| """Distillation training v4 — fixed intensity target + 6.25 Hz (G=8, 1 frame per dots.tts token). |
| |
| Changes vs v3: |
| - group_size 8: emotion frame rate 12.5 -> 6.25 Hz (exactly 1 frame per 6.25 Hz LLM token) |
| - intensity target redesigned: |
| tgt(t) = p_emo(t) * clip(rms_raw(t)/GLOBAL_REF, 0, 1) |
| p_emo = max teacher prob over the 6 emotional classes (neutral/other/unk EXCLUDED, |
| so a confidently-neutral clip gets LOW intensity — the v3 gate saturated at ~0.99 |
| because it included neutral and was sqrt-ed) |
| rms_raw: raw 50 Hz RMS envelope, normalized by a GLOBAL reference level |
| (median over the pool of per-utterance P90 RMS) so cross-utterance loudness |
| differences survive (v3 min-max erased them) |
| - intensity loss: MSE + Pearson-correlation + log-std matching (anti mean-collapse; |
| v3 collapsed to a constant ~0.31 because plain MSE to a noisy target rewards the mean) |
| |
| Cache: /tmp/work/teacher_cache_v4/shard_*.pt (states, probs, rms_raw) |
| Same held-out gate: RAVDESS actors 19-24. 4000 AdamW steps, batch 8, fp16, cuda:0. |
| """ |
| import glob, json, math, os, random, sys, time, warnings |
| warnings.filterwarnings("ignore") |
| import logging; logging.disable(logging.CRITICAL) |
| import numpy as np |
| import torch |
| import torch.nn.functional as F |
|
|
| sys.path.insert(0, "/tmp/work/s2st_emotion") |
| from surgical_emotion import SurgicalConfig, SurgicalEmotionEncoder |
|
|
| FUNASR_DIR = "/tmp/work/emotion2vec_plus_base" |
| CACHE_DIR = "/tmp/work/teacher_cache_v4" |
| OUT_DIR = "/tmp/work/surgical_out_v4" |
| BATCH, LR, STEPS, WARMUP, SEED = 8, 3e-4, 4000, 200, 42 |
| VAL_FRAC, FP16, DEV = 0.05, True, "cuda:0" |
| |
| EMO_IDX = [0, 1, 2, 3, 6, 7] |
| GATE_FLOOR = 0.02 |
|
|
|
|
| def load_cache(): |
| shards = sorted(glob.glob(os.path.join(CACHE_DIR, "shard_*.pt"))) |
| assert shards, "no v4 teacher shards found" |
| cache = [] |
| for s in shards: |
| cache.extend(torch.load(s, map_location="cpu", weights_only=False)) |
| return cache |
|
|
|
|
| def make_batch(pool): |
| items = random.sample(pool, min(BATCH, len(pool))) |
| T = max(it["states"].size(0) for it in items) |
| B = len(items) |
| states = torch.zeros(B, T, 768); probs = torch.zeros(B, T, 9); rms = torch.zeros(B, T) |
| lengths = torch.zeros(B, dtype=torch.long) |
| for j, it in enumerate(items): |
| t = it["states"].size(0) |
| states[j, :t] = it["states"].float(); probs[j, :t] = it["probs"].float() |
| rms[j, :t] = it["rms"].float(); lengths[j] = t |
| return states.to(DEV), probs.to(DEV), rms.to(DEV), lengths.to(DEV) |
|
|
|
|
| def masked_pearson(a, b, m): |
| n = m.sum(1).clamp(min=1) |
| ma = (a * m).sum(1) / n; mb = (b * m).sum(1) / n |
| da = (a - ma[:, None]) * m; db = (b - mb[:, None]) * m |
| cov = (da * db).sum(1) |
| va = (da * da).sum(1); vb = (db * db).sum(1) |
| corr = cov / (va * vb).clamp(min=1e-10).sqrt() |
| return corr, va, vb, n |
|
|
|
|
| def intensity_target(tprobs, trms, global_ref): |
| p6 = tprobs[..., EMO_IDX].max(-1).values |
| env = (trms / global_ref).clamp(0.0, 1.0) |
| return torch.clamp(p6, min=GATE_FLOOR) * env |
|
|
|
|
| def compute_loss(enc, batch, global_ref): |
| states, tprobs, trms, lengths = batch |
| out = enc.adapter(states, lengths) |
| embs, flen = out["frame_embs"], out["frame_lengths"] |
| T, T_out = states.size(1), embs.size(1) |
|
|
| up = F.interpolate(embs.transpose(1, 2), size=T, mode="nearest").transpose(1, 2) |
| m = (torch.arange(T, device=states.device)[None, :] < lengths[:, None]).unsqueeze(-1) |
| cos = F.cosine_similarity(up, states, dim=-1).unsqueeze(-1) |
| l_frame = ((1 - cos) * m).sum() / m.sum().clamp(min=1) |
| l_mse = F.mse_loss(F.layer_norm(up, (768,)), F.layer_norm(states, (768,)), reduction="none") |
| l_mse = (l_mse * m).sum() / m.sum().clamp(min=1) |
|
|
| om = (torch.arange(T_out, device=states.device)[None, :] < flen[:, None]).unsqueeze(-1) |
| utt_s = (embs * om).sum(1) / flen.unsqueeze(-1).float() |
| utt_probs_s = torch.softmax(enc.adapter.emotion_head(utt_s.float()), dim=-1) |
| utt_t = (states * m).sum(1) / lengths.unsqueeze(-1).float() |
| l_utt_cos = (1 - F.cosine_similarity(utt_s, utt_t, dim=-1)).mean() |
| utt_probs_t = torch.softmax(enc.teacher.proj(utt_t), dim=-1) |
| l_utt_kl = F.kl_div(torch.log(utt_probs_s.clamp(min=1e-8)), utt_probs_t, reduction="batchmean") |
|
|
| tprobs_g = F.interpolate(tprobs.transpose(1, 2), size=T_out, mode="area").transpose(1, 2) |
| tprobs_g = tprobs_g / tprobs_g.sum(-1, keepdim=True).clamp(min=1e-8) |
| l_frm = F.kl_div(torch.log(out["frame_probs"].clamp(min=1e-8)), tprobs_g, |
| reduction="none").sum(-1) |
| l_frm = (l_frm * om.squeeze(-1)).sum() / om.sum().clamp(min=1) |
|
|
| |
| tgt50 = intensity_target(tprobs, trms, global_ref) |
| tgt = F.interpolate(tgt50[:, None, :], size=T_out, mode="area")[:, 0, :] |
| omf = om.squeeze(-1).float() |
| si = out["frame_intensity"][:, :, 0].float() |
| l_int_mse = (((si - tgt) ** 2) * omf).sum() / omf.sum().clamp(min=1) |
| corr, va, vb, n = masked_pearson(si, tgt, omf) |
| ok = (va > 1e-8) & (vb > 1e-8) |
| if ok.any(): |
| std_s = (va[ok] / n[ok]).clamp(min=1e-12).sqrt() |
| std_t = (vb[ok] / n[ok]).clamp(min=1e-12).sqrt() |
| l_corr = (1 - corr[ok]).mean() |
| l_var = ((std_s / std_t).clamp(1e-4, 1e4).log() ** 2).mean() |
| else: |
| l_corr = si.sum() * 0.0; l_var = si.sum() * 0.0 |
| l_int = l_int_mse + 1.0 * l_corr + 0.5 * l_var |
|
|
| loss = (1.0 * l_frame + 0.5 * l_mse + 0.5 * l_utt_cos |
| + 1.0 * l_utt_kl + 1.0 * l_frm + 1.0 * l_int) |
| return loss, {"frame": l_frame.item(), "mse": l_mse.item(), "utt_cos": l_utt_cos.item(), |
| "utt_kl": l_utt_kl.item(), "frm_kl": l_frm.item(), |
| "int_mse": l_int_mse.item(), |
| "int_corr": (1 - l_corr.item()) if ok.any() else float("nan"), |
| "int_var": l_var.item()} |
|
|
|
|
| def main(): |
| random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED) |
| os.makedirs(OUT_DIR, exist_ok=True) |
| log = open(os.path.join(OUT_DIR, "train_v4.log"), "a", buffering=1) |
| def P(*a): |
| print(*a, flush=True); print(*a, file=log) |
|
|
| P(f"=== distill v4 start {time.strftime('%H:%M:%S')} device={DEV} G=8 (6.25 Hz) ===") |
|
|
| cache = load_cache() |
| P(f"teacher cache: {len(cache)} utts from {len(glob.glob(CACHE_DIR + '/shard_*.pt'))} shards") |
| random.shuffle(cache) |
| n_val = max(16, int(len(cache) * VAL_FRAC)) |
| val, train = cache[:n_val], cache[n_val:] |
| P(f"train={len(train)} val={len(val)}") |
|
|
| p90s = [torch.quantile(it["rms"].float(), 0.90).item() for it in cache if it["rms"].numel()] |
| global_ref = float(np.median(p90s)) |
| P(f"GLOBAL_REF (median P90 rms): {global_ref:.6f}") |
| with open(os.path.join(OUT_DIR, "target_meta.json"), "w") as f: |
| json.dump({"global_ref": global_ref, "emo_idx": EMO_IDX, "gate_floor": GATE_FLOOR, |
| "formula": "tgt = clamp(max(p[emo6]),floor) * clip(rms/GLOBAL_REF,0,1)", |
| "version": 4, "group_size": 8, "frame_rate_hz": 6.25}, f, indent=1) |
|
|
| enc = SurgicalEmotionEncoder.from_pretrained(FUNASR_DIR, device=DEV) |
| enc.cfg.group_size = 8 |
| enc.adapter = type(enc.adapter)(enc.cfg).to(DEV) |
| adapter = enc.adapter |
| opt = torch.optim.AdamW(adapter.parameters(), lr=LR, weight_decay=0.01) |
| sched = torch.optim.lr_scheduler.LambdaLR( |
| opt, lambda s: min((s + 1) / WARMUP, 0.5 * (1 + math.cos(math.pi * s / STEPS)))) |
| scaler = torch.amp.GradScaler("cuda", enabled=FP16) |
|
|
| best_val = float("inf") |
| for step in range(STEPS): |
| adapter.train() |
| with torch.autocast("cuda", dtype=torch.float16, enabled=FP16): |
| loss, parts = compute_loss(enc, make_batch(train), global_ref) |
| opt.zero_grad(set_to_none=True) |
| scaler.scale(loss).backward() |
| scaler.unscale_(opt) |
| torch.nn.utils.clip_grad_norm_(adapter.parameters(), 1.0) |
| scaler.step(opt); scaler.update(); sched.step() |
| if step % 100 == 0: |
| P(f"step {step:4d} loss {loss.item():.4f} " + |
| " ".join(f"{k}={v:.3f}" for k, v in parts.items())) |
| if step % 400 == 0 or step == STEPS - 1: |
| adapter.eval() |
| vlosses, vcorr = [], [] |
| with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16, enabled=FP16): |
| for _ in range(4): |
| _, vp = compute_loss(enc, make_batch(val), global_ref) |
| vlosses.append(sum(v for k, v in vp.items() if k != "int_corr")) |
| vcorr.append(vp["int_corr"]) |
| vloss = float(np.mean(vlosses)) |
| P(f" >> val step {step}: {vloss:.4f} val_int_corr={np.nanmean(vcorr):.3f}") |
| if vloss < best_val: |
| best_val = vloss |
| enc.save_surgical(os.path.join(OUT_DIR, "surgical_emotion_module.pt")) |
| P(" >> saved best") |
|
|
| enc.save_surgical(os.path.join(OUT_DIR, "surgical_emotion_module_last.pt")) |
| P(f"=== done, best_val={best_val:.4f} ===") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
