LibreASR vs wav2letter++
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
wav2letter++ is a fast open source speech processing toolkit from the Speech Team at Facebook AI Research. It is written entirely in C++ and uses the ArrayFire tensor library and the flashlight machine learning library for maximum efficiency. Our approach is detailed in this arXiv paper. | It is an On-Premises, Streaming Speech Recognition System built with PyTorch and fastai. |
| - | RNN-T network;
Fused language models;
Dynamic Bucketing DataLoader;
Dynamic Quantization;
Tuned language model fusion |
Statistics | |
GitHub Stars - | GitHub Stars 682 |
GitHub Forks - | GitHub Forks 32 |
Stacks 4 | Stacks 1 |
Followers 16 | Followers 3 |
Votes 0 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to wav2letter++, LibreASR?
Speechly
It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.
Kaldi
It is a state-of-the-art automatic speech recognition toolkit. It is intended for use by speech recognition researchers and professionals.
Deepspeech
It is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Botium Speech Processing
It is a unified, developer-friendly API to the best available Speech-To-Text and Text-To-Speech services.
SpeechPy
The purpose of this project is to provide a package for speech processing and feature extraction. This library provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filterbanks.
WhisperFusion
It builds upon the capabilities of the WhisperLive and WhisperSpeech by integrating Mistral, a Large Language Model (LLM), on top of the real-time speech-to-text pipeline. Both LLM and Whisper are optimized to run efficiently as TensorRT engines, maximizing performance and real-time processing capabilities.
Writeout.ai
Transcribe and translate audio files using OpenAI's Whisper API. You can upload any audio file, and the application will send it through the OpenAI Whisper API using Laravel's queued jobs. Translation makes use of the new OpenAI Chat API and chunks the generated VTT file into smaller parts to fit them into the prompt context limit.
