added text cleaning/normalization for wer purposes but it amounts to nothing desu
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133
vall_e/data.py
133
vall_e/data.py
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@ -63,12 +63,133 @@ def sentence_split( s, split_by="sentences", quote_placeholder="<QUOTE>" ):
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sentences = nltk.sent_tokenize(s)
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return [ sentence.replace(quote_placeholder, '"') for sentence in sentences if sentence ]
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# to-do: improve upon this since it's kind of ass
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# this might be better to live in emb.g2p
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def normalize_text( s ):
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s = s.lower()
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s = re.sub(r'[^\w\s]', '', s)
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return s
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# normalization code borrowed from TorToiSe TTS
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# (it's not perfect but it works)
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try:
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from tokenizers.normalizers import Lowercase, NFD, StripAccents
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normalizer = tokenizers.normalizers.Sequence([Lowercase(), NFD(), StripAccents()])
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except Exception as e:
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normalizer = None
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# List of (regular expression, replacement) pairs for abbreviations:
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_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
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('mrs', 'misess'),
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('mr', 'mister'),
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('dr', 'doctor'),
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('st', 'saint'),
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('co', 'company'),
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('jr', 'junior'),
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('maj', 'major'),
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('gen', 'general'),
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('drs', 'doctors'),
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('rev', 'reverend'),
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('lt', 'lieutenant'),
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('hon', 'honorable'),
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('sgt', 'sergeant'),
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('capt', 'captain'),
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('esq', 'esquire'),
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('ltd', 'limited'),
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('col', 'colonel'),
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('ft', 'fort'),
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]]
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def normalize_abbreviations(text):
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for regex, replacement in _abbreviations:
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text = re.sub(regex, replacement, text)
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return text
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def _remove_commas(m):
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return m.group(1).replace(',', '')
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def _expand_decimal_point(m):
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return m.group(1).replace('.', ' point ')
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def _expand_dollars(m):
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match = m.group(1)
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parts = match.split('.')
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if len(parts) > 2:
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return match + ' dollars' # Unexpected format
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dollars = int(parts[0]) if parts[0] else 0
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cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
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if dollars and cents:
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dollar_unit = 'dollar' if dollars == 1 else 'dollars'
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cent_unit = 'cent' if cents == 1 else 'cents'
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return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
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elif dollars:
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dollar_unit = 'dollar' if dollars == 1 else 'dollars'
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return '%s %s' % (dollars, dollar_unit)
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elif cents:
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cent_unit = 'cent' if cents == 1 else 'cents'
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return '%s %s' % (cents, cent_unit)
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else:
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return 'zero dollars'
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# in case the current env does not have it installed, so I don't need it as a hard dependency
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try:
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import inflect
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_inflect = inflect.engine()
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def _expand_ordinal(m):
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return _inflect.number_to_words(m.group(0))
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def _expand_number(m):
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num = int(m.group(0))
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if num > 1000 and num < 3000:
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if num == 2000:
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return 'two thousand'
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elif num > 2000 and num < 2010:
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return 'two thousand ' + _inflect.number_to_words(num % 100)
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elif num % 100 == 0:
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return _inflect.number_to_words(num // 100) + ' hundred'
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else:
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return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
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else:
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return _inflect.number_to_words(num, andword='')
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except Exception as e:
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_inflect = None
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_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
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_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
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_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
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_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
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_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
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_number_re = re.compile(r'[0-9]+')
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_whitespace_re = re.compile(r'\s+')
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_end_punct_re = re.compile(r'[\.\?\!]$')
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_aux_punct_re = re.compile(r'[,;:\?\.\!-]')
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def normalize_numbers(text):
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text = re.sub(_comma_number_re, _remove_commas, text)
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text = re.sub(_pounds_re, r'\1 pounds', text)
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text = re.sub(_dollars_re, _expand_dollars, text)
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text = re.sub(_decimal_number_re, _expand_decimal_point, text)
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if _inflect is not None:
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text = re.sub(_ordinal_re, _expand_ordinal, text)
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text = re.sub(_number_re, _expand_number, text)
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return text
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# full will do aggressive normalization, perfect for WER/CER
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# not full will do basic cleaning
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def normalize_text(text, language="auto", full=True):
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if full:
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if normalizer is not None:
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text = normalizer.normalize_str( text )
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else:
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text = text.lower()
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text = normalize_numbers(text) # expand numbers
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text = normalize_abbreviations(text) # expand abbreviations
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#text = re.sub(_end_punct_re, '', text) # collapse whitespace
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text = re.sub(_aux_punct_re, '', text) # collapse whitespace
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text = text.replace('"', '') # remove quotation marks
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else:
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text = normalize_numbers(text) # expand numbers
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text = normalize_abbreviations(text) # expand abbreviations
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text = re.sub(_whitespace_re, ' ', text) # collapse whitespace
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# to-do: other languages
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return text
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@cache
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def get_random_prompts( validation=False, min_length=0, tokenized=False ):
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@ -135,7 +135,7 @@ def main():
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parser.add_argument("--lora", action="store_true")
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parser.add_argument("--comparison", type=str, default=None)
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parser.add_argument("--transcription-model", type=str, default="openai/whisper-base")
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parser.add_argument("--transcription-model", type=str, default="openai/whisper-large-v3")
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parser.add_argument("--speaker-similarity-model", type=str, default="microsoft/wavlm-large")
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args = parser.parse_args()
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@ -426,7 +426,8 @@ def main():
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calculate = not metrics_path.exists() or (metrics_path.stat().st_mtime < out_path.stat().st_mtime)
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if calculate:
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wer_score, cer_score = wer( out_path, text, language=language, device=tts.device, dtype=tts.dtype, model_name=args.transcription_model )
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wer_score, cer_score = wer( out_path, text, language=language, device=tts.device, dtype=tts.dtype, model_name=args.transcription_model, phonemize=True )
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#wer_score, cer_score = wer( out_path, reference_path, language=language, device=tts.device, dtype=tts.dtype, model_name=args.transcription_model, phonemize=False )
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sim_o_score = sim_o( out_path, prompt_path, device=tts.device, dtype=tts.dtype, model_name=args.speaker_similarity_model )
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metrics = {"wer": wer_score, "cer": cer_score, "sim-o": sim_o_score}
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@ -12,13 +12,19 @@ from pathlib import Path
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from torcheval.metrics.functional import word_error_rate
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from torchmetrics.functional.text import char_error_rate
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def wer( audio, reference, language="auto", normalize=True, phonemize=True, **transcription_kwargs ):
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import warnings
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warnings.simplefilter(action='ignore', category=FutureWarning)
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warnings.simplefilter(action='ignore', category=UserWarning)
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def wer( audio, reference, language="auto", phonemize=True, **transcription_kwargs ):
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if language == "auto":
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language = detect_language( reference )
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transcription = transcribe( audio, language=language, align=False, **transcription_kwargs )
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if language == "auto":
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language = transcription["language"]
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transcription = transcription["text"]
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# reference audio needs transcribing too
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@ -29,13 +35,12 @@ def wer( audio, reference, language="auto", normalize=True, phonemize=True, **tr
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transcription = coerce_to_hiragana( transcription )
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reference = coerce_to_hiragana( reference )
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if normalize:
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transcription = normalize_text( transcription )
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reference = normalize_text( reference )
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if phonemize:
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transcription = encode( transcription, language=language )
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reference = encode( reference, language=language )
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else:
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transcription = normalize_text( transcription, language=language )
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reference = normalize_text( reference, language=language )
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wer_score = word_error_rate([transcription], [reference]).item()
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# un-normalize
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