slu_inference.py

#!/usr/bin/env python3
import argparse
import logging
import sys
from distutils.version import LooseVersion
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.quantization
from typeguard import typechecked

from espnet2.asr.transducer.beam_search_transducer import BeamSearchTransducer
from espnet2.asr.transducer.beam_search_transducer import (
    ExtendedHypothesis as ExtTransHypothesis,
)
from espnet2.asr.transducer.beam_search_transducer import Hypothesis as TransHypothesis
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.lm import LMTask
from espnet2.tasks.slu import SLUTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.batch_beam_search_online_sim import BatchBeamSearchOnlineSim
from espnet.nets.beam_search import BeamSearch, Hypothesis
from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
from espnet.nets.scorer_interface import BatchScorerInterface
from espnet.nets.scorers.ctc import CTCPrefixScorer
from espnet.nets.scorers.length_bonus import LengthBonus
from espnet.utils.cli_utils import get_commandline_args


class Speech2Understand:
    """Speech2Understand class

    Examples:
        >>> import soundfile
        >>> speech2understand = Speech2Understand("slu_config.yml", "slu.pth")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> speech2understand(audio)
        [(text, token, token_int, hypothesis object), ...]

    """

    @typechecked
    def __init__(
        self,
        slu_train_config: Union[Path, str, None] = None,
        slu_model_file: Union[Path, str, None] = None,
        transducer_conf: Optional[dict] = None,
        lm_train_config: Union[Path, str, None] = None,
        lm_file: Union[Path, str, None] = None,
        ngram_scorer: str = "full",
        ngram_file: Union[Path, str, None] = None,
        token_type: Optional[str] = None,
        bpemodel: Optional[str] = None,
        device: str = "cpu",
        maxlenratio: float = 0.0,
        minlenratio: float = 0.0,
        batch_size: int = 1,
        dtype: str = "float32",
        beam_size: int = 20,
        ctc_weight: float = 0.5,
        lm_weight: float = 1.0,
        ngram_weight: float = 0.9,
        penalty: float = 0.0,
        nbest: int = 1,
        normalize_length: bool = False,
        streaming: bool = False,
        quantize_asr_model: bool = False,
        quantize_lm: bool = False,
        quantize_modules: List[str] = ["Linear"],
        quantize_dtype: str = "qint8",
    ):

        task = SLUTask

        if quantize_asr_model or quantize_lm:
            if quantize_dtype == "float16" and torch.__version__ < LooseVersion(
                "1.5.0"
            ):
                raise ValueError(
                    "float16 dtype for dynamic quantization is not supported with "
                    "torch version < 1.5.0. Switch to qint8 dtype instead."
                )

        qconfig_spec = set([getattr(torch.nn, q) for q in quantize_modules])
        quantize_dtype: torch.dtype = getattr(torch, quantize_dtype)

        # 1. Build ASR model
        scorers = {}
        asr_model, asr_train_args = task.build_model_from_file(
            slu_train_config, slu_model_file, device
        )
        asr_model.to(dtype=getattr(torch, dtype)).eval()

        if quantize_asr_model:
            logging.info("Use quantized asr model for decoding.")

            asr_model = torch.quantization.quantize_dynamic(
                asr_model, qconfig_spec=qconfig_spec, dtype=quantize_dtype
            )

        decoder = asr_model.decoder

        ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
        token_list = asr_model.token_list
        scorers.update(
            decoder=decoder,
            ctc=ctc,
            length_bonus=LengthBonus(len(token_list)),
        )

        # 2. Build Language model
        if lm_train_config is not None:
            lm, lm_train_args = LMTask.build_model_from_file(
                lm_train_config, lm_file, device
            )

            if quantize_lm:
                logging.info("Use quantized lm for decoding.")

                lm = torch.quantization.quantize_dynamic(
                    lm, qconfig_spec=qconfig_spec, dtype=quantize_dtype
                )

            scorers["lm"] = lm.lm

        # 3. Build ngram model
        if ngram_file is not None:
            if ngram_scorer == "full":
                from espnet.nets.scorers.ngram import NgramFullScorer

                ngram = NgramFullScorer(ngram_file, token_list)
            else:
                from espnet.nets.scorers.ngram import NgramPartScorer

                ngram = NgramPartScorer(ngram_file, token_list)
        else:
            ngram = None
        scorers["ngram"] = ngram

        # 4. Build BeamSearch object
        if asr_model.use_transducer_decoder:
            beam_search_transducer = BeamSearchTransducer(
                decoder=asr_model.decoder,
                joint_network=asr_model.joint_network,
                beam_size=beam_size,
                lm=scorers["lm"] if "lm" in scorers else None,
                lm_weight=lm_weight,
                token_list=token_list,
                **transducer_conf,
            )
            beam_search = None
        else:
            beam_search_transducer = None

            weights = dict(
                decoder=1.0 - ctc_weight,
                ctc=ctc_weight,
                lm=lm_weight,
                ngram=ngram_weight,
                length_bonus=penalty,
            )
            beam_search = BeamSearch(
                beam_size=beam_size,
                weights=weights,
                scorers=scorers,
                sos=asr_model.sos,
                eos=asr_model.eos,
                vocab_size=len(token_list),
                token_list=token_list,
                pre_beam_score_key=None if ctc_weight == 1.0 else "full",
                normalize_length=normalize_length,
            )

            # TODO(karita): make all scorers batchfied
            if batch_size == 1:
                non_batch = [
                    k
                    for k, v in beam_search.full_scorers.items()
                    if not isinstance(v, BatchScorerInterface)
                ]
                if len(non_batch) == 0:
                    if streaming:
                        beam_search.__class__ = BatchBeamSearchOnlineSim
                        beam_search.set_streaming_config(slu_train_config)
                        logging.info(
                            "BatchBeamSearchOnlineSim implementation is selected."
                        )
                    else:
                        beam_search.__class__ = BatchBeamSearch
                        logging.info("BatchBeamSearch implementation is selected.")
                else:
                    logging.warning(
                        f"As non-batch scorers {non_batch} are found, "
                        f"fall back to non-batch implementation."
                    )

            beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
            for scorer in scorers.values():
                if isinstance(scorer, torch.nn.Module):
                    scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
            logging.info(f"Beam_search: {beam_search}")
            logging.info(f"Decoding device={device}, dtype={dtype}")

        # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
        if token_type is None:
            token_type = asr_train_args.token_type
        if bpemodel is None:
            bpemodel = asr_train_args.bpemodel

        if token_type is None:
            tokenizer = None
        elif token_type == "bpe":
            if bpemodel is not None:
                tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
            else:
                tokenizer = None
        else:
            tokenizer = build_tokenizer(token_type=token_type)
        converter = TokenIDConverter(token_list=token_list)
        logging.info(f"Text tokenizer: {tokenizer}")

        self.asr_model = asr_model
        self.asr_train_args = asr_train_args
        self.converter = converter
        self.tokenizer = tokenizer
        self.beam_search = beam_search
        self.beam_search_transducer = beam_search_transducer
        self.maxlenratio = maxlenratio
        self.minlenratio = minlenratio
        self.device = device
        self.dtype = dtype
        self.nbest = nbest

    @torch.no_grad()
    @typechecked
    def __call__(
        self,
        speech: Union[torch.Tensor, np.ndarray],
        transcript: Optional[torch.Tensor] = None,
    ) -> List[
        Tuple[
            Optional[str],
            List[str],
            List[int],
            Union[Hypothesis, ExtTransHypothesis, TransHypothesis],
        ]
    ]:
        """Inference

        Args:
            data: Input speech data
        Returns:
            text, token, token_int, hyp

        """

        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)

        # data: (Nsamples,) -> (1, Nsamples)
        speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
        # lengths: (1,)
        lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
        if transcript is None:
            batch = {"speech": speech, "speech_lengths": lengths}
            logging.info("speech length: " + str(speech.size(1)))
        else:
            transcript = transcript.unsqueeze(0).to(getattr(torch, self.dtype))
            # lengths: (1,)
            transcript_lengths = transcript.new_full(
                [1], dtype=torch.long, fill_value=transcript.size(1)
            )
            # print(text)
            # print(text_lengths)
            batch = {
                "speech": speech,
                "speech_lengths": lengths,
                "transcript_pad": transcript,
                "transcript_pad_lens": transcript_lengths,
            }

        # a. To device
        batch = to_device(batch, device=self.device)

        # b. Forward Encoder
        enc, _ = self.asr_model.encode(**batch)
        if isinstance(enc, tuple):
            enc = enc[0]
        assert len(enc) == 1, len(enc)

        # c. Passed the encoder result and the beam search
        if self.beam_search_transducer:
            logging.info("encoder output length: " + str(enc[0].shape[0]))
            nbest_hyps = self.beam_search_transducer(enc[0])

            best = nbest_hyps[0]
            logging.info(f"total log probability: {best.score:.2f}")
            logging.info(
                f"normalized log probability: {best.score / len(best.yseq):.2f}"
            )
            logging.info(
                "best hypo: " + "".join(self.converter.ids2tokens(best.yseq[1:])) + "\n"
            )
        else:
            nbest_hyps = self.beam_search(
                x=enc[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
            )
        nbest_hyps = nbest_hyps[: self.nbest]

        results = []
        for hyp in nbest_hyps:
            assert isinstance(hyp, (Hypothesis, TransHypothesis)), type(hyp)

            # remove sos/eos and get results
            last_pos = None if self.asr_model.use_transducer_decoder else -1
            if isinstance(hyp.yseq, list):
                token_int = hyp.yseq[1:last_pos]
            else:
                token_int = hyp.yseq[1:last_pos].tolist()

            # remove blank symbol id, which is assumed to be 0
            token_int = list(filter(lambda x: x != 0, token_int))

            # Change integer-ids to tokens
            token = self.converter.ids2tokens(token_int)

            if self.tokenizer is not None:
                text = self.tokenizer.tokens2text(token)
            else:
                text = None
            results.append((text, token, token_int, hyp))

        return results

    @staticmethod
    def from_pretrained(
        model_tag: Optional[str] = None,
        **kwargs: Optional[Any],
    ):
        """Build Speech2Understand instance from the pretrained model.

        Args:
            model_tag (Optional[str]): Model tag of the pretrained models.
                Currently, the tags of espnet_model_zoo are supported.

        Returns:
            Speech2Understand: Speech2Understand instance.

        """
        if model_tag is not None:
            try:
                from espnet_model_zoo.downloader import ModelDownloader

            except ImportError:
                logging.error(
                    "`espnet_model_zoo` is not installed. "
                    "Please install via `pip install -U espnet_model_zoo`."
                )
                raise
            d = ModelDownloader()
            kwargs.update(**d.download_and_unpack(model_tag))

        return Speech2Understand(**kwargs)


@typechecked
def inference(
    output_dir: str,
    maxlenratio: float,
    minlenratio: float,
    batch_size: int,
    dtype: str,
    beam_size: int,
    ngpu: int,
    seed: int,
    ctc_weight: float,
    lm_weight: float,
    ngram_weight: float,
    penalty: float,
    nbest: int,
    normalize_length: bool,
    num_workers: int,
    log_level: Union[int, str],
    data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
    key_file: Optional[str],
    slu_train_config: Optional[str],
    slu_model_file: Optional[str],
    lm_train_config: Optional[str],
    lm_file: Optional[str],
    word_lm_train_config: Optional[str],
    word_lm_file: Optional[str],
    ngram_file: Optional[str],
    model_tag: Optional[str],
    token_type: Optional[str],
    bpemodel: Optional[str],
    allow_variable_data_keys: bool,
    transducer_conf: Optional[dict],
    streaming: bool,
    quantize_asr_model: bool,
    quantize_lm: bool,
    quantize_modules: List[str],
    quantize_dtype: str,
):
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if word_lm_train_config is not None:
        raise NotImplementedError("Word LM is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")

    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if ngpu >= 1:
        device = "cuda"
    else:
        device = "cpu"

    # 1. Set random-seed
    set_all_random_seed(seed)

    # 2. Build speech2understand
    speech2understand_kwargs = dict(
        slu_train_config=slu_train_config,
        slu_model_file=slu_model_file,
        transducer_conf=transducer_conf,
        lm_train_config=lm_train_config,
        lm_file=lm_file,
        ngram_file=ngram_file,
        token_type=token_type,
        bpemodel=bpemodel,
        device=device,
        maxlenratio=maxlenratio,
        minlenratio=minlenratio,
        dtype=dtype,
        beam_size=beam_size,
        ctc_weight=ctc_weight,
        lm_weight=lm_weight,
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        normalize_length=normalize_length,
        streaming=streaming,
        quantize_asr_model=quantize_asr_model,
        quantize_lm=quantize_lm,
        quantize_modules=quantize_modules,
        quantize_dtype=quantize_dtype,
    )
    speech2understand = Speech2Understand.from_pretrained(
        model_tag=model_tag,
        **speech2understand_kwargs,
    )

    # 3. Build data-iterator
    loader = SLUTask.build_streaming_iterator(
        data_path_and_name_and_type,
        dtype=dtype,
        batch_size=batch_size,
        key_file=key_file,
        num_workers=num_workers,
        preprocess_fn=SLUTask.build_preprocess_fn(
            speech2understand.asr_train_args, False
        ),
        collate_fn=SLUTask.build_collate_fn(speech2understand.asr_train_args, False),
        allow_variable_data_keys=allow_variable_data_keys,
        inference=True,
    )

    # 7 .Start for-loop
    # FIXME(kamo): The output format should be discussed about
    with DatadirWriter(output_dir) as writer:
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}

            # N-best list of (text, token, token_int, hyp_object)
            try:
                results = speech2understand(**batch)
            except TooShortUttError as e:
                logging.warning(f"Utterance {keys} {e}")
                hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
                results = [[" ", ["<space>"], [2], hyp]] * nbest

            # Only supporting batch_size==1
            key = keys[0]
            for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
                # Create a directory: outdir/{n}best_recog
                ibest_writer = writer[f"{n}best_recog"]

                # Write the result to each file
                ibest_writer["token"][key] = " ".join(token)
                ibest_writer["token_int"][key] = " ".join(map(str, token_int))
                ibest_writer["score"][key] = str(hyp.score)

                if text is not None:
                    ibest_writer["text"][key] = text


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="ASR Decoding",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )

    # Note(kamo): Use '_' instead of '-' as separator.
    # '-' is confusing if written in yaml.
    parser.add_argument(
        "--log_level",
        type=lambda x: x.upper(),
        default="INFO",
        choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
        help="The verbose level of logging",
    )

    parser.add_argument("--output_dir", type=str, required=True)
    parser.add_argument(
        "--ngpu",
        type=int,
        default=0,
        help="The number of gpus. 0 indicates CPU mode",
    )
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument(
        "--dtype",
        default="float32",
        choices=["float16", "float32", "float64"],
        help="Data type",
    )
    parser.add_argument(
        "--num_workers",
        type=int,
        default=1,
        help="The number of workers used for DataLoader",
    )

    group = parser.add_argument_group("Input data related")
    group.add_argument(
        "--data_path_and_name_and_type",
        type=str2triple_str,
        required=True,
        action="append",
    )
    group.add_argument("--key_file", type=str_or_none)
    group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)

    group = parser.add_argument_group("The model configuration related")
    group.add_argument(
        "--slu_train_config",
        type=str,
        help="ASR training configuration",
    )
    group.add_argument(
        "--slu_model_file",
        type=str,
        help="ASR model parameter file",
    )
    group.add_argument(
        "--lm_train_config",
        type=str,
        help="LM training configuration",
    )
    group.add_argument(
        "--lm_file",
        type=str,
        help="LM parameter file",
    )
    group.add_argument(
        "--word_lm_train_config",
        type=str,
        help="Word LM training configuration",
    )
    group.add_argument(
        "--word_lm_file",
        type=str,
        help="Word LM parameter file",
    )
    group.add_argument(
        "--ngram_file",
        type=str,
        help="N-gram parameter file",
    )
    group.add_argument(
        "--model_tag",
        type=str,
        help="Pretrained model tag. If specify this option, *_train_config and "
        "*_file will be overwritten",
    )

    group = parser.add_argument_group("Quantization related")
    group.add_argument(
        "--quantize_asr_model",
        type=str2bool,
        default=False,
        help="Apply dynamic quantization to ASR model.",
    )
    group.add_argument(
        "--quantize_lm",
        type=str2bool,
        default=False,
        help="Apply dynamic quantization to LM.",
    )
    group.add_argument(
        "--quantize_modules",
        type=str,
        nargs="*",
        default=["Linear"],
        help="""List of modules to be dynamically quantized.
        E.g.: --quantize_modules=[Linear,LSTM,GRU].
        Each specified module should be an attribute of 'torch.nn', e.g.:
        torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
    )
    group.add_argument(
        "--quantize_dtype",
        type=str,
        default="qint8",
        choices=["float16", "qint8"],
        help="Dtype for dynamic quantization.",
    )

    group = parser.add_argument_group("Beam-search related")
    group.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="The batch size for inference",
    )
    group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
    group.add_argument("--beam_size", type=int, default=20, help="Beam size")
    group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
    group.add_argument(
        "--maxlenratio",
        type=float,
        default=0.0,
        help="Input length ratio to obtain max output length. "
        "If maxlenratio=0.0 (default), it uses a end-detect "
        "function "
        "to automatically find maximum hypothesis lengths."
        "If maxlenratio<0.0, its absolute value is interpreted"
        "as a constant max output length",
    )
    group.add_argument(
        "--minlenratio",
        type=float,
        default=0.0,
        help="Input length ratio to obtain min output length",
    )
    group.add_argument(
        "--ctc_weight",
        type=float,
        default=0.5,
        help="CTC weight in joint decoding",
    )
    group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
    group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
    group.add_argument("--streaming", type=str2bool, default=False)

    group.add_argument(
        "--transducer_conf",
        default=None,
        help="The keyword arguments for transducer beam search.",
    )

    group = parser.add_argument_group("Text converter related")
    group.add_argument(
        "--token_type",
        type=str_or_none,
        default=None,
        choices=["char", "bpe", None],
        help="The token type for ASR model. "
        "If not given, refers from the training args",
    )
    group.add_argument(
        "--bpemodel",
        type=str_or_none,
        default=None,
        help="The model path of sentencepiece. "
        "If not given, refers from the training args",
    )
    group.add_argument(
        "--normalize_length",
        type=str2bool,
        default=False,
        help="If true, best hypothesis is selected by length-normalized scores",
    )

    return parser


def main(cmd=None):
    print(get_commandline_args(), file=sys.stderr)
    parser = get_parser()
    args = parser.parse_args(cmd)
    kwargs = vars(args)
    kwargs.pop("config", None)
    inference(**kwargs)


if __name__ == "__main__":
    main()