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license: apache-2.0 |
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# Dataset Name: Dataset for ASR Speaker-Tagging Corrections (Speaker Diarization) |
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## Description |
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- This dataset is pairs of erroneous ASR output and speaker tagging, which are generated from a ASR system and speaker diarization system. |
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Each source erroneous transcription is paired with human-annotated transcription, which has correct transcription and speaker tagging. |
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- [SEGment-wise Long-form Speech Transcription annotation](#segment-wise-long-form-speech-transcription-annotation-seglst) (`SegLST`), the file format used in the [CHiME challenges](https://www.chimechallenge.org) |
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Example) `session_ge1nse2c.seglst.json` |
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``` |
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[ |
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... |
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{ |
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"session_id": "session_ge1nse2c", |
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"words": "well that is the problem we have erroneous transcript and speaker tagging we want to correct it using large language models", |
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"start_time": 181.88, |
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"end_time": 193.3, |
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"speaker": "speaker1" |
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}, |
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{ |
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"session_id": "session_ge1nse2c", |
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"words": "it seems like a really interesting problem I feel that we can start with very simple methods", |
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"start_time": 194.48, |
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"end_time": 205.03, |
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"speaker": "speaker2" |
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}, |
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... |
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] |
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``` |
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## Structure |
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### Data Split |
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The dataset is divided into training and test splits: |
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- Development Data: 142 entries |
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- 2 to 4 speakers in each session |
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- Approximately 10 ~ 40 mins of recordings |
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- Evaluation Data: 104 entries |
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- 2 to 4 speakers in each session |
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- Approximately 10 ~ 40 mins of recordings |
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### Keys (items) |
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- `session_id`: "session_ge1nse2c", |
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- `words`: Transcription corresponding to the time stamp (start, end). |
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- `start_time`: Start time in second. |
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- `end_time`: End time in second. |
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- `speaker`: Speaker tagging in string "speaker\<N\>" |
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### Source Datasets |
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`err_source_text`: This is the erroneous ASR-Diarization results to be fixed. Has dev, eval folders |
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`ref_annotated_text`: This is the human annotated ground-truth for evaluation. Only dev split is included. |
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- **Development Sources**: |
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- `dev`: 142 sessions |
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- **Evaluation Sources**: |
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- `eval`: 104 Sessions |
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## Access |
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The dataset can be accessed and downloaded through the HuggingFace Datasets library. |
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## Evaluation |
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This dataset can be evaluated by [MeetEval Software](https://github.com/fgnt/meeteval) |
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### From PyPI |
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``` |
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pip install meeteval |
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``` |
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### From source |
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``` |
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git clone https://github.com/fgnt/meeteval |
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pip install -e ./meeteval |
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``` |
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### Evaluate the corrected segLST files: |
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``` |
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python -m meeteval.wer cpwer -h err_source_text/dev/session_ge1nse2c.json -r ref_annotate_text/dev/session_ge1nse2c.json |
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``` |
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Or after installation, you can use the following command alternatively. |
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``` |
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meeteval-wer cpwer -h err_source_text/dev/session_ge1nse2c.json -r ref_annotate_text/dev/session_ge1nse2c.json |
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``` |
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### References |
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```bib |
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@inproceedings{park2024enhancing, |
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title={Enhancing speaker diarization with large language models: A contextual beam search approach}, |
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author={Park, Tae Jin and Dhawan, Kunal and Koluguri, Nithin and Balam, Jagadeesh}, |
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booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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pages={10861--10865}, |
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year={2024}, |
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organization={IEEE} |
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} |
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``` |
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```bib |
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@InProceedings{MeetEval23, |
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title={MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems}, |
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author={von Neumann, Thilo and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}, |
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booktitle={CHiME-2023 Workshop, Dublin, England}, |
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year={2023} |
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} |
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``` |
