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https://github.com/huggingface/lerobot.git
synced 2026-05-21 11:39:50 +00:00
Adding audio tests
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Vendored
+13
@@ -40,5 +40,18 @@ DUMMY_VIDEO_INFO = {
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"video.is_depth_map": False,
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"has_audio": False,
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}
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DUMMY_MICROPHONE_FEATURES = {
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"laptop": {"dtype": "audio", "shape": (1,), "names": ["channels"], "info": None},
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"phone": {"dtype": "audio", "shape": (1,), "names": ["channels"], "info": None},
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}
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DEFAULT_SAMPLE_RATE = 48000
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DUMMY_AUDIO_CHANNELS = 2
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DUMMY_AUDIO_INFO = {
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"has_audio": True,
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"audio.sample_rate": DEFAULT_SAMPLE_RATE,
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"audio.codec": "aac",
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"audio.channels": DUMMY_AUDIO_CHANNELS,
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"audio.channel_layout": "stereo",
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}
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DUMMY_CHW = (3, 96, 128)
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DUMMY_HWC = (96, 128, 3)
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Vendored
+13
-1
@@ -43,6 +43,7 @@ from lerobot.datasets.video_utils import encode_video_frames
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from tests.fixtures.constants import (
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DEFAULT_FPS,
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DUMMY_CAMERA_FEATURES,
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DUMMY_MICROPHONE_FEATURES,
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DUMMY_MOTOR_FEATURES,
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DUMMY_REPO_ID,
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DUMMY_ROBOT_TYPE,
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@@ -131,6 +132,7 @@ def features_factory():
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def _create_features(
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motor_features: dict = DUMMY_MOTOR_FEATURES,
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camera_features: dict = DUMMY_CAMERA_FEATURES,
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audio_features: dict = DUMMY_MICROPHONE_FEATURES,
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use_videos: bool = True,
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) -> dict:
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if use_videos:
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@@ -142,6 +144,7 @@ def features_factory():
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return {
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**motor_features,
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**camera_ft,
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**audio_features,
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**DEFAULT_FEATURES,
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}
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@@ -166,9 +169,10 @@ def info_factory(features_factory):
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audio_path: str = DEFAULT_AUDIO_PATH,
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motor_features: dict = DUMMY_MOTOR_FEATURES,
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camera_features: dict = DUMMY_CAMERA_FEATURES,
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audio_features: dict = DUMMY_MICROPHONE_FEATURES,
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use_videos: bool = True,
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) -> dict:
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features = features_factory(motor_features, camera_features, use_videos)
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features = features_factory(motor_features, camera_features, audio_features, use_videos)
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return {
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"codebase_version": codebase_version,
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"robot_type": robot_type,
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@@ -207,6 +211,14 @@ def stats_factory():
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"std": np.full((3, 1, 1), 0.25, dtype=np.float32).tolist(),
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"count": [10],
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}
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elif dtype == "audio":
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stats[key] = {
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"mean": np.full((shape[0],), 0.0, dtype=np.float32).tolist(),
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"max": np.full((shape[0],), 1, dtype=np.float32).tolist(),
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"min": np.full((shape[0],), -1, dtype=np.float32).tolist(),
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"std": np.full((shape[0],), 0.5, dtype=np.float32).tolist(),
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"count": [10],
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}
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else:
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stats[key] = {
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"max": np.full(shape, 1, dtype=dtype).tolist(),
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