def vad_segment_generator(wavFile, aggressiveness):
logging.debug("Caught the wav file @: %s" % (wavFile))
audio, sample_rate, audio_length = wavSplit.read_wave(wavFile)
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(10, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 10, 300, vad, frames)
return segments, sample_rate, audio_length
def vad_segment_generator(wavFile, aggressiveness):
logging.debug("Caught the wav file @: %s" % (wavFile))
audio, sample_rate, audio_length = wavSplit.read_wave(wavFile)
assert sample_rate == 16000, "Only 16000Hz input WAV files are supported for now!"
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 30, 300, vad, frames)
return segments, sample_rate, audio_length
def vad_segment_generator(wavFile, aggressiveness):
logging.debug("Caught the wav file @: %s" % (wavFile))
audio, sample_rate, audio_length = wavSplit.read_wave(wavFile)
assert sample_rate == 16000, "Only 16000Hz input WAV files are supported for now!"
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 30, 300, vad, frames)
return segments, sample_rate, audio_length
def vad_segment_generator(wavFile, aggressiveness, model_sample_rate):
logging.debug("Caught the wav file @: %s" % (wavFile))
audio, sample_rate, audio_length = wavSplit.read_wave(wavFile)
assert sample_rate == model_sample_rate, \
"Audio sample rate must match sample rate of used model: {}Hz".format(model_sample_rate)
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 30, 300, vad, frames)
return segments, sample_rate, audio_length
def vad_segment_generator(wav_file, aggressiveness):
"""
Generate VAD segments. Filters out non-voiced audio frames.
:param wav_file: Input wav file to run VAD on.0
:param aggressiveness: How aggressive filtering out non-speech is (between 0 and 3)
:return: Returns tuple of
segments: a bytearray of multiple smaller audio frames
(The longer audio split into multiple smaller one's)
sample_rate: Sample rate of the input audio file
audio_length: Duration of the input audio file
"""
logging.debug("Caught the wav file @: %s" % wav_file)
audio, sample_rate, audio_length = wavSplit.read_wave(wav_file)
assert sample_rate == 16000, "Only 16000Hz input WAV files are supported for now!"
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 30, 300, 0.5, vad, frames)
return segments, sample_rate, audio_length
# load and pre-process audio
audio_file = os.path.join(os.getcwd(),
"data/testing/audio/2830-3980-0043.wav")
# audio_file = os.path.join(os.getcwd(),"data/testing/audio/my_name_is_jamie.wav")
# audio_file = os.path.join(os.getcwd(),"data/testing/audio/hello_liv.wav")
aggressiveness = 0
print("Reading and processing: {}".format(audio_file))
audio, sample_rate, audio_length = wavSplit.read_wave(audio_file)
assert sample_rate == 16000, "Only 16000Hz input WAV files are supported for now!"
vad = webrtcvad.Vad(int(aggressiveness))
frames = wavSplit.frame_generator(30, audio, sample_rate)
frames = list(frames)
segments = wavSplit.vad_collector(sample_rate, 30, 300, vad, frames)
# we now have the data in the following segments
# segments, sample_rate, audio_length
print("we have {} frames".format(len(frames)))
start = time.time()
for i, segment in enumerate(segments):
# Run deepspeech on the chunk that just completed VAD
print("Processing chunk %002d" % (i, ))
audio = np.frombuffer(segment, dtype=np.int16)
# Run Deepspeech
print('Running inference...')
output = ds.stt(audio)
print("Transcript: %s" % output)
end = time.time()
print("that took: {}".format(end - start))
