fiddle with init
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@ -313,7 +313,6 @@ class Wav2Vec2Encoder(nn.Module):
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self,
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hidden_states,
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attention_mask=None,
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output_attentions=False,
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output_hidden_states=False,
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):
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all_hidden_states = () if output_hidden_states else None
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@ -361,6 +360,8 @@ class Mel2Vec(nn.Module):
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layerdrop=0,
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mask_time_prob=.65,
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mask_time_length=10,
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disable_custom_linear_init=False,
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linear_init_scale=.02,
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):
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super().__init__()
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self.input_blocks = nn.Sequential(nn.Conv1d(mel_input_channels, inner_dim//2, kernel_size=5, padding=2, stride=2),
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@ -376,6 +377,8 @@ class Mel2Vec(nn.Module):
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self.encoder = Wav2Vec2Encoder(inner_dim, dropout, layers, layerdrop)
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self.mask_time_prob = mask_time_prob
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self.mask_time_length = mask_time_length
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self.disable_custom_linear_init = disable_custom_linear_init
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self.linear_init_scale = linear_init_scale
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self.apply(self.init)
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def init(self, module):
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@ -393,7 +396,9 @@ class Mel2Vec(nn.Module):
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nn.init.uniform_(module.projection.weight, a=-k, b=k)
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nn.init.uniform_(module.projection.bias, a=-k, b=k)
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elif isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=.02)
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if self.disable_custom_linear_init:
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return
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module.weight.data.normal_(mean=0.0, std=self.linear_init_scale)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
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@ -535,7 +540,7 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module):
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class ContrastiveTrainingWrapper(nn.Module):
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def __init__(self, inner_dim=1024, dropout=.1, mask_time_prob=.65, mask_time_length=4, num_negatives=100,
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def __init__(self, inner_dim=1024, dropout=.1, mask_time_prob=.5, mask_time_length=6, num_negatives=100,
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max_gumbel_temperature=2.0, min_gumbel_temperature=.5, gumbel_temperature_decay=.999995, **kwargs):
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super().__init__()
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self.m2v = Mel2Vec(inner_dim=inner_dim, dropout=dropout, mask_time_prob=mask_time_prob,
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