Benchmark the CTC against a RNN-Transducer model

描述

此議題沒有描述。

貢獻者指南

技術棧

cpppythontensorflow

領域

machine learning

議題類型

research

難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。

4

預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。

3-5 days

活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。

stale

清晰度議題是否清楚說明預期改動、驗收標準和下一步。

unclear

前置要求

Understanding of CTC lossFamiliarity with RNN TransducerBasic knowledge of DeepSpeech codebase

新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。

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.