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Merge pull request #3585 from pipecat-ai/aleix/improve-piper-tts-support

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improve Piper TTS support
This commit is contained in:
Aleix Conchillo Flaqué
2026-01-29 08:36:13 -08:00
committed by GitHub
parent b77a50de73 bd00587092
commit f3b72e9263
12 changed files with 349 additions and 32 deletions
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9
.github/workflows/coverage.yaml vendored
View File

@@ -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: |

1
changelog/3585.added.md Normal file
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@@ -0,0 +1 @@
- Added local `PiperTTSService` for offline text-to-speech using Piper voice models. The existing HTTP-based service has been renamed to `PiperHttpTTSService`.

1
changelog/3585.fixed.md Normal file
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@@ -0,0 +1 @@
- Fixed `PiperHttpTTSService` (olf `PiperTTSService`) to resample audio output based on the model's sample rate parsed from the WAV header.

4
examples/foundational/01-say-one-thing-piper.py
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@@ -16,7 +16,7 @@ from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.piper.tts import PiperTTSService
from pipecat.services.piper.tts import PiperHttpTTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -39,7 +39,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
# Create an HTTP session
async with aiohttp.ClientSession() as session:
tts = PiperTTSService(
tts = PiperHttpTTSService(
base_url=os.getenv("PIPER_BASE_URL"), aiohttp_session=session, sample_rate=24000
)

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
View File

@@ -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
View File

@@ -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.

142
src/pipecat/services/piper/tts.py
View File

@@ -6,7 +6,9 @@
"""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
@@ -20,11 +22,128 @@ 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}")
# This assumes a running TTS service running: https://github.com/OHF-Voice/piper1-gpl/blob/main/docs/API_HTTP.md
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
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) -> AsyncIterator[bytes]:
while True:
item = await asyncio.to_thread(async_next, iterator)
if item is None:
return
yield item.audio_int16_bytes
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)),
in_sample_rate=self._voice.config.sample_rate,
):
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
#
# Usage:
#
# $ uv pip install "piper-tts[http]"
# $ uv run python -m piper.http_server -m en_US-ryan-high
#
class PiperHttpTTSService(TTSService):
"""Piper HTTP TTS service implementation.
Provides integration with Piper's HTTP TTS server for text-to-speech
synthesis. Supports streaming audio generation with configurable sample
rates and automatic WAV header removal.
@@ -35,9 +154,7 @@ class PiperTTSService(TTSService):
*,
base_url: str,
aiohttp_session: aiohttp.ClientSession,
# When using Piper, the sample rate of the generated audio depends on the
# voice model being used.
sample_rate: Optional[int] = None,
voice_id: Optional[str] = None,
**kwargs,
):
"""Initialize the Piper TTS service.
@@ -45,10 +162,10 @@ class PiperTTSService(TTSService):
Args:
base_url: Base URL for the Piper TTS HTTP server.
aiohttp_session: aiohttp ClientSession for making HTTP requests.
sample_rate: Output sample rate. If None, uses the voice model's native rate.
voice_id: Piper voice model identifier (e.g. `en_US-ryan-high`).
**kwargs: Additional arguments passed to the parent TTSService.
"""
super().__init__(sample_rate=sample_rate, **kwargs)
super().__init__(**kwargs)
if base_url.endswith("/"):
logger.warning("Base URL ends with a slash, this is not allowed.")
@@ -56,7 +173,7 @@ class PiperTTSService(TTSService):
self._base_url = base_url
self._session = aiohttp_session
self._settings = {"base_url": base_url}
self._model_id = voice_id
def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics.
@@ -83,9 +200,12 @@ class PiperTTSService(TTSService):
try:
await self.start_ttfb_metrics()
async with self._session.post(
self._base_url, json={"text": text}, headers=headers
) as response:
data = {
"text": text,
"voice": self._model_id,
}
async with self._session.post(self._base_url, json=data, headers=headers) as response:
if response.status != 200:
error = await response.text()
yield ErrorFrame(

42
src/pipecat/services/tts_service.py
View File

@@ -24,6 +24,7 @@ from typing import (
from loguru import logger
from pipecat.audio.utils import create_stream_resampler
from pipecat.frames.frames import (
AggregatedTextFrame,
AggregationType,
@@ -202,6 +203,8 @@ class TTSService(AIService):
)
self._text_filters = [text_filter]
self._resampler = create_stream_resampler()
self._stop_frame_task: Optional[asyncio.Task] = None
self._stop_frame_queue: asyncio.Queue = asyncio.Queue()
@@ -505,12 +508,40 @@ class TTSService(AIService):
await self._stop_frame_queue.put(frame)
async def _stream_audio_frames_from_iterator(
self, iterator: AsyncIterator[bytes], *, strip_wav_header: bool
self,
iterator: AsyncIterator[bytes],
*,
strip_wav_header: bool = False,
in_sample_rate: Optional[int] = None,
) -> AsyncGenerator[Frame, None]:
"""Stream audio frames from an async byte iterator with optional resampling.
For WAV data, use `strip_wav_header=True` to strip the header and
auto-detect the source sample rate. For raw PCM data, pass
`in_sample_rate` directly. Audio is resampled to `self.sample_rate` when
the source rate differs.
Args:
iterator: Async iterator yielding audio bytes.
strip_wav_header: Strip WAV header and parse source sample rate from it.
in_sample_rate: Source sample rate for raw PCM data. Overrides
WAV-detected rate if both are provided.
"""
buffer = bytearray()
source_sample_rate = in_sample_rate
need_to_strip_wav_header = strip_wav_header
async def maybe_resample(audio: bytes) -> bytes:
if source_sample_rate and source_sample_rate != self.sample_rate:
return await self._resampler.resample(audio, source_sample_rate, self.sample_rate)
return audio
async for chunk in iterator:
if need_to_strip_wav_header and chunk.startswith(b"RIFF"):
# Parse sample rate from WAV header (bytes 24-28, little-endian uint32).
if len(chunk) >= 44 and source_sample_rate is None:
source_sample_rate = int.from_bytes(chunk[24:28], "little")
chunk = chunk[44:]
need_to_strip_wav_header = False
@@ -520,19 +551,18 @@ class TTSService(AIService):
# Round to nearest even number.
aligned_length = len(buffer) & ~1 # 111111111...11110
if aligned_length > 0:
aligned_chunk = buffer[:aligned_length]
aligned_chunk = await maybe_resample(bytes(buffer[:aligned_length]))
buffer = buffer[aligned_length:] # keep any leftover byte
if len(aligned_chunk) > 0:
frame = TTSAudioRawFrame(bytes(aligned_chunk), self.sample_rate, 1)
yield frame
yield TTSAudioRawFrame(aligned_chunk, self.sample_rate, 1)
if len(buffer) > 0:
# Make sure we don't need an extra padding byte.
if len(buffer) % 2 == 1:
buffer.extend(b"\x00")
frame = TTSAudioRawFrame(bytes(buffer), self.sample_rate, 1)
yield frame
audio = await maybe_resample(bytes(buffer))
yield TTSAudioRawFrame(audio, self.sample_rate, 1)
async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
self._processing_text = False

12
tests/test_piper_tts.py
View File

@@ -21,7 +21,7 @@ from pipecat.frames.frames import (
TTSStoppedFrame,
TTSTextFrame,
)
from pipecat.services.piper.tts import PiperTTSService
from pipecat.services.piper.tts import PiperHttpTTSService
from pipecat.tests.utils import run_test
@@ -67,8 +67,10 @@ async def test_run_piper_tts_success(aiohttp_client):
base_url = str(client.make_url("")).rstrip("/")
async with aiohttp.ClientSession() as session:
# Instantiate PiperTTSService with our mock server
tts_service = PiperTTSService(base_url=base_url, aiohttp_session=session, sample_rate=24000)
# Instantiate PiperHttpTTSService with our mock server
tts_service = PiperHttpTTSService(
base_url=base_url, aiohttp_session=session, sample_rate=24000
)
frames_to_send = [
TTSSpeakFrame(text="Hello world."),
@@ -117,7 +119,9 @@ async def test_run_piper_tts_error(aiohttp_client):
base_url = str(client.make_url("")).rstrip("/")
async with aiohttp.ClientSession() as session:
tts_service = PiperTTSService(base_url=base_url, aiohttp_session=session, sample_rate=24000)
tts_service = PiperHttpTTSService(
base_url=base_url, aiohttp_session=session, sample_rate=24000
)
frames_to_send = [
TTSSpeakFrame(text="Error case."),

27
uv.lock generated
View File

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