DigitalPhonetics/hard-negative-captions

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Python
Created about 1 year ago
Updated 5 months ago

DigitalPhonetics/hard-negative-captions

DigitalPhonetics

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IMS-Toucan

Multilingual and Controllable Text-to-Speech Toolkit of the Speech and Language Technologies Group at the University of Stuttgart.

adviser

ADvISER is a flexible framework to encourage task-oriented dialog system research & development

VoicePAT

VoicePAT is a modular and efficient toolkit for voice privacy research, with main focus on speaker anonymization.

speaker-anonymization

Speaker anonymization pipeline for hiding the identity of the speaker of a recording by changing the voice in it.

reading-comprehension

Comparing attention-based convolutional and recurrent neural networks under adversarial attacks to investigate their success and limitations in machine reading comprehension

cyclegan-emotion-transfer

CycleGAN-based Emotion Style Transfer as Data Augmentation for Speech Emotion Recognition

conversational-tree-search

Code and Data for Conversational Tree Search: A new task that bridges the gap between FAQ-style information retrieval and task-oriented dialog.

IMS-Speech

IMS-Speech is a tool for German, English and Russian speech transcription aiming to facilitate research in various disciplines. We are willing to provide a speech transcription service with an intuitive web interface accessible with a wide range of computing devices and to people with various backgrounds. Our service is available here: https://75474978-c3fa-43a5-aa6c-ee36f2513064.ma.bw-cloud-instance.org/ims-speech/

nlg-eval

Code accompanying the INLG 2018 paper Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity

Intrinsic-Subgraph-Generation-for-VQA

Predicting a subgraph alongside the answer in a graph based VQA model

diagraph

DIAGRAPH: An open-source graphic interface for dialog flow design

multilingual-seq2seq-slu

Materials for the publication "Leveraging Multilingual Self-Supervised Pretrained Models for Sequence-to-Sequence End-to-End Spoken Language Understanding"

BetterFinetuning

Code accompanying our paper on finetuning self-supervised general speech representations with a combination of contrastive and non-contrastive methods.