.py
elevenlabs_verify_fix.py
final_asr_verification.py
final_chunking_test.sh
measure_client_ttfb.py
measure_ttfb_detailed.py
measure_ttfb_detailed_v2.py
pattern_analysis.py
profile_pipeline.py
quick_asr_check.py
setup_local.sh
stress_test_local.py
stress_test_production.py
test_all_features.py
test_chunking_no_emotions.py
test_correct_format.py
test_fix.py
test_original_bug.py
test_true_streaming.py
validate_chunking_asr.py
validate_indic_normalization.py
validate_modal_endpoint.py
validate_text_normalization.py
validate_true_streaming.py
validation
wer_test_with_emotions.py


I’ve located all hot paths and existing benchmark files. Next I’m reading the core function bodies in one pass to patch timing propagation end-to-end without changing unrelated logic.


sed -n '340,920p' veena3modal/services/tts_runtime.py


sed -n '950,1170p' veena3modal/services/tts_runtime.py


sed -n '1,260p' veena3modal/processing/long_text_processor.py


sed -n '260,520p' veena3modal/processing/long_text_processor.py


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