Generative Audio Model

A Generative Audio Model is a generative audio deep learning model that can create audio waveforms, speech signals, or musical compositions from input conditions such as text prompts or latent representations.

• AKA: Audio Generation Model, Sound Synthesis Model, Generative Sound Model.
• Context:
  • It can typically generate Speech Output through text-to-speech synthesis and voice cloning techniques.
  • It can typically create Musical Compositions through melody generation and harmonic structure synthesis.
  • It can typically produce Sound Effects through environmental audio synthesis and foley sound generation.
  • It can typically perform Audio Style Transfer through timbre manipulation and acoustic characteristic modification.
  • It can typically enable Voice Conversion through speaker identity transformation and prosody adjustment.
  • ...
  • It can often support Conditional Generation through text prompt conditioning, MIDI input conditioning, and reference audio conditioning.
  • It can often implement Temporal Coherence through long-range dependency modeling and sequential generation mechanisms.
  • It can often provide Multi-Speaker Capability through speaker embeddings and voice characteristic control.
  • It can often facilitate Real-Time Generation through efficient inference optimization and streaming audio output.
  • ...
  • It can range from being a Low-Fidelity Generative Audio Model to being a High-Fidelity Generative Audio Model, depending on its audio quality output.
  • It can range from being a Speech-Focused Generative Audio Model to being a Music-Focused Generative Audio Model, depending on its audio domain specialization.
  • It can range from being a Autoregressive Generative Audio Model to being a Parallel Generative Audio Model, depending on its generation architecture.
  • It can range from being a Small-Scale Generative Audio Model to being a Large-Scale Generative Audio Model, depending on its model parameter count.
  • ...
• Examples:
  • Speech Synthesis Generative Audio Models, such as:
    • WaveNet Audio Model for neural vocoding tasks.
    • Tacotron Audio Model for end-to-end speech synthesis.
    • VALL-E Audio Model for zero-shot voice cloning.
    • Bark Audio Model for multi-lingual speech generation.
  • Music Generation Audio Models, such as:
    • MusicGen Audio Model for text-to-music generation.
    • AudioLM Audio Model by Google Research for music continuation.
    • Jukebox Audio Model for genre-specific music creation.
    • MusicLM Audio Model for high-fidelity music synthesis.
  • General Audio Generation Models, such as:
    • AudioGen Audio Model for sound effect generation.
    • DiffWave Audio Model for diffusion-based audio synthesis.
    • SoundStream Audio Model for neural audio codecs.
  • Voice Conversion Audio Models, such as:
    • YourTTS Audio Model for multi-speaker synthesis.
    • VITS Audio Model for variational inference synthesis.
    • HiFi-GAN Audio Model for high-fidelity vocoding.
  • ...
• Counter-Examples:
  • Audio Analysis Model, which processes existing audio rather than generating new audio content.
  • Speech Recognition Model, which converts audio to text rather than text to audio.
  • Audio Classification Model, which categorizes audio samples rather than creating them.
  • Audio Enhancement Model, which improves audio quality rather than generating original content.
• See: Generative Model, Text-to-Speech System, Neural Vocoder, Audio Synthesis Algorithm, Deep Learning Model, Multimodal AI System, WaveNet Neural Network.

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