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Merge pull request #32 from Monadical-SAS/feat/gokul

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Clean up and move notebook to notebooks folder
This commit is contained in:
projects-g
2023-07-18 22:45:39 +05:30
committed by GitHub
parent 0f7c71bea3 b34b8e6a96
commit 7a3150027e
5 changed files with 129 additions and 62 deletions
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0
trials/incsum.ipynb → notebooks/incsum.ipynb
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1
requirements.txt
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@@ -57,3 +57,4 @@ stamina==23.1.0
httpx==0.24.1
sortedcontainers==2.4.0
https://github.com/yt-dlp/yt-dlp/archive/master.tar.gz
gpt4all==1.0.5

45
server_executor_cleaned.py
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@@ -10,6 +10,7 @@ from aiohttp import web
from aiortc import MediaStreamTrack, RTCPeerConnection, RTCSessionDescription
from aiortc.contrib.media import MediaRelay
from av import AudioFifo
from gpt4all import GPT4All
from loguru import logger
from whisper_jax import FlaxWhisperPipline
@@ -26,6 +27,28 @@ CHANNELS = 2
RATE = 48000
audio_buffer = AudioFifo()
executor = ThreadPoolExecutor()
transcription_text = ""
llm = GPT4All("/Users/gokulmohanarangan/Library/Application Support/nomic.ai/GPT4All/ggml-vicuna-13b-1.1-q4_2.bin")
def get_title_and_summary():
global transcription_text
output = None
if len(transcription_text) > 1000:
print("Generating title and summary")
prompt = f"""
### Human:
Create a JSON object having 2 fields: title and summary. For the title field generate a short title for the given
text and for the summary field, summarize the given text by creating 3 key points.
{transcription_text}
### Assistant:
"""
transcription_text = ""
output = llm.generate(prompt)
return str(output)
return output
def channel_log(channel, t, message):
@@ -34,8 +57,8 @@ def channel_log(channel, t, message):
def channel_send(channel, message):
# channel_log(channel, ">", message)
if channel:
channel.send(message)
if channel and message:
channel.send(str(message))
def get_transcription(frames):
@@ -50,9 +73,9 @@ def get_transcription(frames):
wf.writeframes(b"".join(frame.to_ndarray()))
wf.close()
whisper_result = pipeline(out_file.getvalue(), return_timestamps=True)
with open("test_exec.txt", "a") as f:
f.write(whisper_result["text"])
whisper_result['start_time'] = [f.time for f in frames]
# whisper_result['start_time'] = [f.time for f in frames]
global transcription_text
transcription_text += whisper_result["text"]
return whisper_result
@@ -75,9 +98,15 @@ class AudioStreamTrack(MediaStreamTrack):
get_transcription, local_frames, executor=executor
)
whisper_result.add_done_callback(
lambda f: channel_send(data_channel,
str(whisper_result.result()))
if (f.result())
lambda f: channel_send(data_channel, whisper_result.result())
if f.result()
else None
)
llm_result = run_in_executor(get_title_and_summary,
executor=executor)
llm_result.add_done_callback(
lambda f: channel_send(data_channel, llm_result.result())
if f.result()
else None
)
return frame

10
stream_client.py
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@@ -1,4 +1,3 @@
import ast
import asyncio
import time
import uuid
@@ -11,9 +10,7 @@ from aiortc import (RTCPeerConnection, RTCSessionDescription)
from aiortc.contrib.media import (MediaPlayer, MediaRelay)
from utils.log_utils import logger
from utils.run_utils import config, Mutex
file_lock = Mutex(open("test_sm_6.txt", "a"))
from utils.run_utils import config
class StreamClient:
@@ -146,10 +143,7 @@ class StreamClient:
async def worker(self, name, queue):
while True:
msg = await self.queue.get()
msg = ast.literal_eval(msg)
with file_lock.lock() as file:
file.write(msg["text"])
yield msg["text"]
yield msg
self.queue.task_done()
async def start(self):

131
trials/gpt2.py
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@@ -1,55 +1,98 @@
# Approach 1
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
# # Approach 1
# 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'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPTNeoForCausalLM.from_pretrained(model_name)
#
# # Approach 2
# import torch
# from transformers import GPT2LMHeadModel, GPT2Tokenizer
#
# 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)
conversation = """
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 .
"""
# Approach 3
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):])
# Approach 2
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers import GPT2Tokenizer, GPT2LMHeadModel
def generate_response(conversation, max_length=100):
input_text = ""
for entry in conversation:
role = entry["role"]
content = entry["content"]
input_text += f"{role}: {content}\n"
# Tokenize the entire conversation
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate text based on the entire conversation
with torch.no_grad():
output = model.generate(input_ids, pad_token_id=tokenizer.eos_token_id)
# Decode the generated text and return it
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response
if __name__ == "__main__":
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model.eval()
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 . "
]
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 . "
"""
conversation = [
{"role": "system", "content": "Summarize this text" },
{"role": "user", "content": " text : " + sample_chunks[0]},
]
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)
response = generate_response(conversation)
print("Response:", response)
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)