mirror of
https://github.com/thorstenMueller/Thorsten-Voice.git
synced 2024-11-23 08:23:16 +01:00
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
# Script is originally taken from https://github.com/imartinez/privateGPT/blob/main/privateGPT.py
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# and i've added some STT and TTS stuff.
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# See full tutorial on my "Thorsten-Voice" Youtube channel: https://youtu.be/qBs85JNyY7I
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from dotenv import load_dotenv
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from langchain.chains import RetrievalQA
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain.vectorstores import Chroma
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from langchain.llms import GPT4All, LlamaCpp
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import os
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from constants import CHROMA_SETTINGS
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import whisper
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from TTS.api import TTS
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##############################
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# Whisper for local STT part #
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##############################
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model = whisper.load_model("base")
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result = model.transcribe("input.wav")
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query = result["text"]
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##################################
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# PrivateGPT for query documents #
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##################################
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load_dotenv()
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embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
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persist_directory = os.environ.get('PERSIST_DIRECTORY')
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model_type = os.environ.get('MODEL_TYPE')
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print("Model type: " + model_type)
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model_path = os.environ.get('MODEL_PATH')
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print("Model path: " + model_path)
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model_n_ctx = os.environ.get('MODEL_N_CTX')
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target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
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embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
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db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
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retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
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llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=None, verbose=False)
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
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print("")
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print(">> Asking my documents: " + query)
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print("")
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res = qa(query)
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print("")
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############################
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# Coqui for local TTS part #
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############################
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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tts = TTS(model_name="tts_models/en/ljspeech/vits--neon")
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tts.tts_to_file(text=res['result'])
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