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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 | """Surgical emotion encoder built on emotion2vec_plus_base.
Surgery performed on the donor model (iic/emotion2vec_plus_base):
1. The frozen backbone (conv frontend + prenet + 8 AltBlocks, 768-d) is kept
intact and exposes its *frame-level* continuous states at 50 Hz
(20 ms/frame, stride 320 @ 16 kHz). The stock pipeline only surfaces the
temporally mean-pooled utterance vector, discarding within-word emotion
dynamics - the surgery re-exposes them.
2. A lightweight trainable implant ("EmotionAdapter") fuses groups of 4
consecutive teacher frames into continuous emotion vectors at 12.5 Hz
(80 ms/frame), preserving intensity dynamics across words.
3. Two distilled heads provide interpretable signals per 12.5 Hz frame:
- emotion probabilities (9 classes, same labels as the teacher)
- a continuous intensity scalar (distilled from teacher confidence)
Output alignment (designed for later dots.tts injection):
dots.tts LLM token rate = 48000 / 7680 = 6.25 Hz (160 ms/token)
emotion frame rate = 12.5 Hz (80 ms/frame) -> exactly 2 emotion
frames per LLM token, aligned on frame boundaries.
Interface:
enc = SurgicalEmotionEncoder.from_pretrained(funasr_model_dir, ckpt_path)
out = enc(wav_16k) # or enc.from_states(...) for cached states
out["frame_embs"] (B, T, 768) @ 12.5 Hz, continuous
out["frame_probs"] (B, T, 9) per-frame emotion probabilities
out["frame_intensity"](B, T, 1) per-frame continuous intensity in [0,1]
out["utt_emb"] (B, 768) utterance-level embedding (mean pooled)
out["utt_probs"] (B, 9)
out["frame_lengths"] (B,) valid frames per sample
"""
from __future__ import annotations
import json
import math
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
TEACHER_SAMPLE_RATE = 16000
TEACHER_FRAME_RATE = 50.0 # samples/frame stride 320 @ 16 kHz (verified empirically)
TEACHER_DIM = 768
N_EMO_LABELS = 9
EMO_LABELS = [
"angry", "disgusted", "fearful", "happy",
"neutral", "other", "sad", "surprised", "<unk>",
]
@dataclass
class SurgicalConfig:
group_size: int = 4 # 50 Hz / 4 = 12.5 Hz
d_model: int = 512 # adapter working width
out_dim: int = 768 # continuous emotion vector dim
n_emotions: int = N_EMO_LABELS
n_conv_layers: int = 3
conv_kernel: int = 9
conv_dilations: tuple = (1, 2, 4)
dropout: float = 0.1
@property
def frame_rate_hz(self) -> float:
return TEACHER_FRAME_RATE / self.group_size
class ConvFusionBlock(nn.Module):
"""Depthwise-separable 1D conv block at teacher frame rate.
Blends neighbouring 50 Hz frames so each pooled 12.5 Hz vector integrates
short-range dynamics before temporal pooling.
"""
def __init__(self, d_model: int, kernel: int, dilation: int, dropout: float):
super().__init__()
padding = dilation * (kernel - 1) // 2
self.dwconv = nn.Conv1d(d_model, d_model, kernel, padding=padding,
dilation=dilation, groups=d_model)
self.pwconv = nn.Conv1d(d_model, 2 * d_model, 1)
self.out_proj = nn.Conv1d(d_model, d_model, 1)
self.norm = nn.LayerNorm(d_model)
self.drop = nn.Dropout(dropout)
def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
# x: (B, T, D); mask: (B, T) True where valid
h = x.transpose(1, 2) # (B, D, T)
h = h * mask.unsqueeze(1).to(h.dtype)
h = self.dwconv(h) * mask.unsqueeze(1).to(h.dtype)
h = self.pwconv(h).transpose(1, 2) # (B, T, 2D)
h = F.glu(h, dim=-1)
h = self.norm(h)
h = h * F.silu(self.out_proj(h.transpose(1, 2)).transpose(1, 2))
h = self.drop(h)
return x + h * mask.unsqueeze(-1).to(h.dtype)
class GroupedSoftPool(nn.Module):
"""Attention pooling over non-overlapping groups of G frames.
The scoring linear is zero-initialised, so at initialisation the pooling
degenerates to a uniform mean over each group (a safe starting point that
distillation then sharpens into selective pooling).
"""
def __init__(self, d_model: int, group_size: int):
super().__init__()
self.group_size = group_size
self.score = nn.Linear(d_model, 1)
nn.init.zeros_(self.score.weight)
nn.init.zeros_(self.score.bias)
def forward(self, x: torch.Tensor, lengths: torch.Tensor):
# x: (B, T, D), lengths: (B,) valid frames at teacher rate
B, T, D = x.shape
G = self.group_size
T_out = (T + G - 1) // G
pad = T_out * G - T
if pad:
x = F.pad(x, (0, 0, 0, pad))
mask = torch.arange(T_out * G, device=x.device)[None, :] < lengths[:, None]
else:
mask = torch.arange(T, device=x.device)[None, :] < lengths[:, None]
x = x.view(B, T_out, G, D)
mask = mask.view(B, T_out, G)
scores = self.score(x).squeeze(-1) # (B, T_out, G)
scores = scores.masked_fill(~mask, float("-inf"))
attn = torch.softmax(scores, dim=-1)
attn = torch.nan_to_num(attn) # all-masked groups
out = torch.einsum("btg,btgd->btd", attn, x) # (B, T_out, D)
out_lengths = torch.clamp((lengths + G - 1) // G, min=1)
return out, out_lengths
class EmotionAdapter(nn.Module):
"""The trainable implant: 50 Hz teacher states -> 12.5 Hz emotion vectors."""
def __init__(self, cfg: SurgicalConfig):
super().__init__()
self.cfg = cfg
self.in_norm = nn.LayerNorm(TEACHER_DIM)
self.in_proj = nn.Linear(TEACHER_DIM, cfg.d_model)
self.fusion = nn.ModuleList([
ConvFusionBlock(cfg.d_model, cfg.conv_kernel, d, cfg.dropout)
for d in cfg.conv_dilations[: cfg.n_conv_layers]
])
self.pool = GroupedSoftPool(cfg.d_model, cfg.group_size)
self.out_norm = nn.LayerNorm(cfg.d_model)
self.out_proj = nn.Linear(cfg.d_model, cfg.out_dim)
self.emotion_head = nn.Linear(cfg.out_dim, cfg.n_emotions)
self.intensity_head = nn.Linear(cfg.out_dim, 1)
def forward(self, states: torch.Tensor, lengths: torch.Tensor) -> dict:
"""states: (B, T, 768) teacher frame states; lengths: (B,) valid T."""
mask = torch.arange(states.size(1), device=states.device)[None, :] < lengths[:, None]
x = self.in_norm(states)
x = self.in_proj(x)
x = F.gelu(x) * mask.unsqueeze(-1).to(x.dtype)
for block in self.fusion:
x = block(x, mask)
x, out_lengths = self.pool(x, lengths)
x = self.out_norm(x)
embs = self.out_proj(x) # (B, T*, 768) @12.5Hz
omask = torch.arange(embs.size(1), device=embs.device)[None, :] < out_lengths[:, None]
embs = embs * omask.unsqueeze(-1).to(embs.dtype)
probs = torch.softmax(self.emotion_head(embs), dim=-1)
intensity = torch.sigmoid(self.intensity_head(embs)) # (B, T*, 1)
return {
"frame_embs": embs,
"frame_probs": probs,
"frame_intensity": intensity,
"frame_lengths": out_lengths,
}
class SurgicalEmotionEncoder(nn.Module):
"""Frozen emotion2vec_plus_base teacher + surgical adapter."""
def __init__(self, teacher_model, cfg: SurgicalConfig):
super().__init__()
self.cfg = cfg
self.teacher = teacher_model
self.teacher.eval()
for p in self.teacher.parameters():
p.requires_grad = False
self.adapter = EmotionAdapter(cfg)
# ---------- teacher extraction ----------
@torch.no_grad()
def extract_teacher_states(self, wav: torch.Tensor, wav_lengths: torch.Tensor):
"""wav: (B, S) float @16 kHz already loudness-normalised per sample;
wav_lengths: (B,). Returns states (B, T, 768) fp32 and lengths (B,)."""
outs = []
for i in range(wav.size(0)):
src = F.layer_norm(wav[i : i + 1, : int(wav_lengths[i])], (int(wav_lengths[i]),))
out = self.teacher.extract_features(src.to(wav.device), padding_mask=None, mask=False)
outs.append(out["x"].squeeze(0))
lengths = torch.tensor([o.size(0) for o in outs], device=wav.device)
T = int(lengths.max().item())
states = torch.zeros(wav.size(0), T, TEACHER_DIM, device=wav.device)
for i, o in enumerate(outs):
states[i, : o.size(0)] = o
return states.float(), lengths
@torch.no_grad()
def teacher_frame_probs(self, states: torch.Tensor, lengths: torch.Tensor):
"""Per-frame teacher probabilities + intensity proxy (max prob)."""
logits = self.teacher.proj(states) # (B, T, 9)
probs = torch.softmax(logits, dim=-1)
inten = probs[..., : N_EMO_LABELS - 1].max(dim=-1, keepdim=True).values
return probs, inten
# ---------- full pipeline ----------
def forward(self, wav: torch.Tensor, wav_lengths: torch.Tensor) -> dict:
states, lengths = self.extract_teacher_states(wav, wav_lengths)
return self.from_states(states, lengths)
def from_states(self, states: torch.Tensor, lengths: torch.Tensor) -> dict:
out = self.adapter(states, lengths)
omask = (
torch.arange(out["frame_embs"].size(1), device=states.device)[None, :]
< out["frame_lengths"][:, None]
).unsqueeze(-1).to(out["frame_embs"].dtype)
embs = out["frame_embs"] * omask
utt_emb = (embs.sum(dim=1) / out["frame_lengths"].unsqueeze(-1)).to(embs.dtype)
utt_logits = self.adapter.emotion_head(utt_emb)
utt_probs = torch.softmax(utt_logits, dim=-1)
out["utt_emb"] = utt_emb
out["utt_probs"] = utt_probs
return out
# ---------- serialization ----------
def save_surgical(self, path: str):
payload = {
"format_version": 1,
"module_type": "surgical_emotion_encoder",
"teacher_source": "iic/emotion2vec_plus_base",
"sample_rate": TEACHER_SAMPLE_RATE,
"teacher_frame_rate_hz": TEACHER_FRAME_RATE,
"frame_rate_hz": self.cfg.frame_rate_hz,
"labels": EMO_LABELS,
"config": {
"group_size": self.cfg.group_size,
"d_model": self.cfg.d_model,
"out_dim": self.cfg.out_dim,
"n_emotions": self.cfg.n_emotions,
"n_conv_layers": self.cfg.n_conv_layers,
"conv_kernel": self.cfg.conv_kernel,
"conv_dilations": list(self.cfg.conv_dilations),
"dropout": self.cfg.dropout,
},
"adapter_state": {k: v.cpu() for k, v in self.adapter.state_dict().items()},
}
torch.save(payload, path)
@classmethod
def from_pretrained(cls, funasr_model_dir: str, ckpt_path: str | None = None,
device: str = "cuda:0"):
import logging
logging.disable(logging.CRITICAL)
from funasr import AutoModel
teacher_wrapper = AutoModel(model=funasr_model_dir, trust_remote_code=False,
device=device, disable_update=True)
teacher = teacher_wrapper.model
cfg = SurgicalConfig()
enc = cls(teacher, cfg).to(device)
if ckpt_path is not None:
payload = torch.load(ckpt_path, map_location=device, weights_only=False)
enc.adapter.load_state_dict(payload["adapter_state"])
enc.eval()
return enc
def resample_to_16k(wav: torch.Tensor, orig_sr: int) -> torch.Tensor:
if orig_sr == TEACHER_SAMPLE_RATE:
return wav
import torchaudio
return torchaudio.functional.resample(wav, orig_sr, TEACHER_SAMPLE_RATE)
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