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audiolla

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Thirty audio engines. One port. Zero cloud. Fire-and-forget async jobs. Webhooks.

You needed Demucs for stems. Then librosa for BPM and key. Then basic-pitch for MIDI transcription. Then pyannote for speaker diarization. Then DeepFilterNet for speech enhancement. Then you spent three days debugging Python version conflicts and now you hate everything.

audiolla is what happens when you stop doing that.

Every audio processing tool worth using — wrapped in one HTTP API, running in one Docker container. POST a file. Get audio, JSON, or MIDI back. Drive it from curl, shell scripts, Python notebooks, Makefiles, or point an LLM agent at the MCP endpoint and let it rip.

No account. No subscription. No per-minute billing. No vendor lock-in. docker run and you're done.


What's in the box

🎛️ Stem separation

Demucs — htdemucs, fine-tuned, 6-stem, MDX variants

🎚️ Mastering

Reference mastering (matchering) + custom pedalboard chains

📊 Analysis

BPM · key · LUFS · beats · onsets · melody · structural segments

🎹 Chords + key

Chord detection + Krumhansl-Schmuckler key estimation

🎵 Audio → MIDI

Polyphonic transcription via Spotify's basic-pitch (ONNX, no TF)

🧹 Restoration

De-reverb · de-echo · de-noise via UVR BS-Roformer + MelBand Roformer

🗣️ Speech

Enhancement (DeepFilterNet) · VAD (silero-vad) · diarization (pyannote)

🖼️ Visuals

Spectrogram + waveform PNGs + 8-mode animated MP4/WebM

🔍 Fingerprint

Chromaprint acoustic fingerprinting (AcoustID-compatible)

✂️ Silence

Detect gaps · trim edges · strip all silence

🎼 MIDI pipeline

Compose from JSON · inspect · transform · render via fluidsynth

🎸 Effects

23-effect pedalboard chain — Compressor, Reverb, PitchShift, filters…

🔧 Transforms

Sox DSP — pitch, tempo, EQ, reverb, gain

📢 Loudness

Measure LUFS · normalize to target

🥁 HPSS

Harmonic/percussive source separation via librosa median filter

🔇 Noise reduction

Spectral noise reduction via noisereduce — stationary + adaptive modes

Time-stretch

Independent tempo factor + pitch shift via librosa phase vocoder

🏷️ Audio tagging

Top-K AudioSet class labels via Audio Spectrogram Transformer

🔗 Audio embeddings

512-dim semantic embeddings via LAION CLAP + optional text similarity

🏷️ Zero-shot classify

CLAP cosine similarity against any free-form text labels — genres, moods, instruments

📋 Audio info

ffprobe metadata — duration, sample rate, channels, codec, bit depth

✂️ Trim

Cut a clip by start/end seconds — any format in, any format out

🎚️ Mix

Combine N staged tracks with per-track gain_db — pure ffmpeg, no model

🔗 Concat

Stitch N audio files end-to-end in order

Speed

Change playback speed without pitch shift (0.1× – 10×) via ffmpeg atempo

🔄 Convert

Re-encode: format, sample rate, channel count in one call

🔍 Similar

Cosine similarity between two audio files via CLAP embeddings

🎹 MIDI quantize

Snap MIDI note timings to a rhythmic grid (16th, 8th, quarter…)

🌅 Fade

Fade-in and/or fade-out with 13 curve shapes

Reverse

Flip audio backwards

🔁 Loop

Repeat audio N times

🎯 BPM match

Auto-detect BPM then stretch to a target — no manual math

📈 Loudness curve

RMS envelope over time — time-stamped dB values for gain automation

🎤 Pitch correct

Auto-tune toward nearest chromatic semitone — configurable strength

🔧 Repair

Declip + dehum — fix clipped peaks and remove power-line hum

🔁 Loop point

Find best seamless loop boundary — score, bar count, candidates list

🥁 Drum machine

Step-sequencer spec → GM drum MIDI — 16-step pattern, swing, tempo

🎼 Chords to MIDI

Chord progression → MIDI file — root+3rd+5th voicings per segment

↔️ Stereo width

Widen or collapse the stereo image via M/S processing

✂️ Split

Split into N equal parts or on silence — returns ZIP of segments

🔊 Pan

Position audio in the stereo field (-1 left → 0 center → 1 right)

🎚️ EQ

Parametric EQ — JSON array of freq/gain_db/width_hz bands

🎵 Key match

Detect source key then pitch-shift to a target key

🎙️ Sidechain duck

Duck music when a trigger track (voice) is loud

🏷️ Metadata

Read and write ID3/Vorbis/FLAC/WAV audio tags via mutagen

🔴 Clip detect

Detect digital clipping — count, ratio, peak dBFS

↔️ Mid/Side

Encode L/R → Mid+Side or decode Mid+Side → L/R

✂️ Beat slice

Slice audio at detected beat positions — returns ZIP of segments

🏟️ Conv reverb

Convolution reverb via impulse response — wet_mix control

🥁 Transient shaper

Attack/sustain dual-compressor — punch up drums, cut room tail

🎚️ Multiband compress

N-band compressor with zero-phase LR4 crossovers — mastering-grade dynamics

🎛️ DJ prep

One call: BPM + key + Camelot wheel position + integrated LUFS

📦 Batch

Run trim/convert/fade/reverse/speed/eq on staged files in sequence

🧩 Presets + pipeline

Curated YAML workflows (master-for-spotify, podcast-cleanup, …) + ad-hoc op chaining server-side

🗂️ Catalog

GET /v1/catalog — machine-readable endpoint list grouped by category for discovery

Async jobs

Every endpoint supports async_job=true — fire-and-forget + webhook callbacks


Related MCP server: MCP Server Whisper

Table of Contents


Run it

# no GPU
docker run --rm -it \
  -v $HOME/.audiolla-data:/data \
  -p 8000:8000 \
  psyb0t/audiolla:latest

# GPU
docker run --rm -it --gpus all \
  -v $HOME/.audiolla-data:/data \
  -e AUDIOLLA_DEVICE=cuda \
  -p 8000:8000 \
  psyb0t/audiolla:latest-cuda

Demucs weights prefetch at container startup (for whichever variants are enabled) and cache in /data/torch_cache/. First boot downloads them; same -v mount next time and they're already there. Other engines (matchering, pedalboard, librosa, sox, fx, midi) have no weights — they're ready as soon as /healthz is green.


Migration from v0.23.x → v1.0.0

v1.0.0 is a breaking API release. Every existing client breaks. The new shape:

  • Every audio endpoint takes a JSON body (no more multipart/form-data except at /v1/files)

  • Input is file_path (FILES_DIR-relative) xor file_url (server-side fetch). Pre-stage the file via PUT /v1/files/{path} first.

  • Output requires output_path xor output_url. No more raw audio bytes in responses.

  • Async path: async_job=true auto-stages to jobs/{id}.{ext} if neither output is given.

  • MCP audio-producing tools dropped audio_base64 (and midi_base64 / image_base64 / video_base64). Same output_path xor output_url requirement.

  • openapi.yaml is now the contract — Pydantic models regenerate from it via make generate. Never hand-edit src/audiolla/schema/_generated.py.

- curl -X POST http://localhost:8000/v1/audio/normalize \
-     -F "file=@track.wav" -F "target_lufs=-14" -o normalized.wav

+ # 1) stage the file (multipart only lives here now)
+ curl -X PUT --data-binary @track.wav \
+     -H 'Content-Type: application/octet-stream' \
+     http://localhost:8000/v1/files/uploads/track.wav

+ # 2) process via JSON body — response is JSON, not bytes
+ curl -X POST http://localhost:8000/v1/audio/normalize \
+     -H 'Content-Type: application/json' \
+     -d '{"file_path":"uploads/track.wav","target_lufs":-14,"output_path":"out/normalized.wav"}'

+ # 3) retrieve the result
+ curl -o normalized.wav http://localhost:8000/v1/files/out/normalized.wav

Why? See the v1.0.0 CHANGELOG entry for the full rationale.

Quick start

Once the container is up, this is a complete audio pipeline in six commands (every audio endpoint is JSON-body now; stage your input file at /v1/files/... first):

# stage your input file
curl -X PUT --data-binary @song.wav \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/song.wav

# rip the vocals out of a track
curl -X POST http://localhost:8000/v1/audio/separate \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/song.wav","engine":"htdemucs","stems":["vocals"],"output_path":"out/vocals.wav"}'
# → {"path":"out/vocals.wav","size":...,"output_format":"wav"}
curl -o vocals.wav http://localhost:8000/v1/files/out/vocals.wav

# what key is it in? what are the chords?
curl -X POST http://localhost:8000/v1/audio/chords \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/song.wav"}'
# → {"key":"F# minor","key_confidence":0.91,"chords":[{"chord":"F#m","start_sec":0.0,...},...]}

# transcribe that vocal melody to MIDI
curl -X PUT --data-binary @out/vocals.wav -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/vocals.wav  # only if not already staged
curl -X POST http://localhost:8000/v1/audio/to_midi/basic-pitch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocals.wav","output_path":"out/melody.mid"}'

# render the MIDI back to audio through a SoundFont
curl -X POST http://localhost:8000/v1/midi/render \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"out/melody.mid","output_path":"out/rendered.wav"}'

# strip background noise from a voice recording
curl -X POST http://localhost:8000/v1/audio/noise-reduce/uvr-denoise \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/interview.wav","output_path":"out/clean.wav"}'

# who's speaking and when?
curl -X POST http://localhost:8000/v1/audio/diarize/pyannote \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/interview.wav"}'
# → {"num_speakers":2,"segments":[{"speaker":"SPEAKER_00","start_sec":0.5,"end_sec":8.2},...]}

Audio in. MIDI out. Chords detected. Speakers identified. De-noised. Re-synthesized. No Python environment to set up. No API keys. No account. Just HTTP.


What it can do

Output defaults to wav. Add "output_format":"mp3" to the JSON body to get mp3 instead (flac, opus, aac, pcm also work).

Every audio endpoint takes an application/json body. The only place multipart still lives is PUT /v1/files/{path} (raw bytes for staging an input file).

Input — every audio endpoint requires exactly one of:

  • file_path — path inside the /v1/files staging area (stage with PUT /v1/files/{path} first)

  • file_url — remote URL the server fetches (disabled by default — see Remote URLs)

Output — audio-producing endpoints require exactly one of:

  • output_path — server writes to /v1/files/<path>, returns JSON {"path":..., "size":..., ...}

  • output_url — server PUTs to a presigned URL, returns JSON {"url":..., "size":..., ...}

Analysis-only endpoints (those that return JSON data, e.g. /v1/audio/analyze, /v1/audio/loudness, /v1/audio/info) don't need output_path / output_url — the response is the result.

Split stems

# stage input
curl -X PUT --data-binary @track.wav \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/track.wav

# vocals only
curl -X POST http://localhost:8000/v1/audio/separate \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","engine":"htdemucs","stems":["vocals"],"output_path":"out/vocals.wav"}'
curl -o vocals.wav http://localhost:8000/v1/files/out/vocals.wav

# all 4 stems as a ZIP
curl -X POST http://localhost:8000/v1/audio/separate \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","engine":"htdemucs","output_path":"out/stems.zip"}'
curl -o stems.zip http://localhost:8000/v1/files/out/stems.zip

Master

# stage track + reference
curl -X PUT --data-binary @track.wav -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/track.wav
curl -X PUT --data-binary @ref.wav -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/ref.wav

# match EQ + loudness to a reference track
curl -X POST http://localhost:8000/v1/audio/master \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","mode":"reference","reference_path":"uploads/ref.wav","output_path":"out/mastered.wav"}'
curl -o mastered.wav http://localhost:8000/v1/files/out/mastered.wav

# run a built-in pedalboard chain (presets: transparent, loud)
curl -X POST http://localhost:8000/v1/audio/master \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","mode":"chain","preset":"loud","output_path":"out/mastered.wav"}'
curl -o mastered.wav http://localhost:8000/v1/files/out/mastered.wav

Analyze

# returns JSON. features: bpm, key, loudness, duration,
# spectral_centroid, rms, zcr. Omit features to get them all.
curl -X POST http://localhost:8000/v1/audio/analyze \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","features":["bpm","key","loudness"]}'

Beats, onsets, melody, segments

# beat grid — returns bpm + beat timestamps
curl -X POST http://localhost:8000/v1/audio/beats \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'

# onset timestamps — note attacks, transients
curl -X POST http://localhost:8000/v1/audio/onsets \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'

# dominant melody contour — pitch in Hz per frame
curl -X POST http://localhost:8000/v1/audio/melody \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'

# structural segmentation — labels recurring sections A, B, C...
curl -X POST http://localhost:8000/v1/audio/segments \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","num_segments":6}'

Beat detection also generates a click-track file when click_track=true (set output_path to receive it) — handy for aligning a mix to a grid. Pass start_bpm=140 to seed the tracker when you already know the rough tempo (faster, more accurate). Melody can be exported as a single-track MIDI file via as_midi=true + output_path.

Silence detection and trimming

# find silent gaps in a recording (no trim_mode → JSON only)
curl -X POST http://localhost:8000/v1/audio/silence \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","threshold_db":-30,"min_duration_sec":1.0}'

# trim all silence and stage the result
curl -X POST http://localhost:8000/v1/audio/silence \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","threshold_db":-30,"min_duration_sec":0.5,"trim_mode":"all","output_path":"out/trimmed.wav"}'
curl -o trimmed.wav http://localhost:8000/v1/files/out/trimmed.wav

# trim only leading/trailing silence
curl -X POST http://localhost:8000/v1/audio/silence \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","threshold_db":-40,"min_duration_sec":0.3,"trim_mode":"edges","output_path":"processed/trimmed.wav"}'

trim_mode=edges — chop leading + trailing silence only. trim_mode=all — remove every detected gap (compress a talk recording, tighten a loop). Without trim_mode, the response is JSON only: silent_ranges, non_silent_ranges, duration — and output_path / output_url is not required.

Visualize (spectrogram, waveform, video)

Visual output splits into two sub-namespaces by output type:

# Static PNG spectrogram (color + scale params)
curl -X POST http://localhost:8000/v1/audio/visualize/image/spectrogram \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","width":1280,"height":720,"output_path":"out/spec.png"}'
curl -o spec.png http://localhost:8000/v1/files/out/spec.png

# Static PNG waveform (color param)
curl -X POST http://localhost:8000/v1/audio/visualize/image/waveform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","width":1280,"height":240,"output_path":"out/wave.png"}'
curl -o wave.png http://localhost:8000/v1/files/out/wave.png

# Animated MP4 spectrum analyser (fps + container params)
curl -X POST http://localhost:8000/v1/audio/visualize/video/spectrum \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","width":1280,"height":720,"fps":30,"container":"mp4","output_path":"out/viz.mp4"}'
curl -o viz.mp4 http://localhost:8000/v1/files/out/viz.mp4

/image/spectrogram: produces a PNG (staged via output_path or PUT to output_url). Params: width, height, color (default intensity), scale (log/lin).

/image/waveform: produces a PNG. Params: width, height, color (default lime).

/video/{mode}: spectrum (scrolling FFT), waves (oscilloscope), cqt (constant-Q transform), freqs (bar-graph analyzer), volume (VU meter), vectorscope (stereo X/Y scope), phasemeter, histogram. Params: width, height, fps, container (mp4 default, webm).

Acoustic fingerprint

# Chromaprint fingerprint — identifies a recording regardless of encoding
curl -X POST http://localhost:8000/v1/audio/fingerprint \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'
# → {"duration": 215.34, "fingerprint": "AQADtEqRRIuQ..."}

# include the raw integer array (for custom similarity scoring)
curl -X POST http://localhost:8000/v1/audio/fingerprint \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","return_raw":true}'

The base64 fingerprint string is compatible with the AcoustID lookup service.

De-reverb, de-echo, de-noise

AI audio restoration via UVR ecosystem models — BS-Roformer and MelBand Roformer. All three are unified under POST /v1/audio/restore/{engine}.

# Remove room reverb (BS-Roformer, SDR 19+)
curl -X POST http://localhost:8000/v1/audio/restore/uvr-dereverb \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"out/dry.wav"}'

# Remove echo — normal mode
curl -X POST http://localhost:8000/v1/audio/restore/uvr-deecho \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"out/noecho.wav"}'

# Remove echo — aggressive mode (same engine, harder suppression)
curl -X POST http://localhost:8000/v1/audio/restore/uvr-deecho \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","aggressive":true,"output_path":"out/noecho.wav"}'

# Remove broadband background noise — ML (MelBand Roformer, SDR 28)
curl -X POST http://localhost:8000/v1/audio/restore/uvr-denoise \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"out/clean.wav"}'

All support output_format, output_path, output_url. For DSP-based noise reduction (no GPU) use noise-reduce/noise-reduce.

UVR engines also work through /v1/audio/separateuvr-vocal-bsr (BS-Roformer, SDR 13) and uvr-karaoke return vocal + instrumental stems like Demucs but often with higher quality.

Audio-to-MIDI transcription

Polyphonic audio-to-MIDI via Spotify's basic-pitch (ONNX backend, no TensorFlow). Play guitar, hum a melody, record a piano riff — get a MIDI file back with all the notes.

# Any audio → MIDI file (staged)
curl -X POST http://localhost:8000/v1/audio/to_midi/basic-pitch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/guitar_riff.wav","output_path":"out/riff.mid"}'
curl -o riff.mid http://localhost:8000/v1/files/out/riff.mid

# Tune the detection thresholds
curl -X POST http://localhost:8000/v1/audio/to_midi/basic-pitch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/piano.wav","onset_threshold":0.6,"frame_threshold":0.3,"minimum_note_length_ms":80,"output_path":"out/piano.mid"}'

# Write directly to a different staging path
curl -X POST http://localhost:8000/v1/audio/to_midi/basic-pitch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"recordings/bass.wav","output_path":"midi/bass_notes.mid"}'
# → {"path":"midi/bass_notes.mid","size":...,"engine":"basic-pitch","output_format":"mid"}

Optional params: onset_threshold (0–1, default 0.5), frame_threshold (0–1, default 0.3), minimum_note_length_ms (default 58), minimum_frequency / maximum_frequency (Hz, default unconstrained), multiple_pitch_bends (bool, default false), melodia_trick (bool, default true — helps with melodic content). Default engine: basic-pitch.

The MIDI file is piped straight into /v1/midi/inspect or /v1/midi/render — audio → MIDI → audio is a complete round-trip.

Neural speech and vocal enhancement

DeepFilterNet DF3 — deep learning noise suppression trained on speech. Better than broadband de-noise for voice recordings; more surgical than UVR's de-noise on vocals specifically.

# Enhance a vocal recording
curl -X POST http://localhost:8000/v1/audio/enhance/deepfilter \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal_recording.wav","output_path":"out/enhanced.wav"}'
curl -o enhanced.wav http://localhost:8000/v1/files/out/enhanced.wav

# Stage the output as mp3
curl -X POST http://localhost:8000/v1/audio/enhance/deepfilter \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"vocals/raw.wav","output_format":"mp3","output_path":"vocals/enhanced.mp3"}'

Supports output_format, output_path, output_url.

Generate music + SFX

Text-to-audio generation under POST /v1/audio/generate/{engine}. v1.0.0 ships five engines spanning music + sound effects, with different licence / VRAM / sound profiles — all CUDA-only.

# Stable Audio Open 1.0 — 47s cap, no vocals, great for loops + SFX
curl -X POST http://localhost:8000/v1/audio/generate/stable-audio-open \
  -H 'Content-Type: application/json' \
  -d '{"prompt":"130 bpm tech house drum loop, punchy kick, crisp hats, no vocals","duration_sec":10,"seed":42,"output_path":"out/loop.wav"}'
curl -o loop.wav http://localhost:8000/v1/files/out/loop.wav

# MusicGen 300M — 30s cap, instrumental, CC-BY-NC (opt-in required)
curl -X POST http://localhost:8000/v1/audio/generate/musicgen-small \
  -H 'Content-Type: application/json' \
  -d '{"prompt":"lo-fi hip-hop beat with vinyl crackle, 90 bpm","duration_sec":15,"output_path":"out/beat.wav"}'

# Riffusion — spectrogram-to-audio via Griffin-Lim, ~5s, lo-fi character
curl -X POST http://localhost:8000/v1/audio/generate/riffusion \
  -H 'Content-Type: application/json' \
  -d '{"prompt":"ambient drone with metallic resonance","output_path":"out/drone.wav"}'

# AudioLDM 2 — general SFX (no opt-in gate, CC-BY 4.0 commercial-OK)
curl -X POST http://localhost:8000/v1/audio/generate/audioldm2 \
  -H 'Content-Type: application/json' \
  -d '{"prompt":"heavy rain on a metal roof with distant thunder","duration_sec":10,"num_inference_steps":50,"output_path":"out/rain.wav"}'

Engine details:

Engine

Licence

Max length

VRAM (fp16)

Output

stable-audio-open

Stability Community Licence (commercial OK below the revenue threshold)

47 s hard cap

~12 GB

44.1 kHz stereo. Loops, SFX, ambient textures — instrumental only

musicgen-small

CC-BY-NC 4.0 (non-commercial only — opt-in via AUDIOLLA_ENABLE_NONCOMMERCIAL=1)

30 s hard cap

~3 GB

32 kHz mono. Meta MusicGen 300M, instrumental

musicgen-medium

CC-BY-NC 4.0 (same opt-in)

30 s hard cap

~6-8 GB

32 kHz mono. Higher quality than -small

riffusion

CreativeML OpenRAIL-M (commercial OK with the licence's usage restrictions)

~5 s per pass

~3 GB

22.05 kHz mono. SD-style spectrogram, Griffin-Lim reconstruction — lo-fi / loop-y character

audioldm2

CC-BY 4.0 (commercial use OK — no opt-in gate)

30 s hard cap

~8-10 GB (CPU offload)

16 kHz mono. General SFX: ambience, foley, animal, mechanical, impact sounds. Slow (200-step DDIM default; pass num_inference_steps=50 for ~4x speedup)

All engines support async_job=true, webhook_url, output_path, output_url, and seed for reproducibility. stable-audio-open and audioldm2 additionally accept num_inference_steps (trade quality for speed). Model weights download on first call to HF_HOME (default /data/hf inside the container — ~7 GB across all five). Subsequent calls are inference-only. All five are flagged cuda_only — non-CUDA hosts get HTTP 400.

Licence opt-in for MusicGen. MusicGen weights are CC-BY-NC 4.0. The engine code ships with the image but refuses to load the model unless the operator explicitly sets AUDIOLLA_ENABLE_NONCOMMERCIAL=1 in the server's environment. Same pattern matchering (GPL v3) follows — licence-encumbered code in the image, conscious opt-in to actually use it. Read the MusicGen weights licence before opting in. AudioLDM 2 is CC-BY 4.0 (commercial use allowed, no opt-in gate) — it's the only generator in this set that's commercial-safe without flipping any flags.

Deferred to a future release (researched but not shipped in v1.0.0):

  • ACE-Step v1 (3.5B, Apache 2.0, full songs with vocals up to 4 min) — requires AceStepPipeline from diffusers>=0.38, which itself requires a pre-release safetensors. Doesn't pass the project's hash-locked supply-chain gate. Revisit when safetensors 0.8.x ships stable, or vendor ACE-Step's pipeline directly.

  • DiffRhythm full v1.2 (Apache 2.0) — unpackaged research repo (no setup.py / PyPI release). Revisit when upstream ships a package or we vendor under thirdparty/.

  • Stable Audio Open Small (Stability Community Licence, 11 s SFX-specialist) — requires stable-audio-tools which pins python >=3.10, <3.11; audiolla is on Python 3.12, hard incompatibility. Revisit when stable-audio-tools widens the Python constraint or diffusers grows a pipeline for it.

  • TangoFlux (ICLR 2026, 44.1 kHz, 30 s, fast) — git-only install (no PyPI package). Could be SHA-pinned in the hash-locked supply chain; deferred for now to keep the heavy-deps stack PyPI-only.

  • AudioGen (Meta, CC-BY-NC) — audiocraft==1.3.0 pins transformers<=4.31.0, hard conflict with audiolla's 4.51.3. Would require an isolated subprocess / sidecar container.

  • YuE 7B (Apache 2.0, full songs with vocals) — needs 16-24 GB VRAM at fp16, doesn't fit 12 GB GPUs without int4 quant tooling.

Chord and key detection

Krumhansl-Schmuckler key estimation + chroma-template chord segmentation via librosa. No extra deps beyond the librosa stack.

curl -X POST http://localhost:8000/v1/audio/chords \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'
# → {
#     "key": "C major",
#     "key_confidence": 0.87,
#     "duration": 183.4,
#     "chords": [
#       {"chord": "C", "start_sec": 0.0, "end_sec": 2.3, "confidence": 0.91},
#       {"chord": "Am", "start_sec": 2.3, "end_sec": 4.6, "confidence": 0.85},
#       ...
#     ]
#   }

# Tune the hop length (lower = finer time resolution)
curl -X POST http://localhost:8000/v1/audio/chords \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","hop_length":256}'

Optional params: hop_length (default 512), segment_min_duration_sec (default 0.5 — merge very short chord segments).

Voice activity detection

silero-vad — ONNX-based VAD, fast and accurate on both speech and music. Returns timestamped speech and non-speech segments.

curl -X POST http://localhost:8000/v1/audio/vad \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/interview.wav"}'
# → {
#     "speech_ratio": 0.73,
#     "duration": 120.0,
#     "threshold": 0.5,
#     "speech_segments": [
#       {"start_sec": 1.2, "end_sec": 8.4},
#       ...
#     ],
#     "non_speech_segments": [
#       {"start_sec": 0.0, "end_sec": 1.2},
#       ...
#     ]
#   }

# Tighter detection
curl -X POST http://localhost:8000/v1/audio/vad \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/podcast.wav","threshold":0.7,"min_speech_duration_ms":300,"min_silence_duration_ms":200}'

Optional params: threshold (0–1, default 0.5), min_speech_duration_ms (default 250), min_silence_duration_ms (default 100).

Speaker diarization

pyannote/speaker-diarization-3.1 — state-of-the-art speaker diarization from HuggingFace Hub. Returns per-speaker timestamped segments and speaker count.

Note: This engine requires a HuggingFace account. You must accept the model terms at https://huggingface.co/pyannote/speaker-diarization-3.1 and then set HF_TOKEN (or the older alias HUGGINGFACE_TOKEN — the entrypoint mirrors them both ways) when starting the container. A read-only token with model access is enough. The same token also unlocks the gated text-to-audio engines (stable-audio-open, musicgen-small, musicgen-medium) provided you've accepted their licences on huggingface.co.

docker run ... \
  -e HF_TOKEN=hf_your_token_here \
  psyb0t/audiolla:latest
curl -X POST http://localhost:8000/v1/audio/diarize/pyannote \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/interview.wav"}'
# → {
#     "num_speakers": 2,
#     "speakers": ["SPEAKER_00", "SPEAKER_01"],
#     "duration": 120.0,
#     "segments": [
#       {"speaker": "SPEAKER_00", "start_sec": 0.5, "end_sec": 8.2, "duration_sec": 7.7},
#       {"speaker": "SPEAKER_01", "start_sec": 8.5, "end_sec": 14.1, "duration_sec": 5.6},
#       ...
#     ]
#   }

# Hint the expected speaker count
curl -X POST http://localhost:8000/v1/audio/diarize/pyannote \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/roundtable.wav","num_speakers":4}'

# Or constrain the range
curl -X POST http://localhost:8000/v1/audio/diarize/pyannote \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/panel.wav","min_speakers":2,"max_speakers":6}'

Optional params: num_speakers (exact count hint), min_speakers, max_speakers.

Transform

# pitch shift up 2 semitones + add reverb, export mp3.
# operations is a JSON array — ops: gain, equalizer, compand, reverb,
# pitch, tempo, rate, channels, trim, pad.
curl -X POST http://localhost:8000/v1/audio/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","operations":[{"op":"pitch","params":{"n_semitones":2}},{"op":"reverb","params":{"reverberance":50}}],"output_format":"mp3","output_path":"out/out.mp3"}'
curl -o out.mp3 http://localhost:8000/v1/files/out/out.mp3

Loudness measurement

# Measure integrated LUFS — returns JSON, no audio output
curl -X POST http://localhost:8000/v1/audio/loudness \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'
# → {"loudness_lufs": -18.4}

Loudness curve

RMS envelope over time — returns a list of {time_sec, rms_db} points. Useful for generating gain automation curves, finding loud and quiet sections, or visualising dynamic range before mastering.

# Default hop (512 samples) — fine-grained envelope
curl -X POST http://localhost:8000/v1/audio/loudness/curve \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}' | jq '.curve[:5]'
# → [
#     {"time_sec": 0.0,   "rms_db": -18.4},
#     {"time_sec": 0.012, "rms_db": -17.9},
#     ...
#   ]

# Coarser envelope (2048-sample hop)
curl -X POST http://localhost:8000/v1/audio/loudness/curve \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","hop_length":2048}' | jq '{duration, sample_rate, points}'

Response fields: curve (array of {time_sec, rms_db}), duration (seconds), sample_rate, points (total curve length). Optional param: hop_length (default 512).

Loudness normalization

# Normalize to -14 LUFS (streaming platform standard)
curl -X POST http://localhost:8000/v1/audio/normalize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","target_lufs":-14,"output_path":"out/normalized.wav"}'
curl -o normalized.wav http://localhost:8000/v1/files/out/normalized.wav

# Write to a different staging path
curl -X POST http://localhost:8000/v1/audio/normalize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","target_lufs":-23,"output_path":"mastered/norm.wav"}'

target_lufs is required. The response JSON carries loudness_lufs with the measured pre-normalization level alongside path / url / size.

HPSS (harmonic/percussive split)

Median-filter harmonic/percussive source separation via librosa. Harmonic = tonal content (pitched instruments, pads); percussive = transients (drums, percussion). No ML — pure DSP, fast, no GPU needed.

# Get both stems in a ZIP
curl -X POST http://localhost:8000/v1/audio/separate/hpss \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"out/stems.zip"}'
curl -o stems.zip http://localhost:8000/v1/files/out/stems.zip
# → stems.zip contains harmonic.wav + percussive.wav

# Wider margin = harder separation (more aggressive)
curl -X POST http://localhost:8000/v1/audio/separate/hpss \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","margin":3.0,"output_path":"out/stems.zip"}'

# Output to a different staging path
curl -X POST http://localhost:8000/v1/audio/separate/hpss \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"hpss/stems.zip"}'

Params: margin (default 1.0 — ≥1.0, higher = more aggressive), kernel_size (default 31 — odd int, median filter width), output_format (default wav).

Spectral noise reduction

Noise reduction with two engine options under the same endpoint — pick DSP for no-GPU fast cleanup or ML for higher-quality removal.

# DSP (noisereduce) — no GPU, pure spectral subtraction + Wiener filtering
curl -X POST http://localhost:8000/v1/audio/noise-reduce/noise-reduce \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","output_path":"out/clean.wav"}'

# Stationary mode — constant hum, hiss, fan noise
curl -X POST http://localhost:8000/v1/audio/noise-reduce/noise-reduce \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","stationary":true,"output_path":"out/clean.wav"}'

# Partial reduction — subtle noise floor cleanup
curl -X POST http://localhost:8000/v1/audio/noise-reduce/noise-reduce \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","prop_decrease":0.5,"output_path":"out/clean.wav"}'

# ML (UVR MelBand Roformer, SDR 28) — higher quality, GPU-accelerated
curl -X POST http://localhost:8000/v1/audio/noise-reduce/uvr-denoise \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","output_path":"out/clean.wav"}'

DSP params (only apply to noise-reduce engine): stationary (bool, default false), prop_decrease (0–1, default 1.0). Both engines accept output_format, output_path, output_url.

Time-stretch and pitch-shift

Independent tempo factor and semitone offset via librosa phase vocoder. Slow a track down to learn it; shift a vocal up 3 semitones for a different key; transpose a MIDI melody to a different register first, then render.

# Slow down to 80% speed, no pitch change
curl -X POST http://localhost:8000/v1/audio/stretch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","tempo_factor":0.8,"output_path":"out/slow.wav"}'

# Shift up 3 semitones, no tempo change
curl -X POST http://localhost:8000/v1/audio/stretch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","pitch_semitones":3,"output_path":"out/pitched.wav"}'

# Both — pitch-corrected time stretch (traditional chipmunk effect)
curl -X POST http://localhost:8000/v1/audio/stretch \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","tempo_factor":0.5,"pitch_semitones":6,"output_format":"mp3","output_path":"out/stretched.mp3"}'

Params: tempo_factor (default 1.0 — 0.5 = half speed), pitch_semitones (default 0.0 — ±semitones), output_format, output_path.

Pitch correct

Auto-tune audio toward the nearest chromatic semitone using librosa's phase vocoder. Full strength=1.0 snaps hard to pitch; lower values blend the corrected and original signal.

# Hard auto-tune — snap every note to the nearest semitone
curl -X POST http://localhost:8000/v1/audio/pitch-correct \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","output_path":"out/tuned.wav"}'

# Subtle correction — 50% blend
curl -X POST http://localhost:8000/v1/audio/pitch-correct \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","strength":0.5,"output_format":"mp3","output_path":"out/tuned.mp3"}'

# Async for long files, staged output
curl -X POST http://localhost:8000/v1/audio/pitch-correct \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"sessions/take1.wav","strength":1.0,"async_job":true,"output_path":"sessions/take1_tuned.wav"}'

Params: strength (0.0–1.0, default 1.0), output_format, output_path, async_job, webhook_url. Requires librosa-analyze engine.

Repair

Declip clipped peaks and/or remove power-line hum. Declipping uses cubic interpolation to reconstruct flattened waveform tops and bottoms. Dehumming applies a notch filter at hum_freq (and harmonics).

# Declip only (default)
curl -X POST http://localhost:8000/v1/audio/repair \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/overdriven.wav","output_path":"out/repaired.wav"}'

# Remove 60 Hz hum (North American power grid)
curl -X POST http://localhost:8000/v1/audio/repair \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","declip":false,"dehum":true,"hum_freq":60.0,"output_path":"out/clean.wav"}'

# Both — declip a 50 Hz humming mic recording
curl -X POST http://localhost:8000/v1/audio/repair \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/problem_track.wav","declip":true,"dehum":true,"hum_freq":50.0,"output_format":"flac","output_path":"out/repaired.flac"}'

Params: declip (bool, default true), dehum (bool, default false), hum_freq (Hz, default 50.0), output_format, output_path, async_job, webhook_url.

Audio tagging

Top-K AudioSet class label classification via Audio Spectrogram Transformer (MIT/ast-finetuned-audioset-10-10-0.4593). Identifies what's in a recording — music, speech, specific instruments, environmental sounds, etc.

curl -X POST http://localhost:8000/v1/audio/tag \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav"}'
# → {
#     "tags": [
#       {"label": "Music", "score": 0.94},
#       {"label": "Drum", "score": 0.87},
#       {"label": "Guitar", "score": 0.71},
#       ...
#     ],
#     "duration": 5.2
#   }

# Get top 20 results instead of the default 10
curl -X POST http://localhost:8000/v1/audio/tag \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/soundscape.wav","top_k":20}'

Requires the HF model cache. First run downloads the weights to /data/hf/. Optional: top_k (default 10).

The image defaults to HF_HUB_OFFLINE=0 so first call lazy-downloads the weights into /data/hf/. For locked-down deployments (no egress), prefetch the model with huggingface-cli download <model> into a mounted /data/hf volume, then start the container with -e HF_HUB_OFFLINE=1.

Audio embeddings

512-dimensional L2-normalized audio embeddings via LAION CLAP (laion/larger_clap_music_and_speech). Useful for semantic audio search, similarity scoring, and clustering.

# Get the embedding vector
curl -X POST http://localhost:8000/v1/audio/embed \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'
# → {"embedding": [0.032, -0.11, ...], "dim": 512, "norm": 1.0}

# Semantic similarity — how well does the audio match a text description?
curl -X POST http://localhost:8000/v1/audio/embed \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","query_text":"energetic rock guitar riff"}'
# → {"embedding": [...], "dim": 512, "norm": 1.0,
#    "query_text": "energetic rock guitar riff", "similarity": 0.73}

similarity is cosine similarity in [-1, 1]. Requires HF model cache — same first-run download caveat as audio tagging.

Zero-shot classification

Given audio and a list of free-form text labels, return cosine similarity scores for each using the existing CLAP model. No extra model download — uses the same clap-embed engine. Works for genres, moods, instruments, sonic descriptors — anything CLAP understands.

# Genre detection
curl -X POST http://localhost:8000/v1/audio/classify \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","labels":["jazz","hip-hop","classical","electronic","rock"]}'
# → {"results": [
#     {"label": "hip-hop", "score": 0.42},
#     {"label": "electronic", "score": 0.38},
#     ...
#   ]}

# Mood / energy
curl -X POST http://localhost:8000/v1/audio/classify \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","labels":["energetic","calm","melancholic","aggressive","uplifting"]}'

# Speaker gender
curl -X POST http://localhost:8000/v1/audio/classify \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/interview.wav","labels":["male voice","female voice","child voice","multiple speakers"]}'

Results are sorted by descending score. Scores are cosine similarities in [-1, 1] — higher = more similar. Requires clap-embed model cache.

Audio info

Probe any audio file for metadata without loading it into memory for processing. Uses ffprobe — handles any format.

curl -X POST http://localhost:8000/v1/audio/info \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}'
# → {
#     "size_bytes": 52428800,
#     "duration_sec": 297.241,
#     "sample_rate": 44100,
#     "channels": 2,
#     "codec": "pcm_s16le",
#     "sample_fmt": "s16",
#     "format": "wav",
#     "bit_depth": 16,
#     "bit_rate": 1411200
#   }

# Works on any staged file
curl -X POST http://localhost:8000/v1/audio/info \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"recordings/interview.mp3"}'
# → {"codec": "mp3", "bit_rate": 192000, ...}

Trim

Cut a precise time range out of any audio file. Common use: extract a chorus, clip a sample, chop a stem at bar boundaries.

# Extract seconds 30–90 from a track
curl -X POST http://localhost:8000/v1/audio/trim \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","start_sec":30.0,"end_sec":90.0,"output_path":"out/chorus.wav"}'

# Clip a specific beat range, export as mp3
curl -X POST http://localhost:8000/v1/audio/trim \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/stem.wav","start_sec":0.0,"end_sec":8.0,"output_format":"mp3","output_path":"out/loop.mp3"}'

# From staged file, write to a different staging path
curl -X POST http://localhost:8000/v1/audio/trim \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"sessions/full.wav","start_sec":120.5,"end_sec":180.0,"output_path":"clips/verse.wav"}'

start_sec defaults to 0. end_sec is required and must be greater than start_sec. Supports all standard output_format values.

Mix

Combine multiple staged or URL-accessible tracks into one. Per-track gain_db lets you balance levels before mixing. Useful for bouncing separated stems back together at custom levels, layering synth parts, or combining click-track + music.

# Mix drums and bass at equal levels
curl -X POST http://localhost:8000/v1/audio/mix \
  -H 'Content-Type: application/json' \
  -d '{"tracks":[{"file_path":"stems/drums.wav"},{"file_path":"stems/bass.wav"}],"output_path":"out/rhythm.wav"}'

# Stems at custom levels (drums -3 dB, bass 0 dB, vocals +2 dB)
curl -X POST http://localhost:8000/v1/audio/mix \
  -H 'Content-Type: application/json' \
  -d '{"tracks":[
    {"file_path":"stems/drums.wav","gain_db":-3},
    {"file_path":"stems/bass.wav","gain_db":0},
    {"file_path":"stems/vocals.wav","gain_db":2}
  ],"output_format":"wav","output_path":"out/custom_mix.wav"}'

# Write to a different staging path
curl -X POST http://localhost:8000/v1/audio/mix \
  -H 'Content-Type: application/json' \
  -d '{"tracks":[{"file_path":"stems/harmonic.wav"},{"file_path":"stems/percussive.wav","gain_db":-6}],"output_path":"mixed/recombined.wav"}'

tracks is a required JSON array. Each entry needs file_path or file_url and an optional gain_db (default 0.0). Requires at least 2 tracks. Shorter tracks are padded with silence to match the longest.

Concat

Stitch N audio files together in order. Handles different sample rates and channel counts automatically (ffmpeg resamples on the fly).

curl -X POST http://localhost:8000/v1/audio/concat \
  -H 'Content-Type: application/json' \
  -d '{"files":[{"file_path":"intro.wav"},{"file_path":"verse.wav"},{"file_path":"outro.wav"}],"output_path":"out/full_track.wav"}'

# output_format change + different staging path
curl -X POST http://localhost:8000/v1/audio/concat \
  -H 'Content-Type: application/json' \
  -d '{"files":[{"file_path":"a.wav"},{"file_path":"b.wav"}],"output_format":"mp3","output_path":"concat/result.mp3"}'

files is a required JSON array of {file_path?, file_url?} objects. Requires at least 2 entries.

Speed

Change playback speed without pitch shifting — useful for auditioning at half/double speed, or creating slow-motion effects. Uses ffmpeg atempo filter chained for extreme multipliers.

# Half speed
curl -X POST http://localhost:8000/v1/audio/speed \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","speed":0.5,"output_path":"out/slow.wav"}'

# Double speed
curl -X POST http://localhost:8000/v1/audio/speed \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","speed":2.0,"output_path":"out/fast.wav"}'

# 4× speed (chains two atempo=2.0 filters internally)
curl -X POST http://localhost:8000/v1/audio/speed \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","speed":4.0,"output_format":"mp3","output_path":"out/fast.mp3"}'

speed is required. Range: 0.1–10.0. Note: this changes duration but not pitch. For pitch-preserving tempo changes use /v1/audio/stretch.

Convert

Re-encode audio to a different format, sample rate, or channel count in a single call.

# WAV → 16 kHz mono FLAC (for speech models)
curl -X POST http://localhost:8000/v1/audio/convert \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","output_format":"flac","sample_rate":16000,"channels":1,"output_path":"out/prepared.flac"}'

# Stereo → mono WAV
curl -X POST http://localhost:8000/v1/audio/convert \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stereo.wav","channels":1,"output_path":"out/mono.wav"}'

# Any format → Opus at 48 kHz
curl -X POST http://localhost:8000/v1/audio/convert \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/audio.mp3","output_format":"opus","sample_rate":48000,"output_path":"out/out.opus"}'

output_format defaults to wav. sample_rate and channels are optional; if omitted, the source values are preserved.

Similar

Compute cosine similarity between two audio files using CLAP embeddings. Returns a score in [-1, 1] — 1 = identical sound, 0 = unrelated, negative = acoustically opposite. Useful for duplicate detection, cover matching, or finding the closest sample in a library.

curl -X POST http://localhost:8000/v1/audio/similar \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/original.wav","reference_file_path":"uploads/remix.wav"}'
# → {"similarity": 0.847, "dim": 512}

# Different staged paths
curl -X POST http://localhost:8000/v1/audio/similar \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stems/vocals.wav","reference_file_path":"stems/vocals_ref.wav"}'

Primary file: file_path / file_url. Reference file: reference_file_path / reference_file_url. Requires clap-embed engine.

MIDI quantize

Snap all note timings in a MIDI file to the nearest rhythmic grid. Cleaner dedicated endpoint than /v1/midi/transform's quantize_grid_beats param.

# Quantize to 16th notes (0.25 beats)
curl -X POST http://localhost:8000/v1/midi/quantize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/sloppy.mid","grid_beats":0.25,"output_path":"out/tight.mid"}'

# 8th note grid
curl -X POST http://localhost:8000/v1/midi/quantize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"recorded.mid","grid_beats":0.5,"output_path":"midi/quantized.mid"}'

grid_beats: grid size in beats — 0.25 = 16th note, 0.5 = 8th, 1.0 = quarter note. Default: 0.25.

Fade

Apply fade-in, fade-out, or both. 13 curve shapes: tri, qsin, esin, hsin, log, ipar, qua, cub, squ, cbr, par, exp, lin.

# 2s fade-in
curl -X POST http://localhost:8000/v1/audio/fade \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","fade_in":2.0,"output_path":"out/faded.wav"}'

# 3s fade-out with exponential curve
curl -X POST http://localhost:8000/v1/audio/fade \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","fade_out":3.0,"curve":"exp","output_path":"out/faded.wav"}'

# Both — 1s in, 2s out
curl -X POST http://localhost:8000/v1/audio/fade \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","fade_in":1.0,"fade_out":2.0,"output_path":"out/faded.wav"}'

At least one of fade_in / fade_out must be > 0.

Reverse

Flip audio backwards via ffmpeg areverse.

curl -X POST http://localhost:8000/v1/audio/reverse \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/sample.wav","output_path":"out/reversed.wav"}'

curl -X POST http://localhost:8000/v1/audio/reverse \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stems/vocals.wav","output_format":"mp3","output_path":"out/reversed.mp3"}'

Loop

Repeat audio N times. Uses ffmpeg aloop filter — no re-encoding overhead per iteration.

# Play 4 times total
curl -X POST http://localhost:8000/v1/audio/loop \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/beat.wav","count":4,"output_path":"out/looped.wav"}'

# 8-bar loop → 32 bars
curl -X POST http://localhost:8000/v1/audio/loop \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stems/drums.wav","count":4,"output_path":"loops/drums32.wav"}'

count must be ≥ 2 (total plays, not extra loops).

BPM match

Detect the source BPM via librosa, then time-stretch to the target — no manual math.

# Stretch anything to 128 BPM
curl -X POST http://localhost:8000/v1/audio/bpm-match \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loop.wav","target_bpm":128,"output_path":"out/matched.wav"}'

# Match tempo and also shift pitch
curl -X POST http://localhost:8000/v1/audio/bpm-match \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loop.wav","target_bpm":140,"pitch_semitones":2,"output_path":"out/matched.wav"}'

Response JSON includes source_bpm, target_bpm, and tempo_factor alongside the staged path / url. Requires both librosa-analyze and stretch engines.

Stereo width

Widen or collapse the stereo image via M/S processing. width=0.0 → mono, 1.0 → original, >1.0 → wider. Works on mono input too (upmixes first).

# Widen to 1.5×
curl -X POST http://localhost:8000/v1/audio/stereo-width \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mix.wav","width":1.5,"output_path":"out/wide.wav"}'

# Collapse to mono
curl -X POST http://localhost:8000/v1/audio/stereo-width \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mix.wav","width":0.0,"output_path":"out/mono.wav"}'

# Subtle narrowing for mix bus
curl -X POST http://localhost:8000/v1/audio/stereo-width \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"master/mix.wav","width":0.8,"output_path":"master/narrow.wav"}'

Range: [0.0, 3.0].

Split

Split a file into segments. Two modes: equal (N equal time parts) or silence (split on quiet gaps). Returns a ZIP of numbered files.

# Split into 4 equal parts
curl -X POST http://localhost:8000/v1/audio/split \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","mode":"equal","count":4,"output_path":"out/segments.zip"}'

# Split a DJ mix on silence
curl -X POST http://localhost:8000/v1/audio/split \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/djmix.wav","mode":"silence","threshold_db":-40,"min_duration_sec":1.0,"output_path":"out/tracks.zip"}'

# Split to mp3
curl -X POST http://localhost:8000/v1/audio/split \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/album.flac","mode":"equal","count":10,"output_format":"mp3","output_path":"out/parts.zip"}'

mode=equal requires count >= 2. mode=silence uses threshold_db (default -30) and min_duration_sec (default 0.5); requires the silence-detect engine.

Pan

Position audio in the stereo field. Works on mono and stereo input.

# Hard left
curl -X POST http://localhost:8000/v1/audio/pan \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","position":-1.0,"output_path":"out/left.wav"}'

# Slight right (e.g. guitar in mix)
curl -X POST http://localhost:8000/v1/audio/pan \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stems/guitar.wav","position":0.4,"output_path":"out/guitar_panned.wav"}'

# Center (no-op but valid)
curl -X POST http://localhost:8000/v1/audio/pan \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mono.wav","position":0.0,"output_path":"out/stereo.wav"}'

position: -1.0 = hard left, 0.0 = center, 1.0 = hard right.

EQ

Parametric EQ via ffmpeg equalizer filter. Pass any number of bands — each with a center frequency, gain, and optional bandwidth.

# Low-cut + presence boost
curl -X POST http://localhost:8000/v1/audio/eq \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","bands":[{"freq":100,"gain_db":-6,"width_hz":80},{"freq":3000,"gain_db":3,"width_hz":500}],"output_path":"out/eq.wav"}'

# Single band: cut 60 Hz hum
curl -X POST http://localhost:8000/v1/audio/eq \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/recording.wav","bands":[{"freq":60,"gain_db":-20,"width_hz":30}],"output_path":"out/clean.wav"}'

Each band: freq (Hz, required), gain_db (dB, required, range ±30), width_hz (optional, default 100).

Key match

Detect the source key via CLAP chord analysis, then pitch-shift to a target key — one call instead of two.

# Shift everything to C major
curl -X POST http://localhost:8000/v1/audio/key-match \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loop.wav","target_key":"C","output_path":"out/matched.wav"}'

# Match to F# (response includes source_key + semitones shifted)
curl -X POST http://localhost:8000/v1/audio/key-match \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"stems/melody.wav","target_key":"F#","output_path":"matched/melody_fsharp.wav"}'

target_key: root note, e.g. C, F#, Bb, D#. Mode suffix (major/minor/m) is ignored — only the root matters for pitch. Requires chord-detect and stretch engines.

Sidechain duck

Duck a primary track (music) whenever a trigger track (voice) is loud — the classic voiceover-over-music effect. Pure ffmpeg sidechaincompress, no model required.

# stage music + voice first via PUT /v1/files/...

curl -X POST http://localhost:8000/v1/audio/sidechain-duck \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/music.wav","trigger_file_path":"uploads/voice.wav","threshold_db":-20,"ratio":4,"attack_ms":10,"release_ms":200,"output_path":"out/ducked.wav"}'

# Aggressive duck for podcast-style music bed
curl -X POST http://localhost:8000/v1/audio/sidechain-duck \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"music/bed.wav","trigger_file_path":"voice/narration.wav","threshold_db":-30,"ratio":10,"release_ms":400,"output_path":"final/mix.wav"}'

Primary track is compressed whenever the trigger exceeds threshold_db. ratio sets compression intensity. Files must be the same duration for best results; shorter trigger is padded with silence.

Effects chain

Apply an ordered chain of pedalboard effects — full catalog, you pick the order and params. Different from /v1/audio/master (which runs preset mastering chains).

# Compress, then add reverb, then drop -3 dB
curl -X POST http://localhost:8000/v1/audio/fx \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","effects":[
    {"type":"Compressor","params":{"threshold_db":-18,"ratio":4.0}},
    {"type":"Reverb","params":{"room_size":0.5,"wet_level":0.3}},
    {"type":"Gain","params":{"gain_db":-3.0}}
  ],"output_path":"out/out.wav"}'

Allowed effects: Compressor, Limiter, NoiseGate, Gain, Clipping, Distortion, Bitcrush, Reverb, Chorus, Delay, Phaser, PitchShift, HighShelfFilter, LowShelfFilter, PeakFilter, HighpassFilter, LowpassFilter, LadderFilter, IIRFilter, GSMFullRateCompressor, MP3Compressor, Resample, Invert, Convolution.

VST3 / AudioUnit / external plugins are NOT in the allowlist — they load arbitrary native code.

Loop point

Find the best seamless loop boundary in an audio file — audiolla analyses the beat grid and returns the start and end positions where a loop will repeat without a click or gap.

# Find best loop boundary (default: minimum 4 bars)
curl -X POST http://localhost:8000/v1/audio/loop-point \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/beat.wav"}' | jq '{loop_start_sec, loop_end_sec, bars, score, tempo_bpm}'
# → {"loop_start_sec": 0.0, "loop_end_sec": 7.44, "bars": 4,
#    "score": 0.94, "tempo_bpm": 128.0, "candidates": [...]}

# Require at least 8 bars, return top 3 candidates
curl -X POST http://localhost:8000/v1/audio/loop-point \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/long_track.wav","min_loop_bars":8,"num_candidates":3}'

Response fields: loop_start_sec, loop_end_sec, bars, score (0–1, higher = tighter loop), tempo_bpm, candidates (array of ranked alternatives). Optional params: min_loop_bars (default 4), num_candidates (default 5). Requires librosa-analyze engine.

Compose MIDI

POST a JSON song spec, get Standard MIDI File bytes back. Write the spec by hand, generate it from a tracker / DAW / sequencer, script it out of a Python notebook, or have an LLM produce it — audiolla doesn't care. No AI runs server-side; the spec is the music.

# 4-beat C major arpeggio at 120 BPM, piano + kick drum
curl -X POST http://localhost:8000/v1/midi/compose \
  -H 'Content-Type: application/json' \
  -d '{
    "tempo_bpm": 120,
    "tracks": [
      {"name":"Lead","program":0,"channel":0,"notes":[
        {"pitch":60,"start_beats":0.0,"duration_beats":0.5,"velocity":100},
        {"pitch":64,"start_beats":0.5,"duration_beats":0.5,"velocity":100},
        {"pitch":67,"start_beats":1.0,"duration_beats":0.5,"velocity":100},
        {"pitch":72,"start_beats":1.5,"duration_beats":0.5,"velocity":100}
      ]},
      {"name":"Kick","program":0,"channel":9,"notes":[
        {"pitch":36,"start_beats":0.0,"duration_beats":0.1,"velocity":110},
        {"pitch":36,"start_beats":1.0,"duration_beats":0.1,"velocity":110},
        {"pitch":36,"start_beats":2.0,"duration_beats":0.1,"velocity":110},
        {"pitch":36,"start_beats":3.0,"duration_beats":0.1,"velocity":110}
      ]}
    ],
    "output_path": "midi/song.mid"
  }'
curl -o song.mid http://localhost:8000/v1/files/midi/song.mid

# Use a JSON spec file (must include output_path / output_url in the body)
curl -X POST http://localhost:8000/v1/midi/compose \
  -H 'Content-Type: application/json' \
  -d @spec.json

Spec fields: tempo_bpm (default 120), time_signature (default [4,4]), key_signature (optional, e.g. "C", "Am"), ticks_per_beat (default 480), tracks[].{name, program, channel, volume, pan, notes[].{pitch, start_beats, duration_beats, velocity}}. Time is in beats. program is GM program 0-127. Channel 9 is the GM drum channel — pitches there map to the drum kit (36 = kick, 38 = snare, 42 = closed hi-hat, etc.).

Inspect MIDI

# read the structure of any Standard MIDI File
curl -X POST http://localhost:8000/v1/midi/inspect \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid"}'
# → {type, ticks_per_beat, tempo_changes, time_signatures,
#    tracks[{name, note_on_count, channels, programs, length_beats}], ...}

Transform MIDI

# transpose all non-drum tracks up an octave
curl -X POST http://localhost:8000/v1/midi/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","transpose_semitones":12,"output_path":"midi/transposed.mid"}'

# override tempo to 140 BPM and save to staging
curl -X POST http://localhost:8000/v1/midi/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","tempo_bpm":140,"output_path":"midi/fast.mid"}'

# drop the drum track (channel 9)
curl -X POST http://localhost:8000/v1/midi/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","drop_channels":[9],"output_path":"midi/no-drums.mid"}'

# keep only channels 0 and 1
curl -X POST http://localhost:8000/v1/midi/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","keep_channels":[0,1],"output_path":"midi/two-ch.mid"}'

# quantize to 1/16th notes
curl -X POST http://localhost:8000/v1/midi/transform \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","quantize_grid_beats":0.25,"output_path":"midi/quantized.mid"}'

transpose_semitones ±48. quantize_grid_beats is in beats (0.25 = 1/16th at 4/4). keep_channels and drop_channels take a JSON array of channel numbers; only one can be set per request.

Render MIDI to audio

# Synthesise via the bundled FluidR3_GM SoundFont
curl -X POST http://localhost:8000/v1/midi/render \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","output_format":"wav","output_path":"out/song.wav"}'
curl -o song.wav http://localhost:8000/v1/files/out/song.wav

# Use your own SoundFont (must be staged first)
curl -X PUT --data-binary @my.sf2 \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/sf/orchestral.sf2
curl -X POST http://localhost:8000/v1/midi/render \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/song.mid","soundfont_path":"sf/orchestral.sf2","output_format":"flac","output_path":"out/orch.flac"}'

Generate music from a spec

Compose + render in one call — spec in, audio file staged.

# spec.json must include "output_path" or "output_url" alongside the composition fields
curl -X POST http://localhost:8000/v1/midi/generate \
  -H 'Content-Type: application/json' \
  -d @spec.json
curl -o song.wav http://localhost:8000/v1/files/out/song.wav

Drum pattern

Step-sequencer spec → GM drum MIDI. Define a rhythmic pattern as arrays of 0/1 step values for each drum voice; the server maps them to GM channel 9 pitches and bakes a MIDI file. Optional swing shifts even-numbered 16th steps for a shuffled feel.

# 4-on-the-floor kick, snare on 2&4, busy hi-hat — 2 bars at 120 BPM
curl -X POST http://localhost:8000/v1/midi/drum \
  -H "Content-Type: application/json" \
  -d '{
    "tempo_bpm": 120,
    "steps": 16,
    "bars": 2,
    "swing": 0.0,
    "pattern": {
      "kick":  [1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0],
      "snare": [0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
      "hihat": [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
    },
    "output_path": "midi/beat.mid"
  }'
curl -o beat.mid http://localhost:8000/v1/files/midi/beat.mid

# Swing groove — 0.1 = subtle, 0.5 = strong shuffle
curl -X POST http://localhost:8000/v1/midi/drum \
  -H "Content-Type: application/json" \
  -d '{
    "tempo_bpm": 95,
    "steps": 16,
    "bars": 1,
    "swing": 0.2,
    "pattern": {
      "kick":  [1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0],
      "snare": [0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
      "hihat": [1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0]
    },
    "output_path": "midi/groove.mid"
  }'

Body fields: tempo_bpm (default 120), steps (steps per bar, default 16), bars (default 1), swing (0.0–0.5, default 0.0), pattern (object — keys are drum voice names, values are arrays of 0/1). Supported voices: kick, snare, hihat, open_hihat, ride, crash, clap, tom_hi, tom_mid, tom_low, rim, cowbell. Requires midi-compose engine.

Chords to MIDI

Detect the chord progression from an audio file and convert each segment to a MIDI chord (root + 3rd + 5th). Useful for exporting a detected chord chart as playable MIDI, re-harmonising an arrangement, or seeding a DAW session.

# Audio → chord MIDI at the detected tempo
curl -X POST http://localhost:8000/v1/audio/chords-to-midi \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav","output_path":"out/chords.mid"}'

# Override tempo, set velocity and octave
curl -X POST http://localhost:8000/v1/audio/chords-to-midi \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/song.wav","tempo_bpm":120,"velocity":90,"octave":3,"output_path":"out/chords.mid"}'

# Stage the output under a different path
curl -X POST http://localhost:8000/v1/audio/chords-to-midi \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"sessions/song.wav","output_path":"midi/song_chords.mid"}'

Optional params: tempo_bpm (default: detected from audio), velocity (1–127, default 80), octave (0–8, default 4), output_path. Requires chord-detect engine. Each chord segment becomes a MIDI chord event (root + major 3rd/minor 3rd + perfect 5th, duration = segment length).

Audio metadata tags

Read and write ID3 (MP3), Vorbis (OGG/FLAC), and WAV/M4A tags via mutagen. Requires the metadata engine.

# Read tags
curl -X POST http://localhost:8000/v1/audio/metadata \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.mp3"}' | jq '{title, artist, bpm, key, duration_sec}'

# Write tags — returns updated tag set
curl -X POST http://localhost:8000/v1/audio/metadata \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.mp3","tags":{"title":"My Track","artist":"DJ Audiolla","bpm":"128","year":"2026"}}'

Clip detection

Detect digital clipping. No engine required — pure numpy arithmetic.

curl -X POST http://localhost:8000/v1/audio/clip-detect \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loud_master.wav"}' | jq '{clipped, clip_count, clip_ratio, peak_db}'
# → {"clipped":true,"clip_count":4219,"clip_ratio":0.0048,"peak_db":0.0}

Mid/Side encode and decode

Encode L/R stereo to Mid+Side or decode back. Useful for stereo width surgery without touching the pedalboard chain.

# Encode L/R → M/S
curl -X POST http://localhost:8000/v1/audio/mid-side \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/stereo.wav","mode":"encode","output_path":"out/ms_encoded.wav"}'

# Decode back to L/R
curl -X POST http://localhost:8000/v1/audio/mid-side \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"out/ms_encoded.wav","mode":"decode","output_path":"out/restored.wav"}'

Beat slice

Detect beat positions with librosa and return a ZIP of numbered WAV/MP3 slices — one file per beat interval.

curl -X POST http://localhost:8000/v1/audio/beat-slice \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loop.wav","output_format":"wav","output_path":"out/slices.zip"}'
curl -o slices.zip http://localhost:8000/v1/files/out/slices.zip
# → slices.zip: beat_001.wav, beat_002.wav, beat_003.wav …

# Stage the ZIP at a different path
curl -X POST http://localhost:8000/v1/audio/beat-slice \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/loop.wav","output_path":"beats/loop_slices.zip"}'
# → {"path":"beats/loop_slices.zip","beat_count":32,...}

Convolution reverb

Apply an impulse response (IR) to audio via pedalboard's Convolution. Any WAV file can be used as the IR.

# Upload your IR first
curl -X PUT --data-binary @plate_reverb.wav \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/ir/plate.wav

# Apply — wet_mix: 0.0=dry only, 1.0=wet only
curl -X POST http://localhost:8000/v1/audio/conv-reverb \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/dry_vocal.wav","ir_file_path":"ir/plate.wav","wet_mix":0.25,"output_format":"wav","output_path":"out/reverbed.wav"}'

Transient shaper

Attack/sustain dual-compressor blending. Positive attack_gain_db makes drums punchier; negative sustain_gain_db cuts room tail.

# Punchy drums: boost attack, cut sustain
curl -X POST http://localhost:8000/v1/audio/transient \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/drums.wav","attack_gain_db":6,"sustain_gain_db":-4,"output_path":"out/punchy_drums.wav"}'

# Soft attack (pad-like)
curl -X POST http://localhost:8000/v1/audio/transient \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/synth.wav","attack_gain_db":-6,"sustain_gain_db":0,"output_path":"out/softened.wav"}'

Multiband compression

Split the signal into N+1 frequency bands and compress each one independently. Bands are split with zero-phase LR4-equivalent crossovers, so a bypassed chain reconstructs the original. Mastering-engineer staple — tame bass thump without squashing vocal sibilance, level out a busy mid-range, etc.

# 3-band mastering pass: low/mid/high
curl -X POST http://localhost:8000/v1/audio/multiband-compress \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mixdown.wav","crossovers_hz":[200,3000],"bands":[
    {"threshold_db":-18,"ratio":4,"attack_ms":15,"release_ms":150,"makeup_db":1.5},
    {"threshold_db":-14,"ratio":3,"attack_ms":8, "release_ms":80, "makeup_db":1.0},
    {"threshold_db":-10,"ratio":2,"attack_ms":3, "release_ms":40, "makeup_db":0.5}
  ],"output_path":"out/mastered.wav"}'

crossovers_hz length is N, bands length is N+1. Each band: required threshold_db + ratio, optional attack_ms (default 10), release_ms (default 100), makeup_db (default 0).

DJ prep

One call returns everything a DJ needs about a track. Requires librosa-analyze + chord-detect. LUFS is reported when a loudness engine is available.

curl -X POST http://localhost:8000/v1/audio/dj-prep \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/track.wav"}' | jq .
# → {"bpm":128.0,"key":"A minor","camelot":"8A","integrated_lufs":-9.4}

Camelot wheel positions let you quickly find harmonically compatible tracks for mixing.

De-ess

Split-band high-frequency de-esser — attenuates sibilance above frequency_hz without affecting the rest of the signal. Implemented with a Butterworth HPF, envelope follower, and per-channel gain reduction. No engine required.

# Default settings (threshold -20 dB, 6 kHz, 4:1 ratio)
curl -X POST http://localhost:8000/v1/audio/deess \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","output_path":"out/deessed.wav"}'

# Gentle pass on a mix
curl -X POST http://localhost:8000/v1/audio/deess \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mix.wav","threshold_db":-15,"frequency_hz":7000,"ratio":2.5,"output_path":"out/mix_deessed.wav"}'

# Stage output under a different path
curl -X POST http://localhost:8000/v1/audio/deess \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/vocal.wav","output_path":"sessions/vocal_deessed.wav"}'
# → {"path":"sessions/vocal_deessed.wav","threshold_db":-20.0,"frequency_hz":6000.0,"ratio":4.0,...}

Optional params: threshold_db (≤ 0, default -20), frequency_hz (2000–15000, default 6000), ratio (1.0–20.0, default 4.0), output_format (wav/mp3/flac…), output_path.

Stereo field analysis

Measure stereo width, phase correlation, mid/side balance, and mono compatibility. No engine required — pure numpy.

curl -X POST http://localhost:8000/v1/audio/stereo-field \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/stereo_mix.wav"}' | jq .
# → {
#     "correlation": 0.72,       # Pearson L/R correlation [-1,1]
#     "width": 0.41,             # side_rms / mid_rms
#     "balance_db": -0.3,        # L vs R level difference
#     "mono_compatible": true,   # correlation >= 0.5
#     "mid_level_db": -12.1,
#     "side_level_db": -18.4,
#     "phase_issues": false,
#     "channels": 2,
#     "sample_rate": 44100,
#     "duration": 210.5
#   }

# Analyze a different staged file
curl -X POST http://localhost:8000/v1/audio/stereo-field \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"masters/track.wav"}' | jq '{correlation, width, mono_compatible}'

Mono files return correlation=1.0, width=0.0, mono_compatible=true. Use correlation < 0 as a red flag for phase-cancelled material that will collapse on mono playback.

Audio thumbnail

Extract the most energetic segment of an audio file — the passage with the highest onset density in a given window. Useful for generating preview clips, podcast teasers, or DJ cue points. Requires librosa-analyze.

# Default 30-second thumbnail
curl -X POST http://localhost:8000/v1/audio/thumbnail \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/long_track.wav","output_path":"out/preview.wav"}'

# 10-second teaser
curl -X POST http://localhost:8000/v1/audio/thumbnail \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/podcast.wav","duration_sec":10,"output_format":"mp3","output_path":"out/teaser.mp3"}'

# Stage + get timestamps
curl -X POST http://localhost:8000/v1/audio/thumbnail \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/album_track.wav","duration_sec":20,"output_path":"previews/track_thumb.wav"}'
# → {"path":"previews/track_thumb.wav","start_sec":47.3,"end_sec":67.3,"duration_sec":20.0,...}

Optional params: duration_sec (1–300, default 30), output_format, output_path. When output_path is set the response JSON includes start_sec and end_sec so you know exactly where in the source the thumbnail was extracted.

MIDI humanize

Add subtle timing and velocity variations to a MIDI file to make it sound less mechanical. Jitter is uniformly distributed and, when a seed is provided, fully deterministic. Requires midi-compose.

# Gentle humanize with defaults (±10 ms timing, ±10% velocity)
curl -X POST http://localhost:8000/v1/midi/humanize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/rigid.mid","output_path":"midi/human.mid"}'

# Heavier feel with a fixed seed for reproducible results
curl -X POST http://localhost:8000/v1/midi/humanize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/drums.mid","timing_ms":20,"velocity_pct":15,"seed":42,"output_path":"midi/drums_human.mid"}'

# Stage output under a different path
curl -X POST http://localhost:8000/v1/midi/humanize \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"midi/pattern.mid","timing_ms":8,"output_path":"midi/pattern_human.mid"}'
# → {"path":"midi/pattern_human.mid","timing_ms":8.0,"velocity_pct":10.0,...}

Optional params: timing_ms (0–500, default 10), velocity_pct (0–50, default 10), seed (any int, optional), output_path. Non-MIDI input returns 400. Requires midi-compose.

Batch operations

Run multiple operations on staged files in one HTTP call. Operations run sequentially; each gets an independent result entry even if earlier ops fail.

Supported ops: convert, normalize, trim, fade, reverse, speed, eq.

# Stage input
curl -X PUT http://localhost:8000/v1/files/work/track.wav --data-binary @track.wav

# Batch: trim, convert to MP3, reverse in one call
curl -X POST http://localhost:8000/v1/batch \
  -H "Content-Type: application/json" \
  -d '[
    {"op":"trim","file_path":"work/track.wav","output_path":"work/chorus.wav","start_sec":30,"end_sec":60},
    {"op":"convert","file_path":"work/track.wav","output_path":"work/track.mp3","output_format":"mp3"},
    {"op":"reverse","file_path":"work/track.wav","output_path":"work/reversed.wav"}
  ]' | jq '.results[].status'
# → "ok" "ok" "ok"

Async jobs and webhooks

Every audio endpoint accepts async_job=true — the request returns immediately with a job ID and the work happens in the background. Poll for status or register a webhook.

# Pre-stage input (one-time)
curl -X PUT --data-binary @track.wav \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/track.wav

# Submit async with staging path — result written to /v1/files/stems/...
curl -X POST http://localhost:8000/v1/audio/separate \
  -H 'Content-Type: application/json' \
  -d '{
    "file_path":"uploads/track.wav",
    "engine":"htdemucs",
    "stems":["vocals"],
    "async_job":true,
    "webhook_url":"https://my-server.com/hooks/audio",
    "output_path":"stems/track-vocals.wav"
  }'
# → {"job_id":"abc123","status":"pending","status_url":"/v1/jobs/abc123"}

# Submit async with presigned S3 PUT URL — result uploaded on completion
curl -X POST http://localhost:8000/v1/audio/master \
  -H 'Content-Type: application/json' \
  -d '{
    "file_path":"uploads/track.wav",
    "mode":"chain",
    "preset":"transparent",
    "async_job":true,
    "output_url":"https://bucket.s3.amazonaws.com/result.wav?X-Amz-..."
  }'
# → {"job_id":"def456","status":"pending","status_url":"/v1/jobs/def456"}

# Poll
curl http://localhost:8000/v1/jobs/abc123 | jq '{status, duration_sec, result}'

# List all jobs (optional ?status=pending|running|completed|failed|cancelled)
curl http://localhost:8000/v1/jobs

# Cancel a running job
curl -X DELETE http://localhost:8000/v1/jobs/abc123

Webhook payload (POST to your URL when the job completes):

{
  "id": "abc123",
  "endpoint": "/v1/audio/separate",
  "status": "completed",
  "duration_sec": 12.4,
  "result": {"path": "stems/track-vocals.wav", "size": 3145728, ...}
}

Delivery has 4 attempts with exponential backoff (0 s, 1 s, 2 s, 4 s). Completed jobs stay in memory for AUDIOLLA_JOB_TTL seconds (default 1 hour) then are swept.

Stage files

A simple server-side file store under /v1/files. Upload, list, download, delete.

# upload
curl -X PUT http://localhost:8000/v1/files/mytrack.wav \
  --data-binary @track.wav

# list
curl http://localhost:8000/v1/files

# download
curl http://localhost:8000/v1/files/mytrack.wav -o copy.wav

# delete
curl -X DELETE http://localhost:8000/v1/files/mytrack.wav

Once staged, reference the file by path on any audio endpoint via file_path:

# Analyze a staged file
curl -X POST http://localhost:8000/v1/audio/analyze \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"mytrack.wav","features":["bpm"]}'

# Separate stems and write the result back to staging
curl -X POST http://localhost:8000/v1/audio/separate \
  -H 'Content-Type: application/json' \
  -d '{
    "file_path":"mytrack.wav",
    "engine":"htdemucs",
    "stems":["vocals"],
    "output_path":"stems/mytrack-vocals.wav"
  }'
# → {"path":"stems/mytrack-vocals.wav","size":...,"output_format":"wav",...}

Remote URLs

Disabled by default. To allow the server to fetch file_url or PUT to output_url, set the policy at container start:

docker run ... \
  -e AUDIOLLA_FETCH_MODE=allowlist \
  -e AUDIOLLA_FETCH_HOSTS="*.s3.amazonaws.com,*.r2.cloudflarestorage.com" \
  psyb0t/audiolla:latest

Then:

# Fetch from S3, master, PUT result back to a presigned S3 URL
curl -X POST http://localhost:8000/v1/audio/master \
  -H 'Content-Type: application/json' \
  -d '{
    "file_url":"https://my-bucket.s3.amazonaws.com/in.wav",
    "reference_url":"https://my-bucket.s3.amazonaws.com/ref.wav",
    "mode":"reference",
    "output_url":"https://my-bucket.s3.amazonaws.com/out.wav?X-Amz-Signature=..."
  }'
# → {"url":"...","size":...,"output_format":"wav",...}

Policy modes:

  • disabled (default) — file_url / output_url rejected with 400

  • allowlist — only hosts matching AUDIOLLA_FETCH_HOSTS allowed

  • denylist — anything except listed hosts allowed (pair with AUDIOLLA_FETCH_ALLOW_PRIVATE=false to block private IPs / metadata services)

Always-on protections:

  • DNS-resolved private / loopback / link-local IPs rejected (toggleable)

  • Only https by default; http opt-in via AUDIOLLA_FETCH_SCHEMES

  • Redirects re-validated through the same policy

  • Hard timeout + size cap = AUDIOLLA_MAX_UPLOAD_BYTES

  • Every fetch / upload URL logged

See Configuration for all AUDIOLLA_FETCH_* env vars.


Engines

Slug

What it does

htdemucs

4-stem separation: drums, bass, other, vocals. Best speed/quality tradeoff.

htdemucs_ft

Same 4 stems, fine-tuned weights. Higher quality, ~4x slower. CUDA-only — rejected with 400 on the CPU image.

htdemucs_6s

6 stems — also splits guitar and piano. Experimental.

mdx_extra

Strong on vocal isolation. MUSDB-trained, different architecture.

matchering

Reference-based mastering: EQ + loudness matched to a reference track.

pedalboard-chain

Preset mastering chains via pedalboard — transparent (light) or loud (4:1 squash). Backs /v1/audio/master with mode=chain. For arbitrary chains use fx-chain / /v1/audio/fx.

librosa-analyze

BPM, key, LUFS, duration, spectral features, beat grid, onset detection, melody (pyin), structural segmentation via librosa.

sox-transform

Gain, EQ, compression, reverb, pitch shift, tempo via pysox.

fx-chain

Arbitrary pedalboard effects chain — full catalog, your order and params. Backs /v1/audio/fx.

midi-compose

JSON spec → MIDI bytes. Also inspects and transforms existing MIDI files. Backs /v1/midi/{compose,inspect,transform,generate}.

midi-render

MIDI → audio via fluidsynth + SoundFont. Backs /v1/midi/render and /v1/midi/generate.

silence-detect

Locate silent gaps via ffmpeg silencedetect. Optional auto-trim. Backs /v1/audio/silence.

ffmpeg-render

Static PNG spectrogram/waveform + 8-mode animated MP4/WebM video via ffmpeg filters. Backs /v1/audio/visualize/image/* and /v1/audio/visualize/video/{mode}.

audio-fingerprint

Chromaprint acoustic fingerprint via fpcalc. Backs /v1/audio/fingerprint.

uvr-dereverb

BS-Roformer de-reverb — removes room reverb; primary_stem=No Reverb.

uvr-deecho

VR Architecture de-echo — normal and aggressive modes; pass aggressive=true for harder suppression.

uvr-denoise

MelBand Roformer de-noise (SDR 28) — removes broadband background noise.

uvr-karaoke

MelBand Roformer karaoke — remove lead vocals, keep backing; works via /v1/audio/separate.

uvr-vocal-bsr

BS-Roformer vocal/instrumental (SDR 13) — highest-quality vocal separation; works via /v1/audio/separate.

basic-pitch

Polyphonic audio-to-MIDI via Spotify basic-pitch (ONNX backend). Backs /v1/audio/to_midi.

deepfilter

Neural speech and vocal enhancement via DeepFilterNet DF3. Backs /v1/audio/enhance.

chord-detect

Chord and key detection via librosa — Krumhansl-Schmuckler key estimation + chroma template chord segmentation. Backs /v1/audio/chords.

silero-vad

Voice activity detection via silero-vad (ONNX) — returns speech/non-speech segments with timestamps and speech ratio. Backs /v1/audio/vad.

pyannote

Speaker diarization via pyannote/speaker-diarization-3.1 — returns per-speaker timestamped segments. Requires HUGGINGFACE_TOKEN. Backs /v1/audio/diarize.

stretch

Time-stretch + pitch-shift via librosa phase vocoder — independent tempo factor and semitone offset. Backs /v1/audio/stretch.

ast-tag

Audio tagging via Audio Spectrogram Transformer (MIT/ast-finetuned-audioset-10-10-0.4593) — top-K AudioSet class labels. Requires HF model cache. Backs /v1/audio/tag.

clap-embed

512-dim L2-normalized audio embeddings via LAION CLAP (laion/larger_clap_music_and_speech) — semantic audio search. Requires HF model cache. Backs /v1/audio/embed.

hpss

Harmonic/percussive source separation via librosa HPSS median filter — returns harmonic + percussive stems as a ZIP. Backs /v1/audio/separate/hpss.

noise-reduce

Spectral noise reduction via noisereduce — stationary (constant hum/hiss) and non-stationary (adaptive) modes, no GPU required. Backs /v1/audio/noise-reduce/noise-reduce.

metadata

Read/write audio tags (ID3 for MP3, Vorbis for OGG/FLAC, INFO for WAV, MP4 for M4A) via mutagen. No ML weights. Backs /v1/audio/metadata.

stable-audio-open

Text-to-audio — Stability Stable Audio Open 1.0. Stability Community Licence (commercial use OK below the revenue threshold; read the license). 47-second hard cap; best for loops, riffs, ambient textures, SFX, drum beats. No vocals. ~12 GB VRAM at fp16 — CUDA-only. Backs /v1/audio/generate/stable-audio-open.

musicgen-small

Text-to-music — Meta MusicGen 300M. CC-BY-NC 4.0 (non-commercial only; opt-in via AUDIOLLA_ENABLE_NONCOMMERCIAL=1 in the server env). 30 s hard cap; instrumental only. ~3 GB VRAM at fp16 — CUDA-only. Backs /v1/audio/generate/musicgen-small.

musicgen-medium

Text-to-music — Meta MusicGen 1.5B. CC-BY-NC 4.0 (same opt-in). 30 s hard cap; higher quality than -small. ~6-8 GB VRAM at fp16 — CUDA-only. Backs /v1/audio/generate/musicgen-medium.

riffusion

Text-to-music — Riffusion-v1, a Stable Diffusion fine-tune that generates spectrograms (converted to audio via Griffin-Lim). CreativeML OpenRAIL-M (commercial use OK with the licence's usage restrictions). ~5 s per pass, lo-fi character, 22.05 kHz mono. ~3 GB VRAM at fp16 — CUDA-only. Backs /v1/audio/generate/riffusion.

audioldm2

Text-to-audio / SFX — AudioLDM 2 (cvssp/audioldm2). CC-BY 4.0 (commercial use OK — no opt-in gate, the only commercial-safe generator in this set). General-purpose SFX: environmental ambience, animal sounds, foley, mechanical / impact sounds. 16 kHz mono, up to 30 s. Slow (200-step DDIM by default — pass num_inference_steps=50 to trade quality for ~4x speed). ~8-10 GB VRAM at fp16 with CPU offload. CUDA-only. Backs /v1/audio/generate/audioldm2.

Each Demucs variant is its own checkpoint (hosted on dl.fbaipublicfiles.com). The entrypoint prefetches every enabled variant into /data/torch_cache/ at startup so the first separation request doesn't sit there downloading.

AUDIOLLA_ENABLED_ENGINES — restrict which engines are available. AUDIOLLA_PRELOAD — load specific engines into memory at startup instead of waiting for the first request.


Workflows — presets + pipeline

Two ways to chain operations server-side without re-uploading the audio between calls:

Curated presets — server-side YAML workflows shipped in presets/. Run one with a single POST:

# Pre-stage input
curl -X PUT --data-binary @mix.wav \
  -H 'Content-Type: application/octet-stream' \
  http://localhost:8000/v1/files/uploads/mix.wav

# Master a mix for Spotify (-14 LUFS) — multiband compress + normalise
curl -X POST http://localhost:8000/v1/presets/master-for-spotify \
  -H 'Content-Type: application/json' \
  -d '{"file_path":"uploads/mix.wav","output_path":"out/mastered.wav"}'
# → {"path":"out/mastered.wav","size":...,"steps":[...]}
curl -o mastered.wav http://localhost:8000/v1/files/out/mastered.wav

# List available presets
curl http://localhost:8000/v1/presets | jq '.data[] | {name, description}'

# Inspect a preset's steps before running
curl http://localhost:8000/v1/presets/podcast-cleanup | jq '.steps'

Shipped presets: master-for-spotify (3-band master + -14 LUFS), podcast-cleanup (DeepFilterNet + de-ess + -16 LUFS), vocal-cleanup (UVR dereverb + denoise + de-ess + light comp). Add your own as a YAML file in presets/.

Ad-hoc pipeline — chain any registered ops in a single call:

# Restore + multiband + normalise in one request — intermediates stay
# server-side, no re-upload between steps.
curl -X POST http://localhost:8000/v1/pipeline \
  -H 'Content-Type: application/json' \
  -d '{
    "file_path":"uploads/track.wav",
    "output_path":"out/pipelined.wav",
    "steps":[
      {"op":"restore","params":{"engine":"uvr-denoise"}},
      {"op":"multiband_compress","params":{
        "crossovers_hz":[200,3000],
        "bands":[
          {"threshold_db":-18,"ratio":3},
          {"threshold_db":-14,"ratio":2.5},
          {"threshold_db":-10,"ratio":2}
        ]
      }},
      {"op":"normalize","params":{"target_lufs":-14}}
    ]
  }'
# → {"path":"out/pipelined.wav","size":...,"steps":[...]}

# Discover available ops
curl http://localhost:8000/v1/ops | jq .

The response of pipeline + preset endpoints includes a steps log so you can audit what ran. Both endpoints support async_job=true, output_path, output_url like every other audio-producing endpoint.

API catalog

GET /v1/catalog returns the machine-readable list of every endpoint grouped by category (separation, restoration, dynamics, eq-spatial, mastering, time-pitch, editing, analysis, effects-creative, visualize, midi, metadata, workflow, speech, files, jobs, management). Use it for discovery; LLM agents and codegen scripts both consume it.

curl http://localhost:8000/v1/catalog | jq '.categories[] | {name, endpoint_count: (.endpoints | length)}'

Endpoints

Full wire contract: openapi.yaml.

Audio processing

Every endpoint takes a JSON body. Inputs pick exactly one of file_path (pre-staged file under FILES_DIR) xor file_url (HTTPS URL the server fetches). Audio-producing endpoints additionally require exactly one of output_path (server writes the result under FILES_DIR) xor output_url (presigned PUT — server uploads the encoded bytes). Both missing → 400; both set → 400. Responses are always JSON — no raw audio bytes, no Content-Disposition: attachment, no *_base64 fields.

Method

Path

Default returns

POST

/v1/audio/separate

JSON {path|url, size, ...} — one stem; multi-stem (or all) returns ZIP stream of stems via output_path/output_url

POST

/v1/audio/master

JSON {path|url, size, output_format, ...}

POST

/v1/audio/analyze

JSON — BPM, key, LUFS, spectral features

POST

/v1/audio/beats

JSON — BPM + beat timestamps; optional click-track WAV

POST

/v1/audio/onsets

JSON — onset timestamps

POST

/v1/audio/melody

JSON — dominant melody contour; optional MIDI export

POST

/v1/audio/segments

JSON — structural segment labels (A, B, C…)

POST

/v1/audio/silence

JSON — silent/non-silent ranges; optional trimmed audio

POST

/v1/audio/visualize/image/spectrogram

JSON {path|url, size, ...} — static PNG spectrogram (color, scale params)

POST

/v1/audio/visualize/image/waveform

JSON {path|url, size, ...} — static PNG waveform (color param)

POST

/v1/audio/visualize/video/{mode}

JSON {path|url, size, ...} — animated MP4/WebM video (8 modes: spectrum, waves, cqt, …)

POST

/v1/audio/fingerprint

JSON — Chromaprint fingerprint string

POST

/v1/audio/restore/{engine}

JSON {path|url, size, output_format, ...} — reverb/echo/noise removed; aggressive=true for uvr-deecho hard mode

POST

/v1/audio/to_midi/{engine}

JSON {path|url, size, ...} — polyphonic transcription (MIDI)

POST

/v1/audio/enhance/{engine}

JSON {path|url, size, output_format, ...} — neural speech/vocal enhancement

POST

/v1/audio/generate/{engine}

JSON {path|url, size, output_format, ...} — text-to-audio (engine = stable-audio-open / musicgen-small / musicgen-medium / riffusion / audioldm2); prompt required, optional duration_sec / seed / lyrics / num_inference_steps

POST

/v1/audio/chords

JSON — detected key and chord progression

POST

/v1/audio/vad

JSON — speech/non-speech segments with timestamps and speech ratio

POST

/v1/audio/diarize/{engine}

JSON — per-speaker timestamped segments

POST

/v1/audio/transform

JSON {path|url, size, output_format, ...}

POST

/v1/audio/loudness

JSON — {loudness_lufs} (measure only, no audio)

POST

/v1/audio/loudness/curve

JSON — {curve:[{time_sec,rms_db}],duration,sample_rate,points}; hop_length param

POST

/v1/audio/normalize

JSON {path|url, size, measured_lufs, ...} — requires target_lufs; pre-normalization LUFS reported in measured_lufs field

POST

/v1/audio/separate/hpss

JSON {path|url, size, ...} — ZIP stream containing harmonic.<fmt> + percussive.<fmt>

POST

/v1/audio/noise-reduce/{engine}

JSON {path|url, size, output_format, ...}engine=noise-reduce (DSP, stationary/prop_decrease) or uvr-denoise (ML)

POST

/v1/audio/stretch

JSON {path|url, size, output_format, ...}

POST

/v1/audio/pitch-correct

JSON {path|url, size, output_format, ...}strength [0.0–1.0]; requires librosa-analyze

POST

/v1/audio/repair

JSON {path|url, size, output_format, ...}declip bool, dehum bool, hum_freq Hz

POST

/v1/audio/tag

JSON — top-K AudioSet labels with confidence scores

POST

/v1/audio/embed

JSON — 512-dim embedding; with query_text also returns cosine similarity

POST

/v1/audio/classify

JSON — {results: [{label, score}]} sorted descending; requires clap-embed

POST

/v1/audio/info

JSON — duration, sample_rate, channels, codec, bit_depth, format

POST

/v1/audio/trim

JSON {path|url, size, output_format, ...}start_sec + end_sec required

POST

/v1/audio/mix

JSON {path|url, size, output_format, ...}tracks JSON array required (≥2 entries)

POST

/v1/audio/concat

JSON {path|url, size, output_format, ...}files JSON array required (≥2 entries)

POST

/v1/audio/speed

JSON {path|url, size, output_format, ...}speed float required (0.1–10.0)

POST

/v1/audio/convert

JSON {path|url, size, output_format, ...} — format/sample_rate/channels conversion

POST

/v1/audio/similar

JSON — {similarity, dim}; requires clap-embed

POST

/v1/audio/fade

JSON {path|url, size, output_format, ...}fade_in/fade_out seconds, 13 curve options

POST

/v1/audio/reverse

JSON {path|url, size, output_format, ...} — flips playback direction

POST

/v1/audio/loop

JSON {path|url, size, output_format, ...}count total plays (≥2)

POST

/v1/audio/bpm-match

JSON {path|url, size, output_format, ...}target_bpm required; requires librosa-analyze + stretch

POST

/v1/audio/stereo-width

JSON {path|url, size, output_format, ...}width [0.0–3.0]; M/S stereo processing

POST

/v1/audio/split

JSON {path|url, size, ...} — ZIP stream; mode=equal (requires count) or mode=silence

POST

/v1/audio/pan

JSON {path|url, size, output_format, ...}position [-1.0–1.0]

POST

/v1/audio/eq

JSON {path|url, size, output_format, ...}bands JSON array of {freq, gain_db, width_hz}

POST

/v1/audio/key-match

JSON {path|url, size, output_format, ...}target_key required; requires chord-detect + stretch

POST

/v1/audio/sidechain-duck

JSON {path|url, size, output_format, ...} — primary + trigger_file_*; ffmpeg sidechaincompress

POST

/v1/audio/fx

JSON {path|url, size, output_format, ...}

POST

/v1/audio/metadata

JSON — tag fields (title, artist, bpm, key, duration, sample_rate…); writes tags when tags JSON is provided

POST

/v1/audio/clip-detect

JSON — clipped, clip_count, clip_ratio, peak_db, duration_sec

POST

/v1/audio/mid-side

JSON {path|url, size, output_format, ...}mode=encode (L/R→M/S) or mode=decode (M/S→L/R)

POST

/v1/audio/beat-slice

JSON {path|url, size, ...} — ZIP stream of numbered beat slices; requires librosa-analyze

POST

/v1/audio/conv-reverb

JSON {path|url, size, output_format, ...}ir_file_path / ir_file_url required; wet_mix [0.0–1.0]

POST

/v1/audio/transient

JSON {path|url, size, output_format, ...}attack_gain_db + sustain_gain_db

POST

/v1/audio/multiband-compress

JSON {path|url, size, output_format, ...} — N-band compressor; crossovers_hz + bands JSON arrays

POST

/v1/audio/dj-prep

JSON — bpm, key, camelot, integrated_lufs; requires librosa-analyze + chord-detect

POST

/v1/audio/loop-point

JSON — {loop_start_sec,loop_end_sec,bars,score,tempo_bpm,candidates}; requires librosa-analyze

POST

/v1/audio/chords-to-midi

JSON {path|url, size, ...} — chord progression from audio (MIDI); requires chord-detect

POST

/v1/audio/deess

JSON {path|url, size, output_format, ...} — split-band sibilance attenuation; threshold_db, frequency_hz, ratio

POST

/v1/audio/stereo-field

JSON — {correlation, width, balance_db, mono_compatible, mid_level_db, side_level_db, phase_issues, …}

POST

/v1/audio/thumbnail

JSON {path|url, size, start_sec, end_sec, ...} — most energetic duration_sec segment; requires librosa-analyze

Workflow — presets, pipeline, catalog

Server-side multi-step chains + discovery. See Workflows for narrative + curl examples.

Method

Path

GET

/v1/catalog

machine-readable endpoint list grouped by category (17 categories)

GET

/v1/ops

list of pipeline op slugs (~24) usable in presets + /v1/pipeline

GET

/v1/presets

list curated server-side workflows (name + description)

GET

/v1/presets/{name}

describe one preset including all steps

POST

/v1/presets/{name}

JSON {path|url, size, steps, ...} — run a curated preset; response includes a steps audit log of each op executed

POST

/v1/pipeline

JSON {path|url, size, steps, ...} — ad-hoc steps=[{op, params}, …] chain, server-side intermediates; response includes a steps audit log

Batch

Method

Path

POST

/v1/batch

JSON body: array of op objects {op, file_path, output_path, …}. Returns {results:[…]} — errors per-op, not a 4xx. Supported ops: convert, normalize, trim, fade, reverse, speed, eq.

Async jobs

Every audio endpoint accepts "async_job": true in the JSON body. Optional "webhook_url" for push-style delivery. When async_job=true, the endpoint returns HTTP 202 with {job_id, status: "pending", status_url} instead of executing inline.

Method

Path

GET

/v1/jobs

list jobs; optional ?status=pending|running|completed|failed|cancelled

GET

/v1/jobs/{job_id}

poll one job — returns status, result, duration_sec

DELETE

/v1/jobs/{job_id}

cancel running job or remove completed job

MIDI

Method

Path

Default returns

POST

/v1/midi/compose

JSON {path|url, size, ...} — body is JSON song spec; writes MIDI

POST

/v1/midi/inspect

JSON — tempo, tracks, channels, note counts, time/key signatures

POST

/v1/midi/transform

JSON {path|url, size, ...} — transpose, quantize, tempo override, channel filter; writes MIDI

POST

/v1/midi/quantize

JSON {path|url, size, ...}grid_beats snaps all note timings to a rhythmic grid; writes MIDI

POST

/v1/midi/render

JSON {path|url, size, output_format, ...} — input MIDI via file_path / file_url; writes audio

POST

/v1/midi/generate

JSON {path|url, size, output_format, ...} — body is JSON song spec (compose + render in one); writes audio

POST

/v1/midi/drum

JSON {path|url, size, ...} — body is JSON step-sequencer spec; writes MIDI; requires midi-compose

POST

/v1/midi/humanize

JSON {path|url, size, ...} — timing + velocity jitter; timing_ms, velocity_pct, seed; writes MIDI; requires midi-compose

File staging

Method

Path

GET

/v1/files

list staged files

PUT

/v1/files/{path}

upload

GET

/v1/files/{path}

download

DELETE

/v1/files/{path}

delete

Management

Method

Path

GET

/healthz

liveness — always unauthenticated

GET

/v1/engines

list configured engines + loaded / idle_seconds per engine

GET

/v1/ps

list engines in memory right now

DELETE

/v1/ps/{engine}

evict one engine

POST

/v1/unload

evict everything


MCP

audiolla exposes a Model Context Protocol server at /v1/mcp. Point any MCP-capable LLM agent at it and it gets the full audio processing surface as callable tools — separate stems, detect chords, transcribe to MIDI, diarize speakers, compose music from a JSON spec, read/write tags, submit async jobs — all over JSON-RPC without writing a line of integration code.

Audio-producing MCP tools follow the same contract as REST: callers MUST pass exactly one of output_path (server writes the result under FILES_DIR; client retrieves it via the get_file tool or HTTP GET /v1/files/<path>; response is {path, size, ...}) xor output_url (presigned PUT — server uploads the encoded bytes to the URL; response is {url, size, ...}). Both missing → ValueError; both set → ValueError. Inline base64 audio responses are gone in v1.0.0 — no audio_base64 / midi_base64 / image_base64 / video_base64 / zip_base64 fields exist anymore. Use list_jobs / get_job / cancel_job to manage long-running async work.

Endpoint: http://localhost:8000/v1/mcp

Tools:

Tool

What it does

list_engines

List configured engines and whether they're loaded

list_presets

List curated server-side workflows (name + description)

describe_preset

Show full step list of a preset before running

list_ops

List the ~24 pipeline op slugs available in run_pipeline_tool / presets

run_preset

Run a curated preset against an input file

run_pipeline_tool

Run an ad-hoc [{op, params}, …] chain server-side

generate_music

Text-to-audio — engine = stable-audio-open / musicgen-small / musicgen-medium / riffusion / audioldm2; prompt required, optional lyrics, duration_sec, seed. MusicGen requires AUDIOLLA_ENABLE_NONCOMMERCIAL=1. AudioLDM 2 is CC-BY 4.0 — commercial-safe with no opt-in.

separate

Demucs stem separation — per-stem staging via output_paths={stem:path} xor per-stem PUT via output_urls={stem:url}

master

Reference mastering (matchering) or preset chain (pedalboard)

analyze

BPM, key, LUFS, spectral features via librosa

beats

Beat grid — BPM + timestamps; optional click-track audio

onsets

Note onset timestamps

melody

Dominant melody contour in Hz; optional MIDI export

segments

Structural segmentation — recurring section labels (A, B, C…)

silence

Detect silent gaps; optional auto-trim (edges or all)

visualize

PNG spectrogram/waveform or animated MP4/WebM — engine + mode select output type

fingerprint

Chromaprint acoustic fingerprint (AcoustID-compatible)

restore

Remove reverb/echo/noise via UVR — engine selects model; aggressive=true for harder echo suppression

denoise

Thin shim — prefer restore with engine=uvr-denoise or noise_reduce with engine=uvr-denoise

audio_to_midi

Polyphonic audio-to-MIDI transcription via basic-pitch (ONNX) — writes MIDI to output_path xor output_url

enhance

Neural speech and vocal enhancement via DeepFilterNet DF3

chords

Chord and key detection via librosa — key + per-segment chord labels

vad

Voice activity detection via silero-vad — speech/non-speech segments with timestamps

diarize

Speaker diarization via pyannote — per-speaker timestamped segments

transform

Sox DSP chain — gain, EQ, reverb, pitch, tempo, etc.

loudness

Measure integrated LUFS — returns JSON only

loudness_curve

RMS envelope over time — {curve:[{time_sec,rms_db}],duration,sample_rate,points}

normalize

Normalize audio to a target LUFS level — writes to output_path xor output_url

hpss

Harmonic/percussive separation — writes per-stem audio to output_paths={stem:path} xor output_urls={stem:url}

noise_reduce

Noise reduction — engine=noise-reduce (DSP, stationary/prop_decrease) or engine=uvr-denoise (ML)

stretch

Time-stretch + pitch-shift via librosa phase vocoder

pitch_correct

Auto-tune toward nearest chromatic semitone — strength [0.0–1.0]; requires librosa-analyze

repair_audio

Declip + dehum — declip bool, dehum bool, hum_freq Hz

tag

Audio tagging via AST — top-K AudioSet labels with confidence scores

embed

512-dim CLAP audio embedding; with query_text returns cosine similarity

classify

Zero-shot CLAP classification — cosine similarity against any list of text labels

info

Probe audio metadata — duration, sample_rate, channels, codec, bit_depth

trim

Cut audio to [start_sec, end_sec) — writes to output_path xor output_url

mix

Mix N tracks with per-track gain — tracks list of {file_path/url, gain_db}

concat

Stitch N audio files end-to-end in order — files list of {file_path/url}

speed

Change playback speed without pitch shift — speed float (0.1–10.0)

convert

Re-encode: format, sample_rate, channels in one call

similar

Cosine similarity between two audio files via CLAP — returns {similarity, dim}

midi_quantize

Snap MIDI note timings to a rhythmic grid — grid_beats in beats

fade

Fade-in/fade-out with configurable duration and curve shape

reverse

Flip audio backwards

loop

Repeat audio N times — count total plays

bpm_match

Detect BPM then stretch to target_bpm — returns source/target BPM + tempo_factor

stereo_width

M/S stereo width — width=0 mono, 1 original, >1 wider

split

Split into equal parts or on silence — MCP form deprecated in v1.0.0; use REST POST /v1/audio/split with output_path for per-segment staging

pan

Pan in the stereo field — position [-1.0–1.0]

eq

Parametric EQ — bands list of {freq, gain_db, width_hz}

key_match

Detect key then pitch-shift to target_key — returns source_key + semitones

sidechain_duck

Duck primary track on trigger — threshold_db, ratio, attack_ms, release_ms

fx

Generic pedalboard effects chain — full catalog, your order and params

midi_compose

JSON song spec → MIDI; writes to output_path xor output_url

midi_inspect

Read MIDI structure — tempo, tracks, channels, note counts

midi_transform

Transpose, quantize, tempo override, channel filter on an existing MIDI file

midi_render

MIDI → audio via fluidsynth + SoundFont

midi_generate

One-shot compose + render — spec in, audio out

drum_pattern

Step-sequencer JSON spec → GM drum MIDI; pattern object of voice arrays, swing, steps, bars

chords_to_midi

Chord progression detected from audio → MIDI file; tempo_bpm, velocity, octave params

audio_metadata

Read or write audio tags — pass tags dict to write, omit to read

detect_clipping

Report digital clipping — clipped, clip_count, clip_ratio, peak_db

mid_side

M/S encode (mode=encode) or decode (mode=decode) stereo audio

slice_at_beats

Slice audio at beat positions — writes zip archive to output_path xor output_url; response includes beat_count

convolution_reverb

Apply IR reverb — ir_file_path/ir_file_url + wet_mix [0.0–1.0]

transient_shaper

Attack/sustain shaping — attack_gain_db, sustain_gain_db

multiband_compress

N-band compressor — crossovers_hz list + bands list of per-band specs

dj_prep

BPM + key + Camelot wheel + LUFS in one call

find_loop_point

Find best seamless loop boundary — {loop_start_sec,loop_end_sec,bars,score,tempo_bpm,candidates}

deess

Split-band sibilance attenuation — threshold_db, frequency_hz, ratio

stereo_field

Stereo field analysis — correlation, width, balance_db, mono_compatible, mid/side levels

audio_thumbnail

Extract most energetic segment — duration_sec; writes to output_path xor output_url; response includes start_sec/end_sec

midi_humanize

Add timing + velocity jitter to MIDI — timing_ms, velocity_pct, optional seed for deterministic output

list_jobs

List async jobs; optional status filter

get_job

Poll one async job by job_id

cancel_job

Cancel a running job or remove a completed one

list_files

List staged files

put_file

Upload a file (base64) to the staging area

get_file

Read a staged file back (base64)

delete_file

Remove a staged file

Auth (AUDIOLLA_AUTH_TOKEN) covers /v1/mcp the same as the REST endpoints — pass the bearer token in the Authorization header.


Agent integrations

The skill works in any agent that reads .agents/skills/, and installs natively in the clients below.

Claude Code

claude plugin marketplace add psyb0t/agents
claude plugin install audiolla@psyb0t

Claude Code prompts for the audiolla URL and, if auth is enabled, the token — the token is stored in your OS keychain.

Codex

codex plugin marketplace add psyb0t/agents
codex plugin add audiolla@psyb0t

Installed via the marketplace, the skill invokes as $audiolla:audiolla. Codex also picks the skill up automatically, no install needed, in any repo containing .agents/skills/, where it invokes as plain $audiolla.

OpenClaw

The skill is published to ClawHub on every release:

openclaw skills install @psyb0t/audiolla

For MCP clients that speak local stdio, the @psyb0t/audiolla plugin bridges to the running server's /v1/mcp endpoint:

openclaw plugins install clawhub:@psyb0t/audiolla

Then set AUDIOLLA_URL (and AUDIOLLA_AUTH_TOKEN if the server has auth enabled).


Configuration

Variable

Default

AUDIOLLA_DEVICE

auto

auto, cpu, cuda, or cuda:N

AUDIOLLA_ENGINES_FILE

/app/engines.json

path to engines registry

AUDIOLLA_PRESETS_DIR

/app/presets

directory of *.yaml preset workflows loaded at startup

AUDIOLLA_DATA_DIR

/data

where models and staged files live

AUDIOLLA_UVR_MODELS_DIR

<DATA_DIR>/uvr_models

where UVR model files are cached

AUDIOLLA_AUTH_TOKEN

bearer token; empty means no auth

HF_TOKEN / HUGGINGFACE_TOKEN

HuggingFace access token. The entrypoint mirrors the two names so setting either works. Required for the gated engines: pyannote speaker diarization, stable-audio-open, musicgen-small, musicgen-medium. Accept each model's licence on huggingface.co before using.

LOG_LEVEL

INFO

DEBUG / INFO / WARNING / ERROR / CRITICAL (case-insensitive; WARN aliased to WARNING). Controls every audiolla logger + uvicorn's loggers. Logs are line-delimited JSON — each record carries ts / level / logger / file / line / func / msg plus service / version / pid / host / thread. HTTP requests additionally carry request_id (honoured from inbound X-Request-Id, else generated and echoed on the response), method, path, status, duration_ms, client_ip, user_agent, req_bytes, resp_bytes.

AUDIOLLA_ENABLED_ENGINES

(all)

comma-separated slugs to allow; empty = all

AUDIOLLA_PRELOAD

comma-separated slugs to load at startup

AUDIOLLA_ENGINE_TTL

600

seconds idle before an engine is unloaded (10m also works)

AUDIOLLA_SWEEPER_INTERVAL

60

how often the idle sweeper checks, in seconds

AUDIOLLA_LOAD_TIMEOUT

300

seconds allowed for an engine's cold load before it's treated as failed (also accepts 5m, etc.)

AUDIOLLA_MAX_UPLOAD_BYTES

209715200

upload cap (200 MB) — also caps URL fetch body size

AUDIOLLA_FETCH_MODE

disabled

disabled, allowlist, or denylist — controls server-side fetching for file_url / output_url

AUDIOLLA_FETCH_HOSTS

(none)

comma-separated host patterns (bucket.s3.amazonaws.com, *.s3.amazonaws.com). Required when mode=allowlist.

AUDIOLLA_FETCH_SCHEMES

https

comma-separated schemes — https, http (http opt-in only)

AUDIOLLA_FETCH_ALLOW_PRIVATE

false

allow URLs that resolve to private / loopback / link-local IPs

AUDIOLLA_FETCH_TIMEOUT

30

hard timeout per fetch/upload, in seconds (also accepts 30s, 1m)

AUDIOLLA_FETCH_MAX_REDIRECTS

5

max redirects per fetch; each Location re-validated through the policy

AUDIOLLA_JOB_TTL

3600

Seconds a completed/failed/cancelled job stays in memory before being swept. Also accepts 1h, 30m.

AUDIOLLA_JOB_MAX_CONCURRENT

8

Parsed at startup for future async-job concurrency limiting; not yet enforced anywhere in the current build.

AUDIOLLA_SOUNDFONT

/usr/share/sounds/sf2/FluidR3_GM.sf2 (prod images)

Default SoundFont path for /v1/midi/render. Override per request via soundfont_path.


What's not in here

Why

MAGNeT / JASCO

CC-BY-NC weights, same family as MusicGen (which does ship, gated behind AUDIOLLA_ENABLE_NONCOMMERCIAL=1). ACE-Step and DiffRhythm (both Apache 2.0, full-song generation) are researched but not shipped in v1.0.0 either — see "Deferred to a future release" above for why.

YuE 7B

Apache 2.0 but realistically needs 16-24 GB VRAM at fp16; doesn't fit comfortably on a 12 GB GPU without int4 quant tooling. Revisit when a 2B or quantised variant lands.

Essentia analysis

AGPL v3 — any network service using it has to publish full source. librosa handles the common cases without that.

Streaming separation

Demucs needs the whole file. No chunked or real-time inference.

VST3 plugin hosting

Pedalboard can do it but you'd need to mount your host plugin directory. Out of scope for the default image.

rubberband pitch/time-stretch

GPL v2 + commercial license. Sox handles basic pitch and tempo. Add it yourself if you accept the terms.


Build & dev

make build        # CPU image
make build-cuda   # CUDA image
make run          # CPU image on port 8000
make run-cuda     # CUDA image on port 8000
make dev-image          # build the dev container
make shell              # shell inside it
make lint               # flake8 + mypy
make format             # isort + black
make test-unit          # unit tests (no GPU, no ML deps needed)
make test-unit-cov-gate # fail if coverage on support modules drops below 80%
make test-integration   # integration tests (spins up Docker containers)
make generate           # regenerate src/audiolla/schema/ from openapi.yaml
make clean              # wipe build/cache artifacts
make pkg-lock                 # refresh uv.lock
make pkg-add PKG=name[==ver]  # add a dep
make pkg-update PKG=name      # upgrade one dep
make pkg-upgrade              # upgrade everything
make pkg-remove PKG=name      # remove a dep
make pkg-compile-heavy        # recompile requirements-heavy-{cpu,cuda}.txt

Every make pkg-* bumps [tool.uv] exclude-newer to UTC midnight 7 days before the bump date before touching anything — packages published in the last week are invisible to the resolver. The 7-day floor is the supply-chain attack window: fresh wheels (typosquats, hijacked maintainer releases) typically get caught and yanked within hours-to-days, so the floor gives malicious uploads a week of community scrutiny before they're eligible to enter the lockfile. Everything runs inside the dev container. Host needs docker, make, git.


Supply chain

Both prod images do a two-layer install.

Light deps (fastapi, uvicorn, pydantic, etc.): locked in uv.lock, installed with uv sync --frozen --no-dev. Build fails if the lockfile doesn't match pyproject.toml. Wheel hashes verified by uv.

Heavy ML/DSP deps (torch, demucs, matchering, pedalboard, librosa, sox, numpy, soundfile, huggingface-hub): one hash-locked requirements file per image variant (requirements-heavy-cpu.txt, requirements-heavy-cuda.txt), because the torch wheel differs between CPU and CUDA and lives on a different index. Human specs in scripts/heavy-deps-{cpu,cuda}.in, compiled via make pkg-compile-heavy, installed with uv pip install --require-hashes. Both files are committed.

Base images and the uv binary pinned by @sha256: digest.


License

WTFPL.

matchering and pedalboard are GPL v3. Fine for self-hosted use. Distributing the image as a product needs a GPL compliance review.

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