From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/models/eend/utils/report.py | 125 ++++++++++++++++++++++++-----------------
1 files changed, 74 insertions(+), 51 deletions(-)
diff --git a/funasr/models/eend/utils/report.py b/funasr/models/eend/utils/report.py
index cd100f0..eb1bdac 100644
--- a/funasr/models/eend/utils/report.py
+++ b/funasr/models/eend/utils/report.py
@@ -6,13 +6,13 @@
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')
+ ("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"),
]
@@ -20,9 +20,9 @@
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)
+ 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:]
@@ -30,24 +30,29 @@
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)
+ 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_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_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_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_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
@@ -56,14 +61,16 @@
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_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():
+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
@@ -77,22 +84,31 @@
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)
+ 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
+ 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)
+ 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]
+ if label in mapping_dict["label2dec"].keys():
+ num = mapping_dict["label2dec"][label]
else:
num = -1
return num
@@ -100,22 +116,24 @@
def report_core(self, model, data_loader, device):
res = {}
for item in metrics:
- res[item[0]] = 0.
- res[item[1]] = 0.
+ res[item[0]] = 0.0
+ res[item[1]] = 0.0
with torch.no_grad():
- loss_s = 0.
+ 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, 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]
+ oov_index = torch.where(pred == self.mapping_dict["oov"])[0]
for i in oov_index:
if i > 0:
pred[i] = pred[i - 1]
@@ -123,15 +141,19 @@
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]]
+ 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']
+ 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
@@ -140,20 +162,21 @@
return res, loss_s, stats.keys(), vad_acc
def calc_diarization_error(self, decisions, label, label_delay=0):
- label = label[:len(label) - label_delay, ...]
+ 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)))
+ 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)
+ 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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