jit_pretrained.py

#!/usr/bin/env python3
# Copyright      2021-2022  Xiaomi Corp.   (authors: Fangjun Kuang,
#                                                    Zengwei)
#
# 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.
"""
This script loads torchscript models, exported by `torch.jit.script()`
and uses them to decode waves.
You can use the following command to get the exported models:

./zipformer_mmi/export.py \
  --exp-dir ./zipformer_mmi/exp \
  --bpe-model data/lang_bpe_500/bpe.model \
  --epoch 20 \
  --avg 10 \
  --jit 1

Usage of this script:

(1) 1best
./zipformer_mmi/jit_pretrained.py \
    --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --method 1best \
    /path/to/foo.wav \
    /path/to/bar.wav
(2) nbest
./zipformer_mmi/jit_pretrained.py \
    --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --nbest-scale 1.2 \
    --method nbest \
    /path/to/foo.wav \
    /path/to/bar.wav
(3) nbest-rescoring-LG
./zipformer_mmi/jit_pretrained.py \
    --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --nbest-scale 1.2 \
    --method nbest-rescoring-LG \
    /path/to/foo.wav \
    /path/to/bar.wav
(4) nbest-rescoring-3-gram
./zipformer_mmi/jit_pretrained.py \
    --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --nbest-scale 1.2 \
    --method nbest-rescoring-3-gram \
    /path/to/foo.wav \
    /path/to/bar.wav
(5) nbest-rescoring-4-gram
./zipformer_mmi/jit_pretrained.py \
    --nn-model-filename ./zipformer_mmi/exp/cpu_jit.pt \
    --bpe-model ./data/lang_bpe_500/bpe.model \
    --nbest-scale 1.2 \
    --method nbest-rescoring-4-gram \
    /path/to/foo.wav \
    /path/to/bar.wav
"""

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

import k2
import kaldifeat
import sentencepiece as spm
import torch
import torchaudio
from decode import get_decoding_params
from torch.nn.utils.rnn import pad_sequence
from train import get_params

from icefall.decode import (
    get_lattice,
    nbest_decoding,
    nbest_rescore_with_LM,
    one_best_decoding,
)
from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
from icefall.utils import get_texts


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

    parser.add_argument(
        "--nn-model-filename",
        type=str,
        required=True,
        help="Path to the torchscript model cpu_jit.pt",
    )

    parser.add_argument(
        "--bpe-model",
        type=str,
        help="""Path to bpe.model.""",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="1best",
        help="""Decoding method. Use HP as decoding graph, where H is
        ctc_topo and P is token-level bi-gram lm.
        Supported values are:
        - (1) 1best. Extract the best path from the decoding lattice as the
          decoding result.
        - (2) nbest. Extract n paths from the decoding lattice; the path
          with the highest score is the decoding result.
        - (4) nbest-rescoring-LG. Extract n paths from the decoding lattice,
          rescore them with an word-level 3-gram LM, the path with the
          highest score is the decoding result.
        - (5) nbest-rescoring-3-gram. Extract n paths from the decoding
          lattice, rescore them with an token-level 3-gram LM, the path with
          the highest score is the decoding result.
        - (6) nbest-rescoring-4-gram. Extract n paths from the decoding
          lattice, rescore them with an token-level 4-gram LM, the path with
          the highest score is the decoding result.
        """,
    )

    parser.add_argument(
        "--sample-rate",
        type=int,
        default=16000,
        help="The sample rate of the input sound file",
    )

    parser.add_argument(
        "--lang-dir",
        type=Path,
        default="data/lang_bpe_500",
        help="The lang dir containing word table and LG graph",
    )

    parser.add_argument(
        "--num-paths",
        type=int,
        default=100,
        help="""Number of paths for n-best based decoding method.
        Used only when "method" is one of the following values:
        nbest, nbest-rescoring, and nbest-oracle
        """,
    )

    parser.add_argument(
        "--nbest-scale",
        type=float,
        default=1.2,
        help="""The scale to be applied to `lattice.scores`.
        It's needed if you use any kinds of n-best based rescoring.
        Used only when "method" is one of the following values:
        nbest, nbest-rescoring, and nbest-oracle
        A smaller value results in more unique paths.
        """,
    )

    parser.add_argument(
        "--ngram-lm-scale",
        type=float,
        default=0.1,
        help="""
        Used when method is nbest-rescoring-LG, nbest-rescoring-3-gram,
        and nbest-rescoring-4-gram.
        It specifies the scale for n-gram LM scores.
        (Note: You need to tune it on a dataset.)
        """,
    )

    parser.add_argument(
        "--hp-scale",
        type=float,
        default=1.0,
        help="""The scale to be applied to `ctc_topo_P.scores`.
        """,
    )

    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 read_sound_files(
    filenames: List[str], expected_sample_rate: float = 16000
) -> 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


@torch.no_grad()
def main():
    parser = get_parser()
    args = parser.parse_args()
    logging.info(vars(args))

    params = get_params()
    # add decoding params
    params.update(get_decoding_params())
    params.update(vars(args))

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

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

    model = torch.jit.load(params.nn_model_filename)
    model.eval()
    model.to(device)

    sp = spm.SentencePieceProcessor()
    sp.load(args.bpe_model)

    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 = 16000
    opts.mel_opts.num_bins = 80
    opts.mel_opts.high_freq = -400

    fbank = kaldifeat.Fbank(opts)

    logging.info(f"Reading sound files: {args.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)
    feature_lengths = [f.size(0) for f in features]

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

    bpe_model = spm.SentencePieceProcessor()
    bpe_model.load(str(params.lang_dir / "bpe.model"))
    mmi_graph_compiler = MmiTrainingGraphCompiler(
        params.lang_dir,
        uniq_filename="lexicon.txt",
        device=device,
        oov="<UNK>",
        sos_id=1,
        eos_id=1,
    )
    HP = mmi_graph_compiler.ctc_topo_P
    HP.scores *= params.hp_scale
    if not hasattr(HP, "lm_scores"):
        HP.lm_scores = HP.scores.clone()

    method = params.method
    assert method in (
        "1best",
        "nbest",
        "nbest-rescoring-LG",  # word-level 3-gram lm
        "nbest-rescoring-3-gram",  # token-level 3-gram lm
        "nbest-rescoring-4-gram",  # token-level 4-gram lm
    )
    # loading language model for rescoring
    LM = None
    if method == "nbest-rescoring-LG":
        lg_filename = params.lang_dir / "LG.pt"
        logging.info(f"Loading {lg_filename}")
        LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
        LG = k2.Fsa.from_fsas([LG]).to(device)
        LG.lm_scores = LG.scores.clone()
        LM = LG
    elif method in ["nbest-rescoring-3-gram", "nbest-rescoring-4-gram"]:
        order = method[-6]
        assert order in ("3", "4")
        order = int(order)
        logging.info(f"Loading pre-compiled {order}gram.pt")
        d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
        G = k2.Fsa.from_dict(d)
        G.lm_scores = G.scores.clone()
        LM = G

    # Encoder forward
    nnet_output, encoder_out_lens = model(x=features, x_lens=feature_lengths)

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

    lattice = get_lattice(
        nnet_output=nnet_output,
        decoding_graph=HP,
        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 method in ["1best", "nbest"]:
        if method == "1best":
            best_path = one_best_decoding(
                lattice=lattice, use_double_scores=params.use_double_scores
            )
        else:
            best_path = nbest_decoding(
                lattice=lattice,
                num_paths=params.num_paths,
                use_double_scores=params.use_double_scores,
                nbest_scale=params.nbest_scale,
            )
    else:
        best_path_dict = nbest_rescore_with_LM(
            lattice=lattice,
            LM=LM,
            num_paths=params.num_paths,
            lm_scale_list=[params.ngram_lm_scale],
            nbest_scale=params.nbest_scale,
        )
        best_path = next(iter(best_path_dict.values()))

    # Note: `best_path.aux_labels` contains token IDs, not word IDs
    # since we are using HP, not HLG here.
    #
    # token_ids is a lit-of-list of IDs
    token_ids = get_texts(best_path)
    # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
    hyps = bpe_model.decode(token_ids)
    # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
    hyps = [s.split() for s in hyps]
    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()