LocalAI/docs/content/features/audio-transform.md

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title = "Audio Transform"
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![Audio transform: two inputs (mic plus reference) become one cleaned output; interleaved-stereo on the wire](/images/diagrams/audio-transform-io.png)

The audio-transform endpoints take **audio in** and emit **audio out**, optionally
conditioned on a second reference audio signal. The category is generic by
design - concrete operations include joint **acoustic echo cancellation +
noise suppression + dereverberation** (LocalVQE), voice conversion (reference
= target speaker), pitch shifting, audio super-resolution, and so on.

The first shipping backend is [LocalVQE](https://github.com/localai-org/LocalVQE),
a 1.3 M-parameter GGML-based model that performs joint AEC + noise suppression
+ dereverberation on 16 kHz mono speech, ~9.6× realtime on a desktop CPU. It
is a derivative of the Microsoft DeepVQE paper.

## The mental model

Every audio-transform request carries:

- **`audio`** - the primary input file (required).
- **`reference`** - an auxiliary signal whose meaning is backend-specific (optional).
  - For echo cancellation: the loopback / far-end signal played through the speakers.
  - For voice conversion: the target speaker's reference clip.
  - For pitch / style transfer: a tonal or style reference.
  - When omitted, the backend treats it as silence and degrades gracefully (LocalVQE,
    for example, does denoise + dereverb only when ref is empty).
- **`params`** - a generic `key=value` map forwarded to the backend.
  - LocalVQE keys: `noise_gate=true|false`, `noise_gate_threshold_dbfs=<float>`.

This shape mirrors WebRTC's `ProcessStream(near)` / `ProcessReverseStream(far)`
APM API, NVIDIA Maxine's `NvAFX_Run` paired-stream signature, and the ICASSP
AEC challenge 2-channel WAV convention.

## Batch endpoint

`POST /audio/transformations` (alias `POST /audio/transform`) - multipart
form-data, returns audio bytes.

| Field | Type | Required | Notes |
|---|---|---|---|
| `model` | string | yes | Audio-transform model id (e.g. `localvqe-v1.3-4.8m`) |
| `audio` | file   | yes | Primary input audio |
| `reference` | file | no | Optional auxiliary signal |
| `response_format` | string | no | `wav` (default), `mp3`, `ogg`, `flac` |
| `sample_rate` | int | no | Desired output sample rate |
| `params[<key>]` | string | no | Repeated; forwarded to backend |

First install an audio-transform model from the gallery (the examples below use `localvqe-v1.3-4.8m`):

```bash
local-ai run localvqe-v1.3-4.8m
```

Example (LocalVQE: cancel echo, suppress noise, gate residual):

```bash
curl -X POST http://localhost:8080/audio/transformations \
  -F model=localvqe-v1.3-4.8m \
  -F audio=@mic.wav \
  -F reference=@loopback.wav \
  -F 'params[noise_gate]=true' \
  -F 'params[noise_gate_threshold_dbfs]=-50' \
  -o enhanced.wav
```

When `reference` is omitted, LocalVQE zero-fills the reference channel and
the operation reduces to noise suppression + dereverberation.

## Streaming endpoint

`GET /audio/transformations/stream` - bidirectional WebSocket. The first
client message is a JSON envelope; subsequent client messages are binary
PCM frames; server emits binary PCM frames at the same cadence.

### Wire format

**Client → server** (text frame, first):

```json
{
  "type": "session.update",
  "model": "localvqe-v1.3-4.8m",
  "sample_format": "S16_LE",
  "sample_rate": 16000,
  "frame_samples": 256,
  "params": { "noise_gate": "true" }
}
```

`sample_format` is `S16_LE` (16-bit signed little-endian) or `F32_LE` (32-bit
float little-endian, [-1, 1]). `frame_samples` defaults to the backend's
preferred hop length (256 = 16 ms for LocalVQE).

**Client → server** (binary frames, subsequent): interleaved stereo PCM,
channel 0 = audio (mic), channel 1 = reference. Frame size:
`frame_samples × 2 channels × sample_size`. For `S16_LE` at 256 samples that
is 1024 bytes per frame; for `F32_LE` it is 2048 bytes. If the reference is
silent (no auxiliary signal), send zeros on channel 1.

**Server → client** (binary frames): mono PCM in the same format,
`frame_samples × sample_size` bytes (512 bytes for `S16_LE`, 1024 for `F32_LE`).

**Mid-stream control** (text frame): another `session.update` resets the
streaming state when its `reset` field is true; a `session.close` text frame
ends the session cleanly.

### Latency

LocalVQE has 16 ms algorithmic latency (one hop). At runtime the per-frame CPU
cost depends on the model: ~1.6 ms for the compact 1.3 M models (v1.1/v1.2,
~9.7× realtime) and ~3.3 ms for the wider v1.3 4.8 M model (~4.7× realtime) on
a 4-thread modern desktop, leaving the rest of the budget for network and
downstream playback.

## Backend-specific tuning (LocalVQE)

| `params[<key>]` | Type | Default | Effect |
|---|---|---|---|
| `noise_gate` | bool | `false` | Enable post-OLA RMS-based residual-echo gate |
| `noise_gate_threshold_dbfs` | float | `-45.0` | Gate threshold in dBFS; frames below are zeroed |

The gate is most useful in far-end-only / silent-near-end stretches where the
model's residual would otherwise sound like buffering or amplified noise floor.
A reasonable starting point is `-50` dBFS.

## Configuring a model

LocalVQE ships several weight releases in the gallery: `localvqe-v1.3-4.8m`
(current default - best quality), `localvqe-v1.2-1.3m` and `localvqe-v1.1-1.3m`
(compact, ~¼ the per-hop cost - good for low-core or power-constrained hosts).
All share the same backend and request API; only the `model` filename differs.

```yaml
name: localvqe
backend: localvqe
parameters:
  model: localvqe-v1.3-4.8M-f32.gguf

# Backend-specific defaults can be set in Options[]; per-request
# params[*] form fields override.
#
# `backend` and `device` route through the upstream localvqe options
# builder so you can force a non-default GGML backend (e.g. `Vulkan`) or
# pin to a specific GPU index. Leave both unset to keep the CPU default.
options:
- noise_gate=true
- noise_gate_threshold_dbfs=-50
# - backend=Vulkan
# - device=0
```

## See also

- [Text to Audio (TTS)]({{< relref "text-to-audio.md" >}})
- [Audio to Text]({{< relref "audio-to-text.md" >}})
- [LocalVQE upstream](https://github.com/localai-org/LocalVQE)
- [DeepVQE paper (Indenbom et al., Interspeech 2023)](https://arxiv.org/abs/2306.03177)