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146 lines
5.6 KiB
146 lines
5.6 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import argparse
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import os
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import numpy as np
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from paddle import inference
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from scipy.special import softmax
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from paddlespeech.audio.backends import load as load_audio
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from paddlespeech.audio.datasets import ESC50
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from paddlespeech.audio.features import melspectrogram
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# yapf: disable
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_dir", type=str, required=True, default="./export", help="The directory to static model.")
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parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--wav", type=str, required=True, help="Audio file to infer.")
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parser.add_argument("--batch_size", type=int, default=1, help="Batch size per GPU/CPU for training.")
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parser.add_argument('--use_tensorrt', type=eval, default=False, choices=[True, False], help='Enable to use tensorrt to speed up.')
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parser.add_argument("--precision", type=str, default="fp32", choices=["fp32", "fp16"], help='The tensorrt precision.')
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parser.add_argument('--cpu_threads', type=int, default=10, help='Number of threads to predict when using cpu.')
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parser.add_argument('--enable_mkldnn', type=eval, default=False, choices=[True, False], help='Enable to use mkldnn to speed up when using cpu.')
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parser.add_argument("--log_dir", type=str, default="./log", help="The path to save log.")
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args = parser.parse_args()
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# yapf: enable
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def extract_features(files: str, **kwargs):
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waveforms = []
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srs = []
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max_length = float('-inf')
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for file in files:
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waveform, sr = load_audio(file, sr=None)
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max_length = max(max_length, len(waveform))
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waveforms.append(waveform)
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srs.append(sr)
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feats = []
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for i in range(len(waveforms)):
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# padding
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if len(waveforms[i]) < max_length:
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pad_width = max_length - len(waveforms[i])
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waveforms[i] = np.pad(waveforms[i], pad_width=(0, pad_width))
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feat = melspectrogram(waveforms[i], sr, **kwargs).transpose()
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feats.append(feat)
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return np.stack(feats, axis=0)
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class Predictor(object):
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def __init__(self,
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model_dir,
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device="gpu",
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batch_size=1,
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use_tensorrt=False,
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precision="fp32",
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cpu_threads=10,
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enable_mkldnn=False):
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self.batch_size = batch_size
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model_file = os.path.join(model_dir, "inference.pdmodel")
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params_file = os.path.join(model_dir, "inference.pdiparams")
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assert os.path.isfile(model_file) and os.path.isfile(
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params_file), 'Please check model and parameter files.'
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config = inference.Config(model_file, params_file)
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if device == "gpu":
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# set GPU configs accordingly
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# such as intialize the gpu memory, enable tensorrt
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config.enable_use_gpu(100, 0)
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precision_map = {
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"fp16": inference.PrecisionType.Half,
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"fp32": inference.PrecisionType.Float32,
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}
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precision_mode = precision_map[precision]
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if use_tensorrt:
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config.enable_tensorrt_engine(
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max_batch_size=batch_size,
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min_subgraph_size=30,
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precision_mode=precision_mode)
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elif device == "cpu":
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# set CPU configs accordingly,
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# such as enable_mkldnn, set_cpu_math_library_num_threads
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config.disable_gpu()
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if enable_mkldnn:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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config.set_cpu_math_library_num_threads(cpu_threads)
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elif device == "xpu":
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# set XPU configs accordingly
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config.enable_xpu(100)
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config.switch_use_feed_fetch_ops(False)
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self.predictor = inference.create_predictor(config)
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self.input_handles = [
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self.predictor.get_input_handle(name)
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for name in self.predictor.get_input_names()
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]
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self.output_handle = self.predictor.get_output_handle(
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self.predictor.get_output_names()[0])
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def predict(self, wavs):
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feats = extract_features(wavs)
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self.input_handles[0].copy_from_cpu(feats)
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self.predictor.run()
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logits = self.output_handle.copy_to_cpu()
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probs = softmax(logits, axis=1)
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indices = np.argmax(probs, axis=1)
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return indices
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if __name__ == "__main__":
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# Define predictor to do prediction.
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predictor = Predictor(args.model_dir, args.device, args.batch_size,
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args.use_tensorrt, args.precision, args.cpu_threads,
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args.enable_mkldnn)
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wavs = [args.wav]
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for i in range(len(wavs)):
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wavs[i] = os.path.abspath(os.path.expanduser(wavs[i]))
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assert os.path.isfile(
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wavs[i]), f'Please check input wave file: {wavs[i]}'
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results = predictor.predict(wavs)
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for idx, wav in enumerate(wavs):
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print(f'Wav: {wav} \t Label: {ESC50.label_list[results[idx]]}')
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