PaddleSpeech/paddlespeech/s2t/exps/deepspeech2/bin/test_wav.py

# 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 DeepSpeech2 model."""
import os
import sys
from pathlib import Path

import paddle
import soundfile
from yacs.config import CfgNode

from paddlespeech.audio.transform.transformation import Transformation
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
from paddlespeech.s2t.training.cli import default_argument_parser
from paddlespeech.s2t.utils import mp_tools
from paddlespeech.s2t.utils.checkpoint import Checkpoint
from paddlespeech.s2t.utils.log import Log
from paddlespeech.s2t.utils.utility import print_arguments
from paddlespeech.s2t.utils.utility import UpdateConfig

logger = Log(__name__).getlog()


class DeepSpeech2Tester_hub():
    def __init__(self, config, args):
        self.args = args
        self.config = config
        self.audio_file = args.audio_file

        self.preprocess_conf = config.preprocess_config
        self.preprocess_args = {"train": False}
        self.preprocessing = Transformation(self.preprocess_conf)

        self.text_feature = TextFeaturizer(
            unit_type=config.unit_type,
            vocab=config.vocab_filepath,
            spm_model_prefix=config.spm_model_prefix)
        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')

    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
        decode_batch_size = cfg.decode_batch_size
        self.model.decoder.init_decoder(
            decode_batch_size, vocab_list, cfg.decoding_method,
            cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size,
            cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch)
        result_transcripts = self.model.decode(audio, audio_len)
        return result_transcripts

    @mp_tools.rank_zero_only
    @paddle.no_grad()
    def test(self):
        self.model.eval()
        cfg = self.config
        audio_file = self.audio_file

        audio, sample_rate = soundfile.read(
            self.audio_file, dtype="int16", always_2d=True)

        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}")

        audio_len = paddle.to_tensor(feat.shape[0])
        audio = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)

        result_transcripts = self.compute_result_transcripts(
            audio, audio_len, self.text_feature.vocab_list, cfg.decode)

        logger.info("result_transcripts: " + result_transcripts[0])

    def run_test(self):
        self.resume()
        try:
            self.test()
        except KeyboardInterrupt:
            exit(-1)

    def setup(self):
        """Setup the experiment.
        """
        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')

        self.setup_output_dir()
        self.setup_checkpointer()

        self.setup_model()

    def setup_output_dir(self):
        """Create a directory used for output.
        """
        # output dir
        if self.args.output:
            output_dir = Path(self.args.output).expanduser()
            output_dir.mkdir(parents=True, exist_ok=True)
        else:
            output_dir = Path(
                self.args.checkpoint_path).expanduser().parent.parent
            output_dir.mkdir(parents=True, exist_ok=True)
        self.output_dir = output_dir

    def setup_model(self):
        config = self.config.clone()
        with UpdateConfig(config):
            config.input_dim = config.feat_dim
            config.output_dim = self.text_feature.vocab_size
        model = DeepSpeech2Model.from_config(config)
        self.model = model

    def setup_checkpointer(self):
        """Create a directory used to save checkpoints into.

        It is "checkpoints" inside the output directory.
        """
        # checkpoint dir
        checkpoint_dir = self.output_dir / "checkpoints"
        checkpoint_dir.mkdir(exist_ok=True)

        self.checkpoint_dir = checkpoint_dir

        self.checkpoint = Checkpoint(
            kbest_n=self.config.checkpoint.kbest_n,
            latest_n=self.config.checkpoint.latest_n)

    def resume(self):
        """Resume from the checkpoint at checkpoints in the output
        directory or load a specified checkpoint.
        """
        params_path = self.args.checkpoint_path + ".pdparams"
        model_dict = paddle.load(params_path)
        self.model.set_state_dict(model_dict)


def check(audio_file):
    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_sp(config, args):
    exp = DeepSpeech2Tester_hub(config, args)
    exp.setup()
    exp.run_test()


def main(config, args):
    main_sp(config, args)


if __name__ == "__main__":
    parser = default_argument_parser()
    parser.add_argument("--audio_file", type=str, help='audio file path')
    # save asr result to
    parser.add_argument(
        "--result_file", type=str, help="path of save the asr result")
    args = parser.parse_args()
    print_arguments(args, globals())
    if not os.path.isfile(args.audio_file):
        print("Please input the audio file path")
        sys.exit(-1)
    check(args.audio_file)

    # https://yaml.org/type/float.html
    config = CfgNode(new_allowed=True)
    if args.config:
        config.merge_from_file(args.config)
    if args.decode_cfg:
        decode_confs = CfgNode(new_allowed=True)
        decode_confs.merge_from_file(args.decode_cfg)
        config.decode = decode_confs
    if args.opts:
        config.merge_from_list(args.opts)
    config.freeze()
    print(config)
    if args.dump_config:
        with open(args.dump_config, 'w') as f:
            print(config, file=f)

    main(config, args)