llama.cpp/tools/tts/README.md

llama.cpp/example/tts

This example demonstrates the Text To Speech feature. It uses a model from outeai.

Quickstart

If you have built llama.cpp with SSL support you can simply run the following command and the required models will be downloaded automatically:

$ build/bin/llama-tts --tts-oute-default -p "Hello world" && aplay output.wav

For details about the models and how to convert them to the required format see the following sections.

Model conversion

Checkout or download the model that contains the LLM model:

$ pushd models
$ git clone --branch main --single-branch --depth 1 https://huggingface.co/OuteAI/OuteTTS-0.2-500M
$ cd OuteTTS-0.2-500M && git lfs install && git lfs pull
$ popd

Convert the model to .gguf format:

(venv) python convert_hf_to_gguf.py models/OuteTTS-0.2-500M \
    --outfile models/outetts-0.2-0.5B-f16.gguf --outtype f16

The generated model will be models/outetts-0.2-0.5B-f16.gguf.

We can optionally quantize this to Q8_0 using the following command:

$ build/bin/llama-quantize models/outetts-0.2-0.5B-f16.gguf \
    models/outetts-0.2-0.5B-q8_0.gguf q8_0

The quantized model will be models/outetts-0.2-0.5B-q8_0.gguf.

Next we do something similar for the audio decoder. First download or checkout the model for the voice decoder:

$ pushd models
$ git clone --branch main --single-branch --depth 1 https://huggingface.co/novateur/WavTokenizer-large-speech-75token
$ cd WavTokenizer-large-speech-75token && git lfs install && git lfs pull
$ popd

This model file is a PyTorch checkpoint (.ckpt) and we first need to convert it to huggingface format:

(venv) python tools/tts/convert_pt_to_hf.py \
    models/WavTokenizer-large-speech-75token/wavtokenizer_large_speech_320_24k.ckpt
...
Model has been successfully converted and saved to models/WavTokenizer-large-speech-75token/model.safetensors
Metadata has been saved to models/WavTokenizer-large-speech-75token/index.json
Config has been saved to models/WavTokenizer-large-speech-75tokenconfig.json

Then we can convert the huggingface format to gguf:

(venv) python convert_hf_to_gguf.py models/WavTokenizer-large-speech-75token \
    --outfile models/wavtokenizer-large-75-f16.gguf --outtype f16
...
INFO:hf-to-gguf:Model successfully exported to models/wavtokenizer-large-75-f16.gguf

Running the example

With both of the models generated, the LLM model and the voice decoder model, we can run the example:

$ build/bin/llama-tts -m  ./models/outetts-0.2-0.5B-q8_0.gguf \
    -mv ./models/wavtokenizer-large-75-f16.gguf \
    -p "Hello world"
...
main: audio written to file 'output.wav'

The output.wav file will contain the audio of the prompt. This can be heard by playing the file with a media player. On Linux the following command will play the audio:

$ aplay output.wav

Running the example with llama-server

Running this example with llama-server is also possible and requires two server instances to be started. One will serve the LLM model and the other will serve the voice decoder model.

The LLM model server can be started with the following command:

$ ./build/bin/llama-server -m ./models/outetts-0.2-0.5B-q8_0.gguf --port 8020

And the voice decoder model server can be started using:

./build/bin/llama-server -m ./models/wavtokenizer-large-75-f16.gguf --port 8021 --embeddings --pooling none

Then we can run tts-outetts.py to generate the audio.

First create a virtual environment for python and install the required dependencies (this in only required to be done once):

$ python3 -m venv venv
$ source venv/bin/activate
(venv) pip install requests numpy

And then run the python script using:

(venv) python ./tools/tts/tts-outetts.py http://localhost:8020 http://localhost:8021 "Hello world"
spectrogram generated: n_codes: 90, n_embd: 1282
converting to audio ...
audio generated: 28800 samples
audio written to file "output.wav"

And to play the audio we can again use aplay or any other media player:

$ aplay output.wav