metadata
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: wav_id
dtype: string
- name: turn_index
dtype: int32
- name: text
dtype: string
- name: agent_text
dtype: string
- name: domains
dtype: string
- name: slots
dtype: string
- name: context
sequence:
- name: turn_index
dtype: int32
- name: text
dtype: string
- name: agent_text
dtype: string
- name: domains
dtype: string
- name: slots
dtype: string
splits:
- name: train
num_bytes: 9257842443.25
num_examples: 73950
- name: dev
num_bytes: 1123461990
num_examples: 9104
- name: test
num_bytes: 2235588991.5
num_examples: 17652
download_size: 11280684688
dataset_size: 12616893424.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
license: cc-by-4.0
language:
- en
pretty_name: SpokenWOZ -- Whisper Transcripts
SpokenWOZ With Whisper Transcripts
This is a custom version of the SpokenWOZ dataset with all the original transcripts replaced by transcripts generated by Whisper-large-v3. We manually
transcribed 1000 utterances from the test set and like this estimate the WER of the original ASR to be ~29% and the WER of the Whisper transcripts to
be ~5.3%.
Citation Information
@misc{si2024spokenwozlargescalespeechtextbenchmark,
title={SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents},
author={Shuzheng Si and Wentao Ma and Haoyu Gao and Yuchuan Wu and Ting-En Lin and Yinpei Dai and Hangyu Li and Rui Yan and Fei Huang and Yongbin Li},
year={2024},
eprint={2305.13040},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2305.13040},
}
arxiv.org/abs/2305.13040