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Model: arbml/wav2vec2-large-xlsr-53-arabic-egyptian
Source: Original Platform

dialects_speech_corpus.py

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+"""Arabic Speech Corpus"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import datasets
+
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """\
+
+
+```python
+import soundfile as sf
+
+def map_to_array(batch):
+    speech_array, _ = sf.read(batch["file"])
+    batch["speech"] = speech_array
+    return batch
+
+dataset = dataset.map(map_to_array, remove_columns=["file"])
+```
+"""
+
+_URL = "mgb3.zip"
+corrupt_files = ['familyKids_02_first_12min.wav','sports_04_first_12min.wav',
+'cooking_05_first_12min.wav', 'moviesDrama_07_first_12min.wav','science_06_first_12min.wav',
+'comedy_09_first_12min.wav','cultural_08_first_12min.wav','familyKids_11_first_12min.wav',
+'science_10_first_12min.wav']
+import soundfile as sf
+
+class EgyptianSpeechCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for EgyptianSpeechCorpus."""
+
+    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(EgyptianSpeechCorpusConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
+
+
+def map_to_array(batch):
+    start, stop = batch['segment'].split('_')
+    speech_array, _ = sf.read(batch["file"], start = start, stop = stop)
+    batch["speech"] = speech_array
+    return batch
+
+class EgyptionSpeechCorpus(datasets.GeneratorBasedBuilder):
+    """EgyptianSpeechCorpus dataset."""
+
+    BUILDER_CONFIGS = [
+        EgyptianSpeechCorpusConfig(name="clean", description="'Clean' speech."),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "file": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "segment": datasets.Value("string")
+                }
+            ),
+            supervised_keys=("file", "text"),
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        self.archive_path = '/content/mgb3'
+        return [
+            datasets.SplitGenerator(name="train", gen_kwargs={"archive_path": os.path.join(self.archive_path, "adapt")}),
+            datasets.SplitGenerator(name="dev", gen_kwargs={"archive_path": os.path.join(self.archive_path, "dev")}),
+            datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": os.path.join(self.archive_path, "test")}),
+        ]
+
+    def _generate_examples(self, archive_path):
+        """Generate examples from a Librispeech archive_path."""
+        text_dir = os.path.join(archive_path, "Alaa")
+        wav_dir = os.path.join(self.archive_path, "wav")
+        
+        segments_file = os.path.join(text_dir, "text_noverlap")
+
+        with open(segments_file, "r", encoding="utf-8") as f:
+            for _id, line in enumerate(f):
+                segment = line.split(' ')[0]
+                text = ' '.join(line.split(' ')[1:])
+                wav_file = '_'.join(segment.split('_')[:4]) +'.wav'
+                start, stop = segment.split('_')[4:6]
+                wav_path = os.path.join(wav_dir, wav_file) 
+                if (wav_file in corrupt_files) or (wav_file not in os.listdir(wav_dir)):
+                    continue
+                example = {
+                    "file": wav_path,
+                    "text": text,
+                    "segment":('_').join([start, stop])
+                }
+                yield str(_id), example
+