From 11cb9b21cdfa949293fff3c022573fc5412dd328 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期四, 23 二月 2023 23:25:07 +0800
Subject: [PATCH] sond pipeline
---
egs/mars/sd/scripts/simu_chunk_with_labels.py | 8 +++++---
1 files changed, 5 insertions(+), 3 deletions(-)
diff --git a/egs/mars/sd/scripts/simu_chunk_with_labels.py b/egs/mars/sd/scripts/simu_chunk_with_labels.py
index 96d3c0e..226784b 100644
--- a/egs/mars/sd/scripts/simu_chunk_with_labels.py
+++ b/egs/mars/sd/scripts/simu_chunk_with_labels.py
@@ -93,9 +93,9 @@
def calculate_embedding(spk, spk2utts, utt2xvec, embedding_dim, average_emb_num):
- # process for empty speaker
+ # process for dummy speaker
if spk == "None":
- return np.zeros((embedding_dim, ), dtype=np.float32)
+ return np.zeros((1, embedding_dim), dtype=np.float32)
# calculate averaged speaker embeddings
utt_list = spk2utts[spk]
@@ -103,7 +103,9 @@
xvec_list = [kaldiio.load_mat(utt2xvec[utt]) for utt in utt_list]
else:
xvec_list = [kaldiio.load_mat(utt2xvec[utt]) for utt in random.sample(utt_list, average_emb_num)]
- xvec = np.mean(np.hstack(xvec_list), axis=0)
+ # TODO: rerun the simulation
+ xvec_list = [x / np.linalg.norm(x, axis=-1) for x in xvec_list]
+ xvec = np.mean(np.concatenate(xvec_list, axis=0), axis=0)
return xvec
--
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