Small update

.gitignore

@@ -4,7 +4,8 @@ operators/__pycache__/
 __pycache__/
 *.avi
 *.txt
-
+*.wav
+*.mp3
 
 ## TODO:
 - [ ] Make human direct using voice

graphs/dataflow_vlm_basic.yml

@@ -30,6 +30,7 @@ nodes:
       args: ../operators/robot_minimize.py 
       inputs:
         control: idefics2/control
+        led: reload/led
 
   - id: webcam
     custom:
@@ -51,4 +52,12 @@ nodes:
       inputs:
         text: 
           source: idefics2/speak
-          queue_size: 1
+          queue_size: 1
+  
+  - id: reload
+    operator:
+      python: ../operators/reload.py
+      inputs: 
+        image: dora/timer/millis/500
+      outputs:
+        - led

operators/reload.py

@@ -0,0 +1,18 @@
+from dora import DoraStatus
+import pyarrow as pa
+
+
+class Operator:
+    def __init__(self):
+        self.image = None
+        self.text = None
+
+    def on_event(
+        self,
+        dora_event,
+        send_output,
+    ) -> DoraStatus:
+        if dora_event["type"] == "INPUT":
+            send_output("led", pa.array([255, 0, 0]))
+            pass
+        return DoraStatus.CONTINUE

operators/whisper_op.py

@@ -1,8 +1,8 @@
 import pyarrow as pa
 import whisper
 from pynput import keyboard
-from pynput.keyboard import Key
-from dora import DoraStatus
+from pynput.keyboard import Key, Events
+from dora import Node
 
 import torch
 import numpy as np
@@ -13,57 +13,47 @@ import gc  # garbage collect library
 model = whisper.load_model("base")
 
 SAMPLE_RATE = 16000
-MAX_DURATION = 10
-MIN_DURATION = 6
-
-
-class Operator:
-    """
-    Transforming Speech to Text using OpenAI Whisper model
-    """
-
-    def __init__(self) -> None:
-        self.policy_init = False
-
-    def on_event(
-        self,
-        dora_event,
-        send_output,
-    ) -> DoraStatus:
-        global model
-        if dora_event["type"] == "INPUT":
-            ## Check for keyboard event
-            with keyboard.Events() as events:
-                event = events.get(1.0)
-                if event is not None and event.key == Key.up:
-                    # send_output("led", pa.array([0, 255, 0]))
-
-                    if self.policy_init == False:
-                        self.policy_init = True
-                        duration = MAX_DURATION
-                    else:
-                        duration = MIN_DURATION
-
-                    ## Microphone
-                    audio_data = sd.rec(
-                        int(SAMPLE_RATE * duration),
-                        samplerate=SAMPLE_RATE,
-                        channels=1,
-                        dtype=np.int16,
-                        blocking=True,
-                    )
-
-                    audio = audio_data.ravel().astype(np.float32) / 32768.0
-
-                    ## Speech to text
-                    audio = whisper.pad_or_trim(audio)
-                    result = model.transcribe(audio, language="en")
-                    send_output(
-                        "text", pa.array([result["text"]]), dora_event["metadata"]
-                    )
-                    # send_output("led", pa.array([0, 0, 255]))
-
-                    gc.collect()
-                    torch.cuda.empty_cache()
-
-        return DoraStatus.CONTINUE
+MAX_DURATION = 30
+
+policy_init = True
+
+node = Node()
+
+for dora_event in node:
+    if dora_event["type"] == "INPUT":
+        ## Check for keyboard event
+        with keyboard.Events() as events:
+            event = events.get(1.0)
+            if (
+                event is not None
+                and event.key == Key.alt_r
+                and isinstance(event, Events.Press)
+            ):
+
+                ## Microphone
+                audio_data = sd.rec(
+                    int(SAMPLE_RATE * MAX_DURATION),
+                    samplerate=SAMPLE_RATE,
+                    channels=1,
+                    dtype=np.int16,
+                    blocking=False,
+                )
+
+            elif (
+                event is not None
+                and event.key == Key.alt_r
+                and isinstance(event, Events.Release)
+            ):
+                sd.stop()
+                audio = audio_data.ravel().astype(np.float32) / 32768.0
+
+                ## Speech to text
+                audio = whisper.pad_or_trim(audio)
+                result = model.transcribe(audio, language="en")
+                node.send_output(
+                    "text", pa.array([result["text"]]), dora_event["metadata"]
+                )
+                # send_output("led", pa.array([0, 0, 255]))
+
+                gc.collect()
+                torch.cuda.empty_cache()