moving Deepgram TTS base_url from beta to prod

This commit is contained in:
Kwindla Hultman Kramer
2024-05-28 15:59:26 -07:00
parent 650a2b4da4
commit 3685c19b2d
3 changed files with 105 additions and 3 deletions

View File

@@ -9,6 +9,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed
- Fixed Deepgram Aura TTS base_url and added ErrorFrame reporting.
- GoogleLLMService `api_key` argument is now mandatory.
### Fixed

View File

@@ -0,0 +1,94 @@
#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import aiohttp
import os
import sys
from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator, LLMUserResponseAggregator)
from pipecat.services.deepgram import DeepgramTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.vad.silero import SileroVADAnalyzer
from runner import configure
from loguru import logger
from dotenv import load_dotenv
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main(room_url: str, token):
async with aiohttp.ClientSession() as session:
transport = DailyTransport(
room_url,
token,
"Respond bot",
DailyParams(
audio_out_enabled=True,
transcription_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer()
)
)
tts = DeepgramTTSService(
aiohttp_session=session,
api_key=os.getenv("DEEPGRAM_API_KEY"),
voice="aura-helios-en"
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4-turbo-preview")
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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
pipeline = Pipeline([
transport.input(), # Transport user input
tma_in, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out # Assistant spoken responses
])
task = PipelineTask(pipeline, allow_interruptions=True)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
transport.capture_participant_transcription(participant["id"])
# Kick off the conversation.
messages.append(
{"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMMessagesFrame(messages)])
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
(url, token) = configure()
asyncio.run(main(url, token))

View File

@@ -8,7 +8,7 @@ import aiohttp
from typing import AsyncGenerator
from pipecat.frames.frames import AudioRawFrame, Frame
from pipecat.frames.frames import AudioRawFrame, ErrorFrame, Frame
from pipecat.services.ai_services import TTSService
from loguru import logger
@@ -21,7 +21,7 @@ class DeepgramTTSService(TTSService):
*,
aiohttp_session: aiohttp.ClientSession,
api_key: str,
voice: str = "alpha-asteria-en-v2",
voice: str = "aura-helios-en",
**kwargs):
super().__init__(**kwargs)
@@ -31,11 +31,18 @@ class DeepgramTTSService(TTSService):
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
logger.info(f"Running Deepgram TTS for {text}")
base_url = "https://api.beta.deepgram.com/v1/speak"
base_url = "https://api.deepgram.com/v1/speak"
request_url = f"{base_url}?model={self._voice}&encoding=linear16&container=none&sample_rate=16000"
headers = {"authorization": f"token {self._api_key}"}
body = {"text": text}
async with self._aiohttp_session.post(request_url, headers=headers, json=body) as r:
if r.status != 200:
text = await r.text()
logger.error(f"Error getting audio (status: {r.status}, error: {text})")
yield ErrorFrame(f"Error getting audio (status: {r.status}, error: {text})")
return
async for data in r.content:
frame = AudioRawFrame(audio=data, sample_rate=16000, num_channels=1)
yield frame