MusicGen
Meta's MusicGen is an open source, deep-learning language model that can generate short pieces of music based on text prompts. The model was trained on 20,000 hours of music, including whole tracks and individual instrument samples. The model will generate 12 seconds of audio based on the description you provided. You can optionally provide reference audio from which a broad melody will be extracted. The model will then try to follow both the description and melody provided. All samples are generated with the melody model. You can also use your own GPU or a Google Colab by following the instructions on our repo. MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models. MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better control over the generated output.
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Melodea
Generate music based on a mood or tempo. Start with a chord progression and generate melodies. Customize the music to make it your own. Use the AI to generate melodies and harmonies, and then refine the melodies by recording a vocal topline. The generated music is based on hit pop songs. Export as an audio file, multitrack MIDI file, or chord notation. Private and secure; all files are saved onto your device. No signup or login is necessary. Melodea is an AI music generator, that provides melody and harmony ideas for the pro songwriter. Use the AI to generate melodies and harmonies, and then refine the melodies by recording a vocal topline. The generated music is based on hit pop songs. Start with a mood or tempo, or even your own chord progression. Customize the melodies and harmonies to make them your own. Export as an audio file, multitrack MIDI file, or chord notation. Private and secure; all files are saved onto your device.
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MuseNet
We’ve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text. Since MuseNet knows many different styles, we can blend generations in novel ways. We’re excited to see how musicians and non-musicians alike will use MuseNet to create new compositions! Choose a composer or style, an optional start of a famous piece, and start generating. This lets you explore the variety of musical styles the model can create.
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Qwen3-TTS
Qwen3-TTS is an open source series of advanced text-to-speech models developed by the Qwen team at Alibaba Cloud under the Apache-2.0 license, offering stable, expressive, and real-time speech generation with features such as voice cloning, voice design, and fine-grained control of prosody and acoustic attributes. The models support 10 major languages, including Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian, and multiple dialectal voice profiles with adaptive control over tone, speaking rate, and emotional expression based on text semantics and instructions. Qwen3-TTS uses efficient tokenization and a dual-track architecture that enables ultra-low-latency streaming synthesis (first audio packet in ~97 ms), making it suitable for interactive and real-time use cases, and includes a range of models with different capabilities (e.g., rapid 3-second voice cloning, custom voice timbres, and instruction-based voice design).
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