liuaiting / Hip Hop Seq2seq
DeeCamp 2018,AI有嘻哈 自动写歌词
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Hip-Hop-Seq2seq
Projects of DeeCamp2018, hip-hop lyrics generation using Seq2Seq.
Setup
- python 3.6
- tensorflow==1.4
- jieba
Model
基于nmt模型,在inference部分进行了一些修改。
decoder
设计了两种hook的生成方式:
- hook中每一句的第一个词一样
和你我不说累一切都无所谓 和你我不后悔和你我不流泪 和你我不说累一切都无所谓 和你我不后悔和你我不流泪
- 生成的句子与原始句子押韵(inference时将不押韵的词的概率置0)
你感受冰冷的风 也曾经感受过梦 像一场不敢醒来不痛的梦 你试过苦涩的痛 没想过郁郁而终
Training and Evaluate
在项目主目录下,运行下面的命令训练第一种hook生成模型:
python3 -m seq2seq.train\
--source_train_data="seq2seq/data/v2/train.src"\
--target_train_data="seq2seq/data/v2/train.tgt"\
--source_dev_data="seq2seq/data/v2/dev.src"\
--target_dev_data="seq2seq/data/v2/dev.tgt"\
--src_vocab_file="seq2seq/data/v2/vocab.tgt"\
--tgt_vocab_file="seq2seq/data/v2/vocab.tgt"\
--src_vocab_size=43836\
--tgt_vocab_size=43836\
--share_vocab=True\
--out_dir="seq2seq/model1"\
--decoder_rule="samefirst"\
--num_epochs=60
运行下面的命令训练第二种hook生成模型:
python3 -m seq2seq.train\
--source_train_data="seq2seq/data/v3/train.src"\
--target_train_data="seq2seq/data/v3/train.tgt"\
--source_dev_data="seq2seq/data/v3/dev.src"\
--target_dev_data="seq2seq/data/v3/dev.tgt"\
--src_vocab_file="seq2seq/data/v3/vocab.tgt"\
--tgt_vocab_file="seq2seq/data/v3/vocab.tgt"\
--src_vocab_size=23442\
--tgt_vocab_size=23442\
--share_vocab=True\
--out_dir="seq2seq/model3"\
--decoder_rule="rhyme"\
--num_epochs=60
Inference
训练好模型后,运行下面的命令进行第一种hook的inference:
python3 -m seq2seq.test_inference\
--src_vocab_file="seq2seq/data/v2/vocab.tgt"\
--tgt_vocab_file="seq2seq/data/v2/vocab.tgt"\
--src_vocab_size=43836\
--tgt_vocab_size=43836\
--share_vocab=True\
--out_dir="seq2seq/model1"\
--inference_input_file="seq2seq/data/plana/1.0.txt"\
--inference_output_file="seq2seq/model1/output"\
--decoder_rule="samefirst"
第二种hook的inference:
python3 -m seq2seq.test_inference\
--src_vocab_file="seq2seq/data/v3/vocab.tgt"\
--tgt_vocab_file="seq2seq/data/v3/vocab.tgt"\
--src_vocab_size=23442\
--tgt_vocab_size=23442\
--share_vocab=True\
--out_dir="seq2seq/model3"\
--inference_input_file="seq2seq/data/plana/1.0.txt"\
--inference_output_file="seq2seq/model3/output"\
--rhyme_table_file="seq2seq/data/v3/table_23442.npy"\
--decoder_rule="rhyme"
Run Server
在项目主目录下运行下面的命令开启服务。
bash server.sh
References
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR.
- Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. EMNLP.
- Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. NIPS.
News
Homie快来听,AI的嘻哈也很酷 | DeeCamp Show
2分钟写出一首嘻哈歌曲:95后AI创新团队叫板“中国新说唱”
Demo
视频展示:YouTube
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