funasr_detach/models/bicif_paraformer/model.py

#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import copy
import time
import torch
import logging
from contextlib import contextmanager
from distutils.version import LooseVersion
from typing import Dict, List, Optional, Tuple

from funasr_detach.register import tables
from funasr_detach.models.ctc.ctc import CTC
from funasr_detach.utils import postprocess_utils
from funasr_detach.metrics.compute_acc import th_accuracy
from funasr_detach.utils.datadir_writer import DatadirWriter
from funasr_detach.models.paraformer.model import Paraformer
from funasr_detach.models.paraformer.search import Hypothesis
from funasr_detach.train_utils.device_funcs import force_gatherable
from funasr_detach.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr_detach.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr_detach.utils.load_utils import load_audio_text_image_video, extract_fbank


if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
else:
    # Nothing to do if torch<1.6.0
    @contextmanager
    def autocast(enabled=True):
        yield


@tables.register("model_classes", "BiCifParaformer")
class BiCifParaformer(Paraformer):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Paper1: FunASR: A Fundamental End-to-End Speech Recognition Toolkit
    https://arxiv.org/abs/2305.11013
    Paper2: Achieving timestamp prediction while recognizing with non-autoregressive end-to-end ASR model
    https://arxiv.org/abs/2301.12343
    """

    def __init__(
        self,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)

    def _calc_pre2_loss(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        encoder_out_mask = (
            ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
        ).to(encoder_out.device)
        if self.predictor_bias == 1:
            _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
            ys_pad_lens = ys_pad_lens + self.predictor_bias
        _, _, _, _, pre_token_length2 = self.predictor(
            encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
        )

        # loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
        loss_pre2 = self.criterion_pre(
            ys_pad_lens.type_as(pre_token_length2), pre_token_length2
        )

        return loss_pre2

    def _calc_att_loss(
        self,
        encoder_out: torch.Tensor,
        encoder_out_lens: torch.Tensor,
        ys_pad: torch.Tensor,
        ys_pad_lens: torch.Tensor,
    ):
        encoder_out_mask = (
            ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
        ).to(encoder_out.device)
        if self.predictor_bias == 1:
            _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
            ys_pad_lens = ys_pad_lens + self.predictor_bias
        pre_acoustic_embeds, pre_token_length, _, pre_peak_index, _ = self.predictor(
            encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id
        )

        # 0. sampler
        decoder_out_1st = None
        if self.sampling_ratio > 0.0:
            sematic_embeds, decoder_out_1st = self.sampler(
                encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds
            )
        else:
            sematic_embeds = pre_acoustic_embeds

        # 1. Forward decoder
        decoder_outs = self.decoder(
            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
        )
        decoder_out, _ = decoder_outs[0], decoder_outs[1]

        if decoder_out_1st is None:
            decoder_out_1st = decoder_out
        # 2. Compute attention loss
        loss_att = self.criterion_att(decoder_out, ys_pad)
        acc_att = th_accuracy(
            decoder_out_1st.view(-1, self.vocab_size),
            ys_pad,
            ignore_label=self.ignore_id,
        )
        loss_pre = self.criterion_pre(
            ys_pad_lens.type_as(pre_token_length), pre_token_length
        )

        # Compute cer/wer using attention-decoder
        if self.training or self.error_calculator is None:
            cer_att, wer_att = None, None
        else:
            ys_hat = decoder_out_1st.argmax(dim=-1)
            cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())

        return loss_att, acc_att, cer_att, wer_att, loss_pre

    def calc_predictor(self, encoder_out, encoder_out_lens):
        encoder_out_mask = (
            ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
        ).to(encoder_out.device)
        (
            pre_acoustic_embeds,
            pre_token_length,
            alphas,
            pre_peak_index,
            pre_token_length2,
        ) = self.predictor(
            encoder_out, None, encoder_out_mask, ignore_id=self.ignore_id
        )
        return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index

    def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
        encoder_out_mask = (
            ~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]
        ).to(encoder_out.device)
        ds_alphas, ds_cif_peak, us_alphas, us_peaks = (
            self.predictor.get_upsample_timestamp(
                encoder_out, encoder_out_mask, token_num
            )
        )
        return ds_alphas, ds_cif_peak, us_alphas, us_peaks

    def forward(
        self,
        speech: torch.Tensor,
        speech_lengths: torch.Tensor,
        text: torch.Tensor,
        text_lengths: torch.Tensor,
        **kwargs,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss
        Args:
                speech: (Batch, Length, ...)
                speech_lengths: (Batch, )
                text: (Batch, Length)
                text_lengths: (Batch,)
        """
        if len(text_lengths.size()) > 1:
            text_lengths = text_lengths[:, 0]
        if len(speech_lengths.size()) > 1:
            speech_lengths = speech_lengths[:, 0]

        batch_size = speech.shape[0]

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

        loss_ctc, cer_ctc = None, None
        loss_pre = None
        stats = dict()

        # decoder: CTC branch
        if self.ctc_weight != 0.0:
            loss_ctc, cer_ctc = self._calc_ctc_loss(
                encoder_out, encoder_out_lens, text, text_lengths
            )

            # Collect CTC branch stats
            stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
            stats["cer_ctc"] = cer_ctc

        # decoder: Attention decoder branch
        loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
            encoder_out, encoder_out_lens, text, text_lengths
        )

        loss_pre2 = self._calc_pre2_loss(
            encoder_out, encoder_out_lens, text, text_lengths
        )

        # 3. CTC-Att loss definition
        if self.ctc_weight == 0.0:
            loss = (
                loss_att
                + loss_pre * self.predictor_weight
                + loss_pre2 * self.predictor_weight * 0.5
            )
        else:
            loss = (
                self.ctc_weight * loss_ctc
                + (1 - self.ctc_weight) * loss_att
                + loss_pre * self.predictor_weight
                + loss_pre2 * self.predictor_weight * 0.5
            )

        # Collect Attn branch stats
        stats["loss_att"] = loss_att.detach() if loss_att is not None else None
        stats["acc"] = acc_att
        stats["cer"] = cer_att
        stats["wer"] = wer_att
        stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
        stats["loss_pre2"] = loss_pre2.detach().cpu()

        stats["loss"] = torch.clone(loss.detach())

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + self.predictor_bias).sum())

        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight

    def inference(
        self,
        data_in,
        data_lengths=None,
        key: list = None,
        tokenizer=None,
        frontend=None,
        **kwargs,
    ):

        # init beamsearch
        is_use_ctc = (
            kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
        )
        is_use_lm = (
            kwargs.get("lm_weight", 0.0) > 0.00001
            and kwargs.get("lm_file", None) is not None
        )
        if self.beam_search is None and (is_use_lm or is_use_ctc):
            logging.info("enable beam_search")
            self.init_beam_search(**kwargs)
            self.nbest = kwargs.get("nbest", 1)

        meta_data = {}
        # if isinstance(data_in, torch.Tensor):  # fbank
        #     speech, speech_lengths = data_in, data_lengths
        #     if len(speech.shape) < 3:
        #         speech = speech[None, :, :]
        #     if speech_lengths is None:
        #         speech_lengths = speech.shape[1]
        # else:
        # extract fbank feats
        time1 = time.perf_counter()
        audio_sample_list = load_audio_text_image_video(
            data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000)
        )
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        speech, speech_lengths = extract_fbank(
            audio_sample_list,
            data_type=kwargs.get("data_type", "sound"),
            frontend=frontend,
        )
        time3 = time.perf_counter()
        meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
        meta_data["batch_data_time"] = (
            speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
        )

        speech = speech.to(device=kwargs["device"])
        speech_lengths = speech_lengths.to(device=kwargs["device"])

        # Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        if isinstance(encoder_out, tuple):
            encoder_out = encoder_out[0]

        # predictor
        predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
            predictor_outs[0],
            predictor_outs[1],
            predictor_outs[2],
            predictor_outs[3],
        )
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
        decoder_outs = self.cal_decoder_with_predictor(
            encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length
        )
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        # BiCifParaformer, test no bias cif2
        _, _, us_alphas, us_peaks = self.calc_predictor_timestamp(
            encoder_out, encoder_out_lens, pre_token_length
        )

        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
            x = encoder_out[i, : encoder_out_lens[i], :]
            am_scores = decoder_out[i, : pre_token_length[i], :]
            if self.beam_search is not None:
                nbest_hyps = self.beam_search(
                    x=x,
                    am_scores=am_scores,
                    maxlenratio=kwargs.get("maxlenratio", 0.0),
                    minlenratio=kwargs.get("minlenratio", 0.0),
                )

                nbest_hyps = nbest_hyps[: self.nbest]
            else:

                yseq = am_scores.argmax(dim=-1)
                score = am_scores.max(dim=-1)[0]
                score = torch.sum(score, dim=-1)
                # pad with mask tokens to ensure compatibility with sos/eos tokens
                yseq = torch.tensor(
                    [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
                )
                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
            for nbest_idx, hyp in enumerate(nbest_hyps):
                ibest_writer = None
                if kwargs.get("output_dir") is not None:
                    if not hasattr(self, "writer"):
                        self.writer = DatadirWriter(kwargs.get("output_dir"))
                    ibest_writer = self.writer[f"{nbest_idx+1}best_recog"]

                # 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 != self.eos
                        and x != self.sos
                        and x != self.blank_id,
                        token_int,
                    )
                )

                if tokenizer is not None:
                    # Change integer-ids to tokens
                    token = tokenizer.ids2tokens(token_int)
                    text = tokenizer.tokens2text(token)

                    _, timestamp = ts_prediction_lfr6_standard(
                        us_alphas[i][: encoder_out_lens[i] * 3],
                        us_peaks[i][: encoder_out_lens[i] * 3],
                        copy.copy(token),
                        vad_offset=kwargs.get("begin_time", 0),
                    )

                    text_postprocessed, time_stamp_postprocessed, word_lists = (
                        postprocess_utils.sentence_postprocess(token, timestamp)
                    )

                    result_i = {
                        "key": key[i],
                        "text": text_postprocessed,
                        "timestamp": time_stamp_postprocessed,
                    }

                    if ibest_writer is not None:
                        ibest_writer["token"][key[i]] = " ".join(token)
                        # ibest_writer["text"][key[i]] = text
                        ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
                        ibest_writer["text"][key[i]] = text_postprocessed
                else:
                    result_i = {"key": key[i], "token_int": token_int}
                results.append(result_i)

        return results, meta_data