feat: implement transcriber agent

Uses speech fragments of the VAD agent, emits transcribed text over SPADE's default communication channel to no recipient for now.

ref: N25B-209

src/control_backend/agents/transcription/speech_recognizer.py

@@ -0,0 +1,62 @@
+import abc
+import sys
+
+if sys.platform == "darwin":
+    import mlx.core as mx
+    import mlx_whisper
+    from mlx_whisper.transcribe import ModelHolder
+
+import numpy as np
+import torch
+import whisper
+
+
+class SpeechRecognizer(abc.ABC):
+    @abc.abstractmethod
+    def load_model(self): ...
+
+    @abc.abstractmethod
+    def recognize_speech(self, audio: np.ndarray) -> str: ...
+
+    @staticmethod
+    def best_type():
+        if torch.mps.is_available():
+            print("Choosing MLX Whisper model.")
+            return MLXWhisperSpeechRecognizer()
+        else:
+            print("Choosing reference Whisper model.")
+            return OpenAIWhisperSpeechRecognizer()
+
+
+class MLXWhisperSpeechRecognizer(SpeechRecognizer):
+    def __init__(self):
+        super().__init__()
+        self.model = None
+        self.model_name = "mlx-community/whisper-small.en-mlx"
+
+    def load_model(self):
+        if self.model is not None:
+            return
+        ModelHolder.get_model(
+            self.model_name, mx.float16
+        )  # Should store it in memory for later usage
+
+    def recognize_speech(self, audio: np.ndarray) -> str:
+        self.load_model()
+        return mlx_whisper.transcribe(audio, path_or_hf_repo=self.model_name)["text"]
+
+
+class OpenAIWhisperSpeechRecognizer(SpeechRecognizer):
+    def __init__(self):
+        super().__init__()
+        self.model = None
+
+    def load_model(self):
+        if self.model is not None:
+            return
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model = whisper.load_model("small.en", device=device)
+
+    def recognize_speech(self, audio: np.ndarray) -> str:
+        self.load_model()
+        return self.model.transcribe(audio)["text"]