audiolla

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.

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What's in the box

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Table of Contents

• Run it
• Quick start
• What it can do
  • Split stems
  • Master
  • Analyze
  • Beats, onsets, melody, segments
  • Silence detection and trimming
  • Visualize (spectrogram, waveform, video)
  • Acoustic fingerprint
  • De-reverb, de-echo, de-noise
  • Audio-to-MIDI transcription
  • Neural speech and vocal enhancement
  • Chord and key detection
  • Voice activity detection
  • Speaker diarization
  • Transform
  • Loudness measurement
  • Loudness curve
  • Loudness normalization
  • HPSS (harmonic/percussive split)
  • Spectral noise reduction
  • Time-stretch and pitch-shift
  • Pitch correct
  • Repair
  • Audio tagging
  • Audio embeddings
  • Zero-shot classification
  • Audio info
  • Trim
  • Mix
  • Concat
  • Speed
  • Convert
  • Similar
  • MIDI quantize
  • Fade
  • Reverse
  • Loop
  • BPM match
  • Stereo width
  • Split
  • Pan
  • EQ
  • Key match
  • Sidechain duck
  • Effects chain
  • Loop point
  • Compose MIDI
  • Inspect MIDI
  • Transform MIDI
  • Render MIDI to audio
  • Generate music from a spec
  • Drum pattern
  • Chords to MIDI
  • Audio metadata tags
  • Clip detection
  • Mid/Side encode and decode
  • Beat slice
  • Convolution reverb
  • Transient shaper
  • Multiband compression
  • DJ prep
  • De-ess
  • Stereo field analysis
  • Audio thumbnail
  • MIDI humanize
  • Batch operations
  • Async jobs and webhooks
  • Stage files
  • Remote URLs
• Engines
• Workflows — presets + pipeline
• API catalog
• Endpoints
• MCP
• Agent integrations
• Configuration
• What's not in here
• Build & dev
• Supply chain
• License

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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.

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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.

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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/separate — uvr-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:

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 \