pretrained.py

#!/usr/bin/env python3
# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
#                                                    Mingshuang Luo)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.


import argparse
import logging
import math
from typing import List

import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from conformer import Conformer
from torch.nn.utils.rnn import pad_sequence

from icefall.decode import (
    get_lattice,
    one_best_decoding,
    rescore_with_attention_decoder,
    rescore_with_whole_lattice,
)
from icefall.utils import AttributeDict, get_texts


def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--checkpoint",
        type=str,
        required=True,
        help="Path to the checkpoint. "
        "The checkpoint is assumed to be saved by "
        "icefall.checkpoint.save_checkpoint().",
    )

    parser.add_argument(
        "--words-file",
        type=str,
        help="""Path to words.txt.
        Used only when method is not ctc-decoding.
        """,
    )

    parser.add_argument(
        "--HLG",
        type=str,
        help="""Path to HLG.pt.
        Used only when method is not ctc-decoding.
        """,
    )

    parser.add_argument(
        "--bpe-model",
        type=str,
        help="""Path to bpe.model.
        Used only when method is ctc-decoding.
        """,
    )

    parser.add_argument(
        "--method",
        type=str,
        default="1best",
        help="""Decoding method.
        Possible values are:
        (0) ctc-decoding - Use CTC decoding. It uses a sentence
            piece model, i.e., lang_dir/bpe.model, to convert
            word pieces to words. It needs neither a lexicon
            nor an n-gram LM.
        (1) 1best - Use the best path as decoding output. Only
            the transformer encoder output is used for decoding.
            We call it HLG decoding.
        (2) whole-lattice-rescoring - Use an LM to rescore the
            decoding lattice and then use 1best to decode the
            rescored lattice.
            We call it HLG decoding + n-gram LM rescoring.
        (3) attention-decoder - Extract n paths from the rescored
            lattice and use the transformer attention decoder for
            rescoring.
            We call it HLG decoding + n-gram LM rescoring + attention
            decoder rescoring.
        """,
    )

    parser.add_argument(
        "--G",
        type=str,
        help="""An LM for rescoring.
        Used only when method is
        whole-lattice-rescoring or attention-decoder.
        It's usually a 4-gram LM.
        """,
    )

    parser.add_argument(
        "--num-paths",
        type=int,
        default=100,
        help="""
        Used only when method is attention-decoder.
        It specifies the size of n-best list.""",
    )

    parser.add_argument(
        "--ngram-lm-scale",
        type=float,
        default=1.3,
        help="""
        Used only when method is whole-lattice-rescoring and attention-decoder.
        It specifies the scale for n-gram LM scores.
        (Note: You need to tune it on a dataset.)
        """,
    )

    parser.add_argument(
        "--attention-decoder-scale",
        type=float,
        default=1.2,
        help="""
        Used only when method is attention-decoder.
        It specifies the scale for attention decoder scores.
        (Note: You need to tune it on a dataset.)
        """,
    )

    parser.add_argument(
        "--nbest-scale",
        type=float,
        default=0.5,
        help="""
        Used only when method is attention-decoder.
        It specifies the scale for lattice.scores when
        extracting n-best lists. A smaller value results in
        more unique number of paths with the risk of missing
        the best path.
        """,
    )

    parser.add_argument(
        "--sos-id",
        type=int,
        default=1,
        help="""
        Used only when method is attention-decoder.
        It specifies ID for the SOS token.
        """,
    )

    parser.add_argument(
        "--num-classes",
        type=int,
        default=500,
        help="""
        Vocab size in the BPE model.
        """,
    )

    parser.add_argument(
        "--eos-id",
        type=int,
        default=1,
        help="""
        Used only when method is attention-decoder.
        It specifies ID for the EOS token.
        """,
    )

    parser.add_argument(
        "sound_files",
        type=str,
        nargs="+",
        help="The input sound file(s) to transcribe. "
        "Supported formats are those supported by torchaudio.load(). "
        "For example, wav and flac are supported. "
        "The sample rate has to be 16kHz.",
    )

    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            "sample_rate": 16000,
            # parameters for conformer
            "subsampling_factor": 4,
            "vgg_frontend": False,
            "use_feat_batchnorm": True,
            "feature_dim": 80,
            "nhead": 8,
            "attention_dim": 512,
            "num_decoder_layers": 6,
            # parameters for decoding
            "search_beam": 20,
            "output_beam": 8,
            "min_active_states": 30,
            "max_active_states": 10000,
            "use_double_scores": True,
        }
    )
    return params


def read_sound_files(
    filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
    """Read a list of sound files into a list 1-D float32 torch tensors.
    Args:
      filenames:
        A list of sound filenames.
      expected_sample_rate:
        The expected sample rate of the sound files.
    Returns:
      Return a list of 1-D float32 torch tensors.
    """
    ans = []
    for f in filenames:
        wave, sample_rate = torchaudio.load(f)
        assert (
            sample_rate == expected_sample_rate
        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
        # We use only the first channel
        ans.append(wave[0])
    return ans


def main():
    parser = get_parser()
    args = parser.parse_args()

    params = get_params()
    if args.method != "attention-decoder":
        # to save memory as the attention decoder
        # will not be used
        params.num_decoder_layers = 0

    params.update(vars(args))
    logging.info(f"{params}")

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", 0)

    logging.info(f"device: {device}")

    logging.info("Creating model")
    model = Conformer(
        num_features=params.feature_dim,
        nhead=params.nhead,
        d_model=params.attention_dim,
        num_classes=params.num_classes,
        subsampling_factor=params.subsampling_factor,
        num_decoder_layers=params.num_decoder_layers,
        vgg_frontend=params.vgg_frontend,
        use_feat_batchnorm=params.use_feat_batchnorm,
    )

    checkpoint = torch.load(args.checkpoint, map_location="cpu")
    model.load_state_dict(checkpoint["model"], strict=False)
    model.to(device)
    model.eval()

    logging.info("Constructing Fbank computer")
    opts = kaldifeat.FbankOptions()
    opts.device = device
    opts.frame_opts.dither = 0
    opts.frame_opts.snip_edges = False
    opts.frame_opts.samp_freq = params.sample_rate
    opts.mel_opts.num_bins = params.feature_dim

    fbank = kaldifeat.Fbank(opts)

    logging.info(f"Reading sound files: {params.sound_files}")
    waves = read_sound_files(
        filenames=params.sound_files, expected_sample_rate=params.sample_rate
    )
    waves = [w.to(device) for w in waves]

    logging.info("Decoding started")
    features = fbank(waves)

    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))

    # Note: We don't use key padding mask for attention during decoding
    with torch.no_grad():
        nnet_output, memory, memory_key_padding_mask = model(features)

    batch_size = nnet_output.shape[0]
    supervision_segments = torch.tensor(
        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
        dtype=torch.int32,
    )

    if params.method == "ctc-decoding":
        logging.info("Use CTC decoding")
        bpe_model = spm.SentencePieceProcessor()
        bpe_model.load(params.bpe_model)
        max_token_id = params.num_classes - 1

        H = k2.ctc_topo(
            max_token=max_token_id,
            modified=params.num_classes > 500,
            device=device,
        )

        lattice = get_lattice(
            nnet_output=nnet_output,
            decoding_graph=H,
            supervision_segments=supervision_segments,
            search_beam=params.search_beam,
            output_beam=params.output_beam,
            min_active_states=params.min_active_states,
            max_active_states=params.max_active_states,
            subsampling_factor=params.subsampling_factor,
        )

        best_path = one_best_decoding(
            lattice=lattice, use_double_scores=params.use_double_scores
        )
        token_ids = get_texts(best_path)
        hyps = bpe_model.decode(token_ids)
        hyps = [s.split() for s in hyps]
    elif params.method in [
        "1best",
        "whole-lattice-rescoring",
        "attention-decoder",
    ]:
        logging.info(f"Loading HLG from {params.HLG}")
        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
        HLG = HLG.to(device)
        if not hasattr(HLG, "lm_scores"):
            # For whole-lattice-rescoring and attention-decoder
            HLG.lm_scores = HLG.scores.clone()

        if params.method in [
            "whole-lattice-rescoring",
            "attention-decoder",
        ]:
            logging.info(f"Loading G from {params.G}")
            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
            # Add epsilon self-loops to G as we will compose
            # it with the whole lattice later
            G = G.to(device)
            G = k2.add_epsilon_self_loops(G)
            G = k2.arc_sort(G)
            G.lm_scores = G.scores.clone()

        lattice = get_lattice(
            nnet_output=nnet_output,
            decoding_graph=HLG,
            supervision_segments=supervision_segments,
            search_beam=params.search_beam,
            output_beam=params.output_beam,
            min_active_states=params.min_active_states,
            max_active_states=params.max_active_states,
            subsampling_factor=params.subsampling_factor,
        )

        if params.method == "1best":
            logging.info("Use HLG decoding")
            best_path = one_best_decoding(
                lattice=lattice, use_double_scores=params.use_double_scores
            )
        elif params.method == "whole-lattice-rescoring":
            logging.info("Use HLG decoding + LM rescoring")
            best_path_dict = rescore_with_whole_lattice(
                lattice=lattice,
                G_with_epsilon_loops=G,
                lm_scale_list=[params.ngram_lm_scale],
            )
            best_path = next(iter(best_path_dict.values()))
        elif params.method == "attention-decoder":
            logging.info("Use HLG + LM rescoring + attention decoder rescoring")
            rescored_lattice = rescore_with_whole_lattice(
                lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
            )
            best_path_dict = rescore_with_attention_decoder(
                lattice=rescored_lattice,
                num_paths=params.num_paths,
                model=model,
                memory=memory,
                memory_key_padding_mask=memory_key_padding_mask,
                sos_id=params.sos_id,
                eos_id=params.eos_id,
                nbest_scale=params.nbest_scale,
                ngram_lm_scale=params.ngram_lm_scale,
                attention_scale=params.attention_decoder_scale,
            )
            best_path = next(iter(best_path_dict.values()))

        hyps = get_texts(best_path)
        word_sym_table = k2.SymbolTable.from_file(params.words_file)
        hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
    else:
        raise ValueError(f"Unsupported decoding method: {params.method}")

    s = "\n"
    for filename, hyp in zip(params.sound_files, hyps):
        words = " ".join(hyp)
        s += f"{filename}:\n{words}\n\n"
    logging.info(s)

    logging.info("Decoding Done")


if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)
    main()