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import copy
import numpy as np
import time
import torch
from funasr_detach.models.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) / 60.0
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]] = 0.0
res[item[1]] = 0.0
with torch.no_grad():
loss_s = 0.0
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
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