Text to Audio Models
Explore the best AI models for text to audio
Suno AI
Suno AI is a commercial AI music generation platform that creates complete songs with vocals, lyrics, and instrumental arrangements from text descriptions. Founded in 2023 by a team of former Kensho Technologies engineers, Suno AI offers an accessible web interface that enables users to generate professional-sounding songs by simply describing the desired genre, mood, topic, and style in natural language. The platform uses a proprietary transformer-based architecture that generates all components of a song including melody, harmony, rhythm, instrumentation, vocal performance, and lyrics in a single integrated process. Suno AI supports a remarkably wide range of musical genres from pop and rock to hip-hop, country, classical, electronic, jazz, and experimental styles, producing outputs that often sound indistinguishable from human-created music to casual listeners. Generated songs can be up to several minutes in duration and include realistic singing voices with proper pronunciation, emotional expression, and musical phrasing. The platform allows users to provide custom lyrics or let the AI generate lyrics based on a theme or concept. Suno AI operates on a freemium subscription model with limited free generations and paid tiers for higher volume and commercial usage rights. The platform has gained significant attention for democratizing music creation, enabling people without musical training to produce complete songs. Suno AI is particularly popular among content creators, social media marketers, hobbyist musicians, and anyone needing original music for videos, podcasts, or personal projects without the cost and complexity of traditional music production.
MusicGen
MusicGen is a single-stage transformer-based music generation model developed by Meta AI Research as part of the AudioCraft framework. Released in June 2023 under the MIT license, MusicGen uses a single autoregressive language model operating over compressed discrete audio representations from EnCodec, unlike cascading approaches that require multiple models. The model comes in multiple sizes ranging from 300M to 3.3B parameters, allowing users to balance quality against computational requirements. MusicGen generates high-quality mono and stereo music at 32 kHz from text descriptions, supporting a wide range of genres, instruments, moods, and musical styles. Users can describe desired music using natural language prompts specifying genre, tempo, instrumentation, and atmosphere, and the model produces coherent musical compositions that follow the specified characteristics. Beyond text-to-music generation, MusicGen supports melody conditioning where an existing audio clip guides the melodic structure of the generated output, enabling more controlled music creation. The model achieves strong results across both objective metrics and subjective listening evaluations, producing music that sounds natural and musically coherent for durations up to 30 seconds. As a fully open-source model with code and weights available on GitHub and Hugging Face, MusicGen has become one of the most widely adopted AI music generation tools in both research and creative communities. It integrates easily into existing audio production workflows through the Audiocraft Python library and various community-built interfaces. MusicGen is particularly popular among content creators, game developers, and musicians who need royalty-free background music generated on demand.
Udio
Udio is an AI music generation platform developed by former Google DeepMind researchers that creates high-quality songs with vocals, lyrics, and instrumentals from text prompts. Launched in April 2024, Udio quickly gained attention for producing remarkably realistic and musically coherent outputs that rival professional studio recordings in audio fidelity. The platform uses a proprietary transformer-based architecture that generates all aspects of a musical composition including vocal performances, instrumental arrangements, harmonies, and production effects in a unified process. Udio supports an extensive range of musical genres and styles from mainstream pop and rock to niche genres like lo-fi, synthwave, Afrobeat, and traditional folk music from various cultures. Generated songs feature studio-quality audio at high sample rates with realistic vocal timbres, proper musical dynamics, and professional-sounding mixing and mastering. The platform allows users to provide custom lyrics, specify song structure, and control various musical parameters through text descriptions. Udio also supports audio extensions where users can generate additional sections to extend existing songs, enabling the creation of full-length tracks through iterative generation. The platform operates on a freemium model with free daily generations and paid subscription tiers for commercial use and higher generation limits. Udio is particularly notable for its vocal quality, which includes natural-sounding vibrato, breath sounds, and emotional expressiveness that many competing platforms struggle to achieve. The platform is popular among content creators, independent musicians exploring AI-assisted composition, marketing teams needing original music, and hobbyists who want to create professional-sounding songs without musical training or expensive production equipment.
Bark
Bark is a transformer-based text-to-audio generation model developed by Suno AI that converts text into natural-sounding speech, music, and sound effects. Released as open source under the MIT license in April 2023, Bark goes far beyond traditional text-to-speech systems by generating not only spoken words but also laughter, sighs, music, and ambient sounds from text descriptions. The model uses a GPT-style autoregressive transformer architecture with EnCodec audio tokenizer to generate audio tokens that are then decoded into waveforms. Bark supports multiple languages including English, Chinese, French, German, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, and Turkish, making it one of the most multilingual open-source audio generation models available. The model can clone voice characteristics from short audio samples, allowing users to generate speech in specific voices or speaking styles. Bark operates in a zero-shot manner, meaning it can produce diverse outputs without task-specific fine-tuning. Generation includes natural prosody, emotion, and intonation that closely mimics human speech patterns. The model generates audio at 24 kHz sample rate with reasonable quality for most applications. As a fully open-source project with pre-trained weights available on Hugging Face and GitHub, Bark is widely used by developers building voice applications, content creators producing multilingual audio, and researchers exploring generative audio models. The model is particularly valued for its versatility in handling diverse audio types within a single unified architecture and its accessibility for rapid prototyping of audio generation applications.
AudioCraft
AudioCraft is Meta AI's comprehensive open-source framework for generative audio research and applications, bringing together three specialized models under a single integrated platform: MusicGen for music generation, AudioGen for sound effect synthesis, and EnCodec for neural audio compression. Released in August 2023 under the MIT license, AudioCraft provides a unified codebase that simplifies working with state-of-the-art audio generation models through consistent APIs and shared infrastructure. The framework is built on a transformer-based architecture where audio signals are first compressed into discrete tokens by EnCodec, then generated autoregressively by task-specific language models. MusicGen handles text-to-music generation with melody conditioning support, while AudioGen specializes in environmental sounds, sound effects, and non-musical audio from text descriptions. EnCodec serves as the neural audio codec backbone, compressing audio at various bitrates while maintaining high perceptual quality. AudioCraft supports multiple model sizes, stereo generation, and provides extensive training and inference utilities. The framework includes pre-trained models for immediate use and tools for training custom models on user-provided datasets. As a Python library installable via pip, AudioCraft integrates seamlessly into existing machine learning and audio processing pipelines. It is widely used by researchers studying audio generation, developers building creative audio tools, content creators needing original music and sound effects, and game studios requiring dynamic audio systems. AudioCraft represents Meta's most significant contribution to open-source audio AI and has become the foundation for numerous community projects and commercial applications in the rapidly growing AI audio generation space.
Stable Audio
Stable Audio is Stability AI's commercial text-to-audio generation model that produces high-quality music and sound effects from natural language descriptions. Built on a latent diffusion architecture adapted for audio, Stable Audio represents a significant advancement in AI-generated audio quality, producing outputs with professional-grade clarity and musical coherence. The model uses a variational autoencoder to compress audio spectrograms into a compact latent space, then applies a diffusion process conditioned on text embeddings to generate audio in that latent space, which is decoded back into high-fidelity waveforms. Stable Audio supports generation of music tracks and sound effects up to 90 seconds in duration at 44.1 kHz stereo quality, making it suitable for professional audio production workflows. The model was trained on a licensed music dataset from AudioSparx, addressing copyright concerns that affect many competing models. Users can specify genre, mood, tempo, instrumentation, and other musical attributes through natural language prompts, and the model produces coherent compositions that follow the described characteristics. Stable Audio also supports audio-to-audio workflows where an input audio clip is used as a starting point for generation. Released under the Stability AI Community License, the model is available for non-commercial research use with commercial access through the Stable Audio API and web platform. Stable Audio is particularly valued by content creators, video producers, podcasters, and game developers who need high-quality, original audio content generated quickly without licensing complications.
VALL-E
VALL-E is a neural codec language model for text-to-speech synthesis developed by Microsoft Research, introduced in January 2023. Unlike traditional TTS systems that use mel spectrograms and vocoders, VALL-E treats text-to-speech as a conditional language modeling task, generating discrete audio codec codes from text input conditioned on a short audio prompt. The model uses a combination of autoregressive and non-autoregressive transformer decoders operating on EnCodec audio tokens to synthesize speech that preserves the speaker's voice characteristics, emotional tone, and acoustic environment from just a 3-second reference audio sample. This approach enables remarkable zero-shot voice cloning capabilities where the model can generate speech in any voice after hearing only a brief sample, without requiring speaker-specific fine-tuning. VALL-E was trained on 60,000 hours of English speech data from the LibriLight dataset, giving it exposure to a vast diversity of speakers, accents, and speaking styles. The generated speech maintains natural prosody, appropriate pausing, and emotional expressiveness that closely matches the reference speaker's characteristics. VALL-E represents a paradigm shift in TTS technology by demonstrating that language modeling approaches can effectively solve speech synthesis when paired with neural audio codecs. Released under a research-only license, the model is not available for commercial use, reflecting Microsoft's cautious approach given potential misuse concerns. VALL-E has significantly influenced subsequent research in zero-shot TTS, with its architecture inspiring numerous follow-up models. The model is particularly relevant for researchers studying speech synthesis, voice conversion, and the application of language modeling techniques to audio generation tasks.
Riffusion
Riffusion is an innovative AI music generation model that takes a unique approach to audio synthesis by generating spectrograms as images using a fine-tuned version of Stable Diffusion v1.5. Created as a side project by Seth Forsyth and Hayk Martiros in late 2022, Riffusion demonstrated that image diffusion models could be repurposed for audio generation by training on spectrogram representations of music. The model generates mel spectrograms conditioned on text prompts describing musical genres, instruments, moods, and styles, which are then converted back to audio waveforms using the Griffin-Lim algorithm or neural vocoders. This image-based approach to music generation was groundbreaking at the time of release, showing that the powerful generative capabilities of Stable Diffusion could transfer to the audio domain. Riffusion can produce short music clips in various styles including rock, jazz, electronic, classical, and ambient, with real-time interpolation between different prompts enabling smooth musical transitions. The model has approximately 1 billion parameters inherited from its Stable Diffusion base. Released under the MIT license, Riffusion is fully open source with the fine-tuned model weights, training code, and an interactive web application available on GitHub. While newer purpose-built music generation models like MusicGen and Suno have surpassed Riffusion in output quality and duration, the model remains historically significant as a proof of concept that sparked widespread interest in AI music generation. Riffusion continues to be used by hobbyists and researchers exploring the intersection of image generation and audio synthesis.
MusicLM
MusicLM is a text-to-music generation model developed by Google Research that generates high-fidelity music from text descriptions at 24 kHz. Published in January 2023 alongside a research paper, MusicLM was one of the first models to demonstrate that AI could generate coherent, high-quality music spanning multiple minutes from natural language descriptions alone. The model employs a hierarchical sequence-to-sequence architecture combining SoundStream for audio tokenization and w2v-BERT for audio representation learning, generating music tokens at multiple temporal resolutions that are then decoded into waveforms. MusicLM can produce music in diverse genres and styles based on text prompts describing instruments, tempo, mood, and musical characteristics, maintaining musical coherence and structural consistency across extended durations. The model also supports melody conditioning where users can hum or whistle a melody that guides the generated output, enabling more intuitive music creation workflows. MusicLM generates audio with rich timbral quality and natural-sounding dynamics that represent a significant improvement over earlier text-to-music approaches. As a proprietary Google model, MusicLM is not open source and was initially accessible only through the AI Test Kitchen experimental platform before being integrated into broader Google services. While newer models like MusicGen and Suno have since achieved wider adoption, MusicLM remains historically significant as a pioneering demonstration of high-quality text-to-music generation. The model influenced subsequent research and commercial developments in the AI music generation space and helped establish text-to-music as a viable and rapidly advancing field of AI research.
AudioLDM 2
AudioLDM 2 is a unified audio generation framework developed by researchers at the Chinese University of Hong Kong and the University of Surrey, capable of producing music, sound effects, and speech from text descriptions within a single model. Building on the original AudioLDM, version 2 introduces a universal audio representation called Language of Audio that bridges the gap between different audio types by encoding them into a shared semantic space. The model combines a GPT-2 language model for understanding text inputs with an AudioMAE encoder for audio conditioning, feeding into a latent diffusion model that generates audio spectrograms which are converted to waveforms. This architecture enables AudioLDM 2 to handle diverse audio generation tasks without requiring separate specialized models for each audio type. The model achieves competitive performance across multiple benchmarks including text-to-music, text-to-sound-effects, and text-to-speech evaluations. AudioLDM 2 generates audio at up to 48 kHz with good perceptual quality for both musical and non-musical content. Released in August 2023 under a research license, the model is open source with code and pre-trained weights available on GitHub and Hugging Face. AudioLDM 2 supports audio inpainting, style transfer, and super-resolution in addition to text-conditioned generation. The model is particularly relevant for researchers studying unified audio generation, content creators needing diverse audio types from a single tool, and developers building comprehensive audio generation systems. Its unified approach to handling speech, music, and environmental sounds makes it a versatile foundation for multi-purpose audio applications.