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examples : add README.md to tts example [no ci] (#11155)

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* examples : add README.md to tts example [no ci]

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Fix heading to be consistent with other examples, and add a quickstart
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* squash! examples : add README.md to tts example [no ci]

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# llama.cpp/example/tts
This example demonstrates the Text To Speech feature. It uses a
[model](https://www.outeai.com/blog/outetts-0.2-500m) from
[outeai](https://www.outeai.com/).
## Quickstart
If you have built llama.cpp with `-DLLAMA_CURL=ON` you can simply run the
following command and the required models will be downloaded automatically:
```console
$ 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:
```console
$ 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:
```console
(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:
```console
$ 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 simlar for the audio decoder. First download or checkout
the model for the voice decoder:
```console
$ 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 PyTorch checkpoint (.ckpt) and we first need to convert it to
huggingface format:
```console
(venv) python examples/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:
```console
(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:
```console
$ 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:
```console
$ aplay output.wav
```