b03247f360
Replaces every "task" identifier that referred to the BaseTask abstraction with "worker". Asyncio task plumbing (asyncio.Task, BaseTaskManager, TaskManager, create_task, cancel_task, etc.) stays untouched. Highlights: - Classes: BaseTask → BaseWorker, PipelineTask → PipelineWorker, LLMTask → LLMWorker, LLMContextTask → LLMContextWorker, TaskBus → WorkerBus, TaskRegistry → WorkerRegistry, TaskActivationArgs → WorkerActivationArgs, TaskReadyData → WorkerReadyData, TaskRegistryEntry → WorkerRegistryEntry, TaskObserver → WorkerObserver, all Bus*TaskMessage → Bus*WorkerMessage, BusAddTaskMessage.task field → worker, BusWorkerRegistryMessage.tasks field → workers. - Methods/decorators: activate_task → activate_worker, deactivate_task → deactivate_worker, add_task → add_worker, watch_task → watch_worker, @task_ready → @worker_ready, setup_pipeline_task hook → setup_pipeline_worker. - Params/fields: FrameProcessorSetup.pipeline_task and FunctionCallParams.pipeline_task → pipeline_worker. Parameter names like task_name → worker_name; spawn/run accept worker:. - Files: pipeline/base_task.py → base_worker.py, pipeline/task.py → worker.py (plus a re-export shim at pipeline/task.py), task_observer.py → worker_observer.py, task_ready_decorator.py → worker_ready_decorator.py, pipecat.tasks → pipecat.workers, llm_task.py → llm_worker.py, llm_context_task.py → llm_context_worker.py, examples/multi-task → examples/multi-worker. Back-compat: - PipelineTask kept as a deprecated subclass of PipelineWorker that warns on construction. - pipecat.pipeline.task re-exports PipelineWorker/PipelineTask/etc. so existing user imports keep working. - FrameProcessor.pipeline_task kept as a deprecated property that forwards to pipeline_worker. Local variables in examples that hold a worker (task = PipelineTask(...)) are renamed to worker = PipelineWorker(...). Asyncio-task locals (runner_task, etc.) are preserved.
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.frames.frames import Frame, TranscriptionFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.worker import PipelineWorker
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.transcriptions.language import Language
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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class TranscriptionLogger(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, TranscriptionFrame):
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print(f"Transcription: {frame.text}")
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# Push all frames through
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await self.push_frame(frame, direction)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(audio_in_enabled=True),
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"twilio": lambda: FastAPIWebsocketParams(audio_in_enabled=True),
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"webrtc": lambda: TransportParams(audio_in_enabled=True),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Run example using Speechmatics STT.
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This example will use diarization within our STT service and output the words spoken by
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each individual speaker and wrap them with XML tags.
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If you do not wish to use diarization, then set the `enable_diarization` parameter
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to `False` or omit it altogether. The `text_format` will only be used if diarization is enabled.
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By default, this example will use our ENHANCED operating point, which is optimized for
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high accuracy. You can change this by setting the `operating_point` parameter to a different
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value.
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For more information on operating points, see the Speechmatics documentation:
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https://docs.speechmatics.com/rt-api-ref
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"""
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logger.info(f"Starting bot")
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stt = SpeechmaticsSTTService(
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api_key=os.environ["SPEECHMATICS_API_KEY"],
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settings=SpeechmaticsSTTService.Settings(
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language=Language.EN,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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),
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)
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tl = TranscriptionLogger()
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pipeline = Pipeline([transport.input(), stt, tl])
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worker = PipelineWorker(
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pipeline,
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await worker.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(worker)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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