Create librispeech_asr-timestamped.py

librispeech_asr-timestamped.py

@@ -0,0 +1,330 @@
+# 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 csv
+import os
+
+import datasets
+# from datasets.tasks import AutomaticSpeechRecognition
+
+from huggingface_hub import list_repo_files
+
+
+import pyarrow.parquet as pq
+import pyarrow as pa
+
+
+_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
+"""
+
+_URL = "http://www.openslr.org/12"
+_TRANSCRIPT_URL =  "https://huggingface.co/datasets/distil-whisper/whisper_transcriptions_greedy_timestamped/resolve/main/librispeech_asr/"
+
+_DATA_REPO_ID = "sanchit-gandhi/librispeech-data"
+
+_TRANSCRIPT_URLS = {
+    "clean": {
+        "dev": _TRANSCRIPT_URL + "validation-clean-transcription.csv",
+        "test": _TRANSCRIPT_URL + "test-clean-transcription.csv",
+        "train.100": _TRANSCRIPT_URL + "train-clean-100-transcription.csv",
+        "train.360": _TRANSCRIPT_URL + "train-clean-360-transcription.csv",
+    },
+    "other": {
+        "test": _TRANSCRIPT_URL + "test-other-transcription.csv",
+        "dev": _TRANSCRIPT_URL + "validation-other-transcription.csv",
+        "train.500": _TRANSCRIPT_URL + "train-other-500-transcription.csv",
+    },
+    "all": {
+        "dev.clean": _TRANSCRIPT_URL + "validation-clean-transcription.csv",
+        "dev.other": _TRANSCRIPT_URL + "validation-other-transcription.csv",
+        "test.clean": _TRANSCRIPT_URL + "test-clean-transcription.csv",
+        "test.other": _TRANSCRIPT_URL + "test-other-transcription.csv",
+        "train.clean.100": _TRANSCRIPT_URL + "train-clean-100-transcription.csv",
+        "train.clean.360": _TRANSCRIPT_URL + "train-clean-360-transcription.csv",
+        "train.other.500": _TRANSCRIPT_URL + "train-other-500-transcription.csv",
+    },
+}
+
+
+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.ArrowBasedBuilder):
+    """Librispeech dataset."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        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):
+        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"),
+                    "whisper_transcript": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+            # task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
+            task_templates=[],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_repo_download = f"https://huggingface.co/datasets/{_DATA_REPO_ID}/resolve/main/"
+        all_files = list_repo_files(_DATA_REPO_ID, repo_type="dataset")
+
+        train_clean_100_files = [file for file in all_files if file.startswith("data/train.clean.100")]
+        train_clean_360_files = [file for file in all_files if file.startswith("data/train.clean.360")]
+        train_other_500_files = [file for file in all_files if file.startswith("data/train.other.500")]
+        validation_clean_files = [file for file in all_files if file.startswith("data/validation.clean")]
+        validation_other_files = [file for file in all_files if file.startswith("data/validation.other")]
+        test_clean_files = [file for file in all_files if file.startswith("data/test.clean")]
+        test_other_files = [file for file in all_files if file.startswith("data/test.other")]
+
+        split_to_ids = {
+            "train.clean.100": train_clean_100_files,
+            "train.clean.360": train_clean_360_files,
+            "train.other.500": train_other_500_files,
+            "dev.clean": validation_clean_files,
+            "dev.other": validation_other_files,
+            "test.clean": test_clean_files,
+            "test.other": test_other_files,
+        }
+
+        dl_urls = {}
+        for split, split_ids in split_to_ids.items():
+            dl_urls[split] = [data_repo_download + source_id for source_id in split_ids]
+        archive_paths = dl_manager.download(dl_urls)
+
+        local_extracted_archive_paths = (
+            dl_manager.extract(archive_paths)
+            if not dl_manager.is_streaming
+            else {split: [None] * len(archive_paths[split]) for split in split_to_ids}
+        )
+
+        transcript_archive_path = dl_manager.download(_TRANSCRIPT_URLS[self.config.name])
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local transcription files:
+        # local_extracted_transcript_archive = dl_manager.extract(transcript_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_paths": local_extracted_archive_paths.get("train.clean.100"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.clean.100"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.100"),
+                        "transcript_files": transcript_archive_path["train.100"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.360",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("train.360"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.360"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.360"),
+                        "transcript_files": transcript_archive_path["train.360"],
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("dev"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["dev"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("dev"),
+                        "transcript_files": transcript_archive_path["dev"],
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("test"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["test"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("test"),
+                        "transcript_files": transcript_archive_path["test"],
+                    },
+                )
+            ]
+        elif self.config.name == "other":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.500",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("train.500"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.500"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.500"),
+                        "transcript_files": transcript_archive_path["train.500"],
+                    },
+                )
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("dev"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["dev"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("dev"),
+                        "transcript_files": transcript_archive_path["dev"],
+                    },
+                )
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("test"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["test"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("test"),
+                        "transcript_files": transcript_archive_path["test"],
+                    },
+                )
+            ]
+        elif self.config.name == "all":
+            train_splits = [
+                datasets.SplitGenerator(
+                    name="train.clean.100",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("train.clean.100"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.clean.100"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.clean.100"),
+                        "transcript_files": transcript_archive_path["train.clean.100"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.clean.360",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("train.clean.360"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.clean.360"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.clean.360"),
+                        "transcript_files": transcript_archive_path["train.clean.360"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="train.other.500",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("train.other.500"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["train.other.500"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("train.other.500"),
+                        "transcript_files": transcript_archive_path["train.other.500"],
+                    },
+                ),
+            ]
+            dev_splits = [
+                datasets.SplitGenerator(
+                    name="validation.clean",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("dev.clean"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["dev.clean"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("dev.clean"),
+                        "transcript_files": transcript_archive_path["dev.clean"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="validation.other",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("dev.other"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["dev.other"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("dev.other"),
+                        "transcript_files": transcript_archive_path["dev.other"],
+                    },
+                ),
+            ]
+            test_splits = [
+                datasets.SplitGenerator(
+                    name="test.clean",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("test.clean"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["test.clean"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("test.clean"),
+                        "transcript_files": transcript_archive_path["test.clean"],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test.other",
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_paths.get("test.other"),
+                        "archives": [dl_manager.iter_files(path) for path in archive_paths["test.other"]],
+                        #"local_extracted_transcript_archive": local_extracted_transcript_archive.get("test.other"),
+                        "transcript_files": transcript_archive_path["test.other"],
+                    },
+                ),
+            ]
+
+        return train_splits + dev_splits + test_splits
+
+    def _generate_tables(self, local_extracted_archive_paths, archives, transcript_files):
+        whisper_transcriptions = dict()
+        with open(transcript_files, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter=",")
+            for line in reader:
+                whisper_transcriptions[line["file_id"]] = line["whisper_transcript"]
+
+        idx = 0
+        for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
+            # Here we iterate over all the files within the TAR archive:
+            for audio_file in archive:
+                with open(audio_file, "rb") as f:
+                    pf = pq.ParquetFile(f)
+                    for record_batch in pf.iter_batches():
+                        pa_table = pa.Table.from_batches([record_batch])
+                    whisper_transcript = [whisper_transcriptions.get(str(file_id), None) for file_id in pa_table["id"]]
+                    whisper_transcript = pa.array(whisper_transcript, pa.string())
+                    pa_table = pa_table.append_column("whisper_transcript", whisper_transcript)
+                    yield idx, pa_table
+                    idx += 1