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Merge branch 'pipecat-ai:main' into main

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fatwang2
2025-01-19 09:40:08 +08:00
committed by GitHub
parent 3466842cd4 a169e0cde9
commit 5a88165a26
16 changed files with 658 additions and 135 deletions
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35
CHANGELOG.md
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@@ -5,17 +5,31 @@ All notable changes to **Pipecat** will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
## [0.0.53] - 2025-01-18
### Added
- Added `ElevenLabsHttpTTSService` and the
`07d-interruptible-elevenlabs-http.py` foundational example.
- Introduced pipeline frame observers. Observers can view all the frames that go
through the pipeline without the need to inject processors in the
pipeline. This can be useful, for example, to implement frame loggers or
debuggers among other things.
debuggers among other things. The example
`examples/foundational/30-observer.py` shows how to add an observer to a
pipeline for debugging.
- Added `30-observer.py` to show how to add an Observer to a pipeline for
debugging.
- Introduced heartbeat frames. The pipeline task can now push periodic
heartbeats down the pipeline when `enable_heartbeats=True`. Heartbeats are
system frames that are supposed to make it all the way to the end of the
pipeline. When a heartbeat frame is received the traversing time (i.e. the
time it took to go through the whole pipeline) will be displayed (with TRACE
logging) otherwise a warning will be shown. The example
`examples/foundational/31-heartbeats.py` shows how to enable heartbeats and
forces warnings to be displayed.
- Added `LLMTextFrame` and `TTSTextFrame` which should be pushed by LLM and TTS
services respectively instead of `TextFrame`s.
- Added `OpenRouter` for OpenRouter integration with an OpenAI-compatible
interface. Added foundational example `14m-function-calling-openrouter.py`.
@@ -56,6 +70,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed
- Modified `UserIdleProcessor` to start monitoring only after first
conversation activity (`UserStartedSpeakingFrame` or
`BotStartedSpeakingFrame`) instead of immediately.
- Modified `OpenAIAssistantContextAggregator` to support controlled completions
and to emit context update callbacks via `FunctionCallResultProperties`.
@@ -79,6 +97,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Fixed
- Fixed an issue where `DeepgramSTTService` was not generating metrics using
pipeline's VAD.
- Fixed `UserIdleProcessor` not properly propagating `EndFrame`s through the
pipeline.
- Fixed an issue where websocket based TTS services could incorrectly terminate
their connection due to a retry counter not resetting.
@@ -95,6 +119,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Fixed an issue where setting the voice and model for `RimeHttpTTSService`
wasn't working.
- Fixed an issue where `IdleFrameProcessor` and `UserIdleProcessor` were getting
initialized before the start of the pipeline.
## [0.0.52] - 2024-12-24
### Added

6
README.md
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@@ -53,6 +53,12 @@ To keep things lightweight, only the core framework is included by default. If y
pip install "pipecat-ai[option,...]"
```
Or you can install all of them with:
```shell
pip install "pipecat-ai[all]"
```
Available options include:
| Category | Services | Install Command Example |

43
examples/foundational/31-heartbeats.py Normal file
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@@ -0,0 +1,43 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import sys
from loguru import logger
from pipecat.frames.frames import Frame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
class NullProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
async def main():
"""This test shows heartbeat monitoring by displaying a warning when
heartbeats are not received.
"""
pipeline = Pipeline([NullProcessor()])
task = PipelineTask(pipeline, PipelineParams(enable_heartbeats=True))
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

5
pyproject.toml
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@@ -32,8 +32,7 @@ dependencies = [
"protobuf~=5.29.3",
"pydantic~=2.10.5",
"pyloudnorm~=0.1.1",
"resampy~=0.4.3",
"tenacity~=9.0.0"
"resampy~=0.4.3"
]
[project.urls]
@@ -63,7 +62,7 @@ fireworks = [ "openai~=1.59.6" ]
krisp = [ "pipecat-ai-krisp~=0.3.0" ]
koala = [ "pvkoala~=2.0.3" ]
langchain = [ "langchain~=0.3.14", "langchain-community~=0.3.14", "langchain-openai~=0.3.0" ]
livekit = [ "livekit~=0.19.1", "livekit-api~=0.8.1" ]
livekit = [ "livekit~=0.19.1", "livekit-api~=0.8.1", "tenacity~=9.0.0" ]
lmnt = [ "websockets~=13.1" ]
local = [ "pyaudio~=0.2.14" ]
moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers~=4.48.0" ]

4
src/pipecat/audio/utils.py
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@@ -80,14 +80,14 @@ def ulaw_to_pcm(ulaw_bytes: bytes, in_sample_rate: int, out_sample_rate: int):
in_pcm_bytes = audioop.ulaw2lin(ulaw_bytes, 2)
# Resample
out_pcm_bytes = audioop.ratecv(in_pcm_bytes, 2, 1, in_sample_rate, out_sample_rate, None)[0]
out_pcm_bytes = resample_audio(in_pcm_bytes, in_sample_rate, out_sample_rate)
return out_pcm_bytes
def pcm_to_ulaw(pcm_bytes: bytes, in_sample_rate: int, out_sample_rate: int):
# Resample
in_pcm_bytes = audioop.ratecv(pcm_bytes, 2, 1, in_sample_rate, out_sample_rate, None)[0]
in_pcm_bytes = resample_audio(pcm_bytes, in_sample_rate, out_sample_rate)
# Convert PCM to μ-law
ulaw_bytes = audioop.lin2ulaw(in_pcm_bytes, 2)

35
src/pipecat/frames/frames.py
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@@ -5,6 +5,7 @@
#
from dataclasses import dataclass, field
from enum import Enum
from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Literal, Mapping, Optional, Tuple
from pipecat.audio.vad.vad_analyzer import VADParams
@@ -18,6 +19,23 @@ if TYPE_CHECKING:
from pipecat.observers.base_observer import BaseObserver
class KeypadEntry(str, Enum):
"""DTMF entries."""
ONE = "1"
TWO = "2"
THREE = "3"
FOUR = "4"
FIVE = "5"
SIX = "6"
SEVEN = "7"
EIGHT = "8"
NINE = "9"
ZERO = "0"
POUND = "#"
STAR = "*"
def format_pts(pts: int | None):
return nanoseconds_to_str(pts) if pts else None
@@ -375,6 +393,13 @@ class TransportMessageFrame(DataFrame):
return f"{self.name}(message: {self.message})"
@dataclass
class InputDTMFFrame(DataFrame):
"""A DTMF button input"""
button: KeypadEntry
#
# System frames
#
@@ -424,6 +449,16 @@ class FatalErrorFrame(ErrorFrame):
fatal: bool = field(default=True, init=False)
@dataclass
class HeartbeatFrame(SystemFrame):
"""This frame is used by the pipeline task as a mechanism to know if the
pipeline is running properly.
"""
timestamp: int
@dataclass
class EndTaskFrame(SystemFrame):
"""This is used to notify the pipeline task that the pipeline should be

220
src/pipecat/pipeline/task.py
View File

@@ -19,6 +19,7 @@ from pipecat.frames.frames import (
EndTaskFrame,
ErrorFrame,
Frame,
HeartbeatFrame,
MetricsFrame,
StartFrame,
StopTaskFrame,
@@ -26,14 +27,19 @@ from pipecat.frames.frames import (
from pipecat.metrics.metrics import ProcessingMetricsData, TTFBMetricsData
from pipecat.observers.base_observer import BaseObserver
from pipecat.pipeline.base_pipeline import BasePipeline
from pipecat.pipeline.task_observer import TaskObserver
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.utils.utils import obj_count, obj_id
HEARTBEAT_SECONDS = 1.0
HEARTBEAT_MONITOR_SECONDS = HEARTBEAT_SECONDS * 5
class PipelineParams(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
allow_interruptions: bool = False
enable_heartbeats: bool = False
enable_metrics: bool = False
enable_usage_metrics: bool = False
send_initial_empty_metrics: bool = True
@@ -58,25 +64,10 @@ class Source(FrameProcessor):
match direction:
case FrameDirection.UPSTREAM:
await self._handle_upstream_frame(frame)
await self._up_queue.put(frame)
case FrameDirection.DOWNSTREAM:
await self.push_frame(frame, direction)
async def _handle_upstream_frame(self, frame: Frame):
if isinstance(frame, EndTaskFrame):
# Tell the task we should end nicely.
await self._up_queue.put(EndTaskFrame())
elif isinstance(frame, CancelTaskFrame):
# Tell the task we should end right away.
await self._up_queue.put(CancelTaskFrame())
elif isinstance(frame, ErrorFrame):
logger.error(f"Error running app: {frame}")
if frame.fatal:
# Cancel all tasks downstream.
await self.push_frame(CancelFrame())
# Tell the task we should stop.
await self._up_queue.put(StopTaskFrame())
class Sink(FrameProcessor):
"""This is the sink processor that is linked at the end of the pipeline
@@ -91,33 +82,7 @@ class Sink(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
# We really just want to know when the EndFrame reached the sink.
if isinstance(frame, EndFrame):
await self._down_queue.put(frame)
class Observer(BaseObserver):
"""This is a pipeline frame observer that is used as a proxy to the user
provided observers. That is, this is the only observer passed to the frame
processors. Then, every time a frame is pushed this observer will call all
the observers registered to the pipeline task.
"""
def __init__(self, observers: List[BaseObserver] = []):
self._observers = observers
async def on_push_frame(
self,
src: FrameProcessor,
dst: FrameProcessor,
frame: Frame,
direction: FrameDirection,
timestamp: int,
):
for observer in self._observers:
await observer.on_push_frame(src, dst, frame, direction, timestamp)
await self._down_queue.put(frame)
class PipelineTask:
@@ -135,9 +100,18 @@ class PipelineTask:
self._params = params
self._finished = False
# This queue receives frames coming from the pipeline upstream.
self._up_queue = asyncio.Queue()
# This queue receives frames coming from the pipeline downstream.
self._down_queue = asyncio.Queue()
# This queue is the queue used to push frames to the pipeline.
self._push_queue = asyncio.Queue()
# This is the heartbeat queue. When a heartbeat frame is received in the
# down queue we add it to the heartbeat queue for processing.
self._heartbeat_queue = asyncio.Queue()
# This event is used to indicate an EndFrame has been received in the
# down queue.
self._endframe_event = asyncio.Event()
self._source = Source(self._up_queue)
self._source.link(pipeline)
@@ -145,36 +119,52 @@ class PipelineTask:
self._sink = Sink(self._down_queue)
pipeline.link(self._sink)
self._observer = Observer(params.observers)
self._observer = TaskObserver(params.observers)
def has_finished(self):
"""Indicates whether the tasks has finished. That is, all processors
have stopped.
"""
return self._finished
async def stop_when_done(self):
"""This is a helper function that sends an EndFrame to the pipeline in
order to stop the task after everything in it has been processed.
"""
logger.debug(f"Task {self} scheduled to stop when done")
await self.queue_frame(EndFrame())
async def cancel(self):
"""
Stops the running pipeline immediately.
"""
logger.debug(f"Canceling pipeline task {self}")
# Make sure everything is cleaned up downstream. This is sent
# out-of-band from the main streaming task which is what we want since
# we want to cancel right away.
await self._source.push_frame(CancelFrame())
self._process_push_task.cancel()
self._process_up_task.cancel()
await self._process_push_task
await self._process_up_task
await self._cancel_tasks(True)
async def run(self):
self._process_up_task = asyncio.create_task(self._process_up_queue())
self._process_push_task = asyncio.create_task(self._process_push_queue())
await asyncio.gather(self._process_up_task, self._process_push_task)
"""
Starts running the given pipeline.
"""
tasks = self._create_tasks()
await asyncio.gather(*tasks)
self._finished = True
async def queue_frame(self, frame: Frame):
"""
Queue a frame to be pushed down the pipeline.
"""
await self._push_queue.put(frame)
async def queue_frames(self, frames: Iterable[Frame] | AsyncIterable[Frame]):
"""
Queues multiple frames to be pushed down the pipeline.
"""
if isinstance(frames, AsyncIterable):
async for frame in frames:
await self.queue_frame(frame)
@@ -182,6 +172,43 @@ class PipelineTask:
for frame in frames:
await self.queue_frame(frame)
def _create_tasks(self):
tasks = []
self._process_up_task = asyncio.create_task(self._process_up_queue())
self._process_down_task = asyncio.create_task(self._process_down_queue())
self._process_push_task = asyncio.create_task(self._process_push_queue())
tasks = [self._process_up_task, self._process_down_task, self._process_push_task]
return tasks
def _maybe_start_heartbeat_tasks(self):
if self._params.enable_heartbeats:
self._heartbeat_push_task = asyncio.create_task(self._heartbeat_push_handler())
self._heartbeat_monitor_task = asyncio.create_task(self._heartbeat_monitor_handler())
async def _cancel_tasks(self, cancel_push: bool):
await self._maybe_cancel_heartbeat_tasks()
if cancel_push:
self._process_push_task.cancel()
await self._process_push_task
self._process_up_task.cancel()
await self._process_up_task
self._process_down_task.cancel()
await self._process_down_task
await self._observer.stop()
async def _maybe_cancel_heartbeat_tasks(self):
if self._params.enable_heartbeats:
self._heartbeat_push_task.cancel()
await self._heartbeat_push_task
self._heartbeat_monitor_task.cancel()
await self._heartbeat_monitor_task
def _initial_metrics_frame(self) -> MetricsFrame:
processors = self._pipeline.processors_with_metrics()
data = []
@@ -190,9 +217,20 @@ class PipelineTask:
data.append(ProcessingMetricsData(processor=p.name, value=0.0))
return MetricsFrame(data=data)
async def _wait_for_endframe(self):
await self._endframe_event.wait()
self._endframe_event.clear()
async def _process_push_queue(self):
"""This is the task that runs the pipeline for the first time by sending
a StartFrame and by pushing any other frames queued by the user. It runs
until the tasks is canceled or stopped (e.g. with an EndFrame).
"""
self._clock.start()
self._maybe_start_heartbeat_tasks()
start_frame = StartFrame(
allow_interruptions=self._params.allow_interruptions,
enable_metrics=self._params.enable_metrics,
@@ -224,29 +262,91 @@ class PipelineTask:
await self._source.cleanup()
await self._pipeline.cleanup()
await self._sink.cleanup()
# We just enqueue None to terminate the task gracefully.
self._process_up_task.cancel()
await self._process_up_task
async def _wait_for_endframe(self):
# NOTE(aleix): the Sink element just pushes EndFrames to the down queue,
# so just wait for it. In the future we might do something else here,
# but for now this is fine.
await self._down_queue.get()
# Finally, cancel internal tasks. We don't cancel the push tasks because
# that's us.
await self._cancel_tasks(False)
async def _process_up_queue(self):
"""This is the task that processes frames coming upstream from the
pipeline. These frames might indicate, for example, that we want the
pipeline to be stopped (e.g. EndTaskFrame) in which case we would send
an EndFrame down the pipeline.
"""
while True:
try:
frame = await self._up_queue.get()
if isinstance(frame, EndTaskFrame):
# Tell the task we should end nicely.
await self.queue_frame(EndFrame())
elif isinstance(frame, CancelTaskFrame):
# Tell the task we should end right away.
await self.queue_frame(CancelFrame())
elif isinstance(frame, StopTaskFrame):
await self.queue_frame(StopTaskFrame())
elif isinstance(frame, ErrorFrame):
logger.error(f"Error running app: {frame}")
if frame.fatal:
# Cancel all tasks downstream.
await self.queue_frame(CancelFrame())
# Tell the task we should stop.
await self.queue_frame(StopTaskFrame())
self._up_queue.task_done()
except asyncio.CancelledError:
break
async def _process_down_queue(self):
"""This tasks process frames coming downstream from the pipeline. For
example, heartbeat frames or an EndFrame which would indicate all
processors have handled the EndFrame and therefore we can exit the task
cleanly.
"""
while True:
try:
frame = await self._down_queue.get()
if isinstance(frame, EndFrame):
self._endframe_event.set()
elif isinstance(frame, HeartbeatFrame):
await self._heartbeat_queue.put(frame)
self._down_queue.task_done()
except asyncio.CancelledError:
break
async def _heartbeat_push_handler(self):
"""
This tasks pushes a heartbeat frame every HEARTBEAT_SECONDS.
"""
while True:
try:
# Don't use `queue_frame()` because if an EndFrame is queued the
# task will just stop waiting for the pipeline to finish not
# allowing more frames to be pushed.
await self._source.queue_frame(HeartbeatFrame(timestamp=self._clock.get_time()))
await asyncio.sleep(HEARTBEAT_SECONDS)
except asyncio.CancelledError:
break
async def _heartbeat_monitor_handler(self):
"""This tasks monitors heartbeat frames. If a heartbeat frame has not
been received for a long period a warning will be logged. It also logs
the time that a heartbeat frame takes to processes, that is how long it
takes for the heartbeat frame to traverse all the pipeline.
"""
wait_time = HEARTBEAT_MONITOR_SECONDS
while True:
try:
frame = await asyncio.wait_for(self._heartbeat_queue.get(), timeout=wait_time)
process_time = (self._clock.get_time() - frame.timestamp) / 1_000_000_000
logger.trace(f"{self}: heartbeat frame processed in {process_time} seconds")
self._heartbeat_queue.task_done()
except asyncio.TimeoutError:
logger.warning(
f"{self}: heartbeat frame not received for more than {wait_time} seconds"
)
except asyncio.CancelledError:
break
def __str__(self):
return self.name

97
src/pipecat/pipeline/task_observer.py Normal file
View File

@@ -0,0 +1,97 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
from typing import List
from attr import dataclass
from pipecat.frames.frames import Frame
from pipecat.observers.base_observer import BaseObserver
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@dataclass
class Proxy:
"""This is the data we receive from the main observer and that we put into
a queue for later processing.
"""
queue: asyncio.Queue
task: asyncio.Task
observer: BaseObserver
@dataclass
class ObserverData:
"""This is the data we receive from the main observer and that we put into a
proxy queue for later processing.
"""
src: FrameProcessor
dst: FrameProcessor
frame: Frame
direction: FrameDirection
timestamp: int
class TaskObserver(BaseObserver):
"""This is a pipeline frame observer that is meant to be used as a proxy to
the user provided observers. That is, this is the observer that should be
passed to the frame processors. Then, every time a frame is pushed this
observer will call all the observers registered to the pipeline task.
This observer makes sure that passing frames to observers doesn't block the
pipeline by creating a queue and a task for each user observer. When a frame
is received, it will be put in a queue for efficiency and later processed by
each task.
"""
def __init__(self, observers: List[BaseObserver] = []):
self._proxies: List[Proxy] = self._create_proxies(observers)
async def stop(self):
"""Stops all proxy observer tasks."""
for proxy in self._proxies:
proxy.task.cancel()
await proxy.task
async def on_push_frame(
self,
src: FrameProcessor,
dst: FrameProcessor,
frame: Frame,
direction: FrameDirection,
timestamp: int,
):
for proxy in self._proxies:
await proxy.queue.put(
ObserverData(
src=src, dst=dst, frame=frame, direction=direction, timestamp=timestamp
)
)
def _create_proxies(self, observers) -> List[Proxy]:
proxies = []
for observer in observers:
queue = asyncio.Queue()
task = asyncio.create_task(self._proxy_task_handler(queue, observer))
proxy = Proxy(queue=queue, task=task, observer=observer)
proxies.append(proxy)
return proxies
async def _proxy_task_handler(self, queue: asyncio.Queue, observer: BaseObserver):
while True:
try:
data = await queue.get()
await observer.on_push_frame(
data.src, data.dst, data.frame, data.direction, data.timestamp
)
except asyncio.CancelledError:
break

2
src/pipecat/processors/frame_processor.py
View File

@@ -260,7 +260,7 @@ class FrameProcessor:
async def __internal_push_frame(self, frame: Frame, direction: FrameDirection):
try:
timestamp = self._clock.get_time()
timestamp = self._clock.get_time() if self._clock else 0
if direction == FrameDirection.DOWNSTREAM and self._next:
logger.trace(f"Pushing {frame} from {self} to {self._next}")
if self._observer:

7
src/pipecat/processors/idle_frame_processor.py
View File

@@ -7,7 +7,7 @@
import asyncio
from typing import Awaitable, Callable, List
from pipecat.frames.frames import Frame
from pipecat.frames.frames import Frame, StartFrame
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -31,11 +31,12 @@ class IdleFrameProcessor(FrameProcessor):
self._timeout = timeout
self._types = types
self._create_idle_task()
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, StartFrame):
self._create_idle_task()
await self.push_frame(frame, direction)
# If we are not waiting for any specific frame set the event, otherwise

89
src/pipecat/processors/user_idle_processor.py
View File

@@ -19,10 +19,24 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
class UserIdleProcessor(FrameProcessor):
"""This class is useful to check if the user is interacting with the bot
within a given timeout. If the timeout is reached before any interaction
occurred the provided callback will be called.
"""Monitors user inactivity and triggers callbacks after timeout periods.
Starts monitoring only after the first conversation activity (UserStartedSpeaking
or BotSpeaking).
Args:
callback: Function to call when user is idle
timeout: Seconds to wait before considering user idle
**kwargs: Additional arguments passed to FrameProcessor
Example:
async def handle_idle(processor: "UserIdleProcessor") -> None:
await send_reminder("Are you still there?")
processor = UserIdleProcessor(
callback=handle_idle,
timeout=5.0
)
"""
def __init__(
@@ -36,39 +50,72 @@ class UserIdleProcessor(FrameProcessor):
self._callback = callback
self._timeout = timeout
self._interrupted = False
self._create_idle_task()
self._conversation_started = False
self._idle_task = None
self._idle_event = asyncio.Event()
def _create_idle_task(self):
"""Create the idle task if it hasn't been created yet."""
if self._idle_task is None:
self._idle_task = self.get_event_loop().create_task(self._idle_task_handler())
async def _stop(self):
self._idle_task.cancel()
await self._idle_task
"""Stops and cleans up the idle monitoring task."""
if self._idle_task is not None:
self._idle_task.cancel()
try:
await self._idle_task
except asyncio.CancelledError:
pass # Expected when task is cancelled
self._idle_task = None
async def process_frame(self, frame: Frame, direction: FrameDirection):
"""Processes incoming frames and manages idle monitoring state.
Args:
frame: The frame to process
direction: Direction of the frame flow
"""
await super().process_frame(frame, direction)
# Check for end frames before processing
if isinstance(frame, (EndFrame, CancelFrame)):
await self._stop()
await self.push_frame(frame, direction) # Push the frame down the pipeline
if self._idle_task:
await self._stop() # Stop the idle task, if it exists
return
await self.push_frame(frame, direction)
# We shouldn't call the idle callback if the user or the bot are speaking
if isinstance(frame, UserStartedSpeakingFrame):
self._interrupted = True
self._idle_event.set()
elif isinstance(frame, UserStoppedSpeakingFrame):
self._interrupted = False
self._idle_event.set()
elif isinstance(frame, BotSpeakingFrame):
self._idle_event.set()
# Start monitoring on first conversation activity
if not self._conversation_started and isinstance(
frame, (UserStartedSpeakingFrame, BotSpeakingFrame)
):
self._conversation_started = True
self._create_idle_task()
# Only process these events if conversation has started
if self._conversation_started:
# We shouldn't call the idle callback if the user or the bot are speaking
if isinstance(frame, UserStartedSpeakingFrame):
self._interrupted = True
self._idle_event.set()
elif isinstance(frame, UserStoppedSpeakingFrame):
self._interrupted = False
self._idle_event.set()
elif isinstance(frame, BotSpeakingFrame):
self._idle_event.set()
async def cleanup(self):
await self._stop()
def _create_idle_task(self):
self._idle_event = asyncio.Event()
self._idle_task = self.get_event_loop().create_task(self._idle_task_handler())
"""Cleans up resources when processor is shutting down."""
if self._idle_task: # Only stop if task exists
await self._stop()
async def _idle_task_handler(self):
"""Monitors for idle timeout and triggers callbacks.
Runs in a loop until cancelled.
"""
while True:
try:
await asyncio.wait_for(self._idle_event.wait(), timeout=self._timeout)

23
src/pipecat/serializers/twilio.py
View File

@@ -10,7 +10,14 @@ import json
from pydantic import BaseModel
from pipecat.audio.utils import pcm_to_ulaw, ulaw_to_pcm
from pipecat.frames.frames import AudioRawFrame, Frame, InputAudioRawFrame, StartInterruptionFrame
from pipecat.frames.frames import (
AudioRawFrame,
Frame,
InputAudioRawFrame,
InputDTMFFrame,
KeypadEntry,
StartInterruptionFrame,
)
from pipecat.serializers.base_serializer import FrameSerializer, FrameSerializerType
@@ -48,9 +55,7 @@ class TwilioFrameSerializer(FrameSerializer):
def deserialize(self, data: str | bytes) -> Frame | None:
message = json.loads(data)
if message["event"] != "media":
return None
else:
if message["event"] == "media":
payload_base64 = message["media"]["payload"]
payload = base64.b64decode(payload_base64)
@@ -61,3 +66,13 @@ class TwilioFrameSerializer(FrameSerializer):
audio=deserialized_data, num_channels=1, sample_rate=self._params.sample_rate
)
return audio_frame
elif message["event"] == "dtmf":
digit = message.get("dtmf", {}).get("digit")
try:
return InputDTMFFrame(KeypadEntry(digit))
except ValueError as e:
# Handle case where string doesn't match any enum value
return None
else:
return None

15
src/pipecat/services/deepgram.py
View File

@@ -20,6 +20,7 @@ from pipecat.frames.frames import (
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
UserStartedSpeakingFrame,
UserStoppedSpeakingFrame,
)
from pipecat.processors.frame_processor import FrameDirection
@@ -169,7 +170,7 @@ class DeepgramSTTService(STTService):
return self._settings["vad_events"]
def can_generate_metrics(self) -> bool:
return self.vad_enabled
return True
async def set_model(self, model: str):
await super().set_model(model)
@@ -210,9 +211,12 @@ class DeepgramSTTService(STTService):
logger.debug("Disconnecting from Deepgram")
await self._connection.finish()
async def _on_speech_started(self, *args, **kwargs):
async def start_metrics(self):
await self.start_ttfb_metrics()
await self.start_processing_metrics()
async def _on_speech_started(self, *args, **kwargs):
await self.start_metrics()
await self._call_event_handler("on_speech_started", *args, **kwargs)
async def _on_utterance_end(self, *args, **kwargs):
@@ -243,7 +247,10 @@ class DeepgramSTTService(STTService):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, UserStoppedSpeakingFrame):
if isinstance(frame, UserStartedSpeakingFrame) and not self.vad_enabled:
# Start metrics if Deepgram VAD is disabled & pipeline VAD has detected speech
await self.start_metrics()
elif isinstance(frame, UserStoppedSpeakingFrame):
# https://developers.deepgram.com/docs/finalize
await self._connection.finalize()
logger.debug(f"Triggering finalize event on: {frame.name=}, {direction=}")
logger.trace(f"Triggered finalize event on: {frame.name=}, {direction=}")

163
src/pipecat/services/elevenlabs.py
View File

@@ -7,8 +7,9 @@
import asyncio
import base64
import json
from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Tuple
from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Tuple, Union
import aiohttp
from loguru import logger
from pydantic import BaseModel, model_validator
@@ -16,6 +17,7 @@ from pipecat.frames.frames import (
BotStoppedSpeakingFrame,
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
LLMFullResponseEndFrame,
StartFrame,
@@ -26,7 +28,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.ai_services import WordTTSService
from pipecat.services.ai_services import TTSService, WordTTSService
from pipecat.services.websocket_service import WebsocketService
from pipecat.transcriptions.language import Language
@@ -418,3 +420,160 @@ class ElevenLabsTTSService(WordTTSService, WebsocketService):
yield None
except Exception as e:
logger.error(f"{self} exception: {e}")
class ElevenLabsHttpTTSService(TTSService):
"""ElevenLabs Text-to-Speech service using HTTP streaming.
Args:
api_key: ElevenLabs API key
voice_id: ID of the voice to use
aiohttp_session: aiohttp ClientSession
model: Model ID (default: "eleven_flash_v2_5" for low latency)
base_url: API base URL
output_format: Audio output format (PCM)
params: Additional parameters for voice configuration
"""
class InputParams(BaseModel):
language: Optional[Language] = Language.EN
optimize_streaming_latency: Optional[int] = None
stability: Optional[float] = None
similarity_boost: Optional[float] = None
style: Optional[float] = None
use_speaker_boost: Optional[bool] = None
def __init__(
self,
*,
api_key: str,
voice_id: str,
aiohttp_session: aiohttp.ClientSession,
model: str = "eleven_flash_v2_5",
base_url: str = "https://api.elevenlabs.io",
output_format: ElevenLabsOutputFormat = "pcm_24000",
params: InputParams = InputParams(),
**kwargs,
):
super().__init__(sample_rate=sample_rate_from_output_format(output_format), **kwargs)
self._api_key = api_key
self._base_url = base_url
self._output_format = output_format
self._params = params
self._session = aiohttp_session
self._settings = {
"sample_rate": sample_rate_from_output_format(output_format),
"language": self.language_to_service_language(params.language)
if params.language
else "en",
"output_format": output_format,
"optimize_streaming_latency": params.optimize_streaming_latency,
"stability": params.stability,
"similarity_boost": params.similarity_boost,
"style": params.style,
"use_speaker_boost": params.use_speaker_boost,
}
self.set_model_name(model)
self.set_voice(voice_id)
self._voice_settings = self._set_voice_settings()
def can_generate_metrics(self) -> bool:
return True
def _set_voice_settings(self) -> Optional[Dict[str, Union[float, bool]]]:
"""Configure voice settings if stability and similarity_boost are provided.
Returns:
Dictionary of voice settings or None if required parameters are missing.
"""
voice_settings: Dict[str, Union[float, bool]] = {}
if (
self._settings["stability"] is not None
and self._settings["similarity_boost"] is not None
):
voice_settings["stability"] = float(self._settings["stability"])
voice_settings["similarity_boost"] = float(self._settings["similarity_boost"])
if self._settings["style"] is not None:
voice_settings["style"] = float(self._settings["style"])
if self._settings["use_speaker_boost"] is not None:
voice_settings["use_speaker_boost"] = bool(self._settings["use_speaker_boost"])
else:
if self._settings["style"] is not None:
logger.warning(
"'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
)
if self._settings["use_speaker_boost"] is not None:
logger.warning(
"'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
)
return voice_settings or None
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
"""Generate speech from text using ElevenLabs streaming API.
Args:
text: The text to convert to speech
Yields:
Frames containing audio data and status information
"""
logger.debug(f"Generating TTS: [{text}]")
url = f"{self._base_url}/v1/text-to-speech/{self._voice_id}/stream"
payload = {
"text": text,
"model_id": self._model_name,
}
if self._voice_settings:
payload["voice_settings"] = json.dumps(self._voice_settings)
if self._settings["language"]:
payload["language_code"] = self._settings["language"]
headers = {
"xi-api-key": self._api_key,
"Content-Type": "application/json",
}
# Build query parameters
params = {
"output_format": self._output_format,
}
if self._settings["optimize_streaming_latency"] is not None:
params["optimize_streaming_latency"] = self._settings["optimize_streaming_latency"]
logger.debug(f"ElevenLabs request - payload: {payload}, params: {params}")
try:
await self.start_ttfb_metrics()
async with self._session.post(
url, json=payload, headers=headers, params=params
) as response:
if response.status != 200:
error_text = await response.text()
logger.error(f"{self} error: {error_text}")
yield ErrorFrame(error=f"ElevenLabs API error: {error_text}")
return
await self.start_tts_usage_metrics(text)
yield TTSStartedFrame()
async for chunk in response.content:
if chunk:
await self.stop_ttfb_metrics()
yield TTSAudioRawFrame(chunk, self._settings["sample_rate"], 1)
yield TTSStoppedFrame()
except Exception as e:
logger.error(f"Error in run_tts: {e}")
yield ErrorFrame(error=str(e))
finally:
yield TTSStoppedFrame()

33
src/pipecat/services/fish.py
View File

@@ -4,13 +4,11 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import uuid
from typing import AsyncGenerator, Literal, Optional
from loguru import logger
from pydantic import BaseModel
from tenacity import AsyncRetrying, RetryCallState, stop_after_attempt, wait_exponential
from pipecat.frames.frames import (
BotStoppedSpeakingFrame,
@@ -28,6 +26,7 @@ from pipecat.frames.frames import (
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.ai_services import TTSService
from pipecat.services.websocket_service import WebsocketService
from pipecat.transcriptions.language import Language
try:
@@ -44,7 +43,7 @@ except ModuleNotFoundError as e:
FishAudioOutputFormat = Literal["opus", "mp3", "pcm", "wav"]
class FishAudioTTSService(TTSService):
class FishAudioTTSService(TTSService, WebsocketService):
class InputParams(BaseModel):
language: Optional[Language] = Language.EN
latency: Optional[str] = "normal" # "normal" or "balanced"
@@ -105,7 +104,9 @@ class FishAudioTTSService(TTSService):
async def _connect(self):
await self._connect_websocket()
self._receive_task = self.get_event_loop().create_task(self._receive_task_handler())
self._receive_task = self.get_event_loop().create_task(
self._receive_task_handler(self.push_error)
)
async def _disconnect(self):
await self._disconnect_websocket()
@@ -169,30 +170,6 @@ class FishAudioTTSService(TTSService):
except Exception as e:
logger.error(f"Error processing message: {e}")
async def _reconnect_websocket(self, retry_state: RetryCallState):
logger.warning(f"Fish Audio reconnecting (attempt: {retry_state.attempt_number})")
await self._disconnect_websocket()
await self._connect_websocket()
async def _receive_task_handler(self):
while True:
try:
async for attempt in AsyncRetrying(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
before_sleep=self._reconnect_websocket,
reraise=True,
):
with attempt:
await self._receive_messages()
except asyncio.CancelledError:
break
except Exception as e:
message = f"Fish Audio error receiving messages: {e}"
logger.error(message)
await self.push_error(ErrorFrame(message, fatal=True))
break
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)

16
src/pipecat/transports/services/daily.py
View File

@@ -27,6 +27,7 @@ from pipecat.audio.vad.vad_analyzer import VADAnalyzer, VADParams
from pipecat.frames.frames import (
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
InputAudioRawFrame,
InterimTranscriptionFrame,
@@ -921,6 +922,7 @@ class DailyTransport(BaseTransport):
# these handlers.
self._register_event_handler("on_joined")
self._register_event_handler("on_left")
self._register_event_handler("on_error")
self._register_event_handler("on_app_message")
self._register_event_handler("on_call_state_updated")
self._register_event_handler("on_dialin_connected")
@@ -1035,9 +1037,17 @@ class DailyTransport(BaseTransport):
await self._call_event_handler("on_left")
async def _on_error(self, error):
# TODO(aleix): Report error to input/output transports. The one managing
# the client should report the error.
pass
await self._call_event_handler("on_error", error)
# Push error frame to notify the pipeline
error_frame = ErrorFrame(error)
if self._input:
await self._input.push_error(error_frame)
elif self._output:
await self._output.push_error(error_frame)
else:
logger.error("Both input and output are None while trying to push error")
raise RuntimeError("No valid input or output channel to push error")
async def _on_app_message(self, message: Any, sender: str):
if self._input: