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Awesome GitHub RepositoriesDecoding Graph Builders
Constructs weighted finite-state transducer graphs that combine acoustic models, lexicons, and language models for speech recognition decoding.
Distinguishing note: No candidate covers WFST-based decoding graph construction for ASR; closest candidates focus on generic graph frameworks or sequence decoders.
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Awesome Decoding Graph Builders GitHub Repositories
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- wenet-e2e/wenet
wenet-e2e/wenet
5,035عرض على GitHubWeNet is an end-to-end automatic speech recognition (ASR) toolkit designed for both Chinese and English, built around transformer-based models. It supports streaming and non-streaming inference out of the box, and is structured to be production-ready, with model export and deployment paths for servers and mobile devices. The toolkit distinguishes itself through a chunk-based streaming transformer architecture that processes audio in fixed-size segments for low latency while preserving context across chunks. It jointly trains models with both CTC and attention loss to combine alignment accurac
The ASR toolkit builds a decoding graph by composing acoustic model units, a lexicon, and a language model into a single WFST graph.
Pythonasrautomatic-speech-recognitionconformer
