bigdl-llm: add voice-assistant example that are migrated from langchain use-case document (#8468)

python/llm/example/langchain/README.md

@@ -44,3 +44,29 @@ arguments info:
 - `-q QUESTION`: question to ask. Default is `What is AI?`.
 - `-c CONTEXT_SIZE`: specify the maximum context size. Default is `2048`.
 - `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
+
+### 3. Voice Assistant
+> This example is adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant with only tiny code change.
+
+Some extra dependencies are required to be installed for this example.
+```bash
+pip install SpeechRecognition
+pip install pyttsx3
+pip install PyAudio
+pip install whisper.ai
+pip install soundfile
+```
+
+```bash
+python ./voiceassistant.py -x MODEL_FAMILY -m CONVERTED_MODEL_PATH -t THREAD_NUM
+```
+
+arguments info:
+- `-m CONVERTED_MODEL_PATH`: **required**, path to the converted model
+- `-x MODEL_FAMILY`: **required**, the model family of the model specified in `-m`, available options are `llama`, `gptneox` and `bloom`
+- `-t THREAD_NUM`: specify the number of threads to use for inference. Default is `2`.
+
+When you see output says
+> listening now...
+
+Please say something through your microphone (e.g. What is AI). The programe will automatically detect when you have completed your speech and recogize them.

python/llm/example/langchain/native_int4/voiceassistant.py

@@ -0,0 +1,121 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This would makes sure Python is aware there is more than one sub-package within bigdl,
+# physically located elsewhere.
+# Otherwise there would be module not found error in non-pip's setting as Python would
+# only search the first bigdl package and end up finding only one sub-package.
+
+# Code adapted from https://python.langchain.com/docs/use_cases/chatbots/voice_assistant
+
+
+from langchain import LLMChain, PromptTemplate
+from bigdl.llm.langchain.llms import BigdlNativeLLM
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+import speech_recognition as sr
+import pyttsx3
+import argparse
+
+
+def prepare_chain(args):
+
+    model_path = args.model_path
+    model_family = args.model_family
+    n_threads = args.thread_num
+
+    # Use a easy prompt could bring good-enough result
+    template = """
+    {history}
+    Q: {human_input}
+    A:"""
+    prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
+
+    # We use our BigdlNativeLLM to subsititute OpenAI web-required API
+    callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+    llm = BigdlNativeLLM(
+            model_path=model_path,
+            model_family=model_family,
+            n_threads=n_threads,
+            callback_manager=callback_manager, 
+            verbose=True
+        )
+
+    # Following code are complete the same as the use-case
+    voiceassitant_chain = LLMChain(
+        llm=llm,
+        prompt=prompt,
+        verbose=True,
+        memory=ConversationBufferWindowMemory(k=2),
+    )
+
+    return voiceassitant_chain
+
+
+def listen(voiceassitant_chain):
+    engine = pyttsx3.init()
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        print("Calibrating...")
+        r.adjust_for_ambient_noise(source, duration=5)
+        # optional parameters to adjust microphone sensitivity
+        # r.energy_threshold = 200
+        # r.pause_threshold=0.5
+
+        print("Okay, go!")
+        while 1:
+            text = ""
+            print("listening now...")
+            try:
+                audio = r.listen(source, timeout=5, phrase_time_limit=30)
+                print("Recognizing...")
+                # whisper model options are found here: https://github.com/openai/whisper#available-models-and-languages
+                # other speech recognition models are also available.
+                text = r.recognize_whisper(
+                    audio,
+                    model="medium.en",
+                    show_dict=True,
+                )["text"]
+            except Exception as e:
+                unrecognized_speech_text = (
+                    f"Sorry, I didn't catch that. Exception was: {e}s"
+                )
+                text = unrecognized_speech_text
+            print(text)
+
+            response_text = voiceassitant_chain.predict(human_input=text)
+            print(response_text)
+            engine.say(response_text)
+            engine.runAndWait()
+
+
+def main(args):
+    chain = prepare_chain(args)
+    listen(chain)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='BigDL-LLM Langchain Voice Assistant Example')
+    parser.add_argument('-x','--model-family', type=str, required=True,
+                        help='the model family')
+    parser.add_argument('-m','--model-path', type=str, required=True,
+                        help='the path to the converted llm model')
+    parser.add_argument('-t','--thread-num', type=int, default=2,
+                        help='Number of threads to use for inference')
+    args = parser.parse_args()
+
+    main(args)