add more info to readme

README.md

@@ -1,13 +1,56 @@
-# AMI Corpus
-
-https://groups.inf.ed.ac.uk/ami/corpus/
-
-To be filled!
+# 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")
@@ -18,15 +61,15 @@ gives:
 ```
 DatasetDict({
     train: Dataset({
-        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
         num_rows: 108502
     })
     validation: Dataset({
-        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
         num_rows: 13098
     })
     test: Dataset({
-        features: ['segment_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
+        features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
         num_rows: 12643
     })
 })
@@ -39,10 +82,10 @@ ds["train"][0]
 automatically loads the audio into memory:
 
 ```
-{'segment_id': 'EN2001a',
+{'meeting_id': 'EN2001a',
  'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
  'text': 'OKAY',
- 'audio': {'path': '/home/patrick_huggingface_co/.cache/huggingface/datasets/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
+ '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},
@@ -70,4 +113,66 @@ 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.
+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
+
+