Datasets:
Tasks:
Automatic Speech Recognition
Languages:
Swedish
add initial version of script
Browse files- waxholm.py +270 -0
waxholm.py
ADDED
@@ -0,0 +1,270 @@
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1 |
+
# coding=utf-8
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# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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# Copyright 2022 Jim O'Regan
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Datasets loader for Waxholm speech corpus"""
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from io import BytesIO
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import os
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import soundfile as sf
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import datasets
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from datasets.tasks import AutomaticSpeechRecognition
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from datasets.features import Audio
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TRAIN_LIST = "alloktrainfiles"
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TEST_LIST = "testfiles"
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+
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+
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_DESCRIPTION = """\
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The Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH.
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"""
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+
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_CITATION = """
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@article{bertenstam1995spoken,
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title={Spoken dialogue data collected in the {W}axholm project},
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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},
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journal={STH-QPSR, KTH},
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volume={1},
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pages={49--74},
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year={1995}
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}
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@inproceedings{bertenstam1995waxholm,
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title={The {W}axholm application database.},
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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},
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booktitle={EUROSPEECH},
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year={1995}
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}"""
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+
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_URL = "http://www.speech.kth.se/waxholm/waxholm2.html"
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class WaxholmDataset(datasets.GeneratorBasedBuilder):
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"""Dataset script for Waxholm."""
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VERSION = datasets.Version("1.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="waxholm"),
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]
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def _info(self):
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"text": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000)
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_URL,
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citation=_CITATION,
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task_templates=[
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AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="text")
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],
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)
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def _split_generators(self, dl_manager):
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"split": "train",
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"files": TRAIN_LIST
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"split": "test",
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"files": TEST_LIST
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},
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),
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]
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def _generate_examples(self, split, files):
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with open(f"./waxholm/{files}") as input_file:
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for line in input_file.readlines():
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line = line.strip()
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parts = line.split(".")
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subdir = parts[0]
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audio_file = f"./waxholm/scenes_formatted/{subdir}/{line}"
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if not os.path.exists(audio_file):
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print(f"{audio_file} does not exist: skipping")
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continue
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text_file = f"{audio_file}.mix"
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if not os.path.exists(text_file):
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print(f"{text_file} does not exist: skipping")
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continue
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mix = Mix(text_file)
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samples, sr = smp_read_sf(audio_file)
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buffer = BytesIO()
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sf.write(buffer, samples, sr, format="wav")
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blank = Audio()
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audio_to_pass = blank.encode_example(value = {"bytes": buffer.getvalue(), "sampling_rate": sr, })
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yield line, {
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"id": line,
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"text": mix.text,
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"audio": audio_to_pass
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}
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def fix_text(text: str) -> str:
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replacements = text.maketrans("{}|\\", "äåöÖ")
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return text.translate(replacements)
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class FR:
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def __init__(self, text: str):
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if not text.startswith("FR"):
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raise IOError("Unknown line type (does not begin with 'FR'): " + text)
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parts = [a.strip() for a in text.split("\t")]
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self.frame = parts[0][2:].strip()
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if parts[-1].strip().endswith(" sec"):
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self.seconds = parts[-1].strip()[0:-4]
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for subpart in parts[1:-1]:
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if subpart.startswith("$#"):
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self.type = 'I'
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self.phone_type = fix_text(subpart[0:2])
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self.phone = fix_text(subpart[2:])
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elif subpart.startswith("$"):
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self.type = 'I'
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self.phone_type = fix_text(subpart[0:2])
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self.phone = fix_text(subpart[2:])
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elif subpart.startswith("#"):
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self.type = 'B'
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self.phone_type = fix_text(subpart[0:2])
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self.phone = fix_text(subpart[2:])
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elif subpart.startswith(">pm "):
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self.pm_type = fix_text(subpart[4:5])
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self.pm = fix_text(subpart[5:])
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elif subpart.startswith(">pm. "):
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self.pm_type = fix_text(subpart[4:5])
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self.pm = fix_text(subpart[5:])
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elif subpart.startswith(">w "):
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self.type = 'B'
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self.word = fix_text(subpart[3:])
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self.pseudoword = False
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elif subpart.startswith(">w. "):
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self.type = 'B'
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self.word = fix_text(subpart[4:])
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self.pseudoword = False
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elif subpart.startswith("X"):
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if hasattr(self, 'type'):
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print(self.type, self.type == 'B')
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self.type = getattr(self, 'type', 'B')
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self.word = fix_text(subpart)
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self.pseudoword = True
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elif subpart == "OK":
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self.type = 'E'
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def __repr__(self):
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parts = []
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parts.append(f"type: {self.type}")
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parts.append(f"frame: {self.frame}")
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if self.type != 'E':
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parts.append(f"phone: {self.phone}")
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if 'word' in self.__dict__:
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parts.append(f"word: {self.word}")
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if 'pm_type' in self.__dict__:
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parts.append(f"pm_type: {self.pm_type}")
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if 'pm' in self.__dict__:
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parts.append(f"pm: {self.pm}")
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parts.append(f"sec: {self.seconds}")
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return f"FR(" + ", ".join(parts) + ")"
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class Mix():
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def __init__(self, filepath: str):
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self.fr = []
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with open(filepath) as inpf:
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saw_text = False
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saw_phoneme = False
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saw_labels = False
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for line in inpf.readlines():
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if line.startswith("Waxholm dialog."):
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self.filepath = line[15:].strip()
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if line.startswith("TEXT:"):
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saw_text = True
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continue
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if saw_text:
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self.text = fix_text(line.strip())
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saw_text = False
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if line.startswith("FR "):
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if saw_labels:
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saw_labels = False
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self.fr.append(FR(line))
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if line.startswith("Labels: "):
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self.labels = line[8:].strip()
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saw_labels = True
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if saw_labels and line.startswith(" "):
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self.labels += line.strip()
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+
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+
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def smp_probe(filename: str) -> bool:
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with open(filename, "rb") as f:
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return f.read(9) == b"file=samp"
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+
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+
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def smp_headers(filename: str):
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with open(filename, "rb") as f:
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f.seek(0)
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raw_headers = f.read(1024)
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raw_headers = raw_headers.rstrip(b'\x00')
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asc_headers = raw_headers.decode("ascii")
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asc_headers.rstrip('\x00')
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tmp = [a for a in asc_headers.split("\r\n")]
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back = -1
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while abs(back) > len(tmp) + 1:
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if tmp[back] == '=':
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break
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back -= 1
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tmp = tmp[0:back-1]
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return dict(a.split("=") for a in tmp)
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244 |
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245 |
+
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def smp_read_sf(filename: str):
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headers = smp_headers(filename)
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248 |
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if headers["msb"] == "last":
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ENDIAN = "LITTLE"
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else:
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ENDIAN = "BIG"
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+
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data, sr = sf.read(filename, channels=int(headers["nchans"]),
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samplerate=16000, endian=ENDIAN, start=512,
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dtype="int16", format="RAW", subtype="PCM_16")
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return (data, sr)
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+
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+
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def _write_wav(filename, arr):
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import wave
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with wave.open(filename, "w") as f:
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f.setnchannels(1)
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f.setsampwidth(2)
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f.setframerate(16000)
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f.writeframes(arr)
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#arr, sr = smp_read_sf("/Users/joregan/Playing/waxholm/scenes_formatted//fp2060/fp2060.pr.09.smp")
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#write_wav("out.wav", arr)
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