![]() ![]() Speechbrain is a conversational AI toolkit based on PyTorch. The project encompasses several apps, including the Automatic Speech Recognition app for transcription. Looks like it was actively developed from 2017 to late 2020 but has since been abandoned.įlashlight is a fast, flexible machine learning library written entirely in C++ from the Facebook AI Research and the creators of Torch, TensorFlow, Eigen and Deep Speech. It uses Google's TensorFlow to make the implementation easier. Mozilla DeepSpeech - an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Let's dive in! The ContendersĬoqui STT - this is a fork of Mozilla's DeepSpeech that has been in development since early 2021. along with any other open source transcription projects I could find. Then I randomly stumbled across a tweet about "whisper.cpp" which was a CPU optimized version of Whisper. Both are found in the paper.It was a great holiday project, but once I started traveling again I realized that it was a pain to maintain given that I rarely have access to a beefy CUDA compatible GPU. Meanwhile, more BLEU (Bilingual Evaluation Understudy) scores can be found in Appendix D.3. Additional WER scores corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the large-v2 model (The smaller the numbers, the better the performance). Whisper's performance varies widely depending on the language. We observed that the difference becomes less significant for the small.en and medium.en models. en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. Below are the names of the available models and their approximate memory requirements and relative speed. There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Pip install setuptools-rust Available models and languages You can download and install (or update to) the latest release of Whisper with the following command: The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. ApproachĪ Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. Whisper is a general-purpose speech recognition model. ![]()
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