Duplicate from edinburghcstr/ami

Co-authored-by: Patrick von Platen <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

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 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:** [[email protected]](mailto:[email protected])
+
+## 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
+
+

ami-ihm-kaldi-chunked.py

@@ -0,0 +1,382 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
+labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
+and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
+and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
+sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
+for speech recognition training, and to filter out segments with low-quality transcription. For system training,
+GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
+For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
+and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
+are re-processed by professional human transcribers to ensure high transcription quality.
+"""
+
+import csv
+import os
+
+import datasets
+
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2106-06909,
+  author    = {Guoguo Chen and
+               Shuzhou Chai and
+               Guanbo Wang and
+               Jiayu Du and
+               Wei{-}Qiang Zhang and
+               Chao Weng and
+               Dan Su and
+               Daniel Povey and
+               Jan Trmal and
+               Junbo Zhang and
+               Mingjie Jin and
+               Sanjeev Khudanpur and
+               Shinji Watanabe and
+               Shuaijiang Zhao and
+               Wei Zou and
+               Xiangang Li and
+               Xuchen Yao and
+               Yongqing Wang and
+               Yujun Wang and
+               Zhao You and
+               Zhiyong Yan},
+  title     = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours
+               of Transcribed Audio},
+  journal   = {CoRR},
+  volume    = {abs/2106.06909},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2106.06909},
+  eprinttype = {arXiv},
+  eprint    = {2106.06909},
+  timestamp = {Wed, 29 Dec 2021 14:29:26 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
+labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
+and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
+and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
+sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
+for speech recognition training, and to filter out segments with low-quality transcription. For system training,
+GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
+For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
+and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
+are re-processed by professional human transcribers to ensure high transcription quality.
+"""
+
+_HOMEPAGE = "https://groups.inf.ed.ac.uk/ami/corpus/"
+
+_LICENSE = "CC BY 4.0"
+
+_TRAIN_SAMPLE_IDS = [
+    "EN2001a",
+    "EN2001b",
+    "EN2001d",
+    "EN2001e",
+    "EN2003a",
+    "EN2004a",
+    "EN2005a",
+    "EN2006a",
+    "EN2006b",
+    "EN2009b",
+    "EN2009c",
+    "EN2009d",
+    "ES2002a",
+    "ES2002b",
+    "ES2002c",
+    "ES2002d",
+    "ES2003a",
+    "ES2003b",
+    "ES2003c",
+    "ES2003d",
+    "ES2005a",
+    "ES2005b",
+    "ES2005c",
+    "ES2005d",
+    "ES2006a",
+    "ES2006b",
+    "ES2006c",
+    "ES2006d",
+    "ES2007a",
+    "ES2007b",
+    "ES2007c",
+    "ES2007d",
+    "ES2008a",
+    "ES2008b",
+    "ES2008c",
+    "ES2008d",
+    "ES2009a",
+    "ES2009b",
+    "ES2009c",
+    "ES2009d",
+    "ES2010a",
+    "ES2010b",
+    "ES2010c",
+    "ES2010d",
+    "ES2012a",
+    "ES2012b",
+    "ES2012c",
+    "ES2012d",
+    "ES2013a",
+    "ES2013b",
+    "ES2013c",
+    "ES2013d",
+    "ES2014a",
+    "ES2014b",
+    "ES2014c",
+    "ES2014d",
+    "ES2015a",
+    "ES2015b",
+    "ES2015c",
+    "ES2015d",
+    "ES2016a",
+    "ES2016b",
+    "ES2016c",
+    "ES2016d",
+    "IB4005",
+    "IN1001",
+    "IN1002",
+    "IN1005",
+    "IN1007",
+    "IN1008",
+    "IN1009",
+    "IN1012",
+    "IN1013",
+    "IN1014",
+    "IN1016",
+    "IS1000a",
+    "IS1000b",
+    "IS1000c",
+    "IS1000d",
+    "IS1001a",
+    "IS1001b",
+    "IS1001c",
+    "IS1001d",
+    "IS1002b",
+    "IS1002c",
+    "IS1002d",
+    "IS1003a",
+    "IS1003b",
+    "IS1003c",
+    "IS1003d",
+    "IS1004a",
+    "IS1004b",
+    "IS1004c",
+    "IS1004d",
+    "IS1005a",
+    "IS1005b",
+    "IS1005c",
+    "IS1006a",
+    "IS1006b",
+    "IS1006c",
+    "IS1006d",
+    "IS1007a",
+    "IS1007b",
+    "IS1007c",
+    "IS1007d",
+    "TS3005a",
+    "TS3005b",
+    "TS3005c",
+    "TS3005d",
+    "TS3006a",
+    "TS3006b",
+    "TS3006c",
+    "TS3006d",
+    "TS3007a",
+    "TS3007b",
+    "TS3007c",
+    "TS3007d",
+    "TS3008a",
+    "TS3008b",
+    "TS3008c",
+    "TS3008d",
+    "TS3009a",
+    "TS3009b",
+    "TS3009c",
+    "TS3009d",
+    "TS3010a",
+    "TS3010b",
+    "TS3010c",
+    "TS3010d",
+    "TS3011a",
+    "TS3011b",
+    "TS3011c",
+    "TS3011d",
+    "TS3012a",
+    "TS3012b",
+    "TS3012c",
+    "TS3012d",
+]
+
+_VALIDATION_SAMPLE_IDS = [
+    "ES2011a",
+    "ES2011c",
+    "IB4001",
+    "IB4003",
+    "IB4010",
+    "IS1008a",
+    "IS1008c",
+    "TS3004a",
+    "TS3004c",
+    "ES2011b",
+    "ES2011d",
+    "IB4002",
+    "IB4004",
+    "IB4011",
+    "IS1008b",
+    "IS1008d",
+    "TS3004b",
+    "TS3004d",
+]
+
+_EVAL_SAMPLE_IDS = [
+    "EN2002a",
+    "EN2002b",
+    "EN2002c",
+    "EN2002d",
+    "ES2004a",
+    "ES2004b",
+    "ES2004c",
+    "ES2004d",
+    "IS1009a",
+    "IS1009b",
+    "IS1009c",
+    "IS1009d",
+    "TS3003a",
+    "TS3003b",
+    "TS3003c",
+    "TS3003d",
+]
+
+_SUBSETS = ("ihm",)
+
+_BASE_DATA_URL = "https://huggingface.co/datasets/patrickvonplaten/ami-ihm-kaldi-chunked/resolve/main/"
+
+_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}/{split}/{_id}.tar.gz"
+
+_ANNOTATIONS_ARCHIVE_URL = _BASE_DATA_URL + "annotations/{split}/text"
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class AMIConfig(datasets.BuilderConfig):
+    """BuilderConfig for AMI."""
+
+    def __init__(self, name, *args, **kwargs):
+        """BuilderConfig for AMI"""
+        super().__init__(name=name, *args, **kwargs)
+
+
+class AMI(datasets.GeneratorBasedBuilder):
+    """
+    GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
+    labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
+    and unsupervised training (this implementation contains only labelled data for now).
+    Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
+    and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
+    sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
+    for speech recognition training, and to filter out segments with low-quality transcription. For system training,
+    GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
+    For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
+    and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
+    are re-processed by professional human transcribers to ensure high transcription quality.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        AMIConfig(name=subset) for subset in _SUBSETS
+    ]
+
+    DEFAULT_WRITER_BATCH_SIZE = 128
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "segment_id": datasets.Value("string"),
+                "audio_id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "begin_time": datasets.Value("float32"),
+                "end_time": datasets.Value("float32"),
+                "microphone_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS}
+        dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS}
+        eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS}
+
+        train_audio_archives = dl_manager.download_and_extract(train_audio_files)
+        dev_audio_archives = dl_manager.download_and_extract(dev_audio_files)
+        eval_audio_archives = dl_manager.download_and_extract(eval_audio_files)
+
+        train_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="train"))
+        dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev"))
+        eval_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="eval"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation, "split": "dev"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation, "split": "eval"},
+            ),
+        ]
+
+    def _generate_examples(self, audio, annotation, split):
+        # open annotation file
+        with open(annotation, "r", encoding="utf-8") as f:
+            transcriptions = {}
+            for line in f.readlines():
+                line_items = line.strip().split()
+                _id = line_items[0]
+                text = " ".join(line_items[1:])
+                _, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
+
+                transcriptions[_id] = {
+                    "audio_id": _id,
+                    "segment_id": segment_id,
+                    "text": text,
+                    "begin_time": int(begin_time) / 100,
+                    "end_time": int(end_time) / 100,
+                    "microphone_id": microphone_id,
+                    "speaker_id": speaker_id,
+                }
+
+        for _audio_id, (transcription_id, result) in enumerate(transcriptions.items()):
+            folder_id = result["segment_id"]
+            file_name = "_".join([split, transcription_id.lower()]) + ".wav"
+            audio_file = os.path.join(audio[folder_id], folder_id, file_name)
+            result["audio"] = audio_file
+            yield _audio_id, result

ami.py

@@ -0,0 +1,411 @@
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+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.
+"""
+
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{10.1007/11677482_3,
+author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre},
+title = {The AMI Meeting Corpus: A Pre-Announcement},
+year = {2005},
+isbn = {3540325492},
+publisher = {Springer-Verlag},
+address = {Berlin, Heidelberg},
+url = {https://doi.org/10.1007/11677482_3},
+doi = {10.1007/11677482_3},
+abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting
+recordings. It is being created in the context of a project that is developing meeting
+browsing technology and will eventually be released publicly. Some of the meetings
+it contains are naturally occurring, and some are elicited, particularly using a scenario
+in which the participants play different roles in a design team, taking a design project
+from kick-off to completion over the course of a day. The corpus is being recorded
+using a wide range of devices including close-talking and far-field microphones, individual
+and room-view video cameras, projection, a whiteboard, and individual pens, all of
+which produce output signals that are synchronized with each other. It is also being
+hand-annotated for many different phenomena, including orthographic transcription,
+discourse properties such as named entities and dialogue acts, summaries, emotions,
+and some head and hand gestures. We describe the data set, including the rationale
+behind using elicited material, and explain how the material is being recorded, transcribed
+and annotated.},
+booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction},
+pages = {28–39},
+numpages = {12},
+location = {Edinburgh, UK},
+series = {MLMI'05}
+}
+"""
+
+_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. \n
+"""
+
+_HOMEPAGE = "https://groups.inf.ed.ac.uk/ami/corpus/"
+
+_LICENSE = "CC BY 4.0"
+
+_TRAIN_SAMPLE_IDS = [
+    "EN2001a",
+    "EN2001b",
+    "EN2001d",
+    "EN2001e",
+    "EN2003a",
+    "EN2004a",
+    "EN2005a",
+    "EN2006a",
+    "EN2006b",
+    "EN2009b",
+    "EN2009c",
+    "EN2009d",
+    "ES2002a",
+    "ES2002b",
+    "ES2002c",
+    "ES2002d",
+    "ES2003a",
+    "ES2003b",
+    "ES2003c",
+    "ES2003d",
+    "ES2005a",
+    "ES2005b",
+    "ES2005c",
+    "ES2005d",
+    "ES2006a",
+    "ES2006b",
+    "ES2006c",
+    "ES2006d",
+    "ES2007a",
+    "ES2007b",
+    "ES2007c",
+    "ES2007d",
+    "ES2008a",
+    "ES2008b",
+    "ES2008c",
+    "ES2008d",
+    "ES2009a",
+    "ES2009b",
+    "ES2009c",
+    "ES2009d",
+    "ES2010a",
+    "ES2010b",
+    "ES2010c",
+    "ES2010d",
+    "ES2012a",
+    "ES2012b",
+    "ES2012c",
+    "ES2012d",
+    "ES2013a",
+    "ES2013b",
+    "ES2013c",
+    "ES2013d",
+    "ES2014a",
+    "ES2014b",
+    "ES2014c",
+    "ES2014d",
+    "ES2015a",
+    "ES2015b",
+    "ES2015c",
+    "ES2015d",
+    "ES2016a",
+    "ES2016b",
+    "ES2016c",
+    "ES2016d",
+    "IB4005",
+    "IN1001",
+    "IN1002",
+    "IN1005",
+    "IN1007",
+    "IN1008",
+    "IN1009",
+    "IN1012",
+    "IN1013",
+    "IN1014",
+    "IN1016",
+    "IS1000a",
+    "IS1000b",
+    "IS1000c",
+    "IS1000d",
+    "IS1001a",
+    "IS1001b",
+    "IS1001c",
+    "IS1001d",
+    "IS1002b",
+    "IS1002c",
+    "IS1002d",
+    "IS1003a",
+    "IS1003b",
+    "IS1003c",
+    "IS1003d",
+    "IS1004a",
+    "IS1004b",
+    "IS1004c",
+    "IS1004d",
+    "IS1005a",
+    "IS1005b",
+    "IS1005c",
+    "IS1006a",
+    "IS1006b",
+    "IS1006c",
+    "IS1006d",
+    "IS1007a",
+    "IS1007b",
+    "IS1007c",
+    "IS1007d",
+    "TS3005a",
+    "TS3005b",
+    "TS3005c",
+    "TS3005d",
+    "TS3006a",
+    "TS3006b",
+    "TS3006c",
+    "TS3006d",
+    "TS3007a",
+    "TS3007b",
+    "TS3007c",
+    "TS3007d",
+    "TS3008a",
+    "TS3008b",
+    "TS3008c",
+    "TS3008d",
+    "TS3009a",
+    "TS3009b",
+    "TS3009c",
+    "TS3009d",
+    "TS3010a",
+    "TS3010b",
+    "TS3010c",
+    "TS3010d",
+    "TS3011a",
+    "TS3011b",
+    "TS3011c",
+    "TS3011d",
+    "TS3012a",
+    "TS3012b",
+    "TS3012c",
+    "TS3012d",
+]
+
+_TRAIN_SAMPLES_BROKEN_SDM = ["IS1003b", "IS1007d"]
+
+_VALIDATION_SAMPLE_IDS = [
+    "ES2011a",
+    "ES2011c",
+    "IB4001",
+    "IB4003",
+    "IB4010",
+    "IS1008a",
+    "IS1008c",
+    "TS3004a",
+    "TS3004c",
+    "ES2011b",
+    "ES2011d",
+    "IB4002",
+    "IB4004",
+    "IB4011",
+    "IS1008b",
+    "IS1008d",
+    "TS3004b",
+    "TS3004d",
+]
+
+_EVAL_SAMPLE_IDS = [
+    "EN2002a",
+    "EN2002b",
+    "EN2002c",
+    "EN2002d",
+    "ES2004a",
+    "ES2004b",
+    "ES2004c",
+    "ES2004d",
+    "IS1009a",
+    "IS1009b",
+    "IS1009c",
+    "IS1009d",
+    "TS3003a",
+    "TS3003b",
+    "TS3003c",
+    "TS3003d",
+]
+
+_SAMPLE_IDS = {
+    "train": _TRAIN_SAMPLE_IDS,
+    "dev": _VALIDATION_SAMPLE_IDS,
+    "eval": _EVAL_SAMPLE_IDS,
+}
+
+_SUBSETS = ("ihm", "sdm")
+
+_BASE_DATA_URL = "https://huggingface.co/datasets/edinburghcstr/ami/resolve/main/"
+
+_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}/{split}/{_id}.tar.gz"
+
+_ANNOTATIONS_ARCHIVE_URL = _BASE_DATA_URL + "annotations/{split}/text"
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+class AMIConfig(datasets.BuilderConfig):
+    """BuilderConfig for AMI."""
+
+    def __init__(self, name, *args, **kwargs):
+        """BuilderConfig for AMI"""
+        super().__init__(name=name, *args, **kwargs)
+
+
+class AMI(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        AMIConfig(name=subset) for subset in _SUBSETS
+    ]
+
+    DEFAULT_WRITER_BATCH_SIZE = 128
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "meeting_id": datasets.Value("string"),
+                "audio_id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "begin_time": datasets.Value("float32"),
+                "end_time": datasets.Value("float32"),
+                "microphone_id": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        splits = ["train", "dev", "eval"]
+
+        audio_archives_urls = {}
+        for split in splits:
+            split_ids = _SAMPLE_IDS[split]
+
+            if self.config.name == "sdm" and split == "train":
+                split_ids = [s for s in split_ids if s not in _TRAIN_SAMPLES_BROKEN_SDM]
+
+            audio_archives_urls[split] = [
+                _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split=split, _id=m) for m in split_ids
+            ]
+
+        audio_archives = dl_manager.download(audio_archives_urls)
+        local_extracted_archives_paths = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {
+            split: [None] * len(audio_archives[split]) for split in splits
+        }
+
+        annotations_urls = {split: _ANNOTATIONS_ARCHIVE_URL.format(split=split) for split in splits}
+        annotations = dl_manager.download(annotations_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
+                    "local_extracted_archives_paths": local_extracted_archives_paths["train"],
+                    "annotation": annotations["train"],
+                    "split": "train"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
+                    "local_extracted_archives_paths": local_extracted_archives_paths["dev"],
+                    "annotation": annotations["dev"],
+                    "split": "dev"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["eval"]],
+                    "local_extracted_archives_paths": local_extracted_archives_paths["eval"],
+                    "annotation": annotations["eval"],
+                    "split": "eval"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, annotation, split):
+        # open annotation file
+        assert len(audio_archives) == len(local_extracted_archives_paths)
+
+        with open(annotation, "r", encoding="utf-8") as f:
+            transcriptions = {}
+            for line in f.readlines():
+                line_items = line.strip().split()
+                _id = line_items[0]
+                text = " ".join(line_items[1:])
+                _, meeting_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
+
+                # sdm specific
+                if self.config.name == "sdm" and meeting_id in _TRAIN_SAMPLES_BROKEN_SDM:
+                    continue
+                elif self.config.name == "sdm":
+                    microphone_id = "SDM1"
+                    _id_items = _id.split("_")
+                    _id = "_".join(_id_items[:2] + ["sdm"] + _id_items[3:])
+
+                audio_filename = "_".join([split, _id.lower()]) + ".wav"
+
+                transcriptions[audio_filename] = {
+                    "audio_id": _id,
+                    "meeting_id": meeting_id,
+                    "text": text,
+                    "begin_time": int(begin_time) / 100,
+                    "end_time": int(end_time) / 100,
+                    "microphone_id": microphone_id,
+                    "speaker_id": speaker_id,
+                }
+
+        features = ["meeting_id", "audio_id", "text", "begin_time", "end_time", "microphone_id", "speaker_id"]
+        for archive, local_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_path, audio_file in archive:
+                # adapt for sdm
+                if self.config.name == "sdm":
+                    _audio_path_items = audio_path.split("_")
+                    audio_path = "_".join(_audio_path_items[:3] + ["sdm"] + _audio_path_items[4:])
+
+                # audio_path is like 'EN2001a/train_ami_en2001a_h00_mee068_0414915_0415078.wav'
+                audio_meta = transcriptions[audio_path.split("/")[-1]]
+
+                yield audio_path, {
+                    "audio": {
+                        "path": os.path.join(local_archive_path, audio_path) if local_archive_path else audio_path,
+                        "bytes": audio_file.read(),
+                    },
+                    **{feature: audio_meta[feature] for feature in features}
+                }

annotations/dev/reco2file_and_channel

@@ -0,0 +1,72 @@
+AMI_ES2011a_H00 ES2011a.Headset-0 A
+AMI_ES2011a_H01 ES2011a.Headset-1 A
+AMI_ES2011a_H02 ES2011a.Headset-2 A
+AMI_ES2011a_H03 ES2011a.Headset-3 A
+AMI_ES2011b_H00 ES2011b.Headset-0 A
+AMI_ES2011b_H01 ES2011b.Headset-1 A
+AMI_ES2011b_H02 ES2011b.Headset-2 A
+AMI_ES2011b_H03 ES2011b.Headset-3 A
+AMI_ES2011c_H00 ES2011c.Headset-0 A
+AMI_ES2011c_H01 ES2011c.Headset-1 A
+AMI_ES2011c_H02 ES2011c.Headset-2 A
+AMI_ES2011c_H03 ES2011c.Headset-3 A
+AMI_ES2011d_H00 ES2011d.Headset-0 A
+AMI_ES2011d_H01 ES2011d.Headset-1 A
+AMI_ES2011d_H02 ES2011d.Headset-2 A
+AMI_ES2011d_H03 ES2011d.Headset-3 A
+AMI_IB4001_H00 IB4001.Headset-0 A
+AMI_IB4001_H01 IB4001.Headset-1 A
+AMI_IB4001_H02 IB4001.Headset-2 A
+AMI_IB4001_H03 IB4001.Headset-3 A
+AMI_IB4002_H00 IB4002.Headset-0 A
+AMI_IB4002_H01 IB4002.Headset-1 A
+AMI_IB4002_H02 IB4002.Headset-2 A
+AMI_IB4002_H03 IB4002.Headset-3 A
+AMI_IB4003_H00 IB4003.Headset-0 A
+AMI_IB4003_H01 IB4003.Headset-1 A
+AMI_IB4003_H02 IB4003.Headset-2 A
+AMI_IB4003_H03 IB4003.Headset-3 A
+AMI_IB4004_H00 IB4004.Headset-0 A
+AMI_IB4004_H01 IB4004.Headset-1 A
+AMI_IB4004_H02 IB4004.Headset-2 A
+AMI_IB4004_H03 IB4004.Headset-3 A
+AMI_IB4010_H00 IB4010.Headset-0 A
+AMI_IB4010_H01 IB4010.Headset-1 A
+AMI_IB4010_H02 IB4010.Headset-2 A
+AMI_IB4010_H03 IB4010.Headset-3 A
+AMI_IB4011_H00 IB4011.Headset-0 A
+AMI_IB4011_H01 IB4011.Headset-1 A
+AMI_IB4011_H02 IB4011.Headset-2 A
+AMI_IB4011_H03 IB4011.Headset-3 A
+AMI_IS1008a_H00 IS1008a.Headset-0 A
+AMI_IS1008a_H01 IS1008a.Headset-1 A
+AMI_IS1008a_H02 IS1008a.Headset-2 A
+AMI_IS1008a_H03 IS1008a.Headset-3 A
+AMI_IS1008b_H00 IS1008b.Headset-0 A
+AMI_IS1008b_H01 IS1008b.Headset-1 A
+AMI_IS1008b_H02 IS1008b.Headset-2 A
+AMI_IS1008b_H03 IS1008b.Headset-3 A
+AMI_IS1008c_H00 IS1008c.Headset-0 A
+AMI_IS1008c_H01 IS1008c.Headset-1 A
+AMI_IS1008c_H02 IS1008c.Headset-2 A
+AMI_IS1008c_H03 IS1008c.Headset-3 A
+AMI_IS1008d_H00 IS1008d.Headset-0 A
+AMI_IS1008d_H01 IS1008d.Headset-1 A
+AMI_IS1008d_H02 IS1008d.Headset-2 A
+AMI_IS1008d_H03 IS1008d.Headset-3 A
+AMI_TS3004a_H00 TS3004a.Headset-0 A
+AMI_TS3004a_H01 TS3004a.Headset-1 A
+AMI_TS3004a_H02 TS3004a.Headset-2 A
+AMI_TS3004a_H03 TS3004a.Headset-3 A
+AMI_TS3004b_H00 TS3004b.Headset-0 A
+AMI_TS3004b_H01 TS3004b.Headset-1 A
+AMI_TS3004b_H02 TS3004b.Headset-2 A
+AMI_TS3004b_H03 TS3004b.Headset-3 A
+AMI_TS3004c_H00 TS3004c.Headset-0 A
+AMI_TS3004c_H01 TS3004c.Headset-1 A
+AMI_TS3004c_H02 TS3004c.Headset-2 A
+AMI_TS3004c_H03 TS3004c.Headset-3 A
+AMI_TS3004d_H00 TS3004d.Headset-0 A
+AMI_TS3004d_H01 TS3004d.Headset-1 A
+AMI_TS3004d_H02 TS3004d.Headset-2 A
+AMI_TS3004d_H03 TS3004d.Headset-3 A

annotations/dev/wav.scp

@@ -0,0 +1,72 @@
+AMI_ES2011a_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011a/audio/ES2011a.Headset-0.wav -t wavpcm - |
+AMI_ES2011a_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011a/audio/ES2011a.Headset-1.wav -t wavpcm - |
+AMI_ES2011a_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011a/audio/ES2011a.Headset-2.wav -t wavpcm - |
+AMI_ES2011a_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011a/audio/ES2011a.Headset-3.wav -t wavpcm - |
+AMI_ES2011b_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011b/audio/ES2011b.Headset-0.wav -t wavpcm - |
+AMI_ES2011b_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011b/audio/ES2011b.Headset-1.wav -t wavpcm - |
+AMI_ES2011b_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011b/audio/ES2011b.Headset-2.wav -t wavpcm - |
+AMI_ES2011b_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011b/audio/ES2011b.Headset-3.wav -t wavpcm - |
+AMI_ES2011c_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011c/audio/ES2011c.Headset-0.wav -t wavpcm - |
+AMI_ES2011c_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011c/audio/ES2011c.Headset-1.wav -t wavpcm - |
+AMI_ES2011c_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011c/audio/ES2011c.Headset-2.wav -t wavpcm - |
+AMI_ES2011c_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011c/audio/ES2011c.Headset-3.wav -t wavpcm - |
+AMI_ES2011d_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011d/audio/ES2011d.Headset-0.wav -t wavpcm - |
+AMI_ES2011d_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011d/audio/ES2011d.Headset-1.wav -t wavpcm - |
+AMI_ES2011d_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011d/audio/ES2011d.Headset-2.wav -t wavpcm - |
+AMI_ES2011d_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/ES2011d/audio/ES2011d.Headset-3.wav -t wavpcm - |
+AMI_IB4001_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4001/audio/IB4001.Headset-0.wav -t wavpcm - |
+AMI_IB4001_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4001/audio/IB4001.Headset-1.wav -t wavpcm - |
+AMI_IB4001_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4001/audio/IB4001.Headset-2.wav -t wavpcm - |
+AMI_IB4001_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4001/audio/IB4001.Headset-3.wav -t wavpcm - |
+AMI_IB4002_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4002/audio/IB4002.Headset-0.wav -t wavpcm - |
+AMI_IB4002_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4002/audio/IB4002.Headset-1.wav -t wavpcm - |
+AMI_IB4002_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4002/audio/IB4002.Headset-2.wav -t wavpcm - |
+AMI_IB4002_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4002/audio/IB4002.Headset-3.wav -t wavpcm - |
+AMI_IB4003_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4003/audio/IB4003.Headset-0.wav -t wavpcm - |
+AMI_IB4003_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4003/audio/IB4003.Headset-1.wav -t wavpcm - |
+AMI_IB4003_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4003/audio/IB4003.Headset-2.wav -t wavpcm - |
+AMI_IB4003_H03 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4003/audio/IB4003.Headset-3.wav -t wavpcm - |
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+AMI_IB4010_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IB4010/audio/IB4010.Headset-0.wav -t wavpcm - |
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annotations/eval/reco2file_and_channel

@@ -0,0 +1,63 @@
+AMI_EN2002a_H00 EN2002a.Headset-0 A
+AMI_EN2002a_H01 EN2002a.Headset-1 A
+AMI_EN2002a_H02 EN2002a.Headset-2 A
+AMI_EN2002a_H03 EN2002a.Headset-3 A
+AMI_EN2002b_H00 EN2002b.Headset-0 A
+AMI_EN2002b_H01 EN2002b.Headset-1 A
+AMI_EN2002b_H02 EN2002b.Headset-2 A
+AMI_EN2002b_H03 EN2002b.Headset-3 A
+AMI_EN2002c_H01 EN2002c.Headset-1 A
+AMI_EN2002c_H02 EN2002c.Headset-2 A
+AMI_EN2002c_H03 EN2002c.Headset-3 A
+AMI_EN2002d_H00 EN2002d.Headset-0 A
+AMI_EN2002d_H01 EN2002d.Headset-1 A
+AMI_EN2002d_H02 EN2002d.Headset-2 A
+AMI_EN2002d_H03 EN2002d.Headset-3 A
+AMI_ES2004a_H00 ES2004a.Headset-0 A
+AMI_ES2004a_H01 ES2004a.Headset-1 A
+AMI_ES2004a_H02 ES2004a.Headset-2 A
+AMI_ES2004a_H03 ES2004a.Headset-3 A
+AMI_ES2004b_H00 ES2004b.Headset-0 A
+AMI_ES2004b_H01 ES2004b.Headset-1 A
+AMI_ES2004b_H02 ES2004b.Headset-2 A
+AMI_ES2004b_H03 ES2004b.Headset-3 A
+AMI_ES2004c_H00 ES2004c.Headset-0 A
+AMI_ES2004c_H01 ES2004c.Headset-1 A
+AMI_ES2004c_H02 ES2004c.Headset-2 A
+AMI_ES2004c_H03 ES2004c.Headset-3 A
+AMI_ES2004d_H00 ES2004d.Headset-0 A
+AMI_ES2004d_H01 ES2004d.Headset-1 A
+AMI_ES2004d_H02 ES2004d.Headset-2 A
+AMI_ES2004d_H03 ES2004d.Headset-3 A
+AMI_IS1009a_H00 IS1009a.Headset-0 A
+AMI_IS1009a_H01 IS1009a.Headset-1 A
+AMI_IS1009a_H02 IS1009a.Headset-2 A
+AMI_IS1009a_H03 IS1009a.Headset-3 A
+AMI_IS1009b_H00 IS1009b.Headset-0 A
+AMI_IS1009b_H01 IS1009b.Headset-1 A
+AMI_IS1009b_H02 IS1009b.Headset-2 A
+AMI_IS1009b_H03 IS1009b.Headset-3 A
+AMI_IS1009c_H00 IS1009c.Headset-0 A
+AMI_IS1009c_H01 IS1009c.Headset-1 A
+AMI_IS1009c_H02 IS1009c.Headset-2 A
+AMI_IS1009c_H03 IS1009c.Headset-3 A
+AMI_IS1009d_H00 IS1009d.Headset-0 A
+AMI_IS1009d_H01 IS1009d.Headset-1 A
+AMI_IS1009d_H02 IS1009d.Headset-2 A
+AMI_IS1009d_H03 IS1009d.Headset-3 A
+AMI_TS3003a_H00 TS3003a.Headset-0 A
+AMI_TS3003a_H01 TS3003a.Headset-1 A
+AMI_TS3003a_H02 TS3003a.Headset-2 A
+AMI_TS3003a_H03 TS3003a.Headset-3 A
+AMI_TS3003b_H00 TS3003b.Headset-0 A
+AMI_TS3003b_H01 TS3003b.Headset-1 A
+AMI_TS3003b_H02 TS3003b.Headset-2 A
+AMI_TS3003b_H03 TS3003b.Headset-3 A
+AMI_TS3003c_H00 TS3003c.Headset-0 A
+AMI_TS3003c_H01 TS3003c.Headset-1 A
+AMI_TS3003c_H02 TS3003c.Headset-2 A
+AMI_TS3003c_H03 TS3003c.Headset-3 A
+AMI_TS3003d_H00 TS3003d.Headset-0 A
+AMI_TS3003d_H01 TS3003d.Headset-1 A
+AMI_TS3003d_H02 TS3003d.Headset-2 A
+AMI_TS3003d_H03 TS3003d.Headset-3 A

annotations/eval/wav.scp

@@ -0,0 +1,63 @@
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+AMI_EN2002a_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/EN2002a/audio/EN2002a.Headset-1.wav -t wavpcm - |
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+AMI_EN2002b_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/EN2002b/audio/EN2002b.Headset-0.wav -t wavpcm - |
+AMI_EN2002b_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/EN2002b/audio/EN2002b.Headset-1.wav -t wavpcm - |
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+AMI_EN2002c_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/EN2002c/audio/EN2002c.Headset-1.wav -t wavpcm - |
+AMI_EN2002c_H02 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/EN2002c/audio/EN2002c.Headset-2.wav -t wavpcm - |
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+AMI_IS1009d_H00 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IS1009d/audio/IS1009d.Headset-0.wav -t wavpcm - |
+AMI_IS1009d_H01 sox -c 1 -t wavpcm -e signed-integer /home/patrick_huggingface_co/ami_download/IS1009d/audio/IS1009d.Headset-1.wav -t wavpcm - |
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annotations/train/reco2file_and_channel

@@ -0,0 +1,547 @@
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annotations/train/wav.scp

@@ -0,0 +1,547 @@
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