mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-08 00:24:19 +00:00
removed un-used files
This commit is contained in:
marcoyang1998
parent
84ff2ab67c
commit
66ac3a4ecc
@ -1,359 +0,0 @@
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import argparse
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import logging
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import math
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import warnings
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from pathlib import Path
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from typing import List
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from tqdm import tqdm
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import k2
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import kaldifeat
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import sentencepiece as spm
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import torch
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import torchaudio
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from lhotse import load_manifest, Fbank
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from beam_search import (
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beam_search,
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fast_beam_search_one_best,
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greedy_search,
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greedy_search_batch,
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modified_beam_search,
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)
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from text_normalization import ref_text_normalization, remove_non_alphabetic, upper_only_alpha, upper_all_char, lower_all_char, lower_only_alpha
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from train_bert_encoder import (
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add_model_arguments,
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get_params,
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get_tokenizer,
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get_transducer_model,
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_encode_texts_as_bytes,
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)
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.utils import (
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AttributeDict,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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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=30,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 1.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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""",
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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=9,
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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' and '--iter'",
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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="pruned_transducer_stateless7/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500/bpe.model",
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help="""Path to bpe.model.""",
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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="greedy_search",
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help="""Possible values are:
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- greedy_search
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- beam_search
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- modified_beam_search
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- fast_beam_search
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""",
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)
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parser.add_argument(
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"--manifest-dir",
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type=str,
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default="data/manifests_chunk/youtube_cuts_foxnews.jsonl.gz"
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)
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parser.add_argument(
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"--segment-length",
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type=float,
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default=30.0,
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)
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parser.add_argument(
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"--use-pre-text",
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type=str2bool,
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default=False,
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help="Whether use pre-text when decoding the current chunk"
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)
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parser.add_argument(
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"--num-history",
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type=int,
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default=2,
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help="How many previous chunks to look if using pre-text for decoding"
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)
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add_model_arguments(parser)
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return parser
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def read_sound_files(
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filenames: List[str], expected_sample_rate: float
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) -> List[torch.Tensor]:
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"""Read a list of sound files into a list 1-D float32 torch tensors.
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Args:
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filenames:
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A list of sound filenames.
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expected_sample_rate:
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The expected sample rate of the sound files.
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Returns:
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Return a list of 1-D float32 torch tensors.
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"""
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ans = []
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for f in filenames:
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wave, sample_rate = torchaudio.load(f)
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assert sample_rate == expected_sample_rate, (
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f"expected sample rate: {expected_sample_rate}. "
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f"Given: {sample_rate}"
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)
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# We use only the first channel
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ans.append(wave[0])
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return ans
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@torch.no_grad()
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def main():
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parser = get_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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sp = spm.SentencePieceProcessor()
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sp.load(params.bpe_model)
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# <blk> is defined in local/train_bpe_model.py
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params.blank_id = sp.piece_to_id("<blk>")
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params.unk_id = sp.piece_to_id("<unk>")
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params.vocab_size = sp.get_piece_size()
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if params.iter > 0:
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params.suffix = f"iter-{params.iter}-avg-{params.avg}"
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else:
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params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
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if "beam_search" in params.decoding_method:
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params.suffix += (
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f"-{params.decoding_method}-beam-size-{params.beam_size}"
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)
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if params.use_averaged_model:
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params.suffix += "-use-averaged-model"
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if params.use_pre_text:
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params.suffix += f"-pre-text-{params.pre_text_transform}"
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setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
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logging.info("Decoding started")
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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", 0)
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logging.info(f"device: {device}")
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logging.info("Creating model")
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model = get_transducer_model(params)
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tokenizer = get_tokenizer(params)
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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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if params.iter > 0:
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filenames = find_checkpoints(
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params.exp_dir, iteration=-params.iter
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)[: params.avg + 1]
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if len(filenames) == 0:
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raise ValueError(
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f"No checkpoints found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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elif len(filenames) < params.avg + 1:
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raise ValueError(
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f"Not enough checkpoints ({len(filenames)}) found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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filename_start = filenames[-1]
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filename_end = filenames[0]
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logging.info(
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"Calculating the averaged model over iteration checkpoints"
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f" from {filename_start} (excluded) to {filename_end}"
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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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else:
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assert params.avg > 0, params.avg
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start = params.epoch - params.avg
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assert start >= 1, start
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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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model.to(device)
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model.eval()
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model.device = device
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logging.info("Constructing Fbank computer")
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opts = kaldifeat.FbankOptions()
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opts.device = device
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opts.frame_opts.dither = 0
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opts.frame_opts.snip_edges = False
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opts.frame_opts.samp_freq = 16000
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opts.mel_opts.num_bins = params.feature_dim
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params.res_dir = params.exp_dir / "long_audio_transcribe"
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params.res_dir.mkdir(exist_ok=True)
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# load manifest
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manifest = load_manifest(params.manifest_dir)
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fbank = kaldifeat.Fbank(opts)
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all_hyps = []
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all_ref = []
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results = []
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count = 0
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for cut in tqdm(manifest):
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frames_per_segment = params.segment_length * 100 # number of frames per segment
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feats = cut.compute_features(extractor=Fbank())
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feats = torch.tensor(feats).to(device)
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num_chunks = feats.size(0) // frames_per_segment + 1
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hyp = []
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for i in range(int(num_chunks)):
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start = int(i * frames_per_segment)
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end = int(min((i+1) * frames_per_segment, feats.size(0)))
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x = feats[start:end].unsqueeze(0)
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x_lens = torch.tensor([end-start,], device=device)
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if params.use_pre_text:
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pre_texts = hyp[-params.num_history:]
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pre_texts = [" ".join(pre_texts)]
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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encoded_inputs = tokenizer(
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pre_texts,
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return_tensors='pt',
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padding=True,
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truncation=True,
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max_length=500,
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).to(device)
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memory, memory_key_padding_mask = model.encode_text(
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encoded_inputs=encoded_inputs,
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) # (T,B,C)
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else:
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memory = None
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memory_key_padding_mask = None
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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encoder_out, encoder_out_lens = model.encode_audio(
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feature=x,
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feature_lens=x_lens,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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)
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hyp_tokens = greedy_search_batch(
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model=model,
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encoder_out=encoder_out,
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encoder_out_lens=encoder_out_lens,
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)
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hyp.append(sp.decode(hyp_tokens)[0])
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ref = remove_non_alphabetic(cut.text.upper())
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hyp = remove_non_alphabetic(" ".join(hyp).upper())
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all_hyps.append(hyp)
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all_ref.append(ref)
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results.append((cut.id, ref, hyp))
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count += 1
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if count == 5:
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break
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recog_path = params.res_dir / f"recogs-youtube-{params.method}-{params.suffix}.txt"
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store_transcripts(filename=recog_path, texts=results)
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logging.info(f"The transcripts are stored in {recog_path}")
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errs_filename = params.res_dir / f"errs-youtube-{params.method}-{params.suffix}.txt"
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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"youtube-{params.method}", results, enable_log=True
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)
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logging.info("Wrote detailed error stats to {}".format(errs_filename))
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if __name__=="__main__":
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main()
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File diff suppressed because it is too large
Load Diff
@ -1,903 +0,0 @@
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#!/usr/bin/env python3
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#
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# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
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# Zengwei Yao,
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# Xiaoyu Yang)
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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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"""
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Usage:
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(1) greedy search
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./pruned_transducer_stateless7/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless7/exp \
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--max-duration 600 \
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--decoding-method greedy_search
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(2) modified beam search
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./pruned_transducer_stateless7/decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless7/exp \
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--max-duration 600 \
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--decoding-method modified_beam_search \
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--beam-size 4
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"""
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import argparse
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import logging
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import math
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import warnings
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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, Callable
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import k2
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from asr_datamodule import LibriHeavyAsrDataModule
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from beam_search import (
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greedy_search,
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greedy_search_with_context,
|
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greedy_search_batch,
|
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greedy_search_batch_with_context,
|
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modified_beam_search,
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)
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from dataset import naive_triplet_text_sampling
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from text_normalization import ref_text_normalization, remove_non_alphabetic, upper_only_alpha, upper_all_char, lower_all_char, lower_only_alpha
|
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from train_with_joiner_context import (
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add_model_arguments,
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get_params,
|
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get_transducer_model,
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_encode_texts_as_bytes,
|
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)
|
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|
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from icefall.checkpoint import (
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average_checkpoints,
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average_checkpoints_with_averaged_model,
|
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find_checkpoints,
|
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load_checkpoint,
|
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)
|
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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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setup_logger,
|
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store_transcripts,
|
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str2bool,
|
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write_error_stats,
|
||||
)
|
||||
|
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LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
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parser = argparse.ArgumentParser(
|
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
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parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=30,
|
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help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 1.
|
||||
You can specify --avg to use more checkpoints for model averaging.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--iter",
|
||||
type=int,
|
||||
default=0,
|
||||
help="""If positive, --epoch is ignored and it
|
||||
will use the checkpoint exp_dir/checkpoint-iter.pt.
|
||||
You can specify --avg to use more checkpoints for model averaging.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=9,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch' and '--iter'",
|
||||
)
|
||||
|
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parser.add_argument(
|
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"--use-averaged-model",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Whether to load averaged model. Currently it only supports "
|
||||
"using --epoch. If True, it would decode with the averaged model "
|
||||
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
|
||||
"Actually only the models with epoch number of `epoch-avg` and "
|
||||
"`epoch` are loaded for averaging. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="pruned_transducer_stateless7/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bpe_500/bpe.model",
|
||||
help="Path to the BPE model",
|
||||
)
|
||||
|
||||
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(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Possible values are:
|
||||
- greedy_search
|
||||
- beam_search
|
||||
- modified_beam_search
|
||||
- fast_beam_search
|
||||
- fast_beam_search_nbest
|
||||
- fast_beam_search_nbest_oracle
|
||||
- fast_beam_search_nbest_LG
|
||||
- modified_beam_search_lm_shallow_fusion # for rnn lm shallow fusion
|
||||
- modified_beam_search_LODR
|
||||
If you use fast_beam_search_nbest_LG, you have to specify
|
||||
`--lang-dir`, which should contain `LG.pt`.
|
||||
""",
|
||||
)
|
||||
|
||||
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 --decoding-method is beam_search or
|
||||
modified_beam_search.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=20.0,
|
||||
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 --decoding-method is fast_beam_search,
|
||||
fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--ngram-lm-scale",
|
||||
type=float,
|
||||
default=0.01,
|
||||
help="""
|
||||
Used only when --decoding_method is fast_beam_search_nbest_LG.
|
||||
It specifies the scale for n-gram LM scores.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=8,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=64,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
|
||||
and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
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 --decoding_method is greedy_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-paths",
|
||||
type=int,
|
||||
default=200,
|
||||
help="""Number of paths for nbest decoding.
|
||||
Used only when the decoding method is fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--nbest-scale",
|
||||
type=float,
|
||||
default=0.5,
|
||||
help="""Scale applied to lattice scores when computing nbest paths.
|
||||
Used only when the decoding method is fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-pre-text",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Use pre-text is available during decoding",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-style-prompt",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Use style prompt when evaluation"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-context-embedding",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Use context fuser when evaluation"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--post-normalization",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Normalized the recognition results by uppercasing and removing non-alphabetic symbols. ",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--compute-CER",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="Reports CER. By default, only reports WER",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--style-text-transform",
|
||||
type=str,
|
||||
choices=["mixed-punc", "upper-no-punc", "lower-no-punc","lower-punc"],
|
||||
default="mixed-punc",
|
||||
help="The style of style prompt, i.e style_text"
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--pre-text-transform",
|
||||
type=str,
|
||||
choices=["mixed-punc", "upper-no-punc", "lower-no-punc","lower-punc"],
|
||||
default="mixed-punc",
|
||||
help="The style of content prompt, i.e pre_text"
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
|
||||
def _apply_style_transform(text: List[str], transform: str) -> List[str]:
|
||||
"""Apply transform to a list of text. By default, the text are in
|
||||
ground truth format, i.e mixed-punc.
|
||||
|
||||
Args:
|
||||
text (List[str]): Input text string
|
||||
transform (str): Transform to be applied
|
||||
|
||||
Returns:
|
||||
List[str]: _description_
|
||||
"""
|
||||
if transform == "mixed-punc":
|
||||
return text
|
||||
elif transform == "upper-no-punc":
|
||||
return [upper_only_alpha(s) for s in text]
|
||||
elif transform == "lower-no-punc":
|
||||
return [lower_only_alpha(s) for s in text]
|
||||
elif transform == "lower-punc":
|
||||
return [lower_all_char(s) for s in text]
|
||||
else:
|
||||
raise NotImplementedError(f"Unseen transform: {transform}")
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
batch: dict,
|
||||
word_table: Optional[k2.SymbolTable] = None,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[List[str]]]:
|
||||
"""Decode one batch and return the result in a dict. The dict has the
|
||||
following format:
|
||||
|
||||
- key: It indicates the setting used for decoding. For example,
|
||||
if greedy_search is used, it would be "greedy_search"
|
||||
If beam search with a beam size of 7 is used, it would be
|
||||
"beam_7"
|
||||
- value: It contains the decoding result. `len(value)` equals to
|
||||
batch size. `value[i]` is the decoding result for the i-th
|
||||
utterance in the given batch.
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
batch:
|
||||
It is the return value from iterating
|
||||
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
|
||||
for the format of the `batch`.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
LM:
|
||||
A neural net LM for shallow fusion. Only used when `--use-shallow-fusion`
|
||||
set to true.
|
||||
ngram_lm:
|
||||
A ngram lm. Used in LODR decoding.
|
||||
ngram_lm_scale:
|
||||
The scale of the ngram language model.
|
||||
Returns:
|
||||
Return the decoding result. See above description for the format of
|
||||
the returned dict.
|
||||
"""
|
||||
device = next(model.parameters()).device
|
||||
feature = batch["inputs"]
|
||||
batch_size = feature.size(0)
|
||||
|
||||
if "pre_text" in batch["supervisions"] and params.use_pre_text:
|
||||
pre_texts = batch["supervisions"]["pre_text"]
|
||||
else:
|
||||
pre_texts = ["" for _ in range(batch_size)]
|
||||
|
||||
if params.use_style_prompt:
|
||||
style_texts = batch["supervisions"]["style_text"]
|
||||
else:
|
||||
style_texts = ["" for _ in range(batch_size)] # use empty string
|
||||
|
||||
# Get the text embedding input
|
||||
if params.use_pre_text or params.use_style_prompt:
|
||||
|
||||
# apply style transform to the pre_text and style_text
|
||||
if params.use_style_prompt:
|
||||
pre_texts = _apply_style_transform(pre_texts, params.pre_text_transform)
|
||||
style_texts = _apply_style_transform(style_texts, params.style_text_transform)
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
|
||||
pre_texts, pre_texts_lens, style_lens = _encode_texts_as_bytes(
|
||||
pre_texts,
|
||||
style_texts,
|
||||
device,
|
||||
max_len=1200
|
||||
) # note that the output pre_texts include style_text and actual pre_text
|
||||
|
||||
memory, memory_key_padding_mask = model.encode_text(
|
||||
text=pre_texts,
|
||||
text_lens=pre_texts_lens,
|
||||
style_lens=style_lens,
|
||||
) # (T,B,C)
|
||||
else:
|
||||
memory = None
|
||||
memory_key_padding_mask = None
|
||||
|
||||
# Get the transducer encoder output
|
||||
assert feature.ndim == 3
|
||||
feature = feature.to(device)
|
||||
# at entry, feature is (N, T, C)
|
||||
|
||||
supervisions = batch["supervisions"]
|
||||
feature_lens = supervisions["num_frames"].to(device)
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
encoder_out, encoder_out_lens = model.encode_audio(
|
||||
feature=feature,
|
||||
feature_lens=feature_lens,
|
||||
memory=memory,
|
||||
memory_key_padding_mask=memory_key_padding_mask,
|
||||
)
|
||||
|
||||
hyps = []
|
||||
|
||||
if (
|
||||
params.decoding_method == "greedy_search"
|
||||
and params.max_sym_per_frame == 1
|
||||
):
|
||||
if memory is None or not params.use_context_embedding:
|
||||
hyp_tokens = greedy_search_batch(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
else:
|
||||
memory = memory.permute(1,0,2) # (T,N,C) -> (N,T,C)
|
||||
context = model.context_fuser(memory, padding_mask=memory_key_padding_mask) # (N,C)
|
||||
context = model.joiner.context_proj(context) # (N,C)
|
||||
hyp_tokens = greedy_search_batch_with_context(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
context=context,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.decoding_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 sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
else:
|
||||
batch_size = encoder_out.size(0)
|
||||
if memory is not None:
|
||||
memory = memory.permute(1,0,2) # (T,N,C) -> (N,T,C)
|
||||
context = model.context_fuser(memory, padding_mask=memory_key_padding_mask) # (N,C)
|
||||
context = model.joiner.context_proj(context) # (N,C)
|
||||
|
||||
for i in range(batch_size):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.decoding_method == "greedy_search":
|
||||
if memory is None or not params.use_context_embedding:
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
else:
|
||||
cur_context = context[i:i+1, :]
|
||||
hyp = greedy_search_with_context(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
context=cur_context,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
|
||||
elif params.decoding_method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported decoding method: {params.decoding_method}"
|
||||
)
|
||||
hyps.append(sp.decode(hyp).split())
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
return {"greedy_search": hyps}
|
||||
else:
|
||||
return {f"beam_size_{params.beam_size}": hyps}
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
dl: torch.utils.data.DataLoader,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
word_table: Optional[k2.SymbolTable] = None,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
dl:
|
||||
PyTorch's dataloader containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
word_table:
|
||||
The word symbol table.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
|
||||
fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
|
||||
LM:
|
||||
A neural network LM, used during shallow fusion
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
num_cuts = 0
|
||||
|
||||
try:
|
||||
num_batches = len(dl)
|
||||
except TypeError:
|
||||
num_batches = "?"
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
log_interval = 50
|
||||
else:
|
||||
log_interval = 20
|
||||
|
||||
results = defaultdict(list)
|
||||
for batch_idx, batch in enumerate(dl):
|
||||
texts = batch["supervisions"]["text"] # By default, this should be in mixed-punc format
|
||||
|
||||
# the style of ref_text should match style_text
|
||||
if params.use_style_prompt:
|
||||
texts = _apply_style_transform(texts, params.style_text_transform)
|
||||
|
||||
cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
|
||||
|
||||
hyps_dict = decode_one_batch(
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
decoding_graph=decoding_graph,
|
||||
word_table=word_table,
|
||||
batch=batch,
|
||||
)
|
||||
|
||||
for name, 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_text = ref_text_normalization(
|
||||
ref_text
|
||||
) # remove full-width symbols & some book marks
|
||||
if params.post_normalization:
|
||||
ref_words = ref_text.split()
|
||||
ref_words = [remove_non_alphabetic(w.upper()) for w in ref_words]
|
||||
ref_words = [w for w in ref_words if w != ""]
|
||||
hyp_words = [remove_non_alphabetic(w.upper()) for w in hyp_words]
|
||||
hyp_words = [w for w in hyp_words if w != ""]
|
||||
else:
|
||||
ref_words = ref_text.split()
|
||||
this_batch.append((cut_id, ref_words, hyp_words))
|
||||
|
||||
results[name].extend(this_batch)
|
||||
|
||||
num_cuts += len(texts)
|
||||
|
||||
if batch_idx % log_interval == 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]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
test_set_cers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
|
||||
results = sorted(results)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
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.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
if params.compute_CER:
|
||||
# Write CER statistics
|
||||
recog_path = params.res_dir / f"recogs-{test_set_name}-char-{params.suffix}.txt"
|
||||
store_transcripts(filename=recog_path, texts=results, char_level=True)
|
||||
errs_filename = (
|
||||
params.res_dir / f"errs-CER-{test_set_name}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
cer = write_error_stats(
|
||||
f,
|
||||
f"{test_set_name}-{key}",
|
||||
results,
|
||||
enable_log=True,
|
||||
compute_CER=params.compute_CER,
|
||||
)
|
||||
test_set_cers[key] = cer
|
||||
|
||||
logging.info("Wrote detailed CER stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER 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)
|
||||
|
||||
if params.compute_CER:
|
||||
test_set_cers = sorted(test_set_cers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_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_cers:
|
||||
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_cers:
|
||||
s += "{} CER\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
LibriHeavyAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
assert params.decoding_method in (
|
||||
"greedy_search",
|
||||
"modified_beam_search",
|
||||
)
|
||||
params.res_dir = params.exp_dir / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
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."
|
||||
params.suffix += f"-chunk-{params.chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context_frames}"
|
||||
|
||||
if "beam_search" in params.decoding_method:
|
||||
params.suffix += (
|
||||
f"-{params.decoding_method}-beam-size-{params.beam_size}"
|
||||
)
|
||||
else:
|
||||
params.suffix += f"-context-{params.context_size}"
|
||||
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
|
||||
|
||||
if params.use_pre_text:
|
||||
params.suffix += f"-pre-text-{params.pre_text_transform}"
|
||||
|
||||
if params.use_style_prompt:
|
||||
params.suffix += f"-style-prompt-{params.style_text_transform}"
|
||||
|
||||
if params.use_context_embedding:
|
||||
params.suffix += f"-use-context-fuser"
|
||||
|
||||
if params.post_normalization:
|
||||
params.suffix += "-post-normalization"
|
||||
|
||||
if params.use_averaged_model:
|
||||
params.suffix += "-use-averaged-model"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> and <unk> are defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
if not params.use_averaged_model:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(
|
||||
params.exp_dir, iteration=-params.iter
|
||||
)[: params.avg]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if i >= 1:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
else:
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(
|
||||
params.exp_dir, iteration=-params.iter
|
||||
)[: params.avg + 1]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg + 1:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
filename_start = filenames[-1]
|
||||
filename_end = filenames[0]
|
||||
logging.info(
|
||||
"Calculating the averaged model over iteration checkpoints"
|
||||
f" from {filename_start} (excluded) to {filename_end}"
|
||||
)
|
||||
model.to(device)
|
||||
model.load_state_dict(
|
||||
average_checkpoints_with_averaged_model(
|
||||
filename_start=filename_start,
|
||||
filename_end=filename_end,
|
||||
device=device,
|
||||
)
|
||||
)
|
||||
else:
|
||||
assert params.avg > 0, params.avg
|
||||
start = params.epoch - params.avg
|
||||
assert start >= 1, start
|
||||
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,
|
||||
)
|
||||
)
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
LM = None
|
||||
|
||||
decoding_graph = None
|
||||
word_table = None
|
||||
|
||||
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
|
||||
libriheavy = LibriHeavyAsrDataModule(args)
|
||||
|
||||
test_cuts = libriheavy.test_cuts()
|
||||
medium_test_cuts = libriheavy.medium_test_cuts()
|
||||
#medium_test_cuts = medium_test_cuts.subset(first=500)
|
||||
test_clean_cuts = libriheavy.test_clean_cuts()
|
||||
test_other_cuts = libriheavy.test_other_cuts()
|
||||
ls_test_clean_cuts = libriheavy.librispeech_test_clean_cuts()
|
||||
ls_test_other_cuts = libriheavy.librispeech_test_other_cuts()
|
||||
|
||||
test_dl = libriheavy.valid_dataloaders(test_cuts, text_sampling_func=naive_triplet_text_sampling)
|
||||
medium_test_dl = libriheavy.valid_dataloaders(medium_test_cuts, text_sampling_func=naive_triplet_text_sampling)
|
||||
test_clean_dl = libriheavy.test_dataloaders(test_clean_cuts)
|
||||
test_other_dl = libriheavy.test_dataloaders(test_other_cuts)
|
||||
ls_test_clean_dl = libriheavy.test_dataloaders(ls_test_clean_cuts)
|
||||
ls_test_other_dl = libriheavy.test_dataloaders(ls_test_other_cuts)
|
||||
|
||||
#test_sets = ["test-clean", "test-other", "ls-test-clean", "ls-test-other"]
|
||||
#test_dl = [test_clean_dl, test_other_dl, ls_test_clean_dl, ls_test_other_dl]
|
||||
|
||||
#test_sets = ["test-clean", "test-other"]
|
||||
#test_dl = [test_clean_dl, test_other_dl]
|
||||
|
||||
#test_sets = ["ls-test-clean", "ls-test-other"]
|
||||
#test_dl = [ls_test_clean_dl, ls_test_other_dl]
|
||||
|
||||
test_sets = ["medium_test",]
|
||||
test_dl = [medium_test_dl]
|
||||
|
||||
for test_set, test_dl in zip(test_sets, test_dl):
|
||||
results_dict = decode_dataset(
|
||||
dl=test_dl,
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
word_table=word_table,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user