wip - using cartesia word timestamps for context management
This commit is contained in:
@@ -13,7 +13,8 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
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from pipecat.pipeline.pipeline import Pipeline
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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.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineTask
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from pipecat.pipeline.task import PipelineTask
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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# from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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@@ -36,16 +37,21 @@ async def main(room_url):
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"Say One Thing From an LLM",
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"Say One Thing From an LLM",
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DailyParams(audio_out_enabled=True))
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DailyParams(audio_out_enabled=True))
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tts = ElevenLabsTTSService(
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# tts = ElevenLabsTTSService(
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aiohttp_session=session,
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# aiohttp_session=session,
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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# api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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# voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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)
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# )
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llm = OpenAILLMService(
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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api_key=os.getenv("OPENAI_API_KEY"),
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model="gpt-4o")
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model="gpt-4o")
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
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)
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messages = [
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messages = [
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{
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{
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"role": "system",
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"role": "system",
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@@ -58,11 +64,11 @@ async def main(room_url):
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@transport.event_handler("on_first_participant_joined")
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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async def on_first_participant_joined(transport, participant):
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await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
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# await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
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await task.queue_frames([LLMMessagesFrame(messages)])
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await runner.run(task)
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await runner.run(task)
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if __name__ == "__main__":
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if __name__ == "__main__":
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(url, token) = configure()
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(url, token) = configure()
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asyncio.run(main(url))
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asyncio.run(main(url))
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@@ -72,7 +72,7 @@ async def main(room_url: str, token):
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tma_out # Assistant spoken responses
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tma_out # Assistant spoken responses
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])
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])
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
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@transport.event_handler("on_first_participant_joined")
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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async def on_first_participant_joined(transport, participant):
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@@ -136,9 +136,15 @@ class LLMService(AIService):
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class TTSService(AIService):
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class TTSService(AIService):
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def __init__(self, *, aggregate_sentences: bool = True, **kwargs):
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def __init__(
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self,
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*,
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aggregate_sentences: bool = True,
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push_text_frames: bool = True,
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**kwargs):
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super().__init__(**kwargs)
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super().__init__(**kwargs)
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self._aggregate_sentences: bool = aggregate_sentences
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self._aggregate_sentences: bool = aggregate_sentences
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self._push_text_frames: bool = push_text_frames
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self._current_sentence: str = ""
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self._current_sentence: str = ""
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# Converts the text to audio.
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# Converts the text to audio.
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@@ -176,9 +182,11 @@ class TTSService(AIService):
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await self.process_generator(self.run_tts(text))
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await self.process_generator(self.run_tts(text))
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await self.stop_processing_metrics()
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await self.stop_processing_metrics()
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await self.push_frame(TTSStoppedFrame())
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await self.push_frame(TTSStoppedFrame())
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# We send the original text after the audio. This way, if we are
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if self._push_text_frames:
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# interrupted, the text is not added to the assistant context.
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print("PUSHING TEXT FRAME")
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await self.push_frame(TextFrame(text))
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# We send the original text after the audio. This way, if we are
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# interrupted, the text is not added to the assistant context.
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await self.push_frame(TextFrame(text))
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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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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await super().process_frame(frame, direction)
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@@ -8,11 +8,14 @@ import json
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import uuid
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import uuid
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import base64
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import base64
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import asyncio
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import asyncio
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import time
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from typing import AsyncGenerator
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from typing import AsyncGenerator
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.frames.frames import Frame, AudioRawFrame, StartInterruptionFrame
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from pipecat.frames.frames import (
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Frame, AudioRawFrame, StartInterruptionFrame, StartFrame, EndFrame, TextFrame
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)
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from pipecat.services.ai_services import TTSService
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from pipecat.services.ai_services import TTSService
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from loguru import logger
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from loguru import logger
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@@ -39,9 +42,22 @@ class CartesiaTTSService(TTSService):
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model_id: str = "sonic-english",
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model_id: str = "sonic-english",
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encoding: str = "pcm_s16le",
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encoding: str = "pcm_s16le",
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sample_rate: int = 16000,
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sample_rate: int = 16000,
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language: str = "en",
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**kwargs):
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**kwargs):
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super().__init__(**kwargs)
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super().__init__(**kwargs)
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# Aggregating sentences still gives cleaner-sounding results and fewer
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# artifacts than streaming one word at a time. On average, waiting for
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# a full sentence should only "cost" us 15ms or so with GPT-4o or a Llama 3
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# model, and it's worth it for the better audio quality.
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self._aggregate_sentences = True
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# we don't want to automatically push LLM response text frames, because the
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# context aggregators will add them to the LLM context even if we're
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# interrupted. cartesia gives us word-by-word timestamps. we can use those
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# to generate text frames ourselves aligned with the playout timing of the audio!
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self._push_text_frames = False
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self._api_key = api_key
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self._api_key = api_key
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self._cartesia_version = cartesia_version
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self._cartesia_version = cartesia_version
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self._url = url
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self._url = url
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@@ -52,16 +68,32 @@ class CartesiaTTSService(TTSService):
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"encoding": encoding,
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"encoding": encoding,
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"sample_rate": sample_rate,
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"sample_rate": sample_rate,
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}
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}
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self._language = "en"
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self._language = language
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self._websocket = None
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self._websocket = None
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self._context_id = None
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self._context_id = None
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self._context_id_start_timestamp = None
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self._timestamped_words_buffer = []
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self._receive_task = None
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self._receive_task = None
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self._context_appending_task = None
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self._waiting_for_ttfb = False
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self._waiting_for_ttfb = False
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def can_generate_metrics(self) -> bool:
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def can_generate_metrics(self) -> bool:
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return True
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return True
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async def start(self, frame: StartFrame):
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await super().start(frame)
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await self.connect()
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self._context_appending_task = self.get_event_loop().create_task(self._context_appending_task_handler())
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async def stop(self, frame: EndFrame):
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await super().stop(frame)
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await self.disconnect()
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if self._context_appending_task:
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self._context_appending_task.cancel()
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self._context_appending_task = None
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pass
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async def connect(self):
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async def connect(self):
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try:
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try:
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self._websocket = await websockets.connect(
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self._websocket = await websockets.connect(
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@@ -69,6 +101,7 @@ class CartesiaTTSService(TTSService):
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)
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)
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except Exception as e:
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except Exception as e:
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logger.exception(f"{self} initialization error: {e}")
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logger.exception(f"{self} initialization error: {e}")
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self._websocket = None
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async def disconnect(self):
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async def disconnect(self):
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try:
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try:
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@@ -76,38 +109,73 @@ class CartesiaTTSService(TTSService):
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ws = self._websocket
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ws = self._websocket
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self._websocket = None
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self._websocket = None
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await ws.close()
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await ws.close()
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self._context_id = None
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self._context_id_start_timestamp = None
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if self._receive_task:
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self._receive_task.cancel()
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self._receive_task = None
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except Exception as e:
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except Exception as e:
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logger.exception(f"{self} error closing websocket: {e}")
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logger.exception(f"{self} error closing websocket: {e}")
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async def handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
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async def handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection):
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await super().handle_interruption(frame, direction)
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await super().handle_interruption(frame, direction)
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if self._receive_task:
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self._context_id = None
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self._receive_task.cancel()
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self._context_id_start_timestamp = None
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self._receive_task = None
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self._timestamped_words_buffer = []
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await self.disconnect()
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await self.stop_all_metrics()
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await self.stop_all_metrics()
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async def _receive_task_handler(self):
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async def _receive_task_handler(self):
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async for message in self._websocket:
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try:
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msg = json.loads(message)
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async for message in self._websocket:
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if not msg:
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msg = json.loads(message)
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continue
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if not msg or msg["context_id"] != self._context_id:
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# logger.debug(f"Received message: {msg}")
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continue
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if self._waiting_for_ttfb:
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# logger.debug(f"Received message: {msg['type']} {msg['context_id']}")
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await self.stop_ttfb_metrics()
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if msg["type"] == "done":
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self._waiting_for_ttfb = False
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# unset _context_id but not the _context_id_start_timestamp because we are likely still
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if msg["done"]:
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# playing out audio and need the timestamp to set send context frames
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self._context_id = None
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self._context_id = None
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if self._receive_task:
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if self._receive_task:
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self._receive_task.cancel()
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self._receive_task.cancel()
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self._receive_task = None
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self._receive_task = None
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return
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return
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frame = AudioRawFrame(
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if msg["type"] == "timestamps":
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audio=base64.b64decode(msg["data"]),
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# logger.debug(f"TIMESTAMPS: {msg}")
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sample_rate=self._output_format["sample_rate"],
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self._timestamped_words_buffer.extend(
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num_channels=1
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list(zip(msg["word_timestamps"]["words"], msg["word_timestamps"]["end"]))
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)
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)
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await self.push_frame(frame)
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continue
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if msg["type"] == "chunk":
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if not self._context_id_start_timestamp:
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self._context_id_start_timestamp = time.time()
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if self._waiting_for_ttfb:
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await self.stop_ttfb_metrics()
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self._waiting_for_ttfb = False
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frame = AudioRawFrame(
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audio=base64.b64decode(msg["data"]),
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sample_rate=self._output_format["sample_rate"],
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num_channels=1
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)
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await self.push_frame(frame)
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except Exception as e:
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logger.exception(f"{self} exception: {e}")
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async def _context_appending_task_handler(self):
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try:
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while True:
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await asyncio.sleep(0.1)
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if not self._context_id_start_timestamp:
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continue
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elapsed_seconds = time.time() - self._context_id_start_timestamp
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# pop all words from self._timestamped_words_buffer that are older than the
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# elapsed time and print a message about them to the console
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# ...
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while self._timestamped_words_buffer and self._timestamped_words_buffer[0][1] <= elapsed_seconds:
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word, timestamp = self._timestamped_words_buffer.pop(0)
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print(f"Word '{word}' with timestamp {timestamp:.2f}s has been spoken.")
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await self.push_frame(TextFrame(word))
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except Exception as e:
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logger.exception(f"{self} exception: {e}")
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async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
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async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
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logger.debug(f"Generating TTS: [{text}]")
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logger.debug(f"Generating TTS: [{text}]")
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@@ -124,7 +192,7 @@ class CartesiaTTSService(TTSService):
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self._context_id = str(uuid.uuid4())
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self._context_id = str(uuid.uuid4())
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msg = {
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msg = {
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"transcript": text,
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"transcript": text + " ",
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"continue": True,
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"continue": True,
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"context_id": self._context_id,
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"context_id": self._context_id,
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"model_id": self._model_id,
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"model_id": self._model_id,
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@@ -134,8 +202,10 @@ class CartesiaTTSService(TTSService):
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},
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},
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"output_format": self._output_format,
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"output_format": self._output_format,
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"language": self._language,
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"language": self._language,
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"add_timestamps": True,
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}
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}
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# logger.debug(f"SENDING MESSAGE {json.dumps(msg)}")
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# logger.debug(f"SENDING MESSAGE {json.dumps(msg)}")
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# todo: handle websocket closed
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await self._websocket.send(json.dumps(msg))
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await self._websocket.send(json.dumps(msg))
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if not self._receive_task:
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if not self._receive_task:
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# todo: how do we await this task at the app level, so the program doesn't exit?
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# todo: how do we await this task at the app level, so the program doesn't exit?
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Reference in New Issue
Block a user