54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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from cartesia.tts import AsyncCartesiaTTS
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from typing import AsyncGenerator
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from pipecat.frames.frames import AudioRawFrame, Frame
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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__(
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self,
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*,
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api_key: str,
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voice_name: str,
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**kwargs):
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super().__init__(**kwargs)
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self._api_key = api_key
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self._voice_name = voice_name
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try:
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self._client = AsyncCartesiaTTS(api_key=self._api_key)
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voices = self._client.get_voices()
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voice_id = voices[self._voice_name]["id"]
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self._voice = self._client.get_voice_embedding(voice_id=voice_id)
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except Exception as e:
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logger.error(f"Cartesia initialization error: {e}")
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async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
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logger.debug(f"Transcribing text: [{text}]")
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try:
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chunk_generator = await self._client.generate(
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transcript=text, voice=self._voice, stream=True,
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model_id="upbeat-moon", data_rtype='array', output_format='pcm_16000',
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# a chunk_time of 0.1 seems to be the default. there are small audio pops/gaps which
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# we need to debug
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chunk_time=0.1
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)
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async for chunk in chunk_generator:
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yield AudioRawFrame(chunk['audio'], 16000, 1)
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except Exception as e:
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logger.error(f"Cartesia error: {e}")
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