---
license: mit
datasets:
- s3prl/mini_voxceleb1
language:
- en
metrics:
- accuracy
library_name: fairseq
pipeline_tag: audio-classification
tags:
- speech
- text
- cross-modal
- unified model
- self-supervised learning
- SpeechT5
- Speaker Identification
- Speaker Recognition
---

## SpeechT5 SID Manifest

| [**Github**](https://github.com/microsoft/SpeechT5) | [**Huggingface**](https://huggingface.co/mechanicalsea/speecht5-sid) |

This manifest is an attempt to recreate the Speaker Identification recipe used for training [SpeechT5](https://aclanthology.org/2022.acl-long.393). This manifest was constructed using [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) four speakers, e.g., bdl, clb, rms, slt. There are 932 utterances for training, 100 utterances for validation, and 100 utterance for evaluation.

### Requirements

- [Fairseq](https://github.com/facebookresearch/fairseq)

### Tools

- `manifest/utils` is used to produce manifest as well as conduct training, validation, and evaluation.
- `mainfest/iden_split.txt` and `mainfest/vox1_meta.csv` are officially released files.

### Model and Results

- [`speecht5_sid.pt`](.) are reimplemented Speaker Identification fine-tuning on the released manifest **but with a smaller batch size** (Ensure the manifest is ok).
- `results` are reproduced by the released fine-tuned model.

### Reference

If you find our work is useful in your research, please cite the following paper:

```bibtex
@inproceedings{ao-etal-2022-speecht5,
    title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing},
    author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu},
    booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
    month = {May},
    year = {2022},
    pages={5723--5738},
}
```