{"timestamp": "2026-04-05T07:11:03.637Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('223.176.96.141', 60750)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.637Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 62928)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.637Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 57621)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.638Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 62516)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.638Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 54592)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.638Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('10.56.1.169', 39638) -> ('134.209.150.51', 53705)) State.WAITING -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.639Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 53705)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.639Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('10.56.1.169', 39638) -> ('134.209.150.51', 52855)) State.WAITING -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.639Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('167.71.229.96', 60254) -> ('134.209.150.51', 52855)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.656Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(210) Check CandidatePair(('10.56.1.169', 39638) -> ('134.209.150.51', 62516)) State.FAILED -> State.IN_PROGRESS", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.659Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.708Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.757Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 147, "message": "PIPELINE_MODE: language=hi use_gemini_live=False", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:03.890Z", "level": "INFO", "logger": "src.bot.core.pipeline", "function": "_create_openai_stt_service", "line": 126, "message": "STT_SERVICE_CREATED: provider=openai model=gpt-4o-mini-transcribe-2025-12-15", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.247Z", "level": "INFO", "logger": "src.bot.core.pipeline", "function": "create_tts_service", "line": 382, "message": "TTS_SERVICE_CREATED: speaker=Taru", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.277Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('10.56.1.169', 56292) -> ('134.209.150.51', 51724)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.278Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('10.56.1.169', 56292) -> ('134.209.150.51', 64936)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.279Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('100.101.7.96', 35478)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.279Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('105.168.214.24', 35478)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.279Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 61881)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.280Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 54665)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.281Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 51724)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.281Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 64936)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.282Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('10.56.1.169', 56292) -> ('134.209.150.51', 54464)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.282Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 54464)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.283Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('10.56.1.169', 56292) -> ('134.209.150.51', 57872)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:04.283Z", "level": "INFO", "logger": "aioice.ice", "function": "__log_info", "line": 1199, "message": "Connection(207) Check CandidatePair(('167.71.229.96', 50816) -> ('134.209.150.51', 57872)) State.FROZEN -> State.FAILED", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:05.731Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:05.758Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:11:05.793Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}


[Request interrupted by user]


first can u satsh all changes u added


Bash

Stash all changes


Saved working directory and index state WIP on version_5: c3fdbc6 updated


Stashed. All changes are saved in stash — code is back to the original `version_5` branch state. The currently deployed image still has the old `use_gemini_live = True` (from before our changes), so Hindi users are back on Gemini Live.

Want me to redeploy with the original code so it matches what's running?


use_gemini_live = True dont stash this ... we need serate pipeline for hindi users


Pop stash to get changes back


On branch version_5
Your branch is up to date with 'origin/version_5'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   prompts/prompt_character.txt
	modified:   src/bot/core/config.py
	modified:   src/bot/core/main.py
	modified:   src/bot/services/image_service.py
	modified:   src/bot/services/limit_checker.py
	modified:   src/bot/services/music_service.py
	modified:   src/bot/services/ppt_service.py
	modified:   src/bot/services/video_service.py
	modified:   src/repository/config_v2_repository.py
	modified:   src/service/usage_service.py

no changes added to commit (use "git add" and/or "git commit -a")
Dropped refs/stash@{0} (b347dd3f6ecc45d886df9a6e3e9fb7d6738f4f2d)


Now let me stash only the payments-related changes, keeping the Hindi pipeline fix:


git stash push -m "payments config changes" -- src/bot/core/config.py src/bot/services/image_service.py src/bot/services/limit_checker.py src/bot/services/music_service.py src/bot/services/ppt_service.py src/bot/services/video_service.py src/repository/config_v2_repository.py src/service/usage_service.py

Stash only payments changes, keep pipeline fix


Saved working directory and index state On version_5: payments config changes


Check remaining changes


 prompts/prompt_character.txt |  4 ++--
 src/bot/core/main.py         | 12 ++++++++++--
 2 files changed, 12 insertions(+), 4 deletions(-)


Now only the Hindi pipeline fix remains (`use_gemini_live = language not in (None, "hi")` + payments config loading in main.py). Let me check main.py to make sure only the pipeline change is there, not the payments loading:


Check main.py changes


diff --git a/src/bot/core/main.py b/src/bot/core/main.py
index 801cadd..3f03fa4 100644
--- a/src/bot/core/main.py
+++ b/src/bot/core/main.py
@@ -126,7 +126,14 @@ async def run_bot_webrtc_global_agent(webrtc_connection, device_id: str = None,
         else:
             bot_session.free_remaining = 0

-        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} transport={transport_type}")
+        # Load payments config
+        from src.repository.config_v2_repository import config_v2_repo
+        payments_config = await config_v2_repo.get_payments_config()
+        bot_session.payments_enabled = payments_config.get("payments_enabled", True)
+        cooldowns = payments_config.get("cooldowns", {})
+        bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)
+
+        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} payments={bot_session.payments_enabled} cooldown={bot_session.cooldown_minutes}m transport={transport_type}")

     # Determine which tools to enable
     video_enabled = "generate_video" in persona_tools or "generate_video_from_image" in persona_tools
@@ -135,8 +142,9 @@ async def run_bot_webrtc_global_agent(webrtc_connection, device_id: str = None,
     music_enabled = "generate_music" in persona_tools
     bot_session.video_enabled = video_enabled

-    use_gemini_live = True
+    use_gemini_live = language not in (None, "hi")
     bot_session.use_gemini_live = use_gemini_live
+    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")

     vad_analyzer = create_vad_analyzer(session_id)
     bot_session.vad_analyzer = vad_analyzer


The payments config loading is still here but that's fine — it won't break anything since `bot_session` just won't have those attributes used (stashed files don't reference them). Let me remove the payments loading part to keep it clean:


Edit

        # Load payments config
        from src.repository.config_v2_repository import config_v2_repo
        payments_config = await config_v2_repo.get_payments_config()
        bot_session.payments_enabled = payments_config.get("payments_enabled", True)
        cooldowns = payments_config.get("cooldowns", {})
        bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)

        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} payments={bot_session.payments_enabled} cooldown={bot_session.cooldown_minutes}m transport={transport_type}")

logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} transport={transport_type}")


<tool_use_error>File has been modified since read, either by the user or by a linter. Read it again before attempting to write it.</tool_use_error>

Error: File has been modified since read, either by the user or by a linter. Read it again before attempting to write it.


give changes which are in local not in remte


Bash

Show local changes not in remote


Two files changed locally:

1. **`src/bot/core/main.py`** — Hindi pipeline fix (`use_gemini_live = language not in (None, "hi")`) + payments config loading
2. **`prompts/prompt_character.txt`** — some prompt changes

Stashed (not active):
- Payments config changes across 8 files (limit_checker, image/video/music/ppt services, config_v2_repo, usage_service, bot config)


prompt_character.txt what is this change ?


git diff HEAD -- prompts/prompt_character.txt

Show prompt_character.txt changes


diff --git a/prompts/prompt_character.txt b/prompts/prompt_character.txt
index adef7bf..e26711e 100644
--- a/prompts/prompt_character.txt
+++ b/prompts/prompt_character.txt
@@ -1,5 +1,5 @@
-You are Maya, on a live voice call with a friend. Everything you say gets spoken aloud — keep it natural, short, conversational. No markdown, no formatting, no emojis, no asterisks. Talk like a real person, not a chatbot.
+You are Maya, on a live voice call with a friend. Everything you say gets spoken aloud — keep it natural, short, conversational. No markdown, no formatting, no emojis, no asterisks. Talk like a real person, not a chatbot. Keep it to one line — pitch an idea and ask, all in the same sentence.

-When the user uploads a photo, react to what you see and ask what they want done — don't edit anything yet. When they tell you what to change, call the tool immediately — don't ask for permission, they already told you what they want. If their request is vague, pick something creative and go for it.
+When the user uploads a photo, react to what you see and pitch one idea — don't edit anything yet. When they tell you what to change, call the tool immediately — don't ask for permission, they already told you what they want. If their request is vague, pick something creative and go for it.

 While a tool is running, you'll get stage updates. Don't guess timing or make up progress — just describe what you're doing ("background change kar rahi hoon") and wait for the result. When it's done, react to the result and ask what's next.
\ No newline at end of file


This isn't our change — looks like someone (or you) edited the prompt to:
1. Added "Keep it to one line — pitch an idea and ask, all in the same sentence"
2. Changed "react to what you see and ask what they want done" → "react to what you see and pitch one idea"

Not related to our pipeline/payments work. Probably a manual prompt tweak. Want to keep it or revert it?


revert it


git checkout -- prompts/prompt_character.txt

Revert prompt_character.txt


(Bash completed with no output)


Reverted. Now only `src/bot/core/main.py` has local changes (Hindi pipeline fix).


now check pods uy voice is cuting out


kubectl logs $(kubectl get pods -l app=maya-pipecat-v5 --field-selector=status.phase=Running -o jsonpath='{.items[0].metadata.name}') --tail=100 2>&1 | grep -i "PIPELINE_MODE\|TTS_\|STT_\|ERROR\|FAILED\|Veena\|CustomTTS\|audio_out\|voice" | tail -20

Check TTS/voice errors in logs


{"timestamp": "2026-04-05T07:14:15.676Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=6504a727-86fc-4079-8a42-e2aa55c231d0 device_id=60ef9545cec782a8 processor=CustomTTSService#73 type=TTFB (veena3-tts) value=2.03840s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:16.820Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=6504a727-86fc-4079-8a42-e2aa55c231d0 device_id=60ef9545cec782a8 processor=CustomTTSService#73 type=Processing (veena3-tts) value=3.17943s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:21.692Z", "level": "WARNING", "logger": "pipecat.processors.frameworks.rtvi.observer", "function": "_handle_context", "line": 625, "message": "Caught an error while trying to handle context: sequence item 0: expected str instance, NoneType found", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}


kubectl logs $(kubectl get pods -l app=maya-pipecat-v5 --field-selector=status.phase=Running -o jsonpath='{.items[0].metadata.name}') --tail=500 2>&1 | grep -i "TTFB\|Processing.*veena\|TTS.*error\|TTS.*fail\|CustomTTS" | tail -20

Check TTS latency metrics


{"timestamp": "2026-04-05T07:14:25.584Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=06e68362-0a10-4f90-b7a4-116d33ab9261 device_id=af6d653766405adb processor=CustomTTSService#76 type=Processing (veena3-tts) value=2.14430s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:25.627Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=TTFB (veena3-tts) value=0.37195s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:25.627Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=Processing (veena3-tts) value=0.37214s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:25.872Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=bb23fb16-3f6a-4380-b510-78cbae322f96 device_id=6a1916c2b5b9ad88 processor=CustomTTSService#64 type=TTFB (veena3-tts) value=1.98714s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:25.956Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=bb23fb16-3f6a-4380-b510-78cbae322f96 device_id=6a1916c2b5b9ad88 processor=CustomTTSService#64 type=Processing (veena3-tts) value=2.06081s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:28.056Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=OpenAISTTService#77 type=TTFB (gpt-4o-mini-transcribe-2025-12-15) value=1.76013s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:28.511Z", "level": "WARNING", "logger": "pipecat.pipeline.task", "function": "_print_dangling_tasks", "line": 1080, "message": "Dangling tasks detected: ['SmallWebRTCInputTransport#77::_audio_task_handler', 'SmallWebRTCInputTransport#77::_receive_audio', 'GoogleUserContextAggregator#74::_aggregation_task_handler', 'RTVIProcessor#77::_action_task_handler', 'RTVIProcessor#77::_message_task_handler', 'CustomTTSService#74::_audio_context_task_handler', 'SmallWebRTCOutputTransport#77::_clock_task_handler', 'SmallWebRTCOutputTransport#77::_audio_task_handler']", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:30.200Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=OpenAISTTService#77 type=TTFB (gpt-4o-mini-transcribe-2025-12-15) value=1.96603s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:30.492Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=GeminiLLM-OLD type=TTFB (gemini-2.5-flash) value=0.17819s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:32.359Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=GeminiLLM-OLD type=TTFB (gemini-2.5-flash) value=1.15133s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:33.856Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=TTFB (veena3-tts) value=1.43101s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:33.856Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=Processing (veena3-tts) value=1.43122s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:35.888Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=OpenAISTTService#77 type=TTFB (gpt-4o-mini-transcribe-2025-12-15) value=1.75656s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:37.590Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=GeminiLLM-OLD type=TTFB (gemini-2.5-flash) value=1.07031s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:38.652Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=TTFB (veena3-tts) value=0.92549s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:38.652Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=CustomTTSService#77 type=Processing (veena3-tts) value=0.92571s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:40.881Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=06e68362-0a10-4f90-b7a4-116d33ab9261 device_id=af6d653766405adb processor=GeminiLLM-OLD type=TTFB (gemini-2.5-flash) value=1.23719s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:41.003Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=06e68362-0a10-4f90-b7a4-116d33ab9261 device_id=af6d653766405adb processor=CustomTTSService#76 type=TTS (veena3-tts) characters=39", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:41.408Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=d16da91e-de49-412f-b7ca-c93a8641c428 device_id=320783547e62006c processor=OpenAISTTService#77 type=TTFB (gpt-4o-mini-transcribe-2025-12-15) value=1.96792s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:14:42.071Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=06e68362-0a10-4f90-b7a4-116d33ab9261 device_id=af6d653766405adb processor=CustomTTSService#76 type=TTFB (veena3-tts) value=1.06913s", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}


I can see the issue. The traditional pipeline for Hindi has **high latency at every step**:

- **OpenAI STT**: ~1.7-2.0s TTFB (transcribing speech)
- **Gemini LLM**: ~0.2-1.2s TTFB
- **Veena3 TTS**: ~0.4-2.0s TTFB (converting text to speech)

**Total round-trip: ~3-5 seconds** before the user hears a response. With Gemini Live it was all in one service with much lower latency.

The "voice cutting" is likely because:
1. The high latency makes the conversation feel choppy
2. The VAD (voice activity detection) might be cutting off the user while waiting for the slow STT response
3. The DoubleSentenceAggregator adds more delay by buffering 2 sentences before sending to TTS

This was the same pipeline that was working before March 31 when it got switched to Gemini Live. The voice cutting might be because users got used to the faster Gemini Live experience, or the Veena3 TTS / OpenAI STT has degraded.

What do you want to do? Options:
1. **Switch Hindi back to Gemini Live** (revert our change) — faster but uses Gemini's voice, not Veena3
2. **Keep traditional but remove the DoubleSentenceAggregator** — reduces TTS delay
3. **Keep traditional but switch STT to Deepgram Nova-3** (CF worker) — faster STT than OpenAI


how to chekc ttfb of the veena3 what is log for that


The log format is:

```
METRICS: session_id=... processor=CustomTTSService#XX type=TTFB (veena3-tts) value=X.XXXXXs
```

To check live:


kubectl logs $(kubectl get pods -l app=maya-pipecat-v5 --field-selector=status.phase=Running -o jsonpath='{.items[0].metadata.name}') --tail=1000 2>&1 | grep "TTFB (veena3-tts)" | grep -o 'value=[0-9.]*s' | tail -20

Check recent Veena3 TTFB values


value=2.27546s
value=1.23613s
value=0.85362s
value=1.15310s
value=2.03279s
value=1.52936s
value=2.02391s
value=0.82130s
value=0.06942s
value=0.94419s
value=0.05799s
value=1.62917s
value=1.79564s
value=2.32634s
value=2.00941s
value=1.28511s
value=1.14506s
value=1.52554s


Veena3 TTS TTFB ranges from **0.05s to 2.3s**, averaging around **1.3-1.5s**. Some are fast (50-80ms) but most are 1-2 seconds — that's causing the voice cutting/gaps.


give for stt


kubectl logs $(kubectl get pods -l app=maya-pipecat-v5 --field-selector=status.phase=Running -o jsonpath='{.items[0].metadata.name}') --tail=1000 2>&1 | grep "TTFB (gpt-4o-mini-transcribe" | grep -o 'value=[0-9.]*s' | tail -20

Check OpenAI STT TTFB values


value=2.68679s
value=3.36346s
value=2.39632s
value=2.21977s
value=2.23215s
value=0.59876s
value=2.07073s
value=0.05795s
value=2.28390s
value=2.13463s
value=2.08280s
value=1.84336s


OpenAI STT TTFB: **0.06s to 3.4s**, mostly **2-2.5s**. That's terrible — 2+ seconds just to transcribe what the user said, before LLM even starts thinking.

So total pipeline delay: **STT (~2s) + LLM (~1s) + TTS (~1.5s) = ~4.5s** before the user hears anything. That's why voice feels cut/broken.


what llm we using


From the logs: **Gemini 2.5 Flash** via Google Vertex AI (`GeminiLLM-OLD` / `gemini-2.5-flash`).

The pipeline for Hindi is: OpenAI STT (2s) → Gemini 2.5 Flash (~1s) → Veena3 TTS (1.5s)

The LLM is the fastest part. The bottleneck is STT + TTS adding ~3.5s delay.


1 thing .. if it comes lates also tts y the fuck it is cutting in between and cmng what is the issue?


Good question. The voice cutting mid-sentence is likely **interruptions** — not latency. Let me check.


kubectl logs $(kubectl get pods -l app=maya-pipecat-v5 --field-selector=status.phase=Running -o jsonpath='{.items[0].metadata.name}') --tail=2000 2>&1 | grep -i "interrupt\|bot_stopped\|user_started_speaking\|cancel" | tail -20

Check interruption logs


{"timestamp": "2026-04-05T07:30:06.806Z", "level": "WARNING", "logger": "pipecat.pipeline.task", "function": "wait_for_cancel", "line": 760, "message": "PipelineTask#135: timeout waiting for CancelFrame#128(reason: None) to reach the end of the pipeline (being blocked somewhere?).", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:30:07.561Z", "level": "WARNING", "logger": "pipecat.services.google.gemini_live.llm", "function": "_handle_connection_error", "line": 1476, "message": "Connection error (failure 1/3): 1000 None. The operation was cancelled.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:30:13.023Z", "level": "WARNING", "logger": "pipecat.pipeline.task", "function": "wait_for_cancel", "line": 760, "message": "PipelineTask#132: timeout waiting for CancelFrame#129(reason: None) to reach the end of the pipeline (being blocked somewhere?).", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:30:20.142Z", "level": "WARNING", "logger": "pipecat.pipeline.task", "function": "wait_for_cancel", "line": 760, "message": "PipelineTask#134: timeout waiting for CancelFrame#130(reason: None) to reach the end of the pipeline (being blocked somewhere?).", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:30:44.467Z", "level": "WARNING", "logger": "pipecat.pipeline.task", "function": "wait_for_cancel", "line": 760, "message": "PipelineTask#137: timeout waiting for CancelFrame#131(reason: None) to reach the end of the pipeline (being blocked somewhere?).", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-04-05T07:30:47.767Z", "level": "WARNING", "logger": "pipecat.services.google.gemini_live.llm", "function": "_handle_connection_error", "line": 1476, "message": "Connection error (failure 1/3): 1000 None. The operation was cancelled.", "service": "maya-pipecat-v5", "env": "development", "version": "1.0.0", "status": "info"}


138	    # Determine which tools to enable
139	    video_enabled = "generate_video" in persona_tools or "generate_video_from_image" in persona_tools
140
141	    ppt_enabled = "generate_ppt" in persona_tools
142	    music_enabled = "generate_music" in persona_tools
143	    bot_session.video_enabled = video_enabled
144
145	    use_gemini_live = language not in (None, "hi")
146	    bot_session.use_gemini_live = use_gemini_live
147	    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")
148
149	    vad_analyzer = create_vad_analyzer(session_id)
150	    bot_session.vad_analyzer = vad_analyzer
151
152	    # Create services and store references IMMEDIATELY on bot_session after each creation.

    # Determine which tools to enable
    video_enabled = "generate_video" in persona_tools or "generate_video_from_image" in persona_tools

    ppt_enabled = "generate_ppt" in persona_tools
    music_enabled = "generate_music" in persona_tools
    bot_session.video_enabled = video_enabled

    use_gemini_live = language not in (None, "hi")
    bot_session.use_gemini_live = use_gemini_live
    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")

    vad_analyzer = create_vad_analyzer(session_id)
    bot_session.vad_analyzer = vad_analyzer

    # Create services and store references IMMEDIATELY on bot_session after each creation.


492	def create_pipeline(transport, stt, context_aggregator, rtvi, llm, tts, audio_buffer, transcript, llm_text_processor=None):
493	    """Create the processing pipeline."""
494	    # Build core pipeline: llm -> [optional llm_text_processor] -> tts
495	    llm_to_tts = [llm, llm_text_processor, tts] if llm_text_processor else [llm, tts]
496
497	    pipeline = Pipeline([
498	        transport.input(),
499	        stt,
500	        transcript.user(),
501	        context_aggregator.user(),
502	        rtvi,
503	        *llm_to_tts,
504	        transport.output(),
505	        audio_buffer,
506	        transcript.assistant(),
507	        context_aggregator.assistant(),
508	    ])
509	    mode = "2-sentence aggregation" if llm_text_processor else "default (1 sentence)"
510	    logger.info(f"PIPELINE: Traditional STT mode (Audio → STT → Text → LLM → TTS) | text_aggregation={mode}")
511	    return pipeline
512
513
514	def create_task(pipeline, rtvi, bot_session):
515	    """Create pipeline task with observers."""
516	    return PipelineTask(
517	        pipeline,
518	        params=PipelineParams(
519	            enable_metrics=True,
520	            enable_usage_metrics=True,
521	            allow_interruptions=True
522	        ),
523	        observers=[
524	            SafeRTVIObserver(rtvi),  # Use SafeRTVIObserver to handle startup race condition
525	            InfoMetricsObserver(bot_session),
526	            UserBotLatencyLogObserver()
527	        ],
528	    )
529
530
531	# ---- Gemini Live pipeline (Telugu / multi-language) ----

def create_pipeline(transport, stt, context_aggregator, rtvi, llm, tts, audio_buffer, transcript, llm_text_processor=None):
    """Create the processing pipeline."""
    # Build core pipeline: llm -> [optional llm_text_processor] -> tts
    llm_to_tts = [llm, llm_text_processor, tts] if llm_text_processor else [llm, tts]

    pipeline = Pipeline([
        transport.input(),
        stt,
        transcript.user(),
        context_aggregator.user(),
        rtvi,
        *llm_to_tts,
        transport.output(),
        audio_buffer,
        transcript.assistant(),
        context_aggregator.assistant(),
    ])
    mode = "2-sentence aggregation" if llm_text_processor else "default (1 sentence)"
    logger.info(f"PIPELINE: Traditional STT mode (Audio → STT → Text → LLM → TTS) | text_aggregation={mode}")
    return pipeline

def create_task(pipeline, rtvi, bot_session):
    """Create pipeline task with observers."""
    return PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
            allow_interruptions=True
        ),
        observers=[
            SafeRTVIObserver(rtvi),  # Use SafeRTVIObserver to handle startup race condition
            InfoMetricsObserver(bot_session),
            UserBotLatencyLogObserver()
        ],
    )

# ---- Gemini Live pipeline (Telugu / multi-language) ----


Found it. Two issues causing voice to cut mid-sentence:

1. **`allow_interruptions=True`** (line 521) — VAD detects any background noise or the user's own speaker audio as "user speaking" and immediately **cuts the bot's TTS mid-sentence**. This is the main cause.

2. **`DoubleSentenceAggregator`** — it buffers 2 sentences before sending to TTS. If LLM generates one sentence and pauses, there's a gap. Then the second sentence arrives and plays separately, making it sound choppy.

Also the **VAD settings** are very sensitive:


grep -E "VAD_CONFIDENCE|VAD_STOP_SECS|VAD_START_SECS|VAD_MIN_VOLUME" /home/ubuntu/pipecat/src/config/constants.py

Check VAD settings


VAD_CONFIDENCE = 0.7
VAD_STOP_SECS = 0.8
VAD_START_SECS = 0.2
VAD_MIN_VOLUME = 0.6


So here's what's happening:

- **VAD confidence 0.7** + **start_secs 0.2** = after just 0.2 seconds of sound above threshold, VAD thinks user is speaking
- **`allow_interruptions=True`** = immediately cuts the bot's TTS audio
- On mobile, the phone speaker plays TTS → mic picks it up → VAD detects it as user speech → **bot cuts itself off**

This wasn't an issue with Gemini Live because Gemini Live handles its own echo cancellation and interruption logic internally.

Want me to make VAD less aggressive or disable interruptions?


not that while bot speaking it is cmng like cut cut


Ah ok — the audio itself sounds choppy/stuttery, not interruptions. Let me check the TTS audio config.


1	"""Custom TTS Service for external TTS API."""
2
3	import aiohttp
4	from typing import AsyncGenerator, Optional, Callable
5	from loguru import logger
6
7	from pipecat.frames.frames import Frame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame
8	from pipecat.services.tts_service import TTSService
9	from pipecat.services.settings import TTSSettings
10
11
12	class CustomTTSService(TTSService):
13	    """Custom TTS service that integrates with an external TTS API."""
14
15	    def __init__(
16	        self,
17	        *,
18	        api_url: str,
19	        api_key: Optional[str] = None,
20	        speaker: str = "Nilay",
21	        sample_rate: int = 16000,
22	        model_name: str = "veena3-tts",
23	        on_pcm_chunk: Optional[Callable[[bytes], None]] = None,
24	        **kwargs,
25	    ):
26	        super().__init__(
27	            sample_rate=sample_rate,
28	            settings=TTSSettings(model=model_name, voice=speaker, language=None),
29	            **kwargs,
30	        )
31
32	        if not api_url or api_url == "None":
33	            raise ValueError("api_url is required for CustomTTSService")
34
35	        self._api_url = api_url
36	        self._api_key = api_key
37	        self._speaker = speaker
38	        self._sample_rate = sample_rate
39	        self._on_pcm_chunk = on_pcm_chunk
40
41	        self._session = None
42	        self._connector = None
43
44	        self._model_name = model_name
45	        self._voice_id = speaker
46
47	    def can_generate_metrics(self) -> bool:
48	        return True
49
50	    async def _ensure_session(self):
51	        if self._session is None:
52	            self._connector = aiohttp.TCPConnector(
53	                limit=10,
54	                limit_per_host=5,
55	                ttl_dns_cache=300,
56	                keepalive_timeout=60,
57	                force_close=False,
58	                enable_cleanup_closed=True
59	            )
60	            self._session = aiohttp.ClientSession(
61	                connector=self._connector,
62	                timeout=aiohttp.ClientTimeout(total=30, connect=5)
63	            )
64	            logger.debug("HTTP session created with connection pooling")
65
66	    async def close(self):
67	        if self._session:
68	            await self._session.close()
69	            self._session = None
70	        if self._connector:
71	            await self._connector.close()
72	            self._connector = None
73	        logger.debug("HTTP session and connector closed")
74
75	    async def run_tts(self, text: str, context_id: str = "") -> AsyncGenerator[Frame, None]:
76	        logger.debug(f"Starting TTS generation, text_length={len(text)}")
77
78	        try:
79	            headers = {"Content-Type": "application/json"}
80
81	            if self._api_key:
82	                headers["X-API-Key"] = self._api_key
83
84	            payload = {
85	                "text": text,
86	                "speaker": 'lipakshi',
87	                "stream": True,
88	                "output": "48khz",
89	                "seed": 42
90	            }
91
92	            yield TTSStartedFrame()
93
94	            await self.start_ttfb_metrics()
95	            await self.start_tts_usage_metrics(text)
96
97	            ttfb_stopped = False
98	            await self._ensure_session()
99
100	            async with self._session.post(
101	                self._api_url,
102	                headers=headers,
103	                json=payload,
104	                ssl=False,
105	            ) as response:
106	                if response.status != 200:
107	                    error_text = await response.text()
108	                    logger.error(f"TTS API error status={response.status} error={error_text[:500]}")
109	                    if not ttfb_stopped:
110	                        await self.stop_ttfb_metrics()
111	                        ttfb_stopped = True
112	                    yield TTSStoppedFrame()
113	                    return
114
115	                bytes_per_sample = 2
116	                first_chunk = True
117
118	                async for chunk in response.content.iter_any():
119	                    if chunk:
120	                        if not ttfb_stopped:
121	                            await self.stop_ttfb_metrics()
122	                            ttfb_stopped = True
123	                            logger.debug(f"TTS first byte received, chunk_size={len(chunk)}")
124
125	                        if first_chunk:
126	                            first_chunk = False
127	                            if len(chunk) >= 12 and chunk[:4] == b'RIFF' and chunk[8:12] == b'WAVE':
128	                                data_pos = chunk.find(b'data')
129	                                if data_pos != -1:
130	                                    wav_header_size = data_pos + 8
131	                                    chunk = chunk[wav_header_size:]
132	                                else:
133	                                    logger.warning("WAV header detected but 'data' chunk not found")
134
135	                        if len(chunk) == 0:
136	                            continue
137
138	                        if len(chunk) % bytes_per_sample != 0:
139	                            chunk = chunk[:-(len(chunk) % bytes_per_sample)]
140	                            if len(chunk) == 0:
141	                                continue
142
143	                        if self._on_pcm_chunk:
144	                            try:
145	                                self._on_pcm_chunk(chunk)
146	                            except Exception as e:
147	                                logger.error(f"Audio tap callback error: {e}")
148
149	                        yield TTSAudioRawFrame(
150	                            audio=chunk,
151	                            sample_rate=self._sample_rate,
152	                            num_channels=1,
153	                        )
154
155	            yield TTSStoppedFrame()
156
157	        except Exception as e:
158	            logger.error(f"TTS ERROR: {type(e).__name__}", exc_info=True)
159	            yield TTSStoppedFrame()
160

"""Custom TTS Service for external TTS API."""

import aiohttp
from typing import AsyncGenerator, Optional, Callable
from loguru import logger

from pipecat.frames.frames import Frame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame
from pipecat.services.tts_service import TTSService
from pipecat.services.settings import TTSSettings

class CustomTTSService(TTSService):
    """Custom TTS service that integrates with an external TTS API."""

    def __init__(
        self,
        *,
        api_url: str,
        api_key: Optional[str] = None,
        speaker: str = "Nilay",
        sample_rate: int = 16000,
        model_name: str = "veena3-tts",
        on_pcm_chunk: Optional[Callable[[bytes], None]] = None,
        **kwargs,
    ):
        super().__init__(
            sample_rate=sample_rate,
            settings=TTSSettings(model=model_name, voice=speaker, language=None),
            **kwargs,
        )

        if not api_url or api_url == "None":
            raise ValueError("api_url is required for CustomTTSService")

        self._api_url = api_url
        self._api_key = api_key
        self._speaker = speaker
        self._sample_rate = sample_rate
        self._on_pcm_chunk = on_pcm_chunk

        self._session = None
        self._connector = None

        self._model_name = model_name
        self._voice_id = speaker

    def can_generate_metrics(self) -> bool:
        return True

    async def _ensure_session(self):
        if self._session is None:
            self._connector = aiohttp.TCPConnector(
                limit=10,
                limit_per_host=5,
                ttl_dns_cache=300,
                keepalive_timeout=60,
                force_close=False,
                enable_cleanup_closed=True
            )
            self._session = aiohttp.ClientSession(
                connector=self._connector,
                timeout=aiohttp.ClientTimeout(total=30, connect=5)
            )
            logger.debug("HTTP session created with connection pooling")

    async def close(self):
        if self._session:
            await self._session.close()
            self._session = None
        if self._connector:
            await self._connector.close()
            self._connector = None
        logger.debug("HTTP session and connector closed")

    async def run_tts(self, text: str, context_id: str = "") -> AsyncGenerator[Frame, None]:
        logger.debug(f"Starting TTS generation, text_length={len(text)}")

        try:
            headers = {"Content-Type": "application/json"}

            if self._api_key:
                headers["X-API-Key"] = self._api_key

            payload = {
                "text": text,
                "speaker": 'lipakshi',
                "stream": True,
                "output": "48khz",
                "seed": 42
            }

            yield TTSStartedFrame()

            await self.start_ttfb_metrics()
            await self.start_tts_usage_metrics(text)

            ttfb_stopped = False
            await self._ensure_session()

            async with self._session.post(
                self._api_url,
                headers=headers,
                json=payload,
                ssl=False,
            ) as response:
                if response.status != 200:
                    error_text = await response.text()
                    logger.error(f"TTS API error status={response.status} error={error_text[:500]}")
                    if not ttfb_stopped:
                        await self.stop_ttfb_metrics()
                        ttfb_stopped = True
                    yield TTSStoppedFrame()
                    return

                bytes_per_sample = 2
                first_chunk = True

                async for chunk in response.content.iter_any():
                    if chunk:
                        if not ttfb_stopped:
                            await self.stop_ttfb_metrics()
                            ttfb_stopped = True
                            logger.debug(f"TTS first byte received, chunk_size={len(chunk)}")

                        if first_chunk:
                            first_chunk = False
                            if len(chunk) >= 12 and chunk[:4] == b'RIFF' and chunk[8:12] == b'WAVE':
                                data_pos = chunk.find(b'data')
                                if data_pos != -1:
                                    wav_header_size = data_pos + 8
                                    chunk = chunk[wav_header_size:]
                                else:
                                    logger.warning("WAV header detected but 'data' chunk not found")

                        if len(chunk) == 0:
                            continue

                        if len(chunk) % bytes_per_sample != 0:
                            chunk = chunk[:-(len(chunk) % bytes_per_sample)]
                            if len(chunk) == 0:
                                continue

                        if self._on_pcm_chunk:
                            try:
                                self._on_pcm_chunk(chunk)
                            except Exception as e:
                                logger.error(f"Audio tap callback error: {e}")

                        yield TTSAudioRawFrame(
                            audio=chunk,
                            sample_rate=self._sample_rate,
                            num_channels=1,
                        )

            yield TTSStoppedFrame()

        except Exception as e:
            logger.error(f"TTS ERROR: {type(e).__name__}", exc_info=True)
            yield TTSStoppedFrame()


Found the problem. **Sample rate mismatch:**

- Line 88: TTS API is called with `"output": "48khz"` — returns 48kHz audio
- Line 149: `TTSAudioRawFrame` uses `self._sample_rate` which is set to **16kHz** (from `create_tts_service`)
- Line 89 in pipeline.py: transport output is set to **48000** sample rate

So the TTS returns 48kHz audio but frames are tagged as 16kHz. Pipecat may be resampling or playing at wrong speed, causing choppy/cut audio.

Also check:


grep "SAMPLE_RATE" /home/ubuntu/pipecat/src/config/constants.py | head -5

Check sample rate constants


SAMPLE_RATE = 24000
SAMPLE_RATE_16K = 16000


375	def create_tts_service(speaker: str = None):
376	    """Create TTS service with optional custom speaker/voice.
377
378	    Args:
379	        speaker: TTS voice to use. If None, uses DEFAULT_TTS_SPEAKER.
380	    """
381	    voice = speaker or constants.DEFAULT_TTS_SPEAKER
382	    logger.info(f"TTS_SERVICE_CREATED: speaker={voice}")
383	    return CustomTTSService(
384	        api_url=constants.TTS_API_URL,
385	        api_key=constants.VEENA3_API_KEY,
386	        speaker=voice,
387	        sample_rate=constants.SAMPLE_RATE_16K,
388	    )
389

def create_tts_service(speaker: str = None):
    """Create TTS service with optional custom speaker/voice.

    Args:
        speaker: TTS voice to use. If None, uses DEFAULT_TTS_SPEAKER.
    """
    voice = speaker or constants.DEFAULT_TTS_SPEAKER
    logger.info(f"TTS_SERVICE_CREATED: speaker={voice}")
    return CustomTTSService(
        api_url=constants.TTS_API_URL,
        api_key=constants.VEENA3_API_KEY,
        speaker=voice,
        sample_rate=constants.SAMPLE_RATE_16K,
    )


There it is:

- **TTS API returns**: 48kHz audio (`"output": "48khz"`)
- **CustomTTSService sample_rate**: 16kHz (`SAMPLE_RATE_16K`)
- **Transport audio_out**: 48kHz (`audio_out_sample_rate=48000`)

The TTS audio frames are tagged as 16kHz but contain 48kHz data. Pipecat tries to resample 16kHz→48kHz (3x stretch), but the data is already 48kHz — so it's getting **stretched/distorted and cutting**.

Fix: either change the TTS service sample rate to 48000, or change the API call to output 16kHz. Matching them to 48k is better since transport output is 48k:


Edit

    return CustomTTSService(
        api_url=constants.TTS_API_URL,
        api_key=constants.VEENA3_API_KEY,
        speaker=voice,
        sample_rate=constants.SAMPLE_RATE_16K,
    )

    return CustomTTSService(
        api_url=constants.TTS_API_URL,
        api_key=constants.VEENA3_API_KEY,
        speaker=voice,
        sample_rate=48000,
    )


The file /home/ubuntu/pipecat/src/bot/core/pipeline.py has been updated successfully.

    return CustomTTSService(
        api_url=constants.TTS_API_URL,
        api_key=constants.VEENA3_API_KEY,
        speaker=voice,
        sample_rate=48000,
    )

"""Pipeline creation for bot."""

from loguru import logger
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
from pipecat.processors.transcript_processor import TranscriptProcessor
from pipecat.services.openai.stt import OpenAISTTService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.google.llm_vertex import GoogleVertexLLMService
from pipecat.services.google.gemini_live.vertex.llm import GeminiLiveVertexLLMService
from pipecat.services.groq.llm import GroqLLMService
from pipecat.services.openrouter.llm import OpenRouterLLMService
from typing import Any, Dict
from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
from pipecat.transports.base_transport import TransportParams
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer, VADParams
from pipecat.observers.loggers.user_bot_latency_log_observer import UserBotLatencyLogObserver

from pipecat.services.soniox.stt import SonioxSTTService
from pipecat.transcriptions.language import Language

from pipecat.services.google.tts import GoogleTTSService

from src.config import constants
from pipecat.processors.aggregators.llm_text_processor import LLMTextProcessor
from src.bot.services.custom_tts import CustomTTSService
from src.bot.services.double_sentence_aggregator import DoubleSentenceAggregator
from src.bot.core.observers import InfoMetricsObserver, SafeRTVIObserver
from src.bot.services.image_service import cf_image_function
from src.bot.services.video_service import generate_video_function, generate_video_from_image_function
from src.bot.services.ppt_service import generate_ppt_function
from src.bot.services.music_service import generate_music_function

class CustomOpenRouterLLMService(OpenRouterLLMService):
    """OpenRouter LLM service with provider routing support.

    Overrides build_chat_completion_params to pass OpenRouter-specific fields
    (like 'provider') via extra_body, since the OpenAI SDK rejects unknown
    top-level kwargs but allows extra_body for non-standard API fields.
    """

    def __init__(self, *, provider_order: list[str] = None, allow_fallbacks: bool = True, **kwargs):
        super().__init__(**kwargs)
        self._provider_routing = None
        if provider_order:
            self._provider_routing = {
                "order": provider_order,
                "allow_fallbacks": allow_fallbacks,
            }

    def build_chat_completion_params(self, params_from_context: Dict[str, Any]) -> Dict[str, Any]:
        params = super().build_chat_completion_params(params_from_context)
        if self._provider_routing:
            existing_extra = params.get("extra_body", {}) or {}
            existing_extra["provider"] = self._provider_routing
            params["extra_body"] = existing_extra
        return params

def create_vad_analyzer(session_id: str):
    """Create VAD analyzer with configured parameters."""
    vad = SileroVADAnalyzer(
        params=VADParams(
            confidence=constants.VAD_CONFIDENCE,
            stop_secs=constants.VAD_STOP_SECS,
            start_secs=constants.VAD_START_SECS,
            min_volume=constants.VAD_MIN_VOLUME,
        )
    )
    logger.info(f"VAD_CREATED: session_id={session_id}")
    return vad

def create_transport(webrtc_connection, vad_analyzer):
    """Create SmallWebRTC transport with configured parameters."""
    return SmallWebRTCTransport(
        webrtc_connection,
        TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            video_in_enabled=False,
            audio_in_sample_rate=constants.SAMPLE_RATE_16K,
            audio_out_sample_rate=48000,
            vad_analyzer=vad_analyzer,
        ),
    )

def create_daily_transport(room_url: str, token: str, vad_analyzer):
    """Create Daily transport with configured parameters."""
    from pipecat.transports.daily.transport import DailyTransport, DailyParams

    return DailyTransport(
        room_url,
        token,
        "Maya Global Agent Bot",
        DailyParams(
            api_key=constants.DAILY_API_KEY,
            audio_in_enabled=True,
            audio_out_enabled=True,
            video_in_enabled=False,
            camera_out_enabled=False,
            audio_in_sample_rate=constants.SAMPLE_RATE_16K,
            audio_out_sample_rate=constants.SAMPLE_RATE_16K,
            vad_analyzer=vad_analyzer,
            transcription_enabled=False,
        ),
    )

def _create_openai_stt_service():
    """Create OpenAI STT service."""
    stt = OpenAISTTService(
        api_key=constants.OPENAI_API_KEY,
        settings=OpenAISTTService.Settings(
            model=constants.STT_MODEL,
            language=constants.STT_LANGUAGE,
        ),
    )
    logger.info(f"STT_SERVICE_CREATED: provider=openai model={constants.STT_MODEL}")
    return stt

def _create_cf_nova3_stt_service():
    """Create CF Workers AI Deepgram Nova-3 STT service.

    Connects to a CF Worker that proxies WebSocket to @cf/deepgram/nova-3.
    Uses Pipecat's DeepgramSTTService with base_url pointing to the CF Worker.
    """
    stt = DeepgramSTTService(
        api_key=constants.CF_STT_API_KEY,
        base_url=constants.CF_STT_WORKER_URL,
        encoding="linear16",
        sample_rate=constants.SAMPLE_RATE_16K,
        settings=DeepgramSTTService.Settings(
            model="nova-3-general",
            language=Language.HI,
            interim_results=True,
            punctuate=True,
            smart_format=False,
            endpointing=300,
            utterance_end_ms=1000,
            vad_events=False,
        ),
    )
    logger.info(f"STT_SERVICE_CREATED: provider=cf_nova3 base_url={constants.CF_STT_WORKER_URL}")
    return stt

def create_stt_service():
    """Create STT service — OpenAI Whisper (Deepgram disabled due to SDK incompatibility)."""
    return _create_openai_stt_service()

def create_soniox_stt_service():
    """Create Soniox STT service for Telugu (web sessions)."""
    stt = SonioxSTTService(
        api_key=constants.SONIOX_API_KEY,
        sample_rate=constants.SAMPLE_RATE_16K,
        settings=SonioxSTTService.Settings(
            model="stt-rt-v4",
            language_hints=[Language.TE],
        ),
    )
    logger.info("STT_SERVICE_CREATED: provider=soniox model=stt-rt-v4 language=te")
    return stt

def _select_llm_provider_weighted() -> str:
    """Select LLM provider using weighted traffic routing.

    Uses per-provider percentage weights from config. Each provider gets
    its configured percentage of traffic, remainder goes to gemini (default).

    Example config weights: {"openai": 30, "groq": 5, "openrouter": 10}
    -> 30% openai, 5% groq, 10% openrouter, 55% gemini

    Example config weights: {"groq": 5}
    -> 5% groq, 95% gemini
    """
    import random
    from src.repository.config_repository import config_repo

    weights = config_repo.get_llm_weights()

    # Sanitize: ensure all values are valid numbers, skip bad entries
    valid_providers = ["openai", "groq", "openrouter"]  # Known non-default providers
    sanitized = {}
    for provider, percentage in weights.items():
        if provider not in valid_providers:
            logger.warning(f"LLM_WEIGHTS_UNKNOWN_PROVIDER: provider={provider} skipped")
            continue
        try:
            pct = int(percentage)
            if pct > 0:
                sanitized[provider] = pct
        except (TypeError, ValueError):
            logger.warning(f"LLM_WEIGHTS_INVALID_VALUE: provider={provider} value={percentage} skipped")
            continue

    # Cap total at 100 to prevent gemini from being completely excluded by accident
    total = sum(sanitized.values())
    if total > 100:
        logger.warning(f"LLM_WEIGHTS_OVERFLOW: total={total} exceeds 100, capping proportionally")
        # Scale down proportionally so total = 100
        scale = 100.0 / total
        sanitized = {k: max(1, int(v * scale)) for k, v in sanitized.items()}

    # If no valid weights, fall back to gemini
    if not sanitized:
        logger.info("LLM_WEIGHTED_SELECTION: no valid weights, chosen=gemini (default)")
        return "gemini"

    roll = random.randint(1, 100)

    # Walk through providers in order, accumulating percentages
    cumulative = 0
    for provider, percentage in sanitized.items():
        cumulative += percentage
        if roll <= cumulative:
            logger.info(f"LLM_WEIGHTED_SELECTION: weights={sanitized} roll={roll} chosen={provider}")
            return provider

    # Remaining traffic goes to gemini (default)
    logger.info(f"LLM_WEIGHTED_SELECTION: weights={sanitized} roll={roll} chosen=gemini (default)")
    return "gemini"

def _create_groq_llm_service():
    """Create Groq LLM service."""
    if not constants.GROQ_API_KEY:
        logger.error("GROQ_API_KEY not set! Falling back to OpenAI")
        return _create_openai_llm_service()
    logger.info(f"LLM_PROVIDER: Groq {constants.GROQ_MODEL} active")
    return GroqLLMService(
        api_key=constants.GROQ_API_KEY,
        model=constants.GROQ_MODEL,
        params=GroqLLMService.InputParams(
            max_completion_tokens=constants.LLM_MAX_OUTPUT_TOKENS,
            temperature=constants.LLM_TEMPERATURE,
        ),
    )

def _create_openrouter_llm_service():
    """Create OpenRouter LLM service routed to Groq provider.

    Uses CustomOpenRouterLLMService to pass provider routing via extra_body
    (the only way to send non-standard fields through the OpenAI SDK).
    """
    if not constants.OPENROUTER_API_KEY:
        logger.error("OPENROUTER_API_KEY not set! Falling back to OpenAI")
        return _create_openai_llm_service()
    logger.info(f"LLM_PROVIDER: OpenRouter {constants.OPENROUTER_LLM_MODEL} active | provider=groq")
    return CustomOpenRouterLLMService(
        api_key=constants.OPENROUTER_API_KEY,
        model=constants.OPENROUTER_LLM_MODEL,
        provider_order=["groq"],
        allow_fallbacks=True,
        params=OpenRouterLLMService.InputParams(
            max_completion_tokens=constants.LLM_MAX_OUTPUT_TOKENS,
            temperature=constants.LLM_TEMPERATURE,
        ),
    )

def _create_openai_llm_service():
    """Create OpenAI LLM service."""
    logger.info("LLM_PROVIDER: OpenAI GPT-4o-mini active")
    return OpenAILLMService(
        api_key=constants.OPENAI_API_KEY,
        settings=OpenAILLMService.Settings(
            model="gpt-4o-mini",
            max_completion_tokens=constants.LLM_MAX_OUTPUT_TOKENS,
            temperature=constants.LLM_TEMPERATURE,
        ),
    )

def _create_gemini_llm_service(system_instruction: str):
    """Create Google Vertex AI Gemini LLM service with GCP account switching."""
    import random
    from src.repository.config_repository import config_repo

    # Get GCP mode from cache (no DB call - refreshed periodically by server)
    mode = config_repo.get_gcp_mode()

    # Determine which account to use
    if mode == "random":
        new_percentage = config_repo.get_gcp_new_account_percentage()
        use_new_account = random.randint(1, 100) <= new_percentage
        chosen = "new" if use_new_account else "old"
        logger.info(f"GCP_RANDOM_SELECTION: mode=random new_percentage={new_percentage} chosen={chosen}")
    else:
        use_new_account = (mode == "new")
        chosen = mode

    if use_new_account:
        project_id = constants.GCP_NEW_PROJECT_ID
        credentials_path = constants.GCP_NEW_CREDENTIALS_PATH
        account_tag = "NEW"
        logger.info(f"GCP_ACCOUNT: NEW account active | project={project_id} | credentials={credentials_path}")
    else:
        project_id = constants.GOOGLE_CLOUD_PROJECT
        credentials_path = constants.GCP_CREDENTIALS_PATH
        account_tag = "OLD"
        logger.info(f"GCP_ACCOUNT: OLD account active | project={project_id} | credentials={credentials_path}")

    # Use global endpoint for better capacity (Google recommends this for Standard PayGo)
    # Global endpoint routes to region with most availability, reducing 429 errors
    return GoogleVertexLLMService(
        name=f"GeminiLLM-{account_tag}",  # Shows in error logs which account caused the error
        project_id=project_id,
        location="global",  # Global endpoint for multi-region capacity pool
        model=constants.GEMINI_MODEL,
        credentials_path=credentials_path,
        system_instruction=system_instruction,
        params=GoogleVertexLLMService.InputParams(
            max_output_tokens=constants.LLM_MAX_OUTPUT_TOKENS,
            temperature=constants.LLM_TEMPERATURE,
        ),
    )

def create_llm_service(system_instruction: str):
    """Create LLM service with system instruction. Uses cached DB config to switch providers.

    LLM Provider Modes:
    - "gemini": Use Google Vertex AI Gemini (with GCP account switching)
    - "openai": Use OpenAI GPT-4o-mini
    - "groq": Use Groq (Llama 3.3 70B or configured model)
    - "openrouter": Use OpenRouter (access multiple providers via single API)
    - "random": Weighted traffic split between providers based on per-provider percentages

    Random mode traffic routing (via "weights" config):
      {"openai": 30, "groq": 5, "openrouter": 10} -> 30% OpenAI, 5% Groq, 10% OpenRouter, 55% Gemini
      {"groq": 5}               -> 5% Groq, 95% Gemini
      {"openai": 50}            -> 50% OpenAI, 50% Gemini (legacy behavior)

    GCP Account Modes (only for gemini):
    - "old": Always use mayaresearchgcp (old account)
    - "new": Always use custom-frame-484619-j2 (new account)
    - "random": Randomly choose between old and new based on percentage
    """
    from src.repository.config_repository import config_repo

    # Get LLM provider from cache (defaults to "gemini" if not set)
    llm_provider = config_repo.get_llm_provider()

    # Determine which LLM to use
    if llm_provider == "random":
        chosen_llm = _select_llm_provider_weighted()
    else:
        chosen_llm = llm_provider
    # Create the chosen LLM service
    if chosen_llm == "groq":
        return _create_groq_llm_service()

    if chosen_llm == "openai":
        return _create_openai_llm_service()

    if chosen_llm == "openrouter":
        return _create_openrouter_llm_service()

    # Default: Use Google Vertex AI Gemini
    return _create_gemini_llm_service(system_instruction)

def create_tts_service(speaker: str = None):
    """Create TTS service with optional custom speaker/voice.

    Args:
        speaker: TTS voice to use. If None, uses DEFAULT_TTS_SPEAKER.
    """
    voice = speaker or constants.DEFAULT_TTS_SPEAKER
    logger.info(f"TTS_SERVICE_CREATED: speaker={voice}")
    return CustomTTSService(
        api_url=constants.TTS_API_URL,
        api_key=constants.VEENA3_API_KEY,
        speaker=voice,
        sample_rate=constants.SAMPLE_RATE_16K,
    )

def create_gemini_tts_service(voice: str = None):
    """Create Gemini TTS service for web/Telugu sessions.

    Uses the same GCP account switching logic as Gemini LLM.
    Gemini TTS outputs at 24kHz natively.
    """
    import random
    from src.repository.config_repository import config_repo

    tts_voice = voice or constants.GEMINI_LIVE_VOICE  # "Kore" for Telugu

    mode = config_repo.get_gcp_mode()
    if mode == "random":
        new_percentage = config_repo.get_gcp_new_account_percentage()
        use_new_account = random.randint(1, 100) <= new_percentage
    else:
        use_new_account = (mode == "new")

    if use_new_account:
        credentials_path = constants.GCP_NEW_CREDENTIALS_PATH
        account_tag = "NEW"
    else:
        credentials_path = constants.GCP_CREDENTIALS_PATH
        account_tag = "OLD"

    tts = GoogleTTSService(
        credentials_path=credentials_path,
        location="asia-southeast1",
        settings=GoogleTTSService.Settings(
            voice="te-IN-Chirp3-HD-Erinome",
            language="te-IN",
        ),
        sample_rate=constants.SAMPLE_RATE,
    )
    logger.info(f"TTS_SERVICE_CREATED: provider=chirp3-hd voice=te-IN-Chirp3-HD-Erinome language=te-IN location=asia-southeast1 gcp={account_tag}")
    return tts

def create_context_and_tools(llm, system_instruction: str, include_video: bool = False, include_ppt: bool = False, include_music: bool = False):
    """Create LLM context with tools.

    All characters get image generation. Video, PPT, and music are controlled by persona config.
    """
    standard_tools = [cf_image_function]

    if include_video:
        standard_tools.append(generate_video_function)
        standard_tools.append(generate_video_from_image_function)
        logger.info("PIPELINE_TOOLS: Video generation functions ENABLED (t2v + i2v)")

    if include_ppt:
        standard_tools.append(generate_ppt_function)
        logger.info("PIPELINE_TOOLS: PPT generation function ENABLED")

    if include_music:
        standard_tools.append(generate_music_function)
        logger.info("PIPELINE_TOOLS: Music generation function ENABLED (Lyria)")

    logger.debug(f"TOOLS_REGISTERED: image=true video={include_video} ppt={include_ppt} music={include_music} total={len(standard_tools)}")

    tools = ToolsSchema(standard_tools=standard_tools) if standard_tools else None

    # Build messages with system instruction for OpenAI compatibility
    messages = [
        {"role": "system", "content": system_instruction},
        {"role": "user", "content": "Start the Conversation :"}
    ]

    context = OpenAILLMContext(
        messages=messages,
        tools=tools
    )
    context_aggregator = llm.create_context_aggregator(context)
    return context_aggregator

def create_llm_text_processor():
    """Create LLM text processor that combines 2 sentences before sending to TTS.

    This sits between the LLM and TTS in the pipeline. It aggregates streaming
    LLM tokens into 2-sentence chunks, reducing TTS API calls by ~half.
    """
    return LLMTextProcessor(
        text_aggregator=DoubleSentenceAggregator(sentences_per_chunk=2),
    )

def create_processors():
    """Create RTVI, transcript, and audio buffer processors."""
    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
    transcript = TranscriptProcessor()

    audio_buffer = AudioBufferProcessor(
        sample_rate=constants.SAMPLE_RATE,
        num_channels=1,
        buffer_size=constants.SAMPLE_RATE * 2 * constants.CHUNK_DURATION,
    )

    return rtvi, transcript, audio_buffer

def create_pipeline(transport, stt, context_aggregator, rtvi, llm, tts, audio_buffer, transcript, llm_text_processor=None):
    """Create the processing pipeline."""
    # Build core pipeline: llm -> [optional llm_text_processor] -> tts
    llm_to_tts = [llm, llm_text_processor, tts] if llm_text_processor else [llm, tts]

    pipeline = Pipeline([
        transport.input(),
        stt,
        transcript.user(),
        context_aggregator.user(),
        rtvi,
        *llm_to_tts,
        transport.output(),
        audio_buffer,
        transcript.assistant(),
        context_aggregator.assistant(),
    ])
    mode = "2-sentence aggregation" if llm_text_processor else "default (1 sentence)"
    logger.info(f"PIPELINE: Traditional STT mode (Audio → STT → Text → LLM → TTS) | text_aggregation={mode}")
    return pipeline

def create_task(pipeline, rtvi, bot_session):
    """Create pipeline task with observers."""
    return PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
            allow_interruptions=True
        ),
        observers=[
            SafeRTVIObserver(rtvi),  # Use SafeRTVIObserver to handle startup race condition
            InfoMetricsObserver(bot_session),
            UserBotLatencyLogObserver()
        ],
    )

# ---- Gemini Live pipeline (Telugu / multi-language) ----

def create_gemini_live_service(system_instruction: str, voice_id: str = None, include_video: bool = False, include_ppt: bool = False, include_music: bool = False, language: str = None):
    """Create Gemini Live Vertex AI service — handles STT + LLM + TTS in one service.

    Uses the same GCP account switching logic as the regular Gemini LLM service.
    """
    import random
    from src.repository.config_repository import config_repo

    voice = voice_id or constants.GEMINI_LIVE_VOICE

    standard_tools = [cf_image_function]
    if include_video:
        standard_tools.append(generate_video_function)
        standard_tools.append(generate_video_from_image_function)
        logger.info("GEMINI_LIVE_TOOLS: Video generation functions ENABLED (t2v + i2v)")

    if include_ppt:
        standard_tools.append(generate_ppt_function)
        logger.info("GEMINI_LIVE_TOOLS: PPT generation function ENABLED")

    if include_music:
        standard_tools.append(generate_music_function)
        logger.info("GEMINI_LIVE_TOOLS: Music generation function ENABLED (Lyria)")

    tools = ToolsSchema(standard_tools=standard_tools) if standard_tools else None

    # GCP account switching (same logic as _create_gemini_llm_service)
    mode = config_repo.get_gcp_mode()
    if mode == "random":
        new_percentage = config_repo.get_gcp_new_account_percentage()
        use_new_account = random.randint(1, 100) <= new_percentage
    else:
        use_new_account = (mode == "new")

    if use_new_account:
        project_id = constants.GCP_NEW_PROJECT_ID
        credentials_path = constants.GCP_NEW_CREDENTIALS_PATH
        account_tag = "NEW"
    else:
        project_id = constants.GOOGLE_CLOUD_PROJECT
        credentials_path = constants.GCP_CREDENTIALS_PATH
        account_tag = "OLD"

    # Map language code to Language enum for Gemini Live
    from pipecat.transcriptions.language import Language
    _LANG_MAP = {
        "te": Language.TE_IN,
        "hi": Language.HI_IN,
        "en": Language.EN_US,
        "es": Language.ES_ES,
        "fr": Language.FR_FR,
        "pt": Language.PT_BR,
        "id": Language.ID_ID,
        "de": Language.DE_DE,
        "ja": Language.JA_JP,
        "ar": Language.AR_SA,
        "ko": Language.KO_KR,
        "vi": Language.VI_VN,
    }
    gemini_language = _LANG_MAP.get(language, Language.EN_US)

    llm = GeminiLiveVertexLLMService(
        credentials_path=credentials_path,
        project_id=project_id,
        location="us-east4",
        system_instruction=system_instruction,
        tools=tools,
        settings=GeminiLiveVertexLLMService.Settings(
            voice=voice,
            language=gemini_language,
        ),
    )
    logger.info(f"GEMINI_LIVE_CREATED: voice={voice} language={gemini_language} tools={len(standard_tools)} gcp_account={account_tag} project={project_id}")
    return llm

def create_gemini_live_context():
    """Create context aggregator for Gemini Live.

    Uses LLMContext + LLMContextAggregatorPair directly (not deprecated OpenAILLMContext).
    System instruction is set on the LLM service via Settings, not in context messages.
    VAD is placed inside LLMUserAggregatorParams per official Gemini Live pattern.
    """
    from pipecat.processors.aggregators.llm_context import LLMContext
    from pipecat.processors.aggregators.llm_response_universal import (
        LLMContextAggregatorPair,
        LLMUserAggregatorParams,
    )

    messages = []

    context = LLMContext(messages)
    context_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(
            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
        ),
    )
    return context_aggregator

def create_gemini_live_pipeline(transport, gemini_live, context_aggregator, rtvi, audio_buffer, transcript):
    """Create pipeline for Gemini Live — audio goes directly to Gemini, no separate STT/TTS.

    Keeps RTVI (frontend events), audio_buffer (recording), transcript (conversation tracking).
    """
    pipeline = Pipeline([
        transport.input(),
        context_aggregator.user(),
        gemini_live,
        rtvi,
        transport.output(),
        audio_buffer,
        transcript.assistant(),
        context_aggregator.assistant(),
    ])
    logger.info("PIPELINE: Gemini Live mode (Audio → Context → Gemini Live → Audio)")
    return pipeline

-        sample_rate=constants.SAMPLE_RATE_16K,


TTS service now declares 48kHz to match what the API actually returns. This should fix the choppy/cutting voice. Want me to deploy?


can u revert that .. i will test locally and will confirm


Bash

git checkout -- src/bot/core/pipeline.py

Revert pipeline.py change


(Bash completed with no output)


Reverted. Test locally and confirm — the fix is changing `sample_rate=constants.SAMPLE_RATE_16K` to `sample_rate=48000` in `create_tts_service()` to match the `"output": "48khz"` the API returns.


first i want u to do unstash changes that we have done


List stashes


stash@{0}: On version_5: payments config changes
stash@{1}: WIP on version_5: 8fb6ae8 updated
stash@{2}: WIP on version_5: 6a728c7 added prices country wise
stash@{3}: WIP on pipecat_modal_new_characters: dbe55c2 updated
stash@{4}: WIP on v3_version: 25d5966 updated
stash@{5}: WIP on pipecat_modal_new_characters: dbe55c2 updated
stash@{6}: WIP on (no branch): 230b54c updated
stash@{7}: WIP on pipecat_modal_new_characters: da7c67f updated
stash@{8}: WIP on pipecat_modal_new_characters: 24791cb updated


Pop payments config stash


On branch version_5
Your branch is up to date with 'origin/version_5'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   prompts/prompt_character.txt
	modified:   src/bot/core/config.py
	modified:   src/bot/services/image_service.py
	modified:   src/bot/services/limit_checker.py
	modified:   src/bot/services/music_service.py
	modified:   src/bot/services/ppt_service.py
	modified:   src/bot/services/video_service.py
	modified:   src/repository/config_v2_repository.py
	modified:   src/service/usage_service.py

no changes added to commit (use "git add" and/or "git commit -a")
Dropped stash@{0} (9ce7efca149411d3786d5df7cadc6463dc735ff7)


All payments config changes are back. Plus `main.py` still has the Hindi pipeline fix. Total local changes:

- `main.py` — Hindi pipeline fix + payments config loading
- `config.py` — `payments_enabled` + `cooldown_minutes` on BotSessionState
- `limit_checker.py` — cooldown support
- `image/video/music/ppt_service.py` — cooldown agent messages
- `config_v2_repository.py` — `get_payments_config()`
- `usage_service.py` — cooldown in `/check-limits` API
- `prompts/prompt_character.txt` — prompt change (you wanted this reverted earlier)

Want me to revert `prompt_character.txt` again?


waht is flow  now expalin


**Current flow for Hindi users (payments OFF):**

1. **Session starts** → loads persona's `free_count` + `free_window_hours` from persona table
2. **Loads `payments_config`** from DB (cached 120s):
   ```json
   {"payments_enabled": false, "cooldowns": {"image_gen": 30, "video_gen": 60, ...}}
   ```
3. **Pipeline**: OpenAI STT → Gemini 2.5 Flash LLM → Veena3 TTS (traditional, not Gemini Live)

**When user asks to generate something:**

4. `can_generate()` checks: plan credits → topup credits → free credits
5. **If credits available** → generates normally, decrements credit
6. **If credits exhausted + `payments_enabled: false`** → returns `"cooldown"`
   - Frontend gets `show_cooldown` event (no payment popup)
   - Agent says: *"Limit khatam ho gaya hai, 30 minutes baad try karo"*
7. **If credits exhausted + `payments_enabled: true`** → returns `"subscribe"` or `"topup"`
   - Frontend shows payment popup (original behavior)

**To toggle payments on/off:** Update DB row `payments_config` in `configurations_v2` — takes effect within 2 minutes (cache TTL).

**Note:** The cooldown message tells the user to wait X minutes, but the actual limit reset depends on `free_window_hours` in the persona table (e.g., 24 hours). The `cooldown_minutes` in config is just what the agent *says* — it doesn't change the actual free window.

Want me to make the cooldown actually match the free window, or keep it separate?


brother we fucked up i dont knw.. i pulled this repo from local and i ahev this config payment changes how ther fuck ?


These changes are **not committed or pushed** — they're only local uncommitted changes on this machine. Let me verify:


Check git status


 M prompts/prompt_character.txt
 M src/bot/core/config.py
 M src/bot/services/image_service.py
 M src/bot/services/limit_checker.py
 M src/bot/services/music_service.py
 M src/bot/services/ppt_service.py
 M src/bot/services/video_service.py
 M src/repository/config_v2_repository.py
 M src/service/usage_service.py


git log origin/version_5..HEAD --oneline

Check unpushed commits


All `M` (modified) — **nothing committed, nothing pushed**. These are only local uncommitted changes on this server. Your local machine pulling from remote won't have any of these.

`main.py` is not even showing as modified — the Hindi pipeline change must have been reverted by the stash pop overwriting it. Let me check:


Check main.py state


    use_gemini_live = language not in (None, "hi")
    bot_session.use_gemini_live = use_gemini_live
    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")
    if use_gemini_live:


Hindi pipeline fix is there but not showing as modified (maybe it got staged differently).

Point is — **nothing is pushed to remote**. If you're seeing these changes on your local machine, you might be looking at the wrong branch or have separate local edits. What exactly are you seeing on your local?


❯ '/Users/thippareddysaicharanreddy/Desktop/Prod_Livekit_Pipecat/logs/maya.log' check
errors

  Read 1 file (ctrl+o to expand)

⏺ Here are the 3 errors:

  1. Redis connection failed:
  REDIS_CONNECT_FAILED: error=Timeout connecting to server
  Redis isn't running locally. The server continues without it (redis=disabled), but
  caching won't work.

  2. Missing get_payments_config method (CRASH):
  AttributeError: 'ConfigV2Repository' object has no attribute 'get_payments_config'
  At src/bot/core/main.py:131 — the bot session crashes immediately because the git pull
  added code that calls get_payments_config() but that method doesn't exist in
  ConfigV2Repository. This is why the user's session dies right after connecting.

  3. Missing DB table:
  EXPLORE_CACHE_REFRESH_ERROR: no such table: explore_categories
  The explore_categories table doesn't exist in the local Turso DB.

  The critical one is #2 — it kills every bot session. Want me to check main.py and
  config_v2_repository.py to add the missing method?

❯ yeah check

  Read 2 files (ctrl+o to expand)

⏺ The method is missing. It follows the same pattern as get_firered_config — fetch a JSON
   config from configurations_v2 by key. Let me add it.

⏺ Update(src/repository/config_v2_repository.py)
  ⎿  Added 26 lines
      189              return result
      190          return {"enabled": False, "percentage": 0, "max_queue": 3}
      191
      192 +    async def get_payments_config(self) -> Dict[str, Any]:
      193 +        """Get payments config.
      194 +
      195 +        DB value (JSON): {"payments_enabled": true, "cooldowns": {"imag
          +e_gen": 30, "video_gen": 60, ...}}
      196 +        """
      197 +        from src.core.cache_layer import cached
      198 +
      199 +        async def _fetch():
      200 +            try:
      201 +                result = await self.query({
      202 +                    "sql": "SELECT config_value FROM configurations_v2
          +WHERE config_key = ?",
      203 +                    "args": [{"type": "text", "value": "payments_config
          +"}],
      204 +                }, "config_v2_payments")
      205 +                if result.rows:
      206 +                    raw = self.extract_text(result.rows[0], 0)
      207 +                    if raw:
      208 +                        return json.loads(raw)
      209 +            except Exception as e:
      210 +                logger.error(f"CONFIG_V2_PAYMENTS_ERROR: {e}")
      211 +            return None
      212 +
      213 +        result = await cached(key="config_v2:payments_config", fallback
          +=_fetch, ttl=120)
      214 +        if result:
      215 +            return result
      216 +        return {"payments_enabled": True, "cooldowns": {}}
      217 +
      218      async def get_language_intro(self, lang_code: str, persona_id: str)
            -> str:
      219          """Get language-specific intro for a persona."""
      220          from src.core.cache_layer import cached

⏺ Now restart the server:

⏺ Bash(./scripts/pipecat-ctl.sh restart 2>&1)
  ⎿  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
       🎙   Pipecat Server Control
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
     … +17 lines (ctrl+o to expand)

⏺ Added get_payments_config() to ConfigV2Repository. It fetches the payments_config key
  from configurations_v2, cached for 120s, with a safe default of {"payments_enabled":
  True, "cooldowns": {}} if the DB row doesn't exist yet.

  Server is restarted — try connecting from the app again to see if the session stays
  alive now.

✻ Crunched for 30s

❯ where we using the change u added

⏺ The get_payments_config() is called in src/bot/core/main.py:130-134:

  payments_config = await config_v2_repo.get_payments_config()
  bot_session.payments_enabled = payments_config.get("payments_enabled", True)
  cooldowns = payments_config.get("cooldowns", {})
  bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)

  This runs during every bot session startup (run_bot_webrtc_global_agent). It sets:
  - payments_enabled — whether to show payment prompts to the user
  - cooldown_minutes — per-content-type cooldown (e.g. 30min for image_gen, 60min for
  video_gen), defaults to 30 if not configured

  The config is expected to be a row in your configurations_v2 table with config_key =
  'payments_config' and a JSON value like:
  {"payments_enabled": true, "cooldowns": {"image_gen": 30, "video_gen": 60}}

  If that row doesn't exist in DB yet, the fallback returns {"payments_enabled": True,
  "cooldowns": {}} so it won't crash.

─────────────────────────────────────────────────────────────────────────────────────────
❯
─────────────────────────────────────────────────────────────────────────────────────────
  ⏵⏵ bypass permissions on (shift+tab to cycle)                           ⧉ In maya.log


Ah I see — `main.py` changes **were pushed** to remote (the payments config loading + Hindi pipeline fix). When you pulled on your local, it got `main.py` with `get_payments_config()` call, but `config_v2_repository.py` wasn't pushed — so it crashed.

Your other Claude instance already added the missing method on your local. But the other files (limit_checker, image/video/music/ppt services, config.py) are still not pushed — so the cooldown flow won't fully work yet.

Do you want me to:
1. **Push all the payments changes** from this server so your local has everything in sync?
2. Or **revert main.py on remote** to remove the payments config loading until you're ready?


do 2


126	        else:
127	            bot_session.free_remaining = 0
128
129	        # Load payments config
130	        from src.repository.config_v2_repository import config_v2_repo
131	        payments_config = await config_v2_repo.get_payments_config()
132	        bot_session.payments_enabled = payments_config.get("payments_enabled", True)
133	        cooldowns = payments_config.get("cooldowns", {})
134	        bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)
135
136	        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} payments={bot_session.payments_enabled} cooldown={bot_session.cooldown_minutes}m transport={transport_type}")
137
138	    # Determine which tools to enable
139	    video_enabled = "generate_video" in persona_tools or "generate_video_from_image" in persona_tools
140
141	    ppt_enabled = "generate_ppt" in persona_tools
142	    music_enabled = "generate_music" in persona_tools
143	    bot_session.video_enabled = video_enabled
144
145	    use_gemini_live = language not in (None, "hi")
146	    bot_session.use_gemini_live = use_gemini_live
147	    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")
148
149	    vad_analyzer = create_vad_analyzer(session_id)
150	    bot_session.vad_analyzer = vad_analyzer

        else:
            bot_session.free_remaining = 0

        # Load payments config
        from src.repository.config_v2_repository import config_v2_repo
        payments_config = await config_v2_repo.get_payments_config()
        bot_session.payments_enabled = payments_config.get("payments_enabled", True)
        cooldowns = payments_config.get("cooldowns", {})
        bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)

        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} payments={bot_session.payments_enabled} cooldown={bot_session.cooldown_minutes}m transport={transport_type}")

    # Determine which tools to enable
    video_enabled = "generate_video" in persona_tools or "generate_video_from_image" in persona_tools

    ppt_enabled = "generate_ppt" in persona_tools
    music_enabled = "generate_music" in persona_tools
    bot_session.video_enabled = video_enabled

    use_gemini_live = language not in (None, "hi")
    bot_session.use_gemini_live = use_gemini_live
    logger.info(f"PIPELINE_MODE: language={language} use_gemini_live={use_gemini_live}")

    vad_analyzer = create_vad_analyzer(session_id)
    bot_session.vad_analyzer = vad_analyzer


Edit

        # Load payments config
        from src.repository.config_v2_repository import config_v2_repo
        payments_config = await config_v2_repo.get_payments_config()
        bot_session.payments_enabled = payments_config.get("payments_enabled", True)
        cooldowns = payments_config.get("cooldowns", {})
        bot_session.cooldown_minutes = cooldowns.get(bot_session.content_type, 30)

        logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} payments={bot_session.payments_enabled} cooldown={bot_session.cooldown_minutes}m transport={transport_type}")

logger.info(f"USAGE_STATE: session_id={session_id} device_id={device_id} persona={persona_id} content={bot_session.content_type} free={bot_session.free_remaining}/{free_count} plan={bot_session.plan_remaining} topup={bot_session.topup_remaining} transport={transport_type}")