Add a bunch of test files

stream.py

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+import os
+
+DEBUG = os.environ.get('DEBUG')
+
+import logging
+logging.basicConfig(
+    format='[%(asctime)s] %(levelname)s %(module)s/%(funcName)s: - %(message)s',
+    level=logging.DEBUG if DEBUG else logging.INFO)
+
+import pymumble_py3 as pymumble_py3
+from pymumble_py3.callbacks import PYMUMBLE_CLBK_SOUNDRECEIVED as PCS
+import whisper
+from copy import copy
+import numpy as np
+import time
+
+logging.info('Loading whisper model...')
+model = whisper.load_model('medium')
+logging.info('Done.')
+
+# Connection details for mumble server. Hardcoded for now, will have to be
+# command line arguments eventually
+pwd = ""  # password
+server = "protospace.ca"  # server address
+nick = "python"
+port = 64738  # port number
+
+CHUNK_LENGTH = 24000  # 48000 Hz * 0.5 s
+
+# array of 0.5 sec audio chunks
+audio_chunks = [bytearray()]
+
+def sound_received_handler(user, soundchunk):
+    # pymumble PCM is 16-bit 48000 Hz
+
+    if len(audio_chunks[-1]) < CHUNK_LENGTH:
+        audio_chunks[-1].extend(soundchunk.pcm)
+    else:
+        audio_chunks.append(bytearray())
+
+    if len(audio_chunks) > 10:
+        audio_chunks.pop(0)
+
+
+# Spin up a client and connect to mumble server
+mumble = pymumble_py3.Mumble(server, nick, password=pwd, port=port)
+# set up callback called when PCS event occurs
+mumble.callbacks.set_callback(PCS, sound_received_handler)
+mumble.set_receive_sound(1)  # Enable receiving sound from mumble server
+mumble.start()
+mumble.is_ready()  # Wait for client is ready
+
+# constant capturing sound and sending it to mumble server
+while True:
+    #data = stream.read(CHUNK, exception_on_overflow=False)
+    #mumble.sound_output.add_sound(data)
+
+    if len(audio_chunks) != 10:
+        continue
+
+    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))
+    downsampled_audio_data = downsampled_audio_data.flatten().astype(np.float32) / 32768.0
+
+    d = whisper.pad_or_trim(downsampled_audio_data)
+
+    #print('processed audio in', time.time() - start, 's')
+
+    e = model.transcribe(d)
+
+    print(e['text'])
+
+
+