fal-ai/ltx23-trainer-v2/audio-extend-prefix

LTX 2.3 Trainer — Forward Audio Extension (/audio-extend-prefix)

Overview

The /audio-extend-prefix endpoint trains a LoRA for the LTX 2.3 model that continues an audio clip forward in time. During training, the first few seconds of each clip are kept as a clean "prefix" and the model learns to generate the audio that follows. At inference you supply an opening audio clip and the model produces the continuation. This is audio-only.

Key features:

• Learns to extend audio forward from a clean opening (prefix) window.
• Audio-only — no video is processed or generated.
• The prefix is carved from each clip's own audio automatically.
• Fixed audio length bucket via audio_duration_seconds.
• Validation previews continue a supplied clip forward; the output preview is audio.

Dataset Format

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

• Audio: .wav, .mp3, .flac, .ogg, .aac, .m4a
• Captions: a .txt with the same base name as each audio clip (optional but recommended).

File names must be unique across the archive. Aim for at least 10 clips. Clips shorter than audio_duration_seconds are skipped, so make each clip at least that long — and since conditioning_seconds must be smaller than audio_duration_seconds, every kept clip leaves a continuation to learn.

Input Parameters Reference

Dataset

training_data_url (required)

Type: string

URL to the .zip archive of audio clips.

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

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.

conditioning_seconds

Type: number Default: 1.0 (range 0.1–30.0)

Seconds of leading audio kept as the clean prefix the model continues from. Must be less than audio_duration_seconds so there is a continuation to generate (and not so close to it that nothing is left after rounding).

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). Clips shorter than this are skipped; longer clips are trimmed.

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.

Validation

validation

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

Validation samples, each an object with:

• prompt (string) — the text prompt.
• audio_url (string, required) — an audio clip to extend forward. Its opening conditioning_seconds are used as the prefix.

The validation clip must be at least conditioning_seconds long. When validation prompts are provided, conditioning_seconds is also bounded by the preview length: the validation preview generates an audio span sized from audio_duration_seconds (capped at 121 frames divided by validation_frame_rate), and a conditioning_seconds that would fill that span is rejected at request time (HTTP 422). A very low validation_frame_rate shrinks this preview window, so keep conditioning_seconds comfortably below audio_duration_seconds.

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, but they have no effect — no video is processed.

Outputs

• lora_file — the trained 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, audio matched to captions, and each clip fit to the audio_duration_seconds bucket.
2. Training — for each clip, the opening conditioning_seconds of audio are held clean as the prefix and the model learns to generate the rest. Validation previews run at intervals.
3. Output — the 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.
• File matching: each audio clip is paired with the .txt of the same base name.
• Audio fitting: each clip is fit to the audio_duration_seconds bucket (shorter clips skipped, longer ones trimmed; normalized and pitch-fit as configured).
• Prefix carving: the opening conditioning_seconds of each clip are kept clean as conditioning; the remainder is the generation target. This happens automatically.
• Captions: the trigger phrase (if set) is prepended.

Tips for Getting Good Results

Dataset Quality

• Use at least 10 clean clips containing the kind of forward continuation you want learned.
• Clips should be meaningfully longer than conditioning_seconds.
• Pick an audio_duration_seconds that fits most clips so few are skipped.

Caption Best Practices

• Describe the sound and continuation plainly, optionally with a trigger phrase.
• Keep captions consistent across clips.

Good caption: a piano melody continuing into a flowing arpeggio Weak caption: piano

Trigger Phrases

• Use a distinctive trigger phrase to invoke a particular continuation style; include it in every caption and at inference.

Inference Format Matching

At inference, supply an opening audio clip at least conditioning_seconds long, use the same caption style and trigger phrase, and keep durations in line with audio_duration_seconds.

Recommended Starting Configuration

json
{
  "training_data_url": "https://example.com/audio_clips.zip",
  "trigger_phrase": "",
  "rank": 32,
  "number_of_steps": 2000,
  "learning_rate": 0.0002,
  "conditioning_seconds": 1.0,
  "audio_duration_seconds": 5.0,
  "validation": [
    { "prompt": "a piano melody continuing", "audio_url": "https://example.com/opening.wav" }
  ]
}

Diagnosing Issues

• Continuation drifts from the prefix: add more representative clips; try a slightly longer conditioning_seconds for more context.
• Validation rejected for short clip: provide a clip at least conditioning_seconds long, or lower conditioning_seconds.
• Window covers the whole clip: reduce conditioning_seconds relative to audio_duration_seconds.
• Overfitting: fewer steps, lower rank, more clips.

Validation Prompt Tips

• Use a fresh opening clip to gauge generalization.
• Keep the prompt aligned with the continuation you expect.

Common Pitfalls

• conditioning_seconds too close to (or above) audio_duration_seconds — nothing left to generate.
• audio_duration_seconds so long that most clips are skipped.
• Background noise polluting the learned continuation.