speaker_helper.text module

Text segmentation for low-latency streaming synthesis.

Module summary

In streaming mode the time to first audio (TTFA) is dominated by how much text the engine must synthesise before it can emit anything. Splitting a paragraph into sentence-sized units lets speaker-helper synthesise and emit the first unit quickly while later units are still being produced. This module holds the deterministic, dependency-free splitter used for that.

Usage example

>>> from speaker_helper.text import split_sentences
>>> split_sentences("Bonjour. Comment ça va ? Bien !")
['Bonjour.', 'Comment ça va ?', 'Bien !']

Author

Warith HARCHAOUI — https://linkedin.com/in/warith-harchaoui

speaker_helper.text.chunk_for_streaming(text, first_chunk_sentences=1)

Group sentences into synthesis chunks, keeping the first one small.

Parameters:

• text (str) – The text to synthesise.

• first_chunk_sentences (int, optional) – How many sentences to place in the first chunk. Keeping this at 1 (the default) minimises TTFA; larger values reduce per-chunk overhead at the cost of a slower first emission.

Returns:

Chunks of text to synthesise in order. The first chunk holds first_chunk_sentences sentences; every remaining sentence is its own chunk so downstream audio keeps flowing steadily.

Return type:

list[str]

Examples

>>> chunk_for_streaming("A. B. C.", first_chunk_sentences=1)
['A.', 'B.', 'C.']
>>> chunk_for_streaming("A. B. C.", first_chunk_sentences=2)
['A. B.', 'C.']

speaker_helper.text.split_sentences(text)

Split text into sentence-sized, non-empty, stripped units.

Parameters:

text (str) – Arbitrary text, possibly multi-sentence and multi-line.

Returns:

Sentences in order, each stripped of surrounding whitespace, with their terminating punctuation preserved. Text without any terminator returns a single-element list (the whole stripped input); empty or whitespace-only input returns an empty list.

Return type:

list[str]

Examples

>>> split_sentences("Un. Deux. Trois.")
['Un.', 'Deux.', 'Trois.']
>>> split_sentences("no terminator here")
['no terminator here']
>>> split_sentences("   ")
[]