Merge pull request #1366 from adnansiddiquei/neuphonic-tts-plugin
Add integration for Neuphonic TTS
This commit is contained in:
Aleix Conchillo Flaqué
@@ -29,6 +29,9 @@ DAILY_SAMPLE_ROOM_URL=https://...
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ELEVENLABS_API_KEY=...
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ELEVENLABS_VOICE_ID=...
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# Neuphonic
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NEUPHONIC_API_KEY=...
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# Fal
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FAL_KEY=...
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102
examples/foundational/07u-interruptible-neuphonic-http.py
Normal file
102
examples/foundational/07u-interruptible-neuphonic-http.py
Normal file
@@ -0,0 +1,102 @@
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#
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# Copyright (c) 2024–2025, 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 os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.neuphonic import NeuphonicHttpTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = NeuphonicHttpTTSService(
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api_key=os.getenv("NEUPHONIC_API_KEY"),
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voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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102
examples/foundational/07u-interruptible-neuphonic.py
Normal file
102
examples/foundational/07u-interruptible-neuphonic.py
Normal file
@@ -0,0 +1,102 @@
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#
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# Copyright (c) 2024–2025, 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 os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.neuphonic import NeuphonicTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = NeuphonicTTSService(
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api_key=os.getenv("NEUPHONIC_API_KEY"),
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voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -45,6 +45,7 @@ aws = [ "boto3~=1.35.99" ]
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azure = [ "azure-cognitiveservices-speech~=1.42.0"]
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canonical = [ "aiofiles~=24.1.0" ]
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cartesia = [ "cartesia~=1.3.1", "websockets~=13.1" ]
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neuphonic = [ "pyneuphonic~=1.5.13", "websockets~=13.1" ]
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cerebras = []
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deepseek = []
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daily = [ "daily-python~=0.15.0" ]
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339
src/pipecat/services/neuphonic.py
Normal file
339
src/pipecat/services/neuphonic.py
Normal file
@@ -0,0 +1,339 @@
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#
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# Copyright (c) 2024–2025, 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, Mapping, Optional
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from loguru import logger
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from pydantic import BaseModel
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from pipecat.frames.frames import (
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BotStoppedSpeakingFrame,
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CancelFrame,
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EndFrame,
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ErrorFrame,
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Frame,
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LLMFullResponseEndFrame,
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StartFrame,
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StartInterruptionFrame,
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TTSAudioRawFrame,
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TTSSpeakFrame,
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TTSStartedFrame,
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TTSStoppedFrame,
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)
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.ai_services import InterruptibleTTSService, TTSService
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from pipecat.transcriptions.language import Language
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# See .env.example for Neuphonic configuration needed
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try:
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import websockets
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from pyneuphonic import Neuphonic, TTSConfig
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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 Neuphonic, you need to `pip install pipecat-ai[neuphonic]`. Also, set `NEUPHONIC_API_KEY` environment variable."
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)
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raise Exception(f"Missing module: {e}")
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def language_to_neuphonic_lang_code(language: Language) -> Optional[str]:
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BASE_LANGUAGES = {
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Language.DE: "de",
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Language.EN: "en",
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Language.ES: "es",
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Language.NL: "nl",
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Language.AR: "ar",
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}
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result = BASE_LANGUAGES.get(language)
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# If not found in base languages, try to find the base language from a variant
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if not result:
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# Convert enum value to string and get the base language part (e.g. es-ES -> es)
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lang_str = str(language.value)
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base_code = lang_str.split("-")[0].lower()
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# Look up the base code in our supported languages
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result = base_code if base_code in BASE_LANGUAGES.values() else None
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return result
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class NeuphonicTTSService(InterruptibleTTSService):
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class InputParams(BaseModel):
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language: Optional[Language] = Language.EN
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speed: Optional[float] = 1.0
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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: Optional[str] = None,
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url: str = "wss://api.neuphonic.com",
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sample_rate: Optional[int] = 22050,
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encoding: str = "pcm_linear",
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params: InputParams = InputParams(),
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**kwargs,
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):
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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,
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**kwargs,
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)
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self._api_key = api_key
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self._url = url
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self._settings = {
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"lang_code": self.language_to_service_language(params.language),
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"speed": params.speed,
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"encoding": encoding,
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"sampling_rate": sample_rate,
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}
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self.set_voice(voice_id)
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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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def language_to_service_language(self, language: Language) -> Optional[str]:
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return language_to_neuphonic_lang_code(language)
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async def _update_settings(self, settings: Mapping[str, Any]):
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if "voice_id" in settings:
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self.set_voice(settings["voice_id"])
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await super()._update_settings(settings)
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await self._disconnect()
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await self._connect()
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logger.info(f"Switching TTS to settings: [{self._settings}]")
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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": "<STOP>"}
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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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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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# If we received a TTSSpeakFrame and the LLM response included text (it
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# might be that it's only a function calling response) we pause
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# processing more frames until we receive a BotStoppedSpeakingFrame.
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if isinstance(frame, TTSSpeakFrame):
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await self.pause_processing_frames()
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elif isinstance(frame, LLMFullResponseEndFrame) and self._started:
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await self.pause_processing_frames()
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elif isinstance(frame, BotStoppedSpeakingFrame):
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await self.resume_processing_frames()
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async def _connect(self):
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await self._connect_websocket()
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self._receive_task = self.create_task(self._receive_task_handler(self.push_error))
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self._keepalive_task = self.create_task(self._keepalive_task_handler())
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async def _disconnect(self):
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if self._receive_task:
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await self.cancel_task(self._receive_task)
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self._receive_task = None
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if self._keepalive_task:
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await self.cancel_task(self._keepalive_task)
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self._keepalive_task = None
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await self._disconnect_websocket()
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async def _connect_websocket(self):
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try:
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logger.debug("Connecting to Neuphonic")
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tts_config = {
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**self._settings,
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"voice_id": self._voice_id,
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}
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query_params = [f"api_key={self._api_key}"]
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for key, value in tts_config.items():
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if value is not None:
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query_params.append(f"{key}={value}")
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url = f"{self._url}/speak/{self._settings['lang_code']}?{'&'.join(query_params)}"
|
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|
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self._websocket = await websockets.connect(url)
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|
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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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|
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async def _disconnect_websocket(self):
|
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try:
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await self.stop_all_metrics()
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|
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if self._websocket:
|
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logger.debug("Disconnecting from Neuphonic")
|
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await self._websocket.close()
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self._websocket = None
|
||||
|
||||
self._started = False
|
||||
except Exception as e:
|
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logger.error(f"{self} error closing websocket: {e}")
|
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|
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async def _receive_messages(self):
|
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async for message in self._websocket:
|
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if isinstance(message, str):
|
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msg = json.loads(message)
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if msg.get("data", {}).get("audio") is not None:
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await self.stop_ttfb_metrics()
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||||
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||||
audio = base64.b64decode(msg["data"]["audio"])
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frame = TTSAudioRawFrame(audio, self.sample_rate, 1)
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await self.push_frame(frame)
|
||||
|
||||
async def _keepalive_task_handler(self):
|
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while True:
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await asyncio.sleep(10)
|
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await self._send_text("")
|
||||
|
||||
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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logger.debug(f"Sending text to websocket: {msg}")
|
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await self._websocket.send(json.dumps(msg))
|
||||
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
logger.debug(f"Generating TTS: [{text}]")
|
||||
|
||||
try:
|
||||
if not self._websocket:
|
||||
await self._connect()
|
||||
|
||||
try:
|
||||
if not self._started:
|
||||
await self.start_ttfb_metrics()
|
||||
yield TTSStartedFrame()
|
||||
self._started = True
|
||||
self._cumulative_time = 0
|
||||
|
||||
await self._send_text(text)
|
||||
await self.start_tts_usage_metrics(text)
|
||||
except Exception as e:
|
||||
logger.error(f"{self} error sending message: {e}")
|
||||
yield TTSStoppedFrame()
|
||||
await self._disconnect()
|
||||
await self._connect()
|
||||
return
|
||||
yield None
|
||||
except Exception as e:
|
||||
logger.error(f"{self} exception: {e}")
|
||||
|
||||
|
||||
class NeuphonicHttpTTSService(TTSService):
|
||||
"""Neuphonic Text-to-Speech service using HTTP streaming.
|
||||
|
||||
Args:
|
||||
api_key: Neuphonic API key
|
||||
voice_id: ID of the voice to use
|
||||
url: Base URL for the Neuphonic API (default: "https://api.neuphonic.com")
|
||||
sample_rate: Sample rate for audio output (default: 22050Hz)
|
||||
encoding: Audio encoding format (default: "pcm_linear")
|
||||
params: Additional parameters for TTS generation including language and speed
|
||||
**kwargs: Additional keyword arguments passed to the parent class
|
||||
"""
|
||||
|
||||
class InputParams(BaseModel):
|
||||
language: Optional[Language] = Language.EN
|
||||
speed: Optional[float] = 1.0
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
voice_id: Optional[str] = None,
|
||||
url: str = "https://api.neuphonic.com",
|
||||
sample_rate: Optional[int] = 22050,
|
||||
encoding: str = "pcm_linear",
|
||||
params: InputParams = InputParams(),
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(sample_rate=sample_rate, **kwargs)
|
||||
|
||||
self._api_key = api_key
|
||||
self._url = url
|
||||
self._settings = {
|
||||
"lang_code": self.language_to_service_language(params.language),
|
||||
"speed": params.speed,
|
||||
"encoding": encoding,
|
||||
"sampling_rate": sample_rate,
|
||||
}
|
||||
self.set_voice(voice_id)
|
||||
|
||||
def can_generate_metrics(self) -> bool:
|
||||
return True
|
||||
|
||||
async def start(self, frame: StartFrame):
|
||||
await super().start(frame)
|
||||
|
||||
async def flush_audio(self):
|
||||
pass
|
||||
|
||||
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
|
||||
"""Generate speech from text using Neuphonic streaming API.
|
||||
|
||||
Args:
|
||||
text: The text to convert to speech
|
||||
Yields:
|
||||
Frames containing audio data and status information
|
||||
"""
|
||||
logger.debug(f"Generating TTS: [{text}]")
|
||||
|
||||
client = Neuphonic(api_key=self._api_key, base_url=self._url.replace("https://", ""))
|
||||
|
||||
sse = client.tts.AsyncSSEClient()
|
||||
|
||||
try:
|
||||
await self.start_ttfb_metrics()
|
||||
response = sse.send(text, TTSConfig(**self._settings, voice_id=self._voice_id))
|
||||
|
||||
await self.start_tts_usage_metrics(text)
|
||||
yield TTSStartedFrame()
|
||||
|
||||
async for message in response:
|
||||
if message.status_code != 200:
|
||||
logger.error(f"{self} error: {message.errors}")
|
||||
yield ErrorFrame(error=f"Neuphonic API error: {message.errors}")
|
||||
|
||||
await self.stop_ttfb_metrics()
|
||||
yield TTSAudioRawFrame(message.data.audio, self.sample_rate, 1)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in run_tts: {e}")
|
||||
yield ErrorFrame(error=str(e))
|
||||
finally:
|
||||
yield TTSStoppedFrame()
|
||||
Reference in New Issue
Block a user