fix description, fix meeting id feature, fix urls dict structure

ami.py

@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# 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.
@@ -12,19 +12,14 @@
 # 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.
+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 csv
 import os
 
 import datasets
@@ -292,7 +287,7 @@ class AMI(datasets.GeneratorBasedBuilder):
     def _info(self):
         features = datasets.Features(
             {
-                "segment_id": datasets.Value("string"),
+                "meeting_id": datasets.Value("string"),
                 "audio_id": datasets.Value("string"),
                 "text": datasets.Value("string"),
                 "audio": datasets.Audio(sampling_rate=16_000),
@@ -315,9 +310,9 @@ class AMI(datasets.GeneratorBasedBuilder):
 
         audio_archives_urls = {}
         for split in splits:
-            audio_archives_urls[split] = {
-                m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split=split, _id=m) for m in _SAMPLE_IDS[split]
-            }
+            audio_archives_urls[split] = [
+                _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split=split, _id=m) for m in _SAMPLE_IDS[split]
+            ]
 
         audio_archives = dl_manager.download(audio_archives_urls)
         local_extracted_archives_paths = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {
@@ -331,8 +326,8 @@ class AMI(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"].values()],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["train"].values(),
+                    "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"
                 },
@@ -340,8 +335,8 @@ class AMI(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"].values()],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["dev"].values(),
+                    "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"
                 },
@@ -349,8 +344,8 @@ class AMI(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["eval"].values()],
-                    "local_extracted_archives_paths": local_extracted_archives_paths["eval"].values(),
+                    "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"
                 },
@@ -367,12 +362,12 @@ class AMI(datasets.GeneratorBasedBuilder):
                 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("_")
+                _, meeting_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
                 audio_filename = "_".join([split, _id.lower()]) + ".wav"
 
                 transcriptions[audio_filename] = {
                     "audio_id": _id,
-                    "segment_id": segment_id,
+                    "meeting_id": meeting_id,
                     "text": text,
                     "begin_time": int(begin_time) / 100,
                     "end_time": int(end_time) / 100,
@@ -380,7 +375,7 @@ class AMI(datasets.GeneratorBasedBuilder):
                     "speaker_id": speaker_id,
                 }
 
-        features = ["segment_id", "audio_id", "text", "begin_time", "end_time", "microphone_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:
                 # audio_path is like 'EN2001a/train_ami_en2001a_h00_mee068_0414915_0415078.wav'