STT.py

import whisper
import sounddevice as sd
import numpy as np
import torch

print(torch.cuda.is_available())
# Load Whisper model with GPU support
model = whisper.load_model("small").to("cuda" if torch.cuda.is_available() else "cpu")

# Audio parameters
samplerate = 16000
channels = 1
duration_of_silence = 2  # seconds
threshold = 0.01  # adjust sensitivity as needed

# Check for silence in audio
def is_silent(data, threshold):
    return np.abs(data).mean() < threshold

# Continuous audio recording and transcription
def continuous_transcription():
    while True:
        print("\nListening... (start speaking)")
        audio_chunks = []
        silence_duration = 0

        # Start audio stream
        with sd.InputStream(samplerate=samplerate, channels=channels, dtype='float32') as stream:
            while True:
                audio_chunk, _ = stream.read(int(samplerate * 0.5))  # read 0.5 second chunks
                audio_chunks.append(audio_chunk)

                if is_silent(audio_chunk, threshold):
                    silence_duration += 0.5
                else:
                    silence_duration = 0

                if silence_duration >= duration_of_silence:
                    print("Detected silence, processing audio...")
                    break

        # Concatenate audio data
        audio_data = np.concatenate(audio_chunks).flatten()

        # Ensure audio is in correct format (float32 numpy array)
        audio_data = audio_data.astype(np.float32)

        # Transcribe audio using Whisper directly from numpy array
        result = model.transcribe(audio_data, fp16=torch.cuda.is_available(), language="en")

        # Output transcription
        print("Transcription:", result['text'])

# Run the transcription
continuous_transcription()