Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

Self-Supervised Speech Pre-training and Representation Learning Toolkit

How to use our public wav2vec2 dimensional emotion model

A live speech recognition using Facebooks wav2vec 2.0 model.

Python API & command-line tool to easily transcribe speech-based video files into clean text

Official implementation of INTERSPEECH 2021 paper 'Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings'

⚡ Finetune Wa2vec 2.0 For Speech Recognition

🎤📄 An innovative tool that transforms audio or video files into text transcripts and generates concise meeting minutes. Stay organized and efficient in your meetings, and get ready for Phase 2 where we'll be open for contributions to enable real-time meeting transcription! 🚀

End-to-End Vietnamese Speech Recognition using wav2vec 2.0

GSoC'2021 | TensorFlow implementation of Wav2Vec2

Pytorch implementation of Noisy Student Training for Automatic Speech Recognition and Automatic Pronunciation Error Detection problem

Solution for Zalo AI Challenge 2022 - Lyrics Alignment

Phoneme segmentation using pre-trained speech models

A mini, simple, and fast end-to-end automatic speech recognition toolkit.

Scripts used in the research described in the paper "Multimodal Emotion Recognition with High-level Speech and Text Features" accepted in the ASRU 2021 conference.

In this project, several approaches for training/finetuning an audio gender recognition is provided. The code can simply be used for any other audio classification task by simply changing the number of classes and the input dataset.

fine-tune Wav2vec2. an ASR model released by Facebook

SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

Wav2vec 2.0 Self-Supervised Pretraining

[ICASSP 2023] Mingling or Misalignment? Temporal Shift for Speech Emotion Recognition with Pre-trained Representations