42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
from model import *
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from preprocessing import *
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import pandas as pd
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from cc import ce
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bs = 32
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data = pd.read_csv("/mnt/tmpfs1/LJSpeech-1.1/metadata.csv",
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sep = '|',
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header = None,
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quoting = 3,
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names = ['file_name', 'i', 'normalized_transcription'])
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s = int(len(data) * 0.90)
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train_data = data[:s]
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valid_data = data[s:]
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train_ds = to_dataset(train_data, batch_size = bs)
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valid_ds = to_dataset(valid_data, batch_size = bs)
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m = model(input_dim = fft_length // 2 + 1,
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output_dim = char_to_num.vocabulary_size())
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m.load_weights('model40-latest.keras')
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ckpt1 = kc.ModelCheckpoint('model41-latest.keras',
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monitor = 'val_loss',
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save_best_only = False,
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verbose = 1)
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ckpt2 = kc.ModelCheckpoint('model41-best.keras',
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monitor = 'val_loss',
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save_best_only = True,
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verbose = 1)
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ce1 = ce(valid_ds, m)
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m.fit(train_ds,
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epochs = 40,
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validation_data = valid_ds,
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callbacks = [ckpt1, ckpt2, ce1])
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