Update test-user.py

test-user.py

@@ -1,15 +1,26 @@
 # 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
-"""test set"""
+"""Librispeech automatic speech recognition dataset."""
 
 
-import csv
 import os
-import json
 
 import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
+from datasets.tasks import AutomaticSpeechRecognition
 
 
 _CITATION = """\
@@ -24,50 +35,63 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-Lorem ipsum
+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
 """
 
+_URL = "http://www.openslr.org/12"
+_DL_URL = "http://www.openslr.org/resources/12/"
+
+
+_DL_URLS = {
+    "clean": {
+        "dev": _DL_URL + "dev-clean.tar.gz",
+        "test": _DL_URL + "test-clean.tar.gz",
+        "train.100": _DL_URL + "train-clean-100.tar.gz",
+        "train.360": _DL_URL + "train-clean-360.tar.gz",
+    },
+    "other": {
+        "test": _DL_URL + "test-other.tar.gz",
+        "dev": _DL_URL + "dev-other.tar.gz",
+        "train.500": _DL_URL + "train-other-500.tar.gz",
+    },
+    "all": {
+        "dev.clean": _DL_URL + "dev-clean.tar.gz",
+        "dev.other": _DL_URL + "dev-other.tar.gz",
+        "test.clean": _DL_URL + "test-clean.tar.gz",
+        "test.other": _DL_URL + "test-other.tar.gz",
+        "train.clean.100": _DL_URL + "train-clean-100.tar.gz",
+        "train.clean.360": _DL_URL + "train-clean-360.tar.gz",
+        "train.other.500": _DL_URL + "train-other-500.tar.gz",
+    },
+}
 
-_BASE_URL = "https://huggingface.co/datasets/gcjavi/dataviewer-test"
-_DATA_URL = "test.zip"
-_PROMPTS_URLS = {"test": "test.tsv"}
-
-logger = datasets.logging.get_logger(__name__)
-
-class TestConfig(datasets.BuilderConfig):
-    """Lorem impsum."""
-
-    def __init__(self, name, **kwargs):
-        # self.language = kwargs.pop("language", None)
-        # self.release_date = kwargs.pop("release_date", None)
-        # self.num_clips = kwargs.pop("num_clips", None)
-        # self.num_speakers = kwargs.pop("num_speakers", None)
-        # self.validated_hr = kwargs.pop("validated_hr", None)
-        # self.total_hr = kwargs.pop("total_hr", None)
-        # self.size_bytes = kwargs.pop("size_bytes", None)
-        # self.size_human = size_str(self.size_bytes)
-        description = (
-            f"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor "
-            f"incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud "
-            f"exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure "
-            f"dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. "
-            f"Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt "
-            f"mollit anim id est laborum."
-        )
-        super(TestConfig, self).__init__(
-            name=name,
-            description=description,
-            **kwargs,
-        )
 
-class TestASR(datasets.GeneratorBasedBuilder):
-    """Lorem ipsum."""
+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
+    DEFAULT_CONFIG_NAME = "all"
     BUILDER_CONFIGS = [
-        TestConfig(
-            name="test-dataset",
-        )
+        LibrispeechASRConfig(name="clean", description="'Clean' speech."),
+        LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."),
+        LibrispeechASRConfig(name="all", description="Combined clean and other dataset."),
     ]
 
     def _info(self):
@@ -75,51 +99,181 @@ class TestASR(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "audio_id": datasets.Value("string"),
+                    "file": datasets.Value("string"),
                     "audio": datasets.Audio(sampling_rate=16_000),
-                    "sentence": datasets.Value("string")
+                    "text": datasets.Value("string"),
+                    "speaker_id": datasets.Value("int64"),
+                    "chapter_id": datasets.Value("int64"),
+                    "id": datasets.Value("string"),
                 }
             ),
-            supervised_keys=None,
-            homepage=_BASE_URL,
-            citation=_CITATION
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
         )
 
     def _split_generators(self, dl_manager):
-        audio_path = dl_manager.download(_DATA_URL)
-        local_extracted_archive = dl_manager.extract(audio_path) if not dl_manager.is_streaming else None
-        meta_path = dl_manager.download(_PROMPTS_URLS)
-        return [datasets.SplitGenerator(
-            name=datasets.Split.TEST,
-            gen_kwargs={
-                "meta_path": meta_path["test"],
-                "audio_files": dl_manager.iter_archive(audio_path),
-                "local_extracted_archive": local_extracted_archive,
-            }
-        )]
-
-    def _generate_examples(self, meta_path, audio_files, local_extracted_archive):
-        """Lorem ipsum."""
-        data_fields = list(self._info().features.keys())
-        metadata = {}
-        with open(meta_path, encoding="utf-8") as f:
-            next(f)
-            for row in f:
-                print(row)
-                r = row.split("\t")
-                print(r)
-                audio_id = r[0]
-                sentence = r[1]
-                metadata[audio_id] = {"audio_id": audio_id,
-                                      "sentence": sentence}
-
-        id_ = 0
-        for path, f in audio_files:
-            print(path, f)
-            _, audio_name = os.path.split(path)
-            if audio_name in metadata:
-                result = dict(metadata[audio_name])
-                path = os.path.join(local_extracted_archive, "test", path) if local_extracted_archive else path
-                result["audio"] = {"path": path, "bytes":f.read()}
-                yield id_, result
-                id_ +=1
+        archive_path = dl_manager.download(_DL_URLS[self.config.name])
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+
+        if self.config.name == "clean":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.100",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.100"),
+                        "files": dl_manager.iter_archive(archive_path["train.100"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.360",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.360"]),
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test"),
+                        "files": dl_manager.iter_archive(archive_path["test"]),
+                    },
+                )
+            ]
+        elif self.config.name == "other":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.500"]),
+                    },
+                )
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev"),
+                        "files": dl_manager.iter_archive(archive_path["dev"]),
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test"),
+                        "files": dl_manager.iter_archive(archive_path["test"]),
+                    },
+                )
+            ]
+        elif self.config.name == "all":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.clean.100",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.100"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.100"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.clean.360",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.clean.360"),
+                        "files": dl_manager.iter_archive(archive_path["train.clean.360"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.other.500",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("train.other.500"),
+                        "files": dl_manager.iter_archive(archive_path["train.other.500"]),
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name="validation.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev.clean"),
+                        "files": dl_manager.iter_archive(archive_path["dev.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="validation.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("dev.other"),
+                        "files": dl_manager.iter_archive(archive_path["dev.other"]),
+                    },
+                ),
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name="test.clean",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.clean"),
+                        "files": dl_manager.iter_archive(archive_path["test.clean"]),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test.other",
+                    gen_kwargs={
+                        "local_extracted_archive": local_extracted_archive.get("test.other"),
+                        "files": dl_manager.iter_archive(archive_path["test.other"]),
+                    },
+                ),
+            ]
+
+        return train_splits + dev_splits + test_splits
+
+    def _generate_examples(self, files, local_extracted_archive):
+        """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]]
+                        audio_file = (
+                            os.path.join(local_extracted_archive, audio_file)
+                            if local_extracted_archive
+                            else audio_file
+                        )
+                        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 = []