Espnet
Models by this creator
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kan-bayashi_ljspeech_vits
201
The kan-bayashi/ljspeech_vits model is an ESPnet2 text-to-speech (TTS) model trained on the LJSpeech dataset. It is a VITS (Variational Inference for Text-to-Speech) model, a neural vocoder that generates audio samples directly from the input text. This model was developed by the ESPnet team, a group of researchers focused on building an open-source end-to-end speech processing toolkit. Similar TTS models include the mio/amadeus and facebook/fastspeech2-en-ljspeech models, both of which are also trained on the LJSpeech dataset. These models use different architectures, such as FastSpeech 2 and HiFiGAN vocoder, to generate speech from text. Model inputs and outputs Inputs Text**: The model takes in text as input, which it uses to generate an audio waveform. Outputs Audio waveform**: The model outputs an audio waveform representing the synthesized speech. Capabilities The kan-bayashi/ljspeech_vits model is capable of generating high-quality, natural-sounding speech from input text. The VITS architecture allows the model to generate audio directly from text, without the need for a separate vocoder model. What can I use it for? This TTS model can be used to build applications that require text-to-speech functionality, such as audiobook creation, voice assistants, or text-to-speech tools. The model's performance on the LJSpeech dataset suggests it would be suitable for generating speech in a female, English-speaking voice. Things to try You can experiment with the kan-bayashi/ljspeech_vits model by using it to generate audio from different types of text, such as news articles, books, or even user-generated content. You can also compare its performance to other TTS models, such as the fastspeech2-en-ljspeech or tts-tacotron2-ljspeech models, to see how it fares in terms of speech quality and naturalness.
Updated 5/28/2024
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xeus
97
XEUS is a large-scale multilingual speech encoder developed by the WAVLab at Carnegie Mellon University. It covers over 4,000 languages and is pre-trained on over 1 million hours of publicly available speech datasets. XEUS uses the E-Branchformer architecture and is trained using HuBERT-style masked prediction of discrete speech tokens extracted from WavLabLM. The total model size is 577M parameters. XEUS tops the ML-SUPERB multilingual speech recognition leaderboard, outperforming models like MMS, w2v-BERT 2.0, and XLS-R. It also sets a new state-of-the-art on 4 tasks in the monolingual SUPERB benchmark. Model inputs and outputs Inputs Audio Waveform**: XEUS takes raw audio waveform as input, which it encodes into a sequence of speech representations. Outputs Speech Representations**: The model outputs a sequence of speech representations that can be used for downstream tasks such as speech recognition or translation. These representations capture the semantic and acoustic properties of the input speech. Capabilities XEUS is a powerful multilingual speech encoder that can be leveraged for a variety of speech-related tasks. Its broad language coverage and robust performance on benchmarks make it a compelling choice for those working on multilingual speech applications. What can I use it for? XEUS can be used as a speech encoder in various downstream applications, such as automatic speech recognition, speech-to-text translation, and speech-based semantic understanding. By fine-tuning the model on task-specific data, users can take advantage of its strong multilingual capabilities to build solutions that work across a wide range of languages. Things to try One interesting aspect of XEUS is its use of the E-Branchformer architecture and HuBERT-style training. This allows the model to learn robust speech representations that capture both semantic and acoustic properties of the input. When fine-tuning XEUS on downstream tasks, it would be interesting to explore how the model's performance compares to other multilingual speech models and how the architectural choices impact the final results. Another area to explore is the model's ability to handle low-resource languages. With its coverage of over 4,000 languages, XEUS could be a valuable tool for building speech technologies for endangered or under-resourced languages. Researchers and developers could investigate the model's performance on these languages and explore techniques for further improving its capabilities in low-resource settings.
Updated 8/7/2024