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add more info to readme
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README.md
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# AMI
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**Note**: This dataset corresponds to the data-processing of [KALDI's AMI S5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5).
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This means text is normalized and the audio data is chunked according to the scripts above!
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To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
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```python
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from datasets import load_dataset
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ds = load_dataset("edinburghcstr/ami", "ihm")
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```
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DatasetDict({
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train: Dataset({
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features: ['
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num_rows: 108502
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})
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validation: Dataset({
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features: ['
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num_rows: 13098
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})
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test: Dataset({
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features: ['
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num_rows: 12643
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})
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})
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automatically loads the audio into memory:
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```
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{'
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'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
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'text': 'OKAY',
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'audio': {'path': '/
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'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518,
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0.00030518], dtype=float32),
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'sampling_rate': 16000},
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- [*Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*](https://www.researchgate.net/publication/258075865_Hybrid_acoustic_models_for_distant_and_multichannel_large_vocabulary_speech_recognition)
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- [Multi-Span Acoustic Modelling using Raw Waveform Signals](https://arxiv.org/abs/1906.11047)
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You can run [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh) to reproduce the result.
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# Dataset Card for AMI
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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- [Terms of Usage](#terms-of-usage)
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## Dataset Description
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- **Homepage:** https://groups.inf.ed.ac.uk/ami/corpus/
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- **Repository:** https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5
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- **Paper:**
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- **Leaderboard:**
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- **Point of Contact:** [jonathan@ed.ac.uk](mailto:jonathan@ed.ac.uk)
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## Dataset Description
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The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
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synchronized to a common timeline. These include close-talking and far-field microphones, individual and
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room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
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the participants also have unsynchronized pens available to them that record what is written. The meetings
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were recorded in English using three different rooms with different acoustic properties, and include mostly
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non-native speakers.
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**Note**: This dataset corresponds to the data-processing of [KALDI's AMI S5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5).
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This means text is normalized and the audio data is chunked according to the scripts above!
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To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
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### Example Usage
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```python
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from datasets import load_dataset
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ds = load_dataset("edinburghcstr/ami", "ihm")
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```
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DatasetDict({
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train: Dataset({
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features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
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num_rows: 108502
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})
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validation: Dataset({
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features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
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num_rows: 13098
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})
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test: Dataset({
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features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
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num_rows: 12643
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})
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})
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automatically loads the audio into memory:
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```
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{'meeting_id': 'EN2001a',
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'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
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'text': 'OKAY',
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'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
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'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518,
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0.00030518], dtype=float32),
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'sampling_rate': 16000},
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- [*Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*](https://www.researchgate.net/publication/258075865_Hybrid_acoustic_models_for_distant_and_multichannel_large_vocabulary_speech_recognition)
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- [Multi-Span Acoustic Modelling using Raw Waveform Signals](https://arxiv.org/abs/1906.11047)
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You can run [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh) to reproduce the result.
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### Supported Tasks and Leaderboards
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### Languages
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## Dataset Structure
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### Data Instances
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### Data Fields
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### Data Splits
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#### Transcribed Subsets Size
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## Dataset Creation
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### Curation Rationale
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### Source Data
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#### Initial Data Collection and Normalization
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#### Who are the source language producers?
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### Annotations
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#### Annotation process
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#### Who are the annotators?
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### Personal and Sensitive Information
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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### Other Known Limitations
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## Additional Information
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### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
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Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten),
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and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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## Terms of Usage
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