add initial version of loader script

nst_swedish_tts.py

@@ -0,0 +1,154 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+# Copyright 2023 Jim O'Regan for Språkbanken Tal
+#
+# 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
+"""Datasets loader for NST Swedish TTS data"""
+
+import soundfile as sf
+import os
+from pathlib import Path
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+
+
+
+_HEADER = b'PCM44   \x00\x00\x00\x00\x00\x00\x00S'
+
+_AUDIO_URL = "https://www.nb.no/sbfil/talesyntese/sve.ibm.talesyntese.tar.gz"
+
+_DESCRIPTION = """
+Database for Swedish speech synthesis, originally produced by Nordic Language Technology AS (NST).
+"""
+
+_URL = "https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-18/"
+
+
+def is_pcm(filename) -> bool:
+    """
+    Check the header of a .pcm file
+
+    Args:
+        filename: the file to check
+
+    Returns:
+        True is header is present, False otherwise
+    """
+    with open(filename, "rb") as pcm:
+        pcm.seek(0)
+        cond = (pcm.read(16) == _HEADER)
+        # reset location, just in case
+        pcm.seek(0)
+        return cond
+
+
+IGNORE_SENT = [
+    "stod man på torget kunde man se huset och det var ingen tvekan om att det dominerade sin omgivning och det rådde knappast heller något tvivel om att det förr i tiden hade väckt en hel del avund känslor som någon enstaka gång fortfarande kunde framkallas hos de äldre",
+    "viktor hade skickat ut det innan novell sålde unixware till sco",
+    "det gläder oss självklart"
+]
+IGNORE_ID = [
+    "4913",
+]
+# MAYBE_FIX = {
+#     "4913": "en annan gång tar vi ett annat grepp"
+# }
+
+def read_with_soundfile(filename):
+    return sf.read(filename, channels=2, samplerate=44100, endian="BIG",
+                   dtype="int16", format="RAW", subtype="PCM_16", start=16)
+
+
+class NSTDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="speech", version=VERSION, description="Data for speech recognition"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio": datasets.Audio(sampling_rate=44_100),
+                "pitch_tracker": datasets.Audio(sampling_rate=44_100),
+                "text": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_URL,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        if hasattr(dl_manager, 'manual_dir') and dl_manager.manual_dir is not None:
+            data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+            AUDIO_FILE = os.path.join(data_dir, _AUDIO_URL.split("/")[-1])
+            audio_dir = dl_manager.extract(AUDIO_FILE)
+        else:
+            audio_dir = dl_manager.download_and_extract(_AUDIO_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                    "audio_dir": audio_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, split, audio_dir
+    ):
+        filepath = Path(audio_dir) / "sw_pcms" / "mf"
+        textpath = Path(audio_dir) / "sw_pcms" / "scripts" / "mf" / "sw_all"
+        transcripts = {}
+        counter = 1
+        with open(str(textpath), encoding="latin1") as text:
+            for line in text.readlines():
+                line = line.strip()
+                if line in IGNORE_SENT:
+                    continue
+                else:
+                    id = f"sw_all_mf_01_{counter:04d}"
+                    if str(id) not in IGNORE_ID:
+                        transcripts[id] = line
+                    counter += 1
+        for file in filepath.glob("*.pcm"):
+            stem = file.stem
+            if is_pcm(str(file)):
+                data, _ = read_with_soundfile(str(file))
+                yield stem, {
+                    "audio": {
+                        "array": data[:, 1],
+                        "sampling_rate": 44_100,
+                        "path": str(file),
+                        "id": stem,
+                    },
+                    "pitch_tracker": {
+                        "array": data[:, 0],
+                        "sampling_rate": 44_100,
+                        "path": str(file),
+                        "id": stem,
+                    },
+                    "text": transcripts[stem],
+                }
+