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  - **Point of Contact:** [chaewonkim@kaist.ac.kr](mailto:chaewonkim@kaist.ac.kr)
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  ## Dataset Description
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- This dataset includes manually annotated metadata linking audio files to transcriptions, emotions, and other attributes.
 
 
 
 
 
 
 
 
 
 
 
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  ### Example Usage
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- There are 'train', 'test_freq', 'test_rare', 'valid_freq', and 'valid_rare' splits. Below is the example usage.
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  ```python
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  from datasets import load_dataset
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@@ -43,34 +54,41 @@ transcription = MultiD["valid_freq"][0]["value"] # first transcription
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  ```
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  ### Supported Tasks
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- - `multimodal dialogue generation`
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  - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
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  - `text-to-speech`: The dataset can also be used to train a model for Text-To-Speech (TTS).
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  ### Languages
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  Multidialog contains audio and transcription data in English.
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  ## Dataset Structure
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  ### Data Instances
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  ```python
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  {
 
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  'conv_id': 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b',
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  'utterance_id': 0,
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  'from': 'gpt',
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  'audio':
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  {
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- # in streaming mode 'path' will be 'xs_chunks_0000/YOU0000000315_S0000660.wav'
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- 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav',
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  'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),
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  'sampling_rate': 16000
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  },
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  'value': 'Are you a football fan?',
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  'emotion': 'Neutral',
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- 'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus'
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  }
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  ```
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  ### Data Fields
 
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  * conv_id (string) - unique identifier for each conversation.
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  * utterance_id (float) - uterrance index.
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  * from (string) - who the message is from (human, gpt).
 
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  - **Point of Contact:** [chaewonkim@kaist.ac.kr](mailto:chaewonkim@kaist.ac.kr)
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  ## Dataset Description
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+ 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).
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+
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+ ### Dataset Statistics
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+ | | train | valid_freq | valid_rare | test_freq | test_rare | Total |
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+ |-----------------------|---------|---------|---------|---------|---------|----------|
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+ | \# dialogues | 7,011 | 448 | 443 | 450 | 381 | 8,733 |
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+ | \# utterance | 151,645 | 8,516 | 9,556 | 9,811 | 8,331 | 187,859 |
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+ | avg \# utterance/dialogue | 21.63 | 19.01 | 21.57 | 21.80 | 21.87 | 21.51 |
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+ | avg length/utterance (s) | 6.50 | 6.23 | 6.40 | 6.99 | 6.49 | 6.51 |
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+ | avg length/dialogue (min) | 2.34 | 1.97 | 2.28 | 2.54 | 2.36 | 2.33 |
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+ | total length (hr) | 273.93 | 14.74 | 17.00 | 19.04 | 15.01 | 339.71 |
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+
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  ### Example Usage
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+ There are 'train', 'test_freq', 'test_rare', 'valid_freq', and 'valid_rare' splits. Below is an example usage.
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  ```python
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  from datasets import load_dataset
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  ```
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  ### Supported Tasks
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+ - `multimodal dialogue generation` : The dataset can be used to train an end-to-end multimodal
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  - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
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  - `text-to-speech`: The dataset can also be used to train a model for Text-To-Speech (TTS).
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  ### Languages
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  Multidialog contains audio and transcription data in English.
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+ ### Gold Emotion Dialogue Subset
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+ We provide a gold emotion dialogue subset in the MultiDialog dataset, a more reliable resource for studying emotional dynamics in conversations.
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+ 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.
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+
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+
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  ## Dataset Structure
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  ### Data Instances
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  ```python
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  {
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+ 'file_name': 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav'
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  'conv_id': 't_ffa55df6-114d-4b36-87a1-7af6b8b63d9b',
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  'utterance_id': 0,
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  'from': 'gpt',
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  'audio':
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  {
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+ # in streaming mode 'path' will be 't_152ee99a-fec0-4d37-87a8-b1510a9dc7e5/t_152ee99a-fec0-4d37-87a8-b1510a9dc7e5_0i.wav'
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+ 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/cache_id/t_152ee99a-fec0-4d37-87a8-b1510a9dc7e5/t_152ee99a-fec0-4d37-87a8-b1510a9dc7e5_0i.wav,
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  'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),
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  'sampling_rate': 16000
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  },
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  'value': 'Are you a football fan?',
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  'emotion': 'Neutral',
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+ 'original_full_path': 'valid_freq/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b/t_ffa55df6-114d-4b36-87a1-7af6b8b63d9b_0k.wav'
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  }
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  ```
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  ### Data Fields
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+ * file_name (string) - relative file path to the audio sample in the specific split directory.
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  * conv_id (string) - unique identifier for each conversation.
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  * utterance_id (float) - uterrance index.
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  * from (string) - who the message is from (human, gpt).