mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
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import os
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from random import choice, randint
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import numpy as np
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from keras_nlp.src.metrics import EditDistance
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from data_processor import DataProcessor
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from models import BetaGRUModel, DefaultLSTMModel, GammaLSTMModel, DefaultGRUModel, OmicronLSTM, BetaLSTMModel
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from song_generator import SongGenerator
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dp = DataProcessor("ac_dc.csv", 10)
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X, y = dp.training_data()
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v_X, v_y = dp.validation_data()
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# dp = DataProcessor("ac_dc.csv", None)
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# X, y = dp.training_data("ints", True)
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# v_X, v_y = dp.validation_data("ints", True)
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model = GammaLSTMModel(X, y, v_X, v_y)
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weights_to_load = "gamma-lstm-weights.hdf5"
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weights_path = os.path.join("trained_models", weights_to_load)
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model.keras_model.load_weights(weights_path)
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lines = 1
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words_in_line = 1
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generator = SongGenerator(dp, model)
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eds = []
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for i in range(16):
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original_text = choice(dp.validation_lyrics).split(" ")
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original_begin = randint(0, len(original_text) - lines * words_in_line - 1)
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original_end = original_begin + lines * words_in_line + 10
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original = " ".join(original_text[original_begin:original_end])
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original_ints = dp.texts_to_ints([original])[0]
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original = original_ints[10:10+lines*words_in_line]
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seed = original_ints[:10]
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generated = generator.generate(words_in_line, lines, custom_seed=seed).lower()
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generated = dp.texts_to_ints([generated])[0]
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ed = EditDistance(normalize=True)
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eds.append(ed(generated, original).numpy())
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print(f"Average ED: {np.average(eds)}")
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print(f"Min ED: {np.min(eds)}")
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print(f"Max ED: {np.max(eds)}")
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