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PaddleSpeech/paddlespeech/cls/exps/panns/deploy/predict.py

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