def create_seed(filename,
sample_rate,
quantization_channels,
window_size=WINDOW):
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio_reader.trim_silence(audio)
quantized = mu_law_encode(audio, quantization_channels)
cut_index = tf.cond(tf.size(quantized) < tf.constant(window_size),
lambda: tf.size(quantized),
lambda: tf.constant(window_size))
return quantized[:cut_index]
def create_seed(filename,
sample_rate,
quantization_channels,
window_size,
silence_threshold=SILENCE_THRESHOLD):
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio_reader.trim_silence(audio, silence_threshold)
quantized = mu_law_encode(audio, quantization_channels)
cut_index = tf.cond(
tf.size(quantized) < tf.constant(window_size),
lambda: tf.size(quantized), lambda: tf.constant(window_size))
return quantized
def create_seed(filename,
sample_rate,
quantization_channels,
window_size,
scalar_input,
silence_threshold=SILENCE_THRESHOLD):
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio_reader.trim_silence(audio, silence_threshold)
if scalar_input:
if len(audio) < window_size:
return audio
else:
return audio[:window_size]
else:
quantized = mu_law_encode(audio, quantization_channels)
# 짧으면 짧은 대로 return하는데, padding이라도 해야되지 않나???
cut_index = tf.cond(
tf.size(quantized) < tf.constant(window_size),
lambda: tf.size(quantized), lambda: tf.constant(window_size))
return quantized[:cut_index]
