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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class AudioTower(nn.Module): |
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def __init__(self, audio_tower, args, delay_load=False): |
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super().__init__() |
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self.is_loaded = False |
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self.audio_tower_name = audio_tower |
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self.cfg_only = None |
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def forward(self, sounds): |
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if type(sounds) is list: |
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sound_features = [] |
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audio_output_lengths = [] |
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for sound in sounds: |
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if hasattr(sound, "input_features"): |
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sound = sound["input_features"] |
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sound_feature = self.audio_tower(sound) |
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sound_feature = sound_feature.last_hidden_state |
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sound_feature = sound_feature.to(sound.dtype) |
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sound_features.append(sound_feature) |
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audio_output_lengths.append(sound_feature.shape[1]) |
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sound_features = torch.cat(sound_features, dim=1).squeeze(0) |
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else: |
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raise NotImplementedError("Not implemented for this encoder") |
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return sound_features, audio_output_lengths |
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@property |
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def dummy_feature(self): |
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return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) |
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@property |
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def dtype(self): |
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return self.audio_tower.dtype |
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@property |
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def config(self): |
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if self.is_loaded: |
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return self.audio_tower.config |
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else: |
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return self.cfg_only |
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@property |
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def device(self): |
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return self.audio_tower.device |
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@property |
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def hidden_size(self): |
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return self.config.hidden_size |
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