Supercharge Your NLP: Fine Tune OpenAI's Whisper for Lithuanian!

Table of Contents:

1. Introduction
2. Action Plan for Fine-Tuning Whisper AI
   • 2.1 Loading the Dataset
   • 2.2 Preparing Mandatory Components
   • 2.3 Combining Feature Extractor and Tokenizer
   • 2.4 Preprocessing the Data
   • 2.5 Training and Evaluation
3. Installing Required Packages
4. Logging into Hugging Face
5. Downloading Language Dataset
6. Preparing Feature Extractor, Tokenizer, and Data
7. Combining Elements with Whisper Processor
8. Preparing the Data
9. Training and Evaluation
10. Conclusion

Action Plan for Fine-Tuning Whisper AI

1. Introduction

Whisper AI, developed by Mozilla Foundation, is a state-of-the-art automatic speech recognition (ASR) model that is widely used for various language tasks. In this tutorial, we will walk through the steps to fine-tune the Whisper AI model for a specific language. Fine-tuning allows us to adapt the pre-trained model to accommodate the peculiarities and nuances of the target language. By the end of this tutorial, You will be able to train your own ASR model using the Whisper AI framework.

2. Action Plan for Fine-Tuning Whisper AI

To successfully fine-tune Whisper AI, we will follow a step-by-step action plan. Each step is crucial for the successful implementation of the model and will be discussed in Detail.

2.1 Loading the Dataset

In order to fine-tune the Whisper AI model, we need a dataset that is suitable for the target language. We will start by downloading the language dataset from the Hugging Face model repository. The dataset will be divided into a training set and a test set, which will be used for training and evaluation respectively.

2.2 Preparing Mandatory Components

In this step, we will prepare the mandatory components required for fine-tuning the Whisper AI model. These components include the feature extractor and the tokenizer. The feature extractor is responsible for preprocessing the raw audio inputs and converting them into log mel spectrogram input features. The tokenizer performs sequence-to-sequence mapping and converts the model output into text strings.

2.3 Combining Feature Extractor and Tokenizer

Once the feature extractor and tokenizer are ready, we will combine them using the Whisper processor. The Whisper processor combines the functionality of the feature extractor and tokenizer, and prepares the data for the audio inputs and model predictions.

2.4 Preprocessing the Data

In this step, we will preprocess the data according to the requirements of the Whisper AI model. The preprocessing steps include resampling the audio batch to 16,000 Hz, computing log mel spectrogram inputs, and encoding the target text to label IDs using the tokenizer. These preprocessing steps ensure that the data is in the required format for training the model.

2.5 Training and Evaluation

The final step in the action plan is training and evaluation. We will define a data collator to batch the input features, perform padding, and replace tokens. We will also define the evaluation metric, which in this case is the word error rate (WER). We will load the pre-trained Whisper AI model and define the training configuration. Finally, we will initiate the trainer and start training the model.

3. Installing Required Packages

Before we begin the action plan, we need to install the necessary Python packages. These packages include the dataset, transformers, evaluate, and torch. We will use the dataset package to get the required dataset from the Hugging Face model repository, the transformers package for interaction with the Whisper AI model, the evaluate package for computing the evaluation metric, and the torch package for training the model.

4. Logging into Hugging Face

To access the Whisper AI model and download the language dataset, we need to log into the Hugging Face portal. By logging in, we will be able to generate a token that will be used for authentication. Once we have the token, we can proceed with downloading the dataset for fine-tuning the model.

5. Downloading Language Dataset

Now that We Are logged into the Hugging Face portal, we can start downloading the language dataset. We will choose the language dataset for the target language we want to fine-tune the model to. The dataset will consist of training and test sets, which will be used accordingly.

6. Preparing Feature Extractor, Tokenizer, and Data

In this step, we will prepare the feature extractor and tokenizer using the downloaded language dataset. The feature extractor will be responsible for extracting Relevant features from the audio inputs, while the tokenizer will convert the model output to text strings. We will also combine these components with the Whisper processor to simplify the usage of future extractor and tokenizer.

7. Combining Elements with Whisper Processor

Now that we have prepared the feature extractor and tokenizer, we can combine them with the Whisper processor. The Whisper processor combines the functionality of both components and simplifies their usage on the audio inputs and model predictions. We will specify the model version, language, and task for the Whisper AI model.

8. Preparing the Data

Once the elements are combined with the Whisper processor, we can proceed with preparing the data for training the model. This step involves resampling the audio data to meet the requirements of the Whisper AI model. We will compute the log mel spectrogram for the audio inputs and encode the target text to label IDs using the tokenizer. These preprocessing steps ensure that the data is prepared according to the requirements of the model.

9. Training and Evaluation

The final step in the action plan is training and evaluation. We will initiate a data collator to batch the input features, perform padding, and replace tokens. We will also define the evaluation metric, which in this case is the word error rate (WER). We will load the pre-trained Whisper AI model and define the training configuration. Finally, we will initiate the trainer and start training the model. The training process can take several hours, depending on the GPU capabilities.

10. Conclusion

In this tutorial, we have learned the step-by-step action plan for fine-tuning the Whisper AI model for a specific language. We covered the necessary steps from loading the dataset to training and evaluation. By following this action plan, you will be able to train your own ASR model and adapt it to different languages. Fine-tuning the Whisper AI model opens up possibilities for various language tasks and improvements in accuracy.