Nvlabs
Models by this creator
prismer
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Prismer is a powerful vision-language model developed by the researchers at NVIDIA Labs (NVLABS). It is an ensemble-based model that combines multiple expert models to provide robust and versatile performance across a range of vision-language tasks. Prismer is built upon the principles of the Prismer paper, which introduces a novel approach to leveraging an ensemble of specialized models to enhance the overall capabilities of the system. Similar models like Stable Diffusion, CogVLM, LLaVa-13B, and DeepSeek-VL showcase the growing capabilities of vision-language models in areas such as image generation, multimodal understanding, and real-world applications. Model inputs and outputs Prismer is a versatile model that can handle both visual question answering and image captioning tasks. The model takes in an input image and either a question (for visual question answering) or no additional input (for image captioning). The model's output varies depending on the chosen task, but can include the generated caption, the answer to the visual question, or the expert model labels. Inputs Input Image**: The input image, which can be in .png, .jpg, or .jpeg format. Question** (optional): The question to be answered for the visual question answering task. Use Experts**: A boolean flag to indicate whether the expert models should be used. Output Expert Labels**: A boolean flag to return the output of the individual expert models. Outputs Caption**: The generated caption describing the input image (for the image captioning task). Answer**: The answer to the visual question (for the visual question answering task). Expert Labels**: The output of the individual expert models (if Output Expert Labels is set to true). Capabilities Prismer is a powerful model that can tackle a wide range of vision-language tasks. Its ensemble-based approach allows it to leverage the strengths of multiple specialized models, resulting in robust and versatile performance. The model can accurately caption images, answer visual questions, and provide insights into the internal decision-making process through the expert labels. What can I use it for? Prismer can be used in a variety of applications that require integrating vision and language understanding, such as: Intelligent image search and retrieval Automated image captioning for social media or e-commerce Visual question answering for assistive technologies Multimodal content analysis and understanding Things to try With Prismer, you can experiment with different input images and questions to see how the model responds. Try providing images with varying levels of complexity or ambiguity, and observe how the model's outputs change. You can also explore the expert labels to gain insights into the model's decision-making process and potentially identify areas for further improvement.
Updated 9/19/2024
parakeet-rnnt-1.1b
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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.
Updated 9/19/2024