Unlock the Power of Voice with META AI's Voicebox

Table of Contents

1. Introduction
2. Voice Box: A Versatile and Scalable Text-Guided Generative Model for Speech
3. Overview of Voice Box
4. Cross-Lingual Transfer Capability
5. Limitations of Previous Speech Generative Models
6. Text-Guided Speech Filling Tasks
7. Training of Voice Box
8. Non-Autoregressive Nar Continuous Normalizing Flow (CNF) Model
9. Application of Voice Box for Fine-Grained Alignment Control
10. Audio Model and Duration Model in Voice Box
11. Voice Box's Multitude of Abilities
12. Style Transfer and Vocal Characteristics
13. Voice Box for Communication Across Languages
14. Voice Editing and Noise Removal
15. Conclusion

Voice Box: A Highly Versatile and Scalable Text-Guided Generative Model for Speech

In the realm of natural language processing and computer vision, large-Scale generative models like GPT and DALL·E have revolutionized the field. However, when it comes to speech generative models, there is still a significant gap in terms of scale and task generalization. In this article, we will explore Voice Box, a recently released and highly advanced text-guided generative model for speech developed by Med AI. Voice Box outperforms all other speech generative models, and its capabilities are truly impressive. Let's dive deeper into this groundbreaking model.

Voice Box has the exceptional ability to transform written text into speech in a variety of styles. Whether You require a formal tone or a more casual delivery, Voice Box can meet your needs. Additionally, it assists in audio editing by effortlessly removing unwanted background noise and sounds. Moreover, it can process speech in six different languages, namely English, French, German, Polish, Spanish, and Portuguese, all with cross-lingual transfer capability. The versatility of Voice Box knows no bounds.

Previous research in speech generative models often relied on curated datasets with clean Studio recordings from a limited number of speakers. This approach resulted in limitations when it came to synthesizing speech with diverse characteristics. Voice Box, on the other HAND, takes a different approach by employing text-guided speech filling tasks. It generates natural-sounding speech by leveraging not only the surrounding audio but also text transcripts. To achieve this, Voice Box was trained on more than 50,000 hours of recorded speech and transcripts from public domain audiobooks in multiple languages.

Voice Box is built upon a non-autoregressive continuous normalizing flow (CNF) model, similar to diffusion models. CNFs enable the transformation from a simple distribution to a complex data distribution, parameterized by a neural network. More specifically, Voice Box utilizes the flow matching model, which falls under the category of non-autoregressive generative models. These models can learn highly non-deterministic mappings between text and speech. The AdVantage of Voice Box's architecture is its efficiency and scalability in training CNFs, achieved through a simple vector field regression loss. Unlike autoregressive models, Voice Box can Consume Context not only from the past but also from the future. Furthermore, the number of flow steps can be controlled at inference time, allowing flexibility in trading off quality and runtime efficiency.

Cross-Lingual Transfer Capability

Voice Box stands out among other speech generative models due to its remarkable cross-lingual transfer capability. While most models struggle with generating speech in languages other than the one they were primarily trained on, Voice Box can seamlessly process speech in six different languages. This ability to transfer knowledge across languages opens up a world of possibilities. It enables people to communicate in a more authentic way, bridging the gap between different linguistic communities and breaking down language barriers.

Text-Guided Speech Filling Tasks

Conventional speech generative models face limitations in synthesizing speech with diverse characteristics. However, Voice Box takes a different approach by employing text-guided speech filling tasks. By leveraging both the surrounding audio and text transcripts, Voice Box can generate speech that sounds natural and authentic. The model's ability to fill in missing speech Based on textual input makes it highly versatile for various applications. Whether it's generating Mass speech or transcribing audio, Voice Box shines in tasks that require accurate and context-based speech synthesis.

Training of Voice Box

Voice Box's exceptional performance is the result of extensive training on a vast amount of data. It was trained on over 50,000 hours of recorded speech and transcripts from public domain audiobooks in multiple languages, including English, French, Spanish, German, Polish, and Portuguese. The large-scale training data enables Voice Box to capture the nuances and variations in speech, making it capable of delivering high-quality and contextually accurate speech synthesis.

Non-Autoregressive Nar Continuous Normalizing Flow (CNF) Model

At the Core of Voice Box lies the non-autoregressive Nar Continuous Normalizing Flow (CNF) model. This model is based on flow matching, a technique that allows the transformation from a simple distribution to a complex data distribution. The CNF model in Voice Box is trained to learn the highly non-deterministic mapping between text and speech. It achieves efficient and scalable training through a simple vector field regression loss. Unlike autoregressive models, which can only consider past context, Voice Box can incorporate both past and future context. Additionally, the number of flow steps can be adjusted during inference to balance the trade-off between speech quality and runtime efficiency.

Application of Voice Box for Fine-Grained Alignment Control

One of the remarkable features of Voice Box is its ability to provide fine-grained alignment control between speech and text. By decoupling Voice Box into two components—an audio model and a duration model—it allows for precise control over the alignment between speech and text. The audio model, parameterized with a Continuous Normalizing Flow (CNF) model, addresses the stochastic nature of distribution in speech. The duration model, based on a Transformer model, models the conditional distribution of durations. This fine-grained alignment control opens up possibilities for applications such as voiceover synchronization, where precise timing is crucial.

Audio Model and Duration Model in Voice Box

Voice Box consists of two components: an audio model and a duration model. The audio model is responsible for synthesizing speech based on text input. It utilizes a Continuous Normalizing Flow (CNF) model to capture the complex distribution of speech. The CNF model is trained using the flow matching objective, which finds the optimal transport path. To parameterize the vector field, a Transformer model is employed. The phone sequence embeddings and sequences are concatenated frame by frame and projected using a matrix to obtain the sequence D.

The duration model, on the other hand, focuses on modeling the conditional distribution of durations. Two solutions were implemented for the duration model. The first solution closely follows the audio model and models the conditional distribution. Training is performed using the masked version of the CFM (Continuous Flow Model) loss. The Second solution involves regressing the masked duration based on the context duration and the phonetic transcript. The Transformer model is used for this solution, but with only two input sequences and without using the time embedding. The model is trained using an L1 regression loss on the masked phones.

Voice Box's Multitude of Abilities

Voice Box offers a wide range of capabilities that set it apart from other speech generative models. It can take input text and generate audio in a multitude of different voices, allowing for versatile voice synthesis. Whether you need a specific tone or style, Voice Box can provide it. For example:

• The quick brown fox jumps over the lazy dog.
• Quick brown fox jumps over the lazy dog.

Voice Box's style transfer functionality enables the transposition of vocal characteristics and background noise from a reference clip to a target audio clip based on text input. This paves the way for more natural-sounding virtual assistants and NPCs (non-player characters) in various applications.

Furthermore, Voice Box can be used to produce audio in different languages. For example:

• Los frondos brown fox jumps over the lazy dog.

This capability has great potential for improving language communication, making it more authentic and breaking down barriers.

Voice Box also excels in audio editing tasks. It can remove background noise from a clip, enhancing the quality of the audio and improving the listening experience.

Conclusion

In conclusion, Voice Box is a highly advanced and versatile text-guided generative model for speech. It outperforms other speech generative models in terms of scale and task generalization. Its Cross-Lingual Transfer Capability, along with its ability to perform fine-grained alignment control, makes it a powerful tool in various applications. Whether it's synthesizing speech, adjusting vocal characteristics, communicating across languages, or editing audio, Voice Box provides exceptional performance and flexibility. With its groundbreaking capabilities, Voice Box marks an important milestone in the field of generative artificial intelligence.