Add dataset card

README.md

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+---
+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
+
+