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https://github.com/huggingface/lerobot.git
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CarolinePascal
137 lines
3.8 KiB
Python
137 lines
3.8 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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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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import argparse
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from pathlib import Path
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import numpy as np
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import soundfile as sf
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from lerobot.microphones.anyskin import AnyskinSensorConfig
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from lerobot.microphones.configs import MicrophoneConfig
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from lerobot.microphones.utils import (
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async_microphones_start_recording,
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async_microphones_stop_recording,
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make_microphones_from_configs,
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)
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from lerobot.utils.robot_utils import (
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precise_sleep,
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)
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def main(
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sensors_configs: dict[str, MicrophoneConfig],
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multiprocessing: bool = False,
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):
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recording_dir = Path("outputs/tactile_benchmark")
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recording_dir.mkdir(parents=True, exist_ok=True)
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# Create microphones
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sensors = make_microphones_from_configs(sensors_configs)
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# Connect microphones
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for sensor in sensors.values():
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sensor.connect()
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# Create audio chunks
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data_chunks = {}
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for sensor_key in sensors:
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data_chunks.update({sensor_key: []})
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# Start recording
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async_microphones_start_recording(
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sensors,
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output_files=[recording_dir / f"{sensor_key}_recording.wav" for sensor_key in sensors],
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multiprocessing=multiprocessing,
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)
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# Record audio chunks
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precise_sleep(10.0)
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for sensor_key, sensor in sensors.items():
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data_chunk = sensor.read()
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print(f"{sensor_key} - samples {data_chunk.shape[0]}")
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data_chunks[sensor_key].append(data_chunk)
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# Stop recording
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async_microphones_stop_recording(sensors)
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for sensor_key in sensors:
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data_chunks[sensor_key] = np.concatenate(data_chunks[sensor_key], axis=0)
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# Disconnect microphones
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for sensor in sensors.values():
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sensor.disconnect()
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for sensor_key in sensors:
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data, sample_rate = sf.read(recording_dir / f"{sensor_key}_recording.wav")
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print(f"{sensor_key} - samples {data.shape[0]}")
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print(f"{sensor_key} - sample rate {sample_rate}")
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print(f"{sensor_key} - data {data}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--sensors_ports",
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type=str,
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nargs="+",
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)
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parser.add_argument(
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"--sensors_baud_rate",
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type=int,
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nargs="+",
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)
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parser.add_argument(
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"--sensors_sample_rate",
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type=int,
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nargs="+",
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)
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parser.add_argument(
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"--sensors_channels",
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type=int,
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nargs="+",
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)
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parser.add_argument(
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"--multiprocessing",
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action="store_true",
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)
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args = vars(parser.parse_args())
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args["sensors_configs"] = {}
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for port, baud_rate, sample_rate, channels in zip(
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args["sensors_ports"],
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args["sensors_baud_rate"],
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args["sensors_sample_rate"],
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args["sensors_channels"],
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strict=False,
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):
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channels = [1, 2, 3, 4, 5]
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sensor_config = AnyskinSensorConfig(
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sensor_port=port,
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baud_rate=baud_rate,
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sample_rate=sample_rate,
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channels=channels,
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)
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args["sensors_configs"].update({f"sensor_{port}": sensor_config})
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args.pop("sensors_ports")
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args.pop("sensors_baud_rate")
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args.pop("sensors_sample_rate")
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args.pop("sensors_channels")
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main(**args)
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