Initialisation du repository de Beta

venv/lib/python3.12/site-packages/ctranslate2/specs/whisper_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 WhisperConfig(model_spec.ModelConfig):
+    """Configuration for the Whisper model."""
+
+    def __init__(
+        self,
+        suppress_ids: Optional[List[int]] = None,
+        suppress_ids_begin: Optional[List[int]] = None,
+        lang_ids: Optional[List[int]] = None,
+        alignment_heads: Optional[List[Tuple[int, int]]] = None,
+    ):
+        super().__init__(
+            suppress_ids=suppress_ids,
+            suppress_ids_begin=suppress_ids_begin,
+            lang_ids=lang_ids,
+            alignment_heads=alignment_heads,
+        )
+
+
+class WhisperSpec(model_spec.LanguageModelSpec):
+    """Describes a Whisper model."""
+
+    def __init__(
+        self,
+        num_encoder_layers,
+        num_encoder_heads,
+        num_decoder_layers,
+        num_decoder_heads,
+    ):
+        """Initializes the model specification.
+
+        Args:
+          num_encoder_layers: The number of encoder layers.
+          num_encoder_heads: The number of encoder attention heads.
+          num_decoder_layers: The number of decoder layers.
+          num_decoder_heads: The number of decoder attention heads.
+        """
+        super().__init__()
+        self.encoder = WhisperEncoderSpec(num_encoder_layers, num_encoder_heads)
+        self.decoder = transformer_spec.TransformerDecoderSpec(
+            num_decoder_layers,
+            num_decoder_heads,
+            activation=common_spec.Activation.GELU,
+        )
+        self.decoder.scale_embeddings = False
+
+    @property
+    def name(self):
+        return "WhisperSpec"
+
+    @property
+    def revision(self):
+        return 3
+
+    def get_default_config(self):
+        return WhisperConfig()
+
+    def get_vocabulary_size(self):
+        return self.decoder.embeddings.weight.shape[0]
+
+
+class WhisperEncoderSpec(model_spec.LayerSpec):
+    def __init__(self, num_layers, num_heads):
+        self.num_heads = np.dtype("int16").type(num_heads)
+        self.conv1 = common_spec.Conv1DSpec()
+        self.conv2 = common_spec.Conv1DSpec()
+        self.position_encodings = transformer_spec.PositionEncoderSpec()
+        self.layer_norm = common_spec.LayerNormSpec()
+        self.layer = [
+            transformer_spec.TransformerEncoderLayerSpec() for _ in range(num_layers)
+        ]