def test_melgan_trainable(dict_g, dict_d):
    random_c = tf.random.uniform(shape=[4, 32, 80], dtype=tf.float32)

    args_g = make_pwgan_generator_args(**dict_g)
    args_d = make_pwgan_discriminator_args(**dict_d)

    args_g = ParallelWaveGANGeneratorConfig(**args_g)
    args_d = ParallelWaveGANDiscriminatorConfig(**args_d)

    generator = TFParallelWaveGANGenerator(args_g)
    generator._build()
    discriminator = TFParallelWaveGANDiscriminator(args_d)
    discriminator._build()

    generated_audios = generator(random_c, training=True)
    discriminator(generated_audios)

    generator.summary()
    discriminator.summary()
Exemple #2
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def main():
    """Run training process."""
    parser = argparse.ArgumentParser(
        description="Train MultiBand MelGAN (See detail in examples/multiband_melgan/train_multiband_melgan.py)"
    )
    parser.add_argument(
        "--train-dir",
        default=None,
        type=str,
        help="directory including training data. ",
    )
    parser.add_argument(
        "--dev-dir",
        default=None,
        type=str,
        help="directory including development data. ",
    )
    parser.add_argument(
        "--use-norm", default=1, type=int, help="use norm mels for training or raw."
    )
    parser.add_argument(
        "--outdir", type=str, required=True, help="directory to save checkpoints."
    )
    parser.add_argument(
        "--config", type=str, required=True, help="yaml format configuration file."
    )
    parser.add_argument(
        "--resume",
        default="",
        type=str,
        nargs="?",
        help='checkpoint file path to resume training. (default="")',
    )
    parser.add_argument(
        "--verbose",
        type=int,
        default=1,
        help="logging level. higher is more logging. (default=1)",
    )
    parser.add_argument(
        "--generator_mixed_precision",
        default=0,
        type=int,
        help="using mixed precision for generator or not.",
    )
    parser.add_argument(
        "--discriminator_mixed_precision",
        default=0,
        type=int,
        help="using mixed precision for discriminator or not.",
    )
    parser.add_argument(
        "--pretrained",
        default="",
        type=str,
        nargs="?",
        help='path of .h5 mb-melgan generator to load weights from',
    )
    args = parser.parse_args()

    # return strategy
    STRATEGY = return_strategy()

    # set mixed precision config
    if args.generator_mixed_precision == 1 or args.discriminator_mixed_precision == 1:
        tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})

    args.generator_mixed_precision = bool(args.generator_mixed_precision)
    args.discriminator_mixed_precision = bool(args.discriminator_mixed_precision)

    args.use_norm = bool(args.use_norm)

    # set logger
    if args.verbose > 1:
        logging.basicConfig(
            level=logging.DEBUG,
            stream=sys.stdout,
            format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
        )
    elif args.verbose > 0:
        logging.basicConfig(
            level=logging.INFO,
            stream=sys.stdout,
            format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
        )
    else:
        logging.basicConfig(
            level=logging.WARN,
            stream=sys.stdout,
            format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
        )
        logging.warning("Skip DEBUG/INFO messages")

    # check directory existence
    if not os.path.exists(args.outdir):
        os.makedirs(args.outdir)

    # check arguments
    if args.train_dir is None:
        raise ValueError("Please specify --train-dir")
    if args.dev_dir is None:
        raise ValueError("Please specify either --valid-dir")

    # load and save config
    with open(args.config) as f:
        config = yaml.load(f, Loader=yaml.Loader)
    config.update(vars(args))
    config["version"] = tensorflow_tts.__version__
    with open(os.path.join(args.outdir, "config.yml"), "w") as f:
        yaml.dump(config, f, Dumper=yaml.Dumper)
    for key, value in config.items():
        logging.info(f"{key} = {value}")

    # get dataset
    if config["remove_short_samples"]:
        mel_length_threshold = config["batch_max_steps"] // config[
            "hop_size"
        ] + 2 * config["multiband_melgan_generator_params"].get("aux_context_window", 0)
    else:
        mel_length_threshold = None

    if config["format"] == "npy":
        audio_query = "*-wave.npy"
        mel_query = "*-raw-feats.npy" if args.use_norm is False else "*-norm-feats.npy"
        audio_load_fn = np.load
        mel_load_fn = np.load
    else:
        raise ValueError("Only npy are supported.")

    # define train/valid dataset
    train_dataset = AudioMelDataset(
        root_dir=args.train_dir,
        audio_query=audio_query,
        mel_query=mel_query,
        audio_load_fn=audio_load_fn,
        mel_load_fn=mel_load_fn,
        mel_length_threshold=mel_length_threshold,
    ).create(
        is_shuffle=config["is_shuffle"],
        map_fn=lambda items: collater(
            items,
            batch_max_steps=tf.constant(config["batch_max_steps"], dtype=tf.int32),
            hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
        ),
        allow_cache=config["allow_cache"],
        batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync,
    )

    valid_dataset = AudioMelDataset(
        root_dir=args.dev_dir,
        audio_query=audio_query,
        mel_query=mel_query,
        audio_load_fn=audio_load_fn,
        mel_load_fn=mel_load_fn,
        mel_length_threshold=mel_length_threshold,
    ).create(
        is_shuffle=config["is_shuffle"],
        map_fn=lambda items: collater(
            items,
            batch_max_steps=tf.constant(
                config["batch_max_steps_valid"], dtype=tf.int32
            ),
            hop_size=tf.constant(config["hop_size"], dtype=tf.int32),
        ),
        allow_cache=config["allow_cache"],
        batch_size=config["batch_size"] * STRATEGY.num_replicas_in_sync,
    )

    # define trainer
    trainer = MultiBandMelganTrainer(
        steps=0,
        epochs=0,
        config=config,
        strategy=STRATEGY,
        is_generator_mixed_precision=args.generator_mixed_precision,
        is_discriminator_mixed_precision=args.discriminator_mixed_precision,
    )

    with STRATEGY.scope():
        # define generator and discriminator
        generator = TFMelGANGenerator(
            MultiBandMelGANGeneratorConfig(**config["multiband_melgan_generator_params"]),
            name="multi_band_melgan_generator",
        )

        discriminator = TFParallelWaveGANDiscriminator(
            ParallelWaveGANDiscriminatorConfig(
                **config["parallel_wavegan_discriminator_params"]
            ),
            name="parallel_wavegan_discriminator",
        )

        pqmf = TFPQMF(
            MultiBandMelGANGeneratorConfig(**config["multiband_melgan_generator_params"]), name="pqmf"
        )

        # dummy input to build model.
        fake_mels = tf.random.uniform(shape=[1, 100, 80], dtype=tf.float32)
        y_mb_hat = generator(fake_mels)
        y_hat = pqmf.synthesis(y_mb_hat)
        discriminator(y_hat)
        
        if len(args.pretrained) > 2:
          print("Loading pretrained weights...")
          generator.load_weights(args.pretrained)

        generator.summary()
        discriminator.summary()

        # define optimizer
        generator_lr_fn = getattr(
            tf.keras.optimizers.schedules, config["generator_optimizer_params"]["lr_fn"]
        )(**config["generator_optimizer_params"]["lr_params"])
        discriminator_lr_fn = getattr(
            tf.keras.optimizers.schedules,
            config["discriminator_optimizer_params"]["lr_fn"],
        )(**config["discriminator_optimizer_params"]["lr_params"])

        gen_optimizer = tf.keras.optimizers.Adam(
            learning_rate=generator_lr_fn,
            amsgrad=config["generator_optimizer_params"]["amsgrad"],
        )
        dis_optimizer = RectifiedAdam(
            learning_rate=discriminator_lr_fn, amsgrad=False
        )


    trainer.compile(
        gen_model=generator,
        dis_model=discriminator,
        gen_optimizer=gen_optimizer,
        dis_optimizer=dis_optimizer,
        pqmf=pqmf,
    )

    # start training
    try:
        trainer.fit(
            train_dataset,
            valid_dataset,
            saved_path=os.path.join(config["outdir"], "checkpoints/"),
            resume=args.resume,
        )
    except KeyboardInterrupt:
        trainer.save_checkpoint()
        logging.info(f"Successfully saved checkpoint @ {trainer.steps}steps.")