It was agreed that the Consortium members should make their own arrangements for subject description. The LEAP partners, at the outset of the SHERPA-LEAP project, recognised that any shared taxonomy for subject description would have to be so large and unwieldy - supporting research into specialist subjects ranging from clinical biomedicine to ancient South-East Asian cultures - as to be entirely off-putting to depositors, and unworkable by administrators. Subject description in the SHERPA-LEAP repositories LASSO is a simple OAI-PMH-based aggregation service which was developed in 2008 as a demonstrator by UCL Library Services it offers cross-searching of the institutional repositories of several SHERPA-LEAP member institutions. These differences are reflected in the content of the Consortium's repository cross-searching service, LASSO (LEAP Aggregated Search Service On-line), making it an ideal testbed in which to expose and examine issues relating to the application of text mining techniques across institutions and disciplines. Within the LEAP partnership there is substantial diversity of institutional size and mission, ranging from the large, multi-disciplinary and research-led, to the smaller and highly-specialised, and a substantial range of research interests. SHERPA-LEAP helps London universities to develop and maintain their institutional repositories. SHERPA-LEAP (London E-prints Access Project, a partner in SHERPA) is a Consortium of London-based Higher Education Institutions, led by UCL. Later, assuming that MERLIN's work in the aggregator is successful, a platform-neutral tool allowing repositories to implement the MERLIN metadata enhancement solution will be made generally available. The testbed for MERLIN will be the SHERPA-LEAP Consortium's LASSO aggregation service.
#Merlin project rsearch full#
9th ISCA Speech Synthesis Workshop (SSW9), September 2016, Sunnyvale, CA, USA.The primary objective of the MERLIN project is to demonstrate and evaluate the use of off-the-shelf text mining and thesaurus tools to derive descriptive subject classification from full text repository deposits, at minimum cost, and to use such derived terms to enhance the discovery and navigability of repository content. Zhizheng Wu, Oliver Watts, Simon King, " Merlin: An Open Source Neural Network Speech Synthesis System" in Proc. If you publish work based on Merlin, please cite: Post your questions, suggestions, and discussions to GitHub Issues.
#Merlin project rsearch how to#
On how to install Merlin and build SLT demo voice.įor a more in-depth tutorial about building voices with Merlin, you can check out: Now, you can also follow Josh Meyer's blog post for detailed instructions To run the example system builds, see egs/README.txtĪs a first demo, please follow the scripts in egs/slt_arctic These instructions are valid for UNIX systems including various flavors of Linux Getting started with Merlin To install Merlin, cd merlin and run the below steps:įor detailed instructions, to build the toolkit: see INSTALL and CSTR blog post. sklearn, keras, h5py (optional, required if you use keras models).tensorflow (optional, required if you use tensorflow models).Merlin is compatible with: Python 2.7-3.6.
#Merlin project rsearch license#
Merlin is free software, distributed under an Apache License Version 2.0, allowing unrestricted commercial and non-commercial use alike. Merlin comes with recipes (in the spirit of the Kaldi automatic speech recognition toolkit) to show you how to build state-of-the art systems. The system is written in Python and relies on the Theano numerical computation library. It must be used in combination with a front-end text processor (e.g., Festival) and a vocoder (e.g., STRAIGHT or WORLD). Merlin is a toolkit for building Deep Neural Network models for statistical parametric speech synthesis. This repository contains the Neural Network (NN) based Speech Synthesis Systemĭeveloped at the Centre for Speech Technology Research (CSTR), University of
Merlin: The Neural Network (NN) based Speech Synthesis System
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