Files
ai-video-fullstack/backend/services/fixed_speech.py

74 lines
2.3 KiB
Python

"""Shared client-visible output for deterministic fixed speech."""
from __future__ import annotations
from collections.abc import Awaitable
from typing import Any
from pipecat.frames.frames import OutputTransportMessageUrgentFrame, TTSSpeakFrame
from pipecat.utils.time import time_now_iso8601
from services.brains.base import BrainRuntime
from services.runtime_variables import DynamicVariableStore
class FixedSpeechOutput:
"""Display and synthesize fixed speech without waiting for playback."""
def __init__(
self,
store: DynamicVariableStore,
runtime: BrainRuntime,
) -> None:
self._store = store
self._runtime = runtime
self._client_ready = False
self._pending_transcripts: list[dict[str, Any]] = []
async def mark_client_ready(self) -> None:
self._client_ready = True
pending = self._pending_transcripts
self._pending_transcripts = []
for message in pending:
await self.emit(message)
async def speak(
self,
text: str,
*,
source: str,
node_id: str | None = None,
record_history: bool = True,
) -> Awaitable[None] | None:
content = text.strip()
if not content:
return None
if record_history:
self._store.record("agent", content)
transcript = {
"type": "transcript",
"role": "assistant",
"content": content,
"timestamp": time_now_iso8601(),
"source": source,
**({"nodeId": node_id} if node_id else {}),
}
if self._client_ready:
await self.emit(transcript)
else:
self._pending_transcripts.append(transcript)
track_speech = getattr(self._runtime.call_end, "track_speech", None)
playback_completion: Awaitable[None] | None = None
if callable(track_speech):
playback_completion = track_speech()
await self._runtime.queue_frame(
TTSSpeakFrame(content, append_to_context=False)
)
return playback_completion
async def emit(self, message: dict[str, Any]) -> None:
await self._runtime.queue_frame(
OutputTransportMessageUrgentFrame(message=message)
)