chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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import os
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from azure.identity import AzureCliCredential
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from samples.concepts.audio.audio_recorder import AudioRecorder
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from semantic_kernel.connectors.ai.open_ai import (
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AzureAudioToText,
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AzureChatCompletion,
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OpenAIChatPromptExecutionSettings,
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)
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from semantic_kernel.contents import AudioContent, ChatHistory
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# This simple sample demonstrates how to use the AzureChatCompletion and AzureAudioToText services
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# to create a chat bot that can communicate with the user using audio input.
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# The user can enage a long conversation with the chat bot by speaking to it.
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# Resources required for this sample:
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# 1. An Azure OpenAI model deployment (e.g. GPT-4o-mini).
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# 2. An Azure Speech to Text deployment (e.g. whisper).
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# Additional dependencies required for this sample:
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# - pyaudio: `pip install pyaudio` or `uv pip install pyaudio` if you are using uv and have a virtual env activated.
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# - keyboard: `pip install keyboard` or `uv pip install keyboard` if you are using uv and have a virtual env activated.
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logging.basicConfig(level=logging.WARNING)
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AUDIO_FILEPATH = os.path.join(os.path.dirname(__file__), "output.wav")
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system_message = """
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You are a chat bot. Your name is Mosscap and
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you have one goal: figure out what people need.
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Your full name, should you need to know it, is
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Splendid Speckled Mosscap. You communicate
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effectively, but you tend to answer with long
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flowery prose.
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"""
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credential = AzureCliCredential()
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chat_service = AzureChatCompletion(credential=credential)
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audio_to_text_service = AzureAudioToText(credential=credential)
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history = ChatHistory()
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history.add_user_message("Hi there, who are you?")
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history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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async def chat() -> bool:
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try:
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print("User:> ", end="", flush=True)
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with AudioRecorder(output_filepath=AUDIO_FILEPATH) as recorder:
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recorder.start_recording()
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user_input = await audio_to_text_service.get_text_content(AudioContent.from_audio_file(AUDIO_FILEPATH))
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print(user_input.text)
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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if "exit" in user_input.text.lower():
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print("\n\nExiting chat...")
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return False
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history.add_user_message(user_input.text)
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chunks = chat_service.get_streaming_chat_message_content(
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chat_history=history,
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settings=OpenAIChatPromptExecutionSettings(
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max_tokens=2000,
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temperature=0.7,
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top_p=0.8,
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),
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)
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print("Mosscap:> ", end="")
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answer = ""
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async for message in chunks:
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print(str(message), end="")
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answer += str(message)
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print("\n")
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history.add_assistant_message(str(answer))
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return True
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async def main() -> None:
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print(
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"Instruction: when it's your turn to speak, press the spacebar to start recording."
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" Release the spacebar to stop recording."
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)
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chatting = True
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while chatting:
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chatting = await chat()
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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from azure.identity import AzureCliCredential
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from samples.concepts.audio.audio_player import AudioPlayer
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from semantic_kernel.connectors.ai.open_ai import (
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AzureChatCompletion,
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AzureTextToAudio,
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OpenAIChatPromptExecutionSettings,
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OpenAITextToAudioExecutionSettings,
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)
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from semantic_kernel.contents import ChatHistory
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# This simple sample demonstrates how to use the AzureChatCompletion and AzureTextToAudio services
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# to create a chat bot that can communicate with the user using audio output.
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# The chatbot will engage in a conversation with the user and respond using audio output.
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# Resources required for this sample:
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# 1. An Azure OpenAI model deployment (e.g. GPT-4o-mini).
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# 2. An Azure Text to Speech deployment (e.g. tts).
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# Additional dependencies required for this sample:
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# - pyaudio: `pip install pyaudio` or `uv pip install pyaudio` if you are using uv and have a virtual env activated.
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# - keyboard: `pip install keyboard` or `uv pip install keyboard` if you are using uv and have a virtual env activated.
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logging.basicConfig(level=logging.WARNING)
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system_message = """
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You are a chat bot. Your name is Mosscap and
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you have one goal: figure out what people need.
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Your full name, should you need to know it, is
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Splendid Speckled Mosscap. You communicate
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effectively, but you tend to answer with long
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flowery prose.
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"""
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credential = AzureCliCredential()
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chat_service = AzureChatCompletion(credential=credential)
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text_to_audio_service = AzureTextToAudio(credential=credential)
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history = ChatHistory()
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history.add_user_message("Hi there, who are you?")
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history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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async def chat() -> bool:
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try:
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user_input = input("User:> ")
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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if user_input == "exit":
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print("\n\nExiting chat...")
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return False
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history.add_user_message(user_input)
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# No need to stream the response since we can only pass the
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# response to the text to audio service as a whole
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response = await chat_service.get_chat_message_content(
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chat_history=history,
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settings=OpenAIChatPromptExecutionSettings(
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max_tokens=2000,
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temperature=0.7,
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top_p=0.8,
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),
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)
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# Need to set the response format to wav since the audio player only supports wav files
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audio_content = await text_to_audio_service.get_audio_content(
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response.content, OpenAITextToAudioExecutionSettings(response_format="wav")
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)
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AudioPlayer(audio_content=audio_content).play()
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print(f"Mosscap:> {response.content}")
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history.add_message(response)
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return True
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async def main() -> None:
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chatting = True
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while chatting:
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chatting = await chat()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,114 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import logging
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import os
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from azure.identity import AzureCliCredential
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from samples.concepts.audio.audio_player import AudioPlayer
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from samples.concepts.audio.audio_recorder import AudioRecorder
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from semantic_kernel.connectors.ai.open_ai import (
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AzureAudioToText,
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AzureChatCompletion,
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AzureTextToAudio,
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OpenAIChatPromptExecutionSettings,
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OpenAITextToAudioExecutionSettings,
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)
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from semantic_kernel.contents import AudioContent, ChatHistory
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# This simple sample demonstrates how to use the AzureChatCompletion, AzureTextToAudio, and AzureAudioToText
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# services to create a chat bot that can communicate with the user using both audio input and output.
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# The chatbot will engage in a conversation with the user by audio only.
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# This sample combines the functionality of the samples/concepts/audio/01-chat_with_audio_input.py and
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# samples/concepts/audio/02-chat_with_audio_output.py samples.
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# Resources required for this sample:
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# 1. An Azure OpenAI model deployment (e.g. GPT-4o-mini).
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# 2. An Azure Text to Speech deployment (e.g. tts).
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# 3. An Azure Speech to Text deployment (e.g. whisper).
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# Additional dependencies required for this sample:
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# - pyaudio: `pip install pyaudio` or `uv pip install pyaudio` if you are using uv and have a virtual env activated.
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# - keyboard: `pip install keyboard` or `uv pip install keyboard` if you are using uv and have a virtual env activated.
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logging.basicConfig(level=logging.WARNING)
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AUDIO_FILEPATH = os.path.join(os.path.dirname(__file__), "output.wav")
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system_message = """
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You are a chat bot. Your name is Mosscap and
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you have one goal: figure out what people need.
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Your full name, should you need to know it, is
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Splendid Speckled Mosscap. You communicate
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effectively, but you tend to answer with long
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flowery prose.
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"""
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credential = AzureCliCredential()
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chat_service = AzureChatCompletion(credential=credential)
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text_to_audio_service = AzureTextToAudio(credential=credential)
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audio_to_text_service = AzureAudioToText(credential=credential)
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history = ChatHistory()
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history.add_user_message("Hi there, who are you?")
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history.add_assistant_message("I am Mosscap, a chat bot. I'm trying to figure out what people need.")
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async def chat() -> bool:
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try:
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print("User:> ", end="", flush=True)
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with AudioRecorder(output_filepath=AUDIO_FILEPATH) as recorder:
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recorder.start_recording()
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user_input = await audio_to_text_service.get_text_content(AudioContent.from_audio_file(AUDIO_FILEPATH))
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print(user_input.text)
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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if "exit" in user_input.text.lower():
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print("\n\nExiting chat...")
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return False
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history.add_user_message(user_input.text)
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# No need to stream the response since we can only pass the
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# response to the text to audio service as a whole
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response = await chat_service.get_chat_message_content(
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chat_history=history,
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settings=OpenAIChatPromptExecutionSettings(
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max_tokens=2000,
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temperature=0.7,
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top_p=0.8,
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),
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)
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# Need to set the response format to wav since the audio player only supports wav files
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audio_content = await text_to_audio_service.get_audio_content(
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response.content, OpenAITextToAudioExecutionSettings(response_format="wav")
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)
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print("Mosscap:> ", end="", flush=True)
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AudioPlayer(audio_content=audio_content).play(text=response.content)
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history.add_message(response)
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return True
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async def main() -> None:
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print(
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"Instruction: when it's your turn to speak, press the spacebar to start recording."
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" Release the spacebar to stop recording."
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)
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chatting = True
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while chatting:
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chatting = await chat()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,38 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from samples.concepts.audio.audio_player import AudioPlayer
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from semantic_kernel import Kernel
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from semantic_kernel.connectors.ai import PromptExecutionSettings
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from semantic_kernel.connectors.ai.open_ai import OpenAITextToAudio
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from semantic_kernel.functions import KernelArguments
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"""
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This simple sample demonstrates how to use the AzureTextToAudio services
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with a prompt and prompt rendering.
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Resources required for this sample: An Azure Text to Speech deployment (e.g. tts).
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Additional dependencies required for this sample:
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- pyaudio: run `pip install pyaudio` or `uv pip install pyaudio` if you are using uv.
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"""
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async def main():
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kernel = Kernel()
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kernel.add_service(OpenAITextToAudio(service_id="tts"))
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result = await kernel.invoke_prompt(
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prompt="speak the following phrase: {{$phrase}}",
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arguments=KernelArguments(
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phrase="a painting of a flower vase",
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settings=PromptExecutionSettings(service_id="tts", voice="coral"),
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),
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)
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if result:
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AudioPlayer(audio_content=result.value[0]).play()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,99 @@
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# Copyright (c) Microsoft. All rights reserved.
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import io
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import logging
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import wave
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from typing import ClassVar
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import pyaudio
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from pydantic import BaseModel
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from semantic_kernel.contents import AudioContent
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logging.basicConfig(level=logging.WARNING)
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logger: logging.Logger = logging.getLogger(__name__)
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class AudioPlayer(BaseModel):
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"""A class to play an audio file to the default audio output device."""
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# Audio replay parameters
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CHUNK: ClassVar[int] = 1024
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audio_content: AudioContent
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def play(self, text: str | None = None) -> None:
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"""Play the audio content to the default audio output device.
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Args:
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text (str, optional): The text to display while playing the audio. Defaults to None.
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"""
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audio_stream = io.BytesIO(self.audio_content.data)
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with wave.open(audio_stream, "rb") as wf:
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audio = pyaudio.PyAudio()
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stream = audio.open(
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format=audio.get_format_from_width(wf.getsampwidth()),
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channels=wf.getnchannels(),
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rate=wf.getframerate(),
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output=True,
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)
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if text:
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# Simulate the output of text while playing the audio
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data_frames = []
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data = wf.readframes(self.CHUNK)
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while data:
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data_frames.append(data)
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data = wf.readframes(self.CHUNK)
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if len(data_frames) < len(text):
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logger.warning(
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"The audio is too short to play the entire text. ",
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"The text will be displayed without synchronization.",
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)
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print(text)
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else:
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for data_frame, text_frame in self._zip_text_and_audio(text, data_frames):
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stream.write(data_frame)
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print(text_frame, end="", flush=True)
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print()
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else:
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data = wf.readframes(self.CHUNK)
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while data:
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stream.write(data)
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data = wf.readframes(self.CHUNK)
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stream.stop_stream()
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stream.close()
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audio.terminate()
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def _zip_text_and_audio(self, text: str, audio_frames: list) -> zip:
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"""Zip the text and audio frames together so that they can be displayed in sync.
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This is done by evenly distributing empty strings between each character and
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append the remaining empty strings at the end.
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Args:
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text (str): The text to display while playing the audio.
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audio_frames (list): The audio frames to play.
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Returns:
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zip: The zipped text and audio frames.
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"""
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text_frames = list(text)
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empty_string_count = len(audio_frames) - len(text_frames)
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empty_string_spacing = len(text_frames) // empty_string_count
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modified_text_frames = []
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current_empty_string_count = 0
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for i, text_frame in enumerate(text_frames):
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modified_text_frames.append(text_frame)
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if current_empty_string_count < empty_string_count and i % empty_string_spacing == 0:
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modified_text_frames.append("")
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current_empty_string_count += 1
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if current_empty_string_count < empty_string_count:
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modified_text_frames.extend([""] * (empty_string_count - current_empty_string_count))
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return zip(audio_frames, modified_text_frames)
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@@ -0,0 +1,73 @@
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# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import os
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import wave
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from typing import ClassVar
|
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|
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import keyboard
|
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import pyaudio
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from pydantic import BaseModel
|
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class AudioRecorder(BaseModel):
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"""A class to record audio from the microphone and save it to a WAV file.
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To start recording, press the spacebar. To stop recording, release the spacebar.
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||||
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To use as a context manager, that automatically removes the output file after exiting the context:
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```
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with AudioRecorder(output_filepath="output.wav") as recorder:
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recorder.start_recording()
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# Do something with the recorded audio
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||||
...
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||||
```
|
||||
"""
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||||
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# Audio recording parameters
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||||
FORMAT: ClassVar[int] = pyaudio.paInt16
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||||
CHANNELS: ClassVar[int] = 1
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||||
RATE: ClassVar[int] = 44100
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||||
CHUNK: ClassVar[int] = 1024
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||||
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||||
output_filepath: str
|
||||
|
||||
def start_recording(self) -> None:
|
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# Wait for the spacebar to be pressed to start recording
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||||
keyboard.wait("space")
|
||||
|
||||
# Start recording
|
||||
audio = pyaudio.PyAudio()
|
||||
stream = audio.open(
|
||||
format=self.FORMAT,
|
||||
channels=self.CHANNELS,
|
||||
rate=self.RATE,
|
||||
input=True,
|
||||
frames_per_buffer=self.CHUNK,
|
||||
)
|
||||
frames = []
|
||||
|
||||
while keyboard.is_pressed("space"):
|
||||
data = stream.read(self.CHUNK)
|
||||
frames.append(data)
|
||||
|
||||
# Recording stopped as the spacebar is released
|
||||
stream.stop_stream()
|
||||
stream.close()
|
||||
|
||||
# Save the recorded data as a WAV file
|
||||
with wave.open(self.output_filepath, "wb") as wf:
|
||||
wf.setnchannels(self.CHANNELS)
|
||||
wf.setsampwidth(audio.get_sample_size(self.FORMAT))
|
||||
wf.setframerate(self.RATE)
|
||||
wf.writeframes(b"".join(frames))
|
||||
|
||||
audio.terminate()
|
||||
|
||||
def remove_output_file(self) -> None:
|
||||
os.remove(self.output_filepath)
|
||||
|
||||
def __enter__(self) -> "AudioRecorder":
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback) -> None:
|
||||
self.remove_output_file()
|
||||
Reference in New Issue
Block a user