You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/paddlespeech/s2t/exps/u2/bin/test_wav.py

149 lines
5.1 KiB

3 years ago
# 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.
"""Evaluation for U2 model."""
import os
import sys
from pathlib import Path
import paddle
import soundfile
from paddlespeech.s2t.exps.u2.config import get_cfg_defaults
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.models.u2 import U2Model
from paddlespeech.s2t.training.cli import default_argument_parser
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import UpdateConfig
logger = Log(__name__).getlog()
# TODO(hui zhang): dynamic load
class U2Infer():
def __init__(self, config, args):
self.args = args
self.config = config
self.audio_file = args.audio_file
self.sr = config.collator.target_sample_rate
self.preprocess_conf = config.collator.augmentation_config
self.preprocess_args = {"train": False}
self.preprocessing = Transformation(self.preprocess_conf)
self.text_feature = TextFeaturizer(
unit_type=config.collator.unit_type,
vocab_filepath=config.collator.vocab_filepath,
spm_model_prefix=config.collator.spm_model_prefix)
3 years ago
paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
3 years ago
# model
model_conf = config.model
with UpdateConfig(model_conf):
model_conf.input_dim = config.collator.feat_dim
model_conf.output_dim = self.text_feature.vocab_size
model = U2Model.from_config(model_conf)
self.model = model
self.model.eval()
# load model
params_path = self.args.checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
self.model.set_state_dict(model_dict)
def run(self):
check(args.audio_file)
with paddle.no_grad():
# read
audio, sample_rate = soundfile.read(
self.audio_file, dtype="int16", always_2d=True)
if sample_rate != self.sr:
logger.error(
f"sample rate error: {sample_rate}, need {self.sr} ")
sys.exit(-1)
audio = audio[:, 0]
logger.info(f"audio shape: {audio.shape}")
# fbank
feat = self.preprocessing(audio, **self.preprocess_args)
logger.info(f"feat shape: {feat.shape}")
ilen = paddle.to_tensor(feat.shape[0])
xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
cfg = self.config.decoding
result_transcripts = self.model.decode(
xs,
ilen,
text_feature=self.text_feature,
decoding_method=cfg.decoding_method,
lang_model_path=cfg.lang_model_path,
beam_alpha=cfg.alpha,
beam_beta=cfg.beta,
beam_size=cfg.beam_size,
cutoff_prob=cfg.cutoff_prob,
cutoff_top_n=cfg.cutoff_top_n,
num_processes=cfg.num_proc_bsearch,
ctc_weight=cfg.ctc_weight,
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
rsl = result_transcripts[0][0]
utt = Path(self.audio_file).name
logger.info(f"hyp: {utt} {result_transcripts[0][0]}")
return rsl
def check(audio_file):
if not os.path.isfile(audio_file):
print("Please input the right audio file path")
sys.exit(-1)
logger.info("checking the audio file format......")
try:
sig, sample_rate = soundfile.read(audio_file)
except Exception as e:
logger.error(str(e))
logger.error(
"can not open the wav file, please check the audio file format")
sys.exit(-1)
logger.info("The sample rate is %d" % sample_rate)
assert (sample_rate == 16000)
logger.info("The audio file format is right")
def main(config, args):
U2Infer(config, args).run()
if __name__ == "__main__":
parser = default_argument_parser()
# save asr result to
parser.add_argument(
"--result_file", type=str, help="path of save the asr result")
parser.add_argument(
"--audio_file", type=str, help="path of the input audio file")
args = parser.parse_args()
config = get_cfg_defaults()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
main(config, args)