organize trails

trials/api.py

@@ -1,11 +1,13 @@
 import requests
 import spacy
 
+# Enter the Machine where the LLM is hosted
+LLM_MACHINE_IP = ""
 # This is the URL of text-generation-webui
-URL = "http://216.153.52.83:5000/api/v1/generate"
+URL = f"http://{LLM_MACHINE_IP}:5000/api/v1/generate"
 
 headers = {
-    "Content-Type": "application/json"
+        "Content-Type": "application/json"
 }
 
 
@@ -23,7 +25,7 @@ def split_text_file(filename, token_count):
 
     while start_index < total_tokens:
         end_index = start_index + token_count
-        part_tokens = doc[start_index:end_index-5]
+        part_tokens = doc[start_index:end_index - 5]
         part = ' '.join(token.text for token in part_tokens)
         parts.append(part)
         start_index = end_index
@@ -33,7 +35,6 @@ def split_text_file(filename, token_count):
 
 final_summary = ""
 parts = split_text_file("transcript.txt", 1600)
-previous_summary = ""
 
 for part in parts:
     prompt = f"""
@@ -52,5 +53,5 @@ for part in parts:
     except Exception as e:
         print(str(e))
 
-with open("sum.txt", "w") as sum:
+with open("summary.txt", "w") as sum:
-    sum.write(" ".join(final_summary))
+    sum.write(" ".join(final_summary))

trials/gpt2.py

@@ -1,65 +1,66 @@
-# # Approach 1
+# Approach 1
-# from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+from transformers import GPTNeoForCausalLM, GPT2Tokenizer
-#
-# model_name = 'EleutherAI/gpt-neo-1.3B'
-# tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-# model = GPTNeoForCausalLM.from_pretrained(model_name)
-#
-# conversation = """
-# Summarize the following conversation in 3 key sentences:
-#
-# 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 .
-# """
-#
-# input_ids = tokenizer.encode(conversation, return_tensors='pt')
-#
-# output = model.generate(input_ids,
-#                         max_length=30,
-#                         num_return_sequences=1)
-#
-# caption = tokenizer.decode(output[0], skip_special_tokens=True)
-# print("Caption:", caption[len(input_ids):])
 
-#
+model_name = 'EleutherAI/gpt-neo-1.3B'
-# # Approach 2
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-# import torch
+model = GPTNeoForCausalLM.from_pretrained(model_name)
-# from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
-#
+conversation = """
-# model_name = "gpt2"
+Summarize the following conversation in 3 key sentences:
-# tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
-# model = GPT2LMHeadModel.from_pretrained(model_name)
+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 .
-# model.eval()
+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 .
-# text = """
+Thomas serves as a member of the Stanford graduate School of Business Advisory Council and Princeton University School of Engineering Advisory Council .
-# 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 . "
+Please welcome to the stage , Thomas Curian and Alexander Wang . This is a super exciting conversation . Thanks for being here , Thomas .
-# """
+"""
-#
+
-# tokenizer.pad_token = tokenizer.eos_token
+input_ids = tokenizer.encode(conversation, return_tensors='pt')
-# input_ids = tokenizer.encode(text,
+
-#                              max_length=100,
+output = model.generate(input_ids,
-#                              truncation=True,
+                        max_length=30,
-#                              return_tensors="pt")
+                        num_return_sequences=1)
-# attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
+
-# output = model.generate(input_ids,
+caption = tokenizer.decode(output[0], skip_special_tokens=True)
-#                         max_new_tokens=20,
+print("Caption:", caption[len(input_ids):])
-#                         num_return_sequences=1,
+
-#                         num_beams=2,
+
-#                         attention_mask=attention_mask)
+# Approach 2
-#
+import torch
-# chapter_titles = [tokenizer.decode(output[i], skip_special_tokens=True) for i in range(output.shape[0])]
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
-# for i, title in enumerate(chapter_titles):
+
-#     print("Caption: ", title)
+model_name = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+model.eval()
+
+text = """
+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 . "
+"""
+
+tokenizer.pad_token = tokenizer.eos_token
+input_ids = tokenizer.encode(text,
+                             max_length=100,
+                             truncation=True,
+                             return_tensors="pt")
+attention_mask = torch.ones(input_ids.shape, dtype=torch.long)
+output = model.generate(input_ids,
+                        max_new_tokens=20,
+                        num_return_sequences=1,
+                        num_beams=2,
+                        attention_mask=attention_mask)
+
+chapter_titles = [tokenizer.decode(output[i], skip_special_tokens=True) for i in range(output.shape[0])]
+for i, title in enumerate(chapter_titles):
+    print("Caption: ", title)
 
 # Approach 3
 
 import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
 
 def generate_response(conversation, max_length=100):
     input_text = ""
@@ -79,20 +80,22 @@ def generate_response(conversation, max_length=100):
     response = tokenizer.decode(output[0], skip_special_tokens=True)
     return response
 
+
 if __name__ == "__main__":
+
+    # Call appropriate approach from the main while experimenting
     model_name = "gpt2"
     model = GPT2LMHeadModel.from_pretrained(model_name)
     tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
     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 . "
+            "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 . "
     ]
 
     conversation = [
-        {"role": "system", "content": "Summarize this text" },
+            {"role": "system", "content": "Summarize this text"},
-        {"role": "user", "content": " text : " + sample_chunks[0]},
+            {"role": "user", "content": " text : " + sample_chunks[0]},
     ]
 
     response = generate_response(conversation)
     print("Response:", response)
-

trials/incsum.py

@@ -1,9 +1,11 @@
+import spacy
+import sys
+
+
 # Observe the incremental summaries by performing summaries in chunks
 with open("transcript.txt") as f:
     transcription = f.read()
 
-import spacy
-
 
 def split_text_file(filename, token_count):
     nlp = spacy.load('en_core_web_md')
@@ -26,8 +28,9 @@ def split_text_file(filename, token_count):
 
     return parts
 
+
 # Set the chunk length here to split the transcript and test
-MAX_CHUNK_LENGTH=1000
+MAX_CHUNK_LENGTH = 1000
 
 chunks = split_text_file("transcript.txt", MAX_CHUNK_LENGTH)
 print("Number of chunks", len(chunks))
@@ -41,19 +44,17 @@ with open("chunks" + str(MAX_CHUNK_LENGTH) + ".txt", "a") as f:
 # ex. python incsum.py 1 => will run approach 1
 # If no input, will run all approaches
 
-import sys
 try:
     index = sys.argv[1]
 except:
     index = None
 
-
 # Approach 1 : facebook/bart-large-cnn
 if index == "1" or index is None:
-    SUMMARY_MODEL="facebook/bart-large-cnn"
+    SUMMARY_MODEL = "facebook/bart-large-cnn"
-    MIN_LENGTH=5
+    MIN_LENGTH = 5
-    MAX_LENGTH=10
+    MAX_LENGTH = 10
-    BEAM_SIZE=2
+    BEAM_SIZE = 2
 
     print("Performing chunk summary : " + SUMMARY_MODEL)
 
@@ -81,7 +82,6 @@ if index == "1" or index is None:
         for summary in summaries:
             f.write(summary + "\n\n")
 
-
 # Approach 2
 if index == "2" or index is None:
     print("Performing chunk summary : " + "gpt-neo-1.3B")
@@ -108,14 +108,14 @@ if index == "2" or index is None:
                                 max_length=max_length,
                                 attention_mask=attention_mask,
                                 pad_token_id=model.config.eos_token_id,
-                                 num_beams=4,
+                                num_beams=4,
-                                 length_penalty=2.0,
+                                length_penalty=2.0,
-                                 early_stopping=True)
+                                early_stopping=True)
         summary_ids = output[0, input_length:]
         summary = tokenizer.decode(summary_ids, skip_special_tokens=True)
         summaries.append(summary)
         with open("gptneo1.3B-summaries.txt", "a") as f:
-                f.write(summary + "\n\n")
+            f.write(summary + "\n\n")
 
 # Approach 3
 if index == "3" or index is None:
@@ -155,4 +155,3 @@ if index == "3" or index is None:
     with open("mpt-7b-summaries.txt", "a") as f:
         for summary in summaries:
             f.write(summary + "\n\n")
-

trials/openai_endpoint.py

@@ -11,14 +11,15 @@ openai.api_key = ""
 # to incremental summarize, user prompt used : "summarize this conversation in a few sentences by taking key points"
 #               max_tokens=300
 
-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 . ",
+sample_chunks = [
-                 " 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 ."]
+        "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 ."]
 
 conversation = [
-    {"role": "system",
+        {"role": "system",
-     "content":  sample_chunks[1]},
+         "content": sample_chunks[1]},
-    {"role": "user",
+        {"role": "user",
-     "content": "summarize this conversation in a few sentences by taking key points"}
+         "content": "summarize this conversation in a few sentences by taking key points"}
 ]
 
 model = "gpt-3.5-turbo"

trials/whisper-jax/whisjax.py

@@ -18,11 +18,11 @@ import nltk
 import yt_dlp as youtube_dl
 from whisper_jax import FlaxWhisperPipline
 
-from utils.file_utils import download_files, upload_files
+from ...utils.file_utils import download_files, upload_files
-from utils.log_utils import logger
+from ...utils.log_utils import logger
-from utils.run_utils import config
+from ...utils.run_utils import config
-from utils.text_utilities import post_process_transcription, summarize
+from ...utils.text_utilities import post_process_transcription, summarize
-from utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
+from ...utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
 
 nltk.download('punkt', quiet=True)
 nltk.download('stopwords', quiet=True)
@@ -30,8 +30,8 @@ nltk.download('stopwords', quiet=True)
 WHISPER_MODEL_SIZE = config['DEFAULT']["WHISPER_MODEL_SIZE"]
 NOW = datetime.now()
 
-if not os.path.exists('./artefacts'):
+if not os.path.exists('../../artefacts'):
-    os.makedirs('./artefacts')
+    os.makedirs('../../artefacts')
 
 
 def init_argparse() -> argparse.ArgumentParser:
@@ -91,7 +91,7 @@ def main():
             # Download the audio
             with youtube_dl.YoutubeDL(ydl_opts) as ydl:
                 ydl.download([args.location])
-            media_file = "./artefacts/audio.mp3"
+            media_file = "../artefacts/audio.mp3"
 
             logger.info("Saved downloaded YouTube video to: " + media_file)
         else:

trials/whisper-jax/whisjax_realtime.py

@@ -10,11 +10,11 @@ from pynput import keyboard
 from termcolor import colored
 from whisper_jax import FlaxWhisperPipline
 
-from utils.file_utils import upload_files
+from ...utils.file_utils import upload_files
-from utils.log_utils import logger
+from ...utils.log_utils import logger
-from utils.run_utils import config
+from ...utils.run_utils import config
-from utils.text_utilities import post_process_transcription, summarize
+from ...utils.text_utilities import post_process_transcription, summarize
-from utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
+from ...utils.viz_utilities import create_talk_diff_scatter_viz, create_wordcloud
 
 WHISPER_MODEL_SIZE = config['DEFAULT']["WHISPER_MODEL_SIZE"]