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xvector-generator.py

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+import os
+import glob
+import numpy
+import argparse
+import torchaudio
+from speechbrain.pretrained import EncoderClassifier
+import torch
+from tqdm import tqdm
+import torch.nn.functional as F
+
+spk_model = {
+    "speechbrain/spkrec-xvect-voxceleb": 512, 
+    "speechbrain/spkrec-ecapa-voxceleb": 192,
+}
+
+def f2embed(wav_file, classifier, size_embed):
+    signal, fs = torchaudio.load(wav_file)
+    assert fs == 16000, fs
+    with torch.no_grad():
+        embeddings = classifier.encode_batch(signal)
+        embeddings = F.normalize(embeddings, dim=2)
+        embeddings = embeddings.squeeze().cpu().numpy()
+        embeddings = numpy.transpose(embeddings)
+    assert embeddings.shape[0] == size_embed, embeddings.shape[0]
+    return embeddings
+
+def process(args):
+    wavlst = []
+    for split in args.splits.split(","):
+        wav_dir = os.path.join(args.arctic_root, split)
+        print("wav_dir", wav_dir)
+        wavlst_split = glob.glob(os.path.join(wav_dir, "*.wav"))
+        print("wavlst_split", wavlst_split)
+        print(f"{split} {len(wavlst_split)} utterances.")
+        wavlst.extend(wavlst_split)
+
+    spkemb_root = args.output_root
+    if not os.path.exists(spkemb_root):
+        print(f"Create speaker embedding directory: {spkemb_root}")
+        os.mkdir(spkemb_root)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    classifier = EncoderClassifier.from_hparams(source=args.speaker_embed, run_opts={"device": device}, savedir=os.path.join('/tmp', args.speaker_embed))
+    size_embed = spk_model[args.speaker_embed]
+    for utt_i in tqdm(wavlst, total=len(wavlst), desc="Extract"):
+        # TODO rename speaker embedding
+        utt_id = "-".join(utt_i.split("/")[-3:]).replace(".wav", "")
+        utt_emb = f2embed(utt_i, classifier, size_embed)
+        numpy.save(os.path.join(spkemb_root, f"{utt_id}.npy"), utt_emb)
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--arctic-root", "-i", required=True, type=str, help="LibriTTS root directory.")
+    parser.add_argument("--output-root", "-o", required=True, type=str, help="Output directory.")
+    parser.add_argument("--speaker-embed", "-s", type=str, required=True, choices=["speechbrain/spkrec-xvect-voxceleb", "speechbrain/spkrec-ecapa-voxceleb"],
+                        help="Pretrained model for extracting speaker embedding.")
+    parser.add_argument("--splits",  type=str, help="Split of four speakers seperate by comma.",
+                        default="cmu_us_bdl_arctic,cmu_us_clb_arctic,cmu_us_rms_arctic,cmu_us_slt_arctic")
+    args = parser.parse_args()
+    print(f"Loading utterances from {args.arctic_root}/{args.splits}, "
+        + f"Save speaker embedding 'npy' to {args.output_root}, "
+        + f"Using speaker model {args.speaker_embed} with {spk_model[args.speaker_embed]} size.")
+    process(args)
+
+if __name__ == "__main__":
+    """
+    python utils/xvector-generator.py \
+        -i /root/data/cmu_arctic/CMUARCTIC \
+        -o /root/data/cmu_arctic/CMUARCTIC/spkrec-xvect \
+        -s speechbrain/spkrec-xvect-voxceleb
+    """
+    main()

xvectors/french_recording-bernard-candide_segment_090.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3a1ce32ce48973c1bd8f91f2a2a03fb7a088d582436b7c105081bb31034a8dd
+size 4224