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fal-ai/ltx23-trainer-v2/ic-lora/a2a

Train an IC-LoRA that transforms one audio clip into another, conditioned at inference on a reference audio clip.
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LTX 2.3 Trainer — Audio-to-Audio IC-LoRA (/ic-lora/a2a)

Overview

The /ic-lora/a2a endpoint trains an IC-LoRA (In-Context LoRA) for the LTX 2.3 model that performs an audio → audio transformation. An IC-LoRA is a small adapter that does not generate from text alone — instead it conditions on a reference audio clip supplied at inference and learns to produce a corresponding target audio clip. You teach it that mapping by providing pairs of audio — a reference and a target — and the LoRA learns to go from one to the other. This is audio-only: there is no video modality.

Use this endpoint for control-driven audio transformations, for example:

  • Style transfer between two kinds of sound.
  • Timbre or character changes (e.g. making a voice sound like a vintage radio).
  • Denoising or restoration demonstrated with clean/noisy pairs.
  • Any reference-to-audio mapping you can demonstrate with paired clips.

Key features:

  • Trains an audio IC-LoRA from paired reference→target audio clips.
  • Audio-only — no video is processed or generated.
  • Fixed audio length bucket via audio_duration_seconds.
  • Validation previews transform a supplied reference audio clip; the output preview is audio.

Dataset Format

Provide a single .zip archive (linked via training_data_url) of paired audio clips:

  • <name>_start.<ext> — the reference audio (the input to transform).
  • <name>_end.<ext> — the target audio (the desired output).
  • <name>.txt — optional caption for the pair.

Audio formats: .wav, .mp3, .flac, .ogg, .aac, .m4a. Each _end must have a matching _start. File names must be unique across the archive. Aim for at least 10 pairs.

Minimum clip duration (per pair): at least one pair must have both its reference (_start) and target (_end) clip at least audio_duration_seconds long. A pair with either side too short is skipped, and if no pair qualifies the request fails with a 422.

Example layout:

sample01_start.wav   sample01_end.wav   sample01.txt
sample02_start.mp3   sample02_end.wav   sample02.txt

At least one pair must fill the audio bucket. A clip shorter than audio_duration_seconds is skipped, and a pair is usable only when both its reference and target are at least audio_duration_seconds long; if no pair qualifies, the request is rejected (HTTP 422). Lower audio_duration_seconds if your clips are short.

Input Parameters Reference

Dataset
training_data_url (required)

Type: string

URL to the .zip archive of _start/_end audio pairs.

trigger_phrase

Type: string Default: ""

Phrase prepended to captions during training; include it at inference.

Training Parameters
rank

Type: integer (8, 16, 32, 64, 128) Default: 32

IC-LoRA capacity.

number_of_steps

Type: integer Default: 2000 (range 100–20000)

Number of optimization steps.

learning_rate

Type: number Default: 0.0002

Optimization step size.

Audio Configuration
audio_duration_seconds

Type: number Default: 5.0 (range 0.5–60.0)

Target audio clip length in seconds (the audio duration bucket). Each clip is bucketed independently — clips shorter than this are skipped; longer clips are trimmed — and the reference (_start) and target (_end) are then paired by row, so a pair is usable only when both sides fill the bucket. At least one pair must qualify or the request fails with a 422. Audio-only modes need this because there is no video frame count to derive a length from.

audio_normalize

Type: boolean Default: true

Peak-normalize audio for consistent loudness across the dataset.

audio_preserve_pitch

Type: boolean Default: true

Preserve pitch when fitting audio to the target duration (instead of trimming/padding).

Dataset Processing
split_input_into_scenes

Type: boolean Default: false

Off for this mode: scene splitting would desync the reference/target pair. Provide pre-split clips instead.

split_input_duration_threshold

Type: number Default: 30.0 (range 1.0–60.0)

Duration threshold for scene splitting (only relevant if splitting were enabled).

Validation
validation

Type: array Default: [] (max 2 entries)

Validation samples, each an object with:

  • prompt (string) — the text prompt.
  • reference_audio_url (string, required) — the reference audio that conditions the generated audio.
validation_negative_prompt

Type: string Default: a built-in quality negative prompt.

validation_frame_rate

Type: integer Default: 24 (range 8–60)

Used together with the audio bucket to size the preview audio length.

stg_scale

Type: number Default: 1.0 (range 0.0–3.0)

debug_dataset

Type: boolean Default: false

Return an archive of the preprocessed data for inspection.

Note: this audio-only mode also accepts the shared video/validation-video fields (number_of_frames, resolution, validation_resolution, etc.), but they have no effect — no video is processed. You can safely leave them at their defaults.

Outputs

  • lora_file — the trained IC-LoRA weights (.safetensors).
  • config_file — JSON describing the trigger phrase and training type.
  • audio — a combined preview of the generated validation audio.
  • debug_dataset — preprocessed-data archive, only when debug_dataset is enabled.

Billing

A successful run is billed max(100, number_of_steps) billable units. Requests that fail before training completes (input-validation errors / HTTP 422, or dataset-download failures) are billed 0 units.

How the Training Works

Pipeline Overview
  1. Preprocessing — the archive is extracted, _start/_end audio pairs and captions matched, and each clip fit to the audio_duration_seconds bucket.
  2. Training — the IC-LoRA trains for number_of_steps, conditioned on the reference audio, learning to produce the target audio. Validation previews run at intervals.
  3. Output — the IC-LoRA, config, and combined audio preview are uploaded.
What Happens to Your Data
  • Archive extraction: the .zip is unpacked; macOS metadata and hidden files are ignored.
  • Pair matching: each <name>_start is paired with its <name>_end and the optional <name>.txt. A target missing its reference causes a clear error.
  • Audio fitting: each clip is fit to the audio_duration_seconds bucket (clips shorter than the bucket are skipped; longer ones trimmed; normalized and pitch-fit as configured).
  • Captions: the trigger phrase (if set) is prepended.
What an IC-LoRA Is

An IC-LoRA performs an in-context transformation: rather than generating from text alone, it conditions on a reference clip supplied at inference and produces a transformed result. Here the result is audio derived from a reference audio clip. The trained file specializes the base model at your reference→target audio mapping. At inference you supply a reference audio clip plus a prompt, and the LoRA produces the corresponding target audio.

Tips for Getting Good Results

Dataset Quality
  • Use at least 10 well-matched reference→target audio pairs; the two should differ only by the transformation you want learned.
  • Keep recordings clean and consistent in level (normalization helps, but garbage in means garbage out).
  • Choose an audio_duration_seconds that fits most of your clips so few are skipped.
Caption Best Practices
  • Describe the target audio plainly, optionally with a trigger phrase.
  • Keep captions consistent in style so the LoRA associates the transformation, not the wording.

Good caption: tonech4nge a warm vintage-radio version of the voice Weak caption: voice

Trigger Phrases
  • A distinctive trigger phrase helps invoke the transformation cleanly; include it in every caption and at inference.
Inference Format Matching

At inference, supply a reference audio clip, use the same trigger phrase and caption style, and keep clip lengths in line with audio_duration_seconds.

json
{
  "training_data_url": "https://example.com/a2a_pairs.zip",
  "trigger_phrase": "tonech4nge",
  "rank": 32,
  "number_of_steps": 2000,
  "learning_rate": 0.0002,
  "audio_duration_seconds": 5.0,
  "validation": [
    { "prompt": "tonech4nge", "reference_audio_url": "https://example.com/ref.wav" }
  ]
}
Diagnosing Issues
  • Transformation too weak: more steps, higher rank, or more consistent pairs.
  • Many clips skipped: lower audio_duration_seconds to match your clips.
  • Output ignores the reference: ensure pairs differ only by the intended transformation; keep captions consistent.
  • Overfitting: fewer steps, lower rank, more pairs.
Validation Prompt Tips
  • Use a reference clip the LoRA has not seen to gauge generalization.
  • Match the caption style and trigger phrase you trained with.
Common Pitfalls
  • Missing or mismatched _start/_end pairs.
  • audio_duration_seconds set so long that most clips are skipped.
  • Reference/target pairs that differ by more than the intended transformation.
LTX 2.3 Trainer (V2) - Audio-to-Audio IC-LoRA (Training) API on fal