facebookresearch/fairseq

Evaluating TTS using STT

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#5,629 创建于 2025年7月19日

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 (0 评论) (0 反应) (0 负责人)Python (29,107 star) (6,224 fork)batch import
enhancementhelp wantedneeds triage

描述

I tried to get a text back from the speech I generated using this model, but the text was a bit of. So maybe having some type of test could help while improving the model.

Basically, "some text" -> speech output -> speech input -> text output -> check if text output is "some text"

贡献者指南

技术栈
python
领域
machine learningaitesting
议题类型
test
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。
1-3 hours
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。
fresh
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
basic knowledge of fairseqfamiliarity with TTS and STT models
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。
30
研究方向
The issue proposes a cyclical evaluation for TTS models: convert text to speech using the model, then transcribe the speech back to text using an STT model, and compare the output text to the input. To implement this, a contributor should first explore if fairseq already has any evaluation scripts or example notebooks. They could also look into integrating an off the shelf STT model like wav2vec 2.0 or Whisper. The main steps would be: (1) generate speech from a set of test texts using the existing TTS model, (2) pass the audio through an STT model, (3) compute metrics like WER or CER. The contributor should also check existing issues or PRs for similar work.