self-supervised-speech-recognition and wav2vec2-huggingface-demo
These are ecosystem siblings—one is a pretrained model checkpoint while the other is a demonstration application showing how to use wav2vec2 models via Hugging Face's Transformers library for inference.
About self-supervised-speech-recognition
mailong25/self-supervised-speech-recognition
speech to text with self-supervised learning based on wav2vec 2.0 framework
This helps speech recognition practitioners develop highly accurate speech-to-text models for any language, even with limited transcribed audio. You provide audio files (both labeled with transcripts and unlabeled) and text data, and it outputs a custom speech recognition model. This is for data scientists, linguists, or AI engineers who need to build robust transcription systems for specific languages or domains.
About wav2vec2-huggingface-demo
bhattbhavesh91/wav2vec2-huggingface-demo
Speech to Text with self-supervised learning based on wav2vec 2.0 framework using Hugging Face's Transformer
This project helps convert spoken audio into written text, making it easier to analyze recordings or create transcripts. You feed it an audio file, and it outputs the corresponding text. This is useful for anyone who needs to quickly transcribe speeches, interviews, or voice notes.
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