#!/usr/bin/env python3
import argparse
import logging
import sys
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from typeguard import typechecked
from espnet2.asr.maskctc_model import MaskCTCInference
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.asr import ASRTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.nets.beam_search import Hypothesis
from espnet.nets.pytorch_backend.transformer.subsampling import TooShortUttError
from espnet.utils.cli_utils import get_commandline_args
[docs]class Speech2Text:
"""Speech2Text class
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]
"""
@typechecked
def __init__(
self,
asr_train_config: Union[Path, str],
asr_model_file: Union[Path, str, None] = None,
token_type: Optional[str] = None,
bpemodel: Optional[str] = None,
device: str = "cpu",
batch_size: int = 1,
dtype: str = "float32",
maskctc_n_iterations: int = 10,
maskctc_threshold_probability: float = 0.99,
):
# 1. Build ASR model
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device
)
asr_model.to(dtype=getattr(torch, dtype)).eval()
token_list = asr_model.token_list
s2t = MaskCTCInference(
asr_model=asr_model,
n_iterations=maskctc_n_iterations,
threshold_probability=maskctc_threshold_probability,
)
s2t.to(device=device, dtype=getattr(torch, dtype)).eval()
# 2. [Optional] Build Text converter: e.g. bpe-sym -> Text
if token_type is None:
token_type = asr_train_args.token_type
if bpemodel is None:
bpemodel = asr_train_args.bpemodel
if token_type is None:
tokenizer = None
elif token_type == "bpe":
if bpemodel is not None:
tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel)
else:
tokenizer = None
else:
tokenizer = build_tokenizer(token_type=token_type)
converter = TokenIDConverter(token_list=token_list)
logging.info(f"Text tokenizer: {tokenizer}")
self.asr_model = asr_model
self.asr_train_args = asr_train_args
self.s2t = s2t
self.converter = converter
self.tokenizer = tokenizer
self.device = device
self.dtype = dtype
@torch.no_grad()
@typechecked
def __call__(
self, speech: Union[torch.Tensor, np.ndarray]
) -> List[Tuple[Optional[str], List[str], List[int], Hypothesis]]:
"""Inference
Args:
data: Input speech data
Returns:
text, token, token_int, hyp
"""
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
# data: (Nsamples,) -> (1, Nsamples)
speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
# lenghts: (1,)
lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
batch = {"speech": speech, "speech_lengths": lengths}
# a. To device
batch = to_device(batch, device=self.device)
# b. Forward Encoder
enc, _ = self.asr_model.encode(**batch)
if isinstance(enc, tuple):
enc = enc[0]
assert len(enc) == 1, len(enc)
# c. Passed the encoder result and the inference algorithm
hyp = self.s2t(enc[0])
assert isinstance(hyp, Hypothesis), type(hyp)
# remove sos/eos and get results
token_int = hyp.yseq[1:-1].tolist()
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != 0, token_int))
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
if self.tokenizer is not None:
text = self.tokenizer.tokens2text(token)
else:
text = None
results = [(text, token, token_int, hyp)]
return results
[docs] @staticmethod
def from_pretrained(
model_tag: Optional[str] = None,
**kwargs: Optional[Any],
):
"""Build Speech2Text instance from the pretrained model.
Args:
model_tag (Optional[str]): Model tag of the pretrained models.
Currently, the tags of espnet_model_zoo are supported.
Returns:
Speech2Text: Speech2Text instance.
"""
if model_tag is not None:
try:
from espnet_model_zoo.downloader import ModelDownloader
except ImportError:
logging.error(
"`espnet_model_zoo` is not installed. "
"Please install via `pip install -U espnet_model_zoo`."
)
raise
d = ModelDownloader()
kwargs.update(**d.download_and_unpack(model_tag))
return Speech2Text(**kwargs)
[docs]@typechecked
def inference(
output_dir: str,
batch_size: int,
dtype: str,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
asr_train_config: str,
asr_model_file: str,
model_tag: Optional[str],
token_type: Optional[str],
bpemodel: Optional[str],
allow_variable_data_keys: bool,
maskctc_n_iterations: int,
maskctc_threshold_probability: float,
):
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
if ngpu >= 1:
device = "cuda"
else:
device = "cpu"
# 1. Set random-seed
set_all_random_seed(seed)
# 2. Build speech2text
speech2text_kwargs = dict(
asr_train_config=asr_train_config,
asr_model_file=asr_model_file,
token_type=token_type,
bpemodel=bpemodel,
device=device,
batch_size=batch_size,
dtype=dtype,
maskctc_n_iterations=maskctc_n_iterations,
maskctc_threshold_probability=maskctc_threshold_probability,
)
speech2text = Speech2Text.from_pretrained(
model_tag=model_tag,
**speech2text_kwargs,
)
# 3. Build data-iterator
loader = ASRTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 7 .Start for-loop
with DatadirWriter(output_dir) as writer:
for keys, batch in loader:
assert isinstance(batch, dict), type(batch)
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
try:
results = speech2text(**batch)
except TooShortUttError as e:
logging.warning(f"Utterance {keys} {e}")
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
results = [[" ", ["<space>"], [2], hyp]]
# Only supporting batch_size==1
key = keys[0]
(text, token, token_int, hyp) = results[0]
# Create a directory: outdir/{n}best_recog
ibest_writer = writer["1best_recog"]
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
if text is not None:
ibest_writer["text"][key] = text
[docs]def get_parser():
parser = config_argparse.ArgumentParser(
description="ASR Decoding",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
# Note(kamo): Use '_' instead of '-' as separator.
# '-' is confusing if written in yaml.
parser.add_argument(
"--log_level",
type=lambda x: x.upper(),
default="INFO",
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
help="The verbose level of logging",
)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument(
"--ngpu",
type=int,
default=0,
help="The number of gpus. 0 indicates CPU mode",
)
parser.add_argument("--seed", type=int, default=0, help="Random seed")
parser.add_argument(
"--dtype",
default="float32",
choices=["float16", "float32", "float64"],
help="Data type",
)
parser.add_argument(
"--num_workers",
type=int,
default=1,
help="The number of workers used for DataLoader",
)
group = parser.add_argument_group("Input data related")
group.add_argument(
"--data_path_and_name_and_type",
type=str2triple_str,
required=True,
action="append",
)
group.add_argument("--key_file", type=str_or_none)
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
group = parser.add_argument_group("The model configuration related")
group.add_argument("--asr_train_config", type=str, required=True)
group.add_argument("--asr_model_file", type=str, required=True)
group.add_argument(
"--model_tag",
type=str,
help="Pretrained model tag. If specify this option, *_train_config and "
"*_file will be overwritten",
)
group = parser.add_argument_group("Decoding related")
group.add_argument(
"--batch_size",
type=int,
default=1,
help="The batch size for inference",
)
group.add_argument("--maskctc_n_iterations", type=int, default=10)
group.add_argument("--maskctc_threshold_probability", type=float, default=0.99)
group = parser.add_argument_group("Text converter related")
group.add_argument(
"--token_type",
type=str_or_none,
default=None,
choices=["char", "bpe", None],
help="The token type for ASR model. "
"If not given, refers from the training args",
)
group.add_argument(
"--bpemodel",
type=str_or_none,
default=None,
help="The model path of sentencepiece. "
"If not given, refers from the training args",
)
return parser
[docs]def main(cmd=None):
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
kwargs = vars(args)
kwargs.pop("config", None)
inference(**kwargs)
if __name__ == "__main__":
main()