Spaces:
Runtime error
Runtime error
File size: 14,455 Bytes
8437114 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Run inference for pre-processed data with a trained model.
"""
import ast
import logging
import math
import os
import sys
import editdistance
import numpy as np
import torch
from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.data.data_utils import post_process
from fairseq.logging.meters import StopwatchMeter, TimeMeter
logging.basicConfig()
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def add_asr_eval_argument(parser):
parser.add_argument("--kspmodel", default=None, help="sentence piece model")
parser.add_argument(
"--wfstlm", default=None, help="wfstlm on dictonary output units"
)
parser.add_argument(
"--rnnt_decoding_type",
default="greedy",
help="wfstlm on dictonary\
output units",
)
try:
parser.add_argument(
"--lm-weight",
"--lm_weight",
type=float,
default=0.2,
help="weight for lm while interpolating with neural score",
)
except:
pass
parser.add_argument(
"--rnnt_len_penalty", default=-0.5, help="rnnt length penalty on word level"
)
parser.add_argument(
"--w2l-decoder",
choices=["viterbi", "kenlm", "fairseqlm"],
help="use a w2l decoder",
)
parser.add_argument("--lexicon", help="lexicon for w2l decoder")
parser.add_argument("--unit-lm", action="store_true", help="if using a unit lm")
parser.add_argument("--kenlm-model", "--lm-model", help="lm model for w2l decoder")
parser.add_argument("--beam-threshold", type=float, default=25.0)
parser.add_argument("--beam-size-token", type=float, default=100)
parser.add_argument("--word-score", type=float, default=1.0)
parser.add_argument("--unk-weight", type=float, default=-math.inf)
parser.add_argument("--sil-weight", type=float, default=0.0)
parser.add_argument(
"--dump-emissions",
type=str,
default=None,
help="if present, dumps emissions into this file and exits",
)
parser.add_argument(
"--dump-features",
type=str,
default=None,
help="if present, dumps features into this file and exits",
)
parser.add_argument(
"--load-emissions",
type=str,
default=None,
help="if present, loads emissions from this file",
)
return parser
def check_args(args):
# assert args.path is not None, "--path required for generation!"
# assert args.results_path is not None, "--results_path required for generation!"
assert (
not args.sampling or args.nbest == args.beam
), "--sampling requires --nbest to be equal to --beam"
assert (
args.replace_unk is None or args.raw_text
), "--replace-unk requires a raw text dataset (--raw-text)"
def get_dataset_itr(args, task, models):
return task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
max_tokens=args.max_tokens,
max_sentences=args.batch_size,
max_positions=(sys.maxsize, sys.maxsize),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
data_buffer_size=args.data_buffer_size,
).next_epoch_itr(shuffle=False)
def process_predictions(
args, hypos, sp, tgt_dict, target_tokens, res_files, speaker, id
):
for hypo in hypos[: min(len(hypos), args.nbest)]:
hyp_pieces = tgt_dict.string(hypo["tokens"].int().cpu())
if "words" in hypo:
hyp_words = " ".join(hypo["words"])
else:
hyp_words = post_process(hyp_pieces, args.post_process)
if res_files is not None:
print(
"{} ({}-{})".format(hyp_pieces, speaker, id),
file=res_files["hypo.units"],
)
print(
"{} ({}-{})".format(hyp_words, speaker, id),
file=res_files["hypo.words"],
)
tgt_pieces = tgt_dict.string(target_tokens)
tgt_words = post_process(tgt_pieces, args.post_process)
if res_files is not None:
print(
"{} ({}-{})".format(tgt_pieces, speaker, id),
file=res_files["ref.units"],
)
print(
"{} ({}-{})".format(tgt_words, speaker, id), file=res_files["ref.words"]
)
if not args.quiet:
logger.info("HYPO:" + hyp_words)
logger.info("TARGET:" + tgt_words)
logger.info("___________________")
hyp_words = hyp_words.split()
tgt_words = tgt_words.split()
return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
def prepare_result_files(args):
def get_res_file(file_prefix):
if args.num_shards > 1:
file_prefix = f"{args.shard_id}_{file_prefix}"
path = os.path.join(
args.results_path,
"{}-{}-{}.txt".format(
file_prefix, os.path.basename(args.path), args.gen_subset
),
)
return open(path, "w", buffering=1)
if not args.results_path:
return None
return {
"hypo.words": get_res_file("hypo.word"),
"hypo.units": get_res_file("hypo.units"),
"ref.words": get_res_file("ref.word"),
"ref.units": get_res_file("ref.units"),
}
def optimize_models(args, use_cuda, models):
"""Optimize ensemble for generation"""
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
need_attn=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
class ExistingEmissionsDecoder(object):
def __init__(self, decoder, emissions):
self.decoder = decoder
self.emissions = emissions
def generate(self, models, sample, **unused):
ids = sample["id"].cpu().numpy()
try:
emissions = np.stack(self.emissions[ids])
except:
print([x.shape for x in self.emissions[ids]])
raise Exception("invalid sizes")
emissions = torch.from_numpy(emissions)
return self.decoder.decode(emissions)
def main(args, task=None, model_state=None):
check_args(args)
if args.max_tokens is None and args.batch_size is None:
args.max_tokens = 4000000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
logger.info("| decoding with criterion {}".format(args.criterion))
task = tasks.setup_task(args)
# Load ensemble
if args.load_emissions:
models, criterions = [], []
task.load_dataset(args.gen_subset)
else:
logger.info("| loading model(s) from {}".format(args.path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
utils.split_paths(args.path, separator="\\"),
arg_overrides=ast.literal_eval(args.model_overrides),
task=task,
suffix=args.checkpoint_suffix,
strict=(args.checkpoint_shard_count == 1),
num_shards=args.checkpoint_shard_count,
state=model_state,
)
optimize_models(args, use_cuda, models)
task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task)
# Set dictionary
tgt_dict = task.target_dictionary
logger.info(
"| {} {} {} examples".format(
args.data, args.gen_subset, len(task.dataset(args.gen_subset))
)
)
# hack to pass transitions to W2lDecoder
if args.criterion == "asg_loss":
raise NotImplementedError("asg_loss is currently not supported")
# trans = criterions[0].asg.trans.data
# args.asg_transitions = torch.flatten(trans).tolist()
# Load dataset (possibly sharded)
itr = get_dataset_itr(args, task, models)
# Initialize generator
gen_timer = StopwatchMeter()
def build_generator(args):
w2l_decoder = getattr(args, "w2l_decoder", None)
if w2l_decoder == "viterbi":
from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
return W2lViterbiDecoder(args, task.target_dictionary)
elif w2l_decoder == "kenlm":
from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
return W2lKenLMDecoder(args, task.target_dictionary)
elif w2l_decoder == "fairseqlm":
from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
return W2lFairseqLMDecoder(args, task.target_dictionary)
else:
print(
"only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment"
)
# please do not touch this unless you test both generate.py and infer.py with audio_pretraining task
generator = build_generator(args)
if args.load_emissions:
generator = ExistingEmissionsDecoder(
generator, np.load(args.load_emissions, allow_pickle=True)
)
logger.info("loaded emissions from " + args.load_emissions)
num_sentences = 0
if args.results_path is not None and not os.path.exists(args.results_path):
os.makedirs(args.results_path)
max_source_pos = (
utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models]
),
)
if max_source_pos is not None:
max_source_pos = max_source_pos[0]
if max_source_pos is not None:
max_source_pos = max_source_pos[0] - 1
if args.dump_emissions:
emissions = {}
if args.dump_features:
features = {}
models[0].bert.proj = None
else:
res_files = prepare_result_files(args)
errs_t = 0
lengths_t = 0
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if "net_input" not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample["target"][:, : args.prefix_size]
gen_timer.start()
if args.dump_emissions:
with torch.no_grad():
encoder_out = models[0](**sample["net_input"])
emm = models[0].get_normalized_probs(encoder_out, log_probs=True)
emm = emm.transpose(0, 1).cpu().numpy()
for i, id in enumerate(sample["id"]):
emissions[id.item()] = emm[i]
continue
elif args.dump_features:
with torch.no_grad():
encoder_out = models[0](**sample["net_input"])
feat = encoder_out["encoder_out"].transpose(0, 1).cpu().numpy()
for i, id in enumerate(sample["id"]):
padding = (
encoder_out["encoder_padding_mask"][i].cpu().numpy()
if encoder_out["encoder_padding_mask"] is not None
else None
)
features[id.item()] = (feat[i], padding)
continue
hypos = task.inference_step(generator, models, sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample["id"].tolist()):
speaker = None
# id = task.dataset(args.gen_subset).ids[int(sample_id)]
id = sample_id
toks = (
sample["target"][i, :]
if "target_label" not in sample
else sample["target_label"][i, :]
)
target_tokens = utils.strip_pad(toks, tgt_dict.pad()).int().cpu()
# Process top predictions
errs, length = process_predictions(
args,
hypos[i],
None,
tgt_dict,
target_tokens,
res_files,
speaker,
id,
)
errs_t += errs
lengths_t += length
wps_meter.update(num_generated_tokens)
t.log({"wps": round(wps_meter.avg)})
num_sentences += (
sample["nsentences"] if "nsentences" in sample else sample["id"].numel()
)
wer = None
if args.dump_emissions:
emm_arr = []
for i in range(len(emissions)):
emm_arr.append(emissions[i])
np.save(args.dump_emissions, emm_arr)
logger.info(f"saved {len(emissions)} emissions to {args.dump_emissions}")
elif args.dump_features:
feat_arr = []
for i in range(len(features)):
feat_arr.append(features[i])
np.save(args.dump_features, feat_arr)
logger.info(f"saved {len(features)} emissions to {args.dump_features}")
else:
if lengths_t > 0:
wer = errs_t * 100.0 / lengths_t
logger.info(f"WER: {wer}")
logger.info(
"| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"
"sentences/s, {:.2f} tokens/s)".format(
num_sentences,
gen_timer.n,
gen_timer.sum,
num_sentences / gen_timer.sum,
1.0 / gen_timer.avg,
)
)
logger.info("| Generate {} with beam={}".format(args.gen_subset, args.beam))
return task, wer
def make_parser():
parser = options.get_generation_parser()
parser = add_asr_eval_argument(parser)
return parser
def cli_main():
parser = make_parser()
args = options.parse_args_and_arch(parser)
main(args)
if __name__ == "__main__":
cli_main()
|