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--- |
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license: mit |
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task_categories: |
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- text-to-speech |
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language: |
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- pt |
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dataset_info: |
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- config_name: audioCorpus |
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features: |
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- name: audio_name |
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dtype: string |
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- name: file_path |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: text |
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dtype: string |
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- name: start_time |
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dtype: float64 |
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- name: end_time |
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dtype: float64 |
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- name: duration |
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dtype: float64 |
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- name: quality |
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dtype: string |
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- name: speech_genre |
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dtype: string |
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- name: speech_style |
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dtype: string |
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- name: variety |
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dtype: string |
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- name: accent |
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dtype: string |
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- name: sex |
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dtype: string |
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- name: age_range |
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dtype: string |
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- name: num_speakers |
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dtype: string |
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- name: speaker_id |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 1321925619.759 |
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num_examples: 7941 |
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- name: validation |
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num_bytes: 81865796.0 |
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num_examples: 500 |
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download_size: 1397176842 |
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dataset_size: 1403791415.759 |
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- config_name: automatic |
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features: |
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- name: path |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: name |
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dtype: string |
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- name: speaker |
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dtype: string |
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- name: start_time |
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dtype: float64 |
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- name: end_time |
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dtype: float64 |
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- name: text |
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dtype: string |
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- name: duration |
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dtype: int64 |
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- name: most_common_speaker |
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dtype: string |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 1355301836.348 |
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num_examples: 8021 |
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- name: validation |
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num_bytes: 66393070.0 |
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num_examples: 382 |
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download_size: 1403414447 |
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dataset_size: 1421694906.348 |
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- config_name: prosodic |
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features: |
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- name: path |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: name |
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dtype: string |
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- name: speaker |
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dtype: string |
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- name: start_time |
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dtype: float64 |
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- name: end_time |
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dtype: float64 |
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- name: normalized_text |
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dtype: string |
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- name: text |
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dtype: string |
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- name: duration |
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dtype: float64 |
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- name: type |
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dtype: string |
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- name: year |
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dtype: int64 |
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- name: gender |
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dtype: string |
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- name: age_range |
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dtype: string |
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- name: total_duration |
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dtype: string |
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- name: quality |
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dtype: string |
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- name: theme |
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dtype: string |
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|
splits: |
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- name: train |
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num_bytes: 1365221237.321 |
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|
num_examples: 7527 |
|
|
- name: validation |
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|
num_bytes: 82345917.0 |
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num_examples: 473 |
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download_size: 1436686693 |
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dataset_size: 1447567154.321 |
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- config_name: test |
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features: |
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- name: path |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: name |
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dtype: string |
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- name: speaker |
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dtype: string |
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|
- name: start_time |
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dtype: string |
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|
- name: end_time |
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|
dtype: string |
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|
- name: text |
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|
dtype: string |
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|
- name: duration |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 2179301.0 |
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num_examples: 29 |
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download_size: 2125875 |
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dataset_size: 2179301.0 |
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configs: |
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- config_name: audioCorpus |
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data_files: |
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- split: train |
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path: audioCorpus/train-* |
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- split: validation |
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path: audioCorpus/validation-* |
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- config_name: automatic |
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data_files: |
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- split: train |
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path: automatic/train-* |
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- split: validation |
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path: automatic/validation-* |
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- config_name: prosodic |
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data_files: |
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- split: train |
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path: prosodic/train-* |
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- split: validation |
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path: prosodic/validation-* |
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- config_name: test |
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data_files: |
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- split: train |
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path: test/train-* |
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--- |
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## How to Load the Dataset |
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There are 4 configurations: **"prosodic"**, **"automatic"**, **"audioCorpus"** and **test**. To load the dataset with the HuggingFace *datasets* library, use the following code: |
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``` python |
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prosodic = load_dataset("nilc-nlp/NURC-SP_ENTOA_TTS", name="prosodic") |
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automatic = load_dataset("nilc-nlp/NURC-SP_ENTOA_TTS", name = "automatic") |
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audioCorpus = load_dataset("nilc-nlp/NURC-SP_ENTOA_TTS", name = "audioCorpus") |
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test = load_dataset("nilc-nlp/NURC-SP_ENTOA_TTS", name="test") |
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``` |
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## Parameters of each configuration |
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### Prosodic Parameters |
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- path: The path to the audio file. |
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- name: The name of the original audio. |
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- speaker: The speaker in the segment (each different speaker in the original source was given an integer id). This field was automatically writter by WhisperX, so it might not be accurate. |
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- start_time: The time the audio segment starts in the original source in seconds. |
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- end_time: The time the audio segment ends in the original source in seconds. |
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- normalized_text: The human-made trancription without prosodic markings for the given audio. |
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- text: The human-made trancription with prosodic markings for the given audio. |
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- duration: The duration of the audio segment in seconds. |
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- type: The type of the audio according to the original NURC-SP classification. |
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- year: The year the audio was recorded |
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- gender: he speaker's sex. Divided into 'F', 'M', 'F e F', 'F e M' and 'M e M' ('F' stands for female and 'M' stands for male). Note that some audio sources have more than one speaker, so in that case the sex refers to the main speaker or speakers. |
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- age_range: The speaker's age range. Divided into 'I' (25 to 35), 'II' (36 to 55) and 'III' (over 55). Note that some audio sources have more than one speaker, so in that case the age range refers to the main speaker or speakers. |
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- total_duration: The duration of the original audio in minutes. |
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- quality: The human-determined quality of the audio |
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- theme: The theme of the speech. |
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- audio: The audio data of the segment. |
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### Automatic Parameters |
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- path: The path to the audio file. |
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- name: The name of the original audio. |
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- speaker: The speaker in the segment (each different speaker in the original source was given an integer id). This field was automatically writter by WhisperX, so it might not be accurate. |
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- start_time: The time the audio segment starts in the original source in seconds. |
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- end_time: The time the audio segment ends in the original source in seconds. |
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- text: The automatic trancription for the given audio. |
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- duration: The duration of the audio segment in seconds. |
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- audio: The audio data of the segment. |
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### AudioCorpus Parameters |
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- audio_name: The name given to the audio in the database. All audios extracted from the same source have the same name. |
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- file_path: The path to the audio file. |
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- text: The human-verified trancription for the given audio. |
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- start_time: The time the audio segment starts in the original source in seconds. |
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- end_time: The time the audio segment ends in the original source in seconds. |
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- duration: The duration of the audio segment in seconds. |
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- quality: Whether or not the audio had parts that could not be transcribed properly. Audios without this characteristic are rated 'high' and audios with it are rated 'low'. |
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- speech_genre: The speech genre of the original source of the segment. Divided into 'dialogue', 'interview' or 'lecture and talks'. |
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- speech_style: The speech style of the original source of the segment. All segments are categorized as 'spontaneous speech'. |
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- variety: The audio language. All segments are categorized as 'pt-br'. |
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- accent: The speaker's accent. All segments are categorized as 'sp-city'. Note that some audio sources have more than one speaker, so in that case the accent refers to the main speaker or speakers. |
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- sex: The speaker's sex. Divided into 'F', 'M', 'F e F', 'F e M' and 'M e M' ('F' stands for female and 'M' stands for male). Note that some audio sources have more than one speaker, so in that case the sex refers to the main speaker or speakers. |
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- age_range: The speaker's age range. Divided into 'I' (25 to 35), 'II' (36 to 55) and 'III' (over 55). Note that some audio sources have more than one speaker, so in that case the age range refers to the main speaker or speakers. |
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- num_speakers: The number of speakers in the original source of the segment. This field was automatically writter by WhisperX, so it might not be accurate. |
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- speaker_id: The speaker in the segment (each different speaker in the original source was given an integer id). This field was automatically writter by WhisperX, so it might not be accurate. |
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### Test Parameters |
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- path: The path to the audio file. |
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- name: The name of the original audio. |
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- speaker: The speaker in the segment (each different speaker in the original source was given an integer id). This field was automatically writter by WhisperX, so it might not be accurate. |
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- start_time: The time the audio segment starts in the original source in seconds. |
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- end_time: The time the audio segment ends in the original source in seconds. |
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- text: The automatic trancription for the given audio. |
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- duration: The duration of the audio segment in seconds * 16000 (sampling rate). |
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- audio: The audio data of the segment. |
