113 lines
4.5 KiB
Python
113 lines
4.5 KiB
Python
# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, hexisyztem@icloud.com)
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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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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from __future__ import print_function
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import argparse
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import logging
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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import os
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import sys
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import onnxruntime
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import random
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import torch
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from tqdm import tqdm
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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sys.path.append('{}/../..'.format(ROOT_DIR))
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sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
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from cosyvoice.cli.cosyvoice import CosyVoice
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def get_dummy_input(batch_size, seq_len, out_channels, device):
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x = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
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mask = torch.ones((batch_size, 1, seq_len), dtype=torch.float32, device=device)
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mu = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
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t = torch.rand((batch_size), dtype=torch.float32, device=device)
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spks = torch.rand((batch_size, out_channels), dtype=torch.float32, device=device)
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cond = torch.rand((batch_size, out_channels, seq_len), dtype=torch.float32, device=device)
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return x, mask, mu, t, spks, cond
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def get_args():
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parser = argparse.ArgumentParser(description='export your model for deployment')
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parser.add_argument('--model_dir',
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type=str,
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default='pretrained_models/CosyVoice-300M',
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help='local path')
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args = parser.parse_args()
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print(args)
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return args
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def main():
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args = get_args()
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s %(levelname)s %(message)s')
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cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False)
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# 1. export flow decoder estimator
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estimator = cosyvoice.model.flow.decoder.estimator
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device = cosyvoice.model.device
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batch_size, seq_len = 1, 256
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out_channels = cosyvoice.model.flow.decoder.estimator.out_channels
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x, mask, mu, t, spks, cond = get_dummy_input(batch_size, seq_len, out_channels, device)
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torch.onnx.export(
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estimator,
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(x, mask, mu, t, spks, cond),
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'{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
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export_params=True,
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opset_version=18,
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do_constant_folding=True,
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input_names=['x', 'mask', 'mu', 't', 'spks', 'cond'],
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output_names=['estimator_out'],
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dynamic_axes={
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'x': {0: 'batch_size', 2: 'seq_len'},
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'mask': {0: 'batch_size', 2: 'seq_len'},
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'mu': {0: 'batch_size', 2: 'seq_len'},
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'cond': {0: 'batch_size', 2: 'seq_len'},
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't': {0: 'batch_size'},
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'spks': {0: 'batch_size'},
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'estimator_out': {0: 'batch_size', 2: 'seq_len'},
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}
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)
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# 2. test computation consistency
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option = onnxruntime.SessionOptions()
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option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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option.intra_op_num_threads = 1
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providers = ['CUDAExecutionProvider' if torch.cuda.is_available() else 'CPUExecutionProvider']
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estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
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sess_options=option, providers=providers)
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for _ in tqdm(range(10)):
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x, mask, mu, t, spks, cond = get_dummy_input(random.randint(1, 6), random.randint(16, 512), out_channels, device)
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output_pytorch = estimator(x, mask, mu, t, spks, cond)
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ort_inputs = {
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'x': x.cpu().numpy(),
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'mask': mask.cpu().numpy(),
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'mu': mu.cpu().numpy(),
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't': t.cpu().numpy(),
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'spks': spks.cpu().numpy(),
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'cond': cond.cpu().numpy()
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}
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output_onnx = estimator_onnx.run(None, ort_inputs)[0]
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torch.testing.assert_allclose(output_pytorch, torch.from_numpy(output_onnx).to(device), rtol=1e-2, atol=1e-4)
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if __name__ == "__main__":
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main()
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