104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
# 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 torch
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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, CosyVoice2
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from cosyvoice.utils.file_utils import logging
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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 get_optimized_script(model, preserved_attrs=[]):
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script = torch.jit.script(model)
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if preserved_attrs != []:
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script = torch.jit.freeze(script, preserved_attrs=preserved_attrs)
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else:
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script = torch.jit.freeze(script)
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script = torch.jit.optimize_for_inference(script)
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return script
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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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torch._C._jit_set_fusion_strategy([('STATIC', 1)])
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torch._C._jit_set_profiling_mode(False)
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torch._C._jit_set_profiling_executor(False)
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try:
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model = CosyVoice(args.model_dir)
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except Exception:
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try:
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model = CosyVoice2(args.model_dir)
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except Exception:
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raise TypeError('no valid model_type!')
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if not isinstance(model, CosyVoice2):
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# 1. export llm text_encoder
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llm_text_encoder = model.model.llm.text_encoder
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script = get_optimized_script(llm_text_encoder)
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script.save('{}/llm.text_encoder.fp32.zip'.format(args.model_dir))
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script = get_optimized_script(llm_text_encoder.half())
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script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir))
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logging.info('successfully export llm_text_encoder')
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# 2. export llm llm
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llm_llm = model.model.llm.llm
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script = get_optimized_script(llm_llm, ['forward_chunk'])
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script.save('{}/llm.llm.fp32.zip'.format(args.model_dir))
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script = get_optimized_script(llm_llm.half(), ['forward_chunk'])
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script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
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logging.info('successfully export llm_llm')
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# 3. export flow encoder
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flow_encoder = model.model.flow.encoder
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script = get_optimized_script(flow_encoder)
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script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
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script = get_optimized_script(flow_encoder.half())
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script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
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logging.info('successfully export flow_encoder')
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else:
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# 3. export flow encoder
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flow_encoder = model.model.flow.encoder
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script = get_optimized_script(flow_encoder)
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script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
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script = get_optimized_script(flow_encoder.half())
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script.save('{}/flow.encoder.fp16.zip'.format(args.model_dir))
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logging.info('successfully export flow_encoder')
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if __name__ == '__main__':
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main()
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