From 340c55838b56bb98508337f8404e0a74f22a20c2 Mon Sep 17 00:00:00 2001
From: gaochangfeng <54253717+gaochangfeng@users.noreply.github.com>
Date: 星期一, 22 七月 2024 15:28:27 +0800
Subject: [PATCH] EMO_UNK禁用和Merge VAD修复 (#1940)
---
funasr/models/sense_voice/model.py | 5 ++++-
funasr/utils/vad_utils.py | 20 ++++++++++++--------
funasr/auto/auto_model.py | 2 +-
3 files changed, 17 insertions(+), 10 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index d0a7733..acd4c72 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -337,7 +337,7 @@
end_vad = time.time()
# FIX(gcf): concat the vad clips for sense vocie model for better aed
- if kwargs.get("merge_vad", False):
+ if cfg.get("merge_vad", False):
for i in range(len(res)):
res[i]["value"] = merge_vad(
res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index cf4f7fb..5159b82 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -644,6 +644,7 @@
self.embed = torch.nn.Embedding(
7 + len(self.lid_dict) + len(self.textnorm_dict), input_size
)
+ self.emo_dict = {"unk": 25009, "happy": 25001, "sad": 25002, "angry": 25003, "neutral": 25004}
self.criterion_att = LabelSmoothingLoss(
size=self.vocab_size,
@@ -870,7 +871,9 @@
# c. Passed the encoder result and the beam search
ctc_logits = self.ctc.log_softmax(encoder_out)
-
+ if kwargs.get("ban_emo_unk", False):
+ ctc_logits[:, :, self.emo_dict["unk"]] = -float("inf")
+
results = []
b, n, d = encoder_out.size()
if isinstance(key[0], (list, tuple)):
diff --git a/funasr/utils/vad_utils.py b/funasr/utils/vad_utils.py
index eba48a9..f87a85e 100644
--- a/funasr/utils/vad_utils.py
+++ b/funasr/utils/vad_utils.py
@@ -32,8 +32,10 @@
return speech_list, speech_lengths_list
-def merge_vad(vad_result, max_length=15000):
+def merge_vad(vad_result, max_length=15000, min_length=0):
new_result = []
+ if len(vad_result) <= 1:
+ return vad_result
time_step = [t[0] for t in vad_result] + [t[1] for t in vad_result]
time_step = sorted(list(set(time_step)))
if len(time_step) == 0:
@@ -43,13 +45,15 @@
time = time_step[i]
if time_step[i + 1] - bg < max_length:
continue
- if time - bg < max_length * 1.5:
+ if time - bg > min_length:
new_result.append([bg, time])
- else:
- split_num = int(time - bg) // max_length + 1
- spl_l = int(time - bg) // split_num
- for j in range(split_num):
- new_result.append([bg + j * spl_l, bg + (j + 1) * spl_l])
+ # if time - bg < max_length * 1.5:
+ # new_result.append([bg, time])
+ # else:
+ # split_num = int(time - bg) // max_length + 1
+ # spl_l = int(time - bg) // split_num
+ # for j in range(split_num):
+ # new_result.append([bg + j * spl_l, bg + (j + 1) * spl_l])
bg = time
new_result.append([bg, time_step[-1]])
- return new_result
+ return new_result
\ No newline at end of file
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