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feat: allow uploading recording as part of node transition

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This commit is contained in:
Abhishek Kumar 2026-04-10 11:54:00 +05:30
parent bb5f56bfb7
commit 65c76ca7ff
36 changed files with 2255 additions and 201 deletions
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39
api/services/pipecat/event_handlers.py
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@ -11,12 +11,17 @@ from api.services.pipecat.in_memory_buffers import (
InMemoryLogsBuffer,
)
from api.services.pipecat.pipeline_metrics_aggregator import PipelineMetricsAggregator
from api.services.pipecat.recording_playback import queue_recording_audio
from api.services.pipecat.tracing_config import get_trace_url
from api.services.workflow.pipecat_engine import PipecatEngine
from api.tasks.arq import enqueue_job
from api.tasks.function_names import FunctionNames
from api.utils.hold_audio import play_hold_audio_loop
from pipecat.frames.frames import Frame, LLMContextFrame, TTSSpeakFrame
from pipecat.frames.frames import (
Frame,
LLMContextFrame,
TTSSpeakFrame,
)
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.utils.enums import EndTaskReason
@ -32,6 +37,7 @@ def register_event_handlers(
pipeline_metrics_aggregator: PipelineMetricsAggregator,
audio_config=AudioConfig,
pre_call_fetch_task: asyncio.Task | None = None,
fetch_recording_audio=None,
):
"""Register all event handlers for transport and task events.
@ -112,12 +118,31 @@ def register_event_handlers(
# so that render_template() has the complete _call_context_vars.
await engine.set_node(engine.workflow.start_node_id)
greeting = engine.get_start_greeting()
if greeting:
logger.debug(
"Both pipeline_started and client_connected received - playing greeting via TTS"
)
await task.queue_frame(TTSSpeakFrame(greeting))
greeting_info = engine.get_start_greeting()
if greeting_info:
greeting_type, greeting_value = greeting_info
if (
greeting_type == "audio"
and greeting_value
and fetch_recording_audio
):
logger.debug(f"Playing audio greeting recording: {greeting_value}")
audio_data = await fetch_recording_audio(greeting_value)
if audio_data:
await queue_recording_audio(
audio_data,
sample_rate=audio_config.pipeline_sample_rate or 16000,
queue_frame=task.queue_frame,
)
else:
logger.warning(
f"Failed to fetch audio greeting {greeting_value}, "
"falling back to LLM generation"
)
await engine.llm.queue_frame(LLMContextFrame(engine.context))
else:
logger.debug("Playing text greeting via TTS")
await task.queue_frame(TTSSpeakFrame(greeting_value))
else:
logger.debug(
"Both pipeline_started and client_connected received - triggering initial LLM generation"