A Tensorflow implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model.
- Tensroflow 1.0
- Python 2.7
- Librosa
We used the pinao music of 74 minutes as the training corpus, and you can use any corpus containing ".wav" files to instead as well.
- 2-tier SampleRNN
- 3-tier SampleRNN
- Quantization in linear.
- Quantization in mu-law.
python train.py \
--data_dir=./pinao-corpus \
--silence_threshold=0.1 \
--sample_size=102408 \
--big_frame_size=8 \
--frame_size=2 \
--q_levels=256 \
--rnn_type=GRU \
--dim=1024 \
--n_rnn=1 \
--seq_len=520 \
--emb_size=256 \
--batch_size=64 \
--optimizer=adam \
--num_gpus=4
or
sh run.sh
This work is based on the flowing implementations with some modifications:
- tensorflow-wavenet, a TensorFlow implementation of WaveNet
- sampleRNN_ICLR2017, a Theano implementation of sampleRNN