File size: 1,992 Bytes
034b730 ffc2aa4 034b730 3c8b0eb ffc2aa4 3c8b0eb 034b730 ffc2aa4 3c8b0eb ffc2aa4 034b730 3c8b0eb 034b730 8852f54 034b730 ffc2aa4 3c8b0eb ffc2aa4 3c8b0eb ffc2aa4 3c8b0eb 8852f54 3c8b0eb 8852f54 034b730 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import pyarrow as pa
import whisper
from pynput import keyboard
from pynput.keyboard import Key
from dora import DoraStatus
import torch
import numpy as np
import pyarrow as pa
import sounddevice as sd
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
|