91 lines
2.5 KiB
Python
91 lines
2.5 KiB
Python
import os
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DEBUG = os.environ.get('DEBUG')
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import logging
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logging.basicConfig(
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format='[%(asctime)s] %(levelname)s %(module)s/%(funcName)s: - %(message)s',
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level=logging.DEBUG if DEBUG else logging.INFO)
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import pymumble_py3 as pymumble_py3
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from pymumble_py3.callbacks import PYMUMBLE_CLBK_SOUNDRECEIVED as PCS
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import whisper
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from copy import copy
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import numpy as np
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import time
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logging.info('Loading whisper model...')
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model = whisper.load_model('medium')
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logging.info('Done.')
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# Connection details for mumble server. Hardcoded for now, will have to be
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# command line arguments eventually
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pwd = "" # password
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server = "protospace.ca" # server address
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nick = "python"
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port = 64738 # port number
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CHUNK_LENGTH = 24000 # 48000 Hz * 0.5 s
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# array of 0.5 sec audio chunks
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audio_chunks = [bytearray()]
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def sound_received_handler(user, soundchunk):
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# pymumble PCM is 16-bit 48000 Hz
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if len(audio_chunks[-1]) < CHUNK_LENGTH:
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audio_chunks[-1].extend(soundchunk.pcm)
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else:
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audio_chunks.append(bytearray())
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if len(audio_chunks) > 10:
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audio_chunks.pop(0)
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# Spin up a client and connect to mumble server
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mumble = pymumble_py3.Mumble(server, nick, password=pwd, port=port)
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# set up callback called when PCS event occurs
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mumble.callbacks.set_callback(PCS, sound_received_handler)
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mumble.set_receive_sound(1) # Enable receiving sound from mumble server
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mumble.start()
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mumble.is_ready() # Wait for client is ready
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# constant capturing sound and sending it to mumble server
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while True:
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#data = stream.read(CHUNK, exception_on_overflow=False)
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#mumble.sound_output.add_sound(data)
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if len(audio_chunks) != 10:
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continue
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start = time.time()
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a = copy(audio_chunks)
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b = b''.join(a)
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c = np.frombuffer(b, np.int16)
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# Define a low-pass filter kernel
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fs = 48000
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cutoff_freq = fs / 6
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nyquist_freq = fs / 2
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num_taps = 101
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taps = np.sinc(2 * cutoff_freq / fs * (np.arange(num_taps) - (num_taps - 1) / 2))
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taps *= np.blackman(num_taps)
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taps /= np.sum(taps)
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# Apply the filter kernel to audio_data using convolution
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filtered_audio_data = np.convolve(c, taps, mode='same')
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# Downsample filtered_audio_data by a factor of 3 using take
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downsampled_audio_data = filtered_audio_data.take(np.arange(0, len(filtered_audio_data), 3))
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downsampled_audio_data = downsampled_audio_data.flatten().astype(np.float32) / 32768.0
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d = whisper.pad_or_trim(downsampled_audio_data)
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#print('processed audio in', time.time() - start, 's')
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e = model.transcribe(d)
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print(e['text'])
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