264 lines
9.1 KiB
Python
264 lines
9.1 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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import asyncio
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import base64
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import json
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from typing import Any, AsyncGenerator, List, Literal, Mapping, Tuple
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from pydantic import BaseModel
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from pipecat.frames.frames import (
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CancelFrame,
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EndFrame,
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Frame,
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StartFrame,
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StartInterruptionFrame,
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TTSAudioRawFrame,
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TTSStartedFrame,
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TTSStoppedFrame)
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.ai_services import AsyncWordTTSService
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from loguru import logger
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# See .env.example for ElevenLabs configuration needed
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try:
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import websockets
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except ModuleNotFoundError as e:
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logger.error(f"Exception: {e}")
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logger.error(
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"In order to use ElevenLabs, you need to `pip install pipecat-ai[elevenlabs]`. Also, set `ELEVENLABS_API_KEY` environment variable.")
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raise Exception(f"Missing module: {e}")
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def sample_rate_from_output_format(output_format: str) -> int:
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match output_format:
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case "pcm_16000":
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return 16000
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case "pcm_22050":
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return 22050
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case "pcm_24000":
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return 24000
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case "pcm_44100":
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return 44100
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return 16000
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def calculate_word_times(
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alignment_info: Mapping[str, Any], cumulative_time: float
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) -> List[Tuple[str, float]]:
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zipped_times = list(zip(alignment_info["chars"], alignment_info["charStartTimesMs"]))
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words = "".join(alignment_info["chars"]).split(" ")
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# Calculate start time for each word. We do this by finding a space character
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# and using the previous word time, also taking into account there might not
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# be a space at the end.
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times = []
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for (i, (a, b)) in enumerate(zipped_times):
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if a == " " or i == len(zipped_times) - 1:
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t = cumulative_time + (zipped_times[i - 1][1] / 1000.0)
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times.append(t)
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word_times = list(zip(words, times))
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return word_times
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class ElevenLabsTTSService(AsyncWordTTSService):
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class InputParams(BaseModel):
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output_format: Literal["pcm_16000", "pcm_22050", "pcm_24000", "pcm_44100"] = "pcm_16000"
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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_id: str,
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model: str = "eleven_turbo_v2_5",
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url: str = "wss://api.elevenlabs.io",
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params: InputParams = InputParams(),
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**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 a
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# full sentence should only "cost" us 15ms or so with GPT-4o or a Llama
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# 3 model, and it's worth it for the better audio quality.
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#
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# We also don't want to automatically push LLM response text frames,
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# because the context aggregators will add them to the LLM context even
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# if we're interrupted. ElevenLabs gives us word-by-word timestamps. We
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# can use those to generate text frames ourselves aligned with the
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# playout timing of the audio!
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#
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# Finally, ElevenLabs doesn't provide information on when the bot stops
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# speaking for a while, so we want the parent class to send TTSStopFrame
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# after a short period not receiving any audio.
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super().__init__(
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aggregate_sentences=True,
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push_text_frames=False,
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push_stop_frames=True,
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stop_frame_timeout_s=2.0,
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sample_rate=sample_rate_from_output_format(params.output_format),
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**kwargs
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)
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self._api_key = api_key
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self._voice_id = voice_id
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self.set_model_name(model)
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self._url = url
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self._params = params
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# Websocket connection to ElevenLabs.
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self._websocket = None
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# Indicates if we have sent TTSStartedFrame. It will reset to False when
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# there's an interruption or TTSStoppedFrame.
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self._started = False
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self._cumulative_time = 0
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def can_generate_metrics(self) -> bool:
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return True
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async def set_model(self, model: str):
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await super().set_model(model)
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logger.debug(f"Switching TTS model to: [{model}]")
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await self._disconnect()
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await self._connect()
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async def set_voice(self, voice: str):
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logger.debug(f"Switching TTS voice to: [{voice}]")
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self._voice_id = voice
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await self._disconnect()
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await self._connect()
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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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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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async def cancel(self, frame: CancelFrame):
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await super().cancel(frame)
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await self._disconnect()
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async def flush_audio(self):
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if self._websocket:
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msg = {"text": " ", "flush": True}
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await self._websocket.send(json.dumps(msg))
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async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
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await super().push_frame(frame, direction)
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if isinstance(frame, (TTSStoppedFrame, StartInterruptionFrame)):
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self._started = False
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if isinstance(frame, TTSStoppedFrame):
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await self.add_word_timestamps([("LLMFullResponseEndFrame", 0)])
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async def _connect(self):
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try:
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voice_id = self._voice_id
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model = self.model_name
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output_format = self._params.output_format
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url = f"{self._url}/v1/text-to-speech/{voice_id}/stream-input?model_id={model}&output_format={output_format}"
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self._websocket = await websockets.connect(url)
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self._receive_task = self.get_event_loop().create_task(self._receive_task_handler())
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self._keepalive_task = self.get_event_loop().create_task(self._keepalive_task_handler())
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# According to ElevenLabs, we should always start with a single space.
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msg = {
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"text": " ",
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"xi_api_key": self._api_key,
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}
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await self._websocket.send(json.dumps(msg))
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except Exception as e:
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logger.error(f"{self} initialization error: {e}")
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self._websocket = None
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async def _disconnect(self):
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try:
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await self.stop_all_metrics()
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if self._websocket:
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await self._websocket.send(json.dumps({"text": ""}))
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await self._websocket.close()
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self._websocket = None
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if self._receive_task:
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self._receive_task.cancel()
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await self._receive_task
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self._receive_task = None
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if self._keepalive_task:
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self._keepalive_task.cancel()
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await self._keepalive_task
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self._keepalive_task = None
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self._started = False
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except Exception as e:
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logger.error(f"{self} error closing websocket: {e}")
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async def _receive_task_handler(self):
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try:
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async for message in self._websocket:
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msg = json.loads(message)
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if msg.get("audio"):
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await self.stop_ttfb_metrics()
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self.start_word_timestamps()
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audio = base64.b64decode(msg["audio"])
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frame = TTSAudioRawFrame(audio, self.sample_rate, 1)
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await self.push_frame(frame)
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if msg.get("alignment"):
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word_times = calculate_word_times(msg["alignment"], self._cumulative_time)
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await self.add_word_timestamps(word_times)
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self._cumulative_time = word_times[-1][1]
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except asyncio.CancelledError:
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pass
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except Exception as e:
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logger.error(f"{self} exception: {e}")
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async def _keepalive_task_handler(self):
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while True:
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try:
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await asyncio.sleep(10)
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await self._send_text("")
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"{self} exception: {e}")
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async def _send_text(self, text: str):
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if self._websocket:
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msg = {"text": text + " "}
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await self._websocket.send(json.dumps(msg))
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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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try:
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if not self._websocket:
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await self._connect()
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try:
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if not self._started:
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await self.push_frame(TTSStartedFrame())
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await self.start_ttfb_metrics()
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self._started = True
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self._cumulative_time = 0
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await self._send_text(text)
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await self.start_tts_usage_metrics(text)
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except Exception as e:
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logger.error(f"{self} error sending message: {e}")
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await self.push_frame(TTSStoppedFrame())
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await self._disconnect()
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await self._connect()
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return
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yield None
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except Exception as e:
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logger.error(f"{self} exception: {e}")
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