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Category/automatic-speech-recognition
Automatic Speech Recognition (ASR) AI models are a critical component of many modern applications, including virtual assistants, dictation software, and transcription services. These models use machine learning techniques to transcribe spoken language into written text, enabling computers to understand and respond to spoken commands.
There are many different types of ASR models, each with its own strengths and weaknesses. Traditional models include hidden Markov models (HMMs) and Gaussian mixture models (GMMs), while more recent models use deep learning techniques such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and transformers.
While ASR models have made significant progress in recent years, they still face challenges in noisy environments, with multiple speakers, and with accented or non-standard speech. Nevertheless, they are becoming increasingly accurate and versatile, enabling new and exciting applications in areas such as healthcare, education, and entertainment.
automatic-speech-recognition
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrates a strong ability to generalize to many datasets and domains without fine-tuning. The model is based on a Transformer encoder-decoder architecture. Whisper models are available for various languages including English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, and many more.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrated a strong ability to generalise to many datasets and domains without fine-tuning. Whisper checks pens are available in five configurations of varying model sizes, including a smallest configuration trained on English-only data and a largest configuration trained on multilingual data. This one is English-only.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labeled data and demonstrates strong abilities to generalize to various datasets and domains without fine-tuning. The model is based on a Transformer encoder-decoder architecture.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without fine-tuning. The primary intended users of these models are AI researchers studying robustness, generalisation, and capabilities of the current model.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrates a strong ability to generalize to many datasets and domains without the need for fine-tuning. The model is based on a Transformer architecture and uses a large-scale weak supervision technique.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labelled data without the need for fine-tuning. It is a Transformer based encoder-decoder model, trained on either English-only or multilingual data, and is available in five configurations of varying model sizes. The models were trained on the tasks of speech recognition and speech translation, predicting transcriptions in the same or different languages as the audio.
automatic-speech-recognition
Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This version has implementation to predict word timestamps and provide a more accurate estimation of speech segments when transcribing with Whisper models.
automatic-speech-recognition
Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This variant contains implementation to predict word timestamps and provide a more accurate estimation of speech segments when transcribing with Whisper models.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It was trained on 680k hours of labelled data and demonstrates a strong ability to generalize to many datasets and domains without fine-tuning. Whisper is a Transformer-based encoder-decoder model trained on English-only or multilingual data. The English-only models were trained on speech recognition, while the multilingual models were trained on both speech recognition and machine translation.
automatic-speech-recognition
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation, trained on 680k hours of labeled data without fine-tuning. It's a Transformer based encoder-decoder model, trained on English-only or multilingual data, predicting transcriptions in the same or different language as the audio. Whisper checkpoints come in five configurations of varying model sizes.
