Merge pull request #3239 from pipecat-ai/mb/update-inworld-tts

Inworld TTS services: Add websocket TTS class, add word-timestamp ali…
This commit is contained in:
Mark Backman
2025-12-16 16:26:43 -05:00
committed by GitHub
6 changed files with 797 additions and 405 deletions

1
changelog/3239.added.md Normal file
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@@ -0,0 +1 @@
- Added `InworldHttpTTSService` which uses Inworld's HTTP based TTS service in either streaming or non-streaming mode. Note: This class was previously named `InworldTTSService`.

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@@ -0,0 +1,8 @@
- Changed Inworld's TTS service implementations:
- Previously, the HTTP implementation was named `InworldTTSService`. That has
been moved to `InworldHttpTTSService`. This service now supports
word-timestamp alignment data in both streaming and non-streaming modes.
- Updated the `InworldTTSService` class to use Inworld's Websocket API. This
class now has support for word-timestamp alignment data and tracks contexts
for each user turn.

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@@ -4,7 +4,6 @@
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import aiohttp
@@ -15,26 +14,26 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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.frames.frames import LLMRunFrame, TTSTextFrame
from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
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
from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.inworld.tts import InworldTTSService
from pipecat.services.inworld.tts import InworldHttpTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_output import BaseOutputTransport
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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,
@@ -58,22 +57,18 @@ transport_params = {
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
logger.info("Starting bot")
# Create an HTTP session
async with aiohttp.ClientSession() as session:
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
# Inworld TTS Service - Unified streaming and non-streaming
# Set streaming=True for real-time audio, streaming=False for complete audio generation
streaming = True # Toggle this to switch between modes
tts = InworldTTSService(
tts = InworldHttpTTSService(
api_key=os.getenv("INWORLD_API_KEY", ""),
aiohttp_session=session,
voice_id="Ashley",
model="inworld-tts-1",
streaming=streaming, # True: real-time chunks, False: complete audio then playback
# Set to False for non-streaming mode or True for streaming mode.
streaming=True,
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
@@ -81,22 +76,25 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
messages = [
{
"role": "system",
"content": "You are very knowledgable about dogs. 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.",
"content": "You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
},
]
context = LLMContext(messages)
context_aggregator = LLMContextAggregatorPair(context)
rtvi = RTVIProcessor()
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
context_aggregator.assistant(), # Assistant spoken responses
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
@@ -106,19 +104,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[
RTVIObserver(rtvi),
DebugLogObserver(
frame_types={
TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
}
),
],
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")
logger.info("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")
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

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@@ -0,0 +1,141 @@
#
# Copyright (c) 20242025, 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.base_smart_turn import SmartTurnParams
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, TTSTextFrame
from pipecat.observers.loggers.debug_log_observer import DebugLogObserver, FrameEndpoint
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
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.inworld.tts import InworldTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_output import BaseOutputTransport
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
}
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info("Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = InworldTTSService(
api_key=os.getenv("INWORLD_API_KEY", ""),
voice_id="Ashley",
model="inworld-tts-1",
temperature=1.1,
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a helpful AI demonstrating Inworld AI's TTS. 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 friendly and helpful way.",
},
]
context = LLMContext(messages)
context_aggregator = LLMContextAggregatorPair(context)
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[
RTVIObserver(rtvi),
DebugLogObserver(
frame_types={
TTSTextFrame: (BaseOutputTransport, FrameEndpoint.SOURCE),
}
),
],
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("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("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()

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@@ -86,6 +86,7 @@ TESTS_07 = [
("07-interruptible-cartesia-http.py", EVAL_SIMPLE_MATH),
("07a-interruptible-speechmatics.py", EVAL_SIMPLE_MATH),
("07aa-interruptible-soniox.py", EVAL_SIMPLE_MATH),
("07ab-interruptible-inworld.py", EVAL_SIMPLE_MATH),
("07ab-interruptible-inworld-http.py", EVAL_SIMPLE_MATH),
("07ac-interruptible-asyncai.py", EVAL_SIMPLE_MATH),
("07ac-interruptible-asyncai-http.py", EVAL_SIMPLE_MATH),

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