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