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from contextlib import contextmanager
from distutils.version import LooseVersion
from typing import Dict, List, Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from funasr_detach.frontends.wav_frontend import WavFrontendMel23
from funasr_detach.models.eend.encoder import EENDOLATransformerEncoder
from funasr_detach.models.eend.encoder_decoder_attractor import EncoderDecoderAttractor
from funasr_detach.models.eend.utils.losses import (
standard_loss,
cal_power_loss,
fast_batch_pit_n_speaker_loss,
)
from funasr_detach.models.eend.utils.power import create_powerlabel
from funasr_detach.models.eend.utils.power import generate_mapping_dict
from funasr_detach.train_utils.device_funcs import force_gatherable
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
pass
else:
# Nothing to do if torch<1.6.0
@contextmanager
def autocast(enabled=True):
yield
def pad_attractor(att, max_n_speakers):
C, D = att.shape
if C < max_n_speakers:
att = torch.cat(
[att, torch.zeros(max_n_speakers - C, D).to(torch.float32).to(att.device)],
dim=0,
)
return att
def pad_labels(ts, out_size):
for i, t in enumerate(ts):
if t.shape[1] < out_size:
ts[i] = F.pad(
t, (0, out_size - t.shape[1], 0, 0), mode="constant", value=0.0
)
return ts
def pad_results(ys, out_size):
ys_padded = []
for i, y in enumerate(ys):
if y.shape[1] < out_size:
ys_padded.append(
torch.cat(
[
y,
torch.zeros(y.shape[0], out_size - y.shape[1])
.to(torch.float32)
.to(y.device),
],
dim=1,
)
)
else:
ys_padded.append(y)
return ys_padded
class DiarEENDOLAModel(nn.Module):
"""EEND-OLA diarization model"""
def __init__(
self,
frontend: Optional[WavFrontendMel23],
encoder: EENDOLATransformerEncoder,
encoder_decoder_attractor: EncoderDecoderAttractor,
n_units: int = 256,
max_n_speaker: int = 8,
attractor_loss_weight: float = 1.0,
mapping_dict=None,
**kwargs,
):
super().__init__()
self.frontend = frontend
self.enc = encoder
self.encoder_decoder_attractor = encoder_decoder_attractor
self.attractor_loss_weight = attractor_loss_weight
self.max_n_speaker = max_n_speaker
if mapping_dict is None:
mapping_dict = generate_mapping_dict(max_speaker_num=self.max_n_speaker)
self.mapping_dict = mapping_dict
# PostNet
self.postnet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True)
self.output_layer = nn.Linear(n_units, mapping_dict["oov"] + 1)
def forward_encoder(self, xs, ilens):
xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1)
pad_shape = xs.shape
xs_mask = [torch.ones(ilen).to(xs.device) for ilen in ilens]
xs_mask = torch.nn.utils.rnn.pad_sequence(
xs_mask, batch_first=True, padding_value=0
).unsqueeze(-2)
emb = self.enc(xs, xs_mask)
emb = torch.split(emb.view(pad_shape[0], pad_shape[1], -1), 1, dim=0)
emb = [e[0][:ilen] for e, ilen in zip(emb, ilens)]
return emb
def forward_post_net(self, logits, ilens):
maxlen = torch.max(ilens).to(torch.int).item()
logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1)
logits = nn.utils.rnn.pack_padded_sequence(
logits, ilens.cpu().to(torch.int64), batch_first=True, enforce_sorted=False
)
outputs, (_, _) = self.postnet(logits)
outputs = nn.utils.rnn.pad_packed_sequence(
outputs, batch_first=True, padding_value=-1, total_length=maxlen
)[0]
outputs = [
output[: ilens[i].to(torch.int).item()] for i, output in enumerate(outputs)
]
outputs = [self.output_layer(output) for output in outputs]
return outputs
def forward(
self,
speech: List[torch.Tensor],
speaker_labels: List[torch.Tensor],
orders: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
# Check that batch_size is unified
assert len(speech) == len(speaker_labels), (len(speech), len(speaker_labels))
speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64)
speaker_labels_lengths = torch.tensor(
[spk.shape[-1] for spk in speaker_labels]
).to(torch.int64)
batch_size = len(speech)
# Encoder
encoder_out = self.forward_encoder(speech, speech_lengths)
# Encoder-decoder attractor
attractor_loss, attractors = self.encoder_decoder_attractor(
[e[order] for e, order in zip(encoder_out, orders)], speaker_labels_lengths
)
speaker_logits = [
torch.matmul(e, att.permute(1, 0))
for e, att in zip(encoder_out, attractors)
]
# pit loss
pit_speaker_labels = fast_batch_pit_n_speaker_loss(
speaker_logits, speaker_labels
)
pit_loss = standard_loss(speaker_logits, pit_speaker_labels)
# pse loss
with torch.no_grad():
power_ts = [
create_powerlabel(
label.cpu().numpy(), self.mapping_dict, self.max_n_speaker
).to(encoder_out[0].device, non_blocking=True)
for label in pit_speaker_labels
]
pad_attractors = [pad_attractor(att, self.max_n_speaker) for att in attractors]
pse_speaker_logits = [
torch.matmul(e, pad_att.permute(1, 0))
for e, pad_att in zip(encoder_out, pad_attractors)
]
pse_speaker_logits = self.forward_post_net(pse_speaker_logits, speech_lengths)
pse_loss = cal_power_loss(pse_speaker_logits, power_ts)
loss = pse_loss + pit_loss + self.attractor_loss_weight * attractor_loss
stats = dict()
stats["pse_loss"] = pse_loss.detach()
stats["pit_loss"] = pit_loss.detach()
stats["attractor_loss"] = attractor_loss.detach()
stats["batch_size"] = batch_size
# Collect total loss stats
stats["loss"] = torch.clone(loss.detach())
# force_gatherable: to-device and to-tensor if scalar for DataParallel
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def estimate_sequential(
self,
speech: torch.Tensor,
n_speakers: int = None,
shuffle: bool = True,
threshold: float = 0.5,
**kwargs,
):
speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64)
emb = self.forward_encoder(speech, speech_lengths)
if shuffle:
orders = [np.arange(e.shape[0]) for e in emb]
for order in orders:
np.random.shuffle(order)
attractors, probs = self.encoder_decoder_attractor.estimate(
[
e[torch.from_numpy(order).to(torch.long).to(speech[0].device)]
for e, order in zip(emb, orders)
]
)
else:
attractors, probs = self.encoder_decoder_attractor.estimate(emb)
attractors_active = []
for p, att, e in zip(probs, attractors, emb):
if n_speakers and n_speakers >= 0:
att = att[:n_speakers,]
attractors_active.append(att)
elif threshold is not None:
silence = torch.nonzero(p < threshold)[0]
n_spk = silence[0] if silence.size else None
att = att[:n_spk,]
attractors_active.append(att)
else:
NotImplementedError("n_speakers or threshold has to be given.")
raw_n_speakers = [att.shape[0] for att in attractors_active]
attractors = [
(
pad_attractor(att, self.max_n_speaker)
if att.shape[0] <= self.max_n_speaker
else att[: self.max_n_speaker]
)
for att in attractors_active
]
ys = [torch.matmul(e, att.permute(1, 0)) for e, att in zip(emb, attractors)]
logits = self.forward_post_net(ys, speech_lengths)
ys = [
self.recover_y_from_powerlabel(logit, raw_n_speaker)
for logit, raw_n_speaker in zip(logits, raw_n_speakers)
]
return ys, emb, attractors, raw_n_speakers
def recover_y_from_powerlabel(self, logit, n_speaker):
pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1)
oov_index = torch.where(pred == self.mapping_dict["oov"])[0]
for i in oov_index:
if i > 0:
pred[i] = pred[i - 1]
else:
pred[i] = 0
pred = [self.inv_mapping_func(i) for i in pred]
decisions = [bin(num)[2:].zfill(self.max_n_speaker)[::-1] for num in pred]
decisions = (
torch.from_numpy(
np.stack([np.array([int(i) for i in dec]) for dec in decisions], axis=0)
)
.to(logit.device)
.to(torch.float32)
)
decisions = decisions[:, :n_speaker]
return decisions
def inv_mapping_func(self, label):
if not isinstance(label, int):
label = int(label)
if label in self.mapping_dict["label2dec"].keys():
num = self.mapping_dict["label2dec"][label]
else:
num = -1
return num
def collect_feats(self, **batch: torch.Tensor) -> Dict[str, torch.Tensor]:
pass
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