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26. August 2019 · Read 6 min
Wanted to create your personalized AI bot to identify what you say what you say? You probably have to have at some point, but would have dropped the idea because either you are too complex or time consuming to train or create it. In this blog you will learn how to create your custom language models with IBM Watson, and the best is that you do not have to write a single line of code. Let's start!
Speech recognition and speech for text services have long been there for a long time, but it does not offer the full skills, for example, they focus on smaller audiences or are unable to. IBM Watson® speech The text on the IBM Cloud provides functions such as transcribing real-time audio in 7 different languages ??with a very high accuracy rate and supports various applications. Best part is that you drive / train the service to your standards, and we will look at this now.
The language to the text service focuses on creating two different types of custom speech recognition models - acoustic models and voice models. We will learn to create both.
An acoustic model allow us to customize a basic model for the acoustic properties of your environment and speakers. In such cases you can create an acoustic model:
Now let's look at how we can create an acoustic model that deals with a loud environment and with speakers that have Indian and Scottish accents.
Let's download some audio sets! You can download some audios with a loud environment from this link. This is a subset of this record. You can download audio with the Indian accent from here and with the Scottish accent from here.
Open the terminal on your computer and enter the following command. Replace the "ApiKey" by your own ApiKey (credentials available on the IBM Cloud) and you can also give the model a name. I named it as "my first acoustic model".
This command creates an adjustment ID. This customization ID is the identification number for the custom acoustic model we will create. This ID is generally used in your applications, so keep it.
Next, we add audio files to this Base Custom model that we have created so we can send you for training. There are 3 different audio files (make sure they are in zipper folders), so we run this command three times with the different ZIP folders we have. To run this Command CD in the same directory as the audio files.Make sure that you replace APIKEY, the name of the audio folder and the customization ID from the previous step and add, replace:
The next step is to train our model with these audio files, so enter the following command and wait about minutes until you're done:
You can check the status of the training by entering the following command:
This service was created for the broader and general audience. The basic model includes many words that we use in everyday talks, but the vocabulary extends when we dive into an industry focus, such as the medical industry or the oil and gas industry. By using the voice model customization, we can customize the vocabulary to enclose a domain-specific terminology. This is done by adding a corpus file. A corpus file contains text / keywords that are normally used in the industry. We will focus on the oil and gas industry in the blog.
First step is to add the corpus text file to the model. So download the file from here. This file contains the majority of the terminology used in an oil and gas industry.
Now run the following command to create a custom speech model, and this also creates an adjustment ID:
In this step, we add the Corpus file to the model, so we can send it for training on these words. Make sure you use the CD to run the same directory as the Corpus file when you run the command to the same directory and add the customization ID from the previous step:
Now let's look at what the model has analyzed. Run the command below:
After running the command, the following results are displayed on the left side. This means that the file contains 2193 words from which the model is not formed on 10 words. As soon as we send the model for training, it is trained on these 10 vocabulary.
Enter the following command to send it to workout and wait minutes:
You can check the training status by entering the following commands:
We finally created our custom models in the cloud, but how can we actually test if you are working and how exactly you are? To test the model and check the accuracy, we will run our models on a blind audio set with a transcription.
A common technique used to test the accuracy of the language models is a Word error rate. The lower the rate is, the better the accuracy. It is calculated with the following formula:
s : Substitutions (replace a word).
I : for inserts (insertion of a word).
D: Deletions (a word omit).
N is the number of words that were actually stated: Who will be wrong if you forgot to normalize the capitalization, punctuation, numbers, etc. on all transcripts.
We write a Python script to test the accuracy of our two models. Python offers a library named Jiwer to automatically calculate the word error rate for speech recognition models.
Steps for testing and obtaining an accuracy rate:
Download some sample audio files from this audio set and get your transcription from here.
Download the following code files to get the results:
After you have performed these 2 files on the samples, you will receive an accuracy rate near 0.04 in an Excel sheet. What is a pretty good accuracy rate.