neon-tts

Maintainer: awerks

Total Score

46

Last updated 5/21/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The neon-tts model is a Mycroft-compatible Text-to-Speech (TTS) plugin developed by Replicate user awerks. It utilizes the Coqui AI Text-to-Speech library to provide support for a wide range of languages, including all major European Union languages. As noted by the maintainer awerks, the model's performance is impressive, with real-time factors (RTF) ranging from 0.05 on high-end AMD/Intel machines to 0.5 on a Raspberry Pi 4. This makes the neon-tts model well-suited for a variety of applications, from desktop assistants to embedded systems.

Model inputs and outputs

The neon-tts model takes two inputs: a text string and a language code. The text is the input that will be converted to speech, and the language code specifies the language of the input text. The model outputs a URI representing the generated audio file.

Inputs

  • text: The text to be converted to speech
  • language: The language of the input text, defaults to "en" (English)

Outputs

  • Output: A URI representing the generated audio file

Capabilities

The neon-tts model is a powerful tool for generating high-quality speech from text. It supports a wide range of languages, making it useful for applications targeting international audiences. The model's impressive performance, with real-time factors as low as 0.05, allows for seamless integration into a variety of systems, from desktop assistants to embedded devices.

What can I use it for?

The neon-tts model can be used in a variety of applications that require text-to-speech functionality. Some potential use cases include:

  • Virtual assistants: Integrate the neon-tts model into a virtual assistant to provide natural-sounding speech output.
  • Accessibility tools: Use the model to convert written content to speech, making it more accessible for users with visual impairments or reading difficulties.
  • Multimedia applications: Incorporate the neon-tts model into video, audio, or gaming applications to add voice narration or spoken dialogue.
  • Educational resources: Create interactive learning materials that use the neon-tts model to read aloud text or provide audio instructions.

Things to try

One interesting aspect of the neon-tts model is its ability to support a wide range of languages, including less common ones like Irish and Maltese. This makes it a versatile tool for creating multilingual applications or content. You could experiment with generating speech in various languages to see how the model handles different linguistic structures and phonologies.

Another interesting feature of the neon-tts model is its low resource requirements, allowing it to run efficiently on devices like the Raspberry Pi. This makes it a compelling choice for embedded systems or edge computing applications where performance and portability are important.



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

🤷

tortoise-tts

afiaka87

Total Score

156

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

xtts-v2

lucataco

Total Score

148

The xtts-v2 model is a multilingual text-to-speech voice cloning system developed by lucataco, the maintainer of this Cog implementation. This model is part of the Coqui TTS project, an open-source text-to-speech library. The xtts-v2 model is similar to other text-to-speech models like whisperspeech-small, styletts2, and qwen1.5-110b, which also generate speech from text. Model inputs and outputs The xtts-v2 model takes three main inputs: text to synthesize, a speaker audio file, and the output language. It then produces a synthesized audio file of the input text spoken in the voice of the provided speaker. Inputs Text**: The text to be synthesized Speaker**: The original speaker audio file (wav, mp3, m4a, ogg, or flv) Language**: The output language for the synthesized speech Outputs Output**: The synthesized audio file Capabilities The xtts-v2 model can generate high-quality multilingual text-to-speech audio by cloning the voice of a provided speaker. This can be useful for a variety of applications, such as creating personalized audio content, improving accessibility, or enhancing virtual assistants. What can I use it for? The xtts-v2 model can be used to create personalized audio content, such as audiobooks, podcasts, or video narrations. It could also be used to improve accessibility by generating audio versions of written content for users with visual impairments or other disabilities. Additionally, the model could be integrated into virtual assistants or chatbots to provide a more natural, human-like voice interface. Things to try One interesting thing to try with the xtts-v2 model is to experiment with different speaker audio files to see how the synthesized voice changes. You could also try using the model to generate audio in various languages and compare the results. Additionally, you could explore ways to integrate the model into your own applications or projects to enhance the user experience.

Read more

Updated Invalid Date

AI model preview image

whisper

openai

Total Score

8.9K

Whisper is a general-purpose speech recognition model developed by OpenAI. It is capable of converting speech in audio to text, with the ability to translate the text to English if desired. Whisper is based on a large Transformer model trained on a diverse dataset of multilingual and multitask speech recognition data. This allows the model to handle a wide range of accents, background noises, and languages. Similar models like whisper-large-v3, incredibly-fast-whisper, and whisper-diarization offer various optimizations and additional features built on top of the core Whisper model. Model inputs and outputs Whisper takes an audio file as input and outputs a text transcription. The model can also translate the transcription to English if desired. The input audio can be in various formats, and the model supports a range of parameters to fine-tune the transcription, such as temperature, patience, and language. Inputs Audio**: The audio file to be transcribed Model**: The specific version of the Whisper model to use, currently only large-v3 is supported Language**: The language spoken in the audio, or None to perform language detection Translate**: A boolean flag to translate the transcription to English Transcription**: The format for the transcription output, such as "plain text" Initial Prompt**: An optional initial text prompt to provide to the model Suppress Tokens**: A list of token IDs to suppress during sampling Logprob Threshold**: The minimum average log probability threshold for a successful transcription No Speech Threshold**: The threshold for considering a segment as silence Condition on Previous Text**: Whether to provide the previous output as a prompt for the next window Compression Ratio Threshold**: The maximum compression ratio threshold for a successful transcription Temperature Increment on Fallback**: The temperature increase when the decoding fails to meet the specified thresholds Outputs Transcription**: The text transcription of the input audio Language**: The detected language of the audio (if language input is None) Tokens**: The token IDs corresponding to the transcription Timestamp**: The start and end timestamps for each word in the transcription Confidence**: The confidence score for each word in the transcription Capabilities Whisper is a powerful speech recognition model that can handle a wide range of accents, background noises, and languages. The model is capable of accurately transcribing audio and optionally translating the transcription to English. This makes Whisper useful for a variety of applications, such as real-time captioning, meeting transcription, and audio-to-text conversion. What can I use it for? Whisper can be used in various applications that require speech-to-text conversion, such as: Captioning and Subtitling**: Automatically generate captions or subtitles for videos, improving accessibility for viewers. Meeting Transcription**: Transcribe audio recordings of meetings, interviews, or conferences for easy review and sharing. Podcast Transcription**: Convert audio podcasts to text, making the content more searchable and accessible. Language Translation**: Transcribe audio in one language and translate the text to another, enabling cross-language communication. Voice Interfaces**: Integrate Whisper into voice-controlled applications, such as virtual assistants or smart home devices. Things to try One interesting aspect of Whisper is its ability to handle a wide range of languages and accents. You can experiment with the model's performance on audio samples in different languages or with various background noises to see how it handles different real-world scenarios. Additionally, you can explore the impact of the different input parameters, such as temperature, patience, and language detection, on the transcription quality and accuracy.

Read more

Updated Invalid Date

AI model preview image

parakeet-rnnt-1.1b

nvlabs

Total Score

1

The parakeet-rnnt-1.1b is an advanced speech recognition model developed by NVIDIA and Suno.ai. It features the FastConformer architecture and is available in both RNNT and CTC versions, making it well-suited for transcribing English speech in noisy audio environments while maintaining accuracy in silent segments. This model outperforms the popular OpenAI Whisper model on the Open ASR Leaderboard, reclaiming the top spot for speech recognition accuracy. Model inputs and outputs Inputs audio_file**: The input audio file to be transcribed by the ASR model, in a supported audio format. Outputs Output**: The transcribed text output from the speech recognition model. Capabilities The parakeet-rnnt-1.1b model is capable of high-accuracy speech transcription, particularly in challenging audio environments. It has been trained on a diverse 65,000-hour dataset, enabling robust performance across a variety of use cases. Compared to the OpenAI Whisper model, the parakeet-rnnt-1.1b achieves lower Word Error Rates (WER) on benchmarks like AMI, Earnings22, Gigaspeech, and Common Voice 9. What can I use it for? The parakeet-rnnt-1.1b model is designed for precision ASR tasks in voice recognition and transcription, making it suitable for a range of applications such as voice-to-text conversion, meeting minutes generation, and closed captioning. It can be integrated into the NeMo toolkit for a broader set of use cases. However, users should be mindful of data privacy and potential biases in speech recognition, ensuring fair and responsible use of the technology. Things to try Experimenting with the parakeet-rnnt-1.1b model in various audio scenarios, such as noisy environments or recordings with silent segments, can help evaluate its performance and suitability for specific use cases. Additionally, testing the model's accuracy and efficiency on different benchmarks can provide valuable insights into its capabilities.

Read more

Updated Invalid Date