46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
from tensorflow import keras
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import tensorflow as tf
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characters = list("abcdefghijklmnopqrstuvwxyz'?! ")
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char_to_num = keras.layers.StringLookup(vocabulary = characters,
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oov_token = "")
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num_to_char = keras.layers.StringLookup(vocabulary = char_to_num.get_vocabulary(),
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oov_token = "",
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invert = True)
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frame_length = 256
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frame_step = 160
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fft_length = 384
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wavs = '/mnt/tmpfs1/LJSpeech-1.1/wavs/'
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def encode_single_sample(wav, label):
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file = tf.io.read_file(wavs + wav + ".wav")
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audio, _ = tf.audio.decode_wav(file)
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audio = tf.squeeze(audio, axis = -1)
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audio = tf.cast(audio, tf.float32)
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spectrogram = tf.signal.stft(audio,
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frame_length = frame_length,
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frame_step = frame_step,
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fft_length = fft_length)
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spectrogram = tf.abs(spectrogram)
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spectrogram = tf.math.pow(spectrogram, 0.5)
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means = tf.math.reduce_mean(spectrogram, 1, keepdims=True)
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stddevs = tf.math.reduce_std(spectrogram, 1, keepdims=True)
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spectrogram = (spectrogram - means) / (stddevs + 1e-10)
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label = tf.strings.lower(label)
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label = tf.strings.unicode_split(label, input_encoding="UTF-8")
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label = char_to_num(label)
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return spectrogram, label
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def to_dataset(df, batch_size = 32):
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ds = tf.data.Dataset.from_tensor_slices((list(df["file_name"]),
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list(df["normalized_transcription"])))
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ds = ds.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE) \
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.padded_batch(batch_size) \
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.prefetch(buffer_size=tf.data.AUTOTUNE)
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return ds
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