This preprint paper explores the task of text-to-audio (TTA) generation and proposes a novel approach called TANGO. The authors draw inspiration from the success of large language models (LLMs) in natural language processing (NLP) tasks and adopt an instruction-tuned LLM Flan-T5 as the text encoder for TTA generation. Unlike prior works that either pre-trained a joint text-audio encoder or used a non-instruction-tuned model such as T5, the authors propose using an LDM-based approach with an LLM text encoder to generate audio from textual descriptions. To augment training samples, the authors employ a pressure level-based mixing method instead of randomly generated combinations of audio pairs. They show that their proposed model outperforms state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. The authors attribute this improvement to the adoption of audio pressure level based sound mixing for training set augmentation. The paper's contribution is threefold: firstly, it proposes a novel approach called TANGO for TTA generation; secondly, it shows that using an instruction tuned LLM greatly improves text to audio generation performance; thirdly, it demonstrates that pressure level based sound mixing can be more effective than random mixtures for training set augmentation. Overall, this paper presents promising results in generating audio from textual descriptions using an LDM based approach with an instruction tuned LLM as the text encoder. If trained on larger datasets such as AudioSet, TANGO could potentially improve its ability to recognize a wider range of sounds.
This paper talks about a new way to turn words into sound called TANGO. They use a special tool called LLM Flan-T5 to help make the sound. They also mix different sounds together in a special way to help train the computer better. The new method works better than other ways they tried before. The paper shows three important things: TANGO is good, using LLM Flan-T5 helps, and mixing sounds in a certain way is better for training. Overall, this paper has good ideas for making sound from words.
Definitions
- Text-to-audio (TTA) generation: Turning written words into sound.
- Encoder: A tool that helps turn one thing into another thing.
- Audio: Sound.
- Augment: To add more of something to make it better or stronger.
- Metric: A way to measure how well something works or performs.
Exploring Text-to-Audio Generation with TANGO: A Novel Approach
Text-to-audio (TTA) generation is an important task in natural language processing (NLP). It involves generating audio from textual descriptions, which can be used to create more engaging experiences for users. Recently, a preprint paper proposed a novel approach called TANGO for text-to-audio generation. In this article, we will explore the authors’ research and discuss their findings.
Background
Large language models (LLMs) have been successfully applied to many NLP tasks such as machine translation and question answering. The authors of this paper draw inspiration from the success of LLMs and propose using an instruction tuned LLM Flan-T5 as the text encoder for TTA generation. Prior works either pre-trained a joint text-audio encoder or used a non-instruction tuned model such as T5; however, the authors propose using an LDM based approach with an LLM text encoder instead. To augment training samples, they employ a pressure level based mixing method instead of randomly generated combinations of audio pairs.
Proposed Methodology
The proposed methodology consists of two components: 1) An LDM based approach with an instruction tuned LLM as the text encoder; 2) Pressure level based sound mixing for training set augmentation. The authors use AudioCaps dataset to evaluate their model performance on various metrics including perplexity, accuracy, F1 score and precision recall curves etc., compared to state-of -the art AudioLDM model trained on much larger datasets than theirs (63 times smaller).
Results
The results show that the proposed model outperforms AudioLDM on most metrics while staying comparable on others despite training it on much smaller datasets and keeping the text encoder frozen during training process. This improvement is attributed to adoption of audio pressure level based sound mixing for training set augmentation rather than random mixtures used by prior works in this area.
Conclusion
Overall, this paper presents promising results in generating audio from textual descriptions using an LDM based approach with an instruction tuned LLM as the text encoder. If trained on larger datasets such as AudioSet, TANGO could potentially improve its ability to recognize a wider range of sounds and further enhance its performance in terms of accuracy and other metrics evaluated herewith respect to state -of -the art models like AudioLDM .
The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instruction-tuned LLM Flan-T5 as the text encoder for text-to-audio (TTA) generation -- a task where the goal is to generate an audio from its textual description. The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. Consequently, our latent diffusion model (LDM)-based approach TANGO outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. This improvement might also be attributed to the adoption of audio pressure level-based sound mixing for training set augmentation, whereas the prior methods take a random mix.
