---
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