Duplicate from Matthijs/cmu-arctic-xvectors

Co-authored-by: Mathijs Hollemans <Matthijs@users.noreply.huggingface.co>

.gitattributes

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+---
+pretty_name: CMU ARCTIC X-Vectors
+task_categories:
+- text-to-speech
+- audio-to-audio
+license: mit
+---
+
+# Speaker embeddings extracted from CMU ARCTIC
+
+There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors.
+
+The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers:
+
+- bdl (US male)
+- slt (US female)
+- jmk (Canadian male)
+- awb (Scottish male)
+- rms (US male)
+- clb (US female)
+- ksp (Indian male)
+
+The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model.
+
+Usage:
+
+```python
+from datasets import load_dataset
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+
+speaker_embeddings = embeddings_dataset[7306]["xvector"]
+speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
+```

cmu-arctic-xvectors.py

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+# coding=utf-8
+
+import os
+import numpy as np
+import datasets
+
+
+_DATA_URL = "https://huggingface.co/datasets/Matthijs/cmu-arctic-xvectors/resolve/main/spkrec-xvect.zip"
+
+
+class ArcticXvectors(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="default",
+            version=datasets.Version("0.0.1", ""),
+            description="",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "filename": datasets.Value("string"),
+                    "xvector": datasets.Sequence(feature=datasets.Value(dtype="float32"), length=512),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = os.path.join(dl_manager.download_and_extract(_DATA_URL), "spkrec-xvect")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"files": dl_manager.iter_files(archive)}
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, file in enumerate(sorted(files)):
+            if os.path.basename(file).endswith(".npy"):
+                yield str(i), {
+                    "filename": os.path.basename(file)[:-4],  # strip off .npy
+                    "xvector": np.load(file),
+                }

spkrec-xvect.zip

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