address changes
This commit is contained in:
Aaron Ng
@@ -6,6 +6,7 @@
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import os
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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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@@ -89,90 +90,89 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""
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logger.info(f"Starting bot")
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsSTTService.InputParams(
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language=Language.EN,
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enable_vad=True,
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enable_diarization=True,
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focus_speakers=["S1"],
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end_of_utterance_silence_trigger=0.5,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
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),
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)
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsTTSService.InputParams(
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voice="sarah",
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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params=BaseOpenAILLMService.InputParams(temperature=0.75),
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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"Give very short replies - do not give longer replies unless strictly necessary. "
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"Respond to what the user said in a concise, funny, creative and helpful way. "
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"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
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"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
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async with aiohttp.ClientSession() as session:
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsSTTService.InputParams(
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language=Language.EN,
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enable_vad=True,
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enable_diarization=True,
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focus_speakers=["S1"],
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end_of_utterance_silence_trigger=0.5,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
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),
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},
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]
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)
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
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)
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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voice_id="sarah",
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aiohttp_session=session,
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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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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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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params=BaseOpenAILLMService.InputParams(temperature=0.75),
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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"Give very short replies - do not give longer replies unless strictly necessary. "
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"Respond to what the user said in a concise, funny, creative and helpful way. "
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"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
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"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
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),
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},
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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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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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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await runner.run(task)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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@@ -6,6 +6,7 @@
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import os
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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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@@ -82,85 +83,85 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""
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logger.info(f"Starting bot")
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsSTTService.InputParams(
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language=Language.EN,
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enable_diarization=True,
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end_of_utterance_silence_trigger=0.5,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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),
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)
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsTTSService.InputParams(
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voice="sarah",
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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params=BaseOpenAILLMService.InputParams(temperature=0.75),
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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"Give very short replies - do not give longer replies unless strictly necessary. "
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"Respond to what the user said in a concise, funny, creative and helpful way. "
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"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
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async with aiohttp.ClientSession() as session:
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stt = SpeechmaticsSTTService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsSTTService.InputParams(
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language=Language.EN,
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enable_diarization=True,
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end_of_utterance_silence_trigger=0.5,
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speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
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),
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},
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]
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)
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
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)
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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voice_id="sarah",
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aiohttp_session=session,
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)
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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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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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params=BaseOpenAILLMService.InputParams(temperature=0.75),
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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"Give very short replies - do not give longer replies unless strictly necessary. "
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"Respond to what the user said in a concise, funny, creative and helpful way. "
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"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
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),
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},
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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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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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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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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Say a short hello to the user."})
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await task.queue_frames([LLMRunFrame()])
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await runner.run(task)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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