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--- |
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annotations_creators: [] |
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language: |
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- en |
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language_creators: [] |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: AMI |
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size_categories: [] |
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source_datasets: [] |
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tags: [] |
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task_categories: |
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- automatic-speech-recognition |
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task_ids: [] |
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--- |
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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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print(ds) |
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``` |
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gives: |
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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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``` |
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```py |
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ds["train"][0] |
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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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'begin_time': 5.570000171661377, |
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'end_time': 5.940000057220459, |
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'microphone_id': 'H00', |
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'speaker_id': 'MEE068'} |
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``` |
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The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly [the `wav2vec2-large-lv60` checkpoint](https://huggingface.co/facebook/wav2vec2-large-lv60). |
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As can be seen in this experiments, training the model for less than 2 epochs gives |
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*Result (WER)*: |
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| "dev" | "eval" | |
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|---|---| |
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| 25.27 | 25.21 | |
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as can be seen [here](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60). |
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The results are in-line with results of published papers: |
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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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