Repo struct change and fix undefined names

src/AudioSep/models/CLAP/open_clip/model.py → src/v_audio_cc/AudioSep/models/CLAP/open_clip/model.py

@@ -780,63 +780,63 @@ def get_audio_embedding(self, data):
 
         return audio_embeds
 
-    def audio_infer(self, audio, hopsize=None, device=None):
-        """Forward one audio and produce the audio embedding
-
-        Parameters
-        ----------
-        audio:  (audio_length)
-            the time-domain audio input, notice that it must be only one input
-        hopsize: int
-            the overlap hopsize as the sliding window
-
-        Returns
-        ----------
-        output_dict: {
-            key: [n, (embedding_shape)] if "HTS-AT"
-            or
-            key: [(embedding_shape)] if "PANN"
-        }
-            the list of key values of the audio branch
-
-        """
-
-        assert not self.training, "the inference mode must be run at eval stage"
-        output_dict = {}
-        # PANN
-        if self.audio_cfg.model_type == "PANN":
-            audio_input = audio.unsqueeze(dim=0)
-            output_dict[key] = self.encode_audio(audio_input, device=device)[
-                key
-            ].squeeze(dim=0)
-        elif self.audio_cfg.model_type == "HTSAT":
-            # repeat
-            audio_len = len(audio)
-            k = self.audio_cfg.clip_samples // audio_len
-            if k > 1:
-                audio = audio.repeat(k)
-                audio_len = len(audio)
-
-            if hopsize is None:
-                hopsize = min(hopsize, audio_len)
-
-            if audio_len > self.audio_cfg.clip_samples:
-                audio_input = [
-                    audio[pos : pos + self.audio_cfg.clip_samples].clone()
-                    for pos in range(
-                        0, audio_len - self.audio_cfg.clip_samples, hopsize
-                    )
-                ]
-                audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
-                audio_input = torch.stack(audio_input)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key]
-            else:
-                audio_input = audio.unsqueeze(dim=0)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[
-                    key
-                ].squeeze(dim=0)
-
-        return output_dict
+    # def audio_infer(self, audio, hopsize=None, device=None):
+    #     """Forward one audio and produce the audio embedding
+
+    #     Parameters
+    #     ----------
+    #     audio:  (audio_length)
+    #         the time-domain audio input, notice that it must be only one input
+    #     hopsize: int
+    #         the overlap hopsize as the sliding window
+
+    #     Returns
+    #     ----------
+    #     output_dict: {
+    #         key: [n, (embedding_shape)] if "HTS-AT"
+    #         or
+    #         key: [(embedding_shape)] if "PANN"
+    #     }
+    #         the list of key values of the audio branch
+
+    #     """
+
+    #     assert not self.training, "the inference mode must be run at eval stage"
+    #     output_dict = {}
+    #     # PANN
+    #     if self.audio_cfg.model_type == "PANN":
+    #         audio_input = audio.unsqueeze(dim=0)
+    #         output_dict[key] = self.encode_audio(audio_input, device=device)[
+    #             key
+    #         ].squeeze(dim=0)
+    #     elif self.audio_cfg.model_type == "HTSAT":
+    #         # repeat
+    #         audio_len = len(audio)
+    #         k = self.audio_cfg.clip_samples // audio_len
+    #         if k > 1:
+    #             audio = audio.repeat(k)
+    #             audio_len = len(audio)
+
+    #         if hopsize is None:
+    #             hopsize = min(hopsize, audio_len)
+
+    #         if audio_len > self.audio_cfg.clip_samples:
+    #             audio_input = [
+    #                 audio[pos : pos + self.audio_cfg.clip_samples].clone()
+    #                 for pos in range(
+    #                     0, audio_len - self.audio_cfg.clip_samples, hopsize
+    #                 )
+    #             ]
+    #             audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
+    #             audio_input = torch.stack(audio_input)
+    #             output_dict[key] = self.encode_audio(audio_input, device=device)[key]
+    #         else:
+    #             audio_input = audio.unsqueeze(dim=0)
+    #             output_dict[key] = self.encode_audio(audio_input, device=device)[
+    #                 key
+    #             ].squeeze(dim=0)
+
+    #     return output_dict
 
 
 def convert_weights_to_fp16(model: nn.Module):

src/AudioSep/utils.py → src/v_audio_cc/AudioSep/utils.py

@@ -86,43 +86,43 @@ def get_audioset632_id_to_lb(ontology_path: str) -> Dict:
     return audioset632_id_to_lb
 
 
-def load_pretrained_panns(
-    model_type: str,
-    checkpoint_path: str,
-    freeze: bool
-) -> nn.Module:
-    r"""Load pretrained pretrained audio neural networks (PANNs).
+# def load_pretrained_panns(
+#     model_type: str,
+#     checkpoint_path: str,
+#     freeze: bool
+# ) -> nn.Module:
+#     r"""Load pretrained pretrained audio neural networks (PANNs).
 
-    Args:
-        model_type: str, e.g., "Cnn14"
-        checkpoint_path, str, e.g., "Cnn14_mAP=0.431.pth"
-        freeze: bool
+#     Args:
+#         model_type: str, e.g., "Cnn14"
+#         checkpoint_path, str, e.g., "Cnn14_mAP=0.431.pth"
+#         freeze: bool
 
-    Returns:
-        model: nn.Module
-    """
+#     Returns:
+#         model: nn.Module
+#     """
 
-    if model_type == "Cnn14":
-        Model = Cnn14
+#     if model_type == "Cnn14":
+#         Model = Cnn14
 
-    elif model_type == "Cnn14_DecisionLevelMax":
-        Model = Cnn14_DecisionLevelMax
+#     elif model_type == "Cnn14_DecisionLevelMax":
+#         Model = Cnn14_DecisionLevelMax
 
-    else:
-        raise NotImplementedError
+#     else:
+#         raise NotImplementedError
 
-    model = Model(sample_rate=32000, window_size=1024, hop_size=320,
-                  mel_bins=64, fmin=50, fmax=14000, classes_num=527)
+#     model = Model(sample_rate=32000, window_size=1024, hop_size=320,
+#                   mel_bins=64, fmin=50, fmax=14000, classes_num=527)
 
-    if checkpoint_path:
-        checkpoint = torch.load(checkpoint_path, map_location="cpu")
-        model.load_state_dict(checkpoint["model"])
+#     if checkpoint_path:
+#         checkpoint = torch.load(checkpoint_path, map_location="cpu")
+#         model.load_state_dict(checkpoint["model"])
 
-    if freeze:
-        for param in model.parameters():
-            param.requires_grad = False
+#     if freeze:
+#         for param in model.parameters():
+#             param.requires_grad = False
 
-    return model
+#     return model
 
 
 def energy(x):