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Add ResembleAITTSService

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This commit is contained in:
Mark Backman
2025-11-25 23:35:03 -05:00
parent f453227ba3
commit ba2b7c05d6
6 changed files with 517 additions and 1 deletions
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4
env.example
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@@ -156,6 +156,10 @@ PLIVO_AUTH_TOKEN=...
# Qwen
QWEN_API_KEY=...
# Resemble AI
RESEMBLE_API_KEY=
RESEMBLE_VOICE_UUID=
# Rime
RIME_API_KEY=...
RIME_VOICE_ID=...

135
examples/foundational/07zi-interruptible-resemble.py Normal file
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@@ -0,0 +1,135 @@
#
# 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.resembleai.tts import ResembleAITTSService
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 = ResembleAITTSService(
api_key=os.getenv("RESEMBLE_API_KEY"),
voice_id=os.getenv("RESEMBLE_VOICE_UUID"),
)
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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@@ -100,6 +100,7 @@ piper = [ "piper-tts>=1.3.0,<2", "requests>=2.32.5,<3" ]
playht = [ "pipecat-ai[websockets-base]" ]
qwen = []
remote-smart-turn = []
resembleai = [ "pipecat-ai[websockets-base]" ]
rime = [ "pipecat-ai[websockets-base]" ]
riva = [ "pipecat-ai[nvidia]" ]
runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.128.0", "pipecat-ai-small-webrtc-prebuilt>=2.0.4"]

0
src/pipecat/services/resembleai/__init__.py Normal file
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366
src/pipecat/services/resembleai/tts.py Normal file
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@@ -0,0 +1,366 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Resemble AI text-to-speech service implementations."""
import base64
import json
from typing import AsyncGenerator, Optional
from loguru import logger
from pipecat.frames.frames import (
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
InterruptionFrame,
StartFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.tts_service import AudioContextWordTTSService
from pipecat.transcriptions.language import Language
from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.tracing.service_decorators import traced_tts
try:
from websockets.asyncio.client import connect as websocket_connect
from websockets.protocol import State
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use Resemble AI, you need to `pip install pipecat-ai[resembleai]`.")
raise Exception(f"Missing module: {e}")
class ResembleAITTSService(AudioContextWordTTSService):
"""Resemble AI TTS service with WebSocket streaming and word timestamps.
Provides text-to-speech using Resemble AI's streaming WebSocket API.
Supports word-level timestamps and audio context management for handling
multiple simultaneous synthesis requests with proper interruption support.
"""
def __init__(
self,
*,
api_key: str,
voice_id: str,
url: str = "wss://websocket.cluster.resemble.ai/stream",
precision: Optional[str] = "PCM_16",
output_format: Optional[str] = "wav",
sample_rate: Optional[int] = 22050,
**kwargs,
):
"""Initialize the Resemble AI TTS service.
Args:
api_key: Resemble AI API key for authentication.
voice_id: Voice UUID to use for synthesis.
url: WebSocket URL for Resemble AI TTS API.
precision: PCM bit depth (PCM_32, PCM_24, PCM_16, or MULAW).
output_format: Audio format (wav or mp3).
sample_rate: Audio sample rate (8000, 16000, 22050, 32000, or 44100). Defaults to 22050.
**kwargs: Additional arguments passed to the parent service.
"""
super().__init__(
sample_rate=sample_rate,
**kwargs,
)
self._api_key = api_key
self._voice_id = voice_id
self._url = url
self._settings = {
"precision": precision,
"output_format": output_format,
"sample_rate": sample_rate,
}
self._websocket = None
self._request_id_counter = 0
self._current_request_id = None
self._receive_task = None
self.set_voice(voice_id)
def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics.
Returns:
True, as Resemble AI service supports metrics generation.
"""
return True
def _build_msg(self, text: str = "") -> str:
"""Build a JSON message for the Resemble AI WebSocket API.
Args:
text: The text or SSML to synthesize.
Returns:
JSON string containing the request payload.
"""
msg = {
"voice_uuid": self._voice_id,
"data": text,
"binary_response": False, # Use JSON frames to get timestamps
"request_id": self._request_id_counter,
"output_format": self._settings["output_format"],
"sample_rate": self._settings["sample_rate"],
"precision": self._settings["precision"],
"no_audio_header": True,
}
self._request_id_counter += 1
return json.dumps(msg)
async def start(self, frame: StartFrame):
"""Start the Resemble AI TTS service.
Args:
frame: The start frame containing initialization parameters.
"""
await super().start(frame)
self._settings["sample_rate"] = self.sample_rate
await self._connect()
async def stop(self, frame: EndFrame):
"""Stop the Resemble AI TTS service.
Args:
frame: The end frame.
"""
await super().stop(frame)
await self._disconnect()
async def cancel(self, frame: CancelFrame):
"""Cancel the Resemble AI TTS service.
Args:
frame: The cancel frame.
"""
await super().cancel(frame)
await self._disconnect()
async def _connect(self):
"""Connect to the Resemble AI WebSocket."""
await self._connect_websocket()
if self._websocket and not self._receive_task:
self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
async def _disconnect(self):
"""Disconnect from the Resemble AI WebSocket."""
if self._receive_task:
await self.cancel_task(self._receive_task)
self._receive_task = None
await self._disconnect_websocket()
async def _connect_websocket(self):
"""Establish WebSocket connection to Resemble AI."""
try:
if self._websocket and self._websocket.state is State.OPEN:
return
logger.debug("Connecting to Resemble AI TTS")
headers = {"Authorization": f"Bearer {self._api_key}"}
self._websocket = await websocket_connect(self._url, additional_headers=headers)
await self._call_event_handler("on_connected")
except Exception as e:
logger.error(f"{self} exception: {e}")
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
self._websocket = None
await self._call_event_handler("on_connection_error", f"{e}")
async def _disconnect_websocket(self):
"""Close WebSocket connection to Resemble AI."""
try:
await self.stop_all_metrics()
if self._websocket:
logger.debug("Disconnecting from Resemble AI")
await self._websocket.close()
except Exception as e:
logger.error(f"{self} exception: {e}")
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
finally:
self._current_request_id = None
self._websocket = None
await self._call_event_handler("on_disconnected")
def _get_websocket(self):
"""Get the current WebSocket connection.
Returns:
The active WebSocket connection.
Raises:
Exception: If websocket is not connected.
"""
if self._websocket:
return self._websocket
raise Exception("Websocket not connected")
async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
"""Handle interruption by stopping current synthesis.
Args:
frame: The interruption frame.
direction: The direction of frame processing.
"""
await super()._handle_interruption(frame, direction)
await self.stop_all_metrics()
# Note: Resemble AI doesn't have an explicit cancel mechanism,
# but we can stop processing by resetting our current request_id
self._current_request_id = None
async def flush_audio(self):
"""Flush any pending audio and finalize the current context."""
if not self._current_request_id:
return
logger.trace(f"{self}: flushing audio")
# For Resemble AI, we just wait for the audio_end message
# which is handled in _process_messages
self._current_request_id = None
async def _process_messages(self):
"""Process incoming WebSocket messages from Resemble AI."""
async for message in self._get_websocket():
try:
msg = json.loads(message)
except json.JSONDecodeError:
logger.error(f"{self} received invalid JSON: {message}")
continue
if not msg:
continue
msg_type = msg.get("type")
request_id = msg.get("request_id")
# Convert request_id to string for audio context tracking
request_id_str = str(request_id)
# Check if this message belongs to a valid audio context
if not self.audio_context_available(request_id_str):
continue
if msg_type == "audio":
await self.stop_ttfb_metrics()
await self.start_word_timestamps()
# Decode base64 audio content
audio_content = msg.get("audio_content", "")
if audio_content:
audio_data = base64.b64decode(audio_content)
frame = TTSAudioRawFrame(
audio=audio_data,
sample_rate=self.sample_rate,
num_channels=1,
)
await self.append_to_audio_context(request_id_str, frame)
# Process timestamps if available
timestamps = msg.get("audio_timestamps", {})
if timestamps:
graph_chars = timestamps.get("graph_chars", [])
graph_times = timestamps.get("graph_times", [])
# Convert graph_times (start, end pairs) to word timestamps
word_times = []
for char, times in zip(graph_chars, graph_times):
if times and len(times) >= 2:
start_time = times[0]
word_times.append((char, start_time))
if word_times:
await self.add_word_timestamps(word_times)
elif msg_type == "audio_end":
await self.stop_ttfb_metrics()
await self.add_word_timestamps([("TTSStoppedFrame", 0), ("Reset", 0)])
await self.remove_audio_context(request_id_str)
# Clear current request if this was it
if self._current_request_id == request_id:
self._current_request_id = None
elif msg_type == "error":
error_name = msg.get("error_name", "Unknown")
error_msg = msg.get("message", "Unknown error")
status_code = msg.get("status_code", 0)
logger.error(f"{self} error: {error_name} (status {status_code}): {error_msg}")
await self.push_frame(TTSStoppedFrame())
await self.stop_all_metrics()
await self.push_error(ErrorFrame(error=f"{self} error: {error_name} - {error_msg}"))
# Clear current request if this was it
if self._current_request_id == request_id:
self._current_request_id = None
# Check if this is an unrecoverable error (connection-level failure)
if status_code in [401, 403]:
# Close and reconnect for auth errors
await self._disconnect_websocket()
await self._connect_websocket()
else:
logger.warning(f"{self} unknown message type: {msg_type}")
async def _receive_messages(self):
"""Main loop for receiving messages from Resemble AI."""
while True:
try:
await self._process_messages()
except Exception as e:
logger.error(f"{self} error in receive loop: {e}")
# Try to reconnect
logger.debug(f"{self} Resemble AI connection lost, reconnecting")
await self._connect_websocket()
@traced_tts
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
"""Generate speech from text using Resemble AI's streaming API.
Args:
text: The text to synthesize into speech.
Yields:
Frame: Audio frames containing the synthesized speech.
"""
logger.debug(f"{self}: Generating TTS [{text}]")
try:
if not self._websocket or self._websocket.state is State.CLOSED:
await self._connect()
if not self._current_request_id:
await self.start_ttfb_metrics()
yield TTSStartedFrame()
# Track the current request_id we're processing
self._current_request_id = self._request_id_counter
# Create audio context using request_id (converted to string)
request_id_str = str(self._request_id_counter)
await self.create_audio_context(request_id_str)
msg = self._build_msg(text=text)
try:
await self._get_websocket().send(msg)
await self.start_tts_usage_metrics(text)
except Exception as e:
logger.error(f"{self} exception: {e}")
yield ErrorFrame(error=f"{self} error: {e}")
yield TTSStoppedFrame()
await self._disconnect()
await self._connect()
return
yield None
except Exception as e:
logger.error(f"{self} exception: {e}")
yield ErrorFrame(error=f"{self} error: {e}")

12
uv.lock generated
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{ name = "websockets" },
]
resembleai = [
{ name = "websockets" },
]
rime = [
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]
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{ name = "pipecat-ai", extras = ["websockets-base"], marker = "extra == 'openai'" },
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