mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2024-10-03 11:26:10 +00:00
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
from random import randint
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import random
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import keras.callbacks
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import numpy as np
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from data_processor import DataProcessor
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class SongGenerator:
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def __init__(self, data_processor: DataProcessor, model):
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self.data_processor = data_processor
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self.model = model
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def sample(self, predictions, temperature=0.1):
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predictions = np.asarray(predictions).astype('float64')
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predictions = np.log(predictions) / temperature
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exp_predictions = np.exp(predictions)
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predictions = exp_predictions / np.sum(exp_predictions)
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probs = np.random.multinomial(1, predictions, 1)
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return np.argmax(probs)
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def generate(self, tokens_per_line=6, lines=4, temp=1.0, custom_seed=None):
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result_indexes = []
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if not custom_seed:
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seed_idx = randint(0, len(self.model.X) - 1)
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seed = self.model.X[seed_idx]
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seed = list(np.reshape(seed, (len(seed))))
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else:
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seed = custom_seed
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for _ in range(lines * tokens_per_line):
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data_in = np.reshape(seed, (1, len(seed), 1))
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#data_in = data_in / float(self.data_processor.vocab_size())
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prediction = self.model.keras_model.predict(data_in, verbose=0)
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#out_index = np.argmax(prediction)
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# r = random.random()
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# curr = 0.0
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# out_index = -1
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#
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# for idx, pred in sorted(enumerate(list(prediction.flatten())), reverse=True, key=lambda x: x[1]):
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# out_index = idx
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# curr += pred
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# if curr >= r:
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# break
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out_index = self.sample(prediction[0] + 10e-5, temp)
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result_indexes.append(out_index)
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seed.append(out_index)
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seed = seed[1:len(seed)]
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result_tokens = self.data_processor.ints_to_text(result_indexes).split(" ")
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if self.data_processor.mode == "chars":
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raise NotImplementedError()
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# Capitalize I words
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for i, token in enumerate(result_tokens):
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if token == "i":
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result_tokens[i] = token.capitalize()
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elif token == "i'm":
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result_tokens[i] = "I'm"
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result = ""
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for line_idx in range(lines):
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result_tokens[line_idx*tokens_per_line] = result_tokens[line_idx*tokens_per_line].capitalize()
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result += " ".join(result_tokens[line_idx*tokens_per_line:line_idx*tokens_per_line + tokens_per_line]) \
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+ "\n"
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return result.rstrip("\n")
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class GeneratorCallback(keras.callbacks.Callback):
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def __init__(self, generator):
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super().__init__()
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self.generator = generator
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def on_epoch_end(self, epoch, logs=None):
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print(self.generator.generate())
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