| """Back-end parity: authors' models/XLSR2_Nes2Net_X_ATT.py Nested_Res2Net_TDNN (incl. LocalWindowAttention) |
| vs ours (_net.py), both loaded from epoch_5_1.422.pth, on SSL features of real In-the-Wild clips (eval mode). |
| The XLS-R front-end is the fairseq model in both (the authors' SSLModel only differs in how it is constructed). |
| usage: python source_parity.py <path to authors' XLSR2_Nes2Net_X_ATT.py> [n_clips]""" |
| import importlib.util, io, os, sys |
| import numpy as np, torch |
| HERE = os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0, HERE) |
| import xlsr_nes2net_la as W |
|
|
| n = int(sys.argv[2]) if len(sys.argv) > 2 else 12 |
| spec = importlib.util.spec_from_file_location("authors_net", sys.argv[1]); A = importlib.util.module_from_spec(spec); spec.loader.exec_module(A) |
| m = W.XLSRNes2NetLA(); m.load() |
| ref = A.Nested_Res2Net_TDNN(Nes_ratio=[8, 8], input_channel=1024, n_output_logits=2, dilation=2, pool_func="mean", SE_ratio=[1]) |
| pre = "Nested_Res2Net_TDNN." |
| sd = {k[len(pre):]: v for k, v in W.load_state().items() if k.startswith(pre)} |
| r = ref.load_state_dict(sd, strict=True); assert not r.missing_keys and not r.unexpected_keys |
| ref = ref.to(m.device).eval() |
|
|
| import pyarrow.parquet as pq, soundfile as sf |
| t = pq.read_table(os.path.join(HERE, "..", "InTheWild", "data", "test-00000-of-00008.parquet"), columns=["audio"]).slice(0, n).to_pylist() |
| x = torch.from_numpy(np.stack([W.pad_fixed(sf.read(io.BytesIO(r["audio"]["bytes"]), dtype="float32")[0]) for r in t])).to(m.device) |
| with torch.no_grad(): |
| feat, ours = m.net(x) |
| theirs = ref(feat) |
| print("ours ", np.round(ours[:, 1].cpu().numpy(), 4)); print("authors", np.round(theirs[:, 1].cpu().numpy(), 4)) |
| print(f"max|logits diff| = {(ours - theirs).abs().max().item():.3g}") |
|
|
