add nvidia riva - fastpitch
This commit is contained in:
vipyne
56
examples/foundational/01c-fastpitch.py
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56
examples/foundational/01c-fastpitch.py
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#
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# Copyright (c) 2024, 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 aiohttp
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import os
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import sys
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from pipecat.frames.frames import EndFrame, TTSSpeakFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.task import PipelineTask
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.services.riva import FastpitchTTSService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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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, _) = await configure(session)
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transport = DailyTransport(
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room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
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)
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tts = FastpitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
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runner = PipelineRunner()
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task = PipelineTask(Pipeline([tts, transport.output()]))
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# Register an event handler so we can play the audio when the
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# participant joins.
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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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participant_name = participant.get("info", {}).get("userName", "")
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await task.queue_frames([TTSSpeakFrame(f"Aloha, {participant_name}!"), EndFrame()])
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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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94
examples/foundational/07r-interruptible-riva.py
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94
examples/foundational/07r-interruptible-riva.py
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#
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# Copyright (c) 2024, 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.frames.frames import LLMMessagesFrame
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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.deepgram import DeepgramSTTService
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from pipecat.services.riva import FastpitchTTSService, ParakeetSTTService
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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, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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# stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
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tts = FastpitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
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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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stt, # STT
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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(pipeline, PipelineParams(allow_interruptions=True))
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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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# 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([LLMMessagesFrame(messages)])
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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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244
src/pipecat/services/riva.py
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244
src/pipecat/services/riva.py
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#
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# Copyright (c) 2024, 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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from typing import AsyncGenerator, List, Optional, Union, Iterator
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from loguru import logger
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from pydantic.main import BaseModel
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from pipecat.frames.frames import (
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CancelFrame,
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EndFrame,
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ErrorFrame,
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Frame,
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InterimTranscriptionFrame,
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StartFrame,
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TranscriptionFrame,
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TTSAudioRawFrame,
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TTSStartedFrame,
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TTSStoppedFrame,
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)
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from pipecat.services.ai_services import STTService, TTSService
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from pipecat.transcriptions.language import Language
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from pipecat.utils.time import time_now_iso8601
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try:
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import riva.client
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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 nvidia riva TTS or STT, you need to `pip install pipecat-ai[riva]`. Also, set `NVIDIA_API_KEY` environment variable."
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)
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raise Exception(f"Missing module: {e}")
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class FastpitchTTSService(TTSService):
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class InputParams(BaseModel):
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language: Optional[str] = "en-US"
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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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server: str = "grpc.nvcf.nvidia.com:443",
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voice_id: str = "English-US.Female-1",
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sample_rate_hz: int = 24000,
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# nvidia riva calls this 'function-id'
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model: str = "0149dedb-2be8-4195-b9a0-e57e0e14f972",
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params: InputParams = InputParams(),
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**kwargs,
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):
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super().__init__(sample_rate=sample_rate_hz, **kwargs)
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self._api_key = api_key
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self.set_model_name("fastpitch-hifigan-tts")
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self.set_voice(voice_id)
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self.voice_id = voice_id
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self.sample_rate_hz = sample_rate_hz
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self.language_code = params.language
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self.nchannels = 1
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self.sampwidth = 2
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self.quality = None
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metadata = [
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["function-id", model],
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["authorization", f"Bearer {api_key}"],
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]
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auth = riva.client.Auth(None, True, server, metadata)
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self.service = riva.client.SpeechSynthesisService(auth)
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async def stop(self, frame: EndFrame):
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await super().stop(frame)
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async def cancel(self, frame: CancelFrame):
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await super().cancel(frame)
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async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
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logger.debug(f"Generating TTS: [{text}]")
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await self.start_ttfb_metrics()
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yield TTSStartedFrame()
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try:
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custom_dictionary_input = {}
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responses = self.service.synthesize_online(
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text,
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self.voice_id,
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self.language_code,
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sample_rate_hz=self.sample_rate_hz,
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audio_prompt_file=None,
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quality=20 if self.quality is None else self.quality,
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custom_dictionary=custom_dictionary_input,
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)
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for resp in responses:
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await self.stop_ttfb_metrics()
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frame = TTSAudioRawFrame(
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audio=resp.audio,
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sample_rate=self.sample_rate_hz,
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num_channels=self.nchannels,
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)
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yield frame
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except Exception as e:
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logger.error(f"{self} exception: {e}")
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await self.start_tts_usage_metrics(text)
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yield TTSStoppedFrame()
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class ParakeetSTTService(STTService):
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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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server: str = "grpc.nvcf.nvidia.com:443",
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# nvidia calls this 'function-id'
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model: str = "1598d209-5e27-4d3c-8079-4751568b1081",
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**kwargs,
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):
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super().__init__(**kwargs)
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self._api_key = api_key
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self.set_model_name("parakeet-ctc-1.1b-asr")
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input_device = 0
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list_devices = False
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profanity_filter = False
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automatic_punctuation = False
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no_verbatim_transcripts = False
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language_code = "en-US"
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model_name = ""
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boosted_lm_words = None
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boosted_lm_score = 4.0
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speaker_diarization = False
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diarization_max_speakers = 3
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start_history = -1
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start_threshold = -1.0
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stop_history = -1
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stop_threshold = -1.0
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stop_history_eou = -1
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stop_threshold_eou = -1.0
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custom_configuration = ""
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ssl_cert = None
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use_ssl = True
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sample_rate_hz: int = 16000
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file_streaming_chunk = 1600
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metadata = [
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["function-id", model],
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["authorization", f"Bearer {api_key}"],
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]
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auth = riva.client.Auth(None, True, server, metadata)
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self.asr_service = riva.client.ASRService(auth)
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config = riva.client.StreamingRecognitionConfig(
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config=riva.client.RecognitionConfig(
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encoding=riva.client.AudioEncoding.LINEAR_PCM,
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language_code=language_code,
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model="",
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max_alternatives=1,
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profanity_filter=profanity_filter,
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enable_automatic_punctuation=automatic_punctuation,
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verbatim_transcripts=not no_verbatim_transcripts,
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sample_rate_hertz=sample_rate_hz,
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audio_channel_count=1,
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),
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interim_results=True,
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)
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self.config = config
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riva.client.add_word_boosting_to_config(config, boosted_lm_words, boosted_lm_score)
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riva.client.add_endpoint_parameters_to_config(
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config,
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start_history,
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start_threshold,
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stop_history,
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stop_history_eou,
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stop_threshold,
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stop_threshold_eou,
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)
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riva.client.add_custom_configuration_to_config(config, custom_configuration)
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# this doesn't work, but something like this perhaps? part 1
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self.audio = []
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self.responses = self.asr_service.streaming_response_generator(
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audio_chunks=[self.audio],
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streaming_config=self.config,
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)
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def can_generate_metrics(self) -> bool:
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return False
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async def start(self, frame: StartFrame):
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await super().start(frame)
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async def stop(self, frame: EndFrame):
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await super().stop(frame)
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async def cancel(self, frame: CancelFrame):
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await super().cancel(frame)
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async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
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# this doesn't work, but something like this perhaps? part 2
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self.audio.append(audio)
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# need to start to run this generator only once somewhere...
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# 'start' function doesn't work...
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# something about the event loop...
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# maybe an audio buffer... though my attempt at that didn't work either
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for response in self.responses:
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if not response.results:
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continue
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partial_transcript = ""
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for result in response.results:
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if result:
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if not result.alternatives:
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continue
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transcript = result.alternatives[0].transcript
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if transcript:
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language = None
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if len(transcript) > 0:
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await self.stop_ttfb_metrics()
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if result.is_final:
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await self.stop_processing_metrics()
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yield TranscriptionFrame(
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transcript, "", time_now_iso8601(), language
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)
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else:
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yield InterimTranscriptionFrame(
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transcript, "", time_now_iso8601(), language
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)
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yield None
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async def _on_speech_started(self, *args, **kwargs):
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await self.start_ttfb_metrics()
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await self.start_processing_metrics()
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