From 5cfdcfc45a042e338c2b2f4a08dab125de3fb5ee Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期二, 01 八月 2023 23:19:02 +0800
Subject: [PATCH] TOLD/SOND: download sv model
---
egs/callhome/diarization/sond/finetune.sh | 17 +++++++++++++----
1 files changed, 13 insertions(+), 4 deletions(-)
diff --git a/egs/callhome/diarization/sond/finetune.sh b/egs/callhome/diarization/sond/finetune.sh
index 92a1ef5..8e161f9 100644
--- a/egs/callhome/diarization/sond/finetune.sh
+++ b/egs/callhome/diarization/sond/finetune.sh
@@ -46,7 +46,7 @@
freeze_param=
# inference related
-inference_model=valid.der.ave_5best.pth
+inference_model=valid.der.ave_5best.pb
inference_config=conf/basic_inference.yaml
inference_tag=""
test_sets="callhome2"
@@ -189,11 +189,14 @@
done
fi
-# Scoring for finetuned model, you may get a DER like
+# Scoring for finetuned model, you may get a DER like:
+# oracle_vad | system_vad
+# 7.28 | 8.06
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
echo "stage 3: Scoring finetuned models"
if [ ! -e dscore ]; then
git clone https://github.com/nryant/dscore.git
+ pip install intervaltree
# add intervaltree to setup.py
fi
for dset in ${test_sets}; do
@@ -226,17 +229,23 @@
# And convert the sph files to wav files (use scripts/dump_pipe_wav.py).
# Then find the wav files to construct wav.scp and put it at data/callhome2/wav.scp.
# After iteratively perform SOAP, you will get DER results like:
-# iters| oracle_vad | system_vad
+# iters : oracle_vad | system_vad
# iter_0: 9.68 | 10.51
# iter_1: 9.26 | 10.14 (reported in the paper)
# iter_2: 9.18 | 10.08
# iter_3: 9.24 | 10.15
# iter_4: 9.27 | 10.17
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+ if [ ! -e ${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ]; then
+ git lfs install
+ git clone https://www.modelscope.cn/damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch.git
+ mv speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch ${expdir}/
+ fi
+
for dset in ${test_sets}; do
echo "stage 4: Evaluating finetuned system on ${dset} set with medfilter_size=83 clustering=EEND-OLA"
sv_exp_dir=${expdir}/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch
- diar_exp=${expdir}/${model_dir}_phase3
+ diar_exp=${expdir}/${model_dir}
_data="${datadir}/${dset}/dumped_files"
_inference_tag="$(basename "${inference_config}" .yaml)${inference_tag}"
_dir="${diar_exp}/${_inference_tag}/${inference_model}/${dset}"
--
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