Create speech_segment.py

speech_segment.py

@@ -0,0 +1,101 @@
+# Lint as: python3
+"""Speech Segment dataset.
+"""
+import os
+from pathlib import Path
+
+import datasets
+import torchaudio
+
+
+class SpeechSegmentConfig(datasets.BuilderConfig):
+    """BuilderConfig for Speech Segment.
+    For long audio files, segment them into smaller segments of fixed length.
+    For short audio files, return the whole audio file.
+    """
+
+    def __init__(self, segment_length, **kwargs):
+        super(SpeechSegmentConfig, self).__init__(**kwargs)
+        self.segment_length = segment_length
+
+
+class SpeechSegment(datasets.GeneratorBasedBuilder):
+    """Speech Segment dataset."""
+
+    BUILDER_CONFIGS = [
+        SpeechSegmentConfig(name="all", segment_length=60.0,),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return (
+            "Specify the data_dir as the path to the folder, will recursively search for .flac and .wav files. "
+            "`datasets.load_dataset('subatomicseer/speech_segment', data_dir='path/to/folder/folder_name')`"
+        )
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "file": datasets.Value("string"),
+                'sample_rate': datasets.Value('int64'),
+                'offset': datasets.Value('int64'),
+                'num_frames': datasets.Value('int64'),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            features=features,
+        )
+
+    def _split_generators(self, dl_manager):
+        base_data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        if not os.path.exists(base_data_dir):
+            raise FileNotFoundError(
+                f"{base_data_dir} does not exist. Manual download instructions: {self.manual_download_instructions}"
+            )
+
+        data_dirs = [str(p) for p in Path(base_data_dir).rglob('*') if p.suffix in ['.flac', '.wav']]
+        print(f"Found {len(data_dirs)} audio files in {base_data_dir}")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dirs": data_dirs},
+            ),
+        ]
+
+    def _generate_examples(self, data_dirs):
+        for key, path in enumerate(data_dirs):
+            path_split = path.split("/")
+            id_ = '/'.join(path_split[-4:]).replace(".flac", "")
+
+            audio_metadata = torchaudio.info(path)
+            segment_length = int(self.config.segment_length * audio_metadata.sample_rate)
+            total_length = audio_metadata.num_frames
+
+            if total_length <= segment_length:
+                yield id_, {
+                    "id": id_,
+                    "file": path,
+                    'sample_rate': audio_metadata.sample_rate,
+                    'offset': 0,
+                    'num_frames': total_length,
+                }
+            else:
+                # generate non-overlapping segments of segment_length
+                offsets = list(range(0, total_length, segment_length))
+                if total_length - offsets[-1] < 1 * audio_metadata.sample_rate:
+                    # if the last segment is less than 2 seconds, discard it
+                    offsets.pop()
+
+                for segment_id, start in enumerate(offsets):
+                    end = start + segment_length - 1
+                    if end > total_length:
+                        end = total_length
+                    yield f'{id_}_{segment_id}', {
+                        "id": f'{id_}_{segment_id}',
+                        "file": path,
+                        'sample_rate': audio_metadata.sample_rate,
+                        'offset': start,
+                        'num_frames': end-start+1,
+                    }