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2. Speech synthesis - Wikipedia

   en.wikipedia.org/wiki/Speech_synthesis

   A text-to-speech system (or "engine") is composed of two parts: [3] a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization.

3. Sora (text-to-video model) - Wikipedia

   en.wikipedia.org/wiki/Sora_(text-to-video_model)

   Sora is a text-to-video model developed by OpenAI. The model generates short video clips based on user prompts, and can also extend existing short videos. Sora was released publicly for ChatGPT Plus and ChatGPT Pro users in December 2024. [1] [2]

4. Text-to-video model - Wikipedia

   en.wikipedia.org/wiki/Text-to-video_model

   A text-to-video model is a machine learning model that uses a natural language description as input to produce a video relevant to the input text. [1] Advancements during the 2020s in the generation of high-quality, text-conditioned videos have largely been driven by the development of video diffusion models .

5. Whisper (speech recognition system) - Wikipedia

   en.wikipedia.org/wiki/Whisper_(speech...

   Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]

6. Artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Artificial_intelligence

   These agents can interact with users, their environment, or other agents. AI agents are used in various applications, including virtual assistants, chatbots, autonomous vehicles, game-playing systems, and industrial robotics. AI agents operate within the constraints of their programming, available computational resources, and hardware limitations.

7. Neural machine translation - Wikipedia

   en.wikipedia.org/wiki/Neural_machine_translation

   Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]