add waveflow model valid for training only

parakeet/models/waveflow/README.md

+# WaveNet with Paddle Fluid
+
+Paddle fluid implementation of WaveNet, a deep generative model of raw audio waveforms.
+WaveNet model is originally proposed in [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499).
+Our implementation is based on the WaveNet architecture described in [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](https://arxiv.org/abs/1807.07281) and can provide various output distributions, including single Gaussian, mixture of Gaussian, and softmax with linearly quantized channels. 
+
+We implement WaveNet model in paddle fluid with dynamic graph, which is convenient for flexible network architectures.
+
+## Project Structure
+```text
+├── configs                 # yaml configuration files of preset model hyperparameters
+├── data.py                 # dataset and dataloader settings for LJSpeech
+├── slurm.py                # optional slurm helper functions if you use slurm to train model
+├── synthesis.py            # script for speech synthesis
+├── train.py                # script for model training
+├── utils.py                # helper functions for e.g., model checkpointing
+├── wavenet.py              # WaveNet model high level APIs
+└── wavenet_modules.py      # WaveNet model implementation
+```
+
+## Usage
+
+There are many hyperparameters to be tuned depending on the specification of model and dataset you are working on. Hyperparameters that are known to work good for the LJSpeech dataset are provided as yaml files in `./configs/` folder. Specifically, we provide `wavenet_ljspeech_single_gaussian.yaml`, `wavenet_ljspeech_mix_gaussian.yaml`, and `wavenet_ljspeech_softmax.yaml` config files for WaveNet with single Gaussian, 10-component mixture of Gaussians, and softmax (with 2048 linearly quantized channels) output distributions, respectively.
+
+Note that `train.py` and `synthesis.py` all accept a `--config` parameter. To ensure consistency, you should use the same config yaml file for both training and synthesizing. You can also overwrite these preset hyperparameters with command line by updating parameters after `--config`. For example `--config=${yaml} --batch_size=8 --layers=20` can overwrite the corresponding hyperparameters in the `${yaml}` config file. For more details about these hyperparameters, check `utils.add_config_options_to_parser`.
+
+Note that you also need to specify some additional parameters for `train.py` and `synthesis.py`, and the details can be found in `train.add_options_to_parser` and `synthesis.add_options_to_parser`, respectively.
+
+### Dataset
+
+Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
+
+```bash
+wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
+tar xjvf LJSpeech-1.1.tar.bz2
+```
+
+In this example, assume that the path of unzipped LJSpeech dataset is `./data/LJSpeech-1.1`.
+
+### Train on single GPU
+
+```bash
+export PYTHONPATH="${PYTHONPATH}:${PWD}/../../.."
+export CUDA_VISIBLE_DEVICES=0
+python -u train.py --config=${yaml} \
+    --root=./data/LJSpeech-1.1 \
+    --name=${ModelName} --batch_size=4 \
+    --parallel=false --use_gpu=true
+```
+
+#### Save and Load checkpoints
+
+Our model will save model parameters as checkpoints in `./runs/wavenet/${ModelName}/checkpoint/` every 10000 iterations by default.
+The saved checkpoint will have the format of `step-${iteration_number}.pdparams` for model parameters and `step-${iteration_number}.pdopt` for optimizer parameters. 
+
+There are three ways to load a checkpoint and resume training (take an example that you want to load a 500000-iteration checkpoint):
+1. Use `--checkpoint=./runs/wavenet/${ModelName}/checkpoint/step-500000` to provide a specific path to load. Note that you only need to provide the base name of the parameter file, which is `step-500000`, no extension name `.pdparams` or `.pdopt` is needed. 
+2. Use `--iteration=500000`.
+3. If you don't specify either `--checkpoint` or `--iteration`, the model will automatically load the latest checkpoint in `./runs/wavenet/${ModelName}/checkpoint`.
+
+### Train on multiple GPUs
+
+```bash
+export PYTHONPATH="${PYTHONPATH}:${PWD}/../../.."
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python -u -m paddle.distributed.launch train.py \
+    --config=${yaml} \
+    --root=./data/LJSpeech-1.1 \
+    --name=${ModelName} --parallel=true --use_gpu=true
+```
+
+Use `export CUDA_VISIBLE_DEVICES=0,1,2,3` to set the GPUs that you want to use to be visible. Then the `paddle.distributed.launch` module will use these visible GPUs to do data parallel training in multiprocessing mode.
+
+### Monitor with Tensorboard
+
+By default, the logs are saved in `./runs/wavenet/${ModelName}/logs/`. You can monitor logs by tensorboard.
+
+```bash
+tensorboard --logdir=${log_dir} --port=8888
+```
+
+### Synthesize from a checkpoint
+
+Check the [Save and load checkpoint](#save-and-load-checkpoints) section on how to load a specific checkpoint.
+The following example will automatically load the latest checkpoint:
+
+```bash
+export PYTHONPATH="${PYTHONPATH}:${PWD}/../../.."
+export CUDA_VISIBLE_DEVICES=0
+python -u synthesis.py --config=${yaml} \
+    --root=./data/LJSpeech-1.1 \
+    --name=${ModelName} --use_gpu=true \
+    --output=./syn_audios \
+    --sample=${SAMPLE} 
+```
+
+In this example, `--output` specifies where to save the synthesized audios and `--sample` specifies which sample in the valid dataset (a split from the whole LJSpeech dataset, by default contains the first 16 audio samples) to synthesize based on the mel-spectrograms computed from the ground truth sample audio, e.g., `--sample=0` means to synthesize the first audio in the valid dataset.

parakeet/models/waveflow/configs/waveflow_ljspeech_sqz16_r64_layer8x8.yaml

+valid_size: 16
+segment_length: 16000
+sample_rate: 22050
+fft_window_shift: 256
+fft_window_size: 1024
+fft_size: 1024
+mel_bands: 80
+mel_fmin: 0.0
+mel_fmax: 8000.0
+
+seed: 1234
+learning_rate: 0.0002
+batch_size: 8
+test_every: 2000
+save_every: 5000
+max_iterations: 2000000
+
+sigma: 1.0
+n_flows: 8
+n_group: 16
+n_layers: 8
+n_channels: 64
+kernel_h: 3
+kernel_w: 3

parakeet/models/waveflow/configs/waveflow_ljspeech_sqz16_r64_layer8x8_s123.yaml

+valid_size: 16
+segment_length: 16000
+sample_rate: 22050
+fft_window_shift: 256
+fft_window_size: 1024
+fft_size: 1024
+mel_bands: 80
+mel_fmin: 0.0
+mel_fmax: 8000.0
+
+seed: 123
+learning_rate: 0.0002
+batch_size: 8
+test_every: 2000
+save_every: 5000
+max_iterations: 2000000
+
+sigma: 1.0
+n_flows: 8
+n_group: 16
+n_layers: 8
+n_channels: 64
+kernel_h: 3
+kernel_w: 3

parakeet/models/waveflow/data.py

+import random
+
+import librosa
+import numpy as np
+from paddle import fluid
+
+import utils
+from parakeet.datasets import ljspeech
+from parakeet.data import dataset
+from parakeet.data.batch import SpecBatcher, WavBatcher
+from parakeet.data.datacargo import DataCargo
+from parakeet.data.sampler import DistributedSampler, BatchSampler
+from scipy.io.wavfile import read
+
+MAX_WAV_VALUE = 32768.0
+
+
+class Dataset(ljspeech.LJSpeech):
+    def __init__(self, config):
+        super(Dataset, self).__init__(config.root)
+        self.config = config
+
+    def _get_example(self, metadatum):
+        fname, _, _ = metadatum
+        wav_path = self.root.joinpath("wavs", fname + ".wav")
+
+        loaded_sr, audio = read(wav_path)
+        assert loaded_sr == self.config.sample_rate
+
+        return audio
+
+
+class Subset(dataset.Dataset): 
+    def __init__(self, dataset, indices, valid):
+        self.dataset = dataset
+        self.indices = indices
+        self.valid = valid
+        self.config = dataset.config
+
+    def get_mel(self, audio):
+        spectrogram = librosa.core.stft(
+            audio, n_fft=self.config.fft_size,
+            hop_length=self.config.fft_window_shift,
+            win_length=self.config.fft_window_size)
+        spectrogram_magnitude = np.abs(spectrogram) 
+
+        # mel_filter_bank shape: [n_mels, 1 + n_fft/2]
+        mel_filter_bank = librosa.filters.mel(
+            sr=self.config.sample_rate,
+            n_fft=self.config.fft_size,
+            n_mels=self.config.mel_bands,
+            fmin=self.config.mel_fmin,
+            fmax=self.config.mel_fmax)
+        # mel shape: [n_mels, num_frames]
+        mel = np.dot(mel_filter_bank, spectrogram_magnitude)
+
+        # Normalize mel.
+        clip_val = 1e-5
+        ref_constant = 1
+        mel = np.log(np.clip(mel, a_min=clip_val, a_max=None) * ref_constant)
+
+        return mel
+
+    def __getitem__(self, idx):
+        audio = self.dataset[self.indices[idx]]
+        segment_length = self.config.segment_length
+
+        if self.valid:
+            # whole audio for valid set
+            pass
+        else:
+            # audio shape: [len]
+            if audio.shape[0] >= segment_length:  
+                max_audio_start = audio.shape[0] - segment_length
+                audio_start = random.randint(0, max_audio_start)
+                audio = audio[audio_start : (audio_start + segment_length)]
+            else:
+                audio = np.pad(audio, (0, segment_length - audio.shape[0]),
+                    mode='constant', constant_values=0)
+
+        # Normalize audio.
+        audio = audio / MAX_WAV_VALUE
+        mel = self.get_mel(audio)
+
+        return audio, mel
+
+    def _batch_examples(self, batch):
+        audio_batch = []
+        mel_batch = []
+        for audio, mel in batch:
+            audio_batch
+
+        audios = [sample[0] for sample in batch]
+        mels = [sample[1] for sample in batch]
+
+        audios = WavBatcher(pad_value=0.0)(audios)
+        mels = SpecBatcher(pad_value=0.0)(mels)
+
+        return audios, mels
+
+    def __len__(self):
+        return len(self.indices)
+
+
+class LJSpeech:
+    def __init__(self, config, nranks, rank):
+        place = fluid.CUDAPlace(rank) if config.use_gpu else fluid.CPUPlace()
+
+        # Whole LJSpeech dataset.
+        ds = Dataset(config)
+
+        # Split into train and valid dataset.
+        indices = list(range(len(ds)))
+        train_indices = indices[config.valid_size:]
+        valid_indices = indices[:config.valid_size]
+        random.shuffle(train_indices)
+
+        # Train dataset.
+        trainset = Subset(ds, train_indices, valid=False)
+        sampler = DistributedSampler(len(trainset), nranks, rank) 
+        total_bs = config.batch_size
+        assert total_bs % nranks == 0
+        train_sampler = BatchSampler(sampler, total_bs // nranks,
+            drop_last=True)
+        trainloader = DataCargo(trainset, batch_sampler=train_sampler)
+
+        trainreader = fluid.io.PyReader(capacity=50, return_list=True)
+        trainreader.decorate_batch_generator(trainloader, place)
+        self.trainloader = (data for _ in iter(int, 1)
+            for data in trainreader())
+
+        # Valid dataset.
+        validset = Subset(ds, valid_indices, valid=True)
+        # Currently only support batch_size = 1 for valid loader.
+        validloader = DataCargo(validset, batch_size=1, shuffle=False)
+
+        validreader = fluid.io.PyReader(capacity=20, return_list=True)
+        validreader.decorate_batch_generator(validloader, place) 
+        self.validloader = validreader

parakeet/models/waveflow/slurm.py

+"""
+Utility module for restarting training when using SLURM.
+"""
+import subprocess
+import os
+import sys
+import shlex
+import re
+import time
+
+
+def job_info():
+    """Get information about the current job using `scontrol show job`.
+    Returns a dict mapping parameter names (e.g. "UserId", "RunTime", etc) to
+    their values, both as strings.
+    """
+    job_id = int(os.environ["SLURM_JOB_ID"])
+
+    command = ["scontrol", "show", "job", str(job_id)]
+    output = subprocess.check_output(command).decode("utf-8")
+
+    # Use a regex to extract the parameter names and values
+    pattern = "([A-Za-z/]*)=([^ \t\n]*)"
+    return dict(re.findall(pattern, output))
+
+
+def parse_hours(text):
+    """Parse a time format HH or DD-HH into a number of hours."""
+    hour_chunks = text.split("-")
+    if len(hour_chunks) == 1:
+        return int(hour_chunks[0])
+    elif len(hour_chunks) == 2:
+        return 24 * int(hour_chunks[0]) + int(hour_chunks[1])
+    else:
+        raise ValueError("Unexpected hour format (expected HH or "
+                         "DD-HH, but got {}).".format(text))
+
+
+def parse_time(text):
+    """Convert slurm time to an integer.
+    Expects time to be of the form:
+    "hours:minutes:seconds" or "day-hours:minutes:seconds".
+    """
+    hours, minutes, seconds = text.split(":")
+    try:
+        return parse_hours(hours) * 3600 + int(minutes) * 60 + int(seconds)
+    except ValueError as e:
+        raise ValueError("Error parsing time {}. Got error {}.".format(
+            text, str(e)))
+
+
+def restart_command():
+    """Using the environment and SLURM command, create a command that, when,
+    run, will enqueue a repeat of the current job using `sbatch`.
+    Return the command as a list of strings, suitable for passing to
+    `subprocess.check_call` or similar functions.
+    Returns:
+        resume_command: list<str>, command to run to restart job.
+        end_time: int or None; the time the job will end or None
+            if the job has unlimited runtime.
+    """
+    # Make sure `RunTime` could be parsed correctly.
+    while job_info()["RunTime"] == "INVALID":
+        time.sleep(1)
+
+    # Get all the necessary information by querying SLURM with this job id
+    info = job_info()
+
+    try:
+        num_cpus = int(info["CPUs/Task"])
+    except KeyError:
+        num_cpus = int(os.environ["SLURM_CPUS_PER_TASK"])
+
+    num_tasks = int(os.environ["SLURM_NTASKS"])
+    nodes = info["NumNodes"]
+    gres, partition = info.get("Gres"), info.get("Partition")
+    stderr, stdout = info.get("StdErr"), info.get("StdOut")
+    job_name = info.get("JobName")
+    command = ["sbatch", "--job-name={}".format(job_name),
+               "--ntasks={}".format(num_tasks),
+               "--exclude=asimov-186"]
+
+    if partition:
+        command.extend(["--partition", partition])
+
+    if gres and gres != "(null)":
+        command.extend(["--gres", gres])
+        num_gpu = int(gres.split(':')[-1])
+        print("number of gpu assigned by slurm is {}".format(num_gpu))
+
+    if stderr:
+        command.extend(["--error", stderr])
+
+    if stdout:
+        command.extend(["--output", stdout])
+
+    python = subprocess.check_output(
+        ["/usr/bin/which", "python3"]).decode("utf-8").strip()
+    dist_setting = ['-m', 'paddle.distributed.launch']
+    wrap_cmd = ["srun", python, '-u'] + dist_setting + sys.argv
+
+    command.append(
+        "--wrap={}".format(" ".join(shlex.quote(arg) for arg in wrap_cmd)))
+    time_limit_string = info["TimeLimit"]
+    if time_limit_string.lower() == "unlimited":
+        print("UNLIMITED detected: restart OFF, infinite learning ON.",
+              flush=True)
+        return command, None
+    time_limit = parse_time(time_limit_string)
+    runtime = parse_time(info["RunTime"])
+    end_time = time.time() + time_limit - runtime
+
+    return command, end_time

parakeet/models/waveflow/synthesis.py

+import os
+import random
+from pprint import pprint
+
+import jsonargparse
+import numpy as np
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+
+import utils
+from wavenet import WaveNet
+
+
+def add_options_to_parser(parser):
+    parser.add_argument('--model', type=str, default='wavenet',
+        help="general name of the model")
+    parser.add_argument('--name', type=str,
+        help="specific name of the training model")
+    parser.add_argument('--root', type=str,
+        help="root path of the LJSpeech dataset")
+
+    parser.add_argument('--use_gpu', type=bool, default=True,
+        help="option to use gpu training")
+
+    parser.add_argument('--iteration', type=int, default=None,
+        help=("which iteration of checkpoint to load, "
+              "default to load the latest checkpoint"))
+    parser.add_argument('--checkpoint', type=str, default=None,
+        help="path of the checkpoint to load")
+
+    parser.add_argument('--output', type=str, default="./syn_audios",
+        help="path to write synthesized audio files")
+    parser.add_argument('--sample', type=int,
+        help="which of the valid samples to synthesize audio")
+
+
+def synthesize(config):
+    pprint(jsonargparse.namespace_to_dict(config))
+
+    # Get checkpoint directory path.
+    run_dir = os.path.join("runs", config.model, config.name)
+    checkpoint_dir = os.path.join(run_dir, "checkpoint")
+
+    # Configurate device.
+    place = fluid.CUDAPlace(0) if config.use_gpu else fluid.CPUPlace()
+
+    with dg.guard(place):
+        # Fix random seed.
+        seed = config.seed
+        random.seed(seed)
+        np.random.seed(seed)
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
+        print("Random Seed: ", seed)
+        
+        # Build model.
+        model = WaveNet(config, checkpoint_dir)
+        model.build(training=False)
+
+        # Obtain the current iteration.
+        if config.checkpoint is None:
+            if config.iteration is None:
+                iteration = utils.load_latest_checkpoint(checkpoint_dir)
+            else:
+                iteration = config.iteration
+        else:
+            iteration = int(config.checkpoint.split('/')[-1].split('-')[-1])
+
+        # Run model inference.
+        model.infer(iteration)
+
+
+if __name__ == "__main__":
+    # Create parser.
+    parser = jsonargparse.ArgumentParser(
+        description="Synthesize audio using WaveNet model",
+        formatter_class='default_argparse')
+    add_options_to_parser(parser)
+    utils.add_config_options_to_parser(parser)
+
+    # Parse argument from both command line and yaml config file.
+    # For conflicting updates to the same field,
+    # the preceding update will be overwritten by the following one.
+    config = parser.parse_args()
+    synthesize(config)

parakeet/models/waveflow/train.py

+import os
+import random
+import subprocess
+import time
+from pprint import pprint
+
+import jsonargparse
+import numpy as np
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+from tensorboardX import SummaryWriter
+
+import slurm
+import utils
+from waveflow import WaveFlow
+
+MAXIMUM_SAVE_TIME = 10 * 60
+
+
+def add_options_to_parser(parser):
+    parser.add_argument('--model', type=str, default='waveflow',
+        help="general name of the model")
+    parser.add_argument('--name', type=str,
+        help="specific name of the training model")
+    parser.add_argument('--root', type=str,
+        help="root path of the LJSpeech dataset")
+
+    parser.add_argument('--parallel', type=bool, default=True,
+        help="option to use data parallel training")
+    parser.add_argument('--use_gpu', type=bool, default=True,
+        help="option to use gpu training")
+
+    parser.add_argument('--iteration', type=int, default=None,
+        help=("which iteration of checkpoint to load, "
+              "default to load the latest checkpoint"))
+    parser.add_argument('--checkpoint', type=str, default=None,
+        help="path of the checkpoint to load")
+    parser.add_argument('--slurm', type=bool, default=False,
+        help="whether you are using slurm to submit training jobs")
+
+
+def train(config):
+    use_gpu = config.use_gpu
+    parallel = config.parallel if use_gpu else False
+
+    # Get the rank of the current training process.
+    rank = dg.parallel.Env().local_rank if parallel else 0
+    nranks = dg.parallel.Env().nranks if parallel else 1
+
+    if rank == 0:
+        # Print the whole config setting.
+        pprint(jsonargparse.namespace_to_dict(config))
+
+    # Make checkpoint directory.
+    run_dir = os.path.join("runs", config.model, config.name)
+    checkpoint_dir = os.path.join(run_dir, "checkpoint")
+    os.makedirs(checkpoint_dir, exist_ok=True)
+
+    # Create tensorboard logger.
+    tb = SummaryWriter(os.path.join(run_dir, "logs")) \
+        if rank == 0 else None
+
+    # Configurate device
+    place = fluid.CUDAPlace(rank) if use_gpu else fluid.CPUPlace()
+
+    with dg.guard(place):
+        # Fix random seed.
+        seed = config.seed
+        random.seed(seed)
+        np.random.seed(seed)
+        fluid.default_startup_program().random_seed = seed
+        fluid.default_main_program().random_seed = seed
+        print("Random Seed: ", seed)
+
+        # Build model.
+        model = WaveFlow(config, checkpoint_dir, parallel, rank, nranks, tb)
+        model.build()
+
+        # Obtain the current iteration.
+        if config.checkpoint is None:
+            if config.iteration is None:
+                iteration = utils.load_latest_checkpoint(checkpoint_dir, rank)
+            else:
+                iteration = config.iteration
+        else:
+            iteration = int(config.checkpoint.split('/')[-1].split('-')[-1])
+
+        # Get restart command if using slurm.
+        if config.slurm:
+            resume_command, death_time = slurm.restart_command()
+            if rank == 0:
+                print("Restart command:", " ".join(resume_command))
+        done = False
+
+        while iteration < config.max_iterations:
+            # Run one single training step.
+            model.train_step(iteration)
+
+            iteration += 1
+
+            if iteration % config.test_every == 0:
+                # Run validation step.
+                model.valid_step(iteration)
+
+            # Check whether reaching the time limit.
+            if config.slurm:
+                done = (death_time is not None and death_time - time.time() <
+                    MAXIMUM_SAVE_TIME)
+
+            if rank == 0 and done:
+                print("Saving progress before exiting.")
+                model.save(iteration)
+
+                print("Running restart command:", " ".join(resume_command))
+                # Submit restart command.
+                subprocess.check_call(resume_command)
+                break
+
+            if rank == 0 and iteration % config.save_every == 0:
+                # Save parameters.
+                model.save(iteration)
+
+    # Close TensorBoard.
+    if rank == 0:
+        tb.close()
+
+
+if __name__ == "__main__":
+    # Create parser.
+    parser = jsonargparse.ArgumentParser(description="Train WaveFlow model",
+        formatter_class='default_argparse')
+    add_options_to_parser(parser)
+    utils.add_config_options_to_parser(parser)
+
+    # Parse argument from both command line and yaml config file.
+    # For conflicting updates to the same field, 
+    # the preceding update will be overwritten by the following one.
+    config = parser.parse_args()
+    train(config) 

parakeet/models/waveflow/utils.py

+import itertools
+import os
+import time
+
+import jsonargparse
+import numpy as np
+import paddle.fluid.dygraph as dg
+
+
+def add_config_options_to_parser(parser):
+    parser.add_argument('--valid_size', type=int,
+        help="size of the valid dataset")
+    parser.add_argument('--segment_length', type=int,
+        help="the length of audio clip for training")
+    parser.add_argument('--sample_rate', type=int,
+        help="sampling rate of audio data file")
+    parser.add_argument('--fft_window_shift', type=int,
+        help="the shift of fft window for each frame")
+    parser.add_argument('--fft_window_size', type=int,
+        help="the size of fft window for each frame")
+    parser.add_argument('--fft_size', type=int,
+        help="the size of fft filter on each frame")
+    parser.add_argument('--mel_bands', type=int,
+        help="the number of mel bands when calculating mel spectrograms")
+    parser.add_argument('--mel_fmin', type=float,
+        help="lowest frequency in calculating mel spectrograms")
+    parser.add_argument('--mel_fmax', type=float,
+        help="highest frequency in calculating mel spectrograms")
+
+    parser.add_argument('--seed', type=int,
+        help="seed of random initialization for the model")
+    parser.add_argument('--learning_rate', type=float)
+    parser.add_argument('--batch_size', type=int,
+        help="batch size for training")
+    parser.add_argument('--test_every', type=int,
+        help="test interval during training")
+    parser.add_argument('--save_every', type=int,
+        help="checkpointing interval during training")
+    parser.add_argument('--max_iterations', type=int,
+        help="maximum training iterations")
+
+    parser.add_argument('--sigma', type=float,
+        help="standard deviation of the latent Gaussian variable")
+    parser.add_argument('--n_flows', type=int,
+        help="number of flows")
+    parser.add_argument('--n_group', type=int,
+        help="number of adjacent audio samples to squeeze into one column")
+    parser.add_argument('--n_layers', type=int,
+        help="number of conv2d layer in one wavenet-like flow architecture")
+    parser.add_argument('--n_channels', type=int,
+        help="number of residual channels in flow")
+    parser.add_argument('--kernel_h', type=int,
+        help="height of the kernel in the conv2d layer")
+    parser.add_argument('--kernel_w', type=int,
+        help="width of the kernel in the conv2d layer")
+
+    parser.add_argument('--config', action=jsonargparse.ActionConfigFile)
+
+
+def pad_to_size(array, length, pad_with=0.0):
+    """
+    Pad an array on the first (length) axis to a given length.
+    """
+    padding = length - array.shape[0]
+    assert padding >= 0, "Padding required was less than zero"
+
+    paddings = [(0, 0)] * len(array.shape)
+    paddings[0] = (0, padding)
+
+    return np.pad(array, paddings, mode='constant', constant_values=pad_with)
+
+
+def calculate_context_size(config):
+    dilations = list(
+        itertools.islice(
+            itertools.cycle(config.dilation_block), config.layers))
+    config.context_size = sum(dilations) + 1
+    print("Context size is", config.context_size)
+
+
+def load_latest_checkpoint(checkpoint_dir, rank=0):
+    checkpoint_path = os.path.join(checkpoint_dir, "checkpoint")
+    # Create checkpoint index file if not exist.
+    if (not os.path.isfile(checkpoint_path)) and rank == 0:
+        with open(checkpoint_path, "w") as handle:
+            handle.write("model_checkpoint_path: step-0")
+
+    # Make sure that other process waits until checkpoint file is created
+    # by process 0.
+    while not os.path.isfile(checkpoint_path):
+        time.sleep(1)
+
+    # Fetch the latest checkpoint index.
+    with open(checkpoint_path, "r") as handle:
+        latest_checkpoint = handle.readline().split()[-1]
+        iteration = int(latest_checkpoint.split("-")[-1])
+
+    return iteration
+
+
+def save_latest_checkpoint(checkpoint_dir, iteration):
+    checkpoint_path = os.path.join(checkpoint_dir, "checkpoint")
+    # Update the latest checkpoint index.
+    with open(checkpoint_path, "w") as handle:
+        handle.write("model_checkpoint_path: step-{}".format(iteration))
+
+
+def load_parameters(checkpoint_dir, rank, model, optimizer=None,
+                    iteration=None, file_path=None):
+    if file_path is None:
+        if iteration is None:
+            iteration = load_latest_checkpoint(checkpoint_dir, rank)
+        if iteration == 0:
+            return
+        file_path = "{}/step-{}".format(checkpoint_dir, iteration)
+
+    model_dict, optimizer_dict = dg.load_dygraph(file_path)
+    model.set_dict(model_dict)
+    print("[checkpoint] Rank {}: loaded model from {}".format(rank, file_path))
+    if optimizer and optimizer_dict:
+        optimizer.set_dict(optimizer_dict)
+        print("[checkpoint] Rank {}: loaded optimizer state from {}".format(
+              rank, file_path))
+
+
+def save_latest_parameters(checkpoint_dir, iteration, model, optimizer=None):
+    file_path = "{}/step-{}".format(checkpoint_dir, iteration)
+    model_dict = model.state_dict()
+    dg.save_dygraph(model_dict, file_path)
+    print("[checkpoint] Saved model to {}".format(file_path))
+
+    if optimizer:
+        opt_dict = optimizer.state_dict()
+        dg.save_dygraph(opt_dict, file_path)
+        print("[checkpoint] Saved optimzier state to {}".format(file_path))

parakeet/models/waveflow/waveflow.py

+import itertools
+import os
+import time
+
+import librosa
+import numpy as np
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+
+import utils
+from data import LJSpeech
+from waveflow_modules import WaveFlowLoss, WaveFlowModule
+
+
+class WaveFlow():
+    def __init__(self, config, checkpoint_dir, parallel=False, rank=0,
+                 nranks=1, tb_logger=None):
+        self.config = config
+        self.checkpoint_dir = checkpoint_dir
+        self.parallel = parallel
+        self.rank = rank
+        self.nranks = nranks
+        self.tb_logger = tb_logger
+
+    def build(self, training=True):
+        config = self.config
+        dataset = LJSpeech(config, self.nranks, self.rank) 
+        self.trainloader = dataset.trainloader
+        self.validloader = dataset.validloader
+
+#        if self.rank == 0:
+#            for i, (audios, mels) in enumerate(self.validloader()):
+#                print("audios {}, mels {}".format(audios.dtype, mels.dtype))
+#                print("{}: rank {}, audios {}, mels {}".format(
+#                    i, self.rank, audios.shape, mels.shape))
+#    
+#            for i, (audios, mels) in enumerate(self.trainloader):
+#                print("{}: rank {}, audios {}, mels {}".format(
+#                    i, self.rank, audios.shape, mels.shape))
+#
+#        exit()
+
+        waveflow = WaveFlowModule("waveflow", config)
+        
+        # Dry run once to create and initalize all necessary parameters.
+        audio = dg.to_variable(np.random.randn(1, 16000).astype(np.float32))
+        mel = dg.to_variable(
+            np.random.randn(1, config.mel_bands, 63).astype(np.float32))
+        waveflow(audio, mel)
+
+        if training:
+            optimizer = fluid.optimizer.AdamOptimizer(
+                learning_rate=config.learning_rate)
+    
+            # Load parameters.
+            utils.load_parameters(self.checkpoint_dir, self.rank,
+                                  waveflow, optimizer,
+                                  iteration=config.iteration,
+                                  file_path=config.checkpoint)
+            print("Rank {}: checkpoint loaded.".format(self.rank))
+    
+            # Data parallelism.
+            if self.parallel:
+                strategy = dg.parallel.prepare_context()
+                waveflow = dg.parallel.DataParallel(waveflow, strategy)
+    
+            self.waveflow = waveflow
+            self.optimizer = optimizer
+            self.criterion = WaveFlowLoss(config.sigma)
+
+        else:
+            # Load parameters.
+            utils.load_parameters(self.checkpoint_dir, self.rank, waveflow,
+                                  iteration=config.iteration,
+                                  file_path=config.checkpoint)
+            print("Rank {}: checkpoint loaded.".format(self.rank))
+
+            self.waveflow = waveflow
+
+    def train_step(self, iteration):
+        self.waveflow.train()
+
+        start_time = time.time()
+        audios, mels = next(self.trainloader)
+        load_time = time.time()
+
+        outputs = self.waveflow(audios, mels)
+        loss = self.criterion(outputs)
+
+        if self.parallel:
+            # loss = loss / num_trainers
+            loss = self.waveflow.scale_loss(loss)
+            loss.backward()
+            self.waveflow.apply_collective_grads()
+        else:
+            loss.backward()
+
+        current_lr = self.optimizer._learning_rate
+
+        self.optimizer.minimize(loss, parameter_list=self.waveflow.parameters())
+        self.waveflow.clear_gradients()
+
+        graph_time = time.time()
+
+        if self.rank == 0:
+            loss_val = float(loss.numpy()) * self.nranks
+            log = "Rank: {} Step: {:^8d} Loss: {:<8.3f} " \
+                  "Time: {:.3f}/{:.3f}".format(
+                  self.rank, iteration, loss_val,
+                  load_time - start_time, graph_time - load_time)
+            print(log)
+
+            tb = self.tb_logger
+            tb.add_scalar("Train-Loss-Rank-0", loss_val, iteration)
+            tb.add_scalar("Learning-Rate", current_lr, iteration)
+
+    @dg.no_grad
+    def valid_step(self, iteration):
+        self.waveflow.eval()
+        tb = self.tb_logger
+
+        total_loss = []
+        sample_audios = []
+        start_time = time.time()
+
+        for i, batch in enumerate(self.validloader()):
+            audios, mels = batch
+            valid_outputs = self.waveflow(audios, mels)
+            valid_z, valid_log_s_list = valid_outputs
+
+            # Visualize latent z and scale log_s.
+            if self.rank == 0 and i == 0:
+                tb.add_histogram("Valid-Latent_z", valid_z.numpy(), iteration)
+                for j, valid_log_s in enumerate(valid_log_s_list):
+                    hist_name = "Valid-{}th-Flow-Log_s".format(j)
+                    tb.add_histogram(hist_name, valid_log_s.numpy(), iteration)
+
+            valid_loss = self.criterion(valid_outputs)
+            total_loss.append(float(valid_loss.numpy()))
+
+        total_time = time.time() - start_time
+        if self.rank == 0:
+            loss_val = np.mean(total_loss)
+            log = "Test | Rank: {} AvgLoss: {:<8.3f} Time {:<8.3f}".format(
+                self.rank, loss_val, total_time)
+            print(log)
+            tb.add_scalar("Valid-Avg-Loss", loss_val, iteration)
+
+    @dg.no_grad
+    def infer(self, iteration):
+        self.waveflow.eval()
+
+        config = self.config
+        sample = config.sample
+
+        output = "{}/{}/iter-{}".format(config.output, config.name, iteration)
+        os.makedirs(output, exist_ok=True)
+
+        filename = "{}/valid_{}.wav".format(output, sample)
+        print("Synthesize sample {}, save as {}".format(sample, filename))
+
+        mels_list = [mels for _, mels, _ in self.validloader()]
+        start_time = time.time()
+        syn_audio = self.waveflow.synthesize(mels_list[sample])
+        syn_time = time.time() - start_time
+        print("audio shape {}, synthesis time {}".format(
+            syn_audio.shape, syn_time))
+        librosa.output.write_wav(filename, syn_audio,
+            sr=config.sample_rate)
+
+    def save(self, iteration):
+        utils.save_latest_parameters(self.checkpoint_dir, iteration,
+                                     self.waveflow, self.optimizer)
+        utils.save_latest_checkpoint(self.checkpoint_dir, iteration)

parakeet/models/waveflow/waveflow_modules.py

+import itertools
+
+import numpy as np
+import paddle.fluid.dygraph as dg
+from paddle import fluid
+from parakeet.modules import conv, modules, weight_norm
+
+
+def set_param_attr(layer, c_in=1):
+    if isinstance(layer, (weight_norm.Conv2DTranspose, weight_norm.Conv2D)):
+        k = np.sqrt(1.0 / (c_in * np.prod(layer._filter_size)))
+        weight_init = fluid.initializer.UniformInitializer(low=-k, high=k)
+        bias_init = fluid.initializer.UniformInitializer(low=-k, high=k)
+    elif isinstance(layer, dg.Conv2D):
+        weight_init = fluid.initializer.ConstantInitializer(0.0)
+        bias_init = fluid.initializer.ConstantInitializer(0.0)
+    else:
+        raise TypeError("Unsupported layer type.")
+
+    layer._param_attr = fluid.ParamAttr(initializer=weight_init)
+    layer._bias_attr = fluid.ParamAttr(initializer=bias_init)
+
+
+def unfold(x, n_group):
+    length = x.shape[-1] 
+    #assert length % n_group == 0
+    new_shape = x.shape[:-1] + [length // n_group, n_group]
+    return fluid.layers.reshape(x, new_shape)
+
+
+class WaveFlowLoss:
+    def __init__(self, sigma=1.0):
+        self.sigma = sigma
+
+    def __call__(self, model_output):
+        z, log_s_list = model_output
+        for i, log_s in enumerate(log_s_list):
+            if i == 0:
+                log_s_total = fluid.layers.reduce_sum(log_s)
+            else:
+                log_s_total = log_s_total + fluid.layers.reduce_sum(log_s)
+
+        loss = fluid.layers.reduce_sum(z * z) / (2 * self.sigma * self.sigma) \
+            - log_s_total
+        loss = loss / np.prod(z.shape)
+        const = 0.5 * np.log(2 * np.pi) + np.log(self.sigma)
+
+        return loss + const
+
+
+class Conditioner(dg.Layer):
+    def __init__(self, name_scope):
+        super(Conditioner, self).__init__(name_scope)
+        upsample_factors = [16, 16]
+        
+        self.upsample_conv2d = []
+        for s in upsample_factors:
+            in_channel = 1
+            conv_trans2d = modules.Conv2DTranspose(
+                self.full_name(),
+                num_filters=1,
+                filter_size=(3, 2 * s),
+                padding=(1, s // 2),
+                stride=(1, s))
+            set_param_attr(conv_trans2d, c_in=in_channel) 
+            self.upsample_conv2d.append(conv_trans2d)
+
+        for i, layer in enumerate(self.upsample_conv2d):
+            self.add_sublayer("conv2d_transpose_{}".format(i), layer)
+
+    def forward(self, x):
+        x = fluid.layers.unsqueeze(x, 1)
+        for layer in self.upsample_conv2d:
+            x = fluid.layers.leaky_relu(layer(x), alpha=0.4)
+
+        return fluid.layers.squeeze(x, [1])
+
+
+class Flow(dg.Layer):
+    def __init__(self, name_scope, config): 
+        super(Flow, self).__init__(name_scope)
+        self.n_layers = config.n_layers
+        self.n_channels = config.n_channels
+        self.kernel_h = config.kernel_h
+        self.kernel_w = config.kernel_w
+
+        # Transform audio: [batch, 1, n_group, time/n_group] 
+        # => [batch, n_channels, n_group, time/n_group]
+        self.start = weight_norm.Conv2D(
+            self.full_name(),
+            num_filters=self.n_channels,
+            filter_size=(1, 1))
+        set_param_attr(self.start, c_in=1)
+
+        # Initializing last layer to 0 makes the affine coupling layers
+        # do nothing at first.  This helps with training stability
+        # output shape: [batch, 2, n_group, time/n_group]
+        self.end = dg.Conv2D(
+            self.full_name(),
+            num_filters=2,
+            filter_size=(1, 1))
+        set_param_attr(self.end)
+
+        # receiptive fileds: (kernel - 1) * sum(dilations) + 1 >= squeeze
+        dilation_dict = {8:   [1, 1, 1, 1, 1, 1, 1, 1],
+                         16:  [1, 1, 1, 1, 1, 1, 1, 1],
+                         32:  [1, 2, 4, 1, 2, 4, 1, 2],
+                         64:  [1, 2, 4, 8, 16, 1, 2, 4],
+                         128: [1, 2, 4, 8, 16, 32, 64, 1]}
+        self.dilation_h_list = dilation_dict[config.n_group]
+
+        self.in_layers = []
+        self.cond_layers = []
+        self.res_skip_layers = []
+        for i in range(self.n_layers):
+            dilation_h = self.dilation_h_list[i]
+            dilation_w = 2 ** i
+
+            in_layer = weight_norm.Conv2D(
+                self.full_name(),
+                num_filters=2 * self.n_channels,
+                filter_size=(self.kernel_h, self.kernel_w),
+                dilation=(dilation_h, dilation_w))
+            set_param_attr(in_layer, c_in=self.n_channels)
+            self.in_layers.append(in_layer)
+
+            cond_layer = weight_norm.Conv2D(
+                self.full_name(),
+                num_filters=2 * self.n_channels,
+                filter_size=(1, 1))
+            set_param_attr(cond_layer, c_in=config.mel_bands)
+            self.cond_layers.append(cond_layer)
+
+            if i < self.n_layers - 1:
+                res_skip_channels = 2 * self.n_channels
+            else:
+                res_skip_channels = self.n_channels
+            res_skip_layer = weight_norm.Conv2D(
+                self.full_name(),
+                num_filters=res_skip_channels,
+                filter_size=(1, 1))
+            set_param_attr(res_skip_layer, c_in=self.n_channels)
+            self.res_skip_layers.append(res_skip_layer)
+
+            self.add_sublayer("in_layer_{}".format(i), in_layer)
+            self.add_sublayer("cond_layer_{}".format(i), cond_layer)
+            self.add_sublayer("res_skip_layer_{}".format(i), res_skip_layer)
+
+    def forward(self, audio, mel):
+        # audio: [bs, 1, n_group, time/group]
+        # mel: [bs, mel_bands, n_group, time/n_group]
+        audio = self.start(audio)
+
+        for i in range(self.n_layers):
+            dilation_h = self.dilation_h_list[i]
+            dilation_w = 2 ** i
+
+            # Pad height dim (n_group): causal convolution
+            # Pad width dim (time): dialated non-causal convolution
+            pad_top, pad_bottom = (self.kernel_h - 1) * dilation_h, 0
+            pad_left = pad_right = int((self.kernel_w-1) * dilation_w / 2)
+            audio_pad = fluid.layers.pad2d(audio, 
+                paddings=[pad_top, pad_bottom, pad_left, pad_right])
+
+            hidden = self.in_layers[i](audio_pad)
+            cond_hidden = self.cond_layers[i](mel)
+            in_acts = hidden + cond_hidden
+            out_acts = fluid.layers.tanh(in_acts[:, :self.n_channels, :]) * \
+                fluid.layers.sigmoid(in_acts[:, self.n_channels:, :])
+            res_skip_acts = self.res_skip_layers[i](out_acts)
+
+            if i < self.n_layers - 1:
+                audio += res_skip_acts[:, :self.n_channels, :, :]
+                skip_acts = res_skip_acts[:, self.n_channels:, :, :]
+            else:
+                skip_acts = res_skip_acts
+
+            if i == 0:
+                output = skip_acts
+            else:
+                output += skip_acts
+
+        return self.end(output)
+
+
+class WaveFlowModule(dg.Layer):
+    def __init__(self, name_scope, config):
+        super(WaveFlowModule, self).__init__(name_scope)
+        self.n_flows = config.n_flows
+        self.n_group = config.n_group
+        assert self.n_group % 2 == 0
+
+        self.conditioner = Conditioner(self.full_name())
+        self.flows = []
+        for i in range(self.n_flows):
+            flow = Flow(self.full_name(), config)
+            self.flows.append(flow)
+            self.add_sublayer("flow_{}".format(i), flow) 
+
+        self.perms = [[15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+                      [15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+                      [15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+                      [15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+                      [7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8],
+                      [7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8],
+                      [7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8],
+                      [7, 6, 5, 4, 3, 2, 1, 0, 15, 14, 13, 12, 11, 10, 9, 8]]
+        
+    def forward(self, audio, mel):
+        mel = self.conditioner(mel)
+        assert mel.shape[2] >= audio.shape[1]
+        # Prune out the tail of audio/mel so that time/n_group == 0.
+        pruned_len = audio.shape[1] // self.n_group * self.n_group
+
+        if audio.shape[1] > pruned_len:
+            audio = audio[:, :pruned_len]
+        if mel.shape[2] > pruned_len:
+            mel = mel[:, :, :pruned_len]
+        
+        # From [bs, mel_bands, time] to [bs, mel_bands, n_group, time/n_group] 
+        mel = fluid.layers.transpose(unfold(mel, self.n_group), [0, 1, 3, 2])
+        # From [bs, time] to [bs, n_group, time/n_group]
+        audio = fluid.layers.transpose(unfold(audio, self.n_group), [0, 2, 1])
+        # [bs, 1, n_group, time/n_group] 
+        audio = fluid.layers.unsqueeze(audio, 1)
+
+        log_s_list = []
+        for i in range(self.n_flows):
+            inputs = audio[:, :, :-1, :]
+            conds = mel[:, :, 1:, :]
+            outputs = self.flows[i](inputs, conds)
+            log_s = outputs[:, :1, :, :]
+            b = outputs[:, 1:, :, :]
+            log_s_list.append(log_s)
+
+            audio_0 = audio[:, :, :1, :]
+            audio_out = audio[:, :, 1:, :] * fluid.layers.exp(log_s) + b
+            audio = fluid.layers.concat([audio_0, audio_out], axis=2)
+
+            # Permute over the height dim.
+            audio_slices = [audio[:, :, j, :] for j in self.perms[i]]
+            audio = fluid.layers.stack(audio_slices, axis=2)
+            mel_slices = [mel[:, :, j, :] for j in self.perms[i]]
+            mel = fluid.layers.stack(mel_slices, axis=2)
+
+        z = fluid.layers.squeeze(audio, [1])
+
+        return z, log_s_list
+
+    def synthesize(self, mels):
+        pass
+
+    def start_new_sequence(self):
+        for layer in self.sublayers():
+            if isinstance(layer, conv.Conv1D):
+                layer.start_new_sequence()