File size: 6,043 Bytes
5125634 | 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 | #!/usr/bin/env python3
"""Evaluate every model folder with Whisper (CER/WER) and TitaNet (speaker similarity).
Folder layout:
<root>/<model>/sample_00.wav ...
Usage:
python evaluate_compare.py --root /path/eval_in \
--models bayan,magpie,xtts,chatterbox,mms \
--text-file ../texts.txt --out ../metrics.json --csv ../results.csv \
--whisper /path/whisper-large-v3 --titanet /path/titanet.nemo \
--speaker-ref-dir /path/wav22k
"""
from __future__ import annotations
import argparse
import csv
import json
import re
import unicodedata
from pathlib import Path
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
from faster_whisper import WhisperModel
from jiwer import cer, wer
from nemo.collections.asr.models import EncDecSpeakerLabelModel
DIACRITICS = re.compile(r"[\u0610-\u061a\u064b-\u065f\u0670\u06d6-\u06ed\u0640]")
NUMBER_WORDS = (
"واحد|واحده|اثنان|اثنين|ثلاثه|اربعه|خمسه|سته|سبعه|ثمانيه|تسعه|عشره|عشر|"
"عشرون|عشرين|ثلاثون|ثلاثين|اربعون|اربعين|خمسون|خمسين|ستون|ستين|"
"سبعون|سبعين|ثمانون|ثمانين|تسعون|تسعين|مايه|مائه|ميه|ميتين|مائتين|مئتين|مائتان|"
"الف|الاف|الفين|الفان|مليون|مليار|مليارات|"
"ثلاثمائه|ثلاثمايه|اربعمائه|اربعمايه|خمسمائه|خمسمايه|ستمائه|ستمايه|"
"سبعمائه|سبعمايه|ثمانمائه|ثمانمايه|تسعمائه|تسعمايه"
)
UNITS = "كم|كيلومتر|كيلومترات|كيلومترا|متر|مترا|امتار|عام|عاما|اعوام|سنوات|سنه|دوله|دول|شخص|اشخاص"
NUM_RE = re.compile(rf"\bو?(?:{NUMBER_WORDS}|{UNITS}|[0-9]+)\b")
def normalize_ar(text: str) -> str:
text = unicodedata.normalize("NFKC", text)
text = DIACRITICS.sub("", text)
text = (
text.replace("أ", "ا").replace("إ", "ا").replace("آ", "ا")
.replace("ى", "ي").replace("ة", "ه").replace("ؤ", "و").replace("ئ", "ي")
)
text = re.sub(r"[^\w\s]", " ", text)
return re.sub(r"\s+", " ", text).strip()
def mask_numbers(text: str) -> str:
out = NUM_RE.sub("NUM", normalize_ar(text))
out = re.sub(r"(?:NUM\s*)+", "NUM ", out)
return re.sub(r"\s+", " ", out).strip()
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--root", required=True)
ap.add_argument("--models", required=True)
ap.add_argument("--text-file", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--csv", required=True)
ap.add_argument("--whisper", required=True)
ap.add_argument("--titanet", required=True)
ap.add_argument("--speaker-ref-dir", required=True)
ap.add_argument("--ref-stride", type=int, default=50)
ap.add_argument("--ref-count", type=int, default=12)
args = ap.parse_args()
root = Path(args.root)
texts = [ln.strip() for ln in Path(args.text_file).read_text(encoding="utf-8").splitlines() if ln.strip()]
whisper = WhisperModel(args.whisper, device="cpu", compute_type="int8")
sv = EncDecSpeakerLabelModel.restore_from(args.titanet, map_location="cpu").eval()
refs = sorted(Path(args.speaker_ref_dir).glob("*.wav"))[:: args.ref_stride][: args.ref_count]
with torch.no_grad():
embs = torch.stack([F.normalize(sv.get_embedding(str(p)).reshape(-1), dim=0) for p in refs])
voice = F.normalize(embs.mean(0), dim=0)
print(f"voice centroid from {len(refs)} clips")
report: dict[str, dict] = {}
for model in [m.strip() for m in args.models.split(",") if m.strip()]:
files = sorted((root / model).glob("sample_*.wav"))
rows = []
for f, ref in zip(files, texts):
segs, _ = whisper.transcribe(str(f), language="ar", beam_size=5, vad_filter=False, condition_on_previous_text=False)
hyp = " ".join(s.text.strip() for s in segs).strip()
c = float(cer(normalize_ar(ref), normalize_ar(hyp)))
c_norm = float(cer(mask_numbers(ref), mask_numbers(hyp)))
w = float(wer(normalize_ar(ref), normalize_ar(hyp)))
with torch.no_grad():
e = F.normalize(sv.get_embedding(str(f)).reshape(-1), dim=0)
sim = float(e @ voice)
dur = float(sf.info(str(f)).duration)
rows.append({"file": f.name, "cer": c, "cer_norm": c_norm, "wer": w,
"sim": sim, "duration": dur, "hyp": hyp, "ref": ref})
print(f"{model}/{f.name} CER={c:.3f} CER*={c_norm:.3f} WER={w:.3f} SIM={sim:.3f}")
report[model] = {
"n": len(rows),
"mean_cer": float(np.mean([r["cer"] for r in rows])),
"mean_cer_norm": float(np.mean([r["cer_norm"] for r in rows])),
"mean_wer": float(np.mean([r["wer"] for r in rows])),
"mean_sim": float(np.mean([r["sim"] for r in rows])),
"total_duration": float(np.sum([r["duration"] for r in rows])),
"samples": rows,
}
Path(args.out).write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
with Path(args.csv).open("w", newline="", encoding="utf-8") as fh:
wr = csv.writer(fh)
wr.writerow(["model", "file", "cer", "cer_numeric_normalized", "wer", "sim", "duration", "hypothesis"])
for model, rep in report.items():
for r in rep["samples"]:
wr.writerow([model, r["file"], f"{r['cer']:.4f}", f"{r['cer_norm']:.4f}",
f"{r['wer']:.4f}", f"{r['sim']:.4f}", f"{r['duration']:.2f}", r["hyp"]])
for model, rep in report.items():
print(f"\n{model}: CER={rep['mean_cer']:.4f} CER*={rep['mean_cer_norm']:.4f} "
f"WER={rep['mean_wer']:.4f} SIM={rep['mean_sim']:.3f} dur={rep['total_duration']:.1f}s")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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