Datasets:

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ftopal

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huggingface-datasets

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[ "# Dataset Card for Nexdata/American_English_Speech_Synthesis_Corpus-Female", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nFemale audio data of American English,. It is recorded by American English native speaker, with authentic accent and sweet sound. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/American_English_Speech_Synthesis_Corpus-Female", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nFemale audio data of American English,. It is recorded by American English native speaker, with authentic accent and sweet sound. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/Scene_Noise_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nScene noise data, with a duration of 1,297 hours. The data covers multiple scenarios, including subways, supermarkets, restaurants, roads, etc.; audio is recorded using professional recorders, high sampling rate, dual-channel format collection; time and type of non-noise are annotated. this data set can be used for noise modeling.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition,noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNoise Data", "### Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Scene_Noise_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nScene noise data, with a duration of 1,297 hours. The data covers multiple scenarios, including subways, supermarkets, restaurants, roads, etc.; audio is recorded using professional recorders, high sampling rate, dual-channel format collection; time and type of non-noise are annotated. this data set can be used for noise modeling.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition,noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNoise Data", "### Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/English_Emotional_Speech_Data_by_Microphone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nEnglish emotional audio data captured by microphone, 20 American native speakers participate in the recording, 2,100 sentences per person; the recorded script covers 10 emotions such as anger, happiness, sadness; the voice is recorded by high-fidelity microphone therefore has high quality; it is used for analytical detection of emotional speech.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, emotion-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/English_Emotional_Speech_Data_by_Microphone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nEnglish emotional audio data captured by microphone, 20 American native speakers participate in the recording, 2,100 sentences per person; the recorded script covers 10 emotions such as anger, happiness, sadness; the voice is recorded by high-fidelity microphone therefore has high quality; it is used for analytical detection of emotional speech.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, emotion-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nEnglish", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/Mandarin_Speech_Data_in_Noisy_Environment_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nSpoken Mandarin audio data under noisy environment captured by mobile phone, it is recorded by 203 speakers from all over China, covering all major dialect regions; and a variety of noise scenes such as subways, supermarkets, restaurants, etc., more suitable for real application scenes; it can be used for automatic speech recognition, machine translation, and voiceprint recognition.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Speech_Data_in_Noisy_Environment_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nSpoken Mandarin audio data under noisy environment captured by mobile phone, it is recorded by 203 speakers from all over China, covering all major dialect regions; and a variety of noise scenes such as subways, supermarkets, restaurants, etc., more suitable for real application scenes; it can be used for automatic speech recognition, machine translation, and voiceprint recognition.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/German_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nGerman audio data captured by mobile phone, 1,796 hours in total, recorded by 3,442 German native speakers. The recorded text is designed by linguistic experts, covering generic, interactive, on-board, home and other categories. The text has been proofread manually with high accuracy; this data can be used for automatic speech recognition, machine translation, and voiceprint recognition.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nGerman", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/German_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nGerman audio data captured by mobile phone, 1,796 hours in total, recorded by 3,442 German native speakers. The recorded text is designed by linguistic experts, covering generic, interactive, on-board, home and other categories. The text has been proofread manually with high accuracy; this data can be used for automatic speech recognition, machine translation, and voiceprint recognition.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nGerman", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/Far-filed_Noise_Speech_Data_in_Home_Environment_by_Mic-Array", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data consists of multiple sets of products, each with a different type of microphone arrays. Noise data is collected from real home scenes of the indoor residence of ordinary residents. The data set can be used for tasks such as voice enhancement and automatic speech recognition in a home scene.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition,noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNoise Data", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Far-filed_Noise_Speech_Data_in_Home_Environment_by_Mic-Array", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data consists of multiple sets of products, each with a different type of microphone arrays. Noise data is collected from real home scenes of the indoor residence of ordinary residents. The data set can be used for tasks such as voice enhancement and automatic speech recognition in a home scene.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition,noisy-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nNoise Data", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

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[ "# Dataset Card for Nexdata/Japanese_Speech_Datae", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1006 Japanese native speakers participated in the recording, coming from eastern, western, and Kyushu regions, while the eastern region accounting for the largest proportion. The recording content is rich and all texts have been manually transferred with high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nJapanese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Japanese_Speech_Datae", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1006 Japanese native speakers participated in the recording, coming from eastern, western, and Kyushu regions, while the eastern region accounting for the largest proportion. The recording content is rich and all texts have been manually transferred with high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nJapanese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "task_categories:automatic-speech-recognition", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Nexdata/American_English_Spontaneous_Speech_Data", "## Description\nThe 1,178-hour American English Spontaneous Speech Data is a collection of speech clips, covering multiple topics. Audio is transcribed into text, and speaker identity and other more attributes are annotated. This data set can be used for voiceprint recognition model training, construction of corpus for machine translation and algorithm research.\n\nFor more details, please refer to the link: URL", "# Specifications", "## Format\n16kHz, 16bit, mono channel;", "## Content category\nincluding self-media, conversation, live, lecture, variety-show.", "## Language\nAmerican English;", "## Annotation\nannotation for the transcription text, speaker identification, gender;", "## Application scenarios\nspeech recognition, video caption generation and video content review;", "## Accuracy\nat a Sentence Accuracy Rate (SAR) of being no less than 95%.", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #language-English #region-us \n", "# Dataset Card for Nexdata/American_English_Spontaneous_Speech_Data", "## Description\nThe 1,178-hour American English Spontaneous Speech Data is a collection of speech clips, covering multiple topics. Audio is transcribed into text, and speaker identity and other more attributes are annotated. This data set can be used for voiceprint recognition model training, construction of corpus for machine translation and algorithm research.\n\nFor more details, please refer to the link: URL", "# Specifications", "## Format\n16kHz, 16bit, mono channel;", "## Content category\nincluding self-media, conversation, live, lecture, variety-show.", "## Language\nAmerican English;", "## Annotation\nannotation for the transcription text, speaker identification, gender;", "## Application scenarios\nspeech recognition, video caption generation and video content review;", "## Accuracy\nat a Sentence Accuracy Rate (SAR) of being no less than 95%.", "# Licensing Information\nCommercial License" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Non-Hispanic_Spanish_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1,630 non-Spanish nationality native Spanish speakers such as Mexicans and Colombians participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, in-vehicle and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nSpanish", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Non-Hispanic_Spanish_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1,630 non-Spanish nationality native Spanish speakers such as Mexicans and Colombians participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, in-vehicle and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nSpanish", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Emotional_Video_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1,003 People - Emotional Video Data. The data diversity includes multiple races, multiple indoor scenes, multiple age groups, multiple languages, multiple emotions (11 types of facial emotions, 15 types of inner emotions). For each sentence in each video, emotion types (including facial emotions and inner emotions), start & end time, and text transcription were annotated.This dataset can be used for tasks such as emotion recognition and sentiment analysis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, sentiment-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nEnglish, Chinese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Emotional_Video_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1,003 People - Emotional Video Data. The data diversity includes multiple races, multiple indoor scenes, multiple age groups, multiple languages, multiple emotions (11 types of facial emotions, 15 types of inner emotions). For each sentence in each video, emotion types (including facial emotions and inner emotions), start & end time, and text transcription were annotated.This dataset can be used for tasks such as emotion recognition and sentiment analysis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification, sentiment-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nEnglish, Chinese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Mandarin_Interactive_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMandarin home interaction mobile phone language audio data (Far-field home collected audio data subset), with duration of 849 hours, recorded in the real home scene; content focuses on home instructions, functional assistants and wake-up words, specially designed for smart home, more close to data application scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Interactive_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMandarin home interaction mobile phone language audio data (Far-field home collected audio data subset), with duration of 849 hours, recorded in the real home scene; content focuses on home instructions, functional assistants and wake-up words, specially designed for smart home, more close to data application scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/French_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1089 French native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nFrench English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/French_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1089 French native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nFrench English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Spanish_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n891 Spanish native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSpanish English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Spanish_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n891 Spanish native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSpanish English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Singaporean_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is recorded by 452 native Singaporean speakers with a balanced gender. It is rich in content and it covers generic command and control;human-machine interaction; smart home command and control;in-car command and control categories. The transcription corpus has been manually proofread to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSingaporean English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Singaporean_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is recorded by 452 native Singaporean speakers with a balanced gender. It is rich in content and it covers generic command and control;human-machine interaction; smart home command and control;in-car command and control categories. The transcription corpus has been manually proofread to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSingaporean English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Australian_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nIt collects 2.956 speakers from Shanghai and is recorded in quiet indoor environment. The recorded content includes multi-domain customer consultation, short messages, numbers, Shanghai POI, etc. The corpus has no repetition and the average sentence length is 12.68 words. Recording devices are mainstream Android phones and iPhones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nAustralian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Australian_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nIt collects 2.956 speakers from Shanghai and is recorded in quiet indoor environment. The recorded content includes multi-domain customer consultation, short messages, numbers, Shanghai POI, etc. The corpus has no repetition and the average sentence length is 12.68 words. Recording devices are mainstream Android phones and iPhones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nAustralian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Indonesian_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1285 Indonesian native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nIndonesian", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Indonesian_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1285 Indonesian native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nIndonesian", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "task_categories:automatic-speech-recognition", "task_categories:voice-activity-detection", "region:us" ]

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[ "# 201-People-Infant-Cry-Speech-Data-by-Mobile-Phone", "## Description\nCrying sound of 201 infants and young children aged 0~3 years old, a number of paragraphs from each of them; It provides data support for detecting children's crying sound in smart home projects.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel", "## Recording Environment\nrelatively quiet indoor environment, without echo", "## Recording Content\ninfant cry", "## Population\n201 people; 105 boys, 96 girls; 132 people aged 0~1, 58 people aged 1~2, 11 people aged 2~3", "## Device\niPhone, Android mobile phone", "## Application scene\nabnormal voice recognition,smart home", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-voice-activity-detection #region-us \n", "# 201-People-Infant-Cry-Speech-Data-by-Mobile-Phone", "## Description\nCrying sound of 201 infants and young children aged 0~3 years old, a number of paragraphs from each of them; It provides data support for detecting children's crying sound in smart home projects.\n\nFor more details, please refer to the link: URL", "## Format\n16kHz, 16bit, uncompressed wav, mono channel", "## Recording Environment\nrelatively quiet indoor environment, without echo", "## Recording Content\ninfant cry", "## Population\n201 people; 105 boys, 96 girls; 132 people aged 0~1, 58 people aged 1~2, 11 people aged 2~3", "## Device\niPhone, Android mobile phone", "## Application scene\nabnormal voice recognition,smart home", "# Licensing Information\nCommercial License" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/American_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1842 American native speakers participated in the recording with authentic accent. The recorded script is designed by linguists, based on scenes, and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/American_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1842 American native speakers participated in the recording with authentic accent. The recorded script is designed by linguists, based on scenes, and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Chinese_Children_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nChildren read English audio data, covering ages from preschool (3-5 years old) to post-school (6-12 years old) , with children's speech features; content accurately matches children's actual scenes of speaking English. It provides data support for children's smart home, automatic speech recognition and oral assessment in intelligent education scene, .\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Children_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nChildren read English audio data, covering ages from preschool (3-5 years old) to post-school (6-12 years old) , with children's speech features; content accurately matches children's actual scenes of speaking English. It provides data support for children's smart home, automatic speech recognition and oral assessment in intelligent education scene, .\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Mandarin_Strong_Accent_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMore than 2,000 Chinese native speakers participated in the recording with equal gender. Speakers are mainly from the southern China, and some of them are from the provinces of northern China with Strong accents. The recording content is rich, covering mobile phone voice assistant interaction, smart home command and control, In-car command and control, numbers and other fields, which is accurately matching the smart home, intelligent car and other practical application scenarios.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Strong_Accent_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMore than 2,000 Chinese native speakers participated in the recording with equal gender. Speakers are mainly from the southern China, and some of them are from the provinces of northern China with Strong accents. The recording content is rich, covering mobile phone voice assistant interaction, smart home command and control, In-car command and control, numbers and other fields, which is accurately matching the smart home, intelligent car and other practical application scenarios.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Korean_Speech_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nKorean audio data with duration of 516 hours, recorded texts include: daily language, various interactive sentences, home commands, on-board commands, etc. Among 1,077 speakers, male and female speakers are 49% and 51%. The duration of each speaker is around half an hour.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Korean_Speech_Data", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nKorean audio data with duration of 516 hours, recorded texts include: daily language, various interactive sentences, home commands, on-board commands, etc. Among 1,077 speakers, male and female speakers are 49% and 51%. The duration of each speaker is around half an hour.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Mandarin_Voiceprint_Recognition_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nEach person's time span is very long, which can better cover the sound features of a person in different periods and different states.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Voiceprint_Recognition_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nEach person's time span is very long, which can better cover the sound features of a person in different periods and different states.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Italian_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n497 Italians recorded in a relatively quiet environment in authentic English. The recorded script is designed by linguists and covers a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nItalian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Italian_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n497 Italians recorded in a relatively quiet environment in authentic English. The recorded script is designed by linguists and covers a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nItalian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Portuguese_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n532 Portuguese recorded in a relatively quiet environment in authentic English. The recorded script is designed by linguists and covers a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nPortuguese English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Portuguese_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n532 Portuguese recorded in a relatively quiet environment in authentic English. The recorded script is designed by linguists and covers a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nPortuguese English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Korean_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe product contains the speech data recorded by 400 native Korean speakers, with roughly equal gender distribution. The corpus covers a wide domain with rich content of generic category, human-machine interaction category, in-car category, smart home category, etc. The corpus text was manually checked to ensure the high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Korean_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe product contains the speech data recorded by 400 native Korean speakers, with roughly equal gender distribution. The corpus covers a wide domain with rich content of generic category, human-machine interaction category, in-car category, smart home category, etc. The corpus text was manually checked to ensure the high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nKorean English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Russian_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is recorded by 498 native Russian speakers with a balanced gender. It is rich in content and it covers generic command and control;human-machine interaction; smart home command and control;in-car command and control categories. The transcription corpus has been manually proofread to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nRussian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Russian_Speaking_English_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThis dataset is recorded by 498 native Russian speakers with a balanced gender. It is rich in content and it covers generic command and control;human-machine interaction; smart home command and control;in-car command and control categories. The transcription corpus has been manually proofread to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nRussian English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Sichuan_Dialect_Conversational_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1730 Sichuan native speakers participated in the recording and face-to-face free talking in a natural way in wide fields without the topic specified. It is natural and fluency in speech, and in line with the actual dialogue scene. We transcribed the speech into text manually to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSichuan Dialect", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Sichuan_Dialect_Conversational_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n1730 Sichuan native speakers participated in the recording and face-to-face free talking in a natural way in wide fields without the topic specified. It is natural and fluency in speech, and in line with the actual dialogue scene. We transcribed the speech into text manually to ensure high accuracy.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nSichuan Dialect", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chines_Digital_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n11,010 People - Chines Digital Speech Data by Mobile Phone was recorded by 11,010 voice recorders in Mandarin. It's collected from each person 30 sentences of 4-8 digits.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chines_Digital_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n11,010 People - Chines Digital Speech Data by Mobile Phone was recorded by 11,010 voice recorders in Mandarin. It's collected from each person 30 sentences of 4-8 digits.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Wake-up_Words_Speech_Data_by_Microphone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMore than 1,000 recorders read the specified wake-up words, covering slow, normal, and fast three speeds. Audios are recorded in the professional recording studio using the microphone.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Wake-up_Words_Speech_Data_by_Microphone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMore than 1,000 recorders read the specified wake-up words, covering slow, normal, and fast three speeds. Audios are recorded in the professional recording studio using the microphone.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Mixed_Speech_with_Chinese_and_English_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data is recorded by 1113 Chinese native speakers with accents covering seven major dialect areas. The recorded text is a mixture of Chinese and English sentences, covering general scenes and human-computer interaction scenes. It is rich in content and accurate in transcription. It can be used for improving the recognition effect of the speech recognition system on Chinese-English mixed reading speech.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese, English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mixed_Speech_with_Chinese_and_English_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe data is recorded by 1113 Chinese native speakers with accents covering seven major dialect areas. The recorded text is a mixture of Chinese and English sentences, covering general scenes and human-computer interaction scenes. It is rich in content and accurate in transcription. It can be used for improving the recognition effect of the speech recognition system on Chinese-English mixed reading speech.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese, English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Mandarin_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n4,787 Chinese native speakers participated in the recording with equal gender. Speakers are from various provinces of China. The recording content is rich, covering mobile phone voice assistant interaction, smart home command and control, In-car command and control, numbers, and other fields, which is accurately matching the smart home, intelligent car, and other practical application scenarios.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Mandarin_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n4,787 Chinese native speakers participated in the recording with equal gender. Speakers are from various provinces of China. The recording content is rich, covering mobile phone voice assistant interaction, smart home command and control, In-car command and control, numbers, and other fields, which is accurately matching the smart home, intelligent car, and other practical application scenarios.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Infant_Laugh_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nLaugh sound of 20 infants and young children aged 0~3 years old, a number of paragraphs from each of them; It provides data support for detecting children's laugh sound in smart home projects.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nInfant Cry", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Infant_Laugh_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nLaugh sound of 20 infants and young children aged 0~3 years old, a number of paragraphs from each of them; It provides data support for detecting children's laugh sound in smart home projects.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\nInfant Cry", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Data_Female_Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n26.1 Hours - Chinese Mandarin Synthesis Corpus-Female, Customer Service, It is recorded by Chinese native speakers, with lively and frindly voice. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Data_Female_Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n26.1 Hours - Chinese Mandarin Synthesis Corpus-Female, Customer Service, It is recorded by Chinese native speakers, with lively and frindly voice. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Male_Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n20 Hours - Chinese Mandarin Synthesis Corpus-Male, Customer Service. It is recorded by Chinese native speakers, the voice of the full of magnetism. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Male_Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n20 Hours - Chinese Mandarin Synthesis Corpus-Male, Customer Service. It is recorded by Chinese native speakers, the voice of the full of magnetism. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Chinese_Mandarin_Average_Tone_Speech_Synthesis_Corpus-Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n150 People - Chinese Mandarin Average Tone Speech Synthesis Corpus-Customer Service. It is recorded by Chinese native speakers,customer service text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Average_Tone_Speech_Synthesis_Corpus-Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n150 People - Chinese Mandarin Average Tone Speech Synthesis Corpus-Customer Service. It is recorded by Chinese native speakers,customer service text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[ "# Dataset Card for Nexdata/Chinese-English_Mixed_Average_Tone_Speech_Synthesis_Corpus-Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n50 People - Chinese-English Mixed Average Tone Speech Synthesis Corpus-Customer Service. It is recorded by Chinese native speakers,customer service text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese, English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese-English_Mixed_Average_Tone_Speech_Synthesis_Corpus-Customer_Service", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n50 People - Chinese-English Mixed Average Tone Speech Synthesis Corpus-Customer Service. It is recorded by Chinese native speakers,customer service text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese, English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Nexdata/Filipino_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n500 Hours - Filipino Speech Data by Mobile Phone，the data were recorded by Filipino speakers with authentic Filipino accents.The text is manually proofread with high accuracy. Match mainstream Android, Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nFilipino", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Filipino_Speech_Data_by_Mobile_Phone", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n500 Hours - Filipino Speech Data by Mobile Phone，the data were recorded by Filipino speakers with authentic Filipino accents.The text is manually proofread with high accuracy. Match mainstream Android, Apple system phones.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\nautomatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).", "### Languages\n\nFilipino", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "task_categories:text-to-speech", "language:zh", "region:us" ]

[]

[ "zh" ]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus", "## Description\n12 Hours - Chinese Mandarin Entertainment anchor Style Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker. six emotional text+modal particles, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n\nFor more details, please refer to the link: URL", "# Specifications", "## Format\n48,000Hz, 24bit, uncompressed wav, mono channel", "## Recording environment\nprofessional recording studio", "## Recording content\nseven emotions (happiness, anger, sadness, surprise, fear, disgust)+sentences with filler word", "## Speaker\nprofessional CharacterVoice; Role: An 18-year-old girl who works as an entertainment anchor and enjoys singing and dancing", "## Device\nmicrophone", "## Language\nMandarin", "## Annotation\nword and pinyin transcription, prosodic boundary annotation, phoneme boundary annotation", "## The amount of data\nThe amount of neutral data is not less than 1.6 hours; the amount of data with filler word is not less than 0.4 hours; and the remaining six types of emotional data is not less than 1.67 hours each", "# Licensing Information\nCommercial License" ]

[ "TAGS\n#task_categories-text-to-speech #language-Chinese #region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Entertainment_anchor_Style_Multi-emotional_Synthesis_Corpus", "## Description\n12 Hours - Chinese Mandarin Entertainment anchor Style Multi-emotional Synthesis Corpus. It is recorded by Chinese native speaker. six emotional text+modal particles, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n\nFor more details, please refer to the link: URL", "# Specifications", "## Format\n48,000Hz, 24bit, uncompressed wav, mono channel", "## Recording environment\nprofessional recording studio", "## Recording content\nseven emotions (happiness, anger, sadness, surprise, fear, disgust)+sentences with filler word", "## Speaker\nprofessional CharacterVoice; Role: An 18-year-old girl who works as an entertainment anchor and enjoys singing and dancing", "## Device\nmicrophone", "## Language\nMandarin", "## Annotation\nword and pinyin transcription, prosodic boundary annotation, phoneme boundary annotation", "## The amount of data\nThe amount of neutral data is not less than 1.6 hours; the amount of data with filler word is not less than 0.4 hours; and the remaining six types of emotional data is not less than 1.67 hours each", "# Licensing Information\nCommercial License" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Average_Tone_Speech_Synthesis_Corpus_General", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n100 People - Chinese Mandarin Average Tone Speech Synthesis Corpus, General. It is recorded by Chinese native speaker. It covers news, dialogue, audio books, poetry, advertising, news broadcasting, entertainment; and the phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Average_Tone_Speech_Synthesis_Corpus_General", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n100 People - Chinese Mandarin Average Tone Speech Synthesis Corpus, General. It is recorded by Chinese native speaker. It covers news, dialogue, audio books, poetry, advertising, news broadcasting, entertainment; and the phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_General", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nChinese Mandarin Synthesis Corpus-Female, General. It is recorded by Chinese native speaker. It covers oral sentences, audio books, news, advertising, customer service and movie commentary, and the phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_General", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nChinese Mandarin Synthesis Corpus-Female, General. It is recorded by Chinese native speaker. It covers oral sentences, audio books, news, advertising, customer service and movie commentary, and the phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_Emotional", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n13.3 Hours - Chinese Mandarin Synthesis Corpus-Female, Emotional. It is recorded by Chinese native speaker,emotional text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_Emotional", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n13.3 Hours - Chinese Mandarin Synthesis Corpus-Female, Emotional. It is recorded by Chinese native speaker,emotional text, and the syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Average_Tone_Speech_Synthesis_Corpus-Three_Styles", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n50 People - Chinese Average Tone Speech Synthesis Corpus-Three Styles.It is recorded by Chinese native speakers. Corpus includes cunstomer service,news and story. The syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Average_Tone_Speech_Synthesis_Corpus-Three_Styles", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n50 People - Chinese Average Tone Speech Synthesis Corpus-Three Styles.It is recorded by Chinese native speakers. Corpus includes cunstomer service,news and story. The syllables, phonemes and tones are balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Songs_in_Acapella__Female", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n103 Chinese Mandarin Songs in Acapella - Female. It is recorded by Chinese professional singer, with sweet voice. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the song synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts,: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Songs_in_Acapella__Female", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n103 Chinese Mandarin Songs in Acapella - Female. It is recorded by Chinese professional singer, with sweet voice. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the song synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts,: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/American_English_Speech_Synthesis_Corpus-Male", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMale audio data of American English. It is recorded by American English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/American_English_Speech_Synthesis_Corpus-Male", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nMale audio data of American English. It is recorded by American English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nAmerican English", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_Customer_Service_Conversational_Speech", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n20 Hours - Chinese Mandarin Synthesis Corpus-Female, Customer Service, Conversational Speech, It is recorded by Chinese native speakers, with sweet voice. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Nexdata/Chinese_Mandarin_Synthesis_Corpus-Female_Customer_Service_Conversational_Speech", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\n20 Hours - Chinese Mandarin Synthesis Corpus-Female, Customer Service, Conversational Speech, It is recorded by Chinese native speakers, with sweet voice. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.\n \nFor more details, please refer to the link: URL", "### Supported Tasks and Leaderboards\n\ntts: The dataset can be used to train a model for Text to Speech (TTS).", "### Languages\n\nChinese Mandarin", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nCommerical License: URL", "### Contributions" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "license:cc0-1.0", "region:us" ]

[]

[]

[ "# About Dataset", "### Context\n\nText summarization is a way to condense the large amount of information into a concise form by the process of selection of important information and discarding unimportant and redundant information. With the amount of textual information present in the world wide web the area of text summarization is becoming very important. The extractive summarization is the one where the exact sentences present in the document are used as summaries. The extractive summarization is simpler and is the general practice among the automatic text summarization researchers at the present time. Extractive summarization process involves giving scores to sentences using some method and then using the sentences that achieve highest scores as summaries. As the exact sentence present in the document is used the semantic factor can be ignored which results in generation of less calculation intensive summarization procedure. This kind of summary is generally completely unsupervised and language independent too. Although this kind of summary does its job in conveying the essential information it may not be necessarily smooth or fluent. Sometimes there can be almost no connection between adjacent sentences in the summary resulting in the text lacking in readability.\n\nContent\nThis dataset for extractive text summarization has four hundred and seventeen political news articles of BBC from 2004 to 2005 in the News Articles folder. For each articles, five summaries are provided in the Summaries folder. The first clause of the text of articles is the respective title.\n\nAcknowledgements\nThis dataset was created using a dataset used for data categorization that onsists of 2225 documents from the BBC news website corresponding to stories in five topical areas from 2004-2005 used in the paper of D. Greene and P. Cunningham. \"Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering\", Proc. ICML 2006; whose all rights, including copyright, in the content of the original articles are owned by the BBC. More at URL\n\nKaggle Link: URL" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# About Dataset", "### Context\n\nText summarization is a way to condense the large amount of information into a concise form by the process of selection of important information and discarding unimportant and redundant information. With the amount of textual information present in the world wide web the area of text summarization is becoming very important. The extractive summarization is the one where the exact sentences present in the document are used as summaries. The extractive summarization is simpler and is the general practice among the automatic text summarization researchers at the present time. Extractive summarization process involves giving scores to sentences using some method and then using the sentences that achieve highest scores as summaries. As the exact sentence present in the document is used the semantic factor can be ignored which results in generation of less calculation intensive summarization procedure. This kind of summary is generally completely unsupervised and language independent too. Although this kind of summary does its job in conveying the essential information it may not be necessarily smooth or fluent. Sometimes there can be almost no connection between adjacent sentences in the summary resulting in the text lacking in readability.\n\nContent\nThis dataset for extractive text summarization has four hundred and seventeen political news articles of BBC from 2004 to 2005 in the News Articles folder. For each articles, five summaries are provided in the Summaries folder. The first clause of the text of articles is the respective title.\n\nAcknowledgements\nThis dataset was created using a dataset used for data categorization that onsists of 2225 documents from the BBC news website corresponding to stories in five topical areas from 2004-2005 used in the paper of D. Greene and P. Cunningham. \"Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering\", Proc. ICML 2006; whose all rights, including copyright, in the content of the original articles are owned by the BBC. More at URL\n\nKaggle Link: URL" ]

[ "language:pt", "region:us" ]

[]

[ "pt" ]

[ "# Dataset Card for #PraCegoVer", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n- Additional Information\n - Dataset Curators\n - Citation Information", "## Dataset Description\n\n- Dataset repository: #PraCegoVer dataset\n- Github repository: PraCegoVer\n- Paper: #PraCegoVer: A Large Dataset for Image Captioning in Portuguese\n- Contact: Gabriel Oliveira", "### Dataset Summary\n\n\\#PraCegoVer is a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images. The dataset has been created to alleviate the lack of datasets with Portuguese captions for visual-linguistic tasks.\n\n\\#PraCegoVer comprehends 533,523 instances that represent public posts collected from Instagram tagged with #PraCegoVer. the data were collected from more than 14 thousand different profiles.\n\nThis dataset contains images of people, and it consists of data collected from public profiles on Instagram. Thus, the images and raw captions might contain sensitive data that reveal racial or ethnic origins, sexual orientations, and religious beliefs. Hence, under Brazilian Law No. 13,709, to avoid the unintended use of our dataset, we decided to restrict its access, ensuring that the dataset will be used for research purposes only.", "### Supported Tasks and Leaderboards\n\n- 'image-captioning', 'image-to-text': the dataset can be used to train models for Image Captioning, which consists in generating a short description of the visual content of a given image. The model performance is typically measured using ROUGE, METEOR, CIDEr-D, CIDEr-R and SPICE.", "### Languages\n\nThe captions in this dataset are in Brazilian Portuguese (pt-BR).", "## Dataset Structure", "### Data Instances\n\n\\#PraCegoVer dataset is composed of the main file 'URL' and a directory with images, 'images'. The instances in 'URL' have the following format:", "### Data Fields\n\n\nuser: anonymized user that created the post;\nfilename: image file name, which indicates the image in the 'images' directory;\nraw_caption: raw caption;\ncaption: clean caption;\ndate: post date.", "### Data Splits\n\nThis dataset comes with two specified train/validation/test splits, one for #PraCegoVer-63K (train/validation/test: 37,881/12,442/12,612) and another for #PraCegoVer-173K (train/validation/test: 104,004/34,452/34,882). These splits are subsets of the whole dataset.", "## Dataset Creation", "### Curation Rationale\n\nAutomatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce.\n\nThen, inspired by the movement PraCegoVer, #PraCegoVer dataset has been created to provide images annotated with descriptions in Portuguese for the image captioning task. With this dataset, we aim to alleviate the lack of datasets with Portuguese captions for visual-linguistic tasks.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data were collected from posts on Instagram that tagged #PraCegoVer. Then, descriptions are extracted from the raw image caption by using regular expressions. The script to download more data is available in the \\#PraCegoVer repository. We collected the data on a daily basis from 2020 to 2021, but the posts can have been created at any time before this period.", "#### Who are the source language producers?\n\nThe language producers are Instagram users that post images tagging #PraCegoVer.", "#### Who are the annotators?\n\nThe Instagram users that tag \\#PraCegoVer spontaneously add a short description of the image content in their posts.", "### Personal and Sensitive Information\n\nThe usernames were anonymized in order to make it difficult to directly identify the individuals. However, we don't anonymize the remaining data, thus the individuals present in the images can be identified. Moreover, the images and raw captions might contain data revealing racial or ethnic origins, sexual orientations, religious beliefs, political opinions, or union memberships.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop better image captioning models in Portuguese. Such models are essential to help the inclusion of visually impaired people on the Internet, making it more inclusive and democratic.\n\nHowever, it is worth noting that the data were collected from public profiles on Instagram, and were not thoroughly validated. Thus, there might be more examples of offensive and insulting content, albeit an exploratory analysis shows that although there exist words that can be offensive they are insignificant because they occur rarely. Moreover, the images and raw captions might contain data revealing racial or ethnic origins, sexual orientations, religious beliefs, political opinions, or union memberships.", "### Discussion of Biases\n\nWe collected the data from public posts on Instagram. Thus, the data is susceptible to the bias of its algorithm and stereotypes. We conducted an initial analysis of the bias within our dataset. Figure 20 from the dataset's paper shows that women are frequently associated with beauty, cosmetic products, and domestic violence. Moreover, black women co-occur more often with terms such as \"racism\", \"discrimination\", \"prejudice\"\u001dand \"consciousness\", whereas white women appear with \"spa\", \"hair\", and \"lipstick\", and indigenous women are mostly associated with beauty products. Similarly, black men frequently appear together with the terms \"Zumbi dos Palmares\", \"consciousness\", \"racism\", \"United States\"\u001dand \"justice\", while white men are associated with \"theatre\", \"wage\", \"benefit\"\u001dand \"social security\". In addition, Table 4 from the dataset's paper shows that women are more frequently associated with physical words (e.g., thin, fat); still, fat people appear more frequently than thin people. Figure 21 from the dataset's paper illustrates that fat women are also related to swearing words, \"mental harassment\", and \"boss\", while thin women are associated with \"vitamin\", \"fruits\", and \"healthy skin\". To sum up, depending on the usage of this dataset, future users may take these aspects into account.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by Gabriel Oliveira, Esther Colombini and Sandra Avila.\n\n\n\n\nIf you use \\#PraCegoVer dataset, please cite as:" ]

[ "TAGS\n#language-Portuguese #region-us \n", "# Dataset Card for #PraCegoVer", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n- Additional Information\n - Dataset Curators\n - Citation Information", "## Dataset Description\n\n- Dataset repository: #PraCegoVer dataset\n- Github repository: PraCegoVer\n- Paper: #PraCegoVer: A Large Dataset for Image Captioning in Portuguese\n- Contact: Gabriel Oliveira", "### Dataset Summary\n\n\\#PraCegoVer is a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images. The dataset has been created to alleviate the lack of datasets with Portuguese captions for visual-linguistic tasks.\n\n\\#PraCegoVer comprehends 533,523 instances that represent public posts collected from Instagram tagged with #PraCegoVer. the data were collected from more than 14 thousand different profiles.\n\nThis dataset contains images of people, and it consists of data collected from public profiles on Instagram. Thus, the images and raw captions might contain sensitive data that reveal racial or ethnic origins, sexual orientations, and religious beliefs. Hence, under Brazilian Law No. 13,709, to avoid the unintended use of our dataset, we decided to restrict its access, ensuring that the dataset will be used for research purposes only.", "### Supported Tasks and Leaderboards\n\n- 'image-captioning', 'image-to-text': the dataset can be used to train models for Image Captioning, which consists in generating a short description of the visual content of a given image. The model performance is typically measured using ROUGE, METEOR, CIDEr-D, CIDEr-R and SPICE.", "### Languages\n\nThe captions in this dataset are in Brazilian Portuguese (pt-BR).", "## Dataset Structure", "### Data Instances\n\n\\#PraCegoVer dataset is composed of the main file 'URL' and a directory with images, 'images'. The instances in 'URL' have the following format:", "### Data Fields\n\n\nuser: anonymized user that created the post;\nfilename: image file name, which indicates the image in the 'images' directory;\nraw_caption: raw caption;\ncaption: clean caption;\ndate: post date.", "### Data Splits\n\nThis dataset comes with two specified train/validation/test splits, one for #PraCegoVer-63K (train/validation/test: 37,881/12,442/12,612) and another for #PraCegoVer-173K (train/validation/test: 104,004/34,452/34,882). These splits are subsets of the whole dataset.", "## Dataset Creation", "### Curation Rationale\n\nAutomatically describing images using natural sentences is an essential task to visually impaired people's inclusion on the Internet. Although there are many datasets in the literature, most of them contain only English captions, whereas datasets with captions described in other languages are scarce.\n\nThen, inspired by the movement PraCegoVer, #PraCegoVer dataset has been created to provide images annotated with descriptions in Portuguese for the image captioning task. With this dataset, we aim to alleviate the lack of datasets with Portuguese captions for visual-linguistic tasks.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data were collected from posts on Instagram that tagged #PraCegoVer. Then, descriptions are extracted from the raw image caption by using regular expressions. The script to download more data is available in the \\#PraCegoVer repository. We collected the data on a daily basis from 2020 to 2021, but the posts can have been created at any time before this period.", "#### Who are the source language producers?\n\nThe language producers are Instagram users that post images tagging #PraCegoVer.", "#### Who are the annotators?\n\nThe Instagram users that tag \\#PraCegoVer spontaneously add a short description of the image content in their posts.", "### Personal and Sensitive Information\n\nThe usernames were anonymized in order to make it difficult to directly identify the individuals. However, we don't anonymize the remaining data, thus the individuals present in the images can be identified. Moreover, the images and raw captions might contain data revealing racial or ethnic origins, sexual orientations, religious beliefs, political opinions, or union memberships.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop better image captioning models in Portuguese. Such models are essential to help the inclusion of visually impaired people on the Internet, making it more inclusive and democratic.\n\nHowever, it is worth noting that the data were collected from public profiles on Instagram, and were not thoroughly validated. Thus, there might be more examples of offensive and insulting content, albeit an exploratory analysis shows that although there exist words that can be offensive they are insignificant because they occur rarely. Moreover, the images and raw captions might contain data revealing racial or ethnic origins, sexual orientations, religious beliefs, political opinions, or union memberships.", "### Discussion of Biases\n\nWe collected the data from public posts on Instagram. Thus, the data is susceptible to the bias of its algorithm and stereotypes. We conducted an initial analysis of the bias within our dataset. Figure 20 from the dataset's paper shows that women are frequently associated with beauty, cosmetic products, and domestic violence. Moreover, black women co-occur more often with terms such as \"racism\", \"discrimination\", \"prejudice\"\u001dand \"consciousness\", whereas white women appear with \"spa\", \"hair\", and \"lipstick\", and indigenous women are mostly associated with beauty products. Similarly, black men frequently appear together with the terms \"Zumbi dos Palmares\", \"consciousness\", \"racism\", \"United States\"\u001dand \"justice\", while white men are associated with \"theatre\", \"wage\", \"benefit\"\u001dand \"social security\". In addition, Table 4 from the dataset's paper shows that women are more frequently associated with physical words (e.g., thin, fat); still, fat people appear more frequently than thin people. Figure 21 from the dataset's paper illustrates that fat women are also related to swearing words, \"mental harassment\", and \"boss\", while thin women are associated with \"vitamin\", \"fruits\", and \"healthy skin\". To sum up, depending on the usage of this dataset, future users may take these aspects into account.", "## Additional Information", "### Dataset Curators\n\nThe dataset was created by Gabriel Oliveira, Esther Colombini and Sandra Avila.\n\n\n\n\nIf you use \\#PraCegoVer dataset, please cite as:" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-reuters-corpus", "language:he", "license:other", "arxiv:2007.15620", "region:us" ]

[ "2007.15620" ]

[ "he" ]

[ "# Splits for the Ben-Mordecai and Elhadad Hebrew NER Corpus (BMC)\n\nIn order to evaluate performance in accordance with the original Ben-Mordecai and Elhadad (2005) work, we provide three 75%-25% random splits. \n* Only the 7 entity categories viable for evaluation were kept (DATE, LOC, MONEY, ORG, PER, PERCENT, TIME) --- all MISC entities were filtered out.\n* Sequence label scheme was changed from IOB to BIOES\n* The dev sets are 10% taken out of the 75%\n\n\nIf you use use the BMC corpus, please cite the original paper as well as our paper which describes the splits:\n\n* Ben-Mordecai and Elhadad (2005):\n\n\n* Bareket and Tsarfaty (2020)" ]

[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|other-reuters-corpus #language-Hebrew #license-other #arxiv-2007.15620 #region-us \n", "# Splits for the Ben-Mordecai and Elhadad Hebrew NER Corpus (BMC)\n\nIn order to evaluate performance in accordance with the original Ben-Mordecai and Elhadad (2005) work, we provide three 75%-25% random splits. \n* Only the 7 entity categories viable for evaluation were kept (DATE, LOC, MONEY, ORG, PER, PERCENT, TIME) --- all MISC entities were filtered out.\n* Sequence label scheme was changed from IOB to BIOES\n* The dev sets are 10% taken out of the 75%\n\n\nIf you use use the BMC corpus, please cite the original paper as well as our paper which describes the splits:\n\n* Ben-Mordecai and Elhadad (2005):\n\n\n* Bareket and Tsarfaty (2020)" ]

[ "license:cc0-1.0", "region:us" ]

[]

[]

[ "# Dataset Card for Animal Crossing New Horizons Catalog", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Context\nThis dataset comes from this spreadsheet, a comprehensive Item Catalog for Animal Crossing New Horizons (ACNH). As described by Wikipedia,\n&gt; ACNH is a life simulation game released by Nintendo for Nintendo Switch on March 20, 2020. It is the fifth main series title in the Animal Crossing series and, with 5 million digital copies sold, has broken the record for Switch title with most digital units sold in a single month. In New Horizons, the player assumes the role of a customizable character who moves to a deserted island. Taking place in real-time, the player can explore the island in a nonlinear fashion, gathering and crafting items, catching insects and fish, and developing the island into a community of anthropomorphic animals.", "### Content\nThere are 30 csvs each listing various items, villagers, clothing, and other collectibles from the game. The data was collected by a dedicated group of AC fans who continue to collaborate and build this spreadsheet for public use. The database contains the original data and full list of contributors and raw data. At the time of writing, the only difference between the spreadsheet and this version is that the Kaggle version omits all columns with images of the items, but is otherwise identical.", "### Acknowledgements\nThanks to every contributor listed on the spreadsheet! Please attribute this spreadsheet and group for any use of the data. They also have a Discord server linked in the spreadsheet in case you want to contact them.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @jessicali9530", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "# Dataset Card for Animal Crossing New Horizons Catalog", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Context\nThis dataset comes from this spreadsheet, a comprehensive Item Catalog for Animal Crossing New Horizons (ACNH). As described by Wikipedia,\n&gt; ACNH is a life simulation game released by Nintendo for Nintendo Switch on March 20, 2020. It is the fifth main series title in the Animal Crossing series and, with 5 million digital copies sold, has broken the record for Switch title with most digital units sold in a single month. In New Horizons, the player assumes the role of a customizable character who moves to a deserted island. Taking place in real-time, the player can explore the island in a nonlinear fashion, gathering and crafting items, catching insects and fish, and developing the island into a community of anthropomorphic animals.", "### Content\nThere are 30 csvs each listing various items, villagers, clothing, and other collectibles from the game. The data was collected by a dedicated group of AC fans who continue to collaborate and build this spreadsheet for public use. The database contains the original data and full list of contributors and raw data. At the time of writing, the only difference between the spreadsheet and this version is that the Kaggle version omits all columns with images of the items, but is otherwise identical.", "### Acknowledgements\nThanks to every contributor listed on the spreadsheet! Please attribute this spreadsheet and group for any use of the data. They also have a Discord server linked in the spreadsheet in case you want to contact them.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @jessicali9530", "### Licensing Information\n\nThe license for this dataset is cc0-1.0", "### Contributions" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-other #region-us \n" ]

[ "task_categories:object-detection", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:n<1K", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "### Dataset Summary\n\n\nThe \"Object Detection for Chess Pieces\" dataset is a toy dataset created (as suggested by the name!) to introduce object detection in a beginner friendly way. It is structured in a one object-one image manner, with the objects being of four classes, namely, Black King, White King, Black Queen and White Queen", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': The dataset can be used to train and evaluate simplistic object detection models", "### Languages\n\n\nThe text (labels) in the dataset is in English\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image and the corresponding objects in bounding boxes.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the 224x224 image.\n* 'label': An integer between 0 and 3 representing the classes with the following mapping:\n* 'bbox': A list of integers having sequence [x\\_center, y\\_center, width, height] for a particular bounding box", "### Data Splits\n\n\nThe data is split into training and validation set. The training set contains 204 images and the validation set 52 images.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was created to be a simple benchmark for object detection", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is obtained by machine generating images from \"python-chess\" library. Please refer this code to understand data generation pipeline", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe annotations were done manually.", "#### Who are the annotators?\n\n\nThe annotations were done manually.", "### Personal and Sensitive Information\n\n\nNone\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe dataset can be considered as a beginner-friendly toy dataset for object detection. It should not be used for benchmarking state of the art object detection models, or be used for a deployed model.", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe dataset only contains four classes for simplicity. The complexity can be increased by considering all types of chess pieces, and by making it a multi-object detection problem\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Faizan Shaikh", "### Licensing Information\n\n\nThe dataset is licensed as CC-BY-SA:2.0" ]

[ "TAGS\n#task_categories-object-detection #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n", "### Dataset Summary\n\n\nThe \"Object Detection for Chess Pieces\" dataset is a toy dataset created (as suggested by the name!) to introduce object detection in a beginner friendly way. It is structured in a one object-one image manner, with the objects being of four classes, namely, Black King, White King, Black Queen and White Queen", "### Supported Tasks and Leaderboards\n\n\n* 'object-detection': The dataset can be used to train and evaluate simplistic object detection models", "### Languages\n\n\nThe text (labels) in the dataset is in English\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA data point comprises an image and the corresponding objects in bounding boxes.", "### Data Fields\n\n\n* 'image': A 'PIL.Image.Image' object containing the 224x224 image.\n* 'label': An integer between 0 and 3 representing the classes with the following mapping:\n* 'bbox': A list of integers having sequence [x\\_center, y\\_center, width, height] for a particular bounding box", "### Data Splits\n\n\nThe data is split into training and validation set. The training set contains 204 images and the validation set 52 images.\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThe dataset was created to be a simple benchmark for object detection", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe data is obtained by machine generating images from \"python-chess\" library. Please refer this code to understand data generation pipeline", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe annotations were done manually.", "#### Who are the annotators?\n\n\nThe annotations were done manually.", "### Personal and Sensitive Information\n\n\nNone\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe dataset can be considered as a beginner-friendly toy dataset for object detection. It should not be used for benchmarking state of the art object detection models, or be used for a deployed model.", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe dataset only contains four classes for simplicity. The complexity can be increased by considering all types of chess pieces, and by making it a multi-object detection problem\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nThe dataset was created by Faizan Shaikh", "### Licensing Information\n\n\nThe dataset is licensed as CC-BY-SA:2.0" ]

[ "license:apache-2.0", "region:us" ]

[]

[]

[ "# Dataset Card for PartiPrompts (P2)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nPartiPrompts (P2) is a rich set of over 1600 prompts in English that we release\nas part of this work. P2 can be used to measure model capabilities across\nvarious categories and challenge aspects.\n\n!parti-prompts\n\nP2 prompts can be simple, allowing us to gauge the progress from scaling. They\ncan also be complex, such as the following 67-word description we created for\nVincent van Gogh’s *The Starry Night* (1889):\n\n*Oil-on-canvas painting of a blue night sky with roiling energy. A fuzzy and\nbright yellow crescent moon shining at the top. Below the exploding yellow stars\nand radiating swirls of blue, a distant village sits quietly on the right.\nConnecting earth and sky is a flame-like cypress tree with curling and swaying\nbranches on the left. A church spire rises as a beacon over rolling blue hills.*", "### Supported Tasks and Leaderboards", "### Languages\n\nThe text descriptions are in English.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nThe license for this dataset is the apache-2.0 license.", "### Contributions\n\nThanks to @nateraw for adding this dataset." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dataset Card for PartiPrompts (P2)", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nPartiPrompts (P2) is a rich set of over 1600 prompts in English that we release\nas part of this work. P2 can be used to measure model capabilities across\nvarious categories and challenge aspects.\n\n!parti-prompts\n\nP2 prompts can be simple, allowing us to gauge the progress from scaling. They\ncan also be complex, such as the following 67-word description we created for\nVincent van Gogh’s *The Starry Night* (1889):\n\n*Oil-on-canvas painting of a blue night sky with roiling energy. A fuzzy and\nbright yellow crescent moon shining at the top. Below the exploding yellow stars\nand radiating swirls of blue, a distant village sits quietly on the right.\nConnecting earth and sky is a flame-like cypress tree with curling and swaying\nbranches on the left. A church spire rises as a beacon over rolling blue hills.*", "### Supported Tasks and Leaderboards", "### Languages\n\nThe text descriptions are in English.", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nThe license for this dataset is the apache-2.0 license.", "### Contributions\n\nThanks to @nateraw for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"nq\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"nq\"\n\nMore Information needed" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test name" ]

[ "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-mit #region-us \n" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is another test name" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "license:afl-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "task_categories:translation", "multilinguality:multilingual", "size_categories:10K<n<100K", "language:en", "language:de", "license:cc-by-nc-sa-4.0", "LibriS2S", "LibrivoxDeEn", "Speech-to-Speech translation", "LREC2022", "arxiv:2204.10593", "arxiv:1910.07924", "region:us" ]

[ "2204.10593", "1910.07924" ]

[ "en", "de" ]

[ "### The data\n\n\nThe English/German audio are available in the folder EN/DE respectively and can be downloaded from this onedrive. In case there are any problems with the download, feel free to open an issue here or on GitHub. \n\nThe repo structure is as follow:\n\n\n* Alignments : Contains all the alignments for each book and chapter\n* DE : Contains the German audio for each chapter per book.\n* EN : Contains the English audio for each chapter per book.\n* Example : contains example files on for the scraping and aligning explanations that were used to build this dataset.\n* LibrivoxDeEn\\_alignments : Contains the base alignments from the LibrivoxDeEn dataset.\n\n\nIn case you feel a part of the data is missing, feel free to open an issue!\nThe full zipfile is about 52 GB of size.", "### Scraping a book from Librivox\n\n\nTo download all chapters from a librivox url the following command can be used:", "### Allign a book from Librivox with the text from LibrivoxDeEn\n\n\nTo allign the previously downloaded book with the txt files and tsv tables provided by LibrivoxDeEn the following command, based on the example provided with this repo, can be used:\n\n\nnote: the example folder in this repo already contains the first two chapters from Undine scraped from librivox and their transcripts and (modified to only contain the first 2 chapters) tsv table retrieved from LibrivoxDeEn.\nAdditional data to align can be scraped by using the same file shown previously and combined with the provided data from LibriVoxDeEn\n\n\nAdditionally with this repo the full alignment for the 8 following books with following LibrivoxDeEn id's are also given:\n9, 10, 13, 18, 23, 108, 110, 120.\n\n\nOther books such as 11, 36, 67 and 54 are also inside of the librivoxDeEn dataset but the chapters do not correspond in a 1:1 mannner(for example: the German version of book 67 has 27 chapters but the English version has 29 and thus need to be re-aligned before the allignment script in this repo will work). Therefore these alignments are given but might have be different if you scrape them yourselves as the re-alignments might be different for you.", "### Metrics on the alignment given in this repo.\n\n\nUsing the alignments given in this repo some metrics were collected and quickly displayed here, for this table and the next figure the books which were manually alligned, although provided in the zip, were not accounted for, but the full table can be found in the original paper.\n\n\n\nnote: the speakers were counted for each book seperatly so some speakers might be counter more than once.\n\n\nthe number of hours for each book aligned in this repo: \n\n<img src=\"URL width=\"500\">\n\n\nwhen using this work, please cite the original paper and the LibrivoxDeEn authors" ]

[ "TAGS\n#task_categories-text-to-speech #task_categories-automatic-speech-recognition #task_categories-translation #multilinguality-multilingual #size_categories-10K<n<100K #language-English #language-German #license-cc-by-nc-sa-4.0 #LibriS2S #LibrivoxDeEn #Speech-to-Speech translation #LREC2022 #arxiv-2204.10593 #arxiv-1910.07924 #region-us \n", "### The data\n\n\nThe English/German audio are available in the folder EN/DE respectively and can be downloaded from this onedrive. In case there are any problems with the download, feel free to open an issue here or on GitHub. \n\nThe repo structure is as follow:\n\n\n* Alignments : Contains all the alignments for each book and chapter\n* DE : Contains the German audio for each chapter per book.\n* EN : Contains the English audio for each chapter per book.\n* Example : contains example files on for the scraping and aligning explanations that were used to build this dataset.\n* LibrivoxDeEn\\_alignments : Contains the base alignments from the LibrivoxDeEn dataset.\n\n\nIn case you feel a part of the data is missing, feel free to open an issue!\nThe full zipfile is about 52 GB of size.", "### Scraping a book from Librivox\n\n\nTo download all chapters from a librivox url the following command can be used:", "### Allign a book from Librivox with the text from LibrivoxDeEn\n\n\nTo allign the previously downloaded book with the txt files and tsv tables provided by LibrivoxDeEn the following command, based on the example provided with this repo, can be used:\n\n\nnote: the example folder in this repo already contains the first two chapters from Undine scraped from librivox and their transcripts and (modified to only contain the first 2 chapters) tsv table retrieved from LibrivoxDeEn.\nAdditional data to align can be scraped by using the same file shown previously and combined with the provided data from LibriVoxDeEn\n\n\nAdditionally with this repo the full alignment for the 8 following books with following LibrivoxDeEn id's are also given:\n9, 10, 13, 18, 23, 108, 110, 120.\n\n\nOther books such as 11, 36, 67 and 54 are also inside of the librivoxDeEn dataset but the chapters do not correspond in a 1:1 mannner(for example: the German version of book 67 has 27 chapters but the English version has 29 and thus need to be re-aligned before the allignment script in this repo will work). Therefore these alignments are given but might have be different if you scrape them yourselves as the re-alignments might be different for you.", "### Metrics on the alignment given in this repo.\n\n\nUsing the alignments given in this repo some metrics were collected and quickly displayed here, for this table and the next figure the books which were manually alligned, although provided in the zip, were not accounted for, but the full table can be found in the original paper.\n\n\n\nnote: the speakers were counted for each book seperatly so some speakers might be counter more than once.\n\n\nthe number of hours for each book aligned in this repo: \n\n<img src=\"URL width=\"500\">\n\n\nwhen using this work, please cite the original paper and the LibrivoxDeEn authors" ]

[ "task_categories:text-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-sa-3.0", "knowledge-verification", "arxiv:2106.05707", "region:us" ]

[ "2106.05707" ]

[ "en" ]

[ "### Dataset Summary\n\n\nWith billions of individual pages on the web providing information on almost every conceivable topic, we should have\nthe ability to collect facts that answer almost every conceivable question. However, only a small fraction of this\ninformation is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to\ntransform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot\nof recent research and media coverage: false information coming from unreliable sources.\n\n\nThe FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.\n\n\nFEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact\nverification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of\nsentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes,\nor does not provide enough information to reach a verdict. The dataset also contains annotation metadata such as\nannotator actions (query keywords, clicks on page, time signatures), and the type of challenge each claim poses.", "### Supported Tasks and Leaderboards\n\n\nThe task is verification of textual claims against textual sources.\n\n\nWhen compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the\npassage to verify each claim is given, and in recent years it typically consists a single sentence, while in\nverification systems it is retrieved from a large set of documents in order to form the evidence.", "### Languages\n\n\nThe dataset is in English ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 187.82 MB\n* Size of the generated dataset: 123.25 MB\n* Total amount of disk used: 311.07 MB\n\n\nAn example of 'wikipedia\\_pages' looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id' (int): ID of the sample.\n* 'label' (ClassLabel): Annotated label for the claim. Can be one of {\"SUPPORTS\", \"REFUTES\", \"NOT ENOUGH INFO\"}.\n* 'claim' (str): Text of the claim.\n* 'evidence' (list of dict): Evidence sets (at maximum three). Each set consists of dictionaries with two fields:\n\t+ 'content' (list of str): List of element IDs serving as the evidence for the claim. Each element ID is in the format\n\t'\"[PAGE ID]*[EVIDENCE TYPE]*[NUMBER ID]\"', where '[EVIDENCE TYPE]' can be: 'sentence', 'cell', 'header\\_cell',\n\t'table\\_caption', 'item'.\n\t+ 'context' (list of list of str): List (for each element ID in 'content') of a list of Wikipedia elements that are\n\tautomatically associated with that element ID and serve as context. This includes an article's title, relevant\n\tsections (the section and sub-section(s) the element is located in), and for cells the closest row and column\n\theader (multiple row/column headers if they follow each other).\n* 'annotator\\_operations' (list of dict): List of operations an annotator used to find the evidence and reach a verdict,\ngiven the claim. Each element in the list is a dictionary with the fields:\n\t+ 'operation' (str): Operation name. Any of the following:\n\t\t- 'start', 'finish': Annotation started/finished. The value is the name of the operation.\n\t\t- 'search': Annotator used the Wikipedia search function. The value is the entered search term or the term selected\n\t\tfrom the automatic suggestions. If the annotator did not select any of the suggestions but instead went into\n\t\tadvanced search, the term is prefixed with \"contains...\".\n\t\t- 'hyperlink': Annotator clicked on a hyperlink in the page. The value is the anchor text of the hyperlink.\n\t\t- 'Now on': The page the annotator has landed after a search or a hyperlink click. The value is the PAGE ID.\n\t\t- 'Page search': Annotator search on a page. The value is the search term.\n\t\t- 'page-search-reset': Annotator cleared the search box. The value is the name of the operation.\n\t\t- 'Highlighting', 'Highlighting deleted': Annotator selected/unselected an element on the page. The value is\n\t\t'ELEMENT ID'.\n\t\t- 'back-button-clicked': Annotator pressed the back button. The value is the name of the operation.\n\t+ 'value' (str): Value associated with the operation.\n\t+ 'time' (float): Time in seconds from the start of the annotation.\n* 'expected\\_challenge' (str): The challenge the claim generator selected will be faced when verifying the claim, one\nout of the following: 'Numerical Reasoning', 'Multi-hop Reasoning', 'Entity Disambiguation',\n'Combining Tables and Text', 'Search terms not in claim', 'Other'.\n* 'challenge' (str): Main challenge to verify the claim, one out of the following: 'Numerical Reasoning',\n'Multi-hop Reasoning', 'Entity Disambiguation', 'Combining Tables and Text', 'Search terms not in claim', 'Other'.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nIf you use this dataset, please cite:", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|wikipedia #language-English #license-cc-by-sa-3.0 #knowledge-verification #arxiv-2106.05707 #region-us \n", "### Dataset Summary\n\n\nWith billions of individual pages on the web providing information on almost every conceivable topic, we should have\nthe ability to collect facts that answer almost every conceivable question. However, only a small fraction of this\ninformation is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to\ntransform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot\nof recent research and media coverage: false information coming from unreliable sources.\n\n\nThe FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.\n\n\nFEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact\nverification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of\nsentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes,\nor does not provide enough information to reach a verdict. The dataset also contains annotation metadata such as\nannotator actions (query keywords, clicks on page, time signatures), and the type of challenge each claim poses.", "### Supported Tasks and Leaderboards\n\n\nThe task is verification of textual claims against textual sources.\n\n\nWhen compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the\npassage to verify each claim is given, and in recent years it typically consists a single sentence, while in\nverification systems it is retrieved from a large set of documents in order to form the evidence.", "### Languages\n\n\nThe dataset is in English ('en').\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 187.82 MB\n* Size of the generated dataset: 123.25 MB\n* Total amount of disk used: 311.07 MB\n\n\nAn example of 'wikipedia\\_pages' looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id' (int): ID of the sample.\n* 'label' (ClassLabel): Annotated label for the claim. Can be one of {\"SUPPORTS\", \"REFUTES\", \"NOT ENOUGH INFO\"}.\n* 'claim' (str): Text of the claim.\n* 'evidence' (list of dict): Evidence sets (at maximum three). Each set consists of dictionaries with two fields:\n\t+ 'content' (list of str): List of element IDs serving as the evidence for the claim. Each element ID is in the format\n\t'\"[PAGE ID]*[EVIDENCE TYPE]*[NUMBER ID]\"', where '[EVIDENCE TYPE]' can be: 'sentence', 'cell', 'header\\_cell',\n\t'table\\_caption', 'item'.\n\t+ 'context' (list of list of str): List (for each element ID in 'content') of a list of Wikipedia elements that are\n\tautomatically associated with that element ID and serve as context. This includes an article's title, relevant\n\tsections (the section and sub-section(s) the element is located in), and for cells the closest row and column\n\theader (multiple row/column headers if they follow each other).\n* 'annotator\\_operations' (list of dict): List of operations an annotator used to find the evidence and reach a verdict,\ngiven the claim. Each element in the list is a dictionary with the fields:\n\t+ 'operation' (str): Operation name. Any of the following:\n\t\t- 'start', 'finish': Annotation started/finished. The value is the name of the operation.\n\t\t- 'search': Annotator used the Wikipedia search function. The value is the entered search term or the term selected\n\t\tfrom the automatic suggestions. If the annotator did not select any of the suggestions but instead went into\n\t\tadvanced search, the term is prefixed with \"contains...\".\n\t\t- 'hyperlink': Annotator clicked on a hyperlink in the page. The value is the anchor text of the hyperlink.\n\t\t- 'Now on': The page the annotator has landed after a search or a hyperlink click. The value is the PAGE ID.\n\t\t- 'Page search': Annotator search on a page. The value is the search term.\n\t\t- 'page-search-reset': Annotator cleared the search box. The value is the name of the operation.\n\t\t- 'Highlighting', 'Highlighting deleted': Annotator selected/unselected an element on the page. The value is\n\t\t'ELEMENT ID'.\n\t\t- 'back-button-clicked': Annotator pressed the back button. The value is the name of the operation.\n\t+ 'value' (str): Value associated with the operation.\n\t+ 'time' (float): Time in seconds from the start of the annotation.\n* 'expected\\_challenge' (str): The challenge the claim generator selected will be faced when verifying the claim, one\nout of the following: 'Numerical Reasoning', 'Multi-hop Reasoning', 'Entity Disambiguation',\n'Combining Tables and Text', 'Search terms not in claim', 'Other'.\n* 'challenge' (str): Main challenge to verify the claim, one out of the following: 'Numerical Reasoning',\n'Multi-hop Reasoning', 'Entity Disambiguation', 'Combining Tables and Text', 'Search terms not in claim', 'Other'.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nIf you use this dataset, please cite:", "### Contributions\n\n\nThanks to @albertvillanova for adding this dataset." ]

[ "task_categories:question-answering", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #language-English #license-mit #region-us \n" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test submission" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test submission" ]

[ "benchmark:gem", "evaluation", "benchmark", "region:us" ]

[]

[]

[ "# GEM Submission\n\nSubmission name: This is a test submission 1" ]

[ "TAGS\n#benchmark-gem #evaluation #benchmark #region-us \n", "# GEM Submission\n\nSubmission name: This is a test submission 1" ]

[ "annotations_creators:Abhinav Walia (Owner)", "language:en", "license:odbl", "region:us" ]

[]

[ "en" ]

[ "# About Dataset\nfrom Kaggle Datasets", "## Context\n\nAnnotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.\n\nTip: Use Pandas Dataframe to load dataset if using Python for convenience.", "## Content\nThis is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.\n\nNumber of tagged entities:\n\n'O': 1146068', geo-nam': 58388, 'org-nam': 48034, 'per-nam': 23790, 'gpe-nam': 20680, 'tim-dat': 12786, 'tim-dow': 11404, 'per-tit': 9800, 'per-fam': 8152, 'tim-yoc': 5290, 'tim-moy': 4262, 'per-giv': 2413, 'tim-clo': 891, 'art-nam': 866, 'eve-nam': 602, 'nat-nam': 300, 'tim-nam': 146, 'eve-ord': 107, 'per-ini': 60, 'org-leg': 60, 'per-ord': 38, 'tim-dom': 10, 'per-mid': 1, 'art-add': 1", "## Essential info about entities\n\n* geo = Geographical Entity\n* org = Organization\n* per = Person\n* gpe = Geopolitical Entity\n* tim = Time indicator\n* art = Artifact\n* eve = Event\n* nat = Natural Phenomenon\n* Total Words Count = 1354149\n* Target Data Column: \"tag\" (ner_tag in this repo)\n\nInspiration: This dataset is getting more interested because of more features added to the recent version of this dataset. Also, it helps to create a broad view of Feature Engineering with respect to this dataset.", "## Modifications\n \nthe ner_dataset.csv was modified to have a similar data Structure as CoNLL-2003 dataset", "## Licensing information\n\nDatabase: Open Database, Contents: Database Contents." ]

[ "TAGS\n#annotations_creators-Abhinav Walia (Owner) #language-English #license-odbl #region-us \n", "# About Dataset\nfrom Kaggle Datasets", "## Context\n\nAnnotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.\n\nTip: Use Pandas Dataframe to load dataset if using Python for convenience.", "## Content\nThis is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.\n\nNumber of tagged entities:\n\n'O': 1146068', geo-nam': 58388, 'org-nam': 48034, 'per-nam': 23790, 'gpe-nam': 20680, 'tim-dat': 12786, 'tim-dow': 11404, 'per-tit': 9800, 'per-fam': 8152, 'tim-yoc': 5290, 'tim-moy': 4262, 'per-giv': 2413, 'tim-clo': 891, 'art-nam': 866, 'eve-nam': 602, 'nat-nam': 300, 'tim-nam': 146, 'eve-ord': 107, 'per-ini': 60, 'org-leg': 60, 'per-ord': 38, 'tim-dom': 10, 'per-mid': 1, 'art-add': 1", "## Essential info about entities\n\n* geo = Geographical Entity\n* org = Organization\n* per = Person\n* gpe = Geopolitical Entity\n* tim = Time indicator\n* art = Artifact\n* eve = Event\n* nat = Natural Phenomenon\n* Total Words Count = 1354149\n* Target Data Column: \"tag\" (ner_tag in this repo)\n\nInspiration: This dataset is getting more interested because of more features added to the recent version of this dataset. Also, it helps to create a broad view of Feature Engineering with respect to this dataset.", "## Modifications\n \nthe ner_dataset.csv was modified to have a similar data Structure as CoNLL-2003 dataset", "## Licensing information\n\nDatabase: Open Database, Contents: Database Contents." ]

[ "license:cc-by-nc-4.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "task_ids:masked-language-modeling", "size_categories:100k<n<1M", "language:de", "region:us" ]

[]

[ "de" ]

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #task_ids-masked-language-modeling #size_categories-100k<n<1M #language-German #region-us \n" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: mrm8488/distilroberta-finetuned-age_news-classification\n* Dataset: ag_news\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @abhishek for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: mrm8488/distilroberta-finetuned-age_news-classification\n* Dataset: ag_news\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @abhishek for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nateraw/bert-base-uncased-ag-news\n* Dataset: ag_news\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @abhishek for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: nateraw/bert-base-uncased-ag-news\n* Dataset: ag_news\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @abhishek for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: transformersbook/xlm-roberta-base-finetuned-panx-de\n* Dataset: xtreme\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: transformersbook/xlm-roberta-base-finetuned-panx-de\n* Dataset: xtreme\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: transformersbook/xlm-roberta-base-finetuned-panx-all\n* Dataset: wikiann\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: transformersbook/xlm-roberta-base-finetuned-panx-all\n* Dataset: wikiann\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: philschmid/distilroberta-base-ner-wikiann\n* Dataset: wikiann\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: philschmid/distilroberta-base-ner-wikiann\n* Dataset: wikiann\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: elastic/distilbert-base-cased-finetuned-conll03-english\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: elastic/distilbert-base-cased-finetuned-conll03-english\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: elastic/distilbert-base-uncased-finetuned-conll03-english\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: elastic/distilbert-base-uncased-finetuned-conll03-english\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: huggingface-course/bert-finetuned-ner\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: huggingface-course/bert-finetuned-ner\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: philschmid/distilroberta-base-ner-conll2003\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: philschmid/distilroberta-base-ner-conll2003\n* Dataset: conll2003\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @douwekiela for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: patrickvonplaten/bert2bert_cnn_daily_mail\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: patrickvonplaten/bert2bert_cnn_daily_mail\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: echarlaix/bart-base-cnn-r2-19.4-d35-hybrid\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: echarlaix/bart-base-cnn-r2-19.4-d35-hybrid\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: echarlaix/bart-base-cnn-r2-18.7-d23-hybrid\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: echarlaix/bart-base-cnn-r2-18.7-d23-hybrid\n* Dataset: cnn_dailymail\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "license:cc-by-4.0", "zenodo", "arxiv:2012.03411", "region:us" ]

[ "2012.03411" ]

[]

[ "# Dataset Card for MultiLingual LibriSpeech", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: MultiLingual LibriSpeech ASR corpus\n- Repository: \n- Paper: MLS: A Large-Scale Multilingual Dataset for Speech Research\n- Leaderboard: Paperswithcode Leaderboard", "### Dataset Summary\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\"><p><b>Deprecated:</b> Not every model supports a fast tokenizer. Take a look at this <a href=\"index#supported-frameworks\">table</a> to check if a model has fast tokenizer support.</p></div>\n\nMultilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.\n\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\"><p><b>Deprecated:</b> Not every model supports a fast tokenizer. Take a look at this <a href=\"index#supported-frameworks\">table</a> to check if a model has fast tokenizer support.</p></div>\n\n\n<div class=\"alert alert-danger d-flex align-items-center\" role=\"alert\">\n <svg class=\"bi flex-shrink-0 me-2\" width=\"24\" height=\"24\" role=\"img\" aria-label=\"Danger:\"><use xlink:href=\"#exclamation-triangle-fill\"/></svg>\n <div>\n An example danger alert with an icon\n </div>\n</div>\n\n<div class=\"alert alert-block alert-warning\"> In general, just avoid the red boxes. </div>\n<div class=\"alert alert-block alert-danger\"> In general, just avoid the red boxes. </div>\n<div class=\"alert alert-danger\" role=\"alert\"> In general, just avoid the red boxes. </div>\n<div class=\"alert\" role=\"alert\"> In general, just avoid the red boxes. </div>\n\n<div class=\"course-tip-orange\">\n<strong>Error:</strong>\n</div>\n\n<div class=\"alert alert-danger\" role=\"alert\">\n <div class=\"row vertical-align\">\n <div class=\"col-xs-1 text-center\">\n <i class=\"fa fa-exclamation-triangle fa-2x\"></i>\n </div>\n <div class=\"col-xs-11\">\n <strong>Error:</strong> \n </div> \n </div> \n</div>\n\n>[!WARNING]\n>This is a warning\n\n_Warning: Be very careful here._\n\n<Deprecated>\nThis is a warning\n</Deprecated>\n\n<Tip warning>\nThis is a warning\n</Tip>\n\n<Tip warning={true}>\nThis is a warning\n</Tip>\n\n\n> Warning\n> This is a warning" ]

[ "TAGS\n#license-cc-by-4.0 #zenodo #arxiv-2012.03411 #region-us \n", "# Dataset Card for MultiLingual LibriSpeech", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: MultiLingual LibriSpeech ASR corpus\n- Repository: \n- Paper: MLS: A Large-Scale Multilingual Dataset for Speech Research\n- Leaderboard: Paperswithcode Leaderboard", "### Dataset Summary\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\"><p><b>Deprecated:</b> Not every model supports a fast tokenizer. Take a look at this <a href=\"index#supported-frameworks\">table</a> to check if a model has fast tokenizer support.</p></div>\n\nMultilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.", "### Supported Tasks and Leaderboards\n\n- 'automatic-speech-recognition', 'audio-speaker-identification': The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at URL and ranks models based on their WER.\n\n\n<div class=\"course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400\"><p><b>Deprecated:</b> Not every model supports a fast tokenizer. Take a look at this <a href=\"index#supported-frameworks\">table</a> to check if a model has fast tokenizer support.</p></div>\n\n\n<div class=\"alert alert-danger d-flex align-items-center\" role=\"alert\">\n <svg class=\"bi flex-shrink-0 me-2\" width=\"24\" height=\"24\" role=\"img\" aria-label=\"Danger:\"><use xlink:href=\"#exclamation-triangle-fill\"/></svg>\n <div>\n An example danger alert with an icon\n </div>\n</div>\n\n<div class=\"alert alert-block alert-warning\"> In general, just avoid the red boxes. </div>\n<div class=\"alert alert-block alert-danger\"> In general, just avoid the red boxes. </div>\n<div class=\"alert alert-danger\" role=\"alert\"> In general, just avoid the red boxes. </div>\n<div class=\"alert\" role=\"alert\"> In general, just avoid the red boxes. </div>\n\n<div class=\"course-tip-orange\">\n<strong>Error:</strong>\n</div>\n\n<div class=\"alert alert-danger\" role=\"alert\">\n <div class=\"row vertical-align\">\n <div class=\"col-xs-1 text-center\">\n <i class=\"fa fa-exclamation-triangle fa-2x\"></i>\n </div>\n <div class=\"col-xs-11\">\n <strong>Error:</strong> \n </div> \n </div> \n</div>\n\n>[!WARNING]\n>This is a warning\n\n_Warning: Be very careful here._\n\n<Deprecated>\nThis is a warning\n</Deprecated>\n\n<Tip warning>\nThis is a warning\n</Tip>\n\n<Tip warning={true}>\nThis is a warning\n</Tip>\n\n\n> Warning\n> This is a warning" ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: human-centered-summarization/financial-summarization-pegasus\n* Dataset: xsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: human-centered-summarization/financial-summarization-pegasus\n* Dataset: xsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/bart-base-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/bart-base-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/bart-large-cnn-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/bart-large-cnn-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "autotrain", "evaluation", "region:us" ]

[]

[]

[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/distilbart-cnn-12-6-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: philschmid/distilbart-cnn-12-6-samsum\n* Dataset: samsum\n\nTo run new evaluation jobs, visit Hugging Face's automatic evaluation service.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]

[ "region:us" ]

[]

[]

[ "## Code snippet to visualise the position of the box" ]

[ "TAGS\n#region-us \n", "## Code snippet to visualise the position of the box" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:pt", "license:other", "arxiv:1905.10348", "region:us" ]

[ "1905.10348" ]

[ "pt" ]

[ "### Dataset Summary\n\n\nThe dataset is a collection of 4043 *Ementa* (summary) court decisions and their metadata from\nthe *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled\naccording to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset\nsupports the task of Legal Judgment Prediction.", "### Supported Tasks and Leaderboards\n\n\nLegal Judgment Prediction", "### Languages\n\n\nBrazilian Portuguese\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present (train, validation and test) for each configuration.", "### Data Fields\n\n\nThe dataset contains the following fields:\n\n\n* 'process\\_number': A number assigned to the decision by the court\n* 'orgao\\_julgador': Judging Body: one of '1ª Câmara Cível', '2ª Câmara Cível', '3ª Câmara Cível', 'Câmara Criminal', '\nTribunal Pleno', 'Seção Especializada Cível'\n* 'publish\\_date': The date, when the decision has been published (14/12/2018 - 03/04/2019). At that time (in 2018-2019),\nthe scraping script was limited and not configurable to get data based on date range. Therefore, only the data from\nthe last months has been scraped.\n* 'judge\\_relator': Judicial panel\n* 'ementa\\_text': Summary of the court decision\n* 'decision\\_description': Suggested input. Corresponds to ementa\\_text - judgment\\_text - unanimity\\_text. Basic\nstatistics (number of words): mean: 119, median: 88, min: 12, max: 1400\n* 'judgment\\_text': The text used for determining the judgment label\n* 'judgment\\_label': Primary suggested label. Labels that can be used to train a model for judgment prediction:\n\t+ 'no': The appeal was denied\n\t+ 'partial': For partially favourable decisions\n\t+ 'yes': For fully favourable decisions\n\t+ removed labels (present in the original dataset):\n\t\t- 'conflito-competencia': Meta-decision. For example, a decision just to tell that Court A should rule this case\n\t\tand not Court B.\n\t\t- 'not-cognized': The appeal was not accepted to be judged by the court\n\t\t- 'prejudicada': The case could not be judged for any impediment such as the appealer died or gave up on the\n\t\tcase for instance.\n* 'unanimity\\_text': Portuguese text to describe whether the decision was unanimous or not.\n* 'unanimity\\_label': Secondary suggested label. Unified labels to describe whether the decision was unanimous or\nnot (in some cases contains ); they can be used for model training as well (Lage-Freitas et al.,\n2019).", "### Data Splits\n\n\nThe data has been split randomly into 80% train (3234), 10% validation (404), 10% test (405).\n\n\nThere are two tasks possible for this dataset.", "#### Judgment\n\n\nLabel Distribution", "#### Unanimity\n\n\nIn this configuration, all cases that have 'not\\_determined' as 'unanimity\\_label' can be removed.\n\n\nLabel Distribution\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was created to further the research on developing models for predicting Brazilian court decisions that are\nalso able to predict whether the decision will be unanimous.", "### Source Data\n\n\nThe data was scraped from *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil).", "#### Initial Data Collection and Normalization\n\n\n*“We developed a Web scraper for collecting data from Brazilian courts. The scraper first searched for the URL that\ncontains the list of court cases […]. Then, the scraper extracted from these HTML files the specific case URLs and\ndownloaded their data […]. Next, it extracted the metadata and the contents of legal cases and stored them in a CSV file\nformat […].”* (Lage-Freitas et al., 2022)", "#### Who are the source language producers?\n\n\nThe source language producer are presumably attorneys, judges, and other legal professionals.", "### Annotations", "#### Annotation process\n\n\nThe dataset was not annotated.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThe court decisions might contain sensitive information about individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nNote that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton\nMatoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset\nconsisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the\ndataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,\ndifferences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to\nhave a look at the conversion script in order to retrace the steps for converting the\noriginal dataset into the present jsonl-format. For further information on the original dataset structure, we refer to\nthe bibliographical references and the original Github repositories and/or web pages provided in this dataset card.\n\n\nAdditional Information\n----------------------\n\n\nLage-Freitas, A., Allende-Cid, H., Santana Jr, O., & Oliveira-Lage, L. (2019). Predicting Brazilian court decisions:\n\n\n* \"In Brazil [...] lower court judges decisions might be appealed to Brazilian courts (*Tribiunais de Justiça*) to be\nreviewed by second instance court judges. In an appellate court, judges decide together upon a case and their\ndecisions are compiled in Agreement reports named *Acóordãos*.\"", "### Dataset Curators\n\n\nThe names of the original dataset curators and creators can be found in references given below, in the section *Citation\nInformation*. Additional changes were made by Joel Niklaus (Email\n; Github) and Veton Matoshi (Email\n; Github).", "### Licensing Information\n\n\nNo licensing information was provided for this dataset. However, please make sure that you use the dataset according to\nBrazilian law.", "### Contributions\n\n\nThanks to @kapllan and @joelniklaus for adding this\ndataset." ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Portuguese #license-other #arxiv-1905.10348 #region-us \n", "### Dataset Summary\n\n\nThe dataset is a collection of 4043 *Ementa* (summary) court decisions and their metadata from\nthe *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled\naccording to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset\nsupports the task of Legal Judgment Prediction.", "### Supported Tasks and Leaderboards\n\n\nLegal Judgment Prediction", "### Languages\n\n\nBrazilian Portuguese\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nThe file format is jsonl and three data splits are present (train, validation and test) for each configuration.", "### Data Fields\n\n\nThe dataset contains the following fields:\n\n\n* 'process\\_number': A number assigned to the decision by the court\n* 'orgao\\_julgador': Judging Body: one of '1ª Câmara Cível', '2ª Câmara Cível', '3ª Câmara Cível', 'Câmara Criminal', '\nTribunal Pleno', 'Seção Especializada Cível'\n* 'publish\\_date': The date, when the decision has been published (14/12/2018 - 03/04/2019). At that time (in 2018-2019),\nthe scraping script was limited and not configurable to get data based on date range. Therefore, only the data from\nthe last months has been scraped.\n* 'judge\\_relator': Judicial panel\n* 'ementa\\_text': Summary of the court decision\n* 'decision\\_description': Suggested input. Corresponds to ementa\\_text - judgment\\_text - unanimity\\_text. Basic\nstatistics (number of words): mean: 119, median: 88, min: 12, max: 1400\n* 'judgment\\_text': The text used for determining the judgment label\n* 'judgment\\_label': Primary suggested label. Labels that can be used to train a model for judgment prediction:\n\t+ 'no': The appeal was denied\n\t+ 'partial': For partially favourable decisions\n\t+ 'yes': For fully favourable decisions\n\t+ removed labels (present in the original dataset):\n\t\t- 'conflito-competencia': Meta-decision. For example, a decision just to tell that Court A should rule this case\n\t\tand not Court B.\n\t\t- 'not-cognized': The appeal was not accepted to be judged by the court\n\t\t- 'prejudicada': The case could not be judged for any impediment such as the appealer died or gave up on the\n\t\tcase for instance.\n* 'unanimity\\_text': Portuguese text to describe whether the decision was unanimous or not.\n* 'unanimity\\_label': Secondary suggested label. Unified labels to describe whether the decision was unanimous or\nnot (in some cases contains ); they can be used for model training as well (Lage-Freitas et al.,\n2019).", "### Data Splits\n\n\nThe data has been split randomly into 80% train (3234), 10% validation (404), 10% test (405).\n\n\nThere are two tasks possible for this dataset.", "#### Judgment\n\n\nLabel Distribution", "#### Unanimity\n\n\nIn this configuration, all cases that have 'not\\_determined' as 'unanimity\\_label' can be removed.\n\n\nLabel Distribution\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nThis dataset was created to further the research on developing models for predicting Brazilian court decisions that are\nalso able to predict whether the decision will be unanimous.", "### Source Data\n\n\nThe data was scraped from *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil).", "#### Initial Data Collection and Normalization\n\n\n*“We developed a Web scraper for collecting data from Brazilian courts. The scraper first searched for the URL that\ncontains the list of court cases […]. Then, the scraper extracted from these HTML files the specific case URLs and\ndownloaded their data […]. Next, it extracted the metadata and the contents of legal cases and stored them in a CSV file\nformat […].”* (Lage-Freitas et al., 2022)", "#### Who are the source language producers?\n\n\nThe source language producer are presumably attorneys, judges, and other legal professionals.", "### Annotations", "#### Annotation process\n\n\nThe dataset was not annotated.", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThe court decisions might contain sensitive information about individuals.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nNote that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton\nMatoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset\nconsisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the\ndataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,\ndifferences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to\nhave a look at the conversion script in order to retrace the steps for converting the\noriginal dataset into the present jsonl-format. For further information on the original dataset structure, we refer to\nthe bibliographical references and the original Github repositories and/or web pages provided in this dataset card.\n\n\nAdditional Information\n----------------------\n\n\nLage-Freitas, A., Allende-Cid, H., Santana Jr, O., & Oliveira-Lage, L. (2019). Predicting Brazilian court decisions:\n\n\n* \"In Brazil [...] lower court judges decisions might be appealed to Brazilian courts (*Tribiunais de Justiça*) to be\nreviewed by second instance court judges. In an appellate court, judges decide together upon a case and their\ndecisions are compiled in Agreement reports named *Acóordãos*.\"", "### Dataset Curators\n\n\nThe names of the original dataset curators and creators can be found in references given below, in the section *Citation\nInformation*. Additional changes were made by Joel Niklaus (Email\n; Github) and Veton Matoshi (Email\n; Github).", "### Licensing Information\n\n\nNo licensing information was provided for this dataset. However, please make sure that you use the dataset according to\nBrazilian law.", "### Contributions\n\n\nThanks to @kapllan and @joelniklaus for adding this\ndataset." ]

[ "size_categories:10K<n<100K", "source_datasets:original", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Dataset Card for MTG Jamendo Dataset", "## Dataset Description\n- Repository: MTG Jamendo dataset repository", "### Dataset Summary\n\nMTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.", "## Dataset structure", "### Data Fields\n\n- 'id': an integer containing the id of the track\n- 'artist_id': an integer containing the id of the artist\n- 'album_id': an integer containing the id of the album\n- 'duration_in_sec': duration of the track as a float\n- 'genres': list of strings, describing genres the track is assigned to\n- 'instruments': list of strings for the main instruments of the track\n- 'moods': list of strings, describing the moods the track is assigned to\n- 'audio': audio of the track", "### Data Splits\n\nThis dataset has 2 balanced splits: _train_ (90%) and _validation_ (10%)", "### Licensing Information\n\nThis dataset version 1.0.0 is released under the Apache-2.0 License." ]

[ "TAGS\n#size_categories-10K<n<100K #source_datasets-original #license-apache-2.0 #region-us \n", "# Dataset Card for MTG Jamendo Dataset", "## Dataset Description\n- Repository: MTG Jamendo dataset repository", "### Dataset Summary\n\nMTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.", "## Dataset structure", "### Data Fields\n\n- 'id': an integer containing the id of the track\n- 'artist_id': an integer containing the id of the artist\n- 'album_id': an integer containing the id of the album\n- 'duration_in_sec': duration of the track as a float\n- 'genres': list of strings, describing genres the track is assigned to\n- 'instruments': list of strings for the main instruments of the track\n- 'moods': list of strings, describing the moods the track is assigned to\n- 'audio': audio of the track", "### Data Splits\n\nThis dataset has 2 balanced splits: _train_ (90%) and _validation_ (10%)", "### Licensing Information\n\nThis dataset version 1.0.0 is released under the Apache-2.0 License." ]

[ "task_categories:image-to-text", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "language:en", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations" ]

[ "TAGS\n#task_categories-image-to-text #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #language-English #region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations" ]