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Awesome GitHub RepositoriesAlignment Error Metrics
Metrics specifically designed to quantify the accuracy of token-level sequence alignments.
Distinct from Phoneme-Based Alignment: Focuses on quantitative error rates for word/token alignment rather than the alignment model architectures themselves
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Awesome Alignment Error Metrics GitHub Repositories
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- facebookresearch/fairseq
facebookresearch/fairseq
32,228在 GitHub 上查看Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ
Measures the quality of predicted word alignments using specialized metrics like Alignment Error Rate.
Python
