Field Notes from "An Adventure in Thousand Token Wood" — Build Small Hackathon 2026
Music videos are deeply personal artifacts. They transform a song from something you hear into something you see and feel. But creating even a simple lyric video — the kind where words appear in sync with the music — is a tedious, manual process. You're keyframing word timings in a video editor, aligning text to beats by ear, hunting for stock footage that "fits the vibe." Hours of work for a 3-minute song.
We built aMuseMe to ask a different question: What if you just dropped an audio file and got a complete, stylized lyric video back? No lyrics needed. No timeline editing. No stock footage hunting. Just music in, video out.
And we did it with 3.5 billion parameters total.
The Idea: Kinetic Typography Meets Small AI
Kinetic typography — words that move, scale, and animate in sync with spoken audio — is one of the most engaging ways to present text on screen. Music lyric videos are a perfect application: every word has an exact timestamp, and every line has an emotional mood that could inform how it looks.
We imagined a pipeline where:
- An AI listens to the song and timestamps every word
- An AI reads the lyrics and decides how to break them into display lines
- An AI illustrates each section with a matching background painting
- A renderer animates it all into a smooth, 30fps HD video
The catch: all four steps had to fit inside the hackathon's 32B parameter budget. No cloud APIs. Everything local.
The Architecture: Four Small Models, One Pipeline
Stage 1: The Listener — Whisper large-v3 (~1.55B)
We use faster-whisper (a CTranslate2-optimized port) to extract word-level timestamps from raw audio. Not sentence-level — word-level. When the singer says "heart" at exactly 4.72 seconds, we know it starts at 4.72s and ends at 5.01s.
This precision is what makes the final video feel alive. Words don't just appear line-by-line; each word lights up at the exact millisecond it's sung.
The tuning rabbit hole: Getting accurate word timestamps from songs (not clean speech) required extensive experimentation:
-
condition_on_previous_text=Truedramatically improves accuracy — Whisper uses its own previous output as context, so it "remembers" the song's vocabulary. But this causes infinite hallucination loops during instrumental breaks (Whisper fills the silence with repeated phantom lyrics). -
VAD (Voice Activity Detection) solves the hallucination problem. We use aggressive thresholds —
min_silence_duration_ms=2000,speech_pad_ms=2000,min_speech_duration_ms=50— so Whisper only sees audio segments where someone is actually singing. - We started with
whisper-base(74M params) for speed, but word boundary accuracy was poor for fast vocals.large-v3was the sweet spot: accurate enough for songs, and still well within the 32B budget.
Stage 2: The Director — MiniCPM5-1B + Outlines
This is the creative brain of the pipeline. Raw Whisper output is a flat list of timestamped words — but a lyric video needs lines. "Every heartbeat echoes feel like grooving my veins" needs to become:
Every heartbeat echoes ← line 1
feel like grooving my veins ← line 2
A rule-based approach (split on silence gaps, cap at 7 words) works, but it produces mechanical, unnatural breaks. An LLM understands phrase structure — it knows "breaking all of these chains" should stay together.
We use MiniCPM5-1B (by OpenBMB, one of the hackathon's anchor sponsors) — a 1B-parameter language model that's small enough to run alongside Whisper and SD-Turbo on a single GPU. For each chunk of ~10 words, the model:
- Splits words into display lines — deciding how many words belong on each line
-
Picks a frame animation —
zoom_infor emphasis,flashfor a dramatic hit,fade_to_blackfor a quiet ending,pan_left/pan_rightfor gentle movement
The structured generation breakthrough: The biggest challenge with small LLMs is output reliability. A 1B model often produces malformed JSON, missing fields, or hallucinated keys. We solved this completely with Outlines — a library that constrains the LLM's token generation to match a Pydantic schema at decode time. The model literally cannot produce invalid JSON. No retries, no regex extraction, no parsing failures.
from outlines import from_transformers, Generator
class Frame(BaseModel):
count: int # how many words on this line
frame_animation: FrameAnim # zoom_in, flash, pan_left, etc.
class SongFrames(BaseModel):
frames: List[Frame]
model = from_transformers(hf_model, tokenizer)
generator = Generator(model, SongFrames) # schema-enforced!
result = generator(prompt, max_new_tokens=256)
# result is ALWAYS valid SongFrames — guaranteed
Stage 3: The Illustrator — SD-Turbo (~865M)
For each pair of lyric lines, we generate a cinematic background image using SD-Turbo (Stability AI's distilled Stable Diffusion model). The magic of SD-Turbo: it generates high-quality images in a single inference step with guidance_scale=0.0.
We merge the lyric text with the user's style prompt:
"neon-lit futuristic city at night, vibrant glowing colors,
cyberpunk aesthetic, breaking all of these chains"
For a 3-minute song with ~15 storyboard images, the entire background generation step takes ~2 seconds on GPU. The backgrounds are then darkened (55% overlay) so white/neon lyric text remains readable on any generated image.
Stage 4: The Renderer — Pillow + FFmpeg
The final stage is a custom frame-by-frame renderer built with Pillow:
- Word-level highlighting: Words in the current line are shown in the theme's active color; unspoken words are dimmed. As each word's timestamp arrives, it lights up.
- Frame-level animations: The LLM-chosen animation (zoom, pan, flash, fade) is applied to the entire text block, creating cinematic movement.
- Smart text wrapping: Long lines automatically break across multiple rows instead of shrinking to unreadable sizes.
- Cross-fade transitions: Background images blend smoothly with 1-second alpha transitions.
The frames are streamed as raw RGB bytes directly to an FFmpeg subprocess via stdin pipe — no temp files written to disk. This avoids the I/O bottleneck that plagues cloud runners and keeps the assembly step near-instantaneous.
What Makes This "Thousand Token Wood"?
Track 2 asks for something delightful that wouldn't exist without AI. aMuseMe isn't an AI chatbot or a productivity tool — it's a creative instrument. You feed it a song, and four small AI models collaborate to produce something that would take a human editor hours:
- Would you show a friend? Absolutely. "Drop your favorite song and get a lyric video in 90 seconds" is an instant demo.
- Is AI load-bearing? Remove any of the four models and the experience collapses. Without Whisper, no word sync. Without MiniCPM5-1B, ugly line breaks and no animation direction. Without SD-Turbo, no visual atmosphere.
- Is it original? We haven't seen another project that chains speech-to-text → structured LLM direction → text-to-image → kinetic typography rendering in a single pipeline. The "AI as video director" concept — where the LLM doesn't just format text but actually makes creative decisions about animation — is, to our knowledge, novel.
- Is it polished? Three visual themes, four font families, a cyberpunk-inspired dark UI, sample songs to try instantly, and a one-click generation button.
Off the Grid: No Cloud APIs
The entire pipeline runs on-device. Whisper, MiniCPM5-1B, SD-Turbo, and Demucs are all local models loaded into GPU memory. No OpenAI API, no Stability API, no cloud dependencies. On HF Spaces, we use ZeroGPU (@spaces.GPU) for efficient shared-GPU allocation, but the computation is still happening on HF's own hardware — not calling out to external services.
What We Learned
Structured generation changes everything for small models. A 1B model that always outputs valid JSON via Outlines is more reliable than a 70B model that you hope will format correctly. The constraint isn't a limitation — it's a superpower.
Word-level sync is the "wow" factor. Line-by-line lyrics feel like karaoke from 2005. Word-by-word highlighting with millisecond precision feels magical. The difference in viewer engagement is enormous.
Whisper needs babysitting for music. VAD, condition-on-previous-text, compression ratio thresholds, temperature scheduling — we spent more time tuning Whisper parameters than writing the renderer. Songs are fundamentally harder than speech.
Pipes over disk. Streaming raw bytes to FFmpeg via stdin was a 10× performance win over writing temp frames to disk. On cloud runners with slow I/O, this is the difference between a 10-second and a 100-second pipeline.
One-step diffusion is a game-changer for pipelines. SD-Turbo generating 15 images in 2 seconds means background generation is no longer a bottleneck. It's fast enough to be a utility, not a feature.
Try it yourself — drop a song and watch the magic happen:
👉 aMuseMe on Hugging Face Spaces