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from transformers import GPT2LMHeadModel, GPT2Tokenizer, XLNetLMHeadModel, XLNetTokenizer |
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gpt2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") |
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gpt2_model = GPT2LMHeadModel.from_pretrained("gpt2") |
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xlnet_tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') |
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xlnet_model = XLNetLMHeadModel.from_pretrained('xlnet-base-cased') |
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def generate_song_lines_gpt2(style): |
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input_text = f"A song in the style of {style}:" |
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input_ids = gpt2_tokenizer.encode(input_text, return_tensors='pt') |
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output = gpt2_model.generate(input_ids, do_sample=True, max_length=100, temperature=0.7, num_return_sequences=5) |
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song_lines = [gpt2_tokenizer.decode(ids) for ids in output] |
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return song_lines |
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def generate_song_lines_xlnet(style): |
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input_text = f"A song in the style of {style}:" |
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input_ids = xlnet_tokenizer.encode(input_text, return_tensors='pt') |
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output = xlnet_model.generate(input_ids, do_sample=True, max_length=100, temperature=0.7, num_return_sequences=5) |
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song_lines = [xlnet_tokenizer.decode(ids) for ids in output] |
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return song_lines |
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def generate_song_gpt2(style): |
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song_lines = generate_song_lines_gpt2(style) |
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song = "\n".join(song_lines) |
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return song |
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def generate_song_xlnet(style): |
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song_lines = generate_song_lines_xlnet(style) |
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song = "\n".join(song_lines) |
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return song |
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Artist = "Taylor Swift" |
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song_gpt2 = generate_song_gpt2(Artist) |
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song_xlnet = generate_song_xlnet(Artist) |
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print("GPT-2 Song:\n", song_gpt2) |
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print("\nXLNet Song:\n", song_xlnet) |
