Generative AI Audio Models and Tools for Creative Production

Generative AI audio models and tools are revolutionizing the creative production landscape. These models can generate high-quality audio in various styles and genres, from electronic dance music to orchestral scores.

Some popular tools for generative AI audio include Amper Music, AIVA, and Jukedeck, which use AI algorithms to create original music tracks. These tools can save time and effort for music producers and composers.

Amper Music, for instance, can generate a custom music track in just a few minutes, using a combination of AI algorithms and user input. This can be especially useful for video game developers, advertisers, and other content creators who need high-quality background music quickly.

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Generative AI audio is a type of technology that uses machine learning techniques to create new audio content. It can be used to generate custom sound effects, music, and even realistic human speech.

Text-to-audio AI models used to depend on acoustic models, which transform text into mel-spectrograms, but they require high-quality audio data for training, which is scarce and costly.

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Sound and voice generative models use machine learning techniques and a vast amount of training audio data to create new audio content, making it possible to create custom sound effects, music, and even realistic human speech.

Generative models can be great for creating background noises for games, videos, and other production scenarios, and they use training data like nature soundscapes, traffic noise, crowds, machinery, and other ambient environments.

These models can also be used to create music, and trainers use large datasets of existing music grouped together based on their unique genres, including instrumental, vocal, and musical notes.

Text-to-speech is another great example of what can be done with AI sound and voice generation models, allowing you to create different voices, which is mainly trained on recordings of human voices speaking in different languages, accents, and emotional tones.

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Generative AI audio relies on various key technologies to create high-quality audio. One of the main types of generative machine learning models used for sound generation is autoregressive models.

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Autoregressive models, variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers are the main types of generative machine learning models used for sound generation.

These models have been applied to the audio domain in interesting ways, with some of the latest advances in representation-based learning and sequence-modeling capabilities enabled by transformer models.

Here are some of the key technologies behind generative AI audio:

Long Short-Term Memory Networks (LSTM) are particularly effective in handling sequential data, making them ideal for music generation, but they require substantial amounts of training data and have high computational costs.

Tokenization is a key technology behind generative AI audio, allowing for the breakdown of audio data into discrete, manageable chunks. This process is essential for learning discrete representations of audio features.

Large language models have shown that abstraction of input data can encode various forms of "meaning" like syntax and semantics. Tokenization is a crucial step in this process.

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Tokenization involves dividing audio data into smaller units, such as phonemes or words, which can then be used to train audio models. This approach has been shown to improve the generalization capabilities of audio models.

The application of tokenization to audio data has been made possible by advances in transformer models, which enable sequence-modeling capabilities.

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BigVGAN v2 features accelerated synthesis speed by using custom CUDA kernels, with up to 3x faster inference speed than the original BigVGAN.

This significant boost in speed is a major breakthrough in audio generation, enabling faster and more efficient processing of audio waveforms.

On a single NVIDIA A100 GPU, BigVGAN v2 can generate audio waveforms up to 240x faster than real time.

This is a game-changer for applications that require rapid audio generation, such as real-time music composition or voiceovers.

Custom CUDA kernels are a key technology behind this speed boost, allowing for optimized inference and faster processing of audio data.

By leveraging the power of CUDA kernels, BigVGAN v2 sets a new standard for speed and efficiency in audio generation.

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Variational Autoencoders (VAE) are a type of generative AI model that's particularly well-suited for music generation tasks. They work by learning latent representations, which enables them to generate new data points with diversity and creativity.

VAEs are designed to balance the trade-off between exploration and exploitation, allowing them to explore the vast space of possible musical outputs while still maintaining some level of coherence. This is particularly useful in music style transfer tasks, where the goal is to generate diverse musical outputs.

One example of a VAE model is the MIDI-VAE, which has demonstrated its potential in music style transfer. The Conditional VAE (CVAE) is another variant that enhances diversity by incorporating conditional information, helping to mitigate mode collapse risks.

However, it's worth noting that the music generated by VAEs may lack the coherence and expressiveness found in outputs from GANs or Transformers. This is an area of ongoing research and development, as scientists and engineers work to improve the capabilities of VAEs in audio generation tasks.

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ElevenLabs is a popular tool for voice cloning and synthesis, generation, and audio dubbing. It provides a range of services, including text-to-speech generation and speech-to-text.

ElevenLabs also offers an interface to create a fully synthetic voice, giving users more control over the generation process. They can choose between faster or more quality-oriented models, and the tool already has an API available.

Murf AI and Speechify are other notable players in the space, but ElevenLabs stands out for its granular control and API access. The choice between these tools ultimately comes down to subscription specifics, interface, and personal preferences for voice quality.

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ElevenLabs is a popular platform for voice cloning and synthesis, offering a range of services including text-to-speech generation, audio dubbing, and speech-to-text.

Unlike Speechify, ElevenLabs provides an interface to create a fully synthetic voice rather than cloning an existing one.

ElevenLabs offers more granular control over generation, allowing you to choose between faster or more quality-oriented models.

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Their voice cloning technology allegedly uses GANs, but the underlying architecture is not directly disclosed.

ElevenLabs has already released its API, unlike Speechify which has yet to do so.

You can choose between ElevenLabs, Speechify, and other popular players like Murf AI, depending on your subscription specifics, interface, and subjective preferences of voice quality.

ElevenLabs provides a tool to check a recording and determine whether it was generated with their models, offering an additional layer of verification.

Their most advanced voice cloning option requires an additional verification step, providing an extra layer of security.

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Suno is a user-friendly platform for generating music that doesn't require complex preparations to start making AI-generated music. It's a great tool for beginners who want to explore the world of music generation without getting bogged down in technical details.

Suno employs transformer architecture to convert text descriptions into music, making it a powerful tool for generating novel musical ideas. This architecture allows Suno to understand the nuances of a user's text prompts and generate music that's tailored to their needs.

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One of the standout features of Suno is its ability to generate vocals and even write lyrics if you ask it to. This means you can give it a simple prompt and it will start from there, or you can provide your own lyrics and define the style you're looking for.

Suno is a great tool for anyone who wants to experiment with music generation without needing to be a tech expert. With its user-friendly interface and powerful capabilities, it's a great way to explore the possibilities of AI-generated music.

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BigVGAN is a universal neural vocoder that specializes in synthesizing audio waveforms using Mel spectrograms as inputs.

It's a fully convolutional architecture with several upsampling blocks using transposed convolution followed by multiple residual dilated convolution layers.

BigVGAN features a novel module called anti-aliased multiperiodicity composition (AMP), which is specifically designed for generating waveforms. AMP applies a periodic activation function called Snake, which provides an inductive bias to the architecture in generating periodic sound waves.

AMP also applies anti-aliasing filters to reduce undesired artifacts in the generated waveforms.

BigVGAN is available as open source through NVIDIA/BigVGAN on GitHub.

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Generative AI audio is revolutionizing various industries, and its applications are vast and exciting.

The advertising sector is slowly transforming with the use of AI to create custom audio elements for ads, including voiceovers tailored to a specific target audience.

Businesses like Agoda are now using sound generation technology to create unique videos, adapting the footage and language based on viewer location, and even adding relevant background imagery.

In education, text-to-speech conversion with natural-sounding voices has become a great asset for visually impaired students, making learning more inclusive for specific learning challenges like dyslexia.

The Learning Ally Audiobook Solution offers numerous downloadable books narrated by AI, removing reading barriers for kids from kindergarten through high school, with 90 percent of students becoming independent readers after using Ally Audiobooks.

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Businesses are now using AI to create custom audio elements for their ads, including catchy tunes and tailored voiceovers for specific target audiences.

Agoda launched a campaign using sound generation technology to transform one single video into 250 unique ones, adapting the footage and language based on viewer location.

Each video shows a different travel destination, with the AI changing the voiceover, lip-syncing the actor's dialogue, and adding relevant background imagery.

This approach allows businesses to create personalized and engaging ads, setting a new standard for the marketing industry.

The use of AI in advertising is transforming the way businesses create ads, and it's exciting to see where this technology will take us next.

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The technology has also been used to bring back Luke Skywalker's voice for "The Book of Boba Fett." This is a game-changer for the film industry, allowing for more creative possibilities and cost savings.

Sensory Fitness gym has also leveraged AI to maximize their revenue and minimize losses. They implemented FrontdeskAI's AI Assistant, Sasha, which has helped generate an additional $1,500 in monthly revenue from new members who wouldn't have joined otherwise.

Sasha answers calls with a natural human voice and handles appointments, reschedules, and inquiries while remembering customer details, resulting in $30,000 in annual cost savings. This is a powerful example of how generative AI audio can be used to improve customer service and increase revenue.

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Generative AI audio is revolutionizing the way we learn. It's making education more inclusive for visually impaired students and those with specific learning challenges like dyslexia.

The Learning Ally Audiobook Solution is a great example of this. It offers downloadable books narrated by AI that remove reading barriers for kids from kindergarten through high school.

90 percent of students who used Ally Audiobooks became independent readers. This is a remarkable statistic that highlights the potential of generative AI audio in education.

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