egs/librispeech/ASR/zipformer/pretrained.py

#!/usr/bin/env python3
# Copyright      2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
#
# 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 a checkpoint and uses it to decode waves.
You can generate the checkpoint with the following command:

Note: This is a example for librispeech dataset, if you are using different
dataset, you should change the argument values according to your dataset.

- For non-streaming model:

./zipformer/export.py \
  --exp-dir ./zipformer/exp \
  --tokens data/lang_bpe_500/tokens.txt \
  --epoch 30 \
  --avg 9

- For streaming model:

./zipformer/export.py \
  --exp-dir ./zipformer/exp \
  --causal 1 \
  --tokens data/lang_bpe_500/tokens.txt \
  --epoch 30 \
  --avg 9

Usage of this script:

- For non-streaming model:

(1) greedy search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --tokens data/lang_bpe_500/tokens.txt \
  --method greedy_search \
  /path/to/foo.wav \
  /path/to/bar.wav

(2) modified beam search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --tokens ./data/lang_bpe_500/tokens.txt \
  --method modified_beam_search \
  /path/to/foo.wav \
  /path/to/bar.wav

(3) fast beam search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --tokens ./data/lang_bpe_500/tokens.txt \
  --method fast_beam_search \
  /path/to/foo.wav \
  /path/to/bar.wav

- For streaming model:

(1) greedy search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --causal 1 \
  --chunk-size 16 \
  --left-context-frames 128 \
  --tokens ./data/lang_bpe_500/tokens.txt \
  --method greedy_search \
  /path/to/foo.wav \
  /path/to/bar.wav

(2) modified beam search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --causal 1 \
  --chunk-size 16 \
  --left-context-frames 128 \
  --tokens ./data/lang_bpe_500/tokens.txt \
  --method modified_beam_search \
  /path/to/foo.wav \
  /path/to/bar.wav

(3) fast beam search
./zipformer/pretrained.py \
  --checkpoint ./zipformer/exp/pretrained.pt \
  --causal 1 \
  --chunk-size 16 \
  --left-context-frames 128 \
  --tokens ./data/lang_bpe_500/tokens.txt \
  --method fast_beam_search \
  /path/to/foo.wav \
  /path/to/bar.wav


You can also use `./zipformer/exp/epoch-xx.pt`.

Note: ./zipformer/exp/pretrained.pt is generated by ./zipformer/export.py
"""


import argparse
import logging
import math
from typing import List

import k2
import kaldifeat
import torch
import torchaudio
from beam_search import (
    fast_beam_search_one_best,
    greedy_search_batch,
    modified_beam_search,
)
from export import num_tokens
from torch.nn.utils.rnn import pad_sequence
from train import add_model_arguments, get_model, get_params


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(
        "--tokens",
        type=str,
        help="""Path to tokens.txt.""",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="greedy_search",
        help="""Possible values are:
          - greedy_search
          - modified_beam_search
          - fast_beam_search
        """,
    )

    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.",
    )

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

    parser.add_argument(
        "--beam-size",
        type=int,
        default=4,
        help="""An integer indicating how many candidates we will keep for each
        frame. Used only when --method is beam_search or
        modified_beam_search.""",
    )

    parser.add_argument(
        "--beam",
        type=float,
        default=4,
        help="""A floating point value to calculate the cutoff score during beam
        search (i.e., `cutoff = max-score - beam`), which is the same as the
        `beam` in Kaldi.
        Used only when --method is fast_beam_search""",
    )

    parser.add_argument(
        "--max-contexts",
        type=int,
        default=4,
        help="""Used only when --method is fast_beam_search""",
    )

    parser.add_argument(
        "--max-states",
        type=int,
        default=8,
        help="""Used only when --method is fast_beam_search""",
    )

    parser.add_argument(
        "--context-size",
        type=int,
        default=2,
        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
    )

    parser.add_argument(
        "--max-sym-per-frame",
        type=int,
        default=1,
        help="""Maximum number of symbols per frame. Used only when
        --method is greedy_search.
        """,
    )

    add_model_arguments(parser)

    return parser


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].contiguous())
    return ans


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

    params = get_params()

    params.update(vars(args))

    token_table = k2.SymbolTable.from_file(params.tokens)

    params.blank_id = token_table["<blk>"]
    params.unk_id = token_table["<unk>"]
    params.vocab_size = num_tokens(token_table) + 1

    logging.info(f"{params}")

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

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

    if params.causal:
        assert (
            "," not in params.chunk_size
        ), "chunk_size should be one value in decoding."
        assert (
            "," not in params.left_context_frames
        ), "left_context_frames should be one value in decoding."

    logging.info("Creating model")
    model = get_model(params)

    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
    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
    opts.mel_opts.high_freq = -400

    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)
    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)

    # model forward
    encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths)

    hyps = []
    msg = f"Using {params.method}"
    logging.info(msg)

    def token_ids_to_words(token_ids: List[int]) -> str:
        text = ""
        for i in token_ids:
            text += token_table[i]
        return text.replace("▁", " ").strip()

    if params.method == "fast_beam_search":
        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
        hyp_tokens = fast_beam_search_one_best(
            model=model,
            decoding_graph=decoding_graph,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam,
            max_contexts=params.max_contexts,
            max_states=params.max_states,
        )
        for hyp in hyp_tokens:
            hyps.append(token_ids_to_words(hyp))
    elif params.method == "modified_beam_search":
        hyp_tokens = modified_beam_search(
            model=model,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
            beam=params.beam_size,
        )

        for hyp in hyp_tokens:
            hyps.append(token_ids_to_words(hyp))
    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
        hyp_tokens = greedy_search_batch(
            model=model,
            encoder_out=encoder_out,
            encoder_out_lens=encoder_out_lens,
        )
        for hyp in hyp_tokens:
            hyps.append(token_ids_to_words(hyp))
    else:
        raise ValueError(f"Unsupported method: {params.method}")

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