mirror of
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492 lines
16 KiB
Python
Executable File
492 lines
16 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
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# Fangjun Kuang,
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# Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import whisper
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from whisper.normalizers import BasicTextNormalizer
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import k2
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import torch
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import torch.nn as nn
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from asr_datamodule import WenetSpeechAsrDataModule
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from model import load_model
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from icefall.checkpoint import load_checkpoint, average_checkpoints_with_averaged_model
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from icefall.decode import (
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get_lattice,
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nbest_decoding,
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nbest_oracle,
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one_best_decoding,
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rescore_with_attention_decoder,
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)
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from icefall.env import get_env_info
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from icefall.lexicon import Lexicon
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from icefall.utils import (
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AttributeDict,
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get_texts,
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setup_logger,
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store_transcripts,
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write_error_stats,
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)
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from zhconv import convert
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from tn.chinese.normalizer import Normalizer
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import re
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def average_checkpoints(
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filenames: List[Path], device: torch.device = torch.device("cpu")
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) -> dict:
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"""Average a list of checkpoints.
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Args:
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filenames:
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Filenames of the checkpoints to be averaged. We assume all
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checkpoints are saved by :func:`save_checkpoint`.
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device:
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Move checkpoints to this device before averaging.
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Returns:
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Return a dict (i.e., state_dict) which is the average of all
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model state dicts contained in the checkpoints.
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"""
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n = len(filenames)
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if "model" in torch.load(filenames[0], map_location=device):
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avg = torch.load(filenames[0], map_location=device)["model"]
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else:
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avg = torch.load(filenames[0], map_location=device)
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# Identify shared parameters. Two parameters are said to be shared
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# if they have the same data_ptr
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uniqued: Dict[int, str] = dict()
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for k, v in avg.items():
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v_data_ptr = v.data_ptr()
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if v_data_ptr in uniqued:
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continue
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uniqued[v_data_ptr] = k
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uniqued_names = list(uniqued.values())
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for i in range(1, n):
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if "model" in torch.load(filenames[i], map_location=device):
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state_dict = torch.load(filenames[i], map_location=device)["model"]
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else:
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state_dict = torch.load(filenames[i], map_location=device)
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for k in uniqued_names:
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avg[k] += state_dict[k]
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for k in uniqued_names:
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if avg[k].is_floating_point():
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avg[k] /= n
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else:
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avg[k] //= n
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return avg
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def remove_punctuation(text: str or List[str]):
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# https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
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punctuation = '!,.;:?、！，。；：？'
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if isinstance(text, str):
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text = re.sub(r'[{}]+'.format(punctuation), '', text).strip()
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return text
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elif isinstance(text, list):
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result_text = []
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for t in text:
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t = re.sub(r'[{}]+'.format(punctuation), '', t).strip()
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result_text.append(t)
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return result_text
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else:
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raise Exception(f'Not support type {type(text)}')
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def to_simple(text: str or List[str]):
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if isinstance(text, str):
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text = convert(text, 'zh-cn')
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return text
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elif isinstance(text, list):
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result_text = []
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for t in text:
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t = convert(t, 'zh-cn')
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result_text.append(t)
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return result_text
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else:
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raise Exception(f'Not support type{type(text)}')
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=-1,
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help="It specifies the checkpoint to use for decoding."
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"Note: Epoch counts from 0.",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=1,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch'. ",
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)
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parser.add_argument(
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"--method",
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type=str,
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default="beam-search",
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help="""Decoding method.
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Supported values are:
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- beam-search
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""",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=1,
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help="beam size for beam search decoding",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="whisper/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--model-name",
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type=str,
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default="large-v2",
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choices=["large-v2", "large-v3", "medium", "small", "tiny"],
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help="""The model name to use.
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""",
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)
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return parser
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def get_params() -> AttributeDict:
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params = AttributeDict(
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{
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"env_info": get_env_info(),
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}
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)
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return params
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def decode_one_batch(
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params: AttributeDict,
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model: nn.Module,
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batch: dict,
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) -> Dict[str, List[List[int]]]:
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"""Decode one batch and return the result in a dict. The dict has the
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following format:
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- key: It indicates the setting used for decoding. For example,
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if decoding method is 1best, the key is the string `no_rescore`.
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If attention rescoring is used, the key is the string
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`ngram_lm_scale_xxx_attention_scale_xxx`, where `xxx` is the
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value of `lm_scale` and `attention_scale`. An example key is
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`ngram_lm_scale_0.7_attention_scale_0.5`
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- value: It contains the decoding result. `len(value)` equals to
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batch size. `value[i]` is the decoding result for the i-th
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utterance in the given batch.
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Args:
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params:
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It's the return value of :func:`get_params`.
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- params.method is "1best", it uses 1best decoding without LM rescoring.
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- params.method is "nbest", it uses nbest decoding without LM rescoring.
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- params.method is "attention-decoder", it uses attention rescoring.
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model:
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The neural model.
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HLG:
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The decoding graph. Used when params.method is NOT ctc-decoding.
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H:
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The ctc topo. Used only when params.method is ctc-decoding.
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batch:
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It is the return value from iterating
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`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
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for the format of the `batch`.
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lexicon:
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It contains the token symbol table and the word symbol table.
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sos_id:
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The token ID of the SOS.
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eos_id:
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The token ID of the EOS.
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Returns:
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Return the decoding result. See above description for the format of
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the returned dict.
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"""
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dtype = torch.float16
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device = torch.device("cuda")
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feature = batch["inputs"]
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assert feature.ndim == 3
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feature = feature.to(device, dtype=dtype).transpose(1, 2)
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supervisions = batch["supervisions"]
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feature_len = supervisions["num_frames"]
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feature_len = feature_len.to(device, dtype=dtype)
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results = model.decode(feature, params.decoding_options)
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hyps = [result.text for result in results]
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hyps = remove_punctuation(hyps)
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hyps = to_simple(hyps)
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hyps = [params.normalizer.normalize(hyp) for hyp in hyps]
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key = "beam-search"
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return {key: hyps}
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def decode_dataset(
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dl: torch.utils.data.DataLoader,
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params: AttributeDict,
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model: nn.Module,
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) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
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"""Decode dataset.
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Args:
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dl:
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PyTorch's dataloader containing the dataset to decode.
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params:
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It is returned by :func:`get_params`.
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model:
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The neural model.
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HLG:
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The decoding graph. Used when params.method is NOT ctc-decoding.
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H:
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The ctc topo. Used only when params.method is ctc-decoding.
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lexicon:
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It contains the token symbol table and the word symbol table.
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sos_id:
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The token ID for SOS.
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eos_id:
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The token ID for EOS.
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Returns:
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Return a dict, whose key may be "no-rescore" if the decoding method is
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1best or it may be "ngram_lm_scale_0.7_attention_scale_0.5" if attention
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rescoring is used. Its value is a list of tuples. Each tuple contains two
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elements: The first is the reference transcript, and the second is the
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predicted result.
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"""
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results = []
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num_cuts = 0
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try:
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num_batches = len(dl)
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except TypeError:
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num_batches = "?"
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results = defaultdict(list)
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for batch_idx, batch in enumerate(dl):
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texts = batch["supervisions"]["text"]
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cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
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hyps_dict = decode_one_batch(
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params=params,
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model=model,
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batch=batch,
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)
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for lm_scale, hyps in hyps_dict.items():
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this_batch = []
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assert len(hyps) == len(texts)
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for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
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ref_words = ref_text.split()
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this_batch.append((cut_id, ref_words, hyp_words))
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results[lm_scale].extend(this_batch)
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num_cuts += len(batch["supervisions"]["text"])
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if batch_idx % 100 == 0:
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batch_str = f"{batch_idx}/{num_batches}"
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logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
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return results
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def save_results(
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params: AttributeDict,
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test_set_name: str,
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results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
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):
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enable_log = True
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test_set_wers = dict()
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for key, results in results_dict.items():
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recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
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results = sorted(results)
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store_transcripts(filename=recog_path, texts=results)
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if enable_log:
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logging.info(f"The transcripts are stored in {recog_path}")
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# The following prints out WERs, per-word error statistics and aligned
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# ref/hyp pairs.
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errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
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# we compute CER for aishell dataset.
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results_char = []
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for res in results:
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results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
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with open(errs_filename, "w") as f:
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wer = write_error_stats(
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f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
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)
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test_set_wers[key] = wer
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if enable_log:
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logging.info("Wrote detailed error stats to {}".format(errs_filename))
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test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
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errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
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with open(errs_info, "w") as f:
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print("settings\tCER", file=f)
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for key, val in test_set_wers:
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print("{}\t{}".format(key, val), file=f)
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s = "\nFor {}, CER of different settings are:\n".format(test_set_name)
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note = "\tbest for {}".format(test_set_name)
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for key, val in test_set_wers:
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s += "{}\t{}{}\n".format(key, val, note)
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note = ""
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logging.info(s)
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@torch.no_grad()
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def main():
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parser = get_parser()
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WenetSpeechAsrDataModule.add_arguments(parser)
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args = parser.parse_args()
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args.exp_dir = Path(args.exp_dir)
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params = get_params()
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params.update(vars(args))
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params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
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setup_logger(f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}")
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options = whisper.DecodingOptions(task="transcribe", language="zh", without_timestamps=True, beam_size=params.beam_size)
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params.decoding_options = options
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params.cleaner = BasicTextNormalizer()
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params.normalizer = Normalizer()
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logging.info("Decoding started")
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logging.info(params)
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device = torch.device("cpu")
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if torch.cuda.is_available():
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device = torch.device("cuda")
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logging.info(f"device: {device}")
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model = load_model(params.model_name)
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if params.epoch > 0:
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if params.avg > 1:
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start = params.epoch - params.avg
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assert start >= 1, start
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checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
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if 'model' not in checkpoint:
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filenames = [f"{params.exp_dir}/epoch-{epoch}.pt" for epoch in range(start, params.epoch + 1)]
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model.load_state_dict(average_checkpoints(filenames))
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else:
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filename_start = f"{params.exp_dir}/epoch-{start}.pt"
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filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
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logging.info(
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f"Calculating the averaged model over epoch range from "
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f"{start} (excluded) to {params.epoch}"
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)
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model.to(device)
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model.load_state_dict(
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average_checkpoints_with_averaged_model(
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filename_start=filename_start,
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filename_end=filename_end,
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device=device,
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)
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)
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# save checkpoints
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filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
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torch.save(model.state_dict(), filename)
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else:
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checkpoint = torch.load(f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location='cpu')
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if 'model' not in checkpoint:
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model.load_state_dict(checkpoint, strict=True)
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else:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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model.to(device)
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model.eval()
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num_param = sum([p.numel() for p in model.parameters()])
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logging.info(f"Number of model parameters: {num_param}")
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# we need cut ids to display recognition results.
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args.return_cuts = True
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wenetspeech = WenetSpeechAsrDataModule(args)
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def remove_short_utt(c: Cut):
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T = ((c.num_frames - 7) // 2 + 1) // 2
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if T <= 0:
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logging.warning(
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f"Exclude cut with ID {c.id} from decoding, num_frames : {c.num_frames}."
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)
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return T > 0
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# dev_cuts = wenetspeech.valid_cuts()
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# dev_cuts = dev_cuts.filter(remove_short_utt)
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# dev_dl = wenetspeech.valid_dataloaders(dev_cuts)
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# test_net_cuts = wenetspeech.test_net_cuts()
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# test_net_cuts = test_net_cuts.filter(remove_short_utt)
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# test_net_dl = wenetspeech.test_dataloaders(test_net_cuts)
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test_meeting_cuts = wenetspeech.test_meeting_cuts()
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test_meeting_cuts = test_meeting_cuts.filter(remove_short_utt)
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test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
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# test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
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# test_dls = [dev_dl, test_net_dl, test_meeting_dl]
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test_sets = ["TEST_MEETING"]
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test_dls = [test_meeting_dl]
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for test_set, test_dl in zip(test_sets, test_dls):
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results_dict = decode_dataset(
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dl=test_dl,
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params=params,
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model=model,
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)
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save_results(params=params, test_set_name=test_set, results_dict=results_dict)
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logging.info("Done!")
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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if __name__ == "__main__":
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main()
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