Upload hyperparams.yaml

hyperparams.yaml

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+
+# #################################
+# Basic inference parameters for speaker-id. We have first a network that
+# computes some embeddings. On the top of that, we employ a classifier.
+#
+# Author:
+#  * Mirco Ravanelli 2021
+# #################################
+
+# pretrain folders:
+pretrained_path: jefson08/spkrec-ecapa-voxceleb-kha
+
+
+# Model parameters
+n_mels: 23
+sample_rate: 16000
+n_classes: 28 # In this case, we have 28 speakers
+emb_dim: 512 # dimensionality of the embeddings
+
+# Feature extraction
+compute_features: !new:speechbrain.lobes.features.Fbank
+    n_mels: !ref <n_mels>
+
+# Mean and std normalization of the input features
+mean_var_norm: !new:speechbrain.processing.features.InputNormalization
+    norm_type: sentence
+    std_norm: False
+
+# To design a custom model, either just edit the simple CustomModel
+# class that's listed here, or replace this `!new` call with a line
+# pointing to a different file you've defined.
+embedding_model: !new:custom_model.Xvector
+    in_channels: !ref <n_mels>
+    activation: !name:torch.nn.LeakyReLU
+    tdnn_blocks: 5
+    tdnn_channels: [512, 512, 512, 512, 1500]
+    tdnn_kernel_sizes: [5, 3, 3, 1, 1]
+    tdnn_dilations: [1, 2, 3, 1, 1]
+    lin_neurons: !ref <emb_dim>
+
+classifier: !new:custom_model.Classifier
+    input_shape: [null, null, !ref <emb_dim>]
+    activation: !name:torch.nn.LeakyReLU
+    lin_blocks: 1
+    lin_neurons: !ref <emb_dim>
+    out_neurons: !ref <n_classes>
+
+label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
+
+# Objects in "modules" dict will have their parameters moved to the correct
+# device, as well as having train()/eval() called on them by the Brain class.
+modules:
+    compute_features: !ref <compute_features>
+    embedding_model: !ref <embedding_model>
+    classifier: !ref <classifier>
+    mean_var_norm: !ref <mean_var_norm>
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        embedding_model: !ref <embedding_model>
+        classifier: !ref <classifier>
+        label_encoder: !ref <label_encoder>
+    paths:
+        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
+        classifier: !ref <pretrained_path>/classifier.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt