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tts: add support for local PiperTTSService

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This commit is contained in:
Aleix Conchillo Flaqué
2026-01-28 23:29:21 -08:00
parent eb1bf1e446
commit 875614ff7a
7 changed files with 291 additions and 10 deletions
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9
.github/workflows/coverage.yaml vendored
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@@ -33,7 +33,14 @@ jobs:
- name: Install dependencies
run: |
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
uv sync --group dev \
--extra anthropic \
--extra aws \
--extra google \
--extra langchain \
--extra livekit \
--extra piper \
--extra websocket
- name: Run tests with coverage
run: |

9
.github/workflows/tests.yaml vendored
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@@ -37,7 +37,14 @@ jobs:
- name: Install dependencies
run: |
uv sync --group dev --extra anthropic --extra aws --extra google --extra langchain --extra livekit --extra websocket
uv sync --group dev \
--extra anthropic \
--extra aws \
--extra google \
--extra langchain \
--extra livekit \
--extra piper \
--extra websocket
- name: Test with pytest
run: |

132
examples/foundational/07zi-interruptible-piper.py Normal file
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@@ -0,0 +1,132 @@
#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
LLMContextAggregatorPair,
LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.piper.tts import PiperTTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = PiperTTSService(voice_id="en_US-ryan-high")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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 spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
},
]
context = LLMContext(messages)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
),
)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt,
user_aggregator, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
assistant_aggregator, # Assistant spoken responses
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = await create_transport(runner_args, transport_params)
await run_bot(transport, runner_args)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

1
pyproject.toml
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@@ -95,6 +95,7 @@ rnnoise = [ "pyrnnoise~=0.4.1" ]
openpipe = [ "openpipe>=4.50.0,<6" ]
openrouter = []
perplexity = []
piper = [ "piper-tts>=1.3.0,<2" ]
playht = [ "pipecat-ai[websockets-base]" ]
qwen = []
remote-smart-turn = []

1
scripts/evals/run-release-evals.py
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@@ -138,6 +138,7 @@ TESTS_07 = [
("07zf-interruptible-gradium.py", EVAL_SIMPLE_MATH),
("07zg-interruptible-camb.py", EVAL_SIMPLE_MATH),
("07zh-interruptible-hathora.py", EVAL_SIMPLE_MATH),
("07zi-interruptible-piper.py", EVAL_SIMPLE_MATH),
# Needs a local XTTS docker instance running.
# ("07i-interruptible-xtts.py", EVAL_SIMPLE_MATH),
# Needs a Krisp license.

122
src/pipecat/services/piper/tts.py
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@@ -6,11 +6,14 @@
"""Piper TTS service implementation."""
from typing import AsyncGenerator, Optional
import asyncio
from pathlib import Path
from typing import AsyncGenerator, AsyncIterator, Optional
import aiohttp
from loguru import logger
from pipecat.audio.utils import create_stream_resampler
from pipecat.frames.frames import (
ErrorFrame,
Frame,
@@ -20,6 +23,123 @@ from pipecat.frames.frames import (
from pipecat.services.tts_service import TTSService
from pipecat.utils.tracing.service_decorators import traced_tts
try:
from piper import PiperVoice
from piper.download_voices import download_voice
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use Piper, you need to `pip install pipecat-ai[piper]`.")
raise Exception(f"Missing module: {e}")
class PiperTTSService(TTSService):
"""Piper TTS service implementation.
Provides local text-to-speech synthesis using Piper voice models. Automatically
downloads voice models if not already present and resamples audio output to
match the configured sample rate.
"""
def __init__(
self,
*,
voice_id: str,
download_dir: Optional[Path] = None,
force_redownload: bool = False,
use_cuda: bool = False,
**kwargs,
):
"""Initialize the Piper TTS service.
Args:
voice_id: Piper voice model identifier (e.g. `en_US-ryan-high`).
download_dir: Directory for storing voice model files. Defaults to
the current working directory.
force_redownload: Re-download the voice model even if it already exists.
use_cuda: Use CUDA for GPU-accelerated inference.
**kwargs: Additional arguments passed to the parent `TTSService`.
"""
super().__init__(**kwargs)
self._voice_id = voice_id
self._resampler = create_stream_resampler()
download_dir = download_dir or Path.cwd()
model_file = f"{voice_id}.onnx"
model_path = Path(download_dir) / model_file
if not model_path.exists():
logger.debug(f"Downloading Piper '{voice_id}' model")
download_voice(voice_id, download_dir, force_redownload=force_redownload)
logger.debug(f"Loading Piper '{voice_id}' model from {model_path}")
self._voice = PiperVoice.load(model_path, use_cuda=use_cuda)
logger.debug(f"Loaded Piper '{voice_id}' model")
def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics.
Returns:
True, as Piper service supports metrics generation.
"""
return True
@traced_tts
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
"""Generate speech from text using Piper.
Args:
text: The text to convert to speech.
Yields:
Frame: Audio frames containing the synthesized speech and status frames.
"""
def async_next(it):
try:
return next(it)
except StopIteration:
return None
async def async_iterator(iterator, sample_rate: int) -> AsyncIterator[bytes]:
while True:
item = await asyncio.to_thread(async_next, iterator)
if item is None:
return
audio_data = await self._resampler.resample(
item.audio_int16_bytes, sample_rate, self.sample_rate
)
yield audio_data
logger.debug(f"{self}: Generating TTS [{text}]")
try:
await self.start_ttfb_metrics()
await self.start_tts_usage_metrics(text)
yield TTSStartedFrame()
async for frame in self._stream_audio_frames_from_iterator(
async_iterator(self._voice.synthesize(text), self._voice.config.sample_rate),
strip_wav_header=False,
):
await self.stop_ttfb_metrics()
yield frame
except Exception as e:
logger.error(f"{self} exception: {e}")
yield ErrorFrame(error=f"Unknown error occurred: {e}")
finally:
logger.debug(f"{self}: Finished TTS [{text}]")
await self.stop_ttfb_metrics()
yield TTSStoppedFrame()
# This assumes a running TTS service running:
# https://github.com/OHF-Voice/piper1-gpl/blob/main/docs/API_HTTP.md

27
uv.lock generated
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