#Automatic-speech-recognition
Showing 15 of 15 repositories tagged #automatic-speech-recognition, ranked by stars
OpenAI Whisper ASR Webservice API
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.
Open-source industrial-grade ASR models supporting Mandarin, Chinese dialects and English, achieving a new SOTA on public Mandarin ASR benchmarks, while also offering outstanding singing lyrics recognition capability.
Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
Offline Speech Recognition with OpenAI Whisper and TensorFlow Lite for Android
A SOTA Industrial-Grade All-in-One ASR system with ASR, VAD, LID, and Punc modules. FireRedASR2 supports Chinese (Mandarin, 20+ dialects/accents), English, code-switching, and both speech and singing ASR. FireRedVAD supports speech/singing/music in 100+ langs. FireRedLID supports 100+ langs and 20+ zh dialects. FireRedPunc supports zh and en.
[LREC-COLING 2024 (Oral), Interspeech 2024 (Oral), NAACL 2025, ACL 2025, EMNLP 2025] A Series of Multilingual Multitask Medical Speech Processing
This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Speech Recognition model based off of FAIR research paper built using Pytorch.
A minimalistic automatic speech recognition streamlit based webapp powered by OpenAI's Whisper "State of the Art" models
Running speech-to-text in a Meta Quest headset using OpenAI's Whisper tiny model
In this repository, I have developed an end to end Automatic speech recognition project. I have developed the neural network model for automatic speech recognition with PyTorch and used MLflow to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.