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6e802a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | # intelligibility.py: Whisper WER, with the field's Whisper English normaliser as an option.
from __future__ import annotations
import logging
import re
from pathlib import Path
from typing import Optional
import numpy as np
logger = logging.getLogger(__name__)
_WORD_RE = re.compile(r"[^a-z']+")
def whisper_text_normalizer(model_id: str = "openai/whisper-base.en"):
from transformers.models.whisper.english_normalizer import (
BasicTextNormalizer, EnglishTextNormalizer)
try:
from transformers import WhisperTokenizer
tok = WhisperTokenizer.from_pretrained(model_id)
spell = getattr(tok, "english_spelling_normalizer", None)
if spell:
return EnglishTextNormalizer(spell)
except Exception as e:
logger.warning("Whisper spelling map unavailable for %s (%s); using BasicTextNormalizer",
model_id, e)
return BasicTextNormalizer()
def _normalize(text: str) -> list[str]:
text = text.lower().strip()
text = _WORD_RE.sub(" ", text)
return [w for w in text.split() if w]
def _edit_distance(a: list[str], b: list[str]) -> tuple[int, int, int, int]:
n, m = len(a), len(b)
if n == 0:
return 0, 0, m, m
if m == 0:
return 0, n, 0, n
dp = np.zeros((n + 1, m + 1), dtype=np.int32)
op = np.zeros((n + 1, m + 1), dtype=np.int8)
dp[:, 0] = np.arange(n + 1)
dp[0, :] = np.arange(m + 1)
op[:, 0] = 2
op[0, :] = 3
op[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
if a[i - 1] == b[j - 1]:
dp[i, j] = dp[i - 1, j - 1]
op[i, j] = 0
else:
sub_cost = dp[i - 1, j - 1] + 1
del_cost = dp[i - 1, j] + 1
ins_cost = dp[i, j - 1] + 1
best = min(sub_cost, del_cost, ins_cost)
dp[i, j] = best
op[i, j] = (
1 if best == sub_cost
else 2 if best == del_cost
else 3
)
i, j = n, m
S = D = I = 0
while i > 0 or j > 0:
o = op[i, j]
if o == 0:
i -= 1
j -= 1
elif o == 1:
S += 1
i -= 1
j -= 1
elif o == 2:
D += 1
i -= 1
else:
I += 1
j -= 1
return S, D, I, int(dp[n, m])
def word_error_rate(reference: str, hypothesis: str,
text_normalizer=None) -> dict:
if text_normalizer is not None:
ref = text_normalizer(reference).split()
hyp = text_normalizer(hypothesis).split()
else:
ref = _normalize(reference)
hyp = _normalize(hypothesis)
S, D, I, dist = _edit_distance(ref, hyp)
n = len(ref)
return {
"wer": (dist / n) if n > 0 else float("nan"),
"substitutions": S,
"deletions": D,
"insertions": I,
"ref_word_count": n,
"normalizer": "whisper" if text_normalizer is not None else "simple",
}
class IntelligibilityScorer:
DEFAULT_MODEL_ID = "openai/whisper-tiny.en"
def __init__(
self,
model_id: str = DEFAULT_MODEL_ID,
device: str = "cuda",
normalizer: str = "simple",
):
if normalizer not in ("simple", "whisper"):
raise ValueError(f"normalizer must be 'simple' or 'whisper', got {normalizer!r}")
self.model_id = model_id
self.device = device
self.normalizer = normalizer
self._model = None
self._processor = None
self._text_norm = None
def _load(self):
if self._model is not None:
return
from transformers import (
WhisperForConditionalGeneration,
WhisperProcessor,
)
logger.info("Loading Whisper %s on %s", self.model_id, self.device)
self._processor = WhisperProcessor.from_pretrained(self.model_id)
self._model = WhisperForConditionalGeneration.from_pretrained(
self.model_id,
).to(self.device).eval()
if self.normalizer == "whisper":
self._text_norm = whisper_text_normalizer(self.model_id)
def transcribe(self, wav_path: str | Path) -> str:
import soundfile as sf
import torch
self._load()
audio, sr = sf.read(str(wav_path), dtype="float32", always_2d=False)
if audio.ndim > 1:
audio = audio.mean(axis=1)
if sr != 16000 and len(audio) > 0:
import librosa
audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
inputs = self._processor(
audio, sampling_rate=16000, return_tensors="pt",
).to(self.device)
gen_kw = {}
if not self.model_id.endswith(".en"):
gen_kw = {"language": "en", "task": "transcribe"}
with torch.no_grad():
ids = self._model.generate(**inputs, max_new_tokens=256, **gen_kw)
text = self._processor.batch_decode(ids, skip_special_tokens=True)[0]
return text.strip()
def score(self, wav_path: str | Path, reference_text: str) -> dict:
hypothesis = self.transcribe(wav_path)
result = word_error_rate(reference_text, hypothesis,
text_normalizer=self._text_norm)
result["hypothesis"] = hypothesis
result["reference"] = reference_text
return result
|