File size: 4,186 Bytes
63dd946 | 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 | # Copyright (c) 2026 The Anuvani-Diarization authors
# SPDX-License-Identifier: MIT OR Apache-2.0
"""Trace pyannote's own front-end batching (`Inference.slide`, `SpeakerDiarization.get_embeddings`) with both
networks replaced by recorders, on CPU, and print the batches as JSON. Used by tests/test_front.py.
The waveform carries its own sample index (sample k has value k + 1, exact in float32 below 2^24), so each
recorded window row tells which samples it holds; zero padding stays 0.
python -s tests/ref_front_trace.py LENGTHS_JSON MASK_SEED OUT_JSON
"""
import json
import os
import sys
from pathlib import Path
# never touch a GPU from the tests
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import numpy as np
sys.path.insert(0, str(Path(__file__).resolve().parent))
import refpkg # noqa: E402
refpkg.load()
import torch # noqa: E402
from pyannote.core import SlidingWindowFeature # noqa: E402
from anuvani_diar_ref.frontend import load_bundle # noqa: E402
REL = Path(__file__).resolve().parents[2] / "anuvani_diar_v1"
def describe(rows):
"""For each (1, WIN) row: first sample index, number of non-padding samples, padding all zero."""
out = []
for r in rows:
r = r.reshape(-1)
nz = np.flatnonzero(r)
valid = int(nz[-1]) + 1 if len(nz) else 0
first = int(r[0]) - 1 if valid else -1
ok = bool(valid == 0 or np.array_equal(r[:valid], np.arange(first + 1, first + 1 + valid, dtype=np.float32)))
out.append([first, valid, ok and not r[valid:].any()])
return out
def main():
lengths = json.loads(sys.argv[1])
rng = np.random.default_rng(int(sys.argv[2]))
model = load_bundle(REL / "bundle", "cpu", 8, 2)
pipe = model["pipeline"]
# the real voiceprint model, on CPU
real_min = pipe._embedding.min_num_samples
seg_rec, emb_rec = [], []
def fake_infer(chunks, soft=False):
x = chunks.numpy()
seg_rec.append(dict(B=int(x.shape[0]), rows=describe(x)))
return np.zeros((x.shape[0], 799, 16), np.float32)
class FakeEmbedding:
sample_rate = 16000
min_num_samples = real_min
dimension = 256
metric = "cosine"
def __call__(self, waveforms, masks=None):
emb_rec.append(dict(B=int(waveforms.shape[0]), rows=describe(waveforms.numpy()),
masks=masks.numpy().astype(np.uint8).tolist()))
return np.zeros((waveforms.shape[0], 256), np.float32)
pipe._segmentation.infer = fake_infer
pipe._embedding = FakeEmbedding()
result = dict(min_num_samples=int(real_min), receptive_field=[model["frames"].start, model["frames"].duration,
model["frames"].step],
mapping=model["mapping"].tolist(), runs=[])
for n in lengths:
assert n < 2 ** 24
audio = np.arange(1, n + 1, dtype=np.float32)
file = {"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": 16000}
seg_rec.clear(); emb_rec.clear()
seg = pipe.get_segmentations(file, soft=False)
c = seg.data.shape[0]
density = rng.choice([0.05, 0.3, 0.6, 0.95])
masks = (rng.random((c, 799, 4)) < density).astype(np.float32)
# exact clean-frame counts at the threshold
if c > 1:
masks[1] = 0
# 2 clean frames: falls back to the full mask
masks[1, :2, 0] = 1
# 3 clean frames: uses the clean mask
masks[1, 10:13, 1] = 1
# overlapped frames
masks[1, 13:20, 1:3] = 1
sw = seg.sliding_window
emb = pipe.get_embeddings(file, SlidingWindowFeature(masks, sw), exclude_overlap=True)
result["runs"].append(dict(n=n, chunks=c, sw=[sw.start, sw.duration, sw.step], seg=list(seg_rec),
emb=list(emb_rec), masks=masks.astype(np.uint8).tolist(),
emb_shape=list(emb.shape)))
Path(sys.argv[3]).write_text(json.dumps(result))
if __name__ == "__main__":
main()
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