forked from mrq/DL-Art-School
degrade gumbel softmax over time
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3853f37257
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c1bdb4f9a1
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@ -533,7 +533,8 @@ 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, **kwargs):
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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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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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mask_time_length=mask_time_length, **kwargs)
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@ -541,6 +542,9 @@ class ContrastiveTrainingWrapper(nn.Module):
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self.num_negatives = num_negatives
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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.max_gumbel_temperature = max_gumbel_temperature
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self.min_gumbel_temperature = min_gumbel_temperature
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self.gumbel_temperature_decay = gumbel_temperature_decay
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self.quantizer = Wav2Vec2GumbelVectorQuantizer(inner_dim)
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@ -569,7 +573,15 @@ class ContrastiveTrainingWrapper(nn.Module):
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logits = logits / temperature
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return logits
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def update_for_step(self, step, *args):
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self.quantizer.temperature = max(
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self.max_gumbel_temperature * self.gumbel_temperature_decay**step,
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self.min_gumbel_temperature,
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)
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def forward(self, mel):
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mel = mel[:, :, :-1] # The MEL computation always pads with 1, throwing off optimal tensor math.
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features_shape = (mel.shape[0], mel.shape[-1]//4)
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mask_time_indices = _compute_mask_indices(features_shape, self.mask_time_prob, self.mask_time_length)
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sampled_negative_indices = torch.tensor(_sample_negative_indices(features_shape, self.num_negatives, mask_time_indices=mask_time_indices), device=mel.device)
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