mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
260 lines
9.3 KiB
Python
260 lines
9.3 KiB
Python
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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from funasr.utils.types import str2bool
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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 ModelExport:
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def __init__(
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self,
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cache_dir: Union[Path, str] = None,
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onnx: bool = True,
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quant: bool = True,
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fallback_num: int = 0,
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audio_in: str = None,
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calib_num: int = 200,
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):
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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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self.quant = quant
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self.fallback_num = fallback_num
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self.frontend = None
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self.audio_in = audio_in
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self.calib_num = calib_num
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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 _torch_quantize(self, model):
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def _run_calibration_data(m):
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# using dummy inputs for a example
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if self.audio_in is not None:
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feats, feats_len = self.load_feats(self.audio_in)
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for i, (feat, len) in enumerate(zip(feats, feats_len)):
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with torch.no_grad():
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m(feat, len)
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else:
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dummy_input = model.get_dummy_inputs()
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m(*dummy_input)
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from torch_quant.module import ModuleFilter
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from torch_quant.quantizer import Backend, Quantizer
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from funasr.export.models.modules.decoder_layer import DecoderLayerSANM
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from funasr.export.models.modules.encoder_layer import EncoderLayerSANM
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module_filter = ModuleFilter(include_classes=[EncoderLayerSANM, DecoderLayerSANM])
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module_filter.exclude_op_types = [torch.nn.Conv1d]
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quantizer = Quantizer(
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module_filter=module_filter,
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backend=Backend.FBGEMM,
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)
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model.eval()
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calib_model = quantizer.calib(model)
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_run_calibration_data(calib_model)
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if self.fallback_num > 0:
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# perform automatic mixed precision quantization
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amp_model = quantizer.amp(model)
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_run_calibration_data(amp_model)
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quantizer.fallback(amp_model, num=self.fallback_num)
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print('Fallback layers:')
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print('\n'.join(quantizer.module_filter.exclude_names))
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quant_model = quantizer.quantize(model)
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return quant_model
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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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if self.quant:
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quant_model = self._torch_quantize(model)
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model_script = torch.jit.trace(quant_model, dummy_input)
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model_script.save(os.path.join(path, f'{model.model_name}_quant.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 parse_audio_in(self, audio_in):
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wav_list, name_list = [], []
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if audio_in.endswith(".scp"):
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f = open(audio_in, 'r')
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lines = f.readlines()[:self.calib_num]
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for line in lines:
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name, path = line.strip().split()
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name_list.append(name)
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wav_list.append(path)
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else:
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wav_list = [audio_in,]
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name_list = ["test",]
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return wav_list, name_list
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def load_feats(self, audio_in: str = None):
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import torchaudio
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wav_list, name_list = self.parse_audio_in(audio_in)
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feats = []
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feats_len = []
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for line in wav_list:
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path = line.strip()
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waveform, sampling_rate = torchaudio.load(path)
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if sampling_rate != self.frontend.fs:
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waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
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new_freq=self.frontend.fs)(waveform)
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fbank, fbank_len = self.frontend(waveform, [waveform.size(1)])
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feats.append(fbank)
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feats_len.append(fbank_len)
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return feats, feats_len
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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 = None,
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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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if mode is None:
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import json
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json_file = os.path.join(model_dir, 'configuration.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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config = os.path.join(model_dir, 'config.yaml')
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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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model, asr_train_args = ASRTask.build_model_from_file(
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config, model_file, cmvn_file, 'cpu'
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)
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self.frontend = model.frontend
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elif mode.startswith('offline'):
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from funasr.tasks.vad import VADTask
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config = os.path.join(model_dir, 'vad.yaml')
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model_file = os.path.join(model_dir, 'vad.pb')
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cmvn_file = os.path.join(model_dir, 'vad.mvn')
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model, vad_infer_args = VADTask.build_model_from_file(
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config, model_file, 'cpu'
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)
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self.export_config["feats_dim"] = 400
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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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model_path = os.path.join(path, f'{model.model_name}.onnx')
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torch.onnx.export(
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model_script,
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dummy_input,
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model_path,
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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 self.quant:
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from onnxruntime.quantization import QuantType, quantize_dynamic
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import onnx
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quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
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onnx_model = onnx.load(model_path)
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nodes = [n.name for n in onnx_model.graph.node]
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nodes_to_exclude = [m for m in nodes if 'output' in m]
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quantize_dynamic(
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model_input=model_path,
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model_output=quant_model_path,
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op_types_to_quantize=['MatMul'],
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per_channel=True,
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reduce_range=False,
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weight_type=QuantType.QUInt8,
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nodes_to_exclude=nodes_to_exclude,
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)
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--model-name', type=str, required=True)
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parser.add_argument('--export-dir', type=str, required=True)
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parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
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parser.add_argument('--quantize', type=str2bool, default=False, help='export quantized model')
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parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
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parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
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parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
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args = parser.parse_args()
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export_model = ModelExport(
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cache_dir=args.export_dir,
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onnx=args.type == 'onnx',
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quant=args.quantize,
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fallback_num=args.fallback_num,
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audio_in=args.audio_in,
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calib_num=args.calib_num,
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)
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export_model.export(args.model_name)
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