docs: add notes about xtts fine-tuning

TTS/bin/synthesize.py

@@ -34,7 +34,7 @@
   tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
   ```
 
-#### Single Speaker Models
+#### Single speaker models
 
 - Run TTS with the default model (`tts_models/en/ljspeech/tacotron2-DDC`):
 
@@ -102,7 +102,7 @@
       --vocoder_config_path path/to/vocoder_config.json
   ```
 
-#### Multi-speaker Models
+#### Multi-speaker models
 
 - List the available speakers and choose a `<speaker_id>` among them:
 
@@ -125,7 +125,7 @@
       --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
   ```
 
-#### Voice Conversion Models
+#### Voice conversion models
 
 ```sh
 tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" \\

docs/source/faq.md

@@ -16,13 +16,19 @@ We tried to collect common issues and questions we receive about 🐸TTS. It is
 - If you need faster models, consider SpeedySpeech, GlowTTS or AlignTTS. Keep in mind that SpeedySpeech requires a pre-trained Tacotron or Tacotron2 model to compute text-to-speech alignments.
 
 ## How can I train my own `tts` model?
+
+```{note} XTTS has separate fine-tuning scripts, see [here](models/xtts.md#training).
+```
+
 0. Check your dataset with notebooks in [dataset_analysis](https://github.com/idiap/coqui-ai-TTS/tree/main/notebooks/dataset_analysis) folder. Use [this notebook](https://github.com/idiap/coqui-ai-TTS/blob/main/notebooks/dataset_analysis/CheckSpectrograms.ipynb) to find the right audio processing parameters. A better set of parameters results in a better audio synthesis.
 
 1. Write your own dataset `formatter` in `datasets/formatters.py` or [format](datasets/formatting_your_dataset) your dataset as one of the supported datasets, like LJSpeech.
     A `formatter` parses the metadata file and converts a list of training samples.
 
 2. If you have a dataset with a different alphabet than English, you need to set your own character list in the ```config.json```.
-    - If you use phonemes for training and your language is supported [here](https://github.com/rhasspy/gruut#supported-languages), you don't need to set your character list.
+    - If you use phonemes for training and your language is supported by
+    [Espeak](https://github.com/espeak-ng/espeak-ng/blob/master/docs/languages.md)
+    or [Gruut](https://github.com/rhasspy/gruut#supported-languages), you don't need to set your character list.
     - You can use `TTS/bin/find_unique_chars.py` to get characters used in your dataset.
 
 3. Write your own text cleaner in ```utils.text.cleaners```. It is not always necessary, except when you have a different alphabet or language-specific requirements.

docs/source/training/finetuning.md

@@ -29,6 +29,9 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
 
 ## Steps to fine-tune a 🐸 TTS model
 
+```{note} XTTS has separate fine-tuning scripts, see [here](../models/xtts.md#training).
+```
+
 1. Setup your dataset.
 
     You need to format your target dataset in a certain way so that 🐸TTS data loader will be able to load it for the

docs/source/training/index.md

@@ -8,3 +8,6 @@ The following pages show you how to train and fine-tune Coqui models:
 training_a_model
 finetuning
 ```
+
+Also see the [XTTS page](../models/xtts.md#training) if you want to fine-tune
+that model.

docs/source/tutorial_for_nervous_beginners.md

@@ -29,6 +29,9 @@ CLI, server or Python API.
 
 ## Training a `tts` Model
 
+```{note} XTTS has separate fine-tuning scripts, see [here](models/xtts.md#training).
+```
+
 A breakdown of a simple script that trains a GlowTTS model on the LJspeech
 dataset. For a more in-depth guide to training and fine-tuning also see [this
 page](training/index.md).