75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import argparse
|
|
import logging
|
|
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
|
import os
|
|
import sys
|
|
import torch
|
|
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append('{}/../..'.format(ROOT_DIR))
|
|
sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
|
|
from cosyvoice.cli.cosyvoice import CosyVoice
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(description='export your model for deployment')
|
|
parser.add_argument('--model_dir',
|
|
type=str,
|
|
default='pretrained_models/CosyVoice-300M',
|
|
help='local path')
|
|
args = parser.parse_args()
|
|
print(args)
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = get_args()
|
|
logging.basicConfig(level=logging.DEBUG,
|
|
format='%(asctime)s %(levelname)s %(message)s')
|
|
|
|
torch._C._jit_set_fusion_strategy([('STATIC', 1)])
|
|
torch._C._jit_set_profiling_mode(False)
|
|
torch._C._jit_set_profiling_executor(False)
|
|
|
|
cosyvoice = CosyVoice(args.model_dir, load_jit=False, load_onnx=False)
|
|
|
|
# 1. export llm text_encoder
|
|
llm_text_encoder = cosyvoice.model.llm.text_encoder.half()
|
|
script = torch.jit.script(llm_text_encoder)
|
|
script = torch.jit.freeze(script)
|
|
script = torch.jit.optimize_for_inference(script)
|
|
script.save('{}/llm.text_encoder.fp16.zip'.format(args.model_dir))
|
|
|
|
# 2. export llm llm
|
|
llm_llm = cosyvoice.model.llm.llm.half()
|
|
script = torch.jit.script(llm_llm)
|
|
script = torch.jit.freeze(script, preserved_attrs=['forward_chunk'])
|
|
script = torch.jit.optimize_for_inference(script)
|
|
script.save('{}/llm.llm.fp16.zip'.format(args.model_dir))
|
|
|
|
# 3. export flow encoder
|
|
flow_encoder = cosyvoice.model.flow.encoder
|
|
script = torch.jit.script(flow_encoder)
|
|
script = torch.jit.freeze(script)
|
|
script = torch.jit.optimize_for_inference(script)
|
|
script.save('{}/flow.encoder.fp32.zip'.format(args.model_dir))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|