Benchmark the CTC against a RNN-Transducer model

描述

此议题没有描述。

贡献者指南

技术栈

cpppythontensorflow

领域

machine learning

议题类型

research

难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。

4

预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。

3-5 days

活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。

stale

清晰度议题是否清楚说明期望改动、验收标准和下一步。

unclear

前置要求

Understanding of CTC lossFamiliarity with RNN TransducerBasic knowledge of DeepSpeech codebase

新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。

15

研究方向

This issue asks to benchmark the Connectionist Temporal Classification (CTC) model against a RNN Transducer model within the DeepSpeech framework. Since the issue lacks description, one must first understand the current implementation (see DeepSpeech's model definition files, likely in Python). Then, implement a RNN Transducer model (e.g., using TensorFlow) and compare training time, word error rate, and inference speed. Existing discussions or linked PRs may provide context; check comments #753 for any hints. The benchmark should be reproducible and documented.