53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
# handles objective metric calculations, such as WER and SIM-O
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#from .emb.transcribe import transcribe
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from .emb.similar import speaker_similarity_embedding
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from .emb.transcribe import transcribe
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from .emb.g2p import detect_language, coerce_to_hiragana, encode
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from .data import normalize_text
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import torch.nn.functional as F
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from pathlib import Path
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from torcheval.metrics.functional import word_error_rate
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# cringe warning message
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try:
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from torchmetrics.text import CharErrorRate
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except Exception as e:
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from torchmetrics import CharErrorRate
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def wer( audio, reference, language="auto", normalize=True, 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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if isinstance( reference, Path ):
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reference = transcribe( reference, language=language, align=False, **transcription_kwargs )["text"]
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if language == "ja":
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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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wer_score = word_error_rate([transcription], [reference]).item()
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cer_score = CharErrorRate()([transcription], [reference]).item()
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return wer_score, cer_score
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def sim_o( audio, reference, **kwargs ):
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audio_emb = speaker_similarity_embedding( audio, **kwargs )
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reference_emb = speaker_similarity_embedding( reference, **kwargs )
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return F.cosine_similarity( audio_emb, reference_emb, dim=-1 ).item() |