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Merge pull request #189 from pipecat-ai/khk-cartesia-etc
Cartesia TTS
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| # | ||
| # Copyright (c) 2024, Daily | ||
| # | ||
| # SPDX-License-Identifier: BSD 2-Clause License | ||
| # | ||
|
|
||
| import asyncio | ||
| import aiohttp | ||
| import os | ||
| import sys | ||
|
|
||
| from pipecat.frames.frames import LLMMessagesFrame | ||
| from pipecat.pipeline.pipeline import Pipeline | ||
| 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.openai import OpenAILLMService | ||
| from pipecat.transports.services.daily import DailyParams, DailyTransport | ||
| from pipecat.vad.silero import SileroVADAnalyzer | ||
|
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| from runner import configure | ||
|
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| from loguru import logger | ||
|
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| from dotenv import load_dotenv | ||
| load_dotenv(override=True) | ||
|
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| logger.remove(0) | ||
| logger.add(sys.stderr, level="DEBUG") | ||
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| async def main(room_url: str, token): | ||
| async with aiohttp.ClientSession() as session: | ||
| transport = DailyTransport( | ||
| room_url, | ||
| token, | ||
| "Respond bot", | ||
| DailyParams( | ||
| audio_out_enabled=True, | ||
| transcription_enabled=True, | ||
| vad_enabled=True, | ||
| vad_analyzer=SileroVADAnalyzer() | ||
| ) | ||
| ) | ||
|
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| tts = CartesiaTTSService( | ||
| api_key=os.getenv("CARTESIA_API_KEY"), | ||
| voice_name="Barbershop Man" | ||
| ) | ||
|
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| llm = OpenAILLMService( | ||
| api_key=os.getenv("OPENAI_API_KEY"), | ||
| model="gpt-4o") | ||
|
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| messages = [ | ||
| { | ||
| "role": "system", | ||
| "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.", | ||
| }, | ||
| ] | ||
|
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| tma_in = LLMUserResponseAggregator(messages) | ||
| tma_out = LLMAssistantResponseAggregator(messages) | ||
|
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| pipeline = Pipeline([ | ||
| transport.input(), # Transport user input | ||
| tma_in, # User responses | ||
| llm, # LLM | ||
| tts, # TTS | ||
| transport.output(), # Transport bot output | ||
| tma_out # Assistant spoken responses | ||
| ]) | ||
|
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| task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True)) | ||
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| @transport.event_handler("on_first_participant_joined") | ||
| async def on_first_participant_joined(transport, participant): | ||
| transport.capture_participant_transcription(participant["id"]) | ||
| # Kick off the conversation. | ||
| messages.append( | ||
| {"role": "system", "content": "Please introduce yourself to the user."}) | ||
| await task.queue_frames([LLMMessagesFrame(messages)]) | ||
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| runner = PipelineRunner() | ||
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| await runner.run(task) | ||
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| if __name__ == "__main__": | ||
| (url, token) = configure() | ||
| asyncio.run(main(url, token)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| # | ||
| # Copyright (c) 2024, Daily | ||
| # | ||
| # SPDX-License-Identifier: BSD 2-Clause License | ||
| # | ||
|
|
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| from cartesia.tts import AsyncCartesiaTTS | ||
|
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| import time | ||
| from typing import AsyncGenerator | ||
|
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| from pipecat.frames.frames import AudioRawFrame, ErrorFrame, Frame | ||
| from pipecat.services.ai_services import TTSService | ||
|
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| from loguru import logger | ||
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| class CartesiaTTSService(TTSService): | ||
|
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| def __init__( | ||
| self, | ||
| *, | ||
| api_key: str, | ||
| voice_name: str, | ||
| **kwargs): | ||
| super().__init__(**kwargs) | ||
|
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| self._api_key = api_key | ||
| self._voice_name = voice_name | ||
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| self._client = None | ||
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| async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]: | ||
| logger.debug(f"Transcribing text: [{text}]") | ||
|
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| try: | ||
| if self._client is None: | ||
| self._client = AsyncCartesiaTTS(api_key=self._api_key) | ||
| voices = self._client.get_voices() | ||
| self._voice_id = voices[self._voice_name]["id"] | ||
| self._voice = self._client.get_voice_embedding(voice_id=self._voice_id) | ||
|
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| chunk_generator = await self._client.generate( | ||
| transcript=text, voice=self._voice, stream=True, | ||
| model_id="upbeat-moon", data_rtype='array', output_format='pcm_16000', | ||
| # a chunk_time of 0.1 seems to be the default. there are small audio pops/gaps which | ||
| # we need to debug | ||
| chunk_time=0.1 | ||
| ) | ||
|
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| async for chunk in chunk_generator: | ||
| # print(f"") | ||
| frame = AudioRawFrame(chunk['audio'], 16000, 1) | ||
| yield frame | ||
| except Exception as e: | ||
| logger.error(f"Exception {e}") |
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