styletts2

Maintainer: adirik

Total Score

4.2K

Last updated 6/19/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

styletts2 is a text-to-speech (TTS) model developed by Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, and Nima Mesgarani. It leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. Unlike its predecessor, styletts2 models styles as a latent random variable through diffusion models, allowing it to generate the most suitable style for the text without requiring reference speech. It also employs large pre-trained SLMs, such as WavLM, as discriminators with a novel differentiable duration modeling for end-to-end training, resulting in improved speech naturalness.

Model inputs and outputs

styletts2 takes in text and generates high-quality speech audio. The model inputs and outputs are as follows:

Inputs

  • Text: The text to be converted to speech.
  • Beta: A parameter that determines the prosody of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text.
  • Alpha: A parameter that determines the timbre of the generated speech, with lower values sampling style based on previous or reference speech and higher values sampling more from the text.
  • Reference: An optional reference speech audio to copy the style from.
  • Diffusion Steps: The number of diffusion steps to use in the generation process, with higher values resulting in better quality but longer generation time.
  • Embedding Scale: A scaling factor for the text embedding, which can be used to produce more pronounced emotion in the generated speech.

Outputs

  • Audio: The generated speech audio in the form of a URI.

Capabilities

styletts2 is capable of generating human-level TTS synthesis on both single-speaker and multi-speaker datasets. It surpasses human recordings on the LJSpeech dataset and matches human performance on the VCTK dataset. When trained on the LibriTTS dataset, styletts2 also outperforms previous publicly available models for zero-shot speaker adaptation.

What can I use it for?

styletts2 can be used for a variety of applications that require high-quality text-to-speech generation, such as audiobook production, voice assistants, language learning tools, and more. The ability to control the prosody and timbre of the generated speech, as well as the option to use reference audio, makes styletts2 a versatile tool for creating personalized and expressive speech output.

Things to try

One interesting aspect of styletts2 is its ability to perform zero-shot speaker adaptation on the LibriTTS dataset. This means that the model can generate speech in the style of speakers it has not been explicitly trained on, by leveraging the diverse speech synthesis offered by the diffusion model. Developers could explore the limits of this zero-shot adaptation and experiment with fine-tuning the model on new speakers to further improve the quality and diversity of the generated speech.



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

AI model preview image

hierspeechpp

adirik

Total Score

4

hierspeechpp is a zero-shot speech synthesizer developed by Replicate user adirik. It is a text-to-speech model that can generate speech from text and a target voice, enabling zero-shot speech synthesis. This model is similar to other text-to-speech models like styletts2, voicecraft, and whisperspeech-small, which also focus on generating speech from text or audio. Model inputs and outputs hierspeechpp takes in text or audio as input and generates an audio file as output. The model allows you to provide a target voice clip, which it will use to synthesize the output speech. This enables zero-shot speech synthesis, where the model can generate speech in the voice of the target speaker without requiring any additional training data. Inputs input_text**: (optional) Text input to the model. If provided, it will be used for the speech content of the output. input_sound**: (optional) Sound input to the model in .wav format. If provided, it will be used for the speech content of the output. target_voice**: A voice clip in .wav format containing the speaker to synthesize. denoise_ratio**: Noise control. 0 means no noise reduction, 1 means maximum noise reduction. text_to_vector_temperature**: Temperature for text-to-vector model. Larger value corresponds to slightly more random output. output_sample_rate**: Sample rate of the output audio file. scale_output_volume**: Scale normalization. If set to true, the output audio will be scaled according to the input sound if provided. seed**: Random seed to use for reproducibility. Outputs Output**: An audio file in .mp3 format containing the synthesized speech. Capabilities hierspeechpp can generate high-quality speech by leveraging a target voice clip. It is capable of zero-shot speech synthesis, meaning it can create speech in the voice of the target speaker without any additional training data. This allows for a wide range of applications, such as voice cloning, audiobook narration, and dubbing. What can I use it for? You can use hierspeechpp for various speech-related tasks, such as creating custom voice interfaces, generating audio content for podcasts or audiobooks, or even dubbing videos in different languages. The zero-shot nature of the model makes it particularly useful for projects where you need to generate speech in a specific voice without access to a large dataset of that speaker's recordings. Things to try One interesting thing to try with hierspeechpp is to experiment with the different input parameters, such as the denoise_ratio and text_to_vector_temperature. By adjusting these settings, you can fine-tune the output to your specific needs, such as reducing background noise or making the speech more natural-sounding. Additionally, you can try using different target voice clips to see how the model adapts to different speakers.

Read more

Updated Invalid Date

AI model preview image

stylemc

adirik

Total Score

2

StyleMC is a text-guided image generation and editing model developed by Replicate creator adirik. It uses a multi-channel approach to enable fast and efficient text-guided manipulation of images. StyleMC can be used to generate and edit images based on textual prompts, allowing users to create new images or modify existing ones in a guided manner. Similar models like GFPGAN focus on practical face restoration, while Deliberate V6, LLaVA-13B, AbsoluteReality V1.8.1, and Reliberate V3 offer more general text-to-image and image-to-image capabilities. StyleMC aims to provide a specialized solution for text-guided image editing and manipulation. Model inputs and outputs StyleMC takes in an input image and a text prompt, and outputs a modified image based on the provided prompt. The model can be used to generate new images from scratch or to edit existing images in a text-guided manner. Inputs Image**: The input image to be edited or manipulated. Prompt**: The text prompt that describes the desired changes to be made to the input image. Change Alpha**: The strength coefficient to apply the style direction with. Custom Prompt**: An optional custom text prompt that can be used instead of the provided prompt. Id Loss Coeff**: The identity loss coefficient, which can be used to control the balance between preserving the original image's identity and applying the desired changes. Outputs Modified Image**: The output image that has been generated or edited based on the provided text prompt and other input parameters. Capabilities StyleMC excels at text-guided image generation and editing. It can be used to create new images from scratch or modify existing images in a variety of ways, such as changing the hairstyle, adding or removing specific features, or altering the overall style or mood of the image. What can I use it for? StyleMC can be particularly useful for creative applications, such as generating concept art, designing characters or scenes, or experimenting with different visual styles. It can also be used for more practical applications, such as editing product images or creating personalized content for social media. Things to try One interesting aspect of StyleMC is its ability to find a global manipulation direction based on a target text prompt. This allows users to explore the range of possible edits that can be made to an image based on a specific textual description, and then apply those changes in a controlled manner. Another feature to try is the video generation capability, which can create an animation of the step-by-step manipulation process. This can be a useful tool for understanding and demonstrating the model's capabilities.

Read more

Updated Invalid Date

🤷

tortoise-tts

afiaka87

Total Score

159

tortoise-tts is a text-to-speech model developed by James Betker, also known as "neonbjb". It is designed to generate highly realistic speech with strong multi-voice capabilities and natural-sounding prosody and intonation. The model is inspired by OpenAI's DALL-E and uses a combination of autoregressive and diffusion models to achieve its results. Compared to similar models like neon-tts, tortoise-tts aims for more expressive and natural-sounding speech. It can also generate "random" voices that don't correspond to any real speaker, which can be quite fascinating to experiment with. However, the tradeoff is that tortoise-tts is relatively slow, taking several minutes to generate a single sentence on consumer hardware. Model inputs and outputs The tortoise-tts model takes in a text prompt and various optional parameters to control the voice and generation process. The key inputs are: Inputs text**: The text to be spoken voice_a**: The primary voice to use, which can be set to "random" for a generated voice voice_b* and *voice_c**: Optional secondary and tertiary voices to blend with voice_a preset**: A set of pre-defined generation settings, such as "fast" for quicker but potentially lower-quality output seed**: A random seed to ensure reproducible results cvvp_amount**: A parameter to control the influence of the CVVP model, which can help reduce the likelihood of multiple speakers The output of the model is a URI pointing to the generated audio file. Capabilities tortoise-tts is capable of generating highly realistic and expressive speech from text. It can mimic a wide range of voices, including those of specific speakers, and can also generate entirely new "random" voices. The model is particularly adept at capturing nuanced prosody and intonation, making the speech sound natural and lifelike. One of the key strengths of tortoise-tts is its ability to blend multiple voices together to create a new composite voice. This allows for interesting experiments in voice synthesis and can lead to unique and unexpected results. What can I use it for? tortoise-tts could be useful for a variety of applications that require high-quality text-to-speech, such as audiobook production, voice-over work, or conversational AI assistants. The model's multi-voice capabilities could also be interesting for creative projects like audio drama or sound design. However, it's important to be mindful of the ethical considerations around voice cloning technology. The maintainer, afiaka87, has addressed these concerns and implemented safeguards, such as a classifier to detect Tortoise-generated audio. Still, it's crucial to use the model responsibly and avoid any potential misuse. Things to try One interesting aspect of tortoise-tts is its ability to generate "random" voices that don't correspond to any real speaker. These synthetic voices can be quite captivating and may inspire creative applications or further research into generative voice synthesis. Experimenting with the blending of multiple voices can also lead to unexpected and fascinating results. By combining different speaker characteristics, you can create unique vocal timbres and expressions. Additionally, the model's focus on expressive prosody and intonation makes it well-suited for projects that require emotive or nuanced speech, such as audiobooks, podcasts, or interactive voice experiences.

Read more

Updated Invalid Date

AI model preview image

texture

adirik

Total Score

1

The texture model, developed by adirik, is a powerful tool for generating textures for 3D objects using text prompts. This model can be particularly useful for creators and designers who want to add realistic textures to their 3D models. Compared to similar models like stylemc, interior-design, text2image, styletts2, and masactrl-sdxl, the texture model is specifically focused on generating textures for 3D objects. Model inputs and outputs The texture model takes a 3D object file, a text prompt, and several optional parameters as inputs to generate a texture for the 3D object. The model's outputs are an array of image URLs representing the generated textures. Inputs Shape Path**: The 3D object file to generate the texture onto Prompt**: The text prompt used to generate the texture Shape Scale**: The factor to scale the 3D object by Guidance Scale**: The factor to scale the guidance image by Texture Resolution**: The resolution of the texture to generate Texture Interpolation Mode**: The texture mapping interpolation mode, with options like "nearest", "bilinear", and "bicubic" Seed**: The seed for the inference Outputs An array of image URLs representing the generated textures Capabilities The texture model can generate high-quality textures for 3D objects based on text prompts. This can be particularly useful for creating realistic-looking 3D models for various applications, such as game development, product design, or architectural visualizations. What can I use it for? The texture model can be used by 3D artists, game developers, product designers, and others who need to add realistic textures to their 3D models. By providing a text prompt, users can quickly generate a variety of textures that can be applied to their 3D objects. This can save a significant amount of time and effort compared to manually creating textures. Additionally, the model's ability to scale the 3D object and adjust the texture resolution and interpolation mode allows for fine-tuning the output to meet the specific needs of the project. Things to try One interesting thing to try with the texture model is experimenting with different text prompts to see the range of textures the model can generate. For example, you could try prompts like "a weathered metal surface" or "a lush, overgrown forest floor" to see how the model responds. Additionally, you could try adjusting the shape scale, guidance scale, and texture resolution to see how those parameters affect the generated textures.

Read more

Updated Invalid Date