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introduce Ruff formatting

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This commit is contained in:
Aleix Conchillo Flaqué
2024-09-23 09:11:37 -07:00
parent dfa4ac81fd
commit eeb8338dce
149 changed files with 2653 additions and 2461 deletions
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49
examples/foundational/15-switch-voices.py
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@@ -28,6 +28,7 @@ from runner import configure
from loguru import logger
from dotenv import load_dotenv
load_dotenv(override=True)
logger.remove(0)
@@ -39,7 +40,11 @@ current_voice = "News Lady"
async def switch_voice(function_name, tool_call_id, args, llm, context, result_callback):
global current_voice
current_voice = args["voice"]
await result_callback({"voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."})
await result_callback(
{
"voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."
}
)
async def news_lady_filter(frame) -> bool:
@@ -66,8 +71,8 @@ async def main():
audio_out_enabled=True,
transcription_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer()
)
vad_analyzer=SileroVADAnalyzer(),
),
)
news_lady = CartesiaTTSService(
@@ -85,9 +90,7 @@ async def main():
voice_id="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
)
llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o")
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
llm.register_function("switch_voice", switch_voice)
tools = [
@@ -106,7 +109,9 @@ async def main():
},
"required": ["voice"],
},
})]
},
)
]
messages = [
{
"role": "system",
@@ -117,18 +122,20 @@ async def main():
context = OpenAILLMContext(messages, tools)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline([
transport.input(), # Transport user input
context_aggregator.user(), # User responses
llm, # LLM
ParallelPipeline( # TTS (one of the following vocies)
[FunctionFilter(news_lady_filter), news_lady], # News Lady voice
[FunctionFilter(british_lady_filter), british_lady], # British Lady voice
[FunctionFilter(barbershop_man_filter), barbershop_man], # Barbershop Man voice
),
transport.output(), # Transport bot output
context_aggregator.assistant(), # Assistant spoken responses
])
pipeline = Pipeline(
[
transport.input(), # Transport user input
context_aggregator.user(), # User responses
llm, # LLM
ParallelPipeline( # TTS (one of the following vocies)
[FunctionFilter(news_lady_filter), news_lady], # News Lady voice
[FunctionFilter(british_lady_filter), british_lady], # British Lady voice
[FunctionFilter(barbershop_man_filter), barbershop_man], # Barbershop Man voice
),
transport.output(), # Transport bot output
context_aggregator.assistant(), # Assistant spoken responses
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
@@ -139,7 +146,9 @@ async def main():
messages.append(
{
"role": "system",
"content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}."})
"content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
}
)
await task.queue_frames([LLMMessagesFrame(messages)])
runner = PipelineRunner()