For any a file opening URL in SharePoint, I use the two following APIs, but failed to get sensitivity label using Double Key Encryption (DKE)

1. https://nextlabstest2.sharepoint.com/_api/SP.RemoteWeb(@a1)/web/GetFileByUrl(@a1)/ListItemAllFields/GetSharingInformation?$select=sensitivityLabelInformation&@a1=any_form_file_opening_url
{
    “sensitivityLabelInformation”: null
}

2.
https://graph.microsoft.com/v1.0/shares/${encodedSharingUrl}/driveItem/sensitivityLabel
{
“@odata.context”: “https://graph.microsoft.com/v1.0/$metadata#microsoft.graph.driveItemSensitivityLabel”,
“displayName”: “Confidential \ RESTRICTED TO US PERSONS”,
“error”: null,
“id”: “04540264-2228-43ae-a3de-17927756f276”,
“protectionEnabled”: false
}

​For any a file opening URL in SharePoint, I use the two following APIs, but failed to get sensitivity label using Double Key Encryption (DKE)

1. https://nextlabstest2.sharepoint.com/_api/SP.RemoteWeb(@a1)/web/GetFileByUrl(@a1)/ListItemAllFields/GetSharingInformation?$select=sensitivityLabelInformation&@a1=any_form_file_opening_url
{
    “sensitivityLabelInformation”: null
}

2.
https://graph.microsoft.com/v1.0/shares/${encodedSharingUrl}/driveItem/sensitivityLabel
{
“@odata.context”: “https://graph.microsoft.com/v1.0/$metadata#microsoft.graph.driveItemSensitivityLabel”,
“displayName”: “Confidential \ RESTRICTED TO US PERSONS”,
“error”: null,
“id”: “04540264-2228-43ae-a3de-17927756f276”,
“protectionEnabled”: false
}  Read More

​

I have a site that has 3 subsites. I want to display selected news posts from these subsites in a news web part on the home page of the main site. I have tried creating terms in the term library 2 days ago. I then created a managed metadata column on the subsite Pages library and assigned terms to each of the posts. I checked the settings, the column is used in “Wiki Page, Web Part Page, Site Page, Repost Page”.

In the news web part I have selected the site, but when I try to add the filters on the home page web part I can find the managed metadata group by typing it into the “Enter a word to find properties”. However, when I type the value into the “Enter search value” nothing is found.

What am I doing wrong?

​I have a site that has 3 subsites. I want to display selected news posts from these subsites in a news web part on the home page of the main site. I have tried creating terms in the term library 2 days ago. I then created a managed metadata column on the subsite Pages library and assigned terms to each of the posts. I checked the settings, the column is used in “Wiki Page, Web Part Page, Site Page, Repost Page”. In the news web part I have selected the site, but when I try to add the filters on the home page web part I can find the managed metadata group by typing it into the “Enter a word to find properties”. However, when I type the value into the “Enter search value” nothing is found. What am I doing wrong?  Read More

​

Hi,

I am trying to find a way to transfer the model token usage data to BigQuery, here are my attampts:

1. OpenAI model- deployment – metrics. It provides token usage directly, but I failed to find a way to export it on a scheduled basis.

2. API management service. Not working when using streaming responses with Azure OpenAI models because it requires more configuration to capture model completions.  As I am developing a chatbot, the streaming feature is necessary.

3. Export native logs by diagnostic settings. Unfortunately there is no token usage related info included.

So I wanna kindly ask for a potential solution for exporting the token usage to BigQuery with streaming function when using openai model. Thanks!

​Hi,I am trying to find a way to transfer the model token usage data to BigQuery, here are my attampts:1. OpenAI model- deployment – metrics. It provides token usage directly, but I failed to find a way to export it on a scheduled basis.2. API management service. Not working when using streaming responses with Azure OpenAI models because it requires more configuration to capture model completions.  As I am developing a chatbot, the streaming feature is necessary.3. Export native logs by diagnostic settings. Unfortunately there is no token usage related info included. So I wanna kindly ask for a potential solution for exporting the token usage to BigQuery with streaming function when using openai model. Thanks!  Read More

​

Hi,

We are going to use both Jira and Azure devops to manage multiple projects. however, we are unable to match the jira hierarchy to Azure devops. in jira we use Project -> Release -> Issue Types (Epic -> story,task,supbtask etc)

We would like to know how we can map this hierarchy in Azure devops boards? what are equal entities for Project, Release, and Issue types in Azure devops 

​Hi,We are going to use both Jira and Azure devops to manage multiple projects. however, we are unable to match the jira hierarchy to Azure devops. in jira we use Project -> Release -> Issue Types (Epic -> story,task,supbtask etc)We would like to know how we can map this hierarchy in Azure devops boards? what are equal entities for Project, Release, and Issue types in Azure devops   Read More

​

Hi everyone,

I need to convert a set of SVG files to JPG format on my Windows 10 PC. I’ve tried looking into a few options, but I’m hoping for some guidance on the easiest and most reliable methods for doing this. I’d prefer not to rely on online SVG to JPEG converters if possible due to file size and privacy concerns.

Does anyone have recommendations for software or command-line tools that can batch convert svg to jpg images on Windows 10? Any tips or steps would be greatly appreciated! 

​Hi everyone,I need to convert a set of SVG files to JPG format on my Windows 10 PC. I’ve tried looking into a few options, but I’m hoping for some guidance on the easiest and most reliable methods for doing this. I’d prefer not to rely on online SVG to JPEG converters if possible due to file size and privacy concerns. Does anyone have recommendations for software or command-line tools that can batch convert svg to jpg images on Windows 10? Any tips or steps would be greatly appreciated!   Read More

​

Hi everyone.

I’ve read some post on this topic like this

https://www.makeuseof.com/allow-block-biometrics-windows-11/

But GPO enabling (via gpedit) or registry key adding you have to perform on server (AD) machine or single client machine that has to login using biometrics or both systems?

That’s not clear for me.

Many thanks.

​Hi everyone.I’ve read some post on this topic like thishttps://www.makeuseof.com/allow-block-biometrics-windows-11/But GPO enabling (via gpedit) or registry key adding you have to perform on server (AD) machine or single client machine that has to login using biometrics or both systems?That’s not clear for me.Many thanks.    Read More

​

Hi,

we are facing strange issue within Intune, when manually deleted SCEP User certificate is not re-enrolled automatically based on configuration profile. Also this configuration profile is NOT marked as non-compliant even after a week of syncs for that device. And what is the most important, SCEP configuration profile definition from point of view of macOS knows, that SCEP certificate is missing because, when you open config profile within Settings/Device Management on macOS, there is error saying “Not found in keychain”.

Documentation https://learn.microsoft.com/en-us/mem/intune/protect/remove-certificates saying exactly following:

Manual deletion of a certificate is a scenario that applies across platforms and certificates provisioned by SCEP or PKCS certificate profiles. For example, a user might delete a certificate from a device, when the device remains targeted by a certificate policy.

In this scenario, after the certificate is deleted, the next time the device checks in with Intune it’s found to be out of compliance as it is missing the expected certificate. Intune then issues a new certificate to restore the device to compliance. No other action is needed to restore the certificate.

So it means that if user delete SCEP User certificate from keychain, doesn’t matter if it was intention or accident, as long as I keep SCEP Configuration profile within Intune for exact device and user, Intune must initiate re-enrolling/re-generating new certificate based on this profile.

This is not happening on our macOS’s laptops and only workaround I’ve got from MS Support is to remove device from Configuration profile and then return it back… But imagine when you have 1000 macOS laptops and 100 users (extreme example, but could happen, i.e. developers trying things) delete their certificates from Keychain. Whole action to removing devices and users from that profile is time wasting. first create special groups to include affected devices and affected users, then add that group to exclusion, wait a long for sync of all macOS’s, then starting to removing those devices and users from group to return configuration profile back.

Also comment from MS Support was, that they cannot escalate the case to different team, because I have selected exact time zone and only they are responsible for that time zone (what a bullshit???) and that my case is already escalated withing his team manager. But his team manager is same low-skilled incompetent as engineer got my support case. And if certificate is returned when I remove and re-add config profile, then case is finished (what another bullshit????) – but from my point of view it’s not finished because it’s not a fix, it’s workaround and very complex, time and money wasting workaround.

Note to Microsoft: STOP hiring !!!! low-skilled incompetent Indian support teams, just because they costs less then European or United States engineers!!!! You are wasting our money, our time, our patience and you want more and more money for your subscriptions and we are getting less and worst services.

​Hi, we are facing strange issue within Intune, when manually deleted SCEP User certificate is not re-enrolled automatically based on configuration profile. Also this configuration profile is NOT marked as non-compliant even after a week of syncs for that device. And what is the most important, SCEP configuration profile definition from point of view of macOS knows, that SCEP certificate is missing because, when you open config profile within Settings/Device Management on macOS, there is error saying “Not found in keychain”. Documentation https://learn.microsoft.com/en-us/mem/intune/protect/remove-certificates saying exactly following:Manually deleted certificatesManual deletion of a certificate is a scenario that applies across platforms and certificates provisioned by SCEP or PKCS certificate profiles. For example, a user might delete a certificate from a device, when the device remains targeted by a certificate policy.In this scenario, after the certificate is deleted, the next time the device checks in with Intune it’s found to be out of compliance as it is missing the expected certificate. Intune then issues a new certificate to restore the device to compliance. No other action is needed to restore the certificate. So it means that if user delete SCEP User certificate from keychain, doesn’t matter if it was intention or accident, as long as I keep SCEP Configuration profile within Intune for exact device and user, Intune must initiate re-enrolling/re-generating new certificate based on this profile. This is not happening on our macOS’s laptops and only workaround I’ve got from MS Support is to remove device from Configuration profile and then return it back… But imagine when you have 1000 macOS laptops and 100 users (extreme example, but could happen, i.e. developers trying things) delete their certificates from Keychain. Whole action to removing devices and users from that profile is time wasting. first create special groups to include affected devices and affected users, then add that group to exclusion, wait a long for sync of all macOS’s, then starting to removing those devices and users from group to return configuration profile back.Also comment from MS Support was, that they cannot escalate the case to different team, because I have selected exact time zone and only they are responsible for that time zone (what a bullshit???) and that my case is already escalated withing his team manager. But his team manager is same low-skilled incompetent as engineer got my support case. And if certificate is returned when I remove and re-add config profile, then case is finished (what another bullshit????) – but from my point of view it’s not finished because it’s not a fix, it’s workaround and very complex, time and money wasting workaround. Note to Microsoft: STOP hiring !!!! low-skilled incompetent Indian support teams, just because they costs less then European or United States engineers!!!! You are wasting our money, our time, our patience and you want more and more money for your subscriptions and we are getting less and worst services.  Read More

​

I’m looking to recycle my old hard drive, but I want to make sure all of my personal data is completely wiped clean before doing so. I’m using a Windows 11 computer, and I want to make sure I handle this correctly to avoid any chance of data recovery. I’ve heard there are different methods and tools available, but I’m not sure which one is the best for securely erasing the drive.

Can anyone guide me on the best way to thoroughly wipe hard drive before recycling on Windows 11? Thanks.

​I’m looking to recycle my old hard drive, but I want to make sure all of my personal data is completely wiped clean before doing so. I’m using a Windows 11 computer, and I want to make sure I handle this correctly to avoid any chance of data recovery. I’ve heard there are different methods and tools available, but I’m not sure which one is the best for securely erasing the drive. Can anyone guide me on the best way to thoroughly wipe hard drive before recycling on Windows 11? Thanks.  Read More

​

i encountered some cases that intune might have been causing error when updating apps in the personal google play store. have you guys also encounter this kind of issue and any suggestion where do i look for in intune admin for troubleshooting?

Thanks

​i encountered some cases that intune might have been causing error when updating apps in the personal google play store. have you guys also encounter this kind of issue and any suggestion where do i look for in intune admin for troubleshooting? Thanks  Read More

​

Introduction

Automatic Speech Recognition (ASR) is a technique that enables machines to process and convert human spoken language to written text, and it is widely used in many areas today. However, low inference speed is one of the major challenges in automatic speech recognition. This project aims to investigate the different ways of accelerating inference for the Whisper model, leveraging Open Neural Network Exchange (ONNX) format, with different Runtimes and specialised hardware accelerators like NPUs. This project is a part of the IXN (Industrial Exchange Network) program, which is a teaching framework offering master and undergraduate students opportunities to work with industry companies. This Project is supported by UCL CS, Microsoft, and Intel, supervised by Professor Dean Mohamedally from the UCL CS department, Mr. Lee Jonathan Stott, and Mr. Chris Noring from Microsoft. intel provides technical support with an Intel PC equipped with an AI accelerator NPU. This project lasts 3 months, from June 2024 to September 2024.

Project Overview

This project explores the benefits of ONNX and NPU accelerators in accelerating the inference of Whisper models and developing a local Whisper model leveraging these techniques for ARM-based systems. Objectives include investigating the end-to-end speech model  – Whisper, and its variants; evaluating the performance of different approaches to using Whisper model; finding different ways of converting Whisper in PyTorch to the ONNX format; applying various optimisations techniques to the ONNX model; comparing the performance of PyTorch models and ONNX models; utilising NPU for inference and comparing its performance with other hardware devices; compiling ONNX model for ARM, testing its compatibility on ARM-based system to offer the model a more practical use in IoT or embedded devices such as phones or chatbots

Technical Details

The Whisper model was initially built and evaluated in PyTorch. To improve inference speed, the model was converted into ONNX format using tools like PyTorch’s torch.onnx.export, the Optimum library, and Microsoft’s Olive tool. These models were then optimized using graph optimizations and quantization techniques to reduce the model size and speed up inference.

To further boost performance, the team explored various inference engines such as ONNX Runtime and OpenVINO, which significantly accelerated the model’s execution. The project also examined the use of NPUs, which, although not delivering the expected performance boost, still offered insights into future improvements.

Visuals and Media

The project included various visualizations and performance charts comparing the inference speeds of the Whisper models with different levels of optimisation across hardware accelerators (CPU, GPU, NPU) and inference engines (ONNX Runtime, OpenVINO). These visual aids were key to understanding the improvements and trade-offs in model performance across platforms.

Figure 1: Accuracy and speed of different Whisper checkpoints

Figure 2: Accuracy and speed of different invocation methods for Whisper-tiny

Figure 3: Average Inference Time per Sample per Second Across Acceleration Methods and

Datasets

Figure 4 : Inference time using CPU, GPU, and NPU on Song Clip dataset

Results and Outcomes

Figure1

As the model size increases from tiny to large, the inference time increases gradually with values of 0.98s, 1.37s, 5.15s, 17.12s, and 29.09s respectively while WER decreases with values of 6.78%, 4.39%, 3.49%, 2.43% and 2.22% in the same order.

Figure2

Among the four methods, the average inference time per sample for the Hugging Face API endpoint was the longest which was 1.82 seconds, while the other three took less than 1 second for inference: 0.91,0.78,0.97 seconds for using Pipeline, OpenAI Whisper library, and Hugging Face transformers library respectively. For accuracy, except the Hugging Face Transformers library which achieved the lowest word error rate of 6.78%, the other three methods produced slightly higher error rates, all around 7%.

Figure3

The project achieved a maximum 5x increase in inference speed for the Whisper model when using the ONNX format optimized with OpenVINO, compared to its PyTorch counterpart. The project demonstrated the feasibility of deploying these optimized models on ARM-based systems, opening doors to their use in IoT devices and real-time speech applications on edge devices.

Figure 4

The plot demonstrates the inference time of Whisper-tiny with different running device config-

urations, tested on the Song Clip dataset. CPU, GPU, and specialised hardware accelerator NPU

were used in this part of the study. The results were sorted in descending order from top to bottom, from the device configuration with the longest inference time to the device configuration with the shortest inference time. The inference time ranges from 1.371s to 0.182s, the configuration that has the fastest inference speed is CPU and the slowest one has setting MULTI: GPU, NPU. From the results, CPU outperforms NPU, and NPU outperforms GPU. Using MULTI or HETERO mode will have faster speed and less inference time than using GPU or NPU alone for certain configurations. For example, HETERO: CPU, NPU, HETERO: CPU, GPU and MULTI: GPU, NPU, CPU have less inference time than NPU and GPU. For the other three cases, results are similar, either CPU or MULTI: GPU, NPU, CPU has the shortest inference time while MULTI: GPU, NPU has the longest.

Lessons Learned

Several critical lessons emerged from this project:

ONNX format is a game changer: Converting models to ONNX significantly improves cross-platform compatibility and inference speed, making it easier to deploy on various devices.
NPUs need further exploration: While promising, NPUs didn’t deliver as much performance improvement as expected. However, this points toward the need for more refined optimization in future work.
Quantization requires careful management: Reducing the precision of model data helped speed up performance, but it also introduced a trade-off in accuracy. Future iterations will need to balance this more effectively.

Collaboration and Teamwork

This project’s success was a testament to the power of collaboration. The combination of academic expertise from UCL and industry knowledge from Microsoft ensured that the project stayed on track and met its goals. Team roles were clearly defined, which allowed for a smooth and productive workflow.

Future Development

The project has laid a solid foundation for further research and development. Several areas are ripe for future exploration:

NPU-specific optimisation: The team plans to explore more advanced methods to harness the full potential of NPUs.
Deployment on real ARM devices: While the models ran successfully in a Docker container, the next step is to deploy them on actual ARM-based devices, such as smartphones or IoT gadgets.
Multilingual testing: Thus far, the project has focused primarily on English speech. Expanding the model to other languages will open up new possibilities for global applications.

Conclusion

This project successfully demonstrated how cutting-edge tools and optimizations can significantly improve the performance of ASR systems. By leveraging ONNX, NPUs, and ARM-based platforms. The project’s outcomes highlight the importance of optimizing models for resource-constrained environments and provide a clear path forward for deploying advanced ASR models in the field.

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