121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This would makes sure Python is aware there is more than one sub-package within bigdl,
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# physically located elsewhere.
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# Otherwise there would be module not found error in non-pip's setting as Python would
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# only search the first bigdl package and end up finding only one sub-package.
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# Code adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant
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from langchain import LLMChain, PromptTemplate
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from bigdl.llm.langchain.llms import BigdlNativeLLM
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from langchain.memory import ConversationBufferWindowMemory
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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import speech_recognition as sr
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import pyttsx3
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import argparse
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def prepare_chain(args):
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model_path = args.model_path
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model_family = args.model_family
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n_threads = args.thread_num
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# Use a easy prompt could bring good-enough result
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template = """
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{history}
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Q: {human_input}
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A:"""
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prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
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# We use our BigdlNativeLLM to subsititute OpenAI web-required API
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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llm = BigdlNativeLLM(
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model_path=model_path,
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model_family=model_family,
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n_threads=n_threads,
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callback_manager=callback_manager,
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verbose=True
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)
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# Following code are complete the same as the use-case
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voiceassitant_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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verbose=True,
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memory=ConversationBufferWindowMemory(k=2),
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)
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return voiceassitant_chain
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def listen(voiceassitant_chain):
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engine = pyttsx3.init()
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r = sr.Recognizer()
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with sr.Microphone() as source:
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print("Calibrating...")
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r.adjust_for_ambient_noise(source, duration=5)
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# optional parameters to adjust microphone sensitivity
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# r.energy_threshold = 200
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# r.pause_threshold=0.5
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print("Okay, go!")
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while 1:
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text = ""
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print("listening now...")
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try:
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audio = r.listen(source, timeout=5, phrase_time_limit=30)
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print("Recognizing...")
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# whisper model options are found here: https://github.com/openai/whisper#available-models-and-languages
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# other speech recognition models are also available.
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text = r.recognize_whisper(
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audio,
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model="medium.en",
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show_dict=True,
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)["text"]
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except Exception as e:
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unrecognized_speech_text = (
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f"Sorry, I didn't catch that. Exception was: {e}s"
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)
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text = unrecognized_speech_text
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print(text)
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response_text = voiceassitant_chain.predict(human_input=text)
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print(response_text)
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engine.say(response_text)
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engine.runAndWait()
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def main(args):
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chain = prepare_chain(args)
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listen(chain)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='BigDL-LLM Langchain Voice Assistant Example')
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parser.add_argument('-x','--model-family', type=str, required=True,
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help='the model family')
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parser.add_argument('-m','--model-path', type=str, required=True,
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help='the path to the converted llm model')
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parser.add_argument('-t','--thread-num', type=int, default=2,
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help='Number of threads to use for inference')
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args = parser.parse_args()
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main(args)
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