wav2vec2

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.

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

A 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!

:zap: Finetune Wa2vec 2.0 For Speech Recognition

A mental-model-first field guide to Audio AI — from raw waveforms to production voice systems. Covers ASR, TTS, audio classification, Transformer architectures (CTC/Seq2Seq), fine-tuning, and real-world pipelines like voice assistants and meeting transcription. Bilingual (EN/FA).

GSoC'2021 | TensorFlow implementation of Wav2Vec2

Wav2vec 2.0 Self-Supervised Pretraining

An ASR (Automatic Speech Recognition) adversarial attack repository.

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

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.

real time japanese speech recognition translator using wav2vec2

Wav2vec resources and models for Brazilian Portuguese

fine-tune Wav2vec2. an ASR model released by Facebook

Turkish Speech Recognition using Facebook's Wav2vec 2.0 models

Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Speech Assessment API in FastAPI with HuggingFace 🤗

Adnabod lleferydd Cymraeg i'r Gymraeg gyda HuggingFace // Speech Recognition for Welsh with HuggingFace

A multimodal SER project combining BERT and ECAPA-TDNN with cross-attention-based fusion on the IEMOCAP dataset.

The project,being part of Kagglex BIPOC Mentorship Program final project, aims to train two separate Hindi ASR models using the Facebook Wav2Vec2 (300M parameters) and OpenAI Whisper-Small models, respectively. The goal is to compare their performance, with a target WER of less than 13%, across various Hindi accents and dialects.