working on a fastapi http transport

[Cartesia] Fix streaming truncation bug with Twilio Fast API WS

CHANGELOG.md

@@ -9,6 +9,23 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- A clock can now be specified to `PipelineTask` (defaults to
+  `SystemClock`). This clock will be passed to each frame processor via the
+  `StartFrame`.
+
+- Added pipeline clocks. A pipeline clock is used by the output transport to
+  know when a frame needs to be presented. For that, all frames now have an
+  optional `pts` field (prensentation timestamp). There's currently just one
+  clock implementation `SystemClock` and the `pts` field is currently only used
+  for `TextFrame`s (audio and image frames will be next).
+
+- `DailyTransport` now supports setting the audio bitrate to improve audio
+  quality through the `DailyParams.audio_out_bitrate` parameter. The new
+  default is 96kbps.
+
+- `DailyTransport` now uses the number of audio output channels (1 or 2) to set
+  mono or stereo audio when needed.
+
 - Interruptions support has been added to `TwilioFrameSerializer` when using
   `FastAPIWebsocketTransport`.
 
@@ -23,6 +40,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+- `CartesiaTTSService` and `ElevenLabsTTSService` now add presentation
+  timestamps to their text output. This allows the output transport to push the
+  text frames downstream at almost the same time the words are spoken. We say
+  "almost" because currently the audio frames don't have presentation timestamp
+  but they should be played at roughly the same time.
+
 - `DailyTransport.on_joined` event now returns the full session data instead of
   just the participant.
 

examples/dialin-chatbot/requirements.txt

@@ -3,3 +3,4 @@ fastapi
 uvicorn
 python-dotenv
 twilio
+python-multipart

examples/foundational/07d-interruptible-elevenlabs.py

@@ -4,8 +4,8 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import aiohttp
 import asyncio
+import aiohttp
 import os
 import sys
 
@@ -15,12 +15,11 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_response import (
     LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.vad.silero import SileroVADAnalyzer
 
-
 from runner import configure
 
 from loguru import logger
@@ -41,7 +40,6 @@ async def main():
             token,
             "Respond bot",
             DailyParams(
-                audio_out_sample_rate=44100,
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
@@ -49,10 +47,9 @@ async def main():
             )
         )
 
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
-            sample_rate=44100,
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
         )
 
         llm = OpenAILLMService(
@@ -74,11 +71,16 @@ async def main():
             tma_in,              # User responses
             llm,                 # LLM
             tts,                 # TTS
-            tma_out,             # Goes before the transport because cartesia has word-level timestamps!
             transport.output(),  # Transport bot output
+            tma_out              # Assistant spoken responses
         ])
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
+        task = PipelineTask(pipeline, PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):

examples/studypal/studypal.py

@@ -147,8 +147,8 @@ Your task is to help the user understand and learn from this article in 2 senten
             tma_in,
             llm,
             tts,
-            tma_out,
             transport.output(),
+            tma_out,
         ])
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))

pyproject.toml

@@ -39,7 +39,7 @@ azure = [ "azure-cognitiveservices-speech~=1.40.0" ]
 cartesia = [ "websockets~=12.0" ]
 daily = [ "daily-python~=0.10.1" ]
 deepgram = [ "deepgram-sdk~=3.5.0" ]
-elevenlabs = [ "elevenlabs~=1.7.0" ]
+elevenlabs = [ "websockets~=12.0" ]
 examples = [ "python-dotenv~=1.0.1", "flask~=3.0.3", "flask_cors~=4.0.1" ]
 fal = [ "fal-client~=0.4.1" ]
 gladia = [ "websockets~=12.0" ]

src/pipecat/clocks/base_clock.py

@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from abc import ABC, abstractmethod
+
+
+class BaseClock(ABC):
+
+    @abstractmethod
+    def get_time(self) -> int:
+        pass
+
+    @abstractmethod
+    def start(self):
+        pass

src/pipecat/clocks/system_clock.py

@@ -0,0 +1,21 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import time
+
+from pipecat.clocks.base_clock import BaseClock
+
+
+class SystemClock(BaseClock):
+
+    def __init__(self):
+        self._time = 0
+
+    def get_time(self) -> int:
+        return time.monotonic_ns() - self._time if self._time > 0 else 0
+
+    def start(self):
+        self._time = time.monotonic_ns()

src/pipecat/frames/frames.py

@@ -8,19 +8,27 @@ from typing import Any, List, Mapping, Optional, Tuple
 
 from dataclasses import dataclass, field
 
+from pipecat.clocks.base_clock import BaseClock
 from pipecat.transcriptions.language import Language
+from pipecat.utils.time import nanoseconds_to_str
 from pipecat.utils.utils import obj_count, obj_id
 from pipecat.vad.vad_analyzer import VADParams
 
 
+def format_pts(pts: int | None):
+    return nanoseconds_to_str(pts) if pts else None
+
+
 @dataclass
 class Frame:
     id: int = field(init=False)
     name: str = field(init=False)
+    pts: Optional[int] = field(init=False)
 
     def __post_init__(self):
         self.id: int = obj_id()
         self.name: str = f"{self.__class__.__name__}#{obj_count(self)}"
+        self.pts: Optional[int] = None
 
     def __str__(self):
         return self.name
@@ -46,7 +54,8 @@ class AudioRawFrame(DataFrame):
         self.num_frames = int(len(self.audio) / (self.num_channels * 2))
 
     def __str__(self):
-        return f"{self.name}(size: {len(self.audio)}, frames: {self.num_frames}, sample_rate: {self.sample_rate}, channels: {self.num_channels})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, size: {len(self.audio)}, frames: {self.num_frames}, sample_rate: {self.sample_rate}, channels: {self.num_channels})"
 
 
 @dataclass
@@ -60,7 +69,8 @@ class ImageRawFrame(DataFrame):
     format: str | None
 
     def __str__(self):
-        return f"{self.name}(size: {self.size}, format: {self.format})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, size: {self.size}, format: {self.format})"
 
 
 @dataclass
@@ -72,7 +82,8 @@ class URLImageRawFrame(ImageRawFrame):
     url: str | None
 
     def __str__(self):
-        return f"{self.name}(url: {self.url}, size: {self.size}, format: {self.format})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, url: {self.url}, size: {self.size}, format: {self.format})"
 
 
 @dataclass
@@ -84,7 +95,8 @@ class VisionImageRawFrame(ImageRawFrame):
     text: str | None
 
     def __str__(self):
-        return f"{self.name}(text: {self.text}, size: {self.size}, format: {self.format})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, text: {self.text}, size: {self.size}, format: {self.format})"
 
 
 @dataclass
@@ -96,7 +108,8 @@ class UserImageRawFrame(ImageRawFrame):
     user_id: str
 
     def __str__(self):
-        return f"{self.name}(user: {self.user_id}, size: {self.size}, format: {self.format})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format})"
 
 
 @dataclass
@@ -109,7 +122,8 @@ class SpriteFrame(Frame):
     images: List[ImageRawFrame]
 
     def __str__(self):
-        return f"{self.name}(size: {len(self.images)})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, size: {len(self.images)})"
 
 
 @dataclass
@@ -121,7 +135,8 @@ class TextFrame(DataFrame):
     text: str
 
     def __str__(self):
-        return f"{self.name}(text: {self.text})"
+        pts = format_pts(self.pts)
+        return f"{self.name}(pts: {pts}, text: {self.text})"
 
 
 @dataclass
@@ -326,6 +341,7 @@ class ControlFrame(Frame):
 @dataclass
 class StartFrame(ControlFrame):
     """This is the first frame that should be pushed down a pipeline."""
+    clock: BaseClock
     allow_interruptions: bool = False
     enable_metrics: bool = False
     enable_usage_metrics: bool = False

src/pipecat/pipeline/task.py

@@ -10,6 +10,8 @@ from typing import AsyncIterable, Iterable
 
 from pydantic import BaseModel
 
+from pipecat.clocks.base_clock import BaseClock
+from pipecat.clocks.system_clock import SystemClock
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
@@ -60,11 +62,16 @@ class Source(FrameProcessor):
 
 class PipelineTask:
 
-    def __init__(self, pipeline: BasePipeline, params: PipelineParams = PipelineParams()):
+    def __init__(
+            self,
+            pipeline: BasePipeline,
+            params: PipelineParams = PipelineParams(),
+            clock: BaseClock = SystemClock()):
         self.id: int = obj_id()
         self.name: str = f"{self.__class__.__name__}#{obj_count(self)}"
 
         self._pipeline = pipeline
+        self._clock = clock
         self._params = params
         self._finished = False
 
@@ -116,11 +123,14 @@ class PipelineTask:
         return MetricsFrame(ttfb=ttfb, processing=processing)
 
     async def _process_down_queue(self):
+        self._clock.start()
+
         start_frame = StartFrame(
             allow_interruptions=self._params.allow_interruptions,
             enable_metrics=self._params.enable_metrics,
             enable_usage_metrics=self._params.enable_metrics,
-            report_only_initial_ttfb=self._params.report_only_initial_ttfb
+            report_only_initial_ttfb=self._params.report_only_initial_ttfb,
+            clock=self._clock
         )
         await self._source.process_frame(start_frame, FrameDirection.DOWNSTREAM)
 

src/pipecat/processors/aggregators/llm_response.py

@@ -109,7 +109,7 @@ class LLMResponseAggregator(FrameProcessor):
             await self.push_frame(frame, direction)
         elif isinstance(frame, self._accumulator_frame):
             if self._aggregating:
-                self._aggregation += f" {frame.text}"
+                self._aggregation += f" {frame.text}" if self._aggregation else frame.text
                 # We have recevied a complete sentence, so if we have seen the
                 # end frame and we were still aggregating, it means we should
                 # send the aggregation.

src/pipecat/processors/frame_processor.py

@@ -9,6 +9,7 @@ import time
 
 from enum import Enum
 
+from pipecat.clocks.base_clock import BaseClock
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
@@ -96,6 +97,9 @@ class FrameProcessor:
         self._next: "FrameProcessor" | None = None
         self._loop: asyncio.AbstractEventLoop = loop or asyncio.get_running_loop()
 
+        # Clock
+        self._clock: BaseClock | None = None
+
         # Properties
         self._allow_interruptions = False
         self._enable_metrics = False
@@ -177,8 +181,12 @@ class FrameProcessor:
     def get_parent(self) -> "FrameProcessor":
         return self._parent
 
+    def get_clock(self) -> BaseClock:
+        return self._clock
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         if isinstance(frame, StartFrame):
+            self._clock = frame.clock
             self._allow_interruptions = frame.allow_interruptions
             self._enable_metrics = frame.enable_metrics
             self._enable_usage_metrics = frame.enable_usage_metrics

src/pipecat/services/ai_services.py

@@ -9,7 +9,7 @@ import io
 import wave
 
 from abc import abstractmethod
-from typing import AsyncGenerator, Optional
+from typing import AsyncGenerator, List, Optional, Tuple
 
 from pipecat.frames.frames import (
     AudioRawFrame,
@@ -37,9 +37,12 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transcriptions.language import Language
 from pipecat.utils.audio import calculate_audio_volume
 from pipecat.utils.string import match_endofsentence
+from pipecat.utils.time import seconds_to_nanoseconds
 from pipecat.utils.utils import exp_smoothing
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 
+from loguru import logger
+
 
 class AIService(FrameProcessor):
     def __init__(self, **kwargs):
@@ -167,7 +170,7 @@ class TTSService(AIService):
             # if True, TTSService will push TTSStoppedFrames, otherwise subclass must do it
             push_stop_frames: bool = False,
             # if push_stop_frames is True, wait for this idle period before pushing TTSStoppedFrame
-            stop_frame_timeout_s: float = 0.8,
+            stop_frame_timeout_s: float = 1.0,
             **kwargs):
         super().__init__(**kwargs)
         self._aggregate_sentences: bool = aggregate_sentences
@@ -303,6 +306,74 @@ class TTSService(AIService):
             pass
 
 
+class AsyncTTSService(TTSService):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    @abstractmethod
+    async def flush_audio(self):
+        pass
+
+
+class AsyncWordTTSService(AsyncTTSService):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._initial_word_timestamp = -1
+        self._words_queue = asyncio.Queue()
+        self._words_task = self.get_event_loop().create_task(self._words_task_handler())
+
+    def start_word_timestamps(self):
+        if self._initial_word_timestamp == -1:
+            self._initial_word_timestamp = self.get_clock().get_time()
+
+    def reset_word_timestamps(self):
+        self._initial_word_timestamp = -1
+        self._word_timestamps = []
+
+    async def add_word_timestamps(self, word_times: List[Tuple[str, float]]):
+        for (word, timestamp) in word_times:
+            await self._words_queue.put((word, seconds_to_nanoseconds(timestamp)))
+
+    async def stop(self, frame: EndFrame):
+        await super().stop(frame)
+        await self._stop_words_task()
+
+    async def cancel(self, frame: CancelFrame):
+        await super().cancel(frame)
+        await self._stop_words_task()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, LLMFullResponseEndFrame) or isinstance(frame, EndFrame):
+            await self.flush_audio()
+
+    async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
+        await super()._handle_interruption(frame, direction)
+        self.reset_word_timestamps()
+
+    async def _stop_words_task(self):
+        if self._words_task:
+            self._words_task.cancel()
+            await self._words_task
+
+    async def _words_task_handler(self):
+        while True:
+            try:
+                (word, timestamp) = await self._words_queue.get()
+                if word == "LLMFullResponseEndFrame" and timestamp == 0:
+                    await self.push_frame(LLMFullResponseEndFrame())
+                else:
+                    frame = TextFrame(word)
+                    frame.pts = self._initial_word_timestamp + timestamp
+                    await self.push_frame(frame)
+                self._words_queue.task_done()
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                logger.exception(f"{self} exception: {e}")
+
+
 class STTService(AIService):
     """STTService is a base class for speech-to-text services."""
 

src/pipecat/services/cartesia.py

@@ -27,7 +27,7 @@ from pipecat.frames.frames import (
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.transcriptions.language import Language
-from pipecat.services.ai_services import TTSService
+from pipecat.services.ai_services import AsyncWordTTSService
 
 from loguru import logger
 
@@ -60,7 +60,7 @@ def language_to_cartesia_language(language: Language) -> str | None:
     return None
 
 
-class CartesiaTTSService(TTSService):
+class CartesiaTTSService(AsyncWordTTSService):
 
     def __init__(
             self,
@@ -74,19 +74,17 @@ class CartesiaTTSService(TTSService):
             sample_rate: int = 16000,
             language: str = "en",
             **kwargs):
-        super().__init__(**kwargs)
-
         # Aggregating sentences still gives cleaner-sounding results and fewer
-        # artifacts than streaming one word at a time. On average, waiting for
-        # a full sentence should only "cost" us 15ms or so with GPT-4o or a Llama 3
-        # model, and it's worth it for the better audio quality.
-        self._aggregate_sentences = True
-
-        # we don't want to automatically push LLM response text frames, because the
-        # context aggregators will add them to the LLM context even if we're
-        # interrupted. cartesia gives us word-by-word timestamps. we can use those
-        # to generate text frames ourselves aligned with the playout timing of the audio!
-        self._push_text_frames = False
+        # artifacts than streaming one word at a time. On average, waiting for a
+        # full sentence should only "cost" us 15ms or so with GPT-4o or a Llama
+        # 3 model, and it's worth it for the better audio quality.
+        #
+        # We also don't want to automatically push LLM response text frames,
+        # because the context aggregators will add them to the LLM context even
+        # if we're interrupted. Cartesia gives us word-by-word timestamps. We
+        # can use those to generate text frames ourselves aligned with the
+        # playout timing of the audio!
+        super().__init__(aggregate_sentences=True, push_text_frames=False, **kwargs)
 
         self._api_key = api_key
         self._cartesia_version = cartesia_version
@@ -102,10 +100,7 @@ class CartesiaTTSService(TTSService):
 
         self._websocket = None
         self._context_id = None
-        self._context_id_start_timestamp = None
-        self._timestamped_words_buffer = []
         self._receive_task = None
-        self._context_appending_task = None
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -140,7 +135,6 @@ class CartesiaTTSService(TTSService):
                 f"{self._url}?api_key={self._api_key}&cartesia_version={self._cartesia_version}"
             )
             self._receive_task = self.get_event_loop().create_task(self._receive_task_handler())
-            self._context_appending_task = self.get_event_loop().create_task(self._context_appending_task_handler())
         except Exception as e:
             logger.exception(f"{self} initialization error: {e}")
             self._websocket = None
@@ -149,10 +143,6 @@ class CartesiaTTSService(TTSService):
         try:
             await self.stop_all_metrics()
 
-            if self._context_appending_task:
-                self._context_appending_task.cancel()
-                await self._context_appending_task
-                self._context_appending_task = None
             if self._receive_task:
                 self._receive_task.cancel()
                 await self._receive_task
@@ -162,18 +152,33 @@ class CartesiaTTSService(TTSService):
                 self._websocket = None
 
             self._context_id = None
-            self._context_id_start_timestamp = None
-            self._timestamped_words_buffer = []
         except Exception as e:
             logger.exception(f"{self} error closing websocket: {e}")
 
     async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
         await super()._handle_interruption(frame, direction)
-        self._context_id = None
-        self._context_id_start_timestamp = None
-        self._timestamped_words_buffer = []
         await self.stop_all_metrics()
         await self.push_frame(LLMFullResponseEndFrame())
+        self._context_id = None
+
+    async def flush_audio(self):
+        if not self._context_id or not self._websocket:
+            return
+        logger.debug("Flushing audio")
+        msg = {
+                "transcript": "",
+                "continue": False,
+                "context_id": self._context_id,
+                "model_id": self._model_id,
+                "voice": {
+                    "mode": "id",
+                    "id": self._voice_id
+                },
+                "output_format": self._output_format,
+                "language": self._language,
+                "add_timestamps": True,
+            }
+        await self._websocket.send(json.dumps(msg))
 
     async def _receive_task_handler(self):
         try:
@@ -188,16 +193,14 @@ class CartesiaTTSService(TTSService):
                     # because we are likely still playing out audio and need the
                     # timestamp to set send context frames.
                     self._context_id = None
-                    self._timestamped_words_buffer.append(("LLMFullResponseEndFrame", 0))
+                    await self.add_word_timestamps([("LLMFullResponseEndFrame", 0)])
                 elif msg["type"] == "timestamps":
-                    # logger.debug(f"TIMESTAMPS: {msg}")
-                    self._timestamped_words_buffer.extend(
-                        list(zip(msg["word_timestamps"]["words"], msg["word_timestamps"]["end"]))
+                    await self.add_word_timestamps(
+                        list(zip(msg["word_timestamps"]["words"], msg["word_timestamps"]["start"]))
                     )
                 elif msg["type"] == "chunk":
                     await self.stop_ttfb_metrics()
-                    if not self._context_id_start_timestamp:
-                        self._context_id_start_timestamp = time.time()
+                    self.start_word_timestamps()
                     frame = AudioRawFrame(
                         audio=base64.b64decode(msg["data"]),
                         sample_rate=self._output_format["sample_rate"],
@@ -216,27 +219,6 @@ class CartesiaTTSService(TTSService):
         except Exception as e:
             logger.exception(f"{self} exception: {e}")
 
-    async def _context_appending_task_handler(self):
-        try:
-            while True:
-                await asyncio.sleep(0.1)
-                if not self._context_id_start_timestamp:
-                    continue
-                elapsed_seconds = time.time() - self._context_id_start_timestamp
-                # Pop all words from self._timestamped_words_buffer that are
-                # older than the elapsed time and print a message about them to
-                # the console.
-                while self._timestamped_words_buffer and self._timestamped_words_buffer[0][1] <= elapsed_seconds:
-                    word, timestamp = self._timestamped_words_buffer.pop(0)
-                    if word == "LLMFullResponseEndFrame" and timestamp == 0:
-                        await self.push_frame(LLMFullResponseEndFrame())
-                        continue
-                    await self.push_frame(TextFrame(word))
-        except asyncio.CancelledError:
-            pass
-        except Exception as e:
-            logger.exception(f"{self} exception: {e}")
-
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         logger.debug(f"Generating TTS: [{text}]")
 

src/pipecat/services/elevenlabs.py

@@ -4,17 +4,30 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-from typing import AsyncGenerator, Literal
+import asyncio
+import base64
+import json
+
+from typing import Any, AsyncGenerator, List, Literal, Mapping, Tuple
 from pydantic import BaseModel
 
-from pipecat.frames.frames import AudioRawFrame, Frame, TTSStartedFrame, TTSStoppedFrame
-from pipecat.services.ai_services import TTSService
+from pipecat.frames.frames import (
+    AudioRawFrame,
+    CancelFrame,
+    EndFrame,
+    Frame,
+    StartFrame,
+    StartInterruptionFrame,
+    TTSStartedFrame,
+    TTSStoppedFrame)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_services import AsyncWordTTSService
 
 from loguru import logger
 
 # See .env.example for ElevenLabs configuration needed
 try:
-    from elevenlabs.client import AsyncElevenLabs
+    import websockets
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error(
@@ -35,7 +48,28 @@ def sample_rate_from_output_format(output_format: str) -> int:
     return 16000
 
 
-class ElevenLabsTTSService(TTSService):
+def calculate_word_times(
+        alignment_info: Mapping[str, Any], cumulative_time: float
+) -> List[Tuple[str, float]]:
+    zipped_times = list(zip(alignment_info["chars"], alignment_info["charStartTimesMs"]))
+
+    words = "".join(alignment_info["chars"]).split(" ")
+
+    # Calculate start time for each word. We do this by finding a space character
+    # and using the previous word time, also taking into account there might not
+    # be a space at the end.
+    times = []
+    for (i, (a, b)) in enumerate(zipped_times):
+        if a == " " or i == len(zipped_times) - 1:
+            t = cumulative_time + (zipped_times[i - 1][1] / 1000.0)
+            times.append(t)
+
+    word_times = list(zip(words, times))
+
+    return word_times
+
+
+class ElevenLabsTTSService(AsyncWordTTSService):
     class InputParams(BaseModel):
         output_format: Literal["pcm_16000", "pcm_22050", "pcm_24000", "pcm_44100"] = "pcm_16000"
 
@@ -45,49 +79,185 @@ class ElevenLabsTTSService(TTSService):
             api_key: str,
             voice_id: str,
             model: str = "eleven_turbo_v2_5",
+            url: str = "wss://api.elevenlabs.io",
             params: InputParams = InputParams(),
             **kwargs):
-        super().__init__(**kwargs)
+        # Aggregating sentences still gives cleaner-sounding results and fewer
+        # artifacts than streaming one word at a time. On average, waiting for a
+        # full sentence should only "cost" us 15ms or so with GPT-4o or a Llama
+        # 3 model, and it's worth it for the better audio quality.
+        #
+        # We also don't want to automatically push LLM response text frames,
+        # because the context aggregators will add them to the LLM context even
+        # if we're interrupted. ElevenLabs gives us word-by-word timestamps. We
+        # can use those to generate text frames ourselves aligned with the
+        # playout timing of the audio!
+        #
+        # Finally, ElevenLabs doesn't provide information on when the bot stops
+        # speaking for a while, so we want the parent class to send TTSStopFrame
+        # after a short period not receiving any audio.
+        super().__init__(
+            aggregate_sentences=True,
+            push_text_frames=False,
+            push_stop_frames=True,
+            stop_frame_timeout_s=2.0,
+            **kwargs
+        )
 
+        self._api_key = api_key
         self._voice_id = voice_id
         self._model = model
+        self._url = url
         self._params = params
-        self._client = AsyncElevenLabs(api_key=api_key)
         self._sample_rate = sample_rate_from_output_format(params.output_format)
 
+        # Websocket connection to ElevenLabs.
+        self._websocket = None
+        # Indicates if we have sent TTSStartedFrame. It will reset to False when
+        # there's an interruption or TTSStoppedFrame.
+        self._started = False
+        self._cumulative_time = 0
+
     def can_generate_metrics(self) -> bool:
         return True
 
     async def set_model(self, model: str):
         logger.debug(f"Switching TTS model to: [{model}]")
         self._model = model
+        await self._disconnect()
+        await self._connect()
 
     async def set_voice(self, voice: str):
         logger.debug(f"Switching TTS voice to: [{voice}]")
         self._voice_id = voice
+        await self._disconnect()
+        await self._connect()
+
+    async def start(self, frame: StartFrame):
+        await super().start(frame)
+        await self._connect()
+
+    async def stop(self, frame: EndFrame):
+        await super().stop(frame)
+        await self._disconnect()
+
+    async def cancel(self, frame: CancelFrame):
+        await super().cancel(frame)
+        await self._disconnect()
+
+    async def flush_audio(self):
+        if self._websocket:
+            msg = {"text": " ", "flush": True}
+            await self._websocket.send(json.dumps(msg))
+
+    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
+        await super().push_frame(frame, direction)
+        if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
+            self._started = False
+            if isinstance(frame, TTSStoppedFrame):
+                await self.add_word_timestamps([("LLMFullResponseEndFrame", 0)])
+
+    async def _connect(self):
+        try:
+            voice_id = self._voice_id
+            model = self._model
+            output_format = self._params.output_format
+            url = f"{self._url}/v1/text-to-speech/{voice_id}/stream-input?model_id={model}&output_format={output_format}"
+            self._websocket = await websockets.connect(url)
+            self._receive_task = self.get_event_loop().create_task(self._receive_task_handler())
+            self._keepalive_task = self.get_event_loop().create_task(self._keepalive_task_handler())
+
+            # According to ElevenLabs, we should always start with a single space.
+            msg = {
+                "text": " ",
+                "xi_api_key": self._api_key,
+            }
+            await self._websocket.send(json.dumps(msg))
+        except Exception as e:
+            logger.exception(f"{self} initialization error: {e}")
+            self._websocket = None
+
+    async def _disconnect(self):
+        try:
+            await self.stop_all_metrics()
+
+            if self._receive_task:
+                self._receive_task.cancel()
+                await self._receive_task
+                self._receive_task = None
+
+            if self._keepalive_task:
+                self._keepalive_task.cancel()
+                await self._keepalive_task
+                self._keepalive_task = None
+
+            if self._websocket:
+                await self._websocket.close()
+                self._websocket = None
+
+            self._started = False
+        except Exception as e:
+            logger.exception(f"{self} error closing websocket: {e}")
+
+    async def _receive_task_handler(self):
+        try:
+            async for message in self._websocket:
+                msg = json.loads(message)
+                if msg.get("audio"):
+                    await self.stop_ttfb_metrics()
+                    self.start_word_timestamps()
+
+                    audio = base64.b64decode(msg["audio"])
+                    frame = AudioRawFrame(audio, self._sample_rate, 1)
+                    await self.push_frame(frame)
+
+                if msg.get("alignment"):
+                    word_times = calculate_word_times(msg["alignment"], self._cumulative_time)
+                    await self.add_word_timestamps(word_times)
+                    self._cumulative_time = word_times[-1][1]
+
+        except asyncio.CancelledError:
+            pass
+        except Exception as e:
+            logger.exception(f"{self} exception: {e}")
+
+    async def _keepalive_task_handler(self):
+        while True:
+            try:
+                await asyncio.sleep(10)
+                await self._send_text("")
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                logger.exception(f"{self} exception: {e}")
+
+    async def _send_text(self, text: str):
+        if self._websocket:
+            msg = {"text": text + " "}
+            await self._websocket.send(json.dumps(msg))
 
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         logger.debug(f"Generating TTS: [{text}]")
 
-        await self.start_tts_usage_metrics(text)
-        await self.start_ttfb_metrics()
+        try:
+            if not self._websocket:
+                await self._connect()
 
-        results = await self._client.generate(
-            text=text,
-            voice=self._voice_id,
-            model=self._model,
-            output_format=self._params.output_format
-        )
+            try:
+                if not self._started:
+                    await self.push_frame(TTSStartedFrame())
+                    await self.start_ttfb_metrics()
+                    self._started = True
+                    self._cumulative_time = 0
 
-        tts_started = False
-        async for audio in results:
-            # This is so we send TTSStartedFrame when we have the first audio
-            # bytes.
-            if not tts_started:
-                await self.push_frame(TTSStartedFrame())
-                tts_started = True
-            await self.stop_ttfb_metrics()
-            frame = AudioRawFrame(audio, self._sample_rate, 1)
-            yield frame
-
-        await self.push_frame(TTSStoppedFrame())
+                await self._send_text(text)
+                await self.start_tts_usage_metrics(text)
+            except Exception as e:
+                logger.error(f"{self} error sending message: {e}")
+                await self.push_frame(TTSStoppedFrame())
+                await self._disconnect()
+                await self._connect()
+                return
+            yield None
+        except Exception as e:
+            logger.exception(f"{self} exception: {e}")

src/pipecat/services/lmnt.py

@@ -20,7 +20,7 @@ from pipecat.frames.frames import (
     TTSStartedFrame,
     TTSStoppedFrame,
 )
-from pipecat.services.ai_services import TTSService
+from pipecat.services.ai_services import AsyncTTSService
 
 from loguru import logger
 
@@ -34,7 +34,7 @@ except ModuleNotFoundError as e:
     raise Exception(f"Missing module: {e}")
 
 
-class LmntTTSService(TTSService):
+class LmntTTSService(AsyncTTSService):
 
     def __init__(
             self,
@@ -44,11 +44,9 @@ class LmntTTSService(TTSService):
             sample_rate: int = 24000,
             language: str = "en",
             **kwargs):
-        super().__init__(**kwargs)
-
         # Let TTSService produce TTSStoppedFrames after a short delay of
         # no activity.
-        self._push_stop_frames = True
+        super().__init__(push_stop_frames=True, **kwargs)
 
         self._api_key = api_key
         self._voice_id = voice_id
@@ -62,6 +60,8 @@ class LmntTTSService(TTSService):
         self._speech = None
         self._connection = None
         self._receive_task = None
+        # Indicates if we have sent TTSStartedFrame. It will reset to False when
+        # there's an interruption or TTSStoppedFrame.
         self._started = False
 
     def can_generate_metrics(self) -> bool:

src/pipecat/transports/base_output.py

@@ -8,6 +8,7 @@
 import asyncio
 import itertools
 import time
+import sys
 
 from PIL import Image
 from typing import List
@@ -30,11 +31,14 @@ from pipecat.frames.frames import (
     SystemFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
+    TextFrame,
     TransportMessageFrame)
 from pipecat.transports.base_transport import TransportParams
 
 from loguru import logger
 
+from pipecat.utils.time import nanoseconds_to_seconds
+
 
 class BaseOutputTransport(FrameProcessor):
 
@@ -64,7 +68,7 @@ class BaseOutputTransport(FrameProcessor):
         # Create sink frame task. This is the task that will actually write
         # audio or video frames. We write audio/video in a task so we can keep
         # generating frames upstream while, for example, the audio is playing.
-        self._create_sink_task()
+        self._create_sink_tasks()
 
         # Create push frame task. This is the task that will push frames in
         # order. We also guarantee that all frames are pushed in the same task.
@@ -149,6 +153,7 @@ class BaseOutputTransport(FrameProcessor):
             await self._sink_queue.put(frame)
             await self.start(frame)
         elif isinstance(frame, EndFrame):
+            await self._sink_clock_queue.put((sys.maxsize, frame.id, frame))
             await self._sink_queue.put(frame)
             await self.stop(frame)
         # Other frames.
@@ -158,6 +163,9 @@ class BaseOutputTransport(FrameProcessor):
             await self._handle_image(frame)
         elif isinstance(frame, TransportMessageFrame) and frame.urgent:
             await self.send_message(frame)
+        # TODO(aleix): Images and audio should support presentation timestamps.
+        elif frame.pts:
+            await self._sink_clock_queue.put((frame.pts, frame.id, frame))
         else:
             await self._sink_queue.put(frame)
 
@@ -166,10 +174,14 @@ class BaseOutputTransport(FrameProcessor):
             return
 
         if isinstance(frame, StartInterruptionFrame):
-            # Stop sink task.
+            # Stop sink tasks.
             self._sink_task.cancel()
             await self._sink_task
-            self._create_sink_task()
+            # Stop sink clock tasks.
+            self._sink_clock_task.cancel()
+            await self._sink_clock_task
+            # Create sink tasks.
+            self._create_sink_tasks()
             # Stop push task.
             self._push_frame_task.cancel()
             await self._push_frame_task
@@ -201,43 +213,83 @@ class BaseOutputTransport(FrameProcessor):
         else:
             await self._sink_queue.put(frame)
 
-    def _create_sink_task(self):
+    #
+    # Sink tasks
+    #
+
+    def _create_sink_tasks(self):
         loop = self.get_event_loop()
         self._sink_queue = asyncio.Queue()
         self._sink_task = loop.create_task(self._sink_task_handler())
+        self._sink_clock_queue = asyncio.PriorityQueue()
+        self._sink_clock_task = loop.create_task(self._sink_clock_task_handler())
+
+    async def _sink_frame_handler(self, frame: Frame):
+        if isinstance(frame, AudioRawFrame):
+            await self.write_raw_audio_frames(frame.audio)
+            await self._internal_push_frame(frame)
+            await self.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
+        elif isinstance(frame, ImageRawFrame):
+            await self._set_camera_image(frame)
+        elif isinstance(frame, SpriteFrame):
+            await self._set_camera_images(frame.images)
+        elif isinstance(frame, TransportMessageFrame):
+            await self.send_message(frame)
+        elif isinstance(frame, TTSStartedFrame):
+            await self._bot_started_speaking()
+            await self._internal_push_frame(frame)
+        elif isinstance(frame, TTSStoppedFrame):
+            await self._bot_stopped_speaking()
+            await self._internal_push_frame(frame)
+        else:
+            await self._internal_push_frame(frame)
 
     async def _sink_task_handler(self):
         running = True
         while running:
             try:
                 frame = await self._sink_queue.get()
-                if isinstance(frame, AudioRawFrame):
-                    await self.write_raw_audio_frames(frame.audio)
-                    await self._internal_push_frame(frame)
-                    await self.push_frame(BotSpeakingFrame(), FrameDirection.UPSTREAM)
-                elif isinstance(frame, ImageRawFrame):
-                    await self._set_camera_image(frame)
-                elif isinstance(frame, SpriteFrame):
-                    await self._set_camera_images(frame.images)
-                elif isinstance(frame, TransportMessageFrame):
-                    await self.send_message(frame)
-                elif isinstance(frame, TTSStartedFrame):
-                    await self._bot_started_speaking()
-                    await self._internal_push_frame(frame)
-                elif isinstance(frame, TTSStoppedFrame):
-                    await self._bot_stopped_speaking()
-                    await self._internal_push_frame(frame)
-                else:
-                    await self._internal_push_frame(frame)
-
+                await self._sink_frame_handler(frame)
                 running = not isinstance(frame, EndFrame)
-
                 self._sink_queue.task_done()
             except asyncio.CancelledError:
                 break
             except Exception as e:
                 logger.exception(f"{self} error processing sink queue: {e}")
 
+    async def _sink_clock_frame_handler(self, frame: Frame):
+        # TODO(aleix): For now we just process TextFrame. But we should process
+        # audio and video as well.
+        if isinstance(frame, TextFrame):
+            await self._internal_push_frame(frame)
+
+    async def _sink_clock_task_handler(self):
+        running = True
+        while running:
+            try:
+                timestamp, _, frame = await self._sink_clock_queue.get()
+
+                # If we hit an EndFrame, we cna finish right away.
+                running = not isinstance(frame, EndFrame)
+
+                # If we have a frame we check it's presentation timestamp. If it
+                # has already passed we process it, otherwise we wait until it's
+                # time to process it.
+                if running:
+                    current_time = self.get_clock().get_time()
+                    if timestamp <= current_time:
+                        await self._sink_clock_frame_handler(frame)
+                    else:
+                        wait_time = nanoseconds_to_seconds(timestamp - current_time)
+                        await asyncio.sleep(wait_time)
+                        await self._sink_frame_handler(frame)
+
+                self._sink_clock_queue.task_done()
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                logger.exception(f"{self} error processing sink clock queue: {e}")
+
     async def _bot_started_speaking(self):
         logger.debug("Bot started speaking")
         self._bot_speaking = True

src/pipecat/transports/base_transport.py

@@ -32,6 +32,7 @@ class TransportParams(BaseModel):
     audio_out_is_live: bool = False
     audio_out_sample_rate: int = 16000
     audio_out_channels: int = 1
+    audio_out_bitrate: int = 96000
     audio_in_enabled: bool = False
     audio_in_sample_rate: int = 16000
     audio_in_channels: int = 1

src/pipecat/transports/network/fastapi_http.py

@@ -0,0 +1,117 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import asyncio
+import json
+import io
+import wave
+
+from typing import Awaitable, Callable
+from pydantic.main import BaseModel
+
+from pipecat.frames.frames import AudioRawFrame, CancelFrame, EndFrame, Frame, StartFrame, StartInterruptionFrame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.serializers.base_serializer import FrameSerializer
+from pipecat.transports.base_input import BaseInputTransport
+from pipecat.transports.base_output import BaseOutputTransport
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+
+from loguru import logger
+
+try:
+    from fastapi import Request, Response
+    from starlette.background import BackgroundTask
+    from sse_starlette.sse import EventSourceResponse
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error(
+        "In order to use FastAPI HTTP SSE, you need to `pip install pipecat-ai[http]`.")
+    raise Exception(f"Missing module: {e}")
+
+
+class FastAPIHTTPParams(TransportParams):
+    serializer: FrameSerializer
+
+
+class FastAPIHTTPInputTransport(BaseInputTransport):
+
+    def __init__(
+            self,
+            params: FastAPIHTTPParams,
+            **kwargs):
+        super().__init__(params, **kwargs)
+
+        self._params = params
+        self._request = None
+
+    # todo: this should probably expect a list of frames, not just one frame
+    async def handle_request(self, request: Request):
+        self._request = request
+        frames_list = await request.json()
+        logger.debug(f"Received frames: {frames_list}")
+        for frame in frames_list:
+            logger.debug(f"Received frame: {frame}")
+            frame = self._params.serializer.deserialize(frame)
+            if frame and isinstance(frame, AudioRawFrame):
+                await self.push_audio_frame(frame)
+            else:
+                await self.push_frame(frame)
+
+
+class FastAPIHTTPOutputTransport(BaseOutputTransport):
+
+    def __init__(self, params: FastAPIHTTPParams, **kwargs):
+        super().__init__(params, **kwargs)
+
+        self._params = params
+        self._event_queue = asyncio.Queue()
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        await self._write_frame(frame)
+
+    async def write_raw_audio_frames(self, frames: bytes):
+        pass
+
+    async def _write_frame(self, frame: Frame):
+        payload = self._params.serializer.serialize(frame)
+        await self._event_queue.put(payload)
+
+    async def event_generator(self):
+        while True:
+            event = await self._event_queue.get()
+            logger.debug(f"Sending event {event}")
+            yield event
+
+
+class FastAPIHTTPTransport(BaseTransport):
+
+    def __init__(
+            self,
+            params: FastAPIHTTPParams,
+            input_name: str | None = None,
+            output_name: str | None = None,
+            loop: asyncio.AbstractEventLoop | None = None):
+        super().__init__(input_name=input_name, output_name=output_name, loop=loop)
+        self._params = params
+        self._request = None
+
+        self._input = FastAPIHTTPInputTransport(
+            self._params, name=self._input_name)
+        self._output = FastAPIHTTPOutputTransport(
+            self._params, name=self._output_name)
+
+    def input(self) -> FrameProcessor:
+        return self._input
+
+    def output(self) -> FrameProcessor:
+        return self._output
+
+    async def handle_request(self, request: Request):
+        self._request = request
+        await self._input.handle_request(request)
+        return EventSourceResponse(self._output.event_generator())

src/pipecat/transports/network/fastapi_websocket.py

@@ -101,7 +101,7 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport):
 
     async def write_raw_audio_frames(self, frames: bytes):
         self._websocket_audio_buffer += frames
-        while len(self._websocket_audio_buffer) >= self._params.audio_frame_size:
+        while len(self._websocket_audio_buffer):
             frame = AudioRawFrame(
                 audio=self._websocket_audio_buffer[:
                                                    self._params.audio_frame_size],

src/pipecat/transports/services/daily.py

@@ -366,6 +366,12 @@ class DailyTransportClient(EventHandler):
                                 }
                             },
                         }
+                    },
+                    "microphone": {
+                        "sendSettings": {
+                            "channelConfig": "stereo" if self._params.audio_out_channels == 2 else "mono",
+                            "bitrate": self._params.audio_out_bitrate,
+                        }
                     }
                 },
             })

src/pipecat/utils/time.py

@@ -9,3 +9,20 @@ import datetime
 
 def time_now_iso8601() -> str:
     return datetime.datetime.now(datetime.timezone.utc).isoformat(timespec="milliseconds")
+
+
+def seconds_to_nanoseconds(seconds: float) -> int:
+    return int(seconds * 1_000_000_000)
+
+
+def nanoseconds_to_seconds(nanoseconds: int) -> float:
+    return nanoseconds / 1_000_000_000
+
+
+def nanoseconds_to_str(nanoseconds: int) -> str:
+    total_seconds = nanoseconds_to_seconds(nanoseconds)
+    hours = int(total_seconds // 3600)
+    minutes = int((total_seconds % 3600) // 60)
+    seconds = int(total_seconds % 60)
+    microseconds = int((total_seconds - int(total_seconds)) * 1_000_000)
+    return f"{hours}:{minutes:02}:{seconds:02}.{microseconds:06}"