restructuring

trials/finetuning/inference_fine_tuned.py

@@ -0,0 +1,24 @@
+# Steps to prepare data and submit/check OpenAI finetuning
+# import subprocess
+# subprocess.run("openai tools fine_tunes.prepare_data -f " + "finetuning_dataset.jsonl")
+# export OPENAI_API_KEY=
+# openai api fine_tunes.create -t <TRAIN_FILE_ID_OR_PATH> -m <BASE_MODEL>
+# openai api fine_tunes.list
+
+
+import openai
+
+# Use your OpenAI API Key
+openai.api_key = ""
+
+sample_chunks = ["You all just came off of your incredible Google Cloud next conference where you released a wide variety of functionality and features and new products across artisan television and also across the entire sort of cloud ecosystem . You want to just first by walking through , first start by walking through all the innovations that you sort of released and what you 're excited about when you come to Google Cloud ? Now our vision is super simple .  If you look at what smartphones did for a consumer , you know they took a computer and internet browser , a communication device , and a camera , and made it so that it 's in everybody 's pocket , so it really brought computation to every person . We feel that , you know , our , what we 're trying to do is take all the technological innovation that Google 's doing , but make it super simple so that everyone can consume it . And so that includes our global data center footprint , all the new types of hardware and large-scale systems we work on , the software that we 're making available for people to do high-scale computation , tools for data processing , tools for cybersecurity , processing , tools for cyber security , tools for machine learning , but make it so simple that everyone can use it . And every step that we do to simplify things for people , we think adoption can grow . And so that 's a lot of what we 've done these last three , four years , and we made a number of announcements that next in machine learning and AI in particular , you know , we look at our work as four elements , how we take our large-scale compute systems that were building for AI and how we make that available to everybody .  Second , what we 're doing with the software stacks and top of it , things like jacks and other things and how we 're making those available to everybody . Third is advances because different people have different levels of expertise . Some people say I need the hardware to build my own large language model or algorithm . Other people say , look , I really need to use a building block . You guys give me .  So , 30s we 've done a lot with AutoML and we announce new capability for image , video , and translation to make it available to everybody .  And then lastly , we 're also building completely packaged solutions for some areas and we announce some new stuff . -> ",
+                 " We 're joined next by Thomas Curian , CEO of Google Cloud , and Alexander Wang , CEO and founder of Scale AI . Thomas joined Google in November 2018 as the CEO of Google Cloud . Prior to Google , Thomas spent 22 years at Oracle , where most recently he was president of product development . Before that , Thomas worked at McKinsey as a business analyst and engagement manager . His nearly 30 years of experience have given him a deep knowledge of engineering enterprise relationships and leadership of large organizations . Thomas 's degrees include an MBA in administration and management from Stanford University , as an RJ Miller scholar and a BSEE in electrical engineering and computer science from Princeton University , where he graduated suma cum laude .  Thomas serves as a member of the Stanford graduate School of Business Advisory Council and Princeton University School of Engineering Advisory Council . Please welcome to the stage , Thomas Curian and Alexander Wang . This is a super exciting conversation . Thanks for being here , Thomas . - > "]
+
+# Give your finetuned model name here
+# "davinci:ft-personal-2023-07-14-10-43-51"
+model_name = ""
+response = openai.Completion.create(
+    model=model_name,
+    prompt=sample_chunks[0])
+
+print(response)

trials/server/server_multithreaded.py

@@ -16,8 +16,8 @@ from av import AudioFifo
 from sortedcontainers import SortedDict
 from whisper_jax import FlaxWhisperPipline
 
-from ..utils.log_utils import logger
-from ..utils.run_utils import config, Mutex
+from reflector.utils.log_utils import logger
+from reflector.utils.run_utils import config, Mutex
 
 WHISPER_MODEL_SIZE = config['DEFAULT']["WHISPER_REAL_TIME_MODEL_SIZE"]
 pcs = set()

trials/title_summary/openai_endpoint.py

@@ -28,7 +28,7 @@ response = openai.ChatCompletion.create(model=model,
                                         n=1,
                                         max_tokens=300)
 
-# Try finetuned model
+# Try fine tuned model
 # model = "davinci:ft-personal-2023-07-14-10-43-51"
 # response = openai.Completion.create(model=model,
 #                                         prompt=sample_chunks[0] + " -> ")