File size: 6,475 Bytes
85ba398 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from pathlib import Path
import torch
from fairseq import checkpoint_utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.speech_to_speech.s2s_transformer import (
S2SpecTTransformerModel,
S2UTTransformerModel,
s2spect_architecture_base,
s2ut_architecture_base,
)
from fairseq.models.speech_to_text import S2TConformerEncoder
from fairseq.models.transformer import Linear
logger = logging.getLogger(__name__)
def build_s2s_conformer_encoder(args):
encoder = S2SConformerEncoder(args)
pretraining_path = getattr(args, "load_pretrained_encoder_from", None)
if pretraining_path is not None:
if not Path(pretraining_path).exists():
logger.warning(
f"skipped pretraining because {pretraining_path} does not exist"
)
else:
encoder = checkpoint_utils.load_pretrained_component_from_model(
component=encoder, checkpoint=pretraining_path
)
logger.info(f"loaded pretrained encoder from: {pretraining_path}")
return encoder
class S2SConformerEncoder(S2TConformerEncoder):
"""Based on S2T transformer encoder, with support
to incorporate target speaker embedding."""
def __init__(self, args):
super().__init__(args)
self.spk_emb_proj = None
if args.target_speaker_embed:
self.spk_emb_proj = Linear(
args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim
)
def forward(
self, src_tokens, src_lengths, tgt_speaker=None, return_all_hiddens=False
):
out = super().forward(src_tokens, src_lengths, return_all_hiddens)
if self.spk_emb_proj:
x = out["encoder_out"][0]
seq_len, bsz, _ = x.size()
tgt_speaker_emb = tgt_speaker.view(1, bsz, -1).expand(seq_len, bsz, -1)
x = self.spk_emb_proj(torch.cat([x, tgt_speaker_emb], dim=2))
out["encoder_out"][0] = x
return out
@register_model("s2ut_conformer")
class S2UTConformerModel(S2UTTransformerModel):
"""
Direct speech-to-speech translation model with Conformer encoder + Transformer discrete unit decoder
"""
@staticmethod
def add_args(parser):
S2UTTransformerModel.add_args(parser)
parser.add_argument(
"--depthwise-conv-kernel-size",
type=int,
metavar="N",
help="kernel size of depthwise convolution layers",
)
parser.add_argument(
"--attn-type",
type=str,
metavar="STR",
help="If not specified uses fairseq MHA. Other valid option is espnet for using conformer",
)
parser.add_argument(
"--pos-enc-type",
type=str,
metavar="STR",
help="Must be specified in addition to attn-type=espnet for rel_pos and rope",
)
@classmethod
def build_encoder(cls, args):
return build_s2s_conformer_encoder(args)
@register_model("s2spect_conformer")
class S2SpecTConformerModel(S2SpecTTransformerModel):
"""
Direct speech-to-speech translation model with Conformer encoder + TTS Transformer decoder
"""
@staticmethod
def add_args(parser):
S2SpecTTransformerModel.add_args(parser)
parser.add_argument("--depthwise-conv-kernel-size", type=int, default=31)
parser.add_argument(
"--attn-type",
type=str,
default=None,
help="If not specified uses fairseq MHA. Other valid option is espnet for using conformer",
)
parser.add_argument(
"--pos-enc-type",
type=str,
default="abs",
help="Must be specified in addition to attn-type=espnet for rel_pos and rope",
)
@classmethod
def build_encoder(cls, args):
return build_s2s_conformer_encoder(args)
@register_model_architecture("s2ut_conformer", "s2ut_conformer")
def s2ut_conformer_architecture_base(args):
args.attn_type = getattr(args, "attn_type", None)
args.pos_enc_type = getattr(args, "pos_enc_type", "abs")
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.input_channels = getattr(args, "input_channels", 1)
args.max_source_positions = getattr(args, "max_source_positions", 6000)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_layers = getattr(args, "encoder_layers", 16)
args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31)
s2ut_architecture_base(args)
@register_model_architecture("s2spect_conformer", "s2spect_conformer")
def s2spect_conformer_architecture_base(args):
args.attn_type = getattr(args, "attn_type", None)
args.pos_enc_type = getattr(args, "pos_enc_type", "abs")
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.input_channels = getattr(args, "input_channels", 1)
args.max_source_positions = getattr(args, "max_source_positions", 6000)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_layers = getattr(args, "encoder_layers", 16)
args.depthwise_conv_kernel_size = getattr(args, "depthwise_conv_kernel_size", 31)
s2spect_architecture_base(args)
@register_model_architecture("s2spect_conformer", "s2spect_conformer_fisher")
def s2spect_architecture_fisher(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.dropout = getattr(args, "dropout", 0.1)
# decoder
args.prenet_dim = getattr(args, "prenet_dim", 32)
s2spect_conformer_architecture_base(args)
|