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4d91d1cadc
moved to brains # Description Please include a summary of the changes and the related issue. Please also include relevant motivation and context. ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate): --------- Co-authored-by: Antoine Dewez <44063631+Zewed@users.noreply.github.com>
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import os
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import tempfile
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import time
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import openai
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from langchain.schema import Document
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from models import File, get_documents_vector_store
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from packages.files.file import compute_sha1_from_content
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async def process_audio(
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file: File, user, original_file_name, integration=None, integration_link=None
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):
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temp_filename = None
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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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documents_vector_store = get_documents_vector_store()
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try:
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upload_file = file.file
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with tempfile.NamedTemporaryFile(
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delete=False,
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suffix=upload_file.filename, # pyright: ignore reportPrivateUsage=none
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) as tmp_file:
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await upload_file.seek(0) # pyright: ignore reportPrivateUsage=none
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content = (
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await upload_file.read() # pyright: ignore reportPrivateUsage=none
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)
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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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temp_filename = tmp_file.name
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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(
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transcript.text.encode("utf-8") # pyright: ignore reportPrivateUsage=none
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)
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file_size = len(
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transcript.text.encode("utf-8") # pyright: ignore reportPrivateUsage=none
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)
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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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)
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texts = text_splitter.split_text(
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transcript.text.encode("utf-8") # pyright: ignore reportPrivateUsage=none
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)
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docs_with_metadata = [
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Document(
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page_content=text,
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metadata={
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"file_sha1": file_sha,
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"file_size": file_size,
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"file_name": file_meta_name,
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"chunk_size": chunk_size,
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"chunk_overlap": chunk_overlap,
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"date": dateshort,
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},
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)
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for text in texts
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]
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documents_vector_store.add_documents(docs_with_metadata)
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finally:
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if temp_filename and os.path.exists(temp_filename):
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os.remove(temp_filename)
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