Create privateGPT_Voice.py (initial version)

Youtube/privateGPT_Voice.py

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+# Script is originally taken from https://github.com/imartinez/privateGPT/blob/main/privateGPT.py
+# and i've added some STT and TTS stuff.
+# See tutorial on my Youtube channel here: 
+
+from dotenv import load_dotenv
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Chroma
+from langchain.llms import GPT4All, LlamaCpp
+import os
+from constants import CHROMA_SETTINGS
+
+import whisper
+from TTS.api import TTS
+
+##############################
+# Whisper for local STT part #
+##############################
+
+model = whisper.load_model("base")
+result = model.transcribe("input.wav")
+query = result["text"]
+
+##################################
+# PrivateGPT for query documents #
+##################################
+load_dotenv()
+
+embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
+persist_directory = os.environ.get('PERSIST_DIRECTORY')
+
+model_type = os.environ.get('MODEL_TYPE')
+print("Model type: " + model_type)
+model_path = os.environ.get('MODEL_PATH')
+print("Model path: " + model_path)
+model_n_ctx = os.environ.get('MODEL_N_CTX')
+target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
+
+embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
+db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
+llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=None, verbose=False)
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
+
+print("")
+print(">> Asking my documents: " + query)
+print("")
+
+res = qa(query)
+print("")
+
+############################
+# Coqui for local TTS part #
+############################
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+tts = TTS(model_name="tts_models/en/ljspeech/vits--neon")
+tts.tts_to_file(text=res['result'])