| import pyarrow as pa | |
| import whisper | |
| from pynput import keyboard | |
| from pynput.keyboard import Key | |
| from dora import DoraStatus | |
| import numpy as np | |
| import pyarrow as pa | |
| import sounddevice as sd | |
| model = whisper.load_model("base") | |
| SAMPLE_RATE = 16000 | |
| MAX_DURATION = 15 | |
| class Operator: | |
| """ | |
| Transforming Speech to Text using OpenAI Whisper model | |
| """ | |
| def on_event( | |
| self, | |
| dora_event, | |
| send_output, | |
| ) -> DoraStatus: | |
| 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: | |
| ## Microphone | |
| audio_data = sd.rec( | |
| int(SAMPLE_RATE * MAX_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"] | |
| ) | |
| return DoraStatus.CONTINUE | |