wehub-resource-sync
269 lines
8.7 KiB
Python
269 lines
8.7 KiB
Python
#!/usr/bin/env python3 -u
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Run inference for pre-processed data with a trained model.
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"""
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import logging
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import math
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import os
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import sentencepiece as spm
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import torch
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from fairseq import checkpoint_utils, options, progress_bar, utils, tasks
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from fairseq.meters import StopwatchMeter, TimeMeter
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from fairseq.utils import import_user_module
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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def add_asr_eval_argument(parser):
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parser.add_argument("--kspmodel", default=None, help="sentence piece model")
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parser.add_argument(
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"--wfstlm", default=None, help="wfstlm on dictonary output units"
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)
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parser.add_argument(
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"--rnnt_decoding_type",
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default="greedy",
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help="wfstlm on dictonary\
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output units",
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)
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parser.add_argument(
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"--lm-weight",
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"--lm_weight",
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type=float,
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default=0.2,
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help="weight for lm while interpolating with neural score",
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)
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parser.add_argument(
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"--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level"
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)
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parser.add_argument(
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"--w2l-decoder", choices=["viterbi", "kenlm"], help="use a w2l decoder"
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)
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parser.add_argument("--lexicon", help="lexicon for w2l decoder")
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parser.add_argument("--kenlm-model", help="kenlm model for w2l decoder")
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parser.add_argument("--beam-threshold", type=float, default=25.0)
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parser.add_argument("--word-score", type=float, default=1.0)
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parser.add_argument("--unk-weight", type=float, default=-math.inf)
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parser.add_argument("--sil-weight", type=float, default=0.0)
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return parser
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def check_args(args):
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assert args.path is not None, "--path required for generation!"
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assert args.results_path is not None, "--results_path required for generation!"
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assert (
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not args.sampling or args.nbest == args.beam
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), "--sampling requires --nbest to be equal to --beam"
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assert (
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args.replace_unk is None or args.raw_text
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), "--replace-unk requires a raw text dataset (--raw-text)"
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def get_dataset_itr(args, task):
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return task.get_batch_iterator(
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dataset=task.dataset(args.gen_subset),
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max_tokens=args.max_tokens,
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max_sentences=args.max_sentences,
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max_positions=(1000000.0, 1000000.0),
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ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
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required_batch_size_multiple=args.required_batch_size_multiple,
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num_shards=args.num_shards,
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shard_id=args.shard_id,
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num_workers=args.num_workers,
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).next_epoch_itr(shuffle=False)
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def process_predictions(
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args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id
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):
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for hypo in hypos[: min(len(hypos), args.nbest)]:
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hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu())
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hyp_words = sp.DecodePieces(hyp_pieces.split())
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print(
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"{} ({}-{})".format(hyp_pieces, speaker, id), file=res_files["hypo.units"]
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)
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print("{} ({}-{})".format(hyp_words, speaker, id), file=res_files["hypo.words"])
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tgt_pieces = tgt_dict.string(target_tokens)
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tgt_words = sp.DecodePieces(tgt_pieces.split())
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print("{} ({}-{})".format(tgt_pieces, speaker, id), file=res_files["ref.units"])
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print("{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"])
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# only score top hypothesis
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if not args.quiet:
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logger.debug("HYPO:" + hyp_words)
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logger.debug("TARGET:" + tgt_words)
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logger.debug("___________________")
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def prepare_result_files(args):
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def get_res_file(file_prefix):
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path = os.path.join(
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args.results_path,
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"{}-{}-{}.txt".format(
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file_prefix, os.path.basename(args.path), args.gen_subset
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),
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)
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return open(path, "w", buffering=1)
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return {
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"hypo.words": get_res_file("hypo.word"),
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"hypo.units": get_res_file("hypo.units"),
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"ref.words": get_res_file("ref.word"),
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"ref.units": get_res_file("ref.units"),
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}
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def load_models_and_criterions(filenames, arg_overrides=None, task=None):
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models = []
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criterions = []
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for filename in filenames:
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if not os.path.exists(filename):
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raise IOError("Model file not found: {}".format(filename))
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state = checkpoint_utils.load_checkpoint_to_cpu(filename, arg_overrides)
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args = state["args"]
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if task is None:
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task = tasks.setup_task(args)
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# build model for ensemble
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model = task.build_model(args)
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model.load_state_dict(state["model"], strict=True)
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models.append(model)
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criterion = task.build_criterion(args)
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if "criterion" in state:
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criterion.load_state_dict(state["criterion"], strict=True)
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criterions.append(criterion)
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return models, criterions, args
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def optimize_models(args, use_cuda, models):
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"""Optimize ensemble for generation
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"""
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for model in models:
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model.make_generation_fast_(
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beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
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need_attn=args.print_alignment,
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)
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if args.fp16:
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model.half()
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if use_cuda:
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model.cuda()
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def main(args):
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check_args(args)
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import_user_module(args)
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if args.max_tokens is None and args.max_sentences is None:
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args.max_tokens = 30000
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logger.info(args)
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use_cuda = torch.cuda.is_available() and not args.cpu
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# Load dataset splits
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task = tasks.setup_task(args)
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task.load_dataset(args.gen_subset)
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logger.info(
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"| {} {} {} examples".format(
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args.data, args.gen_subset, len(task.dataset(args.gen_subset))
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)
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)
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# Set dictionary
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tgt_dict = task.target_dictionary
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logger.info("| decoding with criterion {}".format(args.criterion))
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# Load ensemble
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logger.info("| loading model(s) from {}".format(args.path))
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models, criterions, _model_args = load_models_and_criterions(
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args.path.split(":"),
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arg_overrides=eval(args.model_overrides), # noqa
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task=task,
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)
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optimize_models(args, use_cuda, models)
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# hack to pass transitions to W2lDecoder
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if args.criterion == "asg_loss":
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trans = criterions[0].asg.trans.data
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args.asg_transitions = torch.flatten(trans).tolist()
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# Load dataset (possibly sharded)
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itr = get_dataset_itr(args, task)
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# Initialize generator
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gen_timer = StopwatchMeter()
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generator = task.build_generator(args)
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num_sentences = 0
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if not os.path.exists(args.results_path):
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os.makedirs(args.results_path)
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sp = spm.SentencePieceProcessor()
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sp.Load(os.path.join(args.data, "spm.model"))
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res_files = prepare_result_files(args)
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with progress_bar.build_progress_bar(args, itr) as t:
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wps_meter = TimeMeter()
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for sample in t:
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sample = utils.move_to_cuda(sample) if use_cuda else sample
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if "net_input" not in sample:
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continue
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prefix_tokens = None
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if args.prefix_size > 0:
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prefix_tokens = sample["target"][:, : args.prefix_size]
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gen_timer.start()
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hypos = task.inference_step(generator, models, sample, prefix_tokens)
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num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
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gen_timer.stop(num_generated_tokens)
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for i, sample_id in enumerate(sample["id"].tolist()):
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speaker = task.dataset(args.gen_subset).speakers[int(sample_id)]
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id = task.dataset(args.gen_subset).ids[int(sample_id)]
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target_tokens = (
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utils.strip_pad(sample["target"][i, :], tgt_dict.pad()).int().cpu()
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)
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# Process top predictions
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process_predictions(
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args, hypos[i], sp, tgt_dict, target_tokens, res_files, speaker, id
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)
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wps_meter.update(num_generated_tokens)
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t.log({"wps": round(wps_meter.avg)})
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num_sentences += sample["nsentences"]
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logger.info(
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"| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"
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"sentences/s, {:.2f} tokens/s)".format(
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num_sentences,
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gen_timer.n,
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gen_timer.sum,
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num_sentences / gen_timer.sum,
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1.0 / gen_timer.avg,
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)
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)
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logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam))
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def cli_main():
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parser = options.get_generation_parser()
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parser = add_asr_eval_argument(parser)
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args = options.parse_args_and_arch(parser)
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main(args)
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if __name__ == "__main__":
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cli_main()
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