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# ----------------------------------------------------------------------------
# SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training (https://arxiv.org/abs/2210.03730)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/SpeechUT
# Code based on fairseq: https://github.com/facebookresearch/fairseq/tree/272c4c5197250997148fb12c0db6306035f166a4
#
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# ----------------------------------------------------------------------------
import logging
import contextlib
import torch
import torch.nn as nn
from argparse import Namespace
from dataclasses import dataclass
from typing import Any
from fairseq import checkpoint_utils, tasks
from fairseq.models import BaseFairseqModel, register_model
from fairseq.models.fairseq_encoder import FairseqEncoder
from fairseq.tasks import FairseqTask
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.data.data_utils import lengths_to_padding_mask
from fairseq.models.hubert import HubertAsrConfig
logger = logging.getLogger(__name__)
@dataclass
class SpeechUTS2TConfig(HubertAsrConfig):
### the following config is only for the compatibility to fairseq speech_to_text task
input_feat_per_channel: Any = None
input_channels: Any = None
speaker_to_id: Any = None
@register_model("speechut_st_legacy", dataclass=SpeechUTS2TConfig)
class SpeechUTS2T(BaseFairseqModel):
"""An encoder-decoder model."""
def __init__(self, cfg: SpeechUTS2TConfig, encoder: FairseqEncoder):
super().__init__()
self.cfg = cfg
self.encoder = encoder
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
return state_dict
@classmethod
def build_model(cls, cfg: SpeechUTS2TConfig, task: FairseqTask):
"""Build a new model instance."""
encoder = SpeechUTEncoder(cfg, task)
return cls(cfg, encoder)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
encoder_out = self.encoder(src_tokens, src_lengths, **kwargs)
decoder_out = self.encoder.w2v_model.decoder(
prev_output_tokens, encoder_out=encoder_out, **kwargs
)
return decoder_out
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.encoder.w2v_model.decoder(prev_output_tokens, **kwargs)
def get_normalized_probs(self, net_output, log_probs, sample=None):
"""For decoder decoding."""
return self.encoder.w2v_model.decoder.get_normalized_probs(net_output, log_probs, sample)
@property
def decoder(self):
return self.encoder.w2v_model.decoder
class SpeechUTEncoder(FairseqEncoder):
"""
Modified from fairseq.models.hubert.hubert_asr.HubertEncoder
1. make it compatible with fairseq speech_to_text task
2. make it compatible with encoder-decoder model
"""
def __init__(self, cfg: SpeechUTS2TConfig, task):
self.apply_mask = cfg.apply_mask
arg_overrides = {
"dropout": cfg.dropout,
"activation_dropout": cfg.activation_dropout,
"dropout_input": cfg.dropout_input,
"attention_dropout": cfg.attention_dropout,
"mask_length": cfg.mask_length,
"mask_prob": cfg.mask_prob,
"mask_selection": cfg.mask_selection,
"mask_other": cfg.mask_other,
"no_mask_overlap": cfg.no_mask_overlap,
"mask_channel_length": cfg.mask_channel_length,
"mask_channel_prob": cfg.mask_channel_prob,
"mask_channel_selection": cfg.mask_channel_selection,
"mask_channel_other": cfg.mask_channel_other,
"no_mask_channel_overlap": cfg.no_mask_channel_overlap,
"encoder_layerdrop": cfg.layerdrop,
"feature_grad_mult": cfg.feature_grad_mult,
}
if cfg.w2v_args is None:
state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
w2v_args = state.get("cfg", None)
if w2v_args is None:
w2v_args = convert_namespace_to_omegaconf(state["args"])
cfg.w2v_args = w2v_args
else:
state = None
w2v_args = cfg.w2v_args
if isinstance(w2v_args, Namespace):
cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args)
assert task.data_cfg.standardize_audio() == w2v_args.task.normalize, (
"Fine-tuning works best when data normalization is the same. "
"Please check that --normalize is set or unset for "
"both pre-training and here"
)
pretrain_task = tasks.setup_task(w2v_args.task, load_local_states=False)
assert state is not None and "task_state" in state, f"the stored dictionaries not found in checkpoint!"
# This will load the stored "dictionaries" object
pretrain_task.load_state_dict(state["task_state"])
model = pretrain_task.build_model(w2v_args.model, from_checkpoint=True)
if state is not None and not cfg.no_pretrained_weights:
try:
model.load_state_dict(state["model"], strict=True)
except Exception as e:
logger.warn(e)
model.load_state_dict(state["model"], strict=False)
model.remove_pretraining_modules()
super().__init__(pretrain_task.source_dictionary)
d = w2v_args.model.encoder_embed_dim
self.w2v_model = model
self.final_dropout = nn.Dropout(cfg.final_dropout)
self.freeze_finetune_updates = cfg.freeze_finetune_updates
self.num_updates = 0
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
super().set_num_updates(num_updates)
self.num_updates = num_updates
def forward(self, src_tokens=None, src_lengths=None, **kwargs):
w2v_args = {
"source": src_tokens,
"padding_mask": lengths_to_padding_mask(src_lengths),
"mask": self.apply_mask and self.training,
}
ft = self.freeze_finetune_updates <= self.num_updates
with torch.no_grad() if not ft else contextlib.ExitStack():
x, padding_mask = self.w2v_model.extract_features(**w2v_args)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [padding_mask], # B x T
"padding_mask": [padding_mask],
}
def forward_torchscript(self, net_input):
"""A TorchScript-compatible version of forward.
Forward the encoder out.
"""
_net_input = {
"source": net_input["src_tokens"],
"padding_mask": lengths_to_padding_mask(net_input["src_lengths"]),
"mask": False,
}
x, padding_mask = self.w2v_model.extract_features(**_net_input)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_out = {
"encoder_out" : [x],
"encoder_padding_mask" : [padding_mask],
}
return encoder_out
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out["encoder_out"] is not None:
encoder_out["encoder_out"] = [
x.index_select(1, new_order) for x in encoder_out["encoder_out"]
]
if encoder_out["encoder_padding_mask"] is not None:
encoder_out["encoder_padding_mask"] = [
x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]
]
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return None
def upgrade_state_dict_named(self, state_dict, name):
return state_dict
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.0)
return m
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