TikTok’s $300 billion-valued mum or dad firm, ByteDance, is likely one of the world’s busiest AI builders. It plans to spend billions of {dollars} on AI chips this 12 months, whereas its tech provides Sam Altman’s OpenAI a run for its cash.
ByteDance’s Duobao AI chatbot is presently the preferred AI assistant in China, with 78.6 million month-to-month lively customers as of January.
This makes it the world’s second most-used AI app behind OpenAI’s ChatGPT (with 349.4 million MAUs). The lately launched Doubao-1.5-pro is claimed to match the efficiency of OpenAI’s GPT-4o at a fraction of the price.
As Counterpoint Analysis notes on this breakdown of Duobao’s positioning and performance, “very similar to its worldwide rival ChatGPT, the cornerstone of Doubao’s enchantment is its multimodality, providing superior textual content, picture, and audio processing capabilities”.
It may possibly additionally generate music.
In September, ByteDance added an AI music era operate to the Duobao app, which apparently “helps greater than ten kinds of music types and means that you can write lyrics and compose music with one click on”.
This, although, isn’t the top of ByteDance’s fascination with constructing music AI applied sciences.
On September 18, ByteDance’s Duobao Staff introduced the massive launch of a set of AI music fashions dubbed Seed-Music.
Seed-Music, they claimed, would “empower folks to discover extra prospects in music creation”.
Established in 2023, the ByteDance Doubao (Seed) Staff is “devoted to constructing industry-leading AI basis fashions”.
In accordance with the official launch announcement for Seed-Music in September, the AI music product “helps score-to-song conversion, controllable era, music and lyrics modifying, and low-threshold voice cloning”.
It additionally claims that “it cleverly combines the strengths of language fashions and diffusion fashions and integrates them into the music composition workflow, making it appropriate for various music creation situations for each inexperienced persons and professionals”.
The official Seed-Music web site accommodates a lot of audio clips that show what it will probably do.
You may hear a few of that, beneath:
Extra vital, although, is how Seed-Music was constructed.
Fortunately, the Duobao Staff has printed a tech report that explains the internal workings of their Seed-Music undertaking.
MBW has learn it cowl to cowl.
Within the introduction to ByteDance’s analysis paper, which you’ll be able to learn in full right here, the corporate’s researchers state that, “music is deeply embedded in human tradition” and that “all through human historical past, vocal music has accompanied key moments in life and society: from love calls to seasonal harvests”.
“Our aim is to leverage fashionable generative modeling applied sciences, to not change human creativity, however to decrease the boundaries to music creation.”
ByeDance analysis paper for Seed-Music
The intro continues: “Immediately, vocal music stays central to international tradition. Nevertheless, creating vocal music is a posh, multi-stage course of involving pre-production, writing, recording, modifying, mixing, and mastering, making it difficult for most individuals.”
“Our aim is to leverage fashionable generative modeling applied sciences, to not change human creativity, however to decrease the boundaries to music creation. By providing interactive creation and modifying instruments, we purpose to empower each novices and professionals to have interaction at completely different levels of the music manufacturing course of.”
How Seed-Music works
ByteDance’s researchers clarify that the “unified framework” behind Seed-Music “is constructed upon three elementary representations: audio tokens, symbolic tokens, and vocoder latents”, which every correspond to “a era pipeline.”
The audio token-based pipeline, as illustrated within the chart beneath, works like this: “(1) Enter embedders convert multi-modal controlling inputs, resembling music type description, lyrics, reference audio, or music scores, right into a prefix embedding sequence. (2) The auto-regressive LM generates a sequence of audio tokens. (3) The diffusion transformer mannequin generates steady vocoder latents. (4) The acoustic vocoder produces high-quality 44.1kHz stereo audio.”
In distinction to the audio token-based pipeline, the symbolic token-based Generator, which you’ll be able to see within the chart beneath, is “designed to foretell symbolic tokens for higher interpretability”, which the researchers state is “essential for addressing musicians’ workflows in Seed-Music”.
In accordance with the analysis paper, “Symbolic representations, resembling MIDI, ABC notation and MusicXML, are discrete and could be simply tokenized right into a format suitable with LMs”.
ByteDance’s researchers add within the paper: “In contrast to audio tokens, symbolic representations are interpretable, permitting creators to learn and modify them immediately. Nevertheless, their lack of acoustic particulars means the system has to rely closely on the Renderer’s means to generate nuanced acoustic traits for musical efficiency. Coaching such a Renderer requires large-scale datasets of paired audio and symbolic transcriptions, that are particularly scarce for vocal music.”
The plain query…
By now, you’re in all probability asking the place The Beatles and Michael Jackson’s music come into all of this.
We’re practically there. First, we have to discuss MIRs.
In accordance with the Seed-Music analysis paper, “to extract the symbolic options from audio for coaching the above system,” the group behind the tech used varied “in-house Music Info Retrieval (MIR) fashions”.
In accordance with this very clear clarification over at Dataloop, MIR “is a subcategory of AI fashions that focuses on extracting significant data from music information, resembling audio indicators, lyrics, and metadata”.
Aka: It’s a metadata scraper. Stick a music into the jaws of a MIR mannequin, and it’ll analyze, predict and current information which may embrace pitch, beats-per-minute (BPM), lyrics, chords, and extra.
Music Info Retrieval analysis first gained recognition over its means to assist with the digital classification of genres, moods, tempos, and so on. – key constructing blocks for advice programs utilized by music streaming companies.
Now, although, main generative AI music platforms are reportedly utilizing MIR analysis to enhance their product output.
Are you able to see the place that is going? Sure, after all.
ByteDance’s analysis group has efficiently constructed its personal in-house MIR fashions, which have been utilized by the ByteDance group to “extract the symbolic options from audio” to construct components of its Seed-Music system. These MIR fashions embrace:
AI, are you okay? Are you okay, AI?
Taking a deeper dive into the analysis printed by ByteDance for its Structural evaluation-focused MIR mannequin, we discover a analysis paper titled:
‘To catch a refrain, verse, intro, or the rest: Analyzing a music with structural capabilities’.
It was printed in 2022. You can learn it right here.
In accordance with the paper: “Typical music construction evaluation algorithms purpose to divide a music into segments and to group them with summary labels (e.g., ‘A’, ‘B’, and ‘C’).
“Nevertheless, explicitly figuring out the operate of every phase (e.g., ‘verse’ or ‘refrain’) isn’t tried, however has many purposes”.
On this analysis paper, they “introduce a multi-task deep studying framework to mannequin these structural semantic labels immediately from audio by estimating ‘verseness,’ ‘chorusness,’ and so forth, as a operate of time”.
To conduct this analysis, the ByteDance group used 4 “public datasets”, together with one known as the ‘Isophonics’ dataset, which, it notes, “accommodates 277 songs from The Beatles, Carole King, Michael Jackson, and Queen.”
The supply of the Isophonics dataset utilized by ByteDance’s researchers seems to be Isophonics.web, described as the house for software program and information sources from the Centre for Digital Music (C4DM) at Queen Mary, College of London.
The Isophonics web site notes that its “chord, onset, and segmentation annotations have been utilized by many researchers within the MIR neighborhood.”
The web site explains that “the annotations printed right here fall into 4 classes: chords, keys, structural segmentations, and beats/bars”.
In 2022, ByteDance’s researchers printed a video presentation of their, To catch a refrain, verse, intro, or the rest: Analyzing a music with structural capabilities paper for the Worldwide Convention on Acoustics, Speech, and Sign Processing (ICASSP).
You may see this presentation beneath.
The video’s caption describes a “novel system/methodology that segments a music into sections resembling refrain, verse, intro, outro, bridge, and so on”.
It demonstrates its findings associated to songs by the Beatles, Michael Jackson, Avril Lavigne and different artists:
We should be cautious right here over any suggestion that ByteDance’s AI music-generating expertise might have been “skilled” utilizing songs by standard artists just like the Beatles or Michael Jackson.
But, as you possibly can see, a dataset containing annotations of such songs has clearly been used as part of a ByteDance analysis undertaking on this subject.
Any evaluation or reference to standard songs and their annotations in analysis performed or funded by a multi-billion-dollar expertise firm will certainly elevate a lot of questions for the music {industry} – particularly these employed to guard its copyrights.
“We firmly consider that AI applied sciences ought to help, not disrupt, the livelihoods of musicians and artists. AI ought to function a software for creative expression, as true artwork at all times stems from human intention.”
ByteDance’s Seed-Music researchers
There’s a part devoted to Ethics and Security on the backside of ByteDance’s Seed-Music analysis paper.
In accordance with ByteDance’s researchers, they “firmly consider that AI applied sciences ought to help, not disrupt, the livelihoods of musicians and artists“.
They add: “AI ought to function a software for creative expression, as true artwork at all times stems from human intention. Our aim is to current this expertise as a possibility to advance the music {industry} by decreasing boundaries to entry, providing smarter, sooner modifying instruments, producing new and thrilling sounds, and opening up new prospects for creative exploration.”
The ByteDance researchers additionally define moral points particularly: “We acknowledge that AI instruments are inherently susceptible to bias, and our aim is to offer a software that stays impartial and advantages everybody. To attain this, we purpose to supply a variety of management components that assist reduce preexisting biases.
“By returning creative selections to customers, we consider we are able to promote equality, protect creativity, and improve the worth of their work. With these priorities in thoughts, we hope our breakthroughs in lead sheet tokens spotlight our dedication to empowering musicians and fostering human creativity via AI.”
When it comes to Security / ‘deepfake’ considerations, the researchers clarify that, “within the case of vocal music, we acknowledge how the singing voice evokes one of many strongest expressions of particular person id”.
They add: “To safeguard towards the misuse of this expertise in impersonating others, we undertake a course of much like the protection measures specified by Seed-TTS. This entails a multistep verification methodology for spoken content material and voice to make sure the enrollment of audio tokens accommodates solely the voice of licensed customers.
“We additionally implement a multi-level water-marking scheme and duplication checks throughout the generative course of. Trendy programs for music era might essentially reshape tradition and the connection between creative creation and consumption.
“We’re assured that, with robust consensus between stakeholders, these applied sciences will and revolutionize music creation workflow and profit music novices, professionals, and listeners alike.”Music Enterprise Worldwide
