mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-29 09:18:21 +00:00
Brandon Thomas
76c6dbf12b
This breaks the text pipeline into the following pieces: - input parsing (split known bracketed phoneme sections out first) - normalization (fix spacing, numbers, abbreviations, etc.) - phonetization (do CMUdict lookup or g2p prediction) - sequencing (convert the symbolic representation to integer representation) Each "pipeline" can have a unique combination of operations for each of these pipeline steps. This will grow over time.
74 lines
3.5 KiB
Python
74 lines
3.5 KiB
Python
from typing import List
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from text.pipeline.base_pipeline import BasePipeline
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from text.pipeline_components.input_parsing.parser import ArpabetSequence, GraphemeSequence, ParsedSequence, parse_grapheme_and_arpabet_sequence
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from text.pipeline_components.normalization.english_normalization import normalize_english
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from text.pipeline_components.phonetization.base_phonetization import BasePhonetization
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from text.pipeline_components.phonetization.cmudict import DEFAULT_CMUDICT_PATH, CMUDict
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from text.pipeline_components.phonetization.english_phonetization import EnglishPhonetization
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from text.pipeline_components.sequencing.english_symbols_v1 import SPACE_INTEGER_ENCODING, arpabet_to_sequence, symbols_to_sequence
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class EnglishPipelineV1(BasePipeline):
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"""
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This is a new text pipeline for FakeYou that will be introduced in July 2022.
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It introduces Arpabet and individually upgradable components.
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The code is not 1:1 with our notebooks, but we'll go to efforts to make sure that
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the model input expectations on both sides match one another.
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"""
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def __init__(self, phonetization: BasePhonetization):
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self.phonetization = phonetization
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def user_input_to_sequence(self, input_text: str) -> List[int]:
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"""Convert raw user input into a numerical sequence for ML inference."""
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parsed_sequence = parse_grapheme_and_arpabet_sequence(input_text)
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self._normalize_graphemes(parsed_sequence)
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parsed_sequence = self._convert_phonemes(parsed_sequence)
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return self._convert_to_integer_sequence(parsed_sequence)
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def _normalize_graphemes(self, parsed_sequence: ParsedSequence):
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for i in range(len(parsed_sequence)):
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subsequence = parsed_sequence[i]
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if isinstance(subsequence, GraphemeSequence):
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normalized = normalize_english(subsequence.sequence)
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parsed_sequence[i] = GraphemeSequence(normalized)
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def _convert_phonemes(self, parsed_sequence: ParsedSequence) -> ParsedSequence:
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# NB: We will likely break grapheme runs into multiple arpabet sequences, extending the
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# output list length.
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updated_sequence = []
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for subsequence in parsed_sequence:
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if isinstance(subsequence, ArpabetSequence):
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updated_sequence.append(subsequence)
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elif isinstance(subsequence, GraphemeSequence):
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converted_sequence = self.phonetization.try_convert_to_phonemes(subsequence.sequence)
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updated_sequence.extend(converted_sequence)
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return updated_sequence
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def _convert_to_integer_sequence(self, parsed_sequence: ParsedSequence) -> List[int]:
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integer_sequence = []
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last_index = len(parsed_sequence) - 1
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for i, subsequence in enumerate(parsed_sequence):
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integer_subsequence = []
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if isinstance(subsequence, ArpabetSequence):
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integer_subsequence = arpabet_to_sequence(subsequence.sequence)
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elif isinstance(subsequence, GraphemeSequence):
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integer_subsequence = symbols_to_sequence(subsequence.sequence)
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if integer_subsequence:
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integer_sequence.extend(integer_subsequence)
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if i < last_index and integer_sequence[-1] != SPACE_INTEGER_ENCODING:
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# Separate words with single spaces if not already present.
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integer_sequence.append(SPACE_INTEGER_ENCODING)
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return integer_sequence
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