def main(args): if args.use_ivectors: args.input_dim = args.input_dim + args.ivector_dim input_data = T.ftensor3('input_data') input_mask = T.fmatrix('input_mask') network = models.deep_bidir_lstm_alex(input_var=input_data, mask_var=input_mask, input_dim=args.input_dim, num_units_list=[args.num_nodes] * args.num_layers, output_dim=args.output_dim) network_params = get_all_params(network, trainable=True) print('Loading Parameters...', file=sys.stderr) if args.model: with open(args.model, 'rb') as f: pretrain_network_params_val, pretrain_update_params_val, \ pretrain_total_epoch_cnt = pickle.load(f) set_model_param_value(network_params, pretrain_network_params_val) else: print('Must specfiy network to load', file=sys.stderr) sys.exit(1) ff_fn = ff(input_data=input_data, input_mask=input_mask, network=network) feat_stream = fuel_utils.get_feat_stream(args.data_path, args.dataset, args.batch_size, use_ivectors=args.use_ivectors) uttid_stream = fuel_utils.get_uttid_stream(args.data_path, args.dataset, args.batch_size) writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename) for batch_idx, (feat_batch, uttid_batch) in enumerate( zip(feat_stream.get_epoch_iterator(), uttid_stream.get_epoch_iterator())): input_data, input_mask = feat_batch feat_lens = input_mask.sum(axis=1) print('Feed-forwarding...', file=sys.stderr) net_output = ff_fn(input_data, input_mask) print('Writing outputs...', file=sys.stderr) for out_idx, (output, uttid) in enumerate(zip(net_output[0], uttid_batch[0])): valid_len = feat_lens[out_idx] writer.write(uttid.encode('ascii'), numpy.log(output[:valid_len])) writer.close()
print('Loading Parameters...', file=sys.stderr) if args.model: with open(args.model, 'rb') as f: [ pretrain_network_params_val, pretrain_update_params_val, pretrain_total_epoch_cnt ] = pickle.load(f) set_model_param_value(network_params, pretrain_network_params_val) else: print('Must specfiy network to load', file=sys.stderr) sys.exit(1) ff_fn = ff(network, input_data, input_mask) test_datastream = get_feat_stream(path=args.data_path, which_set=args.dataset, batch_size=args.batch_size) uttid_datastream = get_uttid_stream(path=args.data_path, which_set=args.dataset, batch_size=args.batch_size) writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename) for batch_idx, (feat_batch, uttid_batch) in enumerate( zip(test_datastream.get_epoch_iterator(), uttid_datastream.get_epoch_iterator())): input_data, input_mask = feat_batch feat_lens = input_mask.sum(axis=1) print('Feed-forwarding...', file=sys.stderr) net_output = ff_fn(input_data, input_mask)
print('Loading Parameters...', file=sys.stderr) if args.model: with open(args.model, 'rb') as f: [ pretrain_network_params_val, pretrain_update_params_val, pretrain_total_epoch_cnt ] = pickle.load(f) set_model_param_value(network_params, pretrain_network_params_val) else: print('Must specfiy network to load', file=sys.stderr) sys.exit(1) ff_fn = ff(network, input_data, input_cond, input_mask) test_datastream = get_feat_stream(path=args.data_path, which_set=args.dataset, batch_size=args.batch_size, use_ivectors=True) uttid_datastream = get_uttid_stream(path=args.data_path, which_set=args.dataset, batch_size=args.batch_size) writer = kaldi_io.BaseFloatMatrixWriter(args.wxfilename) for batch_idx, (feat_batch, uttid_batch) in enumerate( zip(test_datastream.get_epoch_iterator(), uttid_datastream.get_epoch_iterator())): input_data, input_mask, input_cond, _ = feat_batch feat_lens = input_mask.sum(axis=1) print('Feed-forwarding...', file=sys.stderr) net_output = ff_fn(input_data, input_cond, input_mask)