File size: 10,575 Bytes
0bb1236 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | """Self-distillation training for the surgical emotion adapter.
Teacher = frozen emotion2vec_plus_base (frame states @ 50 Hz + pooled classifier).
Student = EmotionAdapter producing 12.5 Hz continuous vectors + heads.
Losses (no emotion labels required):
L_frame : cosine + LN-MSE between up-sampled student vectors and teacher states
L_utt_cos : cosine of pooled embeddings
L_utt_kl : KL(teacher pooled probs || student pooled probs)
L_frm_kl : per-12.5Hz-frame KL between teacher group-mean probs and student probs
L_inten : MSE between student intensity and teacher max-prob proxy
Data: RAVDESS Actor_01/02 (already downloaded) + any 16 kHz wav tree.
Teacher states are precomputed once; epochs iterate over the cache.
"""
from __future__ import annotations
import glob
import json
import math
import os
import random
import sys
import time
import numpy as np
import torch
import torch.nn.functional as F
import torchaudio
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from surgical_emotion import (
EMO_LABELS, SurgicalConfig, SurgicalEmotionEncoder,
)
FUNASR_DIR = "/tmp/work/emotion2vec_plus_base"
RAVDESS_DIR = "/tmp/work/ravdess_kag"
OUT_DIR = "/tmp/work/surgical_out"
MAX_SEC = 12.0
BATCH = 8
LR = 3e-4
STEPS = 900
WARMUP = 50
SEED = 42
VAL_FRAC = 0.1
FP16 = True
MAX_TRAIN_FILES = 900
TRAIN_ACTORS = set(range(1, 19)) # actors 1..18 for train/val
TEST_ACTORS = set(range(19, 25)) # actors 19..24 held out for eval
def _actor_of(path: str) -> int:
try:
return int(os.path.basename(path).split("-")[-1].split(".")[0])
except Exception:
return 0
def _is_speech(path: str) -> bool:
# filename: modality-channel-emotion-intensity-statement-repetition-actor
try:
return os.path.basename(path).split("-")[1] == "01" # vocal channel = speech
except Exception:
return False
def gather_wavs():
files = sorted(glob.glob(os.path.join(RAVDESS_DIR, "**", "*.wav"), recursive=True))
files = [f for f in files if _is_speech(f) and _actor_of(f) in TRAIN_ACTORS]
random.shuffle(files)
files = files[:MAX_TRAIN_FILES]
if not files: # fallback: any wav on the machine for a smoke run
files = [FUNASR_DIR + "/example/test.wav"]
return files
def ravdess_label(path: str) -> str:
# modality-vocality-emotion-intensity-statement-repetition-actor.wav
emo = {"01": "neutral", "02": "calm", "03": "happy", "04": "sad",
"05": "angry", "06": "fearful", "07": "disgust", "08": "surprised"}
try:
return emo.get(os.path.basename(path).split("-")[2], "?")
except Exception:
return "?"
def load_wav(path: str):
wav, sr = torchaudio.load(path)
wav = wav.mean(dim=0)
if sr != 16000:
wav = torchaudio.functional.resample(wav, sr, 16000)
return wav
def rms_envelope_50hz(wav: torch.Tensor, T_frames: int) -> torch.Tensor:
"""Short-time RMS at ~50 Hz, smoothed and per-utterance min-maxed to [0,1]."""
n = wav.numel()
frames = torch.zeros(T_frames)
for i in range(T_frames):
seg = wav[i * 320 : (i + 1) * 320]
if seg.numel() == 0:
frames[i] = frames[i - 1] if i else 0.0
else:
frames[i] = float(seg.pow(2).mean().sqrt())
if T_frames >= 9:
k = torch.ones(9) / 9.0
frames = torch.nn.functional.conv1d(
frames[None, None], k[None, None], padding=4)[0, 0]
lo, hi = frames.min(), frames.max()
if hi - lo < 1e-6:
return torch.full_like(frames, 0.5)
return (frames - lo) / (hi - lo)
def main():
random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)
os.makedirs(OUT_DIR, exist_ok=True)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
log = open(os.path.join(OUT_DIR, "train.log"), "a", buffering=1)
def P(*a):
print(*a); print(*a, file=log)
P(f"=== distill start {time.strftime('%H:%M:%S')} device={device} ===")
enc = SurgicalEmotionEncoder.from_pretrained(FUNASR_DIR, device=device)
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)
# ---------------- data ----------------
files = gather_wavs()
P(f"wav files: {len(files)}")
meta = [(f, ravdess_label(f)) for f in files]
# ---------------- precompute teacher states ----------------
cache = []
t0 = time.time()
for i, (f, lab) in enumerate(meta):
try:
wav = load_wav(f)
except Exception:
continue
if wav.numel() < 8000: # <0.5s skip
continue
if wav.numel() > MAX_SEC * 16000:
wav = wav[: int(MAX_SEC * 16000)]
wav = wav / (wav.abs().max().clamp(min=1e-4))
states, lengths = enc.extract_teacher_states(wav[None].to(device), torch.tensor([wav.numel()], device=device))
probs, inten = enc.teacher_frame_probs(states, lengths)
T = int(lengths[0].item())
rms = rms_envelope_50hz(wav, T)
# composite intensity target: confidence-gated expression energy
inten = (inten.squeeze(0)[:, 0].cpu().clamp(min=0, max=1).sqrt() * rms).unsqueeze(-1)
cache.append({
"states": states.squeeze(0).half().cpu(),
"probs": probs.squeeze(0).half().cpu(),
"inten": inten.half().cpu(),
"label": lab, "file": os.path.basename(f),
})
if (i + 1) % 48 == 0:
P(f" teacher {i+1}/{len(meta)} ({time.time()-t0:.0f}s)")
P(f"teacher cache: {len(cache)} utts, {time.time()-t0:.0f}s")
random.shuffle(cache)
n_val = max(8, int(len(cache) * VAL_FRAC))
val, train = cache[:n_val], cache[n_val:]
P(f"train={len(train)} val={len(val)}")
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)
inten = torch.zeros(B, T, 1)
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()
inten[j, :t] = it["inten"].float()
lengths[j] = t
return states.to(device), probs.to(device), inten.to(device), lengths.to(device)
def compute_loss(batch, train_mode):
states, tprobs, tinten, lengths = batch
out = adapter(states, lengths) if train_mode else adapter(states, lengths)
G = SurgicalConfig().group_size
embs, flen = out["frame_embs"], out["frame_lengths"]
T = states.size(1)
T_out = embs.size(1)
# 1) frame reconstruction: upsample student to teacher rate
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)
# 2) utterance losses
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")
# 3) frame-level classification distillation (group-mean teacher probs)
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)
# 4) intensity distillation
tinten_g = F.interpolate(tinten.transpose(1, 2), size=T_out, mode="area").transpose(1, 2)
l_int = (((out["frame_intensity"] - tinten_g) ** 2) * om).sum() / om.sum().clamp(min=1)
loss = (1.0 * l_frame + 0.5 * l_mse + 0.5 * l_utt_cos
+ 1.0 * l_utt_kl + 1.0 * l_frm + 0.5 * 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(), "inten": l_int.item(),
}
# ---------------- train ----------------
best_val = float("inf")
step = 0
while step < STEPS:
adapter.train()
batch = make_batch(train)
with torch.autocast("cuda", dtype=torch.float16, enabled=FP16):
loss, parts = compute_loss(batch, True)
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 % 20 == 0:
P(f"step {step:4d} loss {loss.item():.4f} " +
" ".join(f"{k}={v:.3f}" for k, v in parts.items()))
if step % 100 == 0 or step == STEPS - 1:
adapter.eval()
with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16, enabled=FP16):
vb = make_batch(val)
vloss, vparts = compute_loss(vb, False)
P(f" >> val step {step}: {vloss.item():.4f} " +
" ".join(f"{k}={v:.3f}" for k, v in vparts.items()))
if vloss.item() < best_val:
best_val = vloss.item()
enc.save_surgical(os.path.join(OUT_DIR, "surgical_emotion_module.pt"))
P(" >> saved best")
step += 1
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()
|