DUAL-textless-SQA

This repository is the official implementation for DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering paper, and the release of the Natural Multi-speakers Spoken Question Answering (NMSQA) dataset.

Installation

Model

• [Pretrained DUAL model on HuBERT-128]

Dataset

Download the NMSQA dataset

• [Original dataset]
• [Parquet Format dataset]
• [Huggingface Format dataset]

Data Preparation for Original Dataset

Preprocessed data link (including passage merging and unit-level labels, updated with question code): [link]

• Directory format

  • train
  • dev
  • test

• Files

  • For train and dev split {split}-answer-span.csv: answer time span in seconds meta-{split}.csv: the duration, speaker, and transcription of each utterance {split}-textgrid.tar.gz: force alignment of each utterance {split}_audio.tar.gz: utterance waveform files {split}_hash2question.json: map the hash value to question id
  • For test split lxt_sqa.tar.gz: contains all audio files in audio and transcriptions meta-lxt.csv: the duration, speaker, and transcription of each utterance test/test-SQuAD/test-SQuAD-answer-span.csv: the answer span in the test-SQuAD split test/test-OOD/test-OOD-answer-span.csv: the answer span in the test-OOD split

  NOTE Current the spoken passage is split to segments of utterances. For the standard QA task, you should merge the segments back to the whole passages. The suffix of -1, -2, ..., -n is the segment number of specific passage.

  • Speech Content Encoder Please see details in speeech-content-encoder.
  • Pre-process the QA labels

  python code_answer.py
  

Parquet Format & Huggingface Format dataset

It basically follow the same file format as the Origin SQuAD with the following extra field:

{
   "id": Same as SQuAD,
   "title": Same as SQuAD,
   "context": Same as SQuAD,
   "question": Same as SQuAD,
   "answers":{
      "answer_start": Same as SQuAD,
      "audio_full_answer_end":[], Audio answer end position in second
      "audio_full_answer_start":[], Audio answer start position in second
      "audio_full_neg_answer_end":[], Audio answer end position in second that using the same words but not the correct one
      "audio_full_neg_answer_start":[], Audio answer start position in second that using the same words but not the correct one
      "audio_segment_answer_end":[],
      "audio_segment_answer_start":[],
      "text": Same as SQuAD
   },
   "content_segment_audio_path": Segment Audio Path,
   "content_full_audio_path": Complete Audio Path,
   "content_audio_sampling_rate": Audio Sampling Rate,
   "content_audio_speaker": Audio Speaker,
   "content_segment_text":"",
   "content_segment_normalized_text": Normalized Text for generating audio,
   "question_audio_path": Question Audio Path,
   "question_audio_sampling_rate": Audio Sampling Rate,
   "question_audio_speaker": Audio Speaker,
   "question_normalized_text": Normalized Text for generating audio,
}

Training

python train.py --exp_name [exp name] --config baseline.yaml

Evaluation

python evaluate.py --data_dir [data dir path] --model_path [model checkpoint dir] --output_dir [output dir path] --out_fname [output name]

Results

Contact

Guan-Ting Lin (Email: [email protected]) Eric Lam (Email: [email protected])

Citation

@inproceedings{lin22c_interspeech,
  author={Guan-Ting Lin and Yung-Sung Chuang and Ho-Lam Chung and Shu-wen Yang and Hsuan-Jui Chen and Shuyan Annie Dong and Shang-Wen Li and Abdelrahman Mohamed and Hung-yi Lee and Lin-shan Lee},
  title={{DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={5165--5169},
  doi={10.21437/Interspeech.2022-612}
}