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96 lines
4.1 KiB
96 lines
4.1 KiB
4 years ago
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""export mindir."""
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import json
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from os.path import join
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import argparse
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from warnings import warn
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from hparams import hparams, hparams_debug_string
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from mindspore import context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
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from wavenet_vocoder import WaveNet
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from wavenet_vocoder.util import is_mulaw_quantize, is_scalar_input
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import numpy as np
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from src.loss import PredictNet
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parser = argparse.ArgumentParser(description='TTS training')
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parser.add_argument('--preset', type=str, default='', help='Path of preset parameters (json).')
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parser.add_argument('--speaker_id', type=str, default='',
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help=' Use specific speaker of data in case for multi-speaker datasets.')
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parser.add_argument('--pretrain_ckpt', type=str, default='', help='Pretrained checkpoint path')
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args = parser.parse_args()
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if __name__ == '__main__':
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
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speaker_id = int(args.speaker_id) if args.speaker_id != '' else None
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if args.preset is not None:
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with open(args.preset) as f:
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hparams.parse_json(f.read())
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assert hparams.name == "wavenet_vocoder"
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print(hparams_debug_string())
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fs = hparams.sample_rate
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output_json_path = join(args.checkpoint_dir, "hparams.json")
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with open(output_json_path, "w") as f:
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json.dump(hparams.values(), f, indent=2)
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if is_mulaw_quantize(hparams.input_type):
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if hparams.out_channels != hparams.quantize_channels:
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raise RuntimeError(
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"out_channels must equal to quantize_chennels if input_type is 'mulaw-quantize'")
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if hparams.upsample_conditional_features and hparams.cin_channels < 0:
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s = "Upsample conv layers were specified while local conditioning disabled. "
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s += "Notice that upsample conv layers will never be used."
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warn(s)
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upsample_params = hparams.upsample_params
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upsample_params["cin_channels"] = hparams.cin_channels
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upsample_params["cin_pad"] = hparams.cin_pad
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model = WaveNet(
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out_channels=hparams.out_channels,
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layers=hparams.layers,
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stacks=hparams.stacks,
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residual_channels=hparams.residual_channels,
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gate_channels=hparams.gate_channels,
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skip_out_channels=hparams.skip_out_channels,
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cin_channels=hparams.cin_channels,
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gin_channels=hparams.gin_channels,
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n_speakers=hparams.n_speakers,
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dropout=hparams.dropout,
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kernel_size=hparams.kernel_size,
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cin_pad=hparams.cin_pad,
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upsample_conditional_features=hparams.upsample_conditional_features,
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upsample_params=upsample_params,
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scalar_input=is_scalar_input(hparams.input_type),
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output_distribution=hparams.output_distribution,
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)
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Net = PredictNet(model)
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Net.set_train(False)
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receptive_field = model.receptive_field
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print("Receptive field (samples / ms): {} / {}".format(receptive_field, receptive_field / fs * 1000))
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param_dict = load_checkpoint(args.pretrain_ckpt)
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load_param_into_net(model, param_dict)
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print('Successfully loading the pre-trained model')
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x = np.array(np.random.random((2, 256, 10240)), dtype=np.float32)
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c = np.array(np.random.random((2, 80, 44)), dtype=np.float32)
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g = np.array([0, 0], dtype=np.int64)
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export(Net, Tensor(x), Tensor(c), Tensor(g), file_name="WaveNet", file_format='MINDIR')
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