Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
License:
| annotations_creators: [] | |
| language: | |
| - en | |
| language_creators: [] | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: AMI | |
| size_categories: [] | |
| source_datasets: [] | |
| tags: [] | |
| task_categories: | |
| - automatic-speech-recognition | |
| dataset_info: | |
| - config_name: ihm | |
| features: | |
| - name: meeting_id | |
| dtype: string | |
| - name: audio_id | |
| dtype: string | |
| - name: text | |
| dtype: string | |
| - name: audio | |
| dtype: | |
| audio: | |
| sampling_rate: 16000 | |
| - name: begin_time | |
| dtype: float32 | |
| - name: end_time | |
| dtype: float32 | |
| - name: microphone_id | |
| dtype: string | |
| - name: speaker_id | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 20710074322.672 | |
| num_examples: 108502 | |
| - name: validation | |
| num_bytes: 2196244962.512 | |
| num_examples: 13098 | |
| - name: test | |
| num_bytes: 1587855340.548 | |
| num_examples: 12643 | |
| download_size: 15243022474 | |
| dataset_size: 24494174625.732002 | |
| - config_name: sdm | |
| features: | |
| - name: meeting_id | |
| dtype: string | |
| - name: audio_id | |
| dtype: string | |
| - name: text | |
| dtype: string | |
| - name: audio | |
| dtype: | |
| audio: | |
| sampling_rate: 16000 | |
| - name: begin_time | |
| dtype: float32 | |
| - name: end_time | |
| dtype: float32 | |
| - name: microphone_id | |
| dtype: string | |
| - name: speaker_id | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 13324608404.558 | |
| num_examples: 107319 | |
| - name: validation | |
| num_bytes: 2176476471.684 | |
| num_examples: 13098 | |
| - name: test | |
| num_bytes: 1867748118.586 | |
| num_examples: 12643 | |
| download_size: 13768733115 | |
| dataset_size: 17368832994.828 | |
| configs: | |
| - config_name: ihm | |
| data_files: | |
| - split: train | |
| path: ihm/train-* | |
| - split: validation | |
| path: ihm/validation-* | |
| - split: test | |
| path: ihm/test-* | |
| - config_name: sdm | |
| data_files: | |
| - split: train | |
| path: sdm/train-* | |
| - split: validation | |
| path: sdm/validation-* | |
| - split: test | |
| path: sdm/test-* | |
| # Dataset Card for AMI | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| - [Terms of Usage](#terms-of-usage) | |
| ## Dataset Description | |
| - **Homepage:** https://groups.inf.ed.ac.uk/ami/corpus/ | |
| - **Repository:** https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5 | |
| - **Paper:** | |
| - **Leaderboard:** | |
| - **Point of Contact:** [jonathan@ed.ac.uk](mailto:jonathan@ed.ac.uk) | |
| ## Dataset Description | |
| The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals | |
| synchronized to a common timeline. These include close-talking and far-field microphones, individual and | |
| room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, | |
| the participants also have unsynchronized pens available to them that record what is written. The meetings | |
| were recorded in English using three different rooms with different acoustic properties, and include mostly | |
| non-native speakers. | |
| **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). | |
| This means text is normalized and the audio data is chunked according to the scripts above! | |
| 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: | |
| ### Example Usage | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("edinburghcstr/ami", "ihm") | |
| print(ds) | |
| ``` | |
| gives: | |
| ``` | |
| DatasetDict({ | |
| train: Dataset({ | |
| features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'], | |
| num_rows: 108502 | |
| }) | |
| validation: Dataset({ | |
| features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'], | |
| num_rows: 13098 | |
| }) | |
| test: Dataset({ | |
| features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'], | |
| num_rows: 12643 | |
| }) | |
| }) | |
| ``` | |
| ```py | |
| ds["train"][0] | |
| ``` | |
| automatically loads the audio into memory: | |
| ``` | |
| {'meeting_id': 'EN2001a', | |
| 'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594', | |
| 'text': 'OKAY', | |
| 'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav', | |
| 'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518, | |
| 0.00030518], dtype=float32), | |
| 'sampling_rate': 16000}, | |
| 'begin_time': 5.570000171661377, | |
| 'end_time': 5.940000057220459, | |
| 'microphone_id': 'H00', | |
| 'speaker_id': 'MEE068'} | |
| ``` | |
| 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). | |
| As can be seen in this experiments, training the model for less than 2 epochs gives | |
| *Result (WER)*: | |
| | "dev" | "eval" | | |
| |---|---| | |
| | 25.27 | 25.21 | | |
| as can be seen [here](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60). | |
| The results are in-line with results of published papers: | |
| - [*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) | |
| - [Multi-Span Acoustic Modelling using Raw Waveform Signals](https://arxiv.org/abs/1906.11047) | |
| You can run [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh) to reproduce the result. | |
| ### Supported Tasks and Leaderboards | |
| ### Languages | |
| ## Dataset Structure | |
| ### Data Instances | |
| ### Data Fields | |
| ### Data Splits | |
| #### Transcribed Subsets Size | |
| ## 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 | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| ### Other Known Limitations | |
| ## Additional Information | |
| ### Dataset Curators | |
| ### Licensing Information | |
| ### Citation Information | |
| ### Contributions | |
| Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten), | |
| and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset. | |
| ## Terms of Usage | |