add streaming

ami.py

@@ -283,19 +283,6 @@ class AMIConfig(datasets.BuilderConfig):
 
 
 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")
 
@@ -331,9 +318,9 @@ class AMI(datasets.GeneratorBasedBuilder):
         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_audio_archives = dl_manager.download(train_audio_files)
+        dev_audio_archives = dl_manager.download(dev_audio_files)
+        eval_audio_archives = dl_manager.download(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"))
@@ -342,20 +329,37 @@ class AMI(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation, "split": "train"},
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in train_audio_archives.values()],
+                    "local_extracted_archives_paths": dl_manager.extract(train_audio_archives).values() if not dl_manager.is_streaming else [None] * len(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"},
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in dev_audio_archives.values()],
+                    "local_extracted_archives_paths": dl_manager.extract(dev_audio_archives).values() if not dl_manager.is_streaming else [None] * len(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"},
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in eval_audio_archives.values()],
+                    "local_extracted_archives_paths": dl_manager.extract(eval_audio_archives).values() if not dl_manager.is_streaming else [None] * len(eval_audio_archives),
+                    "annotation": eval_annotation,
+                    "split": "eval"
+                },
             ),
         ]
 
-    def _generate_examples(self, audio, annotation, split):
+    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():
@@ -363,8 +367,9 @@ class AMI(datasets.GeneratorBasedBuilder):
                 _id = line_items[0]
                 text = " ".join(line_items[1:])
                 _, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
+                audio_filename = "_".join([split, _id.lower()]) + ".wav"
 
-                transcriptions[_id] = {
+                transcriptions[audio_filename] = {
                     "audio_id": _id,
                     "segment_id": segment_id,
                     "text": text,
@@ -374,10 +379,21 @@ class AMI(datasets.GeneratorBasedBuilder):
                     "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
+        for archive, local_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in archive:
+                audio_meta = transcriptions[audio_filename.split("/")[-1]]
 
+                yield audio_filename, {
+                    "segment_id": audio_meta["segment_id"],
+                    "audio_id": audio_meta["audio_id"],
+                    "audio": {
+                        "path": os.path.join(local_archive_path,
+                                             audio_filename) if local_archive_path else audio_filename,
+                        "bytes": audio_file.read(),
+                    },
+                    "text": audio_meta["text"],
+                    "begin_time": audio_meta["begin_time"],
+                    "end_time": audio_meta["end_time"],
+                    "microphone_id": audio_meta["microphone_id"],
+                    "speaker_id": audio_meta["speaker_id"],
+                    }