agent/ctc_decoder.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import json
import logging
import math
from argparse import Namespace
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from fairseq import utils
from fairseq.data import Dictionary
from fairseq.data.audio.data_cfg import MultitaskConfig, S2SDataConfig
from fairseq.data.audio.speech_to_speech_dataset import SpeechToSpeechDatasetCreator
from fairseq.data.audio.speech_to_text_dataset import (
    SpeechToTextDataset,
    TextTargetMultitaskData,
)
from fairseq.tasks import LegacyFairseqTask, register_task
from fairseq.tasks.speech_to_text import DummyMultiTask
from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion

logger = logging.getLogger(__name__)


class CTCDecoder(nn.Module):
    def __init__(self, tgt_dict, models):
        super().__init__()
        self.pad = tgt_dict.pad()
        self.eos = tgt_dict.eos()
        self.unk = tgt_dict.unk()
        self.models = models
        self.tgt_dict = tgt_dict

    @torch.no_grad()
    def generate(self, encoder_out, prefix=None, aux_task_name=None, **kwargs):
        model = self.models[0]
        model.eval()

        max_len = model.max_decoder_positions()
        # TODO: incorporate max_len_a and max_len_b

        incremental_state = {}
        pred_out, attn, scores = [], [], []

        prev_output_tokens = None
        decoder_name = f"{aux_task_name}_decoder" if aux_task_name else "decoder"
        ctc_decoder = getattr(model, decoder_name)
        ctc_out = ctc_decoder(encoder_out["encoder_out"][0], **kwargs)
        lprobs = model.get_normalized_probs(
            [ctc_out["encoder_out"].transpose(0, 1)], log_probs=True
        )
        # never select pad, unk
        lprobs[:, :, self.pad] = -math.inf
        lprobs[:, :, self.unk] = -math.inf

        cur_pred_lprob, cur_pred_out = torch.max(lprobs, dim=2)
        scores = cur_pred_lprob
        pred_out = cur_pred_out
        attn = None
        alignment = None

        def _ctc_postprocess(tokens):
            _toks = tokens.int().tolist()
            deduplicated_toks = [
                v for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]
            ]
            hyp = [
                v
                for v in deduplicated_toks
                if (v != 0) and (v != self.tgt_dict.pad_index)
            ]
            return torch.tensor(hyp)

        def _ctc_postprocess_index(tokens):
            _toks = tokens.int().tolist()
            deduplicated_toks = [
                (v, i) for i, v in enumerate(_toks) if i == 0 or v != _toks[i - 1]
            ]
            index = [
                i
                for v, i in deduplicated_toks
                if (v != 0) and (v != self.tgt_dict.pad_index)
            ]
            return index

        if prefix is not None:

            pred_out = torch.cat((prefix, pred_out[:, prefix.size(1) :]), dim=1)

        hypos = [
            [
                {
                    "tokens": _ctc_postprocess(pred_out[b]),
                    "org_tokens": pred_out[b],
                    "lprobs": lprobs,
                    "index": _ctc_postprocess_index(pred_out[b]),
                    "attn": None,
                    "alignment": None,
                    "positional_scores": scores[b],
                    "score": utils.item(scores[b].sum().data),
                }
            ]
            for b in range(pred_out.size(0))
        ]

        return hypos