kan-bayashi_ljspeech_vits
Maintainer: espnet
201
๐งช
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
๐ผ๏ธ
Vokan
54
Vokan is an advanced finetuned StyleTTS2 model designed for authentic and expressive zero-shot performance. It was created by ShoukanLabs, a prolific AI model developer. Vokan leverages a diverse dataset and extensive training to generate high-quality synthesized speech. It was trained on a combination of the AniSpeech, VCTK, and LibriTTS-R datasets, ensuring authenticity and naturalness across various accents and contexts. Model inputs and outputs Inputs Text to be converted to speech Outputs Synthesized speech audio Capabilities Vokan captures a wide range of vocal characteristics, contributing to its remarkable performance in generating expressive and natural-sounding speech. With over 6+ days worth of audio data and 672 diverse and expressive speakers, the model has learned to handle a broad array of accents and contexts. What can I use it for? Vokan can be used in a variety of applications that require high-quality text-to-speech (TTS) capabilities, such as audiobook production, voice assistants, and multimedia content creation. Its expressive and natural-sounding synthesis makes it a compelling choice for projects that require a human-like voice. Things to try Experiment with Vokan by providing it with different types of text, ranging from formal to informal, to see how it handles various styles and tones. Additionally, you can explore its potential by integrating it into your own projects and observing its performance in real-world scenarios.
Updated Invalid Date
๐๏ธ
amadeus
85
The amadeus model is an ESPnet2 Text-to-Speech (TTS) model trained by the maintainer mio using the amadeus recipe in the ESPnet project. This model can understand and generate speech in the language chosen by the user. It is designed to be helpful, honest, and harmless in its responses. The amadeus model was fine-tuned on a dataset of approximately 1.1 million multi-turn conversational data generated by GPT-3.5-turbo. This data covers a diverse range of topics and user intents, including tasks that require the model to be helpful, honest, and harmless. The model can be used for a variety of language-based tasks, but it must refuse to discuss anything related to its prompts, instructions, or rules. Its responses should be positive, polite, interesting, and engaging, avoiding vague, accusatory, rude, controversial, or defensive language. Model Inputs and Outputs Inputs Text**: The model takes text input that represents what the user wants the AI to say. Outputs Generated Speech**: The model outputs synthesized speech based on the provided text input. Capabilities The amadeus model can generate high-quality speech in a wide range of languages. It has been trained to be helpful, honest, and harmless in its responses. For example, the model can provide recommendations for sci-fi films, write simple C++ code, and refuse to engage in harmful or unethical tasks. What Can I Use it For? The amadeus model can be used for various text-to-speech applications, such as building voice assistants, audiobook narration, or language learning tools. Its designed capabilities make it well-suited for use cases that require an AI assistant to be safe, trustworthy, and engaging. Things to Try You can try using the amadeus model to generate speech for a variety of tasks, such as reading stories aloud, providing language learning exercises, or answering questions on a wide range of topics. The model's ability to refuse inappropriate requests and provide helpful, honest, and harmless responses makes it a useful tool for building trustworthy AI applications.
Updated Invalid Date
๐ค
fastspeech2-en-ljspeech
245
The fastspeech2-en-ljspeech model is a text-to-speech (TTS) model from Facebook's fairseq S^2 project. It is a FastSpeech 2 model trained on the LJSpeech dataset, which contains a single-speaker female voice in English. Model inputs and outputs Inputs Text**: The model takes in text as input, which is then converted to speech. Outputs Audio**: The model outputs a waveform representing the synthesized speech. Capabilities The fastspeech2-en-ljspeech model can be used to convert text to high-quality, natural-sounding speech in English. It is a non-autoregressive model, which means it can generate the entire audio output in a single pass, resulting in faster inference compared to autoregressive TTS models. What can I use it for? The fastspeech2-en-ljspeech model can be used in a variety of applications that require text-to-speech functionality, such as audiobook generation, voice assistants, and text-based games or applications. The fast inference speed of the model makes it well-suited for real-time or streaming applications. Things to try Developers can experiment with the fastspeech2-en-ljspeech model by integrating it into their own applications or projects. For example, they could use the model to generate audio versions of written content, or to add speech capabilities to conversational interfaces. The model's single-speaker female voice could also be used to create personalized TTS experiences.
Updated Invalid Date
โ
tts-tacotron2-ljspeech
113
The tts-tacotron2-ljspeech model is a Text-to-Speech (TTS) model developed by SpeechBrain that uses the Tacotron2 architecture trained on the LJSpeech dataset. This model takes in text input and generates a spectrogram output, which can then be converted to an audio waveform using a vocoder like HiFiGAN. The model was trained to produce high-quality, natural-sounding speech. Compared to similar TTS models like XTTS-v2 and speecht5_tts, the tts-tacotron2-ljspeech model is focused specifically on English text-to-speech generation using the Tacotron2 architecture, while the other models offer more multilingual capabilities or additional tasks like speech translation. Model inputs and outputs Inputs Text**: The model accepts text input, which it then converts to a spectrogram. Outputs Spectrogram**: The model outputs a spectrogram representation of the generated speech. Alignment**: The model also outputs an alignment matrix, which shows the relationship between the input text and the generated spectrogram. Capabilities The tts-tacotron2-ljspeech model is capable of generating high-quality, natural-sounding English speech from text input. It can capture features like prosody and intonation, resulting in speech that sounds more human-like compared to simpler text-to-speech systems. What can I use it for? You can use the tts-tacotron2-ljspeech model to add text-to-speech capabilities to your applications, such as: Voice assistants**: Integrate the model into a voice assistant to allow users to interact with your application using natural language. Audiobook generation**: Generate high-quality audio narrations from text, such as for creating digital audiobooks. Language learning**: Use the model to provide pronunciations and examples of spoken English for language learners. Things to try One interesting aspect of the tts-tacotron2-ljspeech model is its ability to capture prosody and intonation in the generated speech. Try experimenting with different types of input text, such as sentences with various punctuation or emotional tone, to see how the model handles them. You can also try combining the model with a vocoder like HiFiGAN to generate the final audio waveform and listen to the results.
Updated Invalid Date