106 lines
5.5 KiB
Python
106 lines
5.5 KiB
Python
import math
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import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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from parakeet.modules.utils import *
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from parakeet.modules.multihead_attention import MultiheadAttention
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from parakeet.modules.ffn import PositionwiseFeedForward
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from parakeet.models.transformer_tts.prenet import PreNet
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from parakeet.models.transformer_tts.post_convnet import PostConvNet
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class Decoder(dg.Layer):
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def __init__(self, num_hidden, config, num_head=4):
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super(Decoder, self).__init__()
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self.num_hidden = num_hidden
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param = fluid.ParamAttr()
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self.alpha = self.create_parameter(shape=(1,), attr=param, dtype='float32',
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default_initializer = fluid.initializer.ConstantInitializer(value=1.0))
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self.pos_inp = get_sinusoid_encoding_table(1024, self.num_hidden, padding_idx=0)
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self.pos_emb = dg.Embedding(size=[1024, num_hidden],
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padding_idx=0,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
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trainable=False))
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self.decoder_prenet = PreNet(input_size = config['audio']['num_mels'],
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hidden_size = num_hidden * 2,
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output_size = num_hidden,
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dropout_rate=0.2)
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k = math.sqrt(1 / num_hidden)
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self.linear = dg.Linear(num_hidden, num_hidden,
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param_attr=fluid.ParamAttr(initializer = fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer = fluid.initializer.Uniform(low=-k, high=k)))
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self.selfattn_layers = [MultiheadAttention(num_hidden, num_hidden//num_head, num_hidden//num_head) for _ in range(3)]
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for i, layer in enumerate(self.selfattn_layers):
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self.add_sublayer("self_attn_{}".format(i), layer)
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self.attn_layers = [MultiheadAttention(num_hidden, num_hidden//num_head, num_hidden//num_head) for _ in range(3)]
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for i, layer in enumerate(self.attn_layers):
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self.add_sublayer("attn_{}".format(i), layer)
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self.ffns = [PositionwiseFeedForward(num_hidden, num_hidden*num_head, filter_size=1) for _ in range(3)]
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for i, layer in enumerate(self.ffns):
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self.add_sublayer("ffns_{}".format(i), layer)
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self.mel_linear = dg.Linear(num_hidden, config['audio']['num_mels'] * config['audio']['outputs_per_step'],
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param_attr=fluid.ParamAttr(initializer = fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer = fluid.initializer.Uniform(low=-k, high=k)))
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self.stop_linear = dg.Linear(num_hidden, 1,
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param_attr=fluid.ParamAttr(initializer = fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer = fluid.initializer.Uniform(low=-k, high=k)))
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self.postconvnet = PostConvNet(config['audio']['num_mels'], config['hidden_size'],
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filter_size = 5, padding = 4, num_conv=5,
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outputs_per_step=config['audio']['outputs_per_step'],
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use_cudnn = True)
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def forward(self, key, value, query, c_mask, positional):
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# get decoder mask with triangular matrix
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if fluid.framework._dygraph_tracer()._train_mode:
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m_mask = get_non_pad_mask(positional)
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mask = get_attn_key_pad_mask((positional==0).astype(np.float32), query)
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triu_tensor = dg.to_variable(get_triu_tensor(query.numpy(), query.numpy())).astype(np.float32)
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mask = mask + triu_tensor
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mask = fluid.layers.cast(mask == 0, np.float32)
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# (batch_size, decoder_len, encoder_len)
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zero_mask = get_attn_key_pad_mask(layers.squeeze(c_mask,[-1]), query)
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else:
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mask = get_triu_tensor(query.numpy(), query.numpy()).astype(np.float32)
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mask = fluid.layers.cast(dg.to_variable(mask == 0), np.float32)
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m_mask, zero_mask = None, None
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# Decoder pre-network
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query = self.decoder_prenet(query)
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# Centered position
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query = self.linear(query)
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# Get position embedding
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positional = self.pos_emb(positional)
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query = positional * self.alpha + query
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#positional dropout
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query = fluid.layers.dropout(query, 0.1)
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# Attention decoder-decoder, encoder-decoder
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selfattn_list = list()
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attn_list = list()
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for selfattn, attn, ffn in zip(self.selfattn_layers, self.attn_layers, self.ffns):
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query, attn_dec = selfattn(query, query, query, mask = mask, query_mask = m_mask)
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query, attn_dot = attn(key, value, query, mask = zero_mask, query_mask = m_mask)
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query = ffn(query)
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selfattn_list.append(attn_dec)
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attn_list.append(attn_dot)
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# Mel linear projection
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mel_out = self.mel_linear(query)
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# Post Mel Network
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out = self.postconvnet(mel_out)
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out = mel_out + out
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# Stop tokens
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stop_tokens = self.stop_linear(query)
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stop_tokens = layers.squeeze(stop_tokens, [-1])
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stop_tokens = layers.sigmoid(stop_tokens)
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return mel_out, out, attn_list, stop_tokens, selfattn_list
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