BasicSR
BasicSR is an open-source neural speech recognition toolkit based on deep learning. It provides an end-to-end speech recognition pipeline to transcribe raw audio into text.
speech-recognition
neural-networks
deep-learning
audio-processing
BasicSR: Open-Source Neural Speech Recognition Toolkit
An open-source neural speech recognition toolkit providing an end-to-end speech recognition pipeline for transcribing raw audio into text.
What is BasicSR?
BasicSR is an open-source neural speech recognition toolkit for researchers and developers. It is built using deep learning techniques to provide an end-to-end speech recognition pipeline. BasicSR takes raw audio as input and outputs transcribed text.
Some key features of BasicSR:
- Implemented with PyTorch and provides modular and customizable neural network components
- Supports model training and inference for speech recognition
- Includes pretrained models for English and Mandarin Chinese
- Provides data preprocessing tools for feature extraction from audio
- Optimized for GPU acceleration and can scale to multiple GPUs
- Active open-source development community for contributions
BasicSR aims to advance speech recognition research by providing an open and flexible toolkit. The goal is to reduce time spent on implementation, so researchers can focus more on novel techniques and model architectures to push the state-of-the-art in speech recognition performance.
BasicSR Features
Features
- End-to-end neural network based speech recognition pipeline
- Supports training acoustic and language models from scratch
- Modular design allows customization and extension
- Open source with permissive license (MIT)
Pricing
- Open Source
Pros
Free and open source
Active development community
Customizable and extensible
Good performance for basic models
Cons
Requires expertise in deep learning and speech recognition
Limited pre-built models and datasets
Not as performant as commercial solutions
Limited documentation and support
Official Links
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