def build_model(cls, args, task):
"""Build a new model instance."""
max_source_positions = getattr(
args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
)
max_target_positions = getattr(
args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim,
)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim
):
raise ValueError(
"--share-decoder-input-output-embed requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
out_channels = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int
)
kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int
)
strides = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_strides, type=int
)
logger.info(
"input feature dimension: {}, channels: {}".format(
task.feat_dim, task.feat_in_channels
)
)
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = (
ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
)
if out_channels is not None
else None
)
rnn_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
rnn_encoder_input_size = (rnn_encoder_input_size + s - 1) // s
rnn_encoder_input_size *= out_channels[-1]
else:
rnn_encoder_input_size = task.feat_dim
if args.encoder_multilayer_rnn_as_single_module and args.encoder_rnn_residual:
args.encoder_rnn_residual = False
logger.info(
"--encoder-rnn-residual is set to False when --encoder-multilayer-rnn-as-single-module=True"
)
scheduled_sampling_rate_scheduler = ScheduledSamplingRateScheduler(
args.scheduled_sampling_probs,
args.start_scheduled_sampling_epoch,
)
encoder = SpeechLSTMEncoder(
pre_encoder=conv_layers,
input_size=rnn_encoder_input_size,
hidden_size=args.encoder_rnn_hidden_size,
num_layers=args.encoder_rnn_layers,
dropout_in=args.encoder_rnn_dropout_in,
dropout_out=args.encoder_rnn_dropout_out,
bidirectional=args.encoder_rnn_bidirectional,
residual=args.encoder_rnn_residual,
src_bucketed=(getattr(task.cfg, "num_batch_buckets", 0) > 0),
max_source_positions=max_source_positions,
multilayer_rnn_as_single_module=args.encoder_multilayer_rnn_as_single_module,
)
decoder = SpeechLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
encoder_output_units=encoder.output_units,
attn_type=args.attention_type,
attn_dim=args.attention_dim,
need_attn=args.need_attention,
residual=args.decoder_rnn_residual,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion_name == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler,
)
pretrained_lm = None
if args.pretrained_lm_checkpoint:
logger.info(
"loading pretrained LM from {}".format(args.pretrained_lm_checkpoint)
)
pretrained_lm = checkpoint_utils.load_model_ensemble(
args.pretrained_lm_checkpoint, task=task
)[0][0]
pretrained_lm.make_generation_fast_()
# freeze pretrained model
for param in pretrained_lm.parameters():
param.requires_grad = False
return cls(encoder, decoder, pretrained_lm)
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
max_source_positions = getattr(args, 'max_source_positions',
DEFAULT_MAX_SOURCE_POSITIONS)
max_target_positions = getattr(args, 'max_target_positions',
DEFAULT_MAX_TARGET_POSITIONS)
def load_pretrained_embedding_from_file(embed_path, dictionary,
embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path, task.target_dictionary,
args.decoder_embed_dim)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim):
raise ValueError(
'--share-decoder-input-output-embed requires '
'--decoder-embed-dim to match --decoder-out-embed-dim')
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
def eval_str_nested_list_or_tuple(x, type=int):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
if isinstance(x, list):
return list(
map(lambda s: eval_str_nested_list_or_tuple(s, type), x))
elif isinstance(x, tuple):
return tuple(
map(lambda s: eval_str_nested_list_or_tuple(s, type), x))
else:
try:
return type(x)
except TypeError:
raise TypeError
out_channels = eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int)
kernel_sizes = eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int)
strides = eval_str_nested_list_or_tuple(args.encoder_conv_strides,
type=int)
logger.info('input feature dimension: {}, channels: {}'.format(
task.feat_dim, task.feat_in_channels))
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
) if out_channels is not None else None
rnn_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
rnn_encoder_input_size = (rnn_encoder_input_size + s - 1) // s
rnn_encoder_input_size *= out_channels[-1]
scheduled_sampling_rate_scheduler = ScheduledSamplingRateScheduler(
args.scheduled_sampling_probs,
args.start_scheduled_sampling_epoch,
)
encoder = SpeechLSTMEncoder(
conv_layers_before=conv_layers,
input_size=rnn_encoder_input_size,
hidden_size=args.encoder_rnn_hidden_size,
num_layers=args.encoder_rnn_layers,
dropout_in=args.encoder_rnn_dropout_in,
dropout_out=args.encoder_rnn_dropout_out,
bidirectional=args.encoder_rnn_bidirectional,
residual=args.encoder_rnn_residual,
max_source_positions=max_source_positions,
)
decoder = SpeechLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
encoder_output_units=encoder.output_units,
attn_type=args.attention_type,
attn_dim=args.attention_dim,
need_attn=args.need_attention,
residual=args.decoder_rnn_residual,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(options.eval_str_list(
args.adaptive_softmax_cutoff, type=int) if args.criterion
== 'adaptive_loss' else None),
max_target_positions=max_target_positions,
scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler,
)
pretrained_lm = None
if args.pretrained_lm_checkpoint:
logger.info('loading pretrained LM from {}'.format(
args.pretrained_lm_checkpoint))
pretrained_lm = checkpoint_utils.load_model_ensemble(
args.pretrained_lm_checkpoint, task=task)[0][0]
pretrained_lm.make_generation_fast_()
# freeze pretrained model
for param in pretrained_lm.parameters():
param.requires_grad = False
return cls(encoder, decoder, pretrained_lm)
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if getattr(args, "max_source_positions", None) is None:
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if getattr(args, "max_target_positions", None) is None:
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
tgt_dict = task.target_dictionary
decoder_embed_tokens = cls.build_embedding(args, tgt_dict,
args.decoder_input_dim,
args.decoder_embed_path)
out_channels = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int)
kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int)
strides = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_strides, type=int)
logger.info("input feature dimension: {}, channels: {}".format(
task.feat_dim, task.feat_in_channels))
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
) if out_channels is not None else None
transformer_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
transformer_encoder_input_size = (
transformer_encoder_input_size + s - 1) // s
transformer_encoder_input_size *= out_channels[-1]
else:
transformer_encoder_input_size = task.feat_dim
encoder_transformer_context = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_transformer_context,
type=int,
)
if encoder_transformer_context is not None:
assert len(encoder_transformer_context) == 2
for i in range(2):
assert (encoder_transformer_context[i] is None
or (isinstance(encoder_transformer_context[i], int)
and encoder_transformer_context[i] >= 0))
scheduled_sampling_rate_scheduler = ScheduledSamplingRateScheduler(
args.scheduled_sampling_probs,
args.start_scheduled_sampling_epoch,
)
encoder = cls.build_encoder(
args,
conv_layers_before=conv_layers,
input_size=transformer_encoder_input_size,
transformer_context=encoder_transformer_context,
)
decoder = cls.build_decoder(
args,
tgt_dict,
decoder_embed_tokens,
scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler,
)
return cls(args, encoder, decoder)
def build_model(cls, cfg, task):
"""Build a new model instance."""
# -- TODO T96535332
# bug caused by interaction between OmegaConf II and argparsing
cfg.decoder.input_dim = int(cfg.decoder.input_dim)
cfg.decoder.output_dim = int(cfg.decoder.output_dim)
# --
if cfg.encoder.layers_to_keep:
cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(","))
if cfg.decoder.layers_to_keep:
cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(","))
if cfg.max_source_positions is None:
cfg.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if cfg.max_target_positions is None:
cfg.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
tgt_dict = task.target_dictionary
decoder_embed_tokens = cls.build_embedding(cfg, tgt_dict,
cfg.decoder.input_dim,
cfg.decoder.embed_path)
if cfg.offload_activations:
cfg.checkpoint_activations = True # offloading implies checkpointing
out_channels = speech_utils.eval_str_nested_list_or_tuple(
cfg.encoder.conv_channels, type=int)
kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(
cfg.encoder.conv_kernel_sizes, type=int)
strides = speech_utils.eval_str_nested_list_or_tuple(
cfg.encoder.conv_strides, type=int)
logger.info("input feature dimension: {}, channels: {}".format(
task.feat_dim, task.feat_in_channels))
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = (ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
) if out_channels is not None else None)
transformer_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
transformer_encoder_input_size = (
transformer_encoder_input_size + s - 1) // s
transformer_encoder_input_size *= out_channels[-1]
else:
transformer_encoder_input_size = task.feat_dim
encoder_transformer_context = speech_utils.eval_str_nested_list_or_tuple(
cfg.encoder.transformer_context,
type=int,
)
if encoder_transformer_context is not None:
assert len(encoder_transformer_context) == 2
for i in range(2):
assert encoder_transformer_context[i] is None or (
isinstance(encoder_transformer_context[i], int)
and encoder_transformer_context[i] >= 0)
scheduled_sampling_rate_scheduler = ScheduledSamplingRateScheduler(
cfg.scheduled_sampling_probs,
cfg.start_scheduled_sampling_epoch,
)
encoder = cls.build_encoder(
cfg,
pre_encoder=conv_layers,
input_size=transformer_encoder_input_size,
transformer_context=encoder_transformer_context,
)
decoder = cls.build_decoder(
cfg,
tgt_dict,
decoder_embed_tokens,
scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler,
)
return cls(cfg, encoder, decoder)