Formatting Your Dataset

For training a TTS model, you need a dataset with speech recordings and transcriptions. The speech must be divided into audio clips and each clip needs transcription.

If you have a single audio file and you need to split it into clips, there are different open-source tools for you. We recommend Audacity. It is an open-source and free audio editing software.

It is also important to use a lossless audio file format to prevent compression artifacts. We recommend using wav file format.

Let’s assume you created the audio clips and their transcription. You can collect all your clips under a folder. Let’s call this folder wavs.

/wavs
  | - audio1.wav
  | - audio2.wav
  | - audio3.wav
  ...

You can either create separate transcription files for each clip or create a text file that maps each audio clip to its transcription. In this file, each line must be delimitered by a special character separating the audio file name from the transcription. And make sure that the delimiter is not used in the transcription text.

We recommend the following format delimited by ||.

# metadata.txt

audio1.wav || This is my sentence.
audio2.wav || This is maybe my sentence.
audio3.wav || This is certainly my sentence.
audio4.wav || Let this be your sentence.
...

In the end, we have the following folder structure

/MyTTSDataset
      |
      | -> metadata.txt
      | -> /wavs
              | -> audio1.wav
              | -> audio2.wav
              | ...

The format above is taken from widely-used the LJSpeech dataset. You can also download and see the dataset. 🐸TTS already provides tooling for the LJSpeech. if you use the same format, you can start training your models right away.

Dataset Quality

Your dataset should have good coverage of the target language. It should cover the phonemic variety, exceptional sounds and syllables. This is extremely important for especially non-phonemic languages like English.

For more info about dataset qualities and properties check our post.

Using Your Dataset in 🐸TTS

After you collect and format your dataset, you need to check two things. Whether you need a formatter and a text_cleaner. The formatter loads the text file (created above) as a list and the text_cleaner performs a sequence of text normalization operations that converts the raw text into the spoken representation (e.g. converting numbers to text, acronyms, and symbols to the spoken format).

If you use a different dataset format then the LJSpeech or the other public datasets that 🐸TTS supports, then you need to write your own formatter.

If your dataset is in a new language or it needs special normalization steps, then you need a new text_cleaner.

What you get out of a formatter is a List[List[]] in the following format.

>>> formatter(metafile_path)
[["audio1.wav", "This is my sentence.", "MyDataset"],
["audio1.wav", "This is maybe a sentence.", "MyDataset"],
...
]

Each sub-list is parsed as ["<filename>", "<transcription>", "<speaker_name">]. <speaker_name> is the dataset name for single speaker datasets and it is mainly used in the multi-speaker models to map the speaker of the each sample. But for now, we only focus on single speaker datasets.

The purpose of a formatter is to parse your metafile and load the audio file paths and transcriptions. Then, its output passes to a Dataset object. It computes features from the audio signals, calls text normalization routines, and converts raw text to phonemes if needed.

See TTS.tts.datasets.TTSDataset, a generic Dataset implementation for the tts models.

See TTS.vocoder.datasets.*, for different Dataset implementations for the vocoder models.

See TTS.utils.audio.AudioProcessor that includes all the audio processing and feature extraction functions used in a Dataset implementation. Feel free to add things as you need.passed