import json
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from typing import Union, Dict
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from pathlib import Path
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from typeguard import check_argument_types
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import os
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import logging
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import torch
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from funasr.export.models import get_model
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import numpy as np
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import random
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torch_version = float(".".join(torch.__version__.split(".")[:2]))
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assert torch_version > 1.9
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class ASRModelExportParaformer:
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def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
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assert check_argument_types()
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self.set_all_random_seed(0)
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if cache_dir is None:
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cache_dir = Path.home() / ".cache" / "export"
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self.cache_dir = Path(cache_dir)
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self.export_config = dict(
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feats_dim=560,
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onnx=False,
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)
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print("output dir: {}".format(self.cache_dir))
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self.onnx = onnx
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def _export(
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self,
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model,
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tag_name: str = None,
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verbose: bool = False,
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):
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export_dir = self.cache_dir / tag_name.replace(' ', '-')
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os.makedirs(export_dir, exist_ok=True)
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# export encoder1
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self.export_config["model_name"] = "model"
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model = get_model(
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model,
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self.export_config,
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)
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model.eval()
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# self._export_onnx(model, verbose, export_dir)
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if self.onnx:
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self._export_onnx(model, verbose, export_dir)
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else:
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self._export_torchscripts(model, verbose, export_dir)
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print("output dir: {}".format(export_dir))
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def _export_torchscripts(self, model, verbose, path, enc_size=None):
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if enc_size:
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dummy_input = model.get_dummy_inputs(enc_size)
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else:
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dummy_input = model.get_dummy_inputs()
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# model_script = torch.jit.script(model)
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model_script = torch.jit.trace(model, dummy_input)
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model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
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def set_all_random_seed(self, seed: int):
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random.seed(seed)
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np.random.seed(seed)
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torch.random.manual_seed(seed)
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def export(self,
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tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
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mode: str = 'paraformer',
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):
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model_dir = tag_name
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if model_dir.startswith('damo/'):
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from modelscope.hub.snapshot_download import snapshot_download
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model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
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asr_train_config = os.path.join(model_dir, 'config.yaml')
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asr_model_file = os.path.join(model_dir, 'model.pb')
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cmvn_file = os.path.join(model_dir, 'am.mvn')
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json_file = os.path.join(model_dir, 'configuration.json')
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if mode is None:
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import json
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with open(json_file, 'r') as f:
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config_data = json.load(f)
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mode = config_data['model']['model_config']['mode']
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if mode.startswith('paraformer'):
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from funasr.tasks.asr import ASRTaskParaformer as ASRTask
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elif mode.startswith('uniasr'):
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from funasr.tasks.asr import ASRTaskUniASR as ASRTask
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model, asr_train_args = ASRTask.build_model_from_file(
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asr_train_config, asr_model_file, cmvn_file, 'cpu'
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)
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self._export(model, tag_name)
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def _export_onnx(self, model, verbose, path, enc_size=None):
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if enc_size:
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dummy_input = model.get_dummy_inputs(enc_size)
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else:
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dummy_input = model.get_dummy_inputs()
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# model_script = torch.jit.script(model)
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model_script = model #torch.jit.trace(model)
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torch.onnx.export(
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model_script,
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dummy_input,
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os.path.join(path, f'{model.model_name}.onnx'),
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verbose=verbose,
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opset_version=14,
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input_names=model.get_input_names(),
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output_names=model.get_output_names(),
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dynamic_axes=model.get_dynamic_axes()
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)
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if __name__ == '__main__':
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import sys
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model_path = sys.argv[1]
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output_dir = sys.argv[2]
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onnx = sys.argv[3]
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onnx = onnx.lower()
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onnx = onnx == 'true'
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# model_path = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
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# output_dir = "../export"
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export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=onnx)
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export_model.export(model_path)
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# export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
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