ehzawad ehzawad

In your current code, the Seq2SeqTrainingArguments class is responsible for saving checkpoints. The save_steps attribute dictates how often a checkpoint is saved. If a checkpoint is saved, you can resume training from that checkpoint at any time. Your current save_steps is set to 1000, meaning a checkpoint will be saved every 1000 steps.

If you want to save checkpoints more frequently, you can decrease the value of save_steps.

Here's the complete refactored version of your code:

import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Union

In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

latest: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
archive: https://docs.nvidia.com/cuda/archive/

To find NVIDIA kernel modules:
bash
Copy code
find /lib/modules/$(uname -r)/kernel/drivers -type f -name 'nvidia*.ko*'
To find NVIDIA libraries in /usr/lib and /usr/lib64:
bash
Copy code
find /usr/lib /usr/lib64 -type f -name 'libnvidia*'

	const products = [
	{ title: 'Cabbage', isFruit: false, id: 1 },
	{ title: 'Garlic', isFruit: false, id: 2 },
	{ title: 'Apple', isFruit: true, id: 3 },
	];

	export function ShoppingList() {
	const listItems = products.map(product =>
	<li
	key={product.id}

	from flask import Flask, render_template, request as rqst, jsonify
	from flask_socketio import SocketIO, emit
	import sys
	from flask_cors import CORS

	import argparse
	import queue
	import sys
	import json
	import asyncio

	import math
	import random

	# Initial state: a random number between 0 and 2π
	def initial_state():
	return random.uniform(0, 2*math.pi)

	# Neighbor: a new state very close to the current one
	def neighbor(state):
	new_state = state + random.gauss(0, 0.1)


	import json
	import gradio as gr
	import numpy as np

	from vosk import KaldiRecognizer, Model

	model = Model(model_path="/home/gns/vosk-api/model/")

	[INFO]: Driver not installed.
	[INFO]: Checking compiler version...
	[INFO]: gcc location: /usr/bin/gcc

	[INFO]: gcc version: gcc version 12.1.0 (Ubuntu 12.1.0-2ubuntu1~22.04)

	[INFO]: Initializing menu
	[INFO]: nvidia-fs.setKOVersion(2.15.3)
	[INFO]: Setup complete
	[INFO]: Installing: Driver

	from transformers import pipeline


	# asr = pipeline("automatic-speech-recognition", model="ehzawad/whisper-tiny-bn")

	# output = asr("/root/ehz_asr/sample_bn.mp3", chunk_length_s=600)


	# print(output)
	# print(type(output))


	from transformers import pipeline
	import gradio as gr

	pipe = pipeline(model="ehzawad/whisper-tiny-bn")

	def transcribe(audio):
	text = pipe(audio)["text"]
	return text