egs/aishell/ASR/whisper/decode.py

#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
#                                            Fangjun Kuang,
#                                            Wei Kang)
#           2024 Yuekai Zhang
#
# 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.
"""
Usage:
# Command for decoding using fine-tuned models:
git lfs install
git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt

python3 ./whisper/decode.py \
  --exp-dir whisper/exp_large_v2 \
  --model-name large-v2 \
  --epoch 999 --avg 1 \
  --manifest-dir data/fbank_whisper \
  --beam-size 10 --max-duration 50

# Command for decoding using pretrained models (before fine-tuning):

python3 ./whisper/decode.py \
  --exp-dir whisper/exp_large_v2 \
  --model-name large-v2 \
  --epoch -1 --avg 1 \
  --manifest-dir data/fbank_whisper \
  --remove-whisper-encoder-input-length-restriction False \
  --beam-size 10 --max-duration 50

"""

import argparse
import logging
import re
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple

import k2
import torch
import torch.nn as nn
import whisper
from asr_datamodule import AishellAsrDataModule
from tn.chinese.normalizer import Normalizer
from whisper.normalizers import BasicTextNormalizer
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
from zhconv import convert

from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
from icefall.env import get_env_info
from icefall.utils import (
    AttributeDict,
    setup_logger,
    store_transcripts,
    str2bool,
    write_error_stats,
)


def average_checkpoints(
    filenames: List[Path], device: torch.device = torch.device("cpu")
) -> dict:
    """Average a list of checkpoints.
    The function is mainly used for deepspeed converted checkpoint averaging, which only include model state_dict.

    Args:
      filenames:
        Filenames of the checkpoints to be averaged. We assume all
        checkpoints are saved by :func:`save_checkpoint`.
      device:
        Move checkpoints to this device before averaging.
    Returns:
      Return a dict (i.e., state_dict) which is the average of all
      model state dicts contained in the checkpoints.
    """
    n = len(filenames)

    if "model" in torch.load(filenames[0], map_location=device, weights_only=False):
        avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
    else:
        avg = torch.load(filenames[0], map_location=device, weights_only=False)

    # Identify shared parameters. Two parameters are said to be shared
    # if they have the same data_ptr
    uniqued: Dict[int, str] = dict()

    for k, v in avg.items():
        v_data_ptr = v.data_ptr()
        if v_data_ptr in uniqued:
            continue
        uniqued[v_data_ptr] = k

    uniqued_names = list(uniqued.values())

    for i in range(1, n):
        if "model" in torch.load(filenames[i], map_location=device, weights_only=False):
            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)["model"]
        else:
            state_dict = torch.load(filenames[i], map_location=device, weights_only=False)
        for k in uniqued_names:
            avg[k] += state_dict[k]

    for k in uniqued_names:
        if avg[k].is_floating_point():
            avg[k] /= n
        else:
            avg[k] //= n

    return avg


def remove_punctuation(text: str or List[str]):
    """Modified from https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py

    Args:
        text: It can be a string or a list of strings.
    Returns:
        Return a string or a list of strings without any punctuation.
    """
    punctuation = "!,.;:?、！，。；：？《》 "
    if isinstance(text, str):
        text = re.sub(r"[{}]+".format(punctuation), "", text).strip()
        return text
    elif isinstance(text, list):
        result_text = []
        for t in text:
            t = re.sub(r"[{}]+".format(punctuation), "", t).strip()
            result_text.append(t)
        return result_text
    else:
        raise Exception(f"Not support type {type(text)}")


def to_simple(text: str or List[str]):
    """Convert traditional Chinese to simplified Chinese.
    Args:
        text: It can be a string or a list of strings.
    Returns:
        Return a string or a list of strings converted to simplified Chinese.
    """
    if isinstance(text, str):
        text = convert(text, "zh-cn")
        return text
    elif isinstance(text, list):
        result_text = []
        for t in text:
            t = convert(t, "zh-cn")
            result_text.append(t)
        return result_text
    else:
        raise Exception(f"Not support type{type(text)}")


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

    parser.add_argument(
        "--epoch",
        type=int,
        default=-1,
        help="It specifies the checkpoint to use for decoding."
        "Note: Epoch counts from 0.",
    )
    parser.add_argument(
        "--avg",
        type=int,
        default=1,
        help="Number of checkpoints to average. Automatically select "
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )

    parser.add_argument(
        "--method",
        type=str,
        default="beam-search",
        help="""Decoding method.
        Supported values are:
          - beam-search
        """,
    )

    parser.add_argument(
        "--beam-size",
        type=int,
        default=1,
        help="beam size for beam search decoding",
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="whisper/exp",
        help="The experiment dir",
    )

    parser.add_argument(
        "--model-name",
        type=str,
        default="large-v2",
        choices=["large-v2", "large-v3", "medium", "small", "base", "tiny"],
        help="""The model name to use.
        """,
    )

    parser.add_argument(
        "--remove-whisper-encoder-input-length-restriction",
        type=str2bool,
        default=True,
        help="replace whisper encoder forward method to remove input length restriction",
    )

    return parser


def get_params() -> AttributeDict:
    params = AttributeDict(
        {
            "env_info": get_env_info(),
        }
    )
    return params


def decode_one_batch(
    params: AttributeDict,
    model: nn.Module,
    batch: dict,
) -> Dict[str, List[List[int]]]:
    """Decode one batch and return the result in a dict. The dict has the
    following format:

        - key: "beam-search"
        - value: A list of lists. Each sublist is a list of token IDs.
    Args:
        params:
            It is returned by :func:`get_params`.
        model:
            The neural model.
        batch:
            It is returned by :meth:`torch.utils.data.DataLoader.__iter__`.
    Returns:
        Return a dict, whose key may be "beam-search".
    """
    dtype = torch.float16
    device = torch.device("cuda")

    feature = batch["inputs"]
    assert feature.ndim == 3
    feature = feature.to(device, dtype=dtype).transpose(1, 2)
    if not params.remove_whisper_encoder_input_length_restriction:
        T = 3000
        if feature.shape[2] < T:
            feature = torch.cat(
                [
                    feature,
                    torch.zeros(
                        feature.shape[0], feature.shape[1], T - feature.shape[2]
                    ).to(device, dtype=dtype),
                ],
                2,
            )

    supervisions = batch["supervisions"]
    feature_len = supervisions["num_frames"]
    feature_len = feature_len.to(device, dtype=dtype)
    results = model.decode(feature, params.decoding_options)
    hyps = [result.text for result in results]

    hyps = remove_punctuation(hyps)
    hyps = to_simple(hyps)
    hyps = [params.normalizer.normalize(hyp) for hyp in hyps]

    return {"beam-search": hyps}


def decode_dataset(
    dl: torch.utils.data.DataLoader,
    params: AttributeDict,
    model: nn.Module,
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
    """Decode dataset.

    Args:
        dl:
            The dataloader.
        params:
            It is returned by :func:`get_params`.
        model:
            The neural model.
    Returns:
        Return a dict, whose key may be "beam-search".
    """
    results = []

    num_cuts = 0

    try:
        num_batches = len(dl)
    except TypeError:
        num_batches = "?"

    results = defaultdict(list)
    for batch_idx, batch in enumerate(dl):
        texts = batch["supervisions"]["text"]
        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]

        hyps_dict = decode_one_batch(
            params=params,
            model=model,
            batch=batch,
        )

        for lm_scale, hyps in hyps_dict.items():
            this_batch = []
            assert len(hyps) == len(texts)
            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
                ref_words = ref_text.split()
                this_batch.append((cut_id, ref_words, hyp_words))

            results[lm_scale].extend(this_batch)

        num_cuts += len(batch["supervisions"]["text"])

        if batch_idx % 100 == 0:
            batch_str = f"{batch_idx}/{num_batches}"

            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
    return results


def save_results(
    params: AttributeDict,
    test_set_name: str,
    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
):

    enable_log = True
    test_set_wers = dict()
    for key, results in results_dict.items():
        recog_path = (
            params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
        )
        results = sorted(results)
        store_transcripts(filename=recog_path, texts=results, char_level=True)
        if enable_log:
            logging.info(f"The transcripts are stored in {recog_path}")

        # The following prints out WERs, per-word error statistics and aligned
        # ref/hyp pairs.
        errs_filename = (
            params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
        )
        # we compute CER for aishell dataset.
        results_char = []
        for res in results:
            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
        with open(errs_filename, "w") as f:
            wer = write_error_stats(
                f,
                f"{test_set_name}-{key}",
                results_char,
                enable_log=enable_log,
                compute_CER=True,
            )
            test_set_wers[key] = wer

        if enable_log:
            logging.info("Wrote detailed error stats to {}".format(errs_filename))

    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
    errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
    with open(errs_info, "w") as f:
        print("settings\tCER", file=f)
        for key, val in test_set_wers:
            print("{}\t{}".format(key, val), file=f)

    s = "\nFor {}, CER of different settings are:\n".format(test_set_name)
    note = "\tbest for {}".format(test_set_name)
    for key, val in test_set_wers:
        s += "{}\t{}{}\n".format(key, val, note)
        note = ""
    logging.info(s)


@torch.no_grad()
def main():
    parser = get_parser()
    AishellAsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)

    params = get_params()
    params.update(vars(args))
    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
    setup_logger(
        f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}"
    )

    options = whisper.DecodingOptions(
        task="transcribe",
        language="zh",
        without_timestamps=True,
        beam_size=params.beam_size,
    )
    params.decoding_options = options
    params.cleaner = BasicTextNormalizer()
    params.normalizer = Normalizer()

    logging.info("Decoding started")
    logging.info(params)

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

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

    if params.remove_whisper_encoder_input_length_restriction:
        replace_whisper_encoder_forward()
    model = whisper.load_model(params.model_name, "cpu")
    if params.epoch > 0:
        if params.avg > 1:
            start = params.epoch - params.avg
            assert start >= 1, start
            checkpoint = torch.load(
                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
            )
            if "model" not in checkpoint:
                # deepspeed converted checkpoint only contains model state_dict
                filenames = [
                    f"{params.exp_dir}/epoch-{epoch}.pt"
                    for epoch in range(start, params.epoch + 1)
                ]
                model.load_state_dict(average_checkpoints(filenames))
            else:
                filename_start = f"{params.exp_dir}/epoch-{start}.pt"
                filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
                logging.info(
                    f"Calculating the averaged model over epoch range from "
                    f"{start} (excluded) to {params.epoch}"
                )
                model.to(device)
                model.load_state_dict(
                    average_checkpoints_with_averaged_model(
                        filename_start=filename_start,
                        filename_end=filename_end,
                        device=device,
                    )
                )
            # save checkpoints
            filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
            torch.save(model.state_dict(), filename)
        else:
            checkpoint = torch.load(
                f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu", weights_only=False
            )
            if "model" not in checkpoint:
                model.load_state_dict(checkpoint, strict=True)
            else:
                load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
    model.to(device)
    model.eval()
    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    # we need cut ids to display recognition results.
    args.return_cuts = True
    aishell = AishellAsrDataModule(args)
    valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())
    test_dl = aishell.test_dataloaders(aishell.test_cuts())
    test_sets = ["valid", "test"]
    test_dls = [valid_dl, test_dl]

    for test_set, test_dl in zip(test_sets, test_dls):
        results_dict = decode_dataset(
            dl=test_dl,
            params=params,
            model=model,
        )

        save_results(params=params, test_set_name=test_set, results_dict=results_dict)

    logging.info("Done!")


torch.set_num_threads(1)
torch.set_num_interop_threads(1)

if __name__ == "__main__":
    main()