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Merge pull request #204 from alibaba-damo-academy/dev_wjm

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0
funasr/modules/eend_ola/__init__.py Normal file
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funasr/modules/eend_ola/encoder.py Normal file
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import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class MultiHeadSelfAttention(nn.Module):
def __init__(self, n_units, h=8, dropout_rate=0.1):
super(MultiHeadSelfAttention, self).__init__()
self.linearQ = nn.Linear(n_units, n_units)
self.linearK = nn.Linear(n_units, n_units)
self.linearV = nn.Linear(n_units, n_units)
self.linearO = nn.Linear(n_units, n_units)
self.d_k = n_units // h
self.h = h
self.dropout = nn.Dropout(dropout_rate)
def __call__(self, x, batch_size, x_mask):
q = self.linearQ(x).view(batch_size, -1, self.h, self.d_k)
k = self.linearK(x).view(batch_size, -1, self.h, self.d_k)
v = self.linearV(x).view(batch_size, -1, self.h, self.d_k)
scores = torch.matmul(
q.permute(0, 2, 1, 3), k.permute(0, 2, 3, 1)) / math.sqrt(self.d_k)
if x_mask is not None:
x_mask = x_mask.unsqueeze(1)
scores = scores.masked_fill(x_mask == 0, -1e9)
self.att = F.softmax(scores, dim=3)
p_att = self.dropout(self.att)
x = torch.matmul(p_att, v.permute(0, 2, 1, 3))
x = x.permute(0, 2, 1, 3).contiguous().view(-1, self.h * self.d_k)
return self.linearO(x)
class PositionwiseFeedForward(nn.Module):
def __init__(self, n_units, d_units, dropout_rate):
super(PositionwiseFeedForward, self).__init__()
self.linear1 = nn.Linear(n_units, d_units)
self.linear2 = nn.Linear(d_units, n_units)
self.dropout = nn.Dropout(dropout_rate)
def __call__(self, x):
return self.linear2(self.dropout(F.relu(self.linear1(x))))
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.reverse = reverse
self.xscale = math.sqrt(self.d_model)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x):
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = torch.zeros(x.size(1), self.d_model)
if self.reverse:
position = torch.arange(
x.size(1) - 1, -1, -1.0, dtype=torch.float32
).unsqueeze(1)
else:
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
self.extend_pe(x)
x = x * self.xscale + self.pe[:, : x.size(1)]
return self.dropout(x)
class TransformerEncoder(nn.Module):
def __init__(self, idim, n_layers, n_units,
e_units=2048, h=8, dropout_rate=0.1, use_pos_emb=False):
super(TransformerEncoder, self).__init__()
self.lnorm_in = nn.LayerNorm(n_units)
self.n_layers = n_layers
self.dropout = nn.Dropout(dropout_rate)
for i in range(n_layers):
setattr(self, '{}{:d}'.format("lnorm1_", i),
nn.LayerNorm(n_units))
setattr(self, '{}{:d}'.format("self_att_", i),
MultiHeadSelfAttention(n_units, h))
setattr(self, '{}{:d}'.format("lnorm2_", i),
nn.LayerNorm(n_units))
setattr(self, '{}{:d}'.format("ff_", i),
PositionwiseFeedForward(n_units, e_units, dropout_rate))
self.lnorm_out = nn.LayerNorm(n_units)
if use_pos_emb:
self.pos_enc = torch.nn.Sequential(
torch.nn.Linear(idim, n_units),
torch.nn.LayerNorm(n_units),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
PositionalEncoding(n_units, dropout_rate),
)
else:
self.linear_in = nn.Linear(idim, n_units)
self.pos_enc = None
def __call__(self, x, x_mask=None):
BT_size = x.shape[0] * x.shape[1]
if self.pos_enc is not None:
e = self.pos_enc(x)
e = e.view(BT_size, -1)
else:
e = self.linear_in(x.reshape(BT_size, -1))
for i in range(self.n_layers):
e = getattr(self, '{}{:d}'.format("lnorm1_", i))(e)
s = getattr(self, '{}{:d}'.format("self_att_", i))(e, x.shape[0], x_mask)
e = e + self.dropout(s)
e = getattr(self, '{}{:d}'.format("lnorm2_", i))(e)
s = getattr(self, '{}{:d}'.format("ff_", i))(e)
e = e + self.dropout(s)
return self.lnorm_out(e)

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funasr/modules/eend_ola/encoder_decoder_attractor.py Normal file
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import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class EncoderDecoderAttractor(nn.Module):
def __init__(self, n_units, encoder_dropout=0.1, decoder_dropout=0.1):
super(EncoderDecoderAttractor, self).__init__()
self.enc0_dropout = nn.Dropout(encoder_dropout)
self.encoder = nn.LSTM(n_units, n_units, 1, batch_first=True, dropout=encoder_dropout)
self.dec0_dropout = nn.Dropout(decoder_dropout)
self.decoder = nn.LSTM(n_units, n_units, 1, batch_first=True, dropout=decoder_dropout)
self.counter = nn.Linear(n_units, 1)
self.n_units = n_units
def forward_core(self, xs, zeros):
ilens = torch.from_numpy(np.array([x.shape[0] for x in xs])).to(torch.float32).to(xs[0].device)
xs = [self.enc0_dropout(x) for x in xs]
xs = nn.utils.rnn.pad_sequence(xs, batch_first=True, padding_value=-1)
xs = nn.utils.rnn.pack_padded_sequence(xs, ilens, batch_first=True, enforce_sorted=False)
_, (hx, cx) = self.encoder(xs)
zlens = torch.from_numpy(np.array([z.shape[0] for z in zeros])).to(torch.float32).to(zeros[0].device)
max_zlen = torch.max(zlens).to(torch.int).item()
zeros = [self.enc0_dropout(z) for z in zeros]
zeros = nn.utils.rnn.pad_sequence(zeros, batch_first=True, padding_value=-1)
zeros = nn.utils.rnn.pack_padded_sequence(zeros, zlens, batch_first=True, enforce_sorted=False)
attractors, (_, _) = self.decoder(zeros, (hx, cx))
attractors = nn.utils.rnn.pad_packed_sequence(attractors, batch_first=True, padding_value=-1,
total_length=max_zlen)[0]
attractors = [att[:zlens[i].to(torch.int).item()] for i, att in enumerate(attractors)]
return attractors
def forward(self, xs, n_speakers):
zeros = [torch.zeros(n_spk + 1, self.n_units).to(torch.float32).to(xs[0].device) for n_spk in n_speakers]
attractors = self.forward_core(xs, zeros)
labels = torch.cat([torch.from_numpy(np.array([[1] * n_spk + [0]], np.float32)) for n_spk in n_speakers], dim=1)
labels = labels.to(xs[0].device)
logit = torch.cat([self.counter(att).view(-1, n_spk + 1) for att, n_spk in zip(attractors, n_speakers)], dim=1)
loss = F.binary_cross_entropy(torch.sigmoid(logit), labels)
attractors = [att[slice(0, att.shape[0] - 1)] for att in attractors]
return loss, attractors
def estimate(self, xs, max_n_speakers=15):
zeros = [torch.zeros(max_n_speakers, self.n_units).to(torch.float32).to(xs[0].device) for _ in xs]
attractors = self.forward_core(xs, zeros)
probs = [torch.sigmoid(torch.flatten(self.counter(att))) for att in attractors]
return attractors, probs

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funasr/modules/eend_ola/utils/losses.py Normal file
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import numpy as np
import torch
import torch.nn.functional as F
from itertools import permutations
from torch import nn
def standard_loss(ys, ts, label_delay=0):
losses = [F.binary_cross_entropy(torch.sigmoid(y), t) * len(y) for y, t in zip(ys, ts)]
loss = torch.sum(torch.stack(losses))
n_frames = torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))).to(torch.float32).to(ys[0].device)
loss = loss / n_frames
return loss
def batch_pit_n_speaker_loss(ys, ts, n_speakers_list):
max_n_speakers = ts[0].shape[1]
olens = [y.shape[0] for y in ys]
ys = nn.utils.rnn.pad_sequence(ys, batch_first=True, padding_value=-1)
ys_mask = [torch.ones(olen).to(ys.device) for olen in olens]
ys_mask = torch.nn.utils.rnn.pad_sequence(ys_mask, batch_first=True, padding_value=0).unsqueeze(-1)
losses = []
for shift in range(max_n_speakers):
ts_roll = [torch.roll(t, -shift, dims=1) for t in ts]
ts_roll = nn.utils.rnn.pad_sequence(ts_roll, batch_first=True, padding_value=-1)
loss = F.binary_cross_entropy(torch.sigmoid(ys), ts_roll, reduction='none')
if ys_mask is not None:
loss = loss * ys_mask
loss = torch.sum(loss, dim=1)
losses.append(loss)
losses = torch.stack(losses, dim=2)
perms = np.array(list(permutations(range(max_n_speakers)))).astype(np.float32)
perms = torch.from_numpy(perms).to(losses.device)
y_ind = torch.arange(max_n_speakers, dtype=torch.float32, device=losses.device)
t_inds = torch.fmod(perms - y_ind, max_n_speakers).to(torch.long)
losses_perm = []
for t_ind in t_inds:
losses_perm.append(
torch.mean(losses[:, y_ind.to(torch.long), t_ind], dim=1))
losses_perm = torch.stack(losses_perm, dim=1)
def select_perm_indices(num, max_num):
perms = list(permutations(range(max_num)))
sub_perms = list(permutations(range(num)))
return [
[x[:num] for x in perms].index(perm)
for perm in sub_perms]
masks = torch.full_like(losses_perm, device=losses.device, fill_value=float('inf'))
for i, t in enumerate(ts):
n_speakers = n_speakers_list[i]
indices = select_perm_indices(n_speakers, max_n_speakers)
masks[i, indices] = 0
losses_perm += masks
min_loss = torch.sum(torch.min(losses_perm, dim=1)[0])
n_frames = torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))).to(losses.device)
min_loss = min_loss / n_frames
min_indices = torch.argmin(losses_perm, dim=1)
labels_perm = [t[:, perms[idx].to(torch.long)] for t, idx in zip(ts, min_indices)]
labels_perm = [t[:, :n_speakers] for t, n_speakers in zip(labels_perm, n_speakers_list)]
return min_loss, labels_perm

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funasr/modules/eend_ola/utils/power.py Normal file
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import numpy as np
import torch
import torch.multiprocessing
import torch.nn.functional as F
from itertools import combinations
from itertools import permutations
def generate_mapping_dict(max_speaker_num=6, max_olp_speaker_num=3):
all_kinds = []
all_kinds.append(0)
for i in range(max_olp_speaker_num):
selected_num = i + 1
coms = np.array(list(combinations(np.arange(max_speaker_num), selected_num)))
for com in coms:
tmp = np.zeros(max_speaker_num)
tmp[com] = 1
item = int(raw_dec_trans(tmp.reshape(1, -1), max_speaker_num)[0])
all_kinds.append(item)
all_kinds_order = sorted(all_kinds)
mapping_dict = {}
mapping_dict['dec2label'] = {}
mapping_dict['label2dec'] = {}
for i in range(len(all_kinds_order)):
dec = all_kinds_order[i]
mapping_dict['dec2label'][dec] = i
mapping_dict['label2dec'][i] = dec
oov_id = len(all_kinds_order)
mapping_dict['oov'] = oov_id
return mapping_dict
def raw_dec_trans(x, max_speaker_num):
num_list = []
for i in range(max_speaker_num):
num_list.append(x[:, i])
base = 1
T = x.shape[0]
res = np.zeros((T))
for num in num_list:
res += num * base
base = base * 2
return res
def mapping_func(num, mapping_dict):
if num in mapping_dict['dec2label'].keys():
label = mapping_dict['dec2label'][num]
else:
label = mapping_dict['oov']
return label
def dec_trans(x, max_speaker_num, mapping_dict):
num_list = []
for i in range(max_speaker_num):
num_list.append(x[:, i])
base = 1
T = x.shape[0]
res = np.zeros((T))
for num in num_list:
res += num * base
base = base * 2
res = np.array([mapping_func(i, mapping_dict) for i in res])
return res
def create_powerlabel(label, mapping_dict, max_speaker_num=6, max_olp_speaker_num=3):
T, C = label.shape
padding_label = np.zeros((T, max_speaker_num))
padding_label[:, :C] = label
out_label = dec_trans(padding_label, max_speaker_num, mapping_dict)
out_label = torch.from_numpy(out_label)
return out_label
def generate_perm_pse(label, n_speaker, mapping_dict, max_speaker_num, max_olp_speaker_num=3):
perms = np.array(list(permutations(range(n_speaker)))).astype(np.float32)
perms = torch.from_numpy(perms).to(label.device).to(torch.int64)
perm_labels = [label[:, perm] for perm in perms]
perm_pse_labels = [create_powerlabel(perm_label.cpu().numpy(), mapping_dict, max_speaker_num).
to(perm_label.device, non_blocking=True) for perm_label in perm_labels]
return perm_labels, perm_pse_labels
def generate_min_pse(label, n_speaker, mapping_dict, max_speaker_num, pse_logit, max_olp_speaker_num=3):
perm_labels, perm_pse_labels = generate_perm_pse(label, n_speaker, mapping_dict, max_speaker_num,
max_olp_speaker_num=max_olp_speaker_num)
losses = [F.cross_entropy(input=pse_logit, target=perm_pse_label.to(torch.long)) * len(pse_logit)
for perm_pse_label in perm_pse_labels]
loss = torch.stack(losses)
min_index = torch.argmin(loss)
selected_perm_label, selected_pse_label = perm_labels[min_index], perm_pse_labels[min_index]
return selected_perm_label, selected_pse_label

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funasr/modules/eend_ola/utils/report.py Normal file
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import copy
import numpy as np
import time
import torch
from eend.utils.power import create_powerlabel
from itertools import combinations
metrics = [
('diarization_error', 'speaker_scored', 'DER'),
('speech_miss', 'speech_scored', 'SAD_MR'),
('speech_falarm', 'speech_scored', 'SAD_FR'),
('speaker_miss', 'speaker_scored', 'MI'),
('speaker_falarm', 'speaker_scored', 'FA'),
('speaker_error', 'speaker_scored', 'CF'),
('correct', 'frames', 'accuracy')
]
def recover_prediction(y, n_speaker):
if n_speaker <= 1:
return y
elif n_speaker == 2:
com_index = torch.from_numpy(
np.array(list(combinations(np.arange(n_speaker), 2)))).to(
y.dtype)
num_coms = com_index.shape[0]
y_single = y[:, :-num_coms]
y_olp = y[:, -num_coms:]
olp_map_index = torch.where(y_olp > 0.5)
olp_map_index = torch.stack(olp_map_index, dim=1)
com_map_index = com_index[olp_map_index[:, -1]]
speaker_map_index = torch.from_numpy(np.array(com_map_index)).view(-1).to(torch.int64)
frame_map_index = olp_map_index[:, 0][:, None].repeat([1, 2]).view(-1).to(
torch.int64)
y_single[frame_map_index] = 0
y_single[frame_map_index, speaker_map_index] = 1
return y_single
else:
olp2_com_index = torch.from_numpy(np.array(list(combinations(np.arange(n_speaker), 2)))).to(y.dtype)
olp2_num_coms = olp2_com_index.shape[0]
olp3_com_index = torch.from_numpy(np.array(list(combinations(np.arange(n_speaker), 3)))).to(y.dtype)
olp3_num_coms = olp3_com_index.shape[0]
y_single = y[:, :n_speaker]
y_olp2 = y[:, n_speaker:n_speaker + olp2_num_coms]
y_olp3 = y[:, -olp3_num_coms:]
olp3_map_index = torch.where(y_olp3 > 0.5)
olp3_map_index = torch.stack(olp3_map_index, dim=1)
olp3_com_map_index = olp3_com_index[olp3_map_index[:, -1]]
olp3_speaker_map_index = torch.from_numpy(np.array(olp3_com_map_index)).view(-1).to(torch.int64)
olp3_frame_map_index = olp3_map_index[:, 0][:, None].repeat([1, 3]).view(-1).to(torch.int64)
y_single[olp3_frame_map_index] = 0
y_single[olp3_frame_map_index, olp3_speaker_map_index] = 1
y_olp2[olp3_frame_map_index] = 0
olp2_map_index = torch.where(y_olp2 > 0.5)
olp2_map_index = torch.stack(olp2_map_index, dim=1)
olp2_com_map_index = olp2_com_index[olp2_map_index[:, -1]]
olp2_speaker_map_index = torch.from_numpy(np.array(olp2_com_map_index)).view(-1).to(torch.int64)
olp2_frame_map_index = olp2_map_index[:, 0][:, None].repeat([1, 2]).view(-1).to(torch.int64)
y_single[olp2_frame_map_index] = 0
y_single[olp2_frame_map_index, olp2_speaker_map_index] = 1
return y_single
class PowerReporter():
def __init__(self, valid_data_loader, mapping_dict, max_n_speaker):
valid_data_loader_cp = copy.deepcopy(valid_data_loader)
self.valid_data_loader = valid_data_loader_cp
del valid_data_loader
self.mapping_dict = mapping_dict
self.max_n_speaker = max_n_speaker
def report(self, model, eidx, device):
self.report_val(model, eidx, device)
def report_val(self, model, eidx, device):
model.eval()
ud_valid_start = time.time()
valid_res, valid_loss, stats_keys, vad_valid_accuracy = self.report_core(model, self.valid_data_loader, device)
# Epoch Display
valid_der = valid_res['diarization_error'] / valid_res['speaker_scored']
valid_accuracy = valid_res['correct'].to(torch.float32) / valid_res['frames'] * 100
vad_valid_accuracy = vad_valid_accuracy * 100
print('Epoch ', eidx + 1, 'Valid Loss ', valid_loss, 'Valid_DER %.5f' % valid_der,
'Valid_Accuracy %.5f%% ' % valid_accuracy, 'VAD_Valid_Accuracy %.5f%% ' % vad_valid_accuracy)
ud_valid = (time.time() - ud_valid_start) / 60.
print('Valid cost time ... ', ud_valid)
def inv_mapping_func(self, label, mapping_dict):
if not isinstance(label, int):
label = int(label)
if label in mapping_dict['label2dec'].keys():
num = mapping_dict['label2dec'][label]
else:
num = -1
return num
def report_core(self, model, data_loader, device):
res = {}
for item in metrics:
res[item[0]] = 0.
res[item[1]] = 0.
with torch.no_grad():
loss_s = 0.
uidx = 0
for xs, ts, orders in data_loader:
xs = [x.to(device) for x in xs]
ts = [t.to(device) for t in ts]
orders = [o.to(device) for o in orders]
loss, pit_loss, mpit_loss, att_loss, ys, logits, labels, attractors = model(xs, ts, orders)
loss_s += loss.item()
uidx += 1
for logit, t, att in zip(logits, labels, attractors):
pred = torch.argmax(torch.softmax(logit, dim=-1), dim=-1) # (T, )
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, self.mapping_dict) 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(att.device).to(
torch.float32)
decisions = decisions[:, :att.shape[0]]
stats = self.calc_diarization_error(decisions, t)
res['speaker_scored'] += stats['speaker_scored']
res['speech_scored'] += stats['speech_scored']
res['frames'] += stats['frames']
for item in metrics:
res[item[0]] += stats[item[0]]
loss_s /= uidx
vad_acc = 0
return res, loss_s, stats.keys(), vad_acc
def calc_diarization_error(self, decisions, label, label_delay=0):
label = label[:len(label) - label_delay, ...]
n_ref = torch.sum(label, dim=-1)
n_sys = torch.sum(decisions, dim=-1)
res = {}
res['speech_scored'] = torch.sum(n_ref > 0)
res['speech_miss'] = torch.sum((n_ref > 0) & (n_sys == 0))
res['speech_falarm'] = torch.sum((n_ref == 0) & (n_sys > 0))
res['speaker_scored'] = torch.sum(n_ref)
res['speaker_miss'] = torch.sum(torch.max(n_ref - n_sys, torch.zeros_like(n_ref)))
res['speaker_falarm'] = torch.sum(torch.max(n_sys - n_ref, torch.zeros_like(n_ref)))
n_map = torch.sum(((label == 1) & (decisions == 1)), dim=-1).to(torch.float32)
res['speaker_error'] = torch.sum(torch.min(n_ref, n_sys) - n_map)
res['correct'] = torch.sum(label == decisions) / label.shape[1]
res['diarization_error'] = (
res['speaker_miss'] + res['speaker_falarm'] + res['speaker_error'])
res['frames'] = len(label)
return res