FunASR/funasr/bin/asr_inference_paraformer.py

#!/usr/bin/env python3
import argparse
import logging
import sys
import time
from pathlib import Path
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union

import numpy as np
import torch
from typeguard import check_argument_types

from funasr.fileio.datadir_writer import DatadirWriter
from funasr.modules.beam_search.beam_search import BeamSearchPara as BeamSearch
from funasr.modules.beam_search.beam_search import Hypothesis
from funasr.modules.scorers.ctc import CTCPrefixScorer
from funasr.modules.scorers.length_bonus import LengthBonus
from funasr.modules.subsampling import TooShortUttError
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
from funasr.tasks.lm import LMTask
from funasr.text.build_tokenizer import build_tokenizer
from funasr.text.token_id_converter import TokenIDConverter
from funasr.torch_utils.device_funcs import to_device
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
from funasr.utils import config_argparse
from funasr.utils.cli_utils import get_commandline_args
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none


class Speech2Text:
    """Speech2Text class

    Examples:
            >>> import soundfile
            >>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
            >>> audio, rate = soundfile.read("speech.wav")
            >>> speech2text(audio)
            [(text, token, token_int, hypothesis object), ...]

    """

    def __init__(
            self,
            asr_train_config: Union[Path, str] = None,
            asr_model_file: Union[Path, str] = None,
            lm_train_config: Union[Path, str] = None,
            lm_file: Union[Path, str] = None,
            token_type: str = None,
            bpemodel: str = None,
            device: str = "cpu",
            maxlenratio: float = 0.0,
            minlenratio: float = 0.0,
            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,
            **kwargs,
    ):
        assert check_argument_types()

        # 1. Build ASR model
        scorers = {}
        asr_model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, device
        )
        logging.info("asr_model: {}".format(asr_model))
        logging.info("asr_train_args: {}".format(asr_train_args))
        asr_model.to(dtype=getattr(torch, dtype)).eval()

        ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos)
        token_list = asr_model.token_list
        scorers.update(
            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
            )
            scorers["lm"] = lm.lm

        # 3. Build ngram model
        # ngram is not supported now
        ngram = None
        scorers["ngram"] = ngram

        # 4. Build BeamSearch object
        # transducer is not supported now
        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",
        )

        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()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray]
    ):
        """Inference

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

        """
        assert check_argument_types()

        # 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))
        lfr_factor = max(1, (speech.size()[-1]//80)-1)
        # lengths: (1,)
        lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
        batch = {"speech": speech, "speech_lengths": lengths}

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

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

        predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
        pre_acoustic_embeds, pre_token_length = predictor_outs[0], predictor_outs[1]
        pre_token_length = torch.tensor([pre_acoustic_embeds.size(1)], device=pre_acoustic_embeds.device)
        decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        nbest_hyps = self.beam_search(
            x=enc[0], am_scores=decoder_out[0], maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
        )

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

            # remove sos/eos and get results
            last_pos = -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, speech.size(1), lfr_factor))

        # assert check_return_type(results)
        return results


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,
        num_workers: int,
        log_level: Union[int, str],
        data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
        key_file: Optional[str],
        asr_train_config: Optional[str],
        asr_model_file: Optional[str],
        lm_train_config: Optional[str],
        lm_file: Optional[str],
        word_lm_train_config: Optional[str],
        token_type: Optional[str],
        bpemodel: Optional[str],
        allow_variable_data_keys: bool,
        **kwargs,
):
    assert check_argument_types()
    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 speech2text
    speech2text_kwargs = dict(
        asr_train_config=asr_train_config,
        asr_model_file=asr_model_file,
        lm_train_config=lm_train_config,
        lm_file=lm_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,
    )
    speech2text = Speech2Text(**speech2text_kwargs)

    # 3. Build data-iterator
    loader = ASRTask.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=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
        collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
        allow_variable_data_keys=allow_variable_data_keys,
        inference=True,
    )

    forward_time_total = 0.0
    length_total = 0.0
    # 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")}

            logging.info("decoding, utt_id: {}".format(keys))
            # N-best list of (text, token, token_int, hyp_object)

            try:
                time_beg = time.time()
                results = speech2text(**batch)
                time_end = time.time()
                forward_time = time_end - time_beg
                lfr_factor = results[0][-1]
                length = results[0][-2]
                results = [results[0][:-2]]
                forward_time_total += forward_time
                length_total += length
                logging.info(
                    "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
                        format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
            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

                logging.info("decoding, predictions: {}".format(text))

    logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
                 format(length_total, forward_time_total, 100 * forward_time_total / (length_total*lfr_factor)))


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(
        "--asr_train_config",
        type=str,
        help="ASR training configuration",
    )
    group.add_argument(
        "--asr_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("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(
        "--frontend_conf",
        default=None,
        help="",
    )

    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",
    )

    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()