Create configuration_xvector.py

configuration_xvector.py

@@ -0,0 +1,58 @@
+from transformers.configuration_utils import PretrainedConfig
+
+
+class XvectorConfig(PretrainedConfig):
+
+    model_type = 'xvector'
+
+    def __init__(
+        self, 
+        n_mels=40, 
+        sample_rate=16000, 
+        win_length=25, 
+        hop_length=10, 
+        mean_norm=True, 
+        std_norm=False, 
+        norm_type='sentence', 
+        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], 
+        hidden_size=512, 
+        num_classes=1251, 
+        loss_fn='aam', 
+        auto_map={
+            "AutoConfig": "configuration_xvector.XvectorConfig", 
+            "AutoModel": "modeling_xvector.XvectorModel", 
+            "AutoModelForAudioClassification": "modeling_xvector.XvectorModelForSequenceClassification"
+        },
+        initializer_range=0.02,
+        **kwargs
+    ):
+        # Compute features
+        self.n_mels = n_mels
+        self.sample_rate = sample_rate
+        self.win_length = win_length
+        self.hop_length = hop_length
+
+        # Mean variance norm
+        self.mean_norm = mean_norm
+        self.std_norm = std_norm
+        self.norm_type = norm_type
+
+        # Embedding model
+        self.tdnn_blocks = tdnn_blocks
+        self.tdnn_channels = tdnn_channels
+        self.tdnn_kernel_sizes = tdnn_kernel_sizes
+        self.tdnn_dilations = tdnn_dilations
+        self.hidden_size = hidden_size
+
+        # Classifier
+        self.num_classes = num_classes
+        self.loss_fn = loss_fn
+
+        # Others
+        self.auto_map = auto_map
+        self.initializer_range = initializer_range
+
+        super().__init__(**kwargs)