add initial version of script

waxholm.py

@@ -0,0 +1,270 @@
+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+# Copyright 2022 Jim O'Regan
+#
+# 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 Waxholm speech corpus"""
+
+from io import BytesIO
+import os
+import soundfile as sf
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+from datasets.features import Audio
+
+TRAIN_LIST = "alloktrainfiles"
+TEST_LIST = "testfiles"
+
+
+_DESCRIPTION = """\
+The Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH.
+"""
+
+
+_CITATION = """
+@article{bertenstam1995spoken,
+  title={Spoken dialogue data collected in the {W}axholm project},
+  author={Bertenstam, Johan and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
+  journal={STH-QPSR, KTH},
+  volume={1},
+  pages={49--74},
+  year={1995}
+}
+@inproceedings{bertenstam1995waxholm,
+  title={The {W}axholm application database.},
+  author={Bertenstam, J and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
+  booktitle={EUROSPEECH},
+  year={1995}
+}"""
+
+
+_URL = "http://www.speech.kth.se/waxholm/waxholm2.html"
+
+
+class WaxholmDataset(datasets.GeneratorBasedBuilder):
+    """Dataset script for Waxholm."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="waxholm"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16_000)
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[
+                AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="text")
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                    "files": TRAIN_LIST
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test",
+                    "files": TEST_LIST
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split, files):
+        with open(f"./waxholm/{files}") as input_file:
+            for line in input_file.readlines():
+                line = line.strip()
+                parts = line.split(".")
+                subdir = parts[0]
+                audio_file = f"./waxholm/scenes_formatted/{subdir}/{line}"
+                if not os.path.exists(audio_file):
+                    print(f"{audio_file} does not exist: skipping")
+                    continue
+                text_file = f"{audio_file}.mix"
+                if not os.path.exists(text_file):
+                    print(f"{text_file} does not exist: skipping")
+                    continue
+                mix = Mix(text_file)
+                samples, sr = smp_read_sf(audio_file)
+                buffer = BytesIO()
+                sf.write(buffer, samples, sr, format="wav")
+                blank = Audio()
+                audio_to_pass = blank.encode_example(value = {"bytes": buffer.getvalue(), "sampling_rate": sr, })
+                yield line, {
+                    "id": line,
+                    "text": mix.text,
+                    "audio": audio_to_pass
+                }
+
+
+def fix_text(text: str) -> str:
+    replacements = text.maketrans("{}|\\", "äåöÖ")
+    return text.translate(replacements)
+
+
+class FR:
+    def __init__(self, text: str):
+        if not text.startswith("FR"):
+            raise IOError("Unknown line type (does not begin with 'FR'): " + text)
+        parts = [a.strip() for a in text.split("\t")]
+        self.frame = parts[0][2:].strip()
+        if parts[-1].strip().endswith(" sec"):
+            self.seconds = parts[-1].strip()[0:-4]
+        for subpart in parts[1:-1]:
+            if subpart.startswith("$#"):
+                self.type = 'I'
+                self.phone_type = fix_text(subpart[0:2])
+                self.phone = fix_text(subpart[2:])
+            elif subpart.startswith("$"):
+                self.type = 'I'
+                self.phone_type = fix_text(subpart[0:2])
+                self.phone = fix_text(subpart[2:])
+            elif subpart.startswith("#"):
+                self.type = 'B'
+                self.phone_type = fix_text(subpart[0:2])
+                self.phone = fix_text(subpart[2:])
+            elif subpart.startswith(">pm "):
+                self.pm_type = fix_text(subpart[4:5])
+                self.pm = fix_text(subpart[5:])
+            elif subpart.startswith(">pm. "):
+                self.pm_type = fix_text(subpart[4:5])
+                self.pm = fix_text(subpart[5:])
+            elif subpart.startswith(">w "):
+                self.type = 'B'
+                self.word = fix_text(subpart[3:])
+                self.pseudoword = False
+            elif subpart.startswith(">w. "):
+                self.type = 'B'
+                self.word = fix_text(subpart[4:])
+                self.pseudoword = False
+            elif subpart.startswith("X"):
+                if hasattr(self, 'type'):
+                    print(self.type, self.type == 'B')
+                self.type = getattr(self, 'type', 'B')
+                self.word = fix_text(subpart)
+                self.pseudoword = True
+            elif subpart == "OK":
+                self.type = 'E'
+
+
+    def __repr__(self):
+        parts = []
+        parts.append(f"type: {self.type}")
+        parts.append(f"frame: {self.frame}")
+        if self.type != 'E':
+            parts.append(f"phone: {self.phone}")
+        if 'word' in self.__dict__:
+            parts.append(f"word: {self.word}")
+        if 'pm_type' in self.__dict__:
+            parts.append(f"pm_type: {self.pm_type}")
+        if 'pm' in self.__dict__:
+            parts.append(f"pm: {self.pm}")
+        parts.append(f"sec: {self.seconds}")
+        return f"FR(" + ", ".join(parts) + ")"
+
+
+class Mix():
+    def __init__(self, filepath: str):
+        self.fr = []
+        with open(filepath) as inpf:
+            saw_text = False
+            saw_phoneme = False
+            saw_labels = False
+            for line in inpf.readlines():
+                if line.startswith("Waxholm dialog."):
+                    self.filepath = line[15:].strip()
+                if line.startswith("TEXT:"):
+                    saw_text = True
+                    continue
+                if saw_text:
+                    self.text = fix_text(line.strip())
+                    saw_text = False
+                if line.startswith("FR "):
+                    if saw_labels:
+                        saw_labels = False
+                    self.fr.append(FR(line))
+                if line.startswith("Labels: "):
+                    self.labels = line[8:].strip()
+                    saw_labels = True
+                if saw_labels and line.startswith(" "):
+                    self.labels += line.strip()
+
+
+def smp_probe(filename: str) -> bool:
+    with open(filename, "rb") as f:
+        return f.read(9) == b"file=samp"
+
+
+def smp_headers(filename: str):
+    with open(filename, "rb") as f:
+        f.seek(0)
+        raw_headers = f.read(1024)
+        raw_headers = raw_headers.rstrip(b'\x00')
+        asc_headers = raw_headers.decode("ascii")
+        asc_headers.rstrip('\x00')
+        tmp = [a for a in asc_headers.split("\r\n")]
+        back = -1
+        while abs(back) > len(tmp) + 1:
+            if tmp[back] == '=':
+                break
+            back -= 1
+        tmp = tmp[0:back-1]
+        return dict(a.split("=") for a in tmp)
+
+
+def smp_read_sf(filename: str):
+    headers = smp_headers(filename)
+    if headers["msb"] == "last":
+        ENDIAN = "LITTLE"
+    else:
+        ENDIAN = "BIG"
+
+    data, sr = sf.read(filename, channels=int(headers["nchans"]),
+                       samplerate=16000, endian=ENDIAN, start=512,
+                       dtype="int16", format="RAW", subtype="PCM_16")
+    return (data, sr)
+
+
+def _write_wav(filename, arr):
+    import wave
+
+    with wave.open(filename, "w") as f:
+        f.setnchannels(1)
+        f.setsampwidth(2)
+        f.setframerate(16000)
+        f.writeframes(arr)
+
+
+#arr, sr = smp_read_sf("/Users/joregan/Playing/waxholm/scenes_formatted//fp2060/fp2060.pr.09.smp")
+#write_wav("out.wav", arr)