NtTestAlert/tortoise-tts
1
0
Fork 0
forked from mrq/tortoise-tts
Code Issues Pull Requests Packages Projects Releases Wiki Activity

AND OTHER DEPS

Browse Source
This commit is contained in:
James Betker 2022-04-18 20:44:22 -06:00
parent 412a441c86
commit 89bb33c839
2 changed files with 1 additions and 48 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
models/clvp.py
View File

@ -2,10 +2,10 @@ import torch
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
from torch import einsum from torch import einsum
from x_transformers import Encoder
from models.arch_util import CheckpointedXTransformerEncoder from models.arch_util import CheckpointedXTransformerEncoder
from models.transformer import Transformer from models.transformer import Transformer
from models.xtransformers import Encoder
def exists(val): def exists(val):

47
models/xtransformers.py
View File

@ -1253,50 +1253,3 @@ class ContinuousTransformerWrapper(nn.Module):
return tuple(res) return tuple(res)
return res[0] return res[0]
class XTransformer(nn.Module):
def __init__(
self,
*,
dim,
tie_token_emb=False,
**kwargs
):
super().__init__()
enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
enc_transformer_kwargs['use_pos_emb'] = enc_kwargs.pop('use_pos_emb', True)
dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
dec_transformer_kwargs['use_pos_emb'] = dec_kwargs.pop('use_pos_emb', True)
self.encoder = TransformerWrapper(
**enc_transformer_kwargs,
attn_layers=Encoder(dim=dim, **enc_kwargs)
)
self.decoder = TransformerWrapper(
**dec_transformer_kwargs,
attn_layers=Decoder(dim=dim, cross_attend=True, **dec_kwargs)
)
if tie_token_emb:
self.decoder.token_emb = self.encoder.token_emb
self.decoder = AutoregressiveWrapper(self.decoder)
@torch.no_grad()
def generate(self, seq_in, seq_out_start, seq_len, src_mask=None, src_attn_mask=None, **kwargs):
encodings = self.encoder(seq_in, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
return self.decoder.generate(seq_out_start, seq_len, context=encodings, context_mask=src_mask, **kwargs)
def forward(self, src, tgt, src_mask=None, tgt_mask=None, src_attn_mask=None):
enc = self.encoder(src, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
out = self.decoder(tgt, context=enc, mask=tgt_mask, context_mask=src_mask)
return out