from copy import copy
import numpy as np
import time
import whisper

print('Loaded audio.py')

CHUNK_LENGTH = 24000  # 48000 Hz * 0.5 s

def process_pcm(audio_chunks, data):
    # pymumble PCM is 16-bit 48000 Hz

    start = time.time()

    audio_chunks.append(data)

    if len(audio_chunks) > 75:
        audio_chunks.pop(0)

    #print('finished chunk in', time.time() - start, 's')

def process_stream(audio_chunks, model):

    if len(audio_chunks) != 75:
        print('Skipping, bad length.')
        time.sleep(0.5)
        return

    start = time.time()
    a = copy(audio_chunks)
    b = b''.join(a)
    c = np.frombuffer(b, np.int16)

    # Define a low-pass filter kernel
    fs = 48000
    cutoff_freq = fs / 6
    nyquist_freq = fs / 2
    num_taps = 101
    taps = np.sinc(2 * cutoff_freq / fs * (np.arange(num_taps) - (num_taps - 1) / 2))
    taps *= np.blackman(num_taps)
    taps /= np.sum(taps)

    # Apply the filter kernel to audio_data using convolution
    filtered_audio_data = np.convolve(c, taps, mode='same')
    # Downsample filtered_audio_data by a factor of 3 using take
    downsampled_audio_data = filtered_audio_data.take(np.arange(0, len(filtered_audio_data), 3)).flatten()

    norm_audio = downsampled_audio_data.astype(np.float32) / 32768.0

    #abs_mean = np.mean(np.abs(downsampled_audio_data ** 3))
    #print('abs mean:', abs_mean)
    #if abs_mean < 0.0:
    #    print('silence detected, skipping')
    #    time.sleep(1)
    #    return

    d = whisper.pad_or_trim(norm_audio)

    #print('processed audio in', time.time() - start, 's')

    start = time.time()
    e = model.transcribe(d, language='en')
    print('transcribed audio in', time.time() - start, 's')

    if time.time() - start > 10:
        with open('downsampled.pcm', 'wb') as f:
            f.write(downsampled_audio_data.astype(np.int16).tobytes())

        print('wrote file, sleeping')
        #breakpoint()
        time.sleep(100)

    print('  ', e['text'])