Upload librispeech_asr_adversarial.py

librispeech_asr_adversarial.py

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+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Adversarial examples based on the Librispeech automatic speech recognition dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import glob
+import os
+
+import datasets
+
+
+# TODO: change
+_CITATION = """\
+@inproceedings{panayotov2015librispeech,
+  title={Librispeech: an ASR corpus based on public domain audio books},
+  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
+  pages={5206--5210},
+  year={2015},
+  organization={IEEE}
+}
+"""
+
+# TODO: change
+_DESCRIPTION = """\
+LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
+prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
+audiobooks from the LibriVox project, and has been carefully segmented and aligned.
+Note that in order to limit the required storage for preparing this dataset, the audio
+is stored in the .flac format and is not converted to a float32 array. To convert, the audio
+file to a float32 array, please make use of the `.map()` function as follows:
+```python
+import soundfile as sf
+def map_to_array(batch):
+    speech_array, _ = sf.read(batch["file"])
+    batch["speech"] = speech_array
+    return batch
+dataset = dataset.map(map_to_array, remove_columns=["file"])
+```
+"""
+# TODO: change
+_DL_URL = "https://drive.google.com/file/d/1oaBhaHlY4TD2JcvenR-6OZNIsyPG8OGN/view?usp=sharing"
+
+
+class LibrispeechASRConfig(datasets.BuilderConfig):
+    """BuilderConfig for LibriSpeechASR."""
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(LibrispeechASRConfig, self).__init__(
+            version=datasets.Version("2.1.0", ""), **kwargs)
+
+
+class LibrispeechASR(datasets.GeneratorBasedBuilder):
+    """Librispeech dataset."""
+
+    BUILDER_CONFIGS = [
+        LibrispeechASRConfig(name="adv", description="'Adversarial' speech."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.features.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("speech", "text"),
+            homepage=_DL_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(
+            _DL_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={
+                                    "archive_path": archive_path["dev"], "split_name": f"dev_{self.config.name}"}),
+        ]
+
+    def _generate_examples(self, archive_path, split_name):
+        """Generate examples from a Librispeech archive_path."""
+        transcripts_glob = os.path.join(archive_path, "**/*.txt")
+        if split_name.endswith("adv-txt"):
+            split_folder = split_name[:-7]
+            use_adv_transcript = True
+        else:
+            assert split_name.endswith("nat-txt")
+            split_folder = split_name[:-7]
+            use_adv_transcript = False
+
+        for transcript_file in glob.glob(transcripts_glob):
+            path = os.path.dirname(transcript_file)
+            audio_path = os.path.join(path, split_folder)
+            with open(os.path.join(path, transcript_file)) as f:
+                for line in f:
+                    line = line.strip()
+                    key, og_transcript, adv_transcript = line.split(",", 2)
+                    transcript = adv_transcript if use_adv_transcript else og_transcript
+                    suffix = "adv" if use_adv_transcript else "nat"
+                    audio_file = f"{key}_{suffix}.wav"
+                    speaker_id, chapter_id = [int(el)
+                                              for el in key.split("-")[:2]]
+                    split_key = key+"_"+suffix+"_"+split_name
+                    example = {
+                        "id": split_key,
+                        "speaker_id": speaker_id,
+                        "chapter_id": chapter_id,
+                        "file": os.path.join(audio_path, audio_file),
+                        "audio": os.path.join(audio_path, audio_file),
+                        "text": transcript,
+                    }
+                    yield split_key, example