| --- |
| license: cc |
| task_categories: |
| - audio-to-audio |
| - text-generation |
| - audio-classification |
| - video-classification |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| |
| |
| |
| |
| |
| --- |
| ## **"Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation", accepted to ACL 2024 (oral presentation).** |
|
|
| **Audio files have been newly processed and re-uploaded on 7/11/2024. Please download the files again for an updated version. |
| |
| - **Homepage:** https://multidialog.github.io |
| - **Paper:** https://arxiv.org/pdf/2406.07867 |
| - **Audio Dataset:** https://huggingface.co/datasets/IVLLab/MultiDialog (this repository) |
| - **Video Dataset:** https://drive.google.com/drive/u/1/folders/1RPMwVHU34yX0R_HbxAWmxF2EHy961HA3 |
| |
| ## Dataset Description |
| |
| - **Homepage:** https://multidialog.github.io |
| - **Repository:** https://github.com/MultiDialog/MultiDialog |
| - **Paper:** https://arxiv.org/pdf/2406.07867 |
| - **Point of Contact:** [jinny960812@kaist.ac.kr](mailto:jinny960812@kaist.ac.kr) |
| - **Point of Contact:** [chaewonkim@kaist.ac.kr](mailto:chaewonkim@kaist.ac.kr) |
| |
| ## Dataset Description |
| This dataset includes manually annotated metadata linking audio files to transcriptions, emotions, and other attributes. For access to video files of MultiDialog, download them [here](https://drive.google.com/drive/folders/1RPMwVHU34yX0R_HbxAWmxF2EHy961HA3?usp=sharing). |
| |
| ### Dataset Statistics |
| | | train | valid_freq | valid_rare | test_freq | test_rare | Total | |
| |-----------------------|---------|---------|---------|---------|---------|----------| |
| | \# dialogues | 7,011 | 448 | 443 | 450 | 381 | 8,733 | |
| | \# utterance | 151,645 | 8,516 | 9,556 | 9,811 | 8,331 | 187,859 | |
| | avg \# utterance/dialogue | 21.63 | 19.01 | 21.57 | 21.80 | 21.87 | 21.51 | |
| | avg length/utterance (s) | 6.50 | 6.23 | 6.40 | 6.99 | 6.49 | 6.51 | |
| | avg length/dialogue (min) | 2.34 | 1.97 | 2.28 | 2.54 | 2.36 | 2.33 | |
| | total length (hr) | 273.93 | 14.74 | 17.00 | 19.04 | 15.01 | 339.71 | |
| |
| |
| ### Example Usage |
| There are 'train', 'test_freq', 'test_rare', 'valid_freq', and 'valid_rare' splits. Below is an example usage. |
| ```python |
| from datasets import load_dataset |
|
|
| MultiD = load_dataset("IVLLab/MultiDialog", "valid_freq", use_auth_token=True) |
|
|
| # see structure |
| print(MultiD) |
|
|
| # load audio sample on the fly |
| audio_input = MultiD["valid_freq"][0]["audio"] # first decoded audio sample |
| transcription = MultiD["valid_freq"][0]["value"] # first transcription |
| ``` |
| |
| ### Supported Tasks |
| - `multimodal dialogue generation` : The dataset can be used to train an end-to-end multimodal |
| - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). |
| - `text-to-speech`: The dataset can also be used to train a model for Text-To-Speech (TTS). |
| |
| ### Languages |
| Multidialog contains audio and transcription data in English. |
| |
| ### Gold Emotion Dialogue Subset |
| We provide a gold emotion dialogue subset in the MultiDialog dataset, a more reliable resource for studying emotional dynamics in conversations. |
| We classify dialogues from actors that exhibit emotion accuracy above 40% as gold emotion dialogue. Please use dialogues from actors with the following ids: a, b, c, e, f, g, i, j, and k. |
| |
| |
| ## Dataset Structure |
| ### Data Instances |
| ```python |
| { |
| 'file_name': 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav' |
| 'conv_id': 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b', |
| 'utterance_id': 0, |
| 'from': 'gpt', |
| 'audio': |
| { |
| # in streaming mode 'path' will be 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav' |
| 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/cache_id/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav, |
| 'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32), |
| 'sampling_rate': 16000 |
| }, |
| 'value': 'Are you a football fan?', |
| 'emotion': 'Neutral', |
| 'original_full_path': 'valid_freq/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav' |
| } |
| ``` |
| |
| ### Data Fields |
| * file_name (string) - relative file path to the audio sample in the specific split directory. |
| * conv_id (string) - unique identifier for each conversation. |
| * utterance_id (float) - uterrance index. |
| * from (string) - who the message is from (human, gpt). |
| * audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. |
| In non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio. |
| segment inside its archive (as files are not downloaded and extracted locally). |
| * value (string) - transcription of the utterance. |
| * emotion (string) - the emotion of the utterance. |
| * original_full_path (string) - the relative path to the original full audio sample in the original data directory. |
| |
| * speaker_id can be obtained from the last letter of 'file_name' excluding '.wav' (e.g. 'k' in the above example) |
| |
| Emotion is assigned from the following labels: |
| "Neutral", "Happy", "Fear", "Angry", "Disgusting", "Surprising", "Sad" |
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