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|
|
| from email.policy import default |
| 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 S2UTTransformerModel |
| from chunk_unity.models.s2t_conformer import ChunkS2TConformerEncoder |
| from fairseq.models.transformer import Linear |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| def build_s2s_chunk_conformer_encoder(args): |
| encoder = ChunkS2SConformerEncoder(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 ChunkS2SConformerEncoder(ChunkS2TConformerEncoder): |
| """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 |
|
|
|
|
| class ChunkS2UTConformerModel(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", |
| ) |
| parser.add_argument( |
| "--chunk-size", |
| type=int, |
| metavar="N", |
| default=-1, |
| help="chunk size", |
| ) |
|
|
| @classmethod |
| def build_encoder(cls, args): |
| return build_s2s_chunk_conformer_encoder(args) |
|
|
