how many audio/voice/sound artificial intelligent generate technologies and models in the world?

The field of audio/voice/sound generation in the artificial intelligence (AI) field has also seen significant advancements in recent years. While the specific number of audio generation technologies and models is difficult to determine, I can mention some notable ones:

1. WaveNet: WaveNet is a deep generative model for audio synthesis developed by DeepMind. It uses autoregressive neural networks to generate high-quality and realistic audio waveforms. WaveNet has been widely used for text-to-speech synthesis and music generation.
2. Tacotron: Tacotron is a sequence-to-sequence model for speech synthesis. It takes text as input and generates corresponding spectrograms, which are then converted into audio waveforms using a vocoder. Tacotron has been influential in producing natural-sounding synthesized speech.
3. SampleRNN: SampleRNN is a recurrent neural network-based model for audio generation. It operates at multiple time scales and can generate high-quality audio samples with long-term dependencies.
4. GAN-based Audio Synthesis: Generative Adversarial Networks (GANs) have been applied to audio synthesis tasks as well. GANs can generate audio signals by learning from a training dataset and capturing the statistical properties of the data. They have been used for tasks such as speech synthesis, music generation, and sound effects synthesis.
5. Deep Voice: Deep Voice is a series of models developed by Baidu Research for text-to-speech synthesis. It combines various neural network architectures and training techniques to generate natural-sounding speech from text inputs.
6. MelGAN: MelGAN is a generative model that focuses on generating mel-spectrograms, which can be converted into high-quality speech audio. It utilizes a modified GAN architecture to generate realistic and intelligible speech signals.
7. WaveRNN: WaveRNN is a model for waveform generation that combines autoregressive techniques with recurrent neural networks. It can generate high-fidelity audio waveforms with fine-grained control over characteristics such as pitch, duration, and timbre.

These are just a few examples of the audio/voice/sound generation technologies and models in the AI field. The field is continuously evolving, and researchers are exploring various techniques to improve the quality, expressiveness, and versatility of generated audio.