volunteers | A volunteer scheduling system for festivals and events | Job Scheduling library

Use a pre-trained automatic speech recognition (ASR) model, e.g. using Python and huggingface, like Facebook's Wav2vec 2.0 model (https://huggingface.co/facebook/wav2vec2-base-960h) or any other ASR model (https://huggingface.co/models?pipeline_tag=automatic-speech-recognition) to get a text transcript of the speech. These are usually language dependent, so you will have to find models that suit your goal.

Process the text you already have into a more similar form to an audio transcript (set to lowercase, remove punctuation etc.).

Then it's up to you how you want to compare the two texts. This also depends on how long the texts can be. If it's just single sentences then you could just compare if they are completely the same. If it's a bit longer you can start with a word-wise matching to see what percentage of the words it gets right/wrong (Word Error Rate). Since many trained ASR models use context to determine the transcript some more advanced (but still character or word-based) text similarity metrics such as BLEU or Levenshtein distance might be more suitable, especially since that handles the problem of extra or left out words for you which might be very difficult to handle in self-created metrics.

Generally you can use the same approaches which are used to evaluate Automatic Speech Recognition models, since you do the same thing (compare a transcript to the expected text). There are repositories and packages for this, e.g. this one and this one.

In any case you need to be aware that a models speech recognition will never be perfect, so a score less than perfect doesn't mean your volunteer didn't follow the script. But if you compare the scores between the volunteers you can get an idea how close they stick to the script as well as generally how clearly they speak.

You should also keep in mind that things like accents, backgrounds noises, audio quality and general similarity between the way your volunteer records and the way the model training data was recorded will influence the scores.

Your C# api controller action is returning a List of objects :

You can attempt to create one more table where the id of through table will be paired with date, and then change it whenever a signal is fired on thorough table.Other option is to manage it manually by overriding methods of the manager save, delete, create and methods of the relation manager add, clean, remove, set

Information that might help you: https://docs.djangoproject.com/en/3.2/ref/signals/#post-save https://docs.djangoproject.com/en/4.0/topics/db/models/#intermediary-manytomany

You can select the first 10 rows of your dataframe with iloc[0:10].

In this case, the solution would look like this, with the least modification to your existing code:

Here is something I did for use with my email whitelist to ensure I didn't get duplicate emails.

To get justify-content to work, the element itself needs to be displayed as flex:

You need to put the logic of comparison inside the some callback:

first, You have a composite unique key on (user_id, provider_id, user_type_id) so user_id by itself is not guaranteed to be unique , but the combination of those 3 column is.

second, your primary key in provide_user is column id , and the best practice is to have FK to PK , not to the uniuque column(s)

so instead of having volunteer_id linked to user_id from provider_user, link it to id column

I did it! The solution found!

Before all, I have to say that I don’t count the solution as correct or corresponding to the ideas of the Gedit editor. It’s a dirty hack, of course. But, strangely, there is no way to change the initial content of placeholders in the snippets — haven’t I just found a standard way to do that?

So. If they don’t allow us to change the text in placeholders, let’s ask the system to do that.

The first thought that stroke me was to print backspace characters. There are two ways to do that: a shell script and a python script. The first approach might look like: $1$(printf '\b') The second one should do the same: $1$<[1]: return '\b'> But both of them don’t work — Gedit prints surrogate squares instead of real backspace characters.

Thus, xdotool is our friend! So is metaprogramming! You will not believe — metaprogramming in a shell script inside a snippet — sed will be writing the scenario for xdotool. Also, I’ve added a feature that changes spaces to underscores for easier typing. Here is the final code of the snippet: