Work on text parsing, testing

This commit is contained in:
Brandon Thomas
2022-06-30 17:47:27 -04:00
parent b20ac5ff89
commit 030d2c9381
12 changed files with 116 additions and 1 deletions
+7
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@@ -13,6 +13,13 @@ The following files are entrypoints and an essential part of FakeYou.com functio
files).
To Run Tests
------------
```
pytest text/
```
Vocodes Server Parameters
-------------------------
-1
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@@ -13,4 +13,3 @@ class ArpabetTransformer:
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@@ -0,0 +1,5 @@
class BaseNormalization:
def normalize(self, raw_input_text):
return raw_input_text
@@ -0,0 +1,6 @@
from text.normalization.BaseNormalization import BaseNormalization
class EnglishNormalization(BaseNormalization):
def normalize(self, raw_input_text):
return raw_input_text
@@ -0,0 +1,6 @@
from text.normalization.BaseNormalization import BaseNormalization
class SpanishNormalization(BaseNormalization):
def normalize(self, raw_input_text):
return raw_input_text
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@@ -0,0 +1,46 @@
"""
Parse raw text sequences into Arpabet and Grapheme sequences so that they can be
treated independently.
Inspired by and including code from original Tacotron2 by Keith Ito.
"""
import re
# Regular expression matching text enclosed in curly braces:
_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
class ArpabetSequence:
def __init__(self, sequence):
self.sequence = sequence
class GraphemeSquence:
def __init__(self, sequence):
self.sequence = sequence
def parse_grapheme_and_arpabet_sequence(input_text):
"""
Parse a string into an array of ArpabetSequence and GraphemeSequence so that each can be treated independently.
"""
sequence = []
# Check for curly braces and treat their contents as ARPAbet:
while len(input_text):
input_text = input_text.strip()
m = _curly_re.match(input_text)
if not m:
sequence.append(GraphemeSquence(input_text))
break
before = m.group(1).strip()
bracketed = m.group(2).strip()
after = m.group(3).strip()
if before:
sequence.append(GraphemeSquence(before))
if bracketed:
sequence.append(ArpabetSequence(bracketed))
input_text = after
return sequence
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@@ -0,0 +1,45 @@
from tts.text.phonetization.parser import ArpabetSequence, GraphemeSquence, parse_grapheme_and_arpabet_sequence
class TestParser:
def test_only_text(self):
parsed = parse_grapheme_and_arpabet_sequence("This is a test")
assert len(parsed) == 1
assert isinstance(parsed[0], GraphemeSquence)
def test_only_arpabet(self):
parsed = parse_grapheme_and_arpabet_sequence("{T EH1 S T IH0 NG}")
assert len(parsed) == 1
assert isinstance(parsed[0], ArpabetSequence)
def test_text_and_arpabet(self):
parsed = parse_grapheme_and_arpabet_sequence("This is a {T EH1 S T}")
assert len(parsed) == 2
assert isinstance(parsed[0], GraphemeSquence)
assert isinstance(parsed[1], ArpabetSequence)
def test_text_and_arpabet_interleved_1(self):
parsed = parse_grapheme_and_arpabet_sequence("{DH IH1 S} is a {T EH1 S T}")
assert len(parsed) == 3
assert isinstance(parsed[0], ArpabetSequence)
assert isinstance(parsed[1], GraphemeSquence)
assert isinstance(parsed[2], ArpabetSequence)
def test_text_and_arpabet_interleved_2(self):
parsed = parse_grapheme_and_arpabet_sequence("Check out {DH IH1 S} {T EH1 S T} of the {S IH1 S T AH0 M}")
assert len(parsed) == 5
assert isinstance(parsed[0], GraphemeSquence)
assert isinstance(parsed[1], ArpabetSequence)
assert isinstance(parsed[2], ArpabetSequence)
assert isinstance(parsed[3], GraphemeSquence)
assert isinstance(parsed[4], ArpabetSequence)
def test_text_and_arpabet_interleved_2(self):
parsed = parse_grapheme_and_arpabet_sequence("Check out {DH IH1 S} {T EH1 S T} of the {S IH1 S T AH0 M}.")
assert len(parsed) == 6
assert isinstance(parsed[0], GraphemeSquence)
assert isinstance(parsed[1], ArpabetSequence)
assert isinstance(parsed[2], ArpabetSequence)
assert isinstance(parsed[3], GraphemeSquence)
assert isinstance(parsed[4], ArpabetSequence)
assert isinstance(parsed[5], GraphemeSquence)
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@@ -96,6 +96,7 @@ def preprocess_text(text, raw_input=True):
pass
pre_sequence += line
print(pre_sequence)
with torch.no_grad():
# save VRAM by not including gradients
sequence = np.array(text_to_sequence(pre_sequence, ['english_cleaners']))[None, :]