Datasets:
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ee802ec
1
Parent(s):
24aa3f7
add streaming
Browse files
ami.py
CHANGED
@@ -283,19 +283,6 @@ class AMIConfig(datasets.BuilderConfig):
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class AMI(datasets.GeneratorBasedBuilder):
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"""
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GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
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labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
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and unsupervised training (this implementation contains only labelled data for now).
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Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
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and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
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sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
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for speech recognition training, and to filter out segments with low-quality transcription. For system training,
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GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
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For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
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and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
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are re-processed by professional human transcribers to ensure high transcription quality.
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"""
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VERSION = datasets.Version("1.0.0")
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@@ -331,9 +318,9 @@ class AMI(datasets.GeneratorBasedBuilder):
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dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS}
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eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS}
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train_audio_archives = dl_manager.
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dev_audio_archives = dl_manager.
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eval_audio_archives = dl_manager.
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train_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="train"))
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dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev"))
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@@ -342,20 +329,37 @@ class AMI(datasets.GeneratorBasedBuilder):
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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),
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]
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def _generate_examples(self,
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# open annotation file
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with open(annotation, "r", encoding="utf-8") as f:
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transcriptions = {}
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for line in f.readlines():
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@@ -363,8 +367,9 @@ class AMI(datasets.GeneratorBasedBuilder):
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_id = line_items[0]
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text = " ".join(line_items[1:])
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_, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
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transcriptions[
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"audio_id": _id,
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"segment_id": segment_id,
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"text": text,
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@@ -374,10 +379,21 @@ class AMI(datasets.GeneratorBasedBuilder):
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"speaker_id": speaker_id,
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}
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for
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audio_file = os.path.join(audio[folder_id], folder_id, file_name)
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result["audio"] = audio_file
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yield _audio_id, result
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class AMI(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS}
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eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS}
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train_audio_archives = dl_manager.download(train_audio_files)
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dev_audio_archives = dl_manager.download(dev_audio_files)
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eval_audio_archives = dl_manager.download(eval_audio_files)
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train_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="train"))
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dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev"))
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"audio_archives": [dl_manager.iter_archive(archive) for archive in train_audio_archives.values()],
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"local_extracted_archives_paths": dl_manager.extract(train_audio_archives).values() if not dl_manager.is_streaming else [None] * len(train_audio_archives),
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"annotation": train_annotation,
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"split": "train"
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"audio_archives": [dl_manager.iter_archive(archive) for archive in dev_audio_archives.values()],
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"local_extracted_archives_paths": dl_manager.extract(dev_audio_archives).values() if not dl_manager.is_streaming else [None] * len(dev_audio_archives),
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"annotation": dev_annotation,
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"split": "dev"
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"audio_archives": [dl_manager.iter_archive(archive) for archive in eval_audio_archives.values()],
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"local_extracted_archives_paths": dl_manager.extract(eval_audio_archives).values() if not dl_manager.is_streaming else [None] * len(eval_audio_archives),
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"annotation": eval_annotation,
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"split": "eval"
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},
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),
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]
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def _generate_examples(self, audio_archives, local_extracted_archives_paths, annotation, split):
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# open annotation file
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assert len(audio_archives) == len(local_extracted_archives_paths)
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with open(annotation, "r", encoding="utf-8") as f:
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transcriptions = {}
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for line in f.readlines():
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_id = line_items[0]
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text = " ".join(line_items[1:])
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_, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_")
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audio_filename = "_".join([split, _id.lower()]) + ".wav"
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transcriptions[audio_filename] = {
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"audio_id": _id,
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"segment_id": segment_id,
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"text": text,
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"speaker_id": speaker_id,
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}
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for archive, local_archive_path in zip(audio_archives, local_extracted_archives_paths):
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for audio_filename, audio_file in archive:
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audio_meta = transcriptions[audio_filename.split("/")[-1]]
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yield audio_filename, {
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"segment_id": audio_meta["segment_id"],
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"audio_id": audio_meta["audio_id"],
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"audio": {
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"path": os.path.join(local_archive_path,
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audio_filename) if local_archive_path else audio_filename,
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"bytes": audio_file.read(),
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},
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"text": audio_meta["text"],
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"begin_time": audio_meta["begin_time"],
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"end_time": audio_meta["end_time"],
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"microphone_id": audio_meta["microphone_id"],
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"speaker_id": audio_meta["speaker_id"],
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}
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