Upload librispeech_asr_test.py

librispeech_asr_test.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
+"""Librispeech automatic speech recognition dataset."""
+
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+_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}
+}
+"""
+
+_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.87
+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"])
+```
+"""
+
+_URL = "http://www.openslr.org/12"
+_DL_URL = "http://www.openslr.org/resources/12/"
+
+_DL_URLS = {
+    "clean": {
+        "test": _DL_URL + "test-clean.tar.gz",
+    },
+    "other": {
+        "test": _DL_URL + "test-other.tar.gz",
+    },
+}
+
+
+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."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    BUILDER_CONFIGS = [
+        LibrispeechASRConfig(name="clean", description="'Clean' speech."),
+        LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "text": datasets.Value("string"),
+                    "speaker_id": datasets.Value("int64"),
+                    "chapter_id": datasets.Value("int64"),
+                    "id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(audio_file_path_column="file", transcription_column="text")],
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DL_URLS[self.config.name])
+
+
+        return  [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"files": dl_manager.iter_archive(archive_path["dev"])}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive_path["test"])}
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        """Generate examples from a LibriSpeech archive_path."""
+        key = 0
+        audio_data = {}
+        transcripts = []
+        for path, f in files:
+            if path.endswith(".flac"):
+                id_ = path.split("/")[-1][: -len(".flac")]
+                audio_data[id_] = f.read()
+            elif path.endswith(".trans.txt"):
+                for line in f:
+                    if line:
+                        line = line.decode("utf-8").strip()
+                        id_, transcript = line.split(" ", 1)
+                        audio_file = f"{id_}.flac"
+                        speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
+                        transcripts.append(
+                            {
+                                "id": id_,
+                                "speaker_id": speaker_id,
+                                "chapter_id": chapter_id,
+                                "file": audio_file,
+                                "text": transcript,
+                            }
+                        )
+            if audio_data and len(audio_data) == len(transcripts):
+                for transcript in transcripts:
+                    audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
+                    yield key, {"audio": audio, **transcript}
+                    key += 1
+                audio_data = {}
+                transcripts = []