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| # Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang) | |
| # 2024 Alibaba Inc (authors: Xiang Lyu, Zetao Hu) | |
| # | |
| # 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. | |
| import json | |
| import torchaudio | |
| import logging | |
| logging.getLogger('matplotlib').setLevel(logging.WARNING) | |
| logging.basicConfig(level=logging.DEBUG, | |
| format='%(asctime)s %(levelname)s %(message)s') | |
| def read_lists(list_file): | |
| lists = [] | |
| with open(list_file, 'r', encoding='utf8') as fin: | |
| for line in fin: | |
| lists.append(line.strip()) | |
| return lists | |
| def read_json_lists(list_file): | |
| lists = read_lists(list_file) | |
| results = {} | |
| for fn in lists: | |
| with open(fn, 'r', encoding='utf8') as fin: | |
| results.update(json.load(fin)) | |
| return results | |
| def load_wav(wav, target_sr): | |
| speech, sample_rate = torchaudio.load(wav, backend='soundfile') | |
| speech = speech.mean(dim=0, keepdim=True) | |
| if sample_rate != target_sr: | |
| assert sample_rate > target_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr) | |
| speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech) | |
| return speech | |
| def convert_onnx_to_trt(trt_model, trt_kwargs, onnx_model, fp16): | |
| import tensorrt as trt | |
| logging.info("Converting onnx to trt...") | |
| network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) | |
| logger = trt.Logger(trt.Logger.INFO) | |
| builder = trt.Builder(logger) | |
| network = builder.create_network(network_flags) | |
| parser = trt.OnnxParser(network, logger) | |
| config = builder.create_builder_config() | |
| config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 33) # 8GB | |
| if fp16: | |
| config.set_flag(trt.BuilderFlag.FP16) | |
| profile = builder.create_optimization_profile() | |
| # load onnx model | |
| with open(onnx_model, "rb") as f: | |
| if not parser.parse(f.read()): | |
| for error in range(parser.num_errors): | |
| print(parser.get_error(error)) | |
| raise ValueError('failed to parse {}'.format(onnx_model)) | |
| # set input shapes | |
| for i in range(len(trt_kwargs['input_names'])): | |
| profile.set_shape(trt_kwargs['input_names'][i], trt_kwargs['min_shape'][i], trt_kwargs['opt_shape'][i], trt_kwargs['max_shape'][i]) | |
| tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT | |
| # set input and output data type | |
| for i in range(network.num_inputs): | |
| input_tensor = network.get_input(i) | |
| input_tensor.dtype = tensor_dtype | |
| for i in range(network.num_outputs): | |
| output_tensor = network.get_output(i) | |
| output_tensor.dtype = tensor_dtype | |
| config.add_optimization_profile(profile) | |
| engine_bytes = builder.build_serialized_network(network, config) | |
| # save trt engine | |
| with open(trt_model, "wb") as f: | |
| f.write(engine_bytes) | |
| logging.info("Succesfully convert onnx to trt...") |