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Update the dataset card

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  1. README.md +4 -6
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@@ -78,7 +78,6 @@ The NPSC dataset contains JSON lines with language training data. The data loade
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  "audio": {"path": "audio/20170110-095504_320246_323590.wav","array": [.......]}
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  }
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- ```
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  ## Data Fields
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  |**Key** | **Type** | **Description** |
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  |:-----------|:------------|:------------|
@@ -90,15 +89,14 @@ The NPSC dataset contains JSON lines with language training data. The data loade
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  | **sentence_text** | String | The sentence text. The transcribed text string of the sentence in non-normalized form. This is the text of the manual transcriptions, without any postprocessing (apart from corrections of known errors). It may contain interrupted words, non-standard words and function words with a pronunciation deviating from the written form. Detailed metadata about the words in the sentence can be found in the word-tokenized version of the corpus in the official release of the dataset. |
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  | **sentence_language_code** | String | The language code of the sentence. The following alternatives exists in the file: ['nb-NO'. 'nn-NO', 'en-US']|
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  | **text** | String | sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.|
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- | **start_time** | Integer | The start time of the sentence. This time is relative to the day of the meeting. |
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  | **end_time** | Integer | End time. See comment above. |
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- | **normsentence_text** | String | Normalised sentence text. This is !!! |
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- | **transsentence_text** | String | Translated sentence text. This is !!! |
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- | **translated** | Integer | A flag indicating if.... |
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  | **audio** | Array | The dataloader will encode the accociated audio files and provide them as an array containing 'path', 'sound array','sampling_rate' |
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-
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  #### Initial Data Collection
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  The procedure for the dataset creation is described in detail in our paper.
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  "audio": {"path": "audio/20170110-095504_320246_323590.wav","array": [.......]}
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  }
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  ## Data Fields
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  |**Key** | **Type** | **Description** |
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  |:-----------|:------------|:------------|
 
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  | **sentence_text** | String | The sentence text. The transcribed text string of the sentence in non-normalized form. This is the text of the manual transcriptions, without any postprocessing (apart from corrections of known errors). It may contain interrupted words, non-standard words and function words with a pronunciation deviating from the written form. Detailed metadata about the words in the sentence can be found in the word-tokenized version of the corpus in the official release of the dataset. |
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  | **sentence_language_code** | String | The language code of the sentence. The following alternatives exists in the file: ['nb-NO'. 'nn-NO', 'en-US']|
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  | **text** | String | sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.|
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+ | **start_time** | Integer | The start time of the sentence in milliseconds. This time is relative to the start of audiofile of the entire meeting, which can be accessed in the official release |
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  | **end_time** | Integer | End time. See comment above. |
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+ | **normsentence_text** | String | Normalized sentence text. In this version of the transcription, numbers and dates are written in digits on standardized formats, and common abbreviations are used. These modifications to the original transcriptions are produced automatically using normalization grammars |
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+ | **transsentence_text** | String | Translated sentence text. Whenever the original transcription is in Bokmål (nb-NO), this field contains a machine-translated version in Nynorsk (nn-NO), and vice versa |
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+ | **translated** | Integer | A flag indicating whether a machine-translated version has been produced or not. Sentences in en-US have not been translated |
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  | **audio** | Array | The dataloader will encode the accociated audio files and provide them as an array containing 'path', 'sound array','sampling_rate' |
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  #### Initial Data Collection
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  The procedure for the dataset creation is described in detail in our paper.
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