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iamleonie merged 4 commits into from
Sep 3, 2026
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Update audio transcription example for LFM2.5-Audio-1.5B #129

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ace6e1a
Update audio transcription example for LFM2.5-Audio-1.5B
iamleonie Sep 1, 2026
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Update examples/laptop-examples/audio-to-text-in-real-time.mdx
iamleonie Sep 3, 2026
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Merge branch 'main' into docs/audio-transcription-cli-lfm25-update
iamleonie Sep 3, 2026
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fix minor merge bug
iamleonie Sep 3, 2026
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60 changes: 37 additions & 23 deletions examples/laptop-examples/audio-to-text-in-real-time.mdx
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Expand Up @@ -6,7 +6,7 @@
Browse the complete example on GitHub
</Card>

This example demonstrates how to use the [LFM2-Audio-1.5B](https://docs.liquid.ai/lfm/models/lfm2-audio-1.5b) model with llama.cpp to transcribe audio files locally in real-time.
This example demonstrates how to use the [LFM2.5-Audio-1.5B](https://docs.liquid.ai/lfm/models/lfm25-audio-1.5b) model with llama.cpp to transcribe audio files locally in real-time.

Intelligent audio assistants on the edge are possible, and this repository is just one step towards that.

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cd cookbook/examples/audio-transcription-cli
```

2. Install uv on your system, if you don't have it already.

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<Accordion title="Click to see installation instructions for uv">
**macOS/Linux:**
Expand All @@ -44,14 +44,26 @@
```
By passing the `--play-audio` flag, you will hear the audio in the background during transcription.

The model is downloaded automatically on first run. Use `--quantization` to trade accuracy for size and speed:

```sh
uv run transcribe --audio './audio-samples/barackobamafederalplaza.mp3' --quantization Q4_0
```

| Quantization | Total size on disk |
| --- | --- |
| `Q4_0` | ~1.1 GB |
| `Q8_0` (default) | ~1.8 GB |
| `F16` | ~3.3 GB |


## Understanding the architecture

This example is a 100% local audio-to-text transcription CLI, that runs on your machine thanks to llama.cpp. Neither inputs audios nor outputs text are sent to any server. Everything runs on your machine.

![](https://raw.githubusercontent.com/Liquid4All/cookbook/main/examples/audio-transcription-cli/media/diagram.gif)

The Python code downloads the necessary llama.cpp builds for your platform automatically, so you don't need to worry about it. Audio support in llama.cpp is still quite experimental, and not fully integrated on the main branch of the llama.cpp project. Because of this, the Liquid AI team has released specialized llama.cpp builds that support the LFM2-Audio-1.5B model, that you will need to run this CLI.
The Python code downloads the necessary llama.cpp builds for your platform automatically, so you don't need to worry about it. Audio support in llama.cpp is still quite experimental, and not fully integrated on the main branch of the llama.cpp project. Because of this, the Liquid AI team has released specialized llama.cpp builds that support the LFM2.5-Audio-1.5B model, that you will need to run this CLI.

<Note>
**Supported Platforms**
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## llama.cpp support for audio models

[llama.cpp](https://github.com/ggerganov/llama.cpp) is a super fast and lightweight open-source inference engine for Language Models. It is written in C++ and can be used to run LLMs on your local machine. For example, our Python CLI used llama.cpp under the hood to deliver fast transcriptions, instead of using either `PyTorch` or the higher-level `transformers` library.

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Did you really mean 'LLMs'?

In the [examples.sh](https://github.com/Liquid4All/cookbook/blob/main/examples/audio-transcription-cli/examples.sh) script you will find 3 examples on how to run inference with LFM2-Audio-1.5 for 3 common use cases:
In the [examples.sh](https://github.com/Liquid4All/cookbook/blob/main/examples/audio-transcription-cli/examples.sh) script you will find 3 examples on how to run inference with LFM2.5-Audio-1.5B for 3 common use cases:

- Audio to text transcription. This is essentially what our Python CLI does under the hood:
```sh
# Audio to Speech Recognition (ASR)
./llama-lfm2-audio \
-m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
--mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
./llama-liquid-audio-cli \
-m $CKPT/LFM2.5-Audio-1.5B-Q8_0.gguf \
-mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q8_0.gguf \
--tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q8_0.gguf \
-sys "Perform ASR." \
--audio $INPUT_WAV
```

- Text to speech.
```sh
# Text To Speech (TTS)
./llama-lfm2-audio \
-m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
--mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
-sys "Perform TTS." \
./llama-liquid-audio-cli \
-m $CKPT/LFM2.5-Audio-1.5B-Q8_0.gguf \
-mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q8_0.gguf \
--tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q8_0.gguf \
-sys "Perform TTS. Use the US male voice." \
-p "My name is Pau Labarta Bajo and I love AI" \
--output $OUTPUT_WAV
```

- Text to speech with voice instructions
- Text to speech with a different voice. The supported voices are `US male`, `UK male`, `US female` and `UK female`.
```sh
./llama-lfm2-audio \
-m $CKPT/LFM2-Audio-1.5B-Q8_0.gguf \
--mmproj $CKPT/mmproj-audioencoder-LFM2-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/audiodecoder-LFM2-Audio-1.5B-Q8_0.gguf \
-sys "Perform TTS.
Use the following voice: A male speaker delivers a very expressive and animated speech, with a low-pitch voice and a slightly close-sounding tone. The recording carries a slight background noise." \
./llama-liquid-audio-cli \
-m $CKPT/LFM2.5-Audio-1.5B-Q8_0.gguf \
-mm $CKPT/mmproj-LFM2.5-Audio-1.5B-Q8_0.gguf \
-mv $CKPT/vocoder-LFM2.5-Audio-1.5B-Q8_0.gguf \
--tts-speaker-file $CKPT/tokenizer-LFM2.5-Audio-1.5B-Q8_0.gguf \
-sys "Perform TTS. Use the UK female voice." \
-p "What is your name man?" \
--output $OUTPUT_WAV
```
Expand All @@ -115,12 +129,12 @@

For example, we can use

- LFM2-Audio-1.5B for audio to text extraction
- LFM2-350M for text cleaning
- LFM2.5-Audio-1.5B for audio to text extraction
- LFM2.5-350M for text cleaning

### What is LFM2-350M?
### What is LFM2.5-350M?

[LFM2-350M](https://docs.liquid.ai/lfm/models/lfm2-350m) is a small text-to-text model that can be used for tasks like text cleaning. To achieve optimal performance for your particular use case, you need to optimize your system and user prompts.
[LFM2.5-350M](https://docs.liquid.ai/lfm/models/lfm25-350m) is a small text-to-text model that can be used for tasks like text cleaning. To achieve optimal performance for your particular use case, you need to optimize your system and user prompts.

## Need help?

Expand Down