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75 lines
3.0 KiB
Python
75 lines
3.0 KiB
Python
import os
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from tempfile import NamedTemporaryFile
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import tempfile
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from io import BytesIO
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import time
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import openai
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from langchain.document_loaders import TextLoader
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from utils import compute_sha1_from_content, documents_vector_store
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from langchain.schema import Document
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from fastapi import UploadFile
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# # Create a function to transcribe audio using Whisper
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# def _transcribe_audio(api_key, audio_file, stats_db):
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# openai.api_key = api_key
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# transcript = ""
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# with BytesIO(audio_file.read()) as audio_bytes:
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# # Get the extension of the uploaded file
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# file_extension = os.path.splitext(audio_file.name)[-1]
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# # Create a temporary file with the uploaded audio data and the correct extension
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# with tempfile.NamedTemporaryFile(delete=True, suffix=file_extension) as temp_audio_file:
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# temp_audio_file.write(audio_bytes.read())
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# temp_audio_file.seek(0) # Move the file pointer to the beginning of the file
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# transcript = openai.Audio.translate("whisper-1", temp_audio_file)
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# return transcript
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async def process_audio(upload_file: UploadFile, stats_db):
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file_sha = ""
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dateshort = time.strftime("%Y%m%d-%H%M%S")
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file_meta_name = f"audiotranscript_{dateshort}.txt"
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# uploaded file to file object
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openai_api_key = os.environ.get("OPENAI_API_KEY")
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# Here, we're writing the uploaded file to a temporary file, so we can use it with your existing code.
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with tempfile.NamedTemporaryFile(delete=False, suffix=upload_file.filename) as tmp_file:
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await upload_file.seek(0)
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content = await upload_file.read()
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tmp_file.write(content)
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tmp_file.flush()
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tmp_file.close()
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with open(tmp_file.name, "rb") as audio_file:
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transcript = openai.Audio.transcribe("whisper-1", audio_file)
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file_sha = compute_sha1_from_content(transcript.text.encode("utf-8"))
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file_size = len(transcript.text.encode("utf-8"))
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print(file_size)
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# Load chunk size and overlap from sidebar
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chunk_size = 500
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chunk_overlap = 0
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text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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print(transcript)
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print("#########")
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texts = text_splitter.split_text(transcript)
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docs_with_metadata = [Document(page_content=text, metadata={"file_sha1": file_sha, "file_size": file_size, "file_name": file_meta_name,
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"chunk_size": chunk_size, "chunk_overlap": chunk_overlap, "date": dateshort}) for text in texts]
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# if st.secrets.self_hosted == "false":
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# add_usage(stats_db, "embedding", "audio", metadata={"file_name": file_meta_name,"file_type": ".txt", "chunk_size": chunk_size, "chunk_overlap": chunk_overlap})
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documents_vector_store.add_documents(docs_with_metadata)
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return documents_vector_store
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