easytranscriber 0.1.0

Speech recognition with accurate word-level timestamps.

easytranscriber is an automatic speech recognition library built for efficient, large-scale transcription with accurate word-level timestamps. The library is backend-agnostic, featuring modular, parallelizable, pipeline components (VAD, transcription, feature/emission extraction, forced alignment), with support for both ctranslate2 and Hugging Face inference backends. Notable features include:

• GPU accelerated forced alignment, using Pytorch's forced alignment API. Forced alignment is based on a GPU implementation of the Viterbi algorithm (Pratap et al., 2024).
• Parallel loading and pre-fetching of audio files for efficient data loading and batch processing.
• Flexible text normalization for improved alignment quality. Users can supply custom regex-based text normalization functions to preprocess ASR outputs before alignment. A mapping from the original text to the normalized text is maintained internally. All of the applied normalizations and transformations are consequently non-destructive and reversible after alignment.
• 35% to 102% faster inference compared to WhisperX. See the benchmarks for more details.
• Batch inference support for both wav2vec2 and Whisper models.

Benchmarks