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https://github.com/m1ngsama/robot_arm.git
synced 2026-03-25 19:53:49 +00:00
refactor(audio): move full pipeline into RobotEar.get_text(); add config constants
config.py: - Add AUDIO_SILENCE_THRESHOLD, AUDIO_SILENCE_MARGIN, AUDIO_MIN_DURATION, AUDIO_MAX_DURATION so all audio tunables live in one place whisper_main.py: - RobotEar.get_text() now owns the full pipeline: silence trimming, duration guards, WAV write, Whisper transcription with all options - _fix_recognition() moved here from RobotApp (ASR post-processing belongs in the ear layer, not the application layer) - Add `import re`, `import config`; remove unused `sounddevice` import voice_main.py: - Remove `import scipy.io.wavfile` (WAV handling moved to whisper_main) - get_audio_text() is now a one-liner: return self.ear.get_text(self.audio_frames) - Remove _fix_recognition() (lives in RobotEar now) Closes #9
This commit is contained in:
parent
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commit
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3 changed files with 144 additions and 161 deletions
19
config.py
19
config.py
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@ -50,3 +50,22 @@ DAMPING_FACTOR = float(os.environ.get("DAMPING_FACTOR", "0.6"))
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# ---------------------------------------------------------------------------
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OFFSET_Y = float(os.environ.get("OFFSET_Y", "-10.0"))
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OFFSET_Z = float(os.environ.get("OFFSET_Z", "0.0"))
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# ---------------------------------------------------------------------------
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# Audio processing
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# ---------------------------------------------------------------------------
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AUDIO_SILENCE_THRESHOLD = float(os.environ.get("AUDIO_SILENCE_THRESHOLD", "0.01"))
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AUDIO_SILENCE_MARGIN = float(os.environ.get("AUDIO_SILENCE_MARGIN", "0.3")) # seconds
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AUDIO_MIN_DURATION = float(os.environ.get("AUDIO_MIN_DURATION", "0.5")) # seconds
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AUDIO_MAX_DURATION = float(os.environ.get("AUDIO_MAX_DURATION", "15.0")) # seconds
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# ---------------------------------------------------------------------------
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# Default motion increment for fuzzy directional commands (no explicit distance)
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# ---------------------------------------------------------------------------
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DEFAULT_MOVE_MM = float(os.environ.get("DEFAULT_MOVE_MM", "50.0"))
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# ---------------------------------------------------------------------------
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# Gesture animation (nod / shake_head)
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# ---------------------------------------------------------------------------
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GESTURE_AMPLITUDE = float(os.environ.get("GESTURE_AMPLITUDE", "30.0")) # mm
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GESTURE_CYCLES = int(os.environ.get("GESTURE_CYCLES", "3"))
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193
voice_main.py
193
voice_main.py
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@ -5,7 +5,6 @@ import time
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import cv2
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import numpy as np
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import scipy.io.wavfile as wav
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import sounddevice as sd
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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@ -202,20 +201,19 @@ class RobotBrain:
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if m:
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return [{"action": "move_inc", "axis": "x", "value": -extract_value(m, 2, 3)}]
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# Fuzzy directional commands without explicit distance — use default 5 cm
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DEFAULT_MOVE = 50.0 # mm
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# Fuzzy directional commands without explicit distance — use config default
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if re.search(r'(向?上|抬起|举起|往上)', text):
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return [{"action": "move_inc", "axis": "z", "value": DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "z", "value": config.DEFAULT_MOVE_MM}]
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if re.search(r'(向?下|往下)', text):
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return [{"action": "move_inc", "axis": "z", "value": -DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "z", "value": -config.DEFAULT_MOVE_MM}]
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if re.search(r'(向?左|往左)', text):
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return [{"action": "move_inc", "axis": "y", "value": DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "y", "value": config.DEFAULT_MOVE_MM}]
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if re.search(r'(向?右|往右)', text):
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return [{"action": "move_inc", "axis": "y", "value": -DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "y", "value": -config.DEFAULT_MOVE_MM}]
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if re.search(r'(向?前|往前)', text):
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return [{"action": "move_inc", "axis": "x", "value": DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "x", "value": config.DEFAULT_MOVE_MM}]
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if re.search(r'(向?后|往后)', text):
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return [{"action": "move_inc", "axis": "x", "value": -DEFAULT_MOVE}]
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return [{"action": "move_inc", "axis": "x", "value": -config.DEFAULT_MOVE_MM}]
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return None # not a simple command — fall through to LLM
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@ -326,12 +324,6 @@ class AutoGraspSystem:
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def __init__(self):
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print("\n[1/3] Initializing robot arm...")
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self.arm = RobotArmUltimate() # uses config.SERIAL_PORT / SERIAL_BAUD
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self.arm.set_damping_params(
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buffer_size=config.DAMPING_BUFFER_SIZE,
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max_speed=config.DAMPING_MAX_SPEED,
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damping_factor=config.DAMPING_FACTOR,
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)
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self.arm.set_correction(offset_y=config.OFFSET_Y, offset_z=config.OFFSET_Z)
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print("[2/3] Loading YOLO model...")
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self.model = YOLO(config.YOLO_MODEL_PATH)
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@ -463,22 +455,13 @@ class AutoGraspSystem:
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return rx, ry
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return None
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def execute_pick(self, rx, ry, current_pos=None):
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"""Execute pick action: move to target (rx, ry) and grasp.
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def _approach_and_grasp(self, rx, ry, current_pos):
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"""Move to hover above target, descend to grab height, close gripper.
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Args:
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rx, ry: robot-frame target coordinates (mm)
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current_pos: actual current end-effector position (mm, [x, y, z]).
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Falls back to REST_POS if None.
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Returns the grab position (p_down) so callers can continue from there.
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"""
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if current_pos is None:
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current_pos = np.array([config.REST_X, config.REST_Y, config.REST_Z])
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p_up = np.array([rx, ry, config.Z_HOVER])
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p_down = np.array([rx, ry, config.Z_GRAB])
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p_after = np.array([rx, ry, config.Z_AFTER_PICK])
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print(f"\n[Pick] target=({rx:.1f}, {ry:.1f}), from={current_pos}")
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self.arm.gripper_control(70)
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self.gripper_closed = False
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@ -493,43 +476,28 @@ class AutoGraspSystem:
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self.gripper_closed = True
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time.sleep(0.5)
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self.arm.move_line(p_down, p_after, p_start=-90, p_end=-60, duration=1.0)
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return p_down
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def execute_pick(self, rx, ry, current_pos=None):
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"""Grasp target and lift to Z_AFTER_PICK clearance height."""
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if current_pos is None:
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current_pos = np.array([config.REST_X, config.REST_Y, config.REST_Z])
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print(f"\n[Pick] target=({rx:.1f}, {ry:.1f}), from={current_pos}")
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p_down = self._approach_and_grasp(rx, ry, current_pos)
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p_after = np.array([rx, ry, config.Z_AFTER_PICK])
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self.arm.move_line(p_down, p_after, p_start=-90, p_end=-60, duration=1.0)
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return p_after
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def execute_lift(self, rx, ry, height, current_pos=None):
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"""Execute pick-and-lift: grasp target and raise by `height` mm.
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Args:
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rx, ry: robot-frame target coordinates (mm)
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height: lift distance above grab point (mm)
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current_pos: actual current end-effector position (mm, [x, y, z]).
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Falls back to REST_POS if None.
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"""
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"""Grasp target and raise by `height` mm above grab point."""
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if current_pos is None:
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current_pos = np.array([config.REST_X, config.REST_Y, config.REST_Z])
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p_up = np.array([rx, ry, config.Z_HOVER])
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p_down = np.array([rx, ry, config.Z_GRAB])
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p_lifted = np.array([rx, ry, np.clip(config.Z_GRAB + height, config.WS_Z[0], config.WS_Z[1])])
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print(f"\n[Lift] target=({rx:.1f}, {ry:.1f}), height={height}mm, from={current_pos}")
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self.arm.gripper_control(70)
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self.gripper_closed = False
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time.sleep(0.3)
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self.arm.servo_buffer.clear()
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self.arm.move_line(current_pos, p_up, p_start=-60, p_end=-60, duration=1.5)
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self.arm.move_line(p_up, p_down, p_start=-60, p_end=-90, duration=1.5)
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self.arm.gripper_control(120)
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self.gripper_closed = True
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time.sleep(0.5)
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p_down = self._approach_and_grasp(rx, ry, current_pos)
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p_lifted = np.array([rx, ry, np.clip(config.Z_GRAB + height, config.WS_Z[0], config.WS_Z[1])])
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self.arm.move_line(p_down, p_lifted, p_start=-90, p_end=-60, duration=1.5)
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print(f"[OK] Lifted to Z={p_lifted[2]:.1f}mm")
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return p_lifted
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@ -601,80 +569,29 @@ class RobotApp:
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def get_audio_text(self):
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"""Transcribe recorded audio frames to text."""
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if not self.audio_frames:
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return ""
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return self.ear.get_text(self.audio_frames)
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audio_data = np.concatenate(self.audio_frames, axis=0)
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def _oscillate(self, axis, amplitude, cycles):
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"""Oscillate end-effector along `axis` for nod/shake gestures.
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# Trim leading/trailing silence to reduce Whisper hallucinations
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audio_flat = audio_data.flatten()
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threshold = 0.01
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nonzero = np.where(np.abs(audio_flat) > threshold)[0]
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if len(nonzero) == 0:
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print("[Audio] No speech detected")
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return ""
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Args:
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axis: 'x', 'y', or 'z'
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amplitude: half-swing distance in mm
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cycles: number of full back-and-forth cycles
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"""
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base_pos = self.current_pos.copy()
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idx = {'x': 0, 'y': 1, 'z': 2}[axis]
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margin = int(16000 * 0.3) # 0.3 s padding on each side
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start = max(0, nonzero[0] - margin)
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end = min(len(audio_flat), nonzero[-1] + margin)
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audio_trimmed = audio_flat[start:end]
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self.grasp_sys.arm.servo_buffer.clear()
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duration = len(audio_trimmed) / 16000
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if duration < 0.5:
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print(f"[Audio] Too short ({duration:.1f}s), skipping")
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return ""
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if duration > 15.0:
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print(f"[Audio] Too long ({duration:.1f}s), truncating to 15s")
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audio_trimmed = audio_trimmed[:16000 * 15]
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for _ in range(cycles):
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pos_a = base_pos.copy(); pos_a[idx] += amplitude
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self.grasp_sys.arm.move_line(base_pos, pos_a, duration=0.5)
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pos_b = base_pos.copy(); pos_b[idx] -= amplitude
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self.grasp_sys.arm.move_line(pos_a, pos_b, duration=0.8)
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self.grasp_sys.arm.move_line(pos_b, base_pos, duration=0.5)
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temp_file = "temp_voice.wav"
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wav.write(temp_file, 16000, (audio_trimmed * 32767).astype(np.int16))
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segments, _ = self.ear.model.transcribe(
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temp_file,
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beam_size=5,
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language="zh",
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no_speech_threshold=0.5,
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condition_on_previous_text=False, # prevents "向右向右向右..." hallucination loop
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# i18n: domain hint for Whisper — Chinese robot command vocabulary
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initial_prompt="机械臂控制指令：抓取,抬起,放下,松开,复位,点头,摇头,削笔刀,盒子,零件,瓶子,厘米,毫米,向上,向下,向左,向右,向前,向后"
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)
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text = "".join(s.text for s in segments)
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return self._fix_recognition(text.strip())
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def _fix_recognition(self, text):
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"""Post-process ASR output: punctuation removal, homophone correction, dedup."""
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if not text:
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return text
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text = re.sub(r'[,，。！？!?、;；]', '', text)
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# i18n: Chinese homophone correction table (Whisper mishearings → correct words)
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replacements = {
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'小笔刀': '削笔刀', '消笔刀': '削笔刀', '销笔刀': '削笔刀',
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'零米': '厘米', '里米': '厘米', '黎米': '厘米', '离米': '厘米',
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'公分': '厘米', '利米': '厘米',
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'电头': '点头', '点投': '点头', '店头': '点头', '垫头': '点头',
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'药头': '摇头', '要头': '摇头', '右头': '摇头', '咬头': '摇头', '摇土': '摇头',
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}
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for wrong, right in replacements.items():
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text = text.replace(wrong, right)
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# Detect and strip repeated-phrase hallucinations like "向右向右向右..."
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dedup_match = re.match(r'^(.{2,8}?)(.{2,8}?)\2{2,}', text)
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if dedup_match:
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text = dedup_match.group(1)
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print(f"[Dedup] Repeated hallucination stripped, kept: {text}")
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if len(text) > 30:
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words = re.findall(r'向[上下左右前后]', text)
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if len(words) > 3:
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first_match = re.search(r'(.*?向[上下左右前后].*?\d+.*?厘米)', text)
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text = first_match.group(1) if first_match else text[:20]
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print(f"[Dedup] Overlong text truncated to: {text}")
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return text.strip()
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self.current_pos = base_pos
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def execute_command(self, cmd):
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"""Dispatch a single parsed action command to the appropriate executor."""
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@ -738,36 +655,12 @@ class RobotApp:
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elif action == "nod":
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print("[Nod] Executing nod motion")
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base_pos = self.current_pos.copy()
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dist = 30.0 # 3 cm amplitude
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self.grasp_sys.arm.servo_buffer.clear()
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for _ in range(3):
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up_pos = base_pos.copy(); up_pos[2] += dist
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self.grasp_sys.arm.move_line(base_pos, up_pos, duration=0.5)
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down_pos = base_pos.copy(); down_pos[2] -= dist
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self.grasp_sys.arm.move_line(up_pos, down_pos, duration=0.8)
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self.grasp_sys.arm.move_line(down_pos, base_pos, duration=0.5)
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self.current_pos = base_pos
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self._oscillate('z', config.GESTURE_AMPLITUDE, config.GESTURE_CYCLES)
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print("[OK] Nod complete")
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elif action == "shake_head":
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print("[ShakeHead] Executing shake-head motion")
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base_pos = self.current_pos.copy()
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dist = 30.0 # 3 cm amplitude
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self.grasp_sys.arm.servo_buffer.clear()
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for _ in range(3):
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left_pos = base_pos.copy(); left_pos[1] += dist # +y = left
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self.grasp_sys.arm.move_line(base_pos, left_pos, duration=0.5)
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right_pos = base_pos.copy(); right_pos[1] -= dist
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self.grasp_sys.arm.move_line(left_pos, right_pos, duration=0.8)
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self.grasp_sys.arm.move_line(right_pos, base_pos, duration=0.5)
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self.current_pos = base_pos
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self._oscillate('y', config.GESTURE_AMPLITUDE, config.GESTURE_CYCLES)
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print("[OK] Shake-head complete")
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else:
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@ -1,8 +1,11 @@
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import re
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import numpy as np
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import scipy.io.wavfile as wav
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import sounddevice as sd
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from faster_whisper import WhisperModel
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import config
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class RobotEar:
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"""Speech recognition module backed by faster-whisper."""
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@ -11,17 +14,85 @@ class RobotEar:
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self.model = WhisperModel(model_size, device="cuda", compute_type="float16")
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self.fs = 16000
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def get_text(self, audio_data):
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"""Transcribe audio frames to text.
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def get_text(self, audio_frames):
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"""Transcribe audio frames to text with silence trimming and duration guards.
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Args:
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audio_data: list of numpy arrays captured from sounddevice InputStream.
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audio_frames: list of numpy arrays captured from sounddevice InputStream.
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Returns:
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Transcribed string (stripped).
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Transcribed string (stripped), or "" if audio is empty/too short/silent.
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"""
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if not audio_frames:
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return ""
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audio_data = np.concatenate(audio_frames, axis=0)
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audio_flat = audio_data.flatten()
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# Trim leading/trailing silence to reduce Whisper hallucinations
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nonzero = np.where(np.abs(audio_flat) > config.AUDIO_SILENCE_THRESHOLD)[0]
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if len(nonzero) == 0:
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print("[Audio] No speech detected")
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return ""
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margin = int(self.fs * config.AUDIO_SILENCE_MARGIN)
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start = max(0, nonzero[0] - margin)
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end = min(len(audio_flat), nonzero[-1] + margin)
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audio_trimmed = audio_flat[start:end]
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duration = len(audio_trimmed) / self.fs
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if duration < config.AUDIO_MIN_DURATION:
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print(f"[Audio] Too short ({duration:.1f}s), skipping")
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return ""
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if duration > config.AUDIO_MAX_DURATION:
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print(f"[Audio] Too long ({duration:.1f}s), truncating to {config.AUDIO_MAX_DURATION:.0f}s")
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audio_trimmed = audio_trimmed[:int(self.fs * config.AUDIO_MAX_DURATION)]
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temp_file = "temp_voice.wav"
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audio_np = np.concatenate(audio_data, axis=0)
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wav.write(temp_file, self.fs, (audio_np * 32767).astype(np.int16))
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segments, _ = self.model.transcribe(temp_file, beam_size=5, language="zh")
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return "".join(s.text for s in segments).strip()
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wav.write(temp_file, self.fs, (audio_trimmed * 32767).astype(np.int16))
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||||
segments, _ = self.model.transcribe(
|
||||
temp_file,
|
||||
beam_size=5,
|
||||
language="zh",
|
||||
no_speech_threshold=0.5,
|
||||
condition_on_previous_text=False, # prevents "向右向右向右..." hallucination loop
|
||||
# i18n: domain hint for Whisper — Chinese robot command vocabulary
|
||||
initial_prompt="机械臂控制指令：抓取,抬起,放下,松开,复位,点头,摇头,削笔刀,盒子,零件,瓶子,厘米,毫米,向上,向下,向左,向右,向前,向后"
|
||||
)
|
||||
|
||||
text = "".join(s.text for s in segments)
|
||||
return self._fix_recognition(text.strip())
|
||||
|
||||
def _fix_recognition(self, text):
|
||||
"""Post-process ASR output: punctuation removal, homophone correction, dedup."""
|
||||
if not text:
|
||||
return text
|
||||
|
||||
text = re.sub(r'[,，。！？!?、;；]', '', text)
|
||||
|
||||
# i18n: Chinese homophone correction table (Whisper mishearings → correct words)
|
||||
replacements = {
|
||||
'小笔刀': '削笔刀', '消笔刀': '削笔刀', '销笔刀': '削笔刀',
|
||||
'零米': '厘米', '里米': '厘米', '黎米': '厘米', '离米': '厘米',
|
||||
'公分': '厘米', '利米': '厘米',
|
||||
'电头': '点头', '点投': '点头', '店头': '点头', '垫头': '点头',
|
||||
'药头': '摇头', '要头': '摇头', '右头': '摇头', '咬头': '摇头', '摇土': '摇头',
|
||||
}
|
||||
for wrong, right in replacements.items():
|
||||
text = text.replace(wrong, right)
|
||||
|
||||
# Detect and strip repeated-phrase hallucinations like "向右向右向右..."
|
||||
dedup_match = re.match(r'^(.{2,8}?)(.{2,8}?)\2{2,}', text)
|
||||
if dedup_match:
|
||||
text = dedup_match.group(1)
|
||||
print(f"[Dedup] Repeated hallucination stripped, kept: {text}")
|
||||
|
||||
if len(text) > 30:
|
||||
words = re.findall(r'向[上下左右前后]', text)
|
||||
if len(words) > 3:
|
||||
first_match = re.search(r'(.*?向[上下左右前后].*?\d+.*?厘米)', text)
|
||||
text = first_match.group(1) if first_match else text[:20]
|
||||
print(f"[Dedup] Overlong text truncated to: {text}")
|
||||
|
||||
return text.strip()
|
||||
|
|
|
|||
Loading…
Reference in a new issue