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)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict,
task.target_dictionary)
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
fconv_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
fconv_encoder_input_size = (fconv_encoder_input_size + s -
1) // s
fconv_encoder_input_size *= out_channels[-1]
encoder = SpeechFConvEncoder(
conv_layers_before=conv_layers,
input_size=fconv_encoder_input_size,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
)
decoder = SpeechFConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
positional_embeddings=args.decoder_positional_embed,
)
return cls(encoder, decoder)
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)
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
encoder = SpeechChunkLSTMEncoder(
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,
src_bucketed=(getattr(task.args, "num_batch_buckets", 0) > 0),
num_targets=getattr(task, "num_targets",
None), # targets for encoder-only model
chunk_width=getattr(task, "chunk_width", None),
chunk_left_context=getattr(task, "chunk_left_context", 0),
training_stage=getattr(task, "training_stage", True),
max_source_positions=max_source_positions,
)
return cls(encoder,
state_prior=getattr(task, "initial_state_prior", None))
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, 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)
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
if getattr(args, "max_source_positions", None) is None:
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
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))
encoder = cls.build_encoder(
args,
conv_layers_before=conv_layers,
input_size=transformer_encoder_input_size,
transformer_context=encoder_transformer_context,
num_targets=getattr(task, "num_targets",
None), # targets for encoder-only model
chunk_width=getattr(task, "chunk_width", None),
chunk_left_context=getattr(task, "chunk_left_context", 0),
training_stage=getattr(task, "training_stage", True),
)
return cls(args,
encoder,
state_prior=getattr(task, "initial_state_prior", None))
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
dict = task.target_dictionary
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
decoder_embed_tokens = build_embedding(dict, args.decoder_embed_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]
encoder = cls.build_encoder(
args,
conv_layers_before=conv_layers,
input_size=transformer_encoder_input_size,
)
decoder = cls.build_decoder(args, dict, decoder_embed_tokens)
return cls(encoder, decoder)