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In this final audio lesson, we'll tackle text-to-audio generation by converting text to speech. Text-to-speech is a challenging task because it is a one-to-many problem. In classification, you have one correct label, maybe a few. In automatic speech recognition, there's one correct transcription for a given utterance. However, there's an infinite amount of ways to say the same sentence. Each person has a different way of speaking, but they are all valid and correct. Think about different voices, dialects, speaking styles, and so on. Despite these challenges, there are open-source models that can handle this task really well, and you're about to use one of them. We'll use a VITS pre-trained model from Kakao Enterprise. This is one of the two models that can fit in this environment. And this model has a permissive license. Once you have the pipeline, all you need to do is to pass some text to it. Let's write some text. Now let's pass this text to the pipeline. Let's give it a listen. Researchers at the Allen Institute for AI are going to face Microsoft. The University of Washington, Carnegie Mellon University, and the Hebrew University of Jerusalem developed a tool that measures atmospheric carbon emitted by cloud servers while training machine learning models. After a model's size, the biggest variables were the server's location and time of day it was active. And just like that, you can convert text into an aerated audio recording. Feel free to paste your text into your computer. Feel free to paste your own text and play with the pipeline. In the next lesson, Yunus will show you how to build an object detector. Let's go on to the next lesson.
Open Source Models with Hugging Face
- Introduction
- Selecting models
- Natural Language Processing (NLP)
- Translation and Summarization
- Sentence Embeddings
- Zero-Shot Audio Classification
- Automatic Speech Recognition
- Text to Speech
- Object Detection
- Image Segmentation
- Image Retrieval
- Image Captioning
- Multimodal Visual Question Answering
- Zero-Shot Image Classification
- Deployment
- Conclusion