LocalAI/docs/content/features/text-to-audio.md

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disableToc = false
title = "🗣 Text to audio (TTS)"
weight = 11
url = "/features/text-to-audio/"
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## API Compatibility

The LocalAI TTS API is compatible with the [OpenAI TTS API](https://platform.openai.com/docs/guides/text-to-speech) and the [Elevenlabs](https://api.elevenlabs.io/docs) API.

## LocalAI API

The `/tts` endpoint can also be used to generate speech from text.

## Usage

Input: `input`, `model`

For example, to generate an audio file, you can send a POST request to the `/tts` endpoint with the instruction as the request body:

```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Hello world",
  "model": "tts"
}'
```

Returns an `audio/wav` file.

## Streaming TTS

LocalAI supports streaming TTS generation, allowing audio to be played as it's generated. This is useful for real-time applications and reduces latency.

To enable streaming, add `"stream": true` to your request:

```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Hello world, this is a streaming test",
  "model": "voxcpm",
  "stream": true
}' | aplay
```

The audio will be streamed chunk-by-chunk as it's generated, allowing playback to start before generation completes. This is particularly useful for long texts or when you want to minimize perceived latency.

You can also pipe the streamed audio directly to audio players like `aplay` (Linux) or save it to a file:

```bash
# Stream to aplay (Linux)
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "This is a longer text that will be streamed as it is generated",
  "model": "voxcpm",
  "stream": true
}' | aplay

# Stream to a file
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Streaming audio to file",
  "model": "voxcpm",
  "stream": true
}' > output.wav
```

Note: Streaming TTS is currently supported by the `voxcpm` backend. Other backends will fall back to non-streaming mode if streaming is not supported.

## Backends

### 🐸 Coqui

Required: Don't use `LocalAI` images ending with the `-core` tag,. Python dependencies are required in order to use this backend.

Coqui works without any configuration, to test it, you can run the following curl command:

```
    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
        "backend": "coqui",
        "model": "tts_models/en/ljspeech/glow-tts",
        "input":"Hello, this is a test!"
        }'
```

You can use the env variable COQUI_LANGUAGE to set the language used by the coqui backend.

You can also use config files to configure tts models (see section below on how to use config files).

### Piper

To install the `piper` audio models manually:

- Download Voices from https://github.com/rhasspy/piper/releases/tag/v0.0.2
- Extract the `.tar.tgz` files (.onnx,.json) inside `models`
- Run the following command to test the model is working

To use the tts endpoint, run the following command. You can specify a backend with the `backend` parameter. For example, to use the `piper` backend:
```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "model":"it-riccardo_fasol-x-low.onnx",
  "backend": "piper",
  "input": "Ciao, sono Ettore"
}' | aplay
```

Note:

- `aplay` is a Linux command. You can use other tools to play the audio file.
- The model name is the filename with the extension.
- The model name is case sensitive.
- LocalAI must be compiled with the `GO_TAGS=tts` flag.

### Transformers-musicgen

LocalAI also has experimental support for `transformers-musicgen` for the generation of short musical compositions. Currently, this is implemented via the same requests used for text to speech:

```
curl --request POST \
  --url http://localhost:8080/tts \
  --header 'Content-Type: application/json' \
  --data '{
    "backend": "transformers-musicgen",
    "model": "facebook/musicgen-medium",
    "input": "Cello Rave"
}' | aplay
```

Future versions of LocalAI will expose additional control over audio generation beyond the text prompt.

### ACE-Step

[ACE-Step 1.5](https://github.com/ACE-Step/ACE-Step-1.5) is a music generation model that can create music from text descriptions, lyrics, or audio samples. It supports both simple text-to-music and advanced music generation with metadata like BPM, key scale, and time signature.

#### Setup

Install the `ace-step-turbo` model from the Model gallery or run `local-ai run models install ace-step-turbo`.

#### Usage

ACE-Step supports two modes: **Simple mode** (text description + vocal language) and **Advanced mode** (caption, lyrics, BPM, key, and more).

**Simple mode:**
```bash
curl http://localhost:8080/v1/audio/speech -H "Content-Type: application/json" -d '{
  "model": "ace-step-turbo",
  "input": "A soft Bengali love song for a quiet evening",
  "vocal_language": "bn"
}' --output music.flac
```

**Advanced mode** (using the `/sound` endpoint):
```bash
curl http://localhost:8080/sound -H "Content-Type: application/json" -d '{
  "model": "ace-step-turbo",
  "caption": "A funky Japanese disco track",
  "lyrics": "[Verse 1]\n...",
  "bpm": 120,
  "keyscale": "Ab major",
  "language": "ja",
  "duration_seconds": 225
}' --output music.flac
```

#### Configuration

You can configure ACE-Step models with various options:

```yaml
name: ace-step-turbo
backend: ace-step
parameters:
  model: acestep-v15-turbo
known_usecases:
  - sound_generation
  - tts
options:
  - "device:auto"
  - "use_flash_attention:true"
  - "init_lm:true"  # Enable LLM for enhanced generation
  - "lm_model_path:acestep-5Hz-lm-0.6B"  # or acestep-5Hz-lm-4B
  - "lm_backend:pt"  # or vllm
  - "temperature:0.85"
  - "top_p:0.9"
  - "inference_steps:8"
  - "guidance_scale:7.0"
```

### VibeVoice

[VibeVoice-Realtime](https://github.com/microsoft/VibeVoice) is a real-time text-to-speech model that generates natural-sounding speech with voice cloning capabilities.

#### Setup

Install the `vibevoice` model in the Model gallery or run `local-ai run models install vibevoice`.

#### Usage

Use the tts endpoint by specifying the vibevoice backend:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "vibevoice",
     "input":"Hello!"
   }' | aplay
```

#### Voice cloning

VibeVoice supports voice cloning through voice preset files. You can configure a model with a specific voice:

```yaml
name: vibevoice
backend: vibevoice
parameters:
  model: microsoft/VibeVoice-Realtime-0.5B
tts:
  voice: "Frank"  # or use audio_path to specify a .pt file path
  # Available English voices: Carter, Davis, Emma, Frank, Grace, Mike
```

Then you can use the model:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "vibevoice",
     "input":"Hello!"
   }' | aplay
```

### Pocket TTS

[Pocket TTS](https://github.com/kyutai-labs/pocket-tts) is a lightweight text-to-speech model designed to run efficiently on CPUs. It supports voice cloning through HuggingFace voice URLs or local audio files.

#### Setup

Install the `pocket-tts` model in the Model gallery or run `local-ai run models install pocket-tts`.

#### Usage

Use the tts endpoint by specifying the pocket-tts backend:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "pocket-tts",
     "input":"Hello world, this is a test."
   }' | aplay
```

#### Voice cloning

Pocket TTS supports voice cloning through built-in voice names, HuggingFace URLs, or local audio files. You can configure a model with a specific voice:

```yaml
name: pocket-tts
backend: pocket-tts
tts:
  voice: "azelma"  # Built-in voice name
  # Or use HuggingFace URL: "hf://kyutai/tts-voices/alba-mackenna/casual.wav"
  # Or use local file path: "path/to/voice.wav"
  # Available built-in voices: alba, marius, javert, jean, fantine, cosette, eponine, azelma
```

You can also pre-load a default voice for faster first generation:

```yaml
name: pocket-tts
backend: pocket-tts
options:
  - "default_voice:azelma"  # Pre-load this voice when model loads
```

Then you can use the model:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "pocket-tts",
     "input":"Hello world, this is a test."
   }' | aplay
```

### Qwen3-TTS

[Qwen3-TTS](https://github.com/QwenLM/Qwen3-TTS) is a high-quality text-to-speech model that supports three modes: custom voice (predefined speakers), voice design (natural language instructions), and voice cloning (from reference audio).

#### Setup

Install the `qwen-tts` model in the Model gallery or run `local-ai run models install qwen-tts`.

#### Usage

Use the tts endpoint by specifying the qwen-tts backend:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "qwen-tts",
     "input":"Hello world, this is a test."
   }' | aplay
```

#### Custom Voice Mode

Qwen3-TTS supports predefined speakers. You can specify a speaker using the `voice` parameter:

```yaml
name: qwen-tts
backend: qwen-tts
parameters:
  model: Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
tts:
  voice: "Vivian"  # Available speakers: Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna, Sohee
```

Available speakers:
- **Chinese**: Vivian, Serena, Uncle_Fu, Dylan, Eric
- **English**: Ryan, Aiden
- **Japanese**: Ono_Anna
- **Korean**: Sohee

#### Voice Design Mode

Voice Design allows you to create custom voices using natural language instructions. Configure the model with an `instruct` option:

```yaml
name: qwen-tts-design
backend: qwen-tts
parameters:
  model: Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign
options:
  - "instruct:体现撒娇稚嫩的萝莉女声，音调偏高且起伏明显，营造出黏人、做作又刻意卖萌的听觉效果。"
```

Then use the model:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "qwen-tts-design",
     "input":"Hello world, this is a test."
   }' | aplay
```

#### Voice Clone Mode

Voice Clone allows you to clone a voice from reference audio. Configure the model with an `AudioPath` and optional `ref_text`:

```yaml
name: qwen-tts-clone
backend: qwen-tts
parameters:
  model: Qwen/Qwen3-TTS-12Hz-1.7B-Base
tts:
  audio_path: "path/to/reference_audio.wav"  # Reference audio file
options:
  - "ref_text:This is the transcript of the reference audio."
  - "x_vector_only_mode:false"  # Set to true to use only speaker embedding (ref_text not required)
```

You can also use URLs or base64 strings for the reference audio. The backend automatically detects the mode based on available parameters (AudioPath → VoiceClone, instruct option → VoiceDesign, voice parameter → CustomVoice).

Then use the model:

```
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "qwen-tts-clone",
     "input":"Hello world, this is a test."
   }' | aplay
```

#### Multi-Voice Clone Mode

Qwen3-TTS also supports loading multiple voices for voice cloning, allowing you to select different voices at request time. Configure multiple voices using the `voices` option:

```yaml
name: qwen-tts-multi-voice
backend: qwen-tts
parameters:
  model: Qwen/Qwen3-TTS-12Hz-1.7B-Base
options:
  - voices:[{"name":"jane","audio":"voices/jane.wav","ref_text":"voices/jane-ref.txt"},{"name":"john","audio":"voices/john.wav","ref_text":"voices/john-ref.txt"}]
```

The `voices` option accepts a JSON array where each voice entry must have:
- `name`: The voice identifier (used in API requests)
- `audio`: Path to the reference audio file (relative to model directory or absolute)
- `ref_text`: Path to the reference text file for the audio it is paired with

Then use the model with voice selection:

```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "qwen-tts-multi-voice",
     "input":"Hello world, this is Jane speaking.",
     "voice": "jane"
   }' | aplay

# Switch to a different voice
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
     "model": "qwen-tts-multi-voice",
     "input":"Hello world, this is John speaking.",
     "voice": "john"
   }' | aplay
```

**Voice Selection Priority:**
1. `voice` parameter in the API request (highest priority)
2. `voice` option in the model configuration
3. Error if voice is not found among configured voices

**Error Handling:**
If you request a voice that doesn't exist in the voices list, the API will return an error with a list of available voices:
```json
{"error": "Voice 'unknown' not found. Available voices: jane, john"}
```

**Backward Compatibility:**
The multi-voice mode is backward compatible with existing single-voice configurations. Models using `audio_path` in the `tts` section will continue to work as before.

You can also use a `config-file` to specify TTS models and their parameters.

In the following example we define a custom config to load the `xtts_v2` model, and specify a voice and language.

```yaml

name: xtts_v2
backend: coqui
parameters:
  language: fr
  model: tts_models/multilingual/multi-dataset/xtts_v2

tts:
  voice: Ana Florence
```

With this config, you can now use the following curl command to generate a text-to-speech audio file:
```bash
curl -L http://localhost:8080/tts \
    -H "Content-Type: application/json" \
    -d '{
"model": "xtts_v2",
"input": "Bonjour, je suis Ana Florence. Comment puis-je vous aider?"
}' | aplay
```

## Response format

To provide some compatibility with OpenAI API regarding `response_format`, ffmpeg must be installed (or a docker image including ffmpeg used) to leverage converting the generated wav file before the api provide its response.

Warning regarding a change in behaviour. Before this addition, the parameter was ignored and a wav file was always returned, with potential codec errors later in the integration (like trying to decode a mp3 file from a wav, which is the default format used by OpenAI)

Supported format thanks to ffmpeg are `wav`, `mp3`, `aac`, `flac`, `opus`, defaulting to `wav` if an unknown or no format is provided.

```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
  "input": "Hello world",
  "model": "tts",
  "response_format": "mp3"
}'
```

If a `response_format` is added in the query (other than `wav`) and ffmpeg is not available, the call will fail.