Merge pull request #226 from alibaba-damo-academy/dev_dzh

Dev dzh

egs_modelscope/speaker_diarization/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/unit_test.py

@@ -0,0 +1,26 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+# 初始化推理 pipeline
+# 当以原始音频作为输入时使用配置文件 sond.yaml，并设置 mode 为sond_demo
+inference_diar_pipline = pipeline(
+    mode="sond_demo",
+    num_workers=0,
+    task=Tasks.speaker_diarization,
+    diar_model_config="sond.yaml",
+    model='damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch',
+    sv_model="damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch",
+    sv_model_revision="master",
+)
+
+# 以 audio_list 作为输入，其中第一个音频为待检测语音，后面的音频为不同说话人的声纹注册语音
+audio_list = [[
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/record.wav",
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_A.wav",
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B.wav",
+    "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B1.wav"
+]]
+
+results = inference_diar_pipline(audio_in=audio_list)
+for rst in results:
+    print(rst["value"])

funasr/models/pooling/statistic_pooling.py

@@ -82,7 +82,7 @@ def windowed_statistic_pooling(
     tt = xs_pad.shape[2]
     num_chunk = int(math.ceil(tt / pooling_stride))
     pad = pooling_size // 2
-    if xs_pad.shape == 4:
+    if len(xs_pad.shape) == 4:
         features = F.pad(xs_pad, (0, 0, pad, pad), "reflect")
     else:
         features = F.pad(xs_pad, (pad, pad), "reflect")