mirror of https://github.com/coqui-ai/TTS.git
101 lines
4.9 KiB
Python
101 lines
4.9 KiB
Python
from math import sqrt
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from torch import nn
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from layers.tacotron2 import Encoder, Decoder, Postnet
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from utils.generic_utils import sequence_mask
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# TODO: match function arguments with tacotron
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class Tacotron2(nn.Module):
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def __init__(self,
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num_chars,
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num_speakers,
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r,
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attn_win=False,
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attn_norm="softmax",
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prenet_type="original",
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prenet_dropout=True,
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forward_attn=False,
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trans_agent=False,
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forward_attn_mask=False,
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location_attn=True,
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separate_stopnet=True):
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super(Tacotron2, self).__init__()
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self.n_mel_channels = 80
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self.n_frames_per_step = r
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self.embedding = nn.Embedding(num_chars, 512)
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std = sqrt(2.0 / (num_chars + 512))
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val = sqrt(3.0) * std # uniform bounds for std
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self.embedding.weight.data.uniform_(-val, val)
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if num_speakers > 1:
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self.speaker_embedding = nn.Embedding(num_speakers, 512)
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self.speaker_embedding.weight.data.normal_(0, 0.3)
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self.encoder = Encoder(512)
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self.decoder = Decoder(512, self.n_mel_channels, r, attn_win,
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attn_norm, prenet_type, prenet_dropout,
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forward_attn, trans_agent, forward_attn_mask,
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location_attn, separate_stopnet)
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self.postnet = Postnet(self.n_mel_channels)
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@staticmethod
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def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
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mel_outputs = mel_outputs.transpose(1, 2)
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mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
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return mel_outputs, mel_outputs_postnet, alignments
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def forward(self, text, text_lengths, mel_specs=None, speaker_ids=None):
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# compute mask for padding
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mask = sequence_mask(text_lengths).to(text.device)
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embedded_inputs = self.embedding(text).transpose(1, 2)
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encoder_outputs = self.encoder(embedded_inputs, text_lengths)
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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speaker_ids)
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mel_outputs, stop_tokens, alignments = self.decoder(
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encoder_outputs, mel_specs, mask)
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mel_outputs_postnet = self.postnet(mel_outputs)
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mel_outputs_postnet = mel_outputs + mel_outputs_postnet
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mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
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mel_outputs, mel_outputs_postnet, alignments)
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return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
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def inference(self, text, speaker_ids=None):
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embedded_inputs = self.embedding(text).transpose(1, 2)
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encoder_outputs = self.encoder.inference(embedded_inputs)
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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speaker_ids)
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mel_outputs, stop_tokens, alignments = self.decoder.inference(
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encoder_outputs)
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mel_outputs_postnet = self.postnet(mel_outputs)
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mel_outputs_postnet = mel_outputs + mel_outputs_postnet
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mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
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mel_outputs, mel_outputs_postnet, alignments)
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return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
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def inference_truncated(self, text, speaker_ids=None):
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"""
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Preserve model states for continuous inference
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"""
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embedded_inputs = self.embedding(text).transpose(1, 2)
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encoder_outputs = self.encoder.inference_truncated(embedded_inputs)
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encoder_outputs = self._add_speaker_embedding(encoder_outputs,
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speaker_ids)
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mel_outputs, stop_tokens, alignments = self.decoder.inference_truncated(
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encoder_outputs)
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mel_outputs_postnet = self.postnet(mel_outputs)
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mel_outputs_postnet = mel_outputs + mel_outputs_postnet
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mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
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mel_outputs, mel_outputs_postnet, alignments)
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return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
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def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
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if hasattr(self, "speaker_embedding") and speaker_ids is None:
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raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
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if hasattr(self, "speaker_embedding") and speaker_ids is not None:
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speaker_embeddings = self.speaker_embedding(speaker_ids)
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speaker_embeddings.unsqueeze_(1)
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speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
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encoder_outputs.size(1),
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-1)
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encoder_outputs = encoder_outputs + speaker_embeddings
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return encoder_outputs
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