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nemo_baseline_models

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popcornell commited on Feb 15

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@@ -25,7 +25,7 @@ on [VoxCeleb1&2 datasets](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.ht
   - MSDD Reference: [Park et al. (2022)](https://arxiv.org/pdf/2203.15974.pdf)
 - MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.
   - TitaNet Reference: [Koluguri et al. (2022)](https://arxiv.org/abs/2110.04410)
-  - TitaNet Model is included in [MSDD-v2 .nemo checkpoint file]((https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/MSDD_v2_PALO_100ms_intrpl_3scales.nemo)).
 - Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.
 - This neural model generates logit values indicating speaker existence.
 - Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.

   - MSDD Reference: [Park et al. (2022)](https://arxiv.org/pdf/2203.15974.pdf)
 - MSDD-v2 speaker diarization system employs a multi-scale embedding approach and utilizes TitaNet speaker embedding extractor.
   - TitaNet Reference: [Koluguri et al. (2022)](https://arxiv.org/abs/2110.04410)
+  - TitaNet Model is included in [MSDD-v2 .nemo checkpoint file](https://huggingface.co/chime-dasr/nemo_baseline_models/blob/main/MSDD_v2_PALO_100ms_intrpl_3scales.nemo).
 - Unlike the system that uses a multi-layer LSTM architecture, we employ a four-layer Transformer architecture with a hidden size of 384.
 - This neural model generates logit values indicating speaker existence.
 - Our diarization model is trained on approximately 3,000 hours of simulated audio mixture data from the same multi-speaker data simulator used in VAD model training, drawing from VoxCeleb1&2 and LibriSpeech datasets.