def build_encoder_layer( self, args, positional_embedding: Optional[RelativePositionalEmbedding] = None ): return super().build_encoder_layer( SpeechTransformerConfig.from_namespace(args), positional_embedding=positional_embedding, )
def build_decoder(cls, args, tgt_dict, embed_tokens, scheduled_sampling_rate_scheduler=None): return super().build_decoder( SpeechTransformerConfig.from_namespace(args), tgt_dict, embed_tokens, scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler, )
def build_encoder(cls, args, conv_layers_before=None, input_size=83, transformer_context=None): return super().build_encoder( SpeechTransformerConfig.from_namespace(args), conv_layers_before=conv_layers_before, input_size=input_size, transformer_context=transformer_context, )
def __init__( self, args, return_fc=False, pre_encoder=None, input_size=83, transformer_context=None, ): self.args = args super().__init__( SpeechTransformerConfig.from_namespace(args), return_fc=return_fc, pre_encoder=pre_encoder, input_size=input_size, transformer_context=transformer_context, )
def __init__( self, args, dictionary, embed_tokens, no_encoder_attn=False, output_projection=None, scheduled_sampling_rate_scheduler=None, ): self.args = args super().__init__( SpeechTransformerConfig.from_namespace(args), dictionary, embed_tokens, no_encoder_attn=no_encoder_attn, output_projection=output_projection, scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler, )
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 if getattr(args, "offload_activations", False): args.checkpoint_activations = True # offloading implies checkpointing args.min_params_to_wrap = getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP) cfg = SpeechTransformerConfig.from_namespace(args) return super().build_model(cfg, task)
def build_output_projection(self, args, dictionary, embed_tokens): super().build_output_projection( SpeechTransformerConfig.from_namespace(args), dictionary, embed_tokens)
def build_embedding(cls, args, dictionary, embed_dim, path=None): return super().build_embedding( SpeechTransformerConfig.from_namespace(args), dictionary, embed_dim, path)
def __init__(self, args, encoder, decoder): cfg = SpeechTransformerConfig.from_namespace(args) super().__init__(cfg, encoder, decoder) self.args = args