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* hacky thing, add tone support for acoustic model * fix experiments for waveflow and wavenet, only write visual log in rank-0 * use emb add in tacotron2 * 1. remove space from numericalized representation; 2. fix decoder paddign mask's unsqueeze dim. * remove bn in postnet * refactoring code * add an option to normalize volume when loading audio. * add an embedding layer. * 1. change the default min value of LogMagnitude to 1e-5; 2. remove stop logit prediction from tacotron2 model. * WIP: baker * add ge2e * fix lstm speaker encoder * fix lstm speaker encoder * fix speaker encoder and add support for 2 more datasets * simplify visualization code * add a simple strategy to support multispeaker for tacotron. * add vctk example for refactored tacotron * fix indentation * fix class name * fix visualizer * fix root path * fix root path * fix root path * fix typos * fix bugs * fix text log extention name * add example for baker and aishell3 * update experiment and display * format code for tacotron_vctk, add plot_waveform to display * add new trainer * minor fix * add global condition support for tacotron2 * add gst layer * add 2 frontend * fix fmax for example/waveflow * update collate function, data loader not does not convert nested list into numpy array. * WIP: add hifigan * WIP:update hifigan * change stft to use conv1d * add audio datasets * change batch_text_id, batch_spec, batch_wav to include valid lengths in the returned value * change wavenet to use on-the-fly prepeocessing * fix typos * resolve conflict * remove imports that are removed * remove files not included in this release * remove imports to deleted modules * move tacotron2_msp * clean code * fix argument order * fix argument name * clean code for data processing * WIP: add README * add more details to thr README, fix some preprocess scripts * add voice cloning notebook * add an optional to alter the loss and model structure of tacotron2, add an alternative config * add plot_multiple_attentions and update visualization code in transformer_tts * format code * remove tacotron2_msp * update tacotron2 from_pretrained, update setup.py * update tacotron2 * update tacotron_aishell3's README * add images for exampels/tacotron2_aishell3's README * update README for examples/ge2e * add STFT back * add extra_config keys into the default config of tacotron * fix typos and docs * update README and doc * update docstrings for tacotron * update doc * update README * add links to downlaod pretrained models * refine READMEs and clean code * add praatio into requirements for running the experiments * format code with pre-commit * simplify text processing code and update notebook |
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| train.py | ||
README.md
WaveFlow with LJSpeech
Dataset
Download the datasaet.
wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
Extract the dataset.
tar xjvf LJSpeech-1.1.tar.bz2
Preprocess the dataset.
Assume the path to save the preprocessed dataset is ljspeech_waveflow. Run the command below to preprocess the dataset.
python preprocess.py --input=LJSpeech-1.1/ --output=ljspeech_waveflow
Train the model
The training script requires 4 command line arguments.
--data is the path of the training dataset, --output is the path of the output directory (we recommend to use a subdirectory in runs to manage different experiments.)
--device should be "cpu" or "gpu", --nprocs is the number of processes to train the model in parallel.
python train.py --data=ljspeech_waveflow/ --output=runs/test --device="gpu" --nprocs=1
If you want distributed training, set a larger --nprocs (e.g. 4). Note that distributed training with cpu is not supported yet.
Synthesize
Synthesize waveform. We assume the --input is a directory containing several mel spectrograms(log magnitude) in .npy format. The output would be saved in --output directory, containing several .wav files, each with the same name as the mel spectrogram does.
--checkpoint_path should be the path of the parameter file (.pdparams) to load. Note that the extention name .pdparmas is not included here.
--device specifies to device to run synthesis on.
python synthesize.py --input=mels/ --output=wavs/ --checkpoint_path='step-2000000' --device="gpu" --verbose
Pretrained Model
Pretrained Model with residual channel equals 128 can be downloaded here. waveflow_ljspeech_ckpt_0.3.zip.