Initialisation du repository de Beta

venv/lib/python3.12/site-packages/ctranslate2/specs/wav2vec2_spec.py

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+from typing import List, Optional, Tuple
+
+import numpy as np
+
+from ctranslate2.specs import common_spec, model_spec, transformer_spec
+
+
+class Wav2Vec2Config(model_spec.ModelConfig):
+    """Configuration for the Wav2Vec2 model."""
+
+    def __init__(self):
+        return
+
+
+class Wav2Vec2Spec(model_spec.LanguageModelSpec):
+    def __init__(
+        self,
+        feat_layers,
+        num_layers,
+        num_heads,
+        vocab_size,
+        return_hidden,
+    ):
+        super().__init__()
+        self.vocab_size = np.dtype("int16").type(vocab_size)
+        self.encoder = Wav2Vec2EncoderSpec(
+            feat_layers,
+            num_layers,
+            num_heads,
+            return_hidden,
+        )
+
+    @property
+    def name(self):
+        return "Wav2Vec2Spec"
+
+    @property
+    def revision(self):
+        return 3
+
+    def get_default_config(self):
+        return Wav2Vec2Config()
+
+    def get_vocabulary_size(self):
+        return int(self.vocab_size.numpy())
+
+
+class Wav2Vec2LayerNormConvLayer(model_spec.LayerSpec):
+    def __init__(self):
+        self.conv = common_spec.Conv1DSpec()
+        self.layer_norm = common_spec.LayerNormSpec()
+
+
+class Wav2Vec2PosEmbedConvLayer(model_spec.LayerSpec):
+    def __init__(self):
+        self.conv = common_spec.Conv1DSpec()
+
+
+class Wav2Vec2EncoderSpec(model_spec.LayerSpec):
+    def __init__(self, feat_layers, num_layers, num_heads, return_hidden):
+        self.num_heads = np.dtype("int16").type(num_heads)
+        self.feat_layer0 = Wav2Vec2LayerNormConvLayer()
+        self.feat_layer = [Wav2Vec2LayerNormConvLayer() for i in range(feat_layers - 1)]
+        self.fp_layer_norm = common_spec.LayerNormSpec()
+        self.fp_projection = common_spec.LinearSpec()
+        self.pos_conv_embed = Wav2Vec2PosEmbedConvLayer()
+        self.layer_norm = common_spec.LayerNormSpec()
+        self.layer = [
+            transformer_spec.TransformerEncoderLayerSpec() for _ in range(num_layers)
+        ]
+        if not return_hidden:
+            self.lm_head = common_spec.LinearSpec()