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sd.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43d6de49a557a63b23147e1b16c10fb624d220b40afc7b620dd1cf9ac540c739
+size 6949114

superb_dummy.py

@@ -217,6 +217,33 @@ class Superb(datasets.GeneratorBasedBuilder):
             url="https://sail.usc.edu/iemocap/",
             data_url="er.json",
         ),
+        SuperbConfig(
+            name="sd",
+            description=textwrap.dedent(
+                """\
+            Speaker Diarization (SD) predicts `who is speaking when` for each timestamp, and multiple speakers can
+            speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be
+            able to represent mixtures of signals. [LibriMix] is adopted where LibriSpeech
+            train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing.
+            We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using
+            alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER)."""
+            ),
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "speech": datasets.Sequence(datasets.Value("float32")),
+                    "speakers": [
+                        {
+                            "speaker_id": datasets.Value("string"),
+                            "start": datasets.Value("int64"),
+                            "end": datasets.Value("int64"),
+                        }
+                    ],
+                }
+            ),
+            url="https://github.com/ftshijt/LibriMix",
+            data_url="sd.json",
+        ),
     ]
 
     def _info(self):