import argparse
|
import logging
|
import os
|
import sys
|
import json
|
from pathlib import Path
|
from typing import Any
|
from typing import List
|
from typing import Optional
|
from typing import Sequence
|
from typing import Tuple
|
from typing import Union
|
from typing import Dict
|
|
import math
|
import numpy as np
|
import torch
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from typeguard import check_argument_types
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from typeguard import check_return_type
|
|
from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.scorers.scorer_interface import BatchScorerInterface
|
from funasr.modules.subsampling import TooShortUttError
|
from funasr.tasks.vad import VADTask
|
from funasr.torch_utils.device_funcs import to_device
|
from funasr.torch_utils.set_all_random_seed import set_all_random_seed
|
from funasr.utils import config_argparse
|
from funasr.utils.cli_utils import get_commandline_args
|
from funasr.utils.types import str2bool
|
from funasr.utils.types import str2triple_str
|
from funasr.utils.types import str_or_none
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from funasr.utils import asr_utils, wav_utils, postprocess_utils
|
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
|
|
header_colors = '\033[95m'
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end_colors = '\033[0m'
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global_asr_language: str = 'zh-cn'
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global_sample_rate: Union[int, Dict[Any, int]] = {
|
'audio_fs': 16000,
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'model_fs': 16000
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}
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|
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class Speech2VadSegment:
|
"""Speech2VadSegment class
|
|
Examples:
|
>>> import soundfile
|
>>> speech2segment = Speech2VadSegment("vad_config.yml", "vad.pt")
|
>>> audio, rate = soundfile.read("speech.wav")
|
>>> speech2segment(audio)
|
[[10, 230], [245, 450], ...]
|
|
"""
|
|
def __init__(
|
self,
|
vad_infer_config: Union[Path, str] = None,
|
vad_model_file: Union[Path, str] = None,
|
vad_cmvn_file: Union[Path, str] = None,
|
device: str = "cpu",
|
batch_size: int = 1,
|
dtype: str = "float32",
|
**kwargs,
|
):
|
assert check_argument_types()
|
|
# 1. Build vad model
|
vad_model, vad_infer_args = VADTask.build_model_from_file(
|
vad_infer_config, vad_model_file, device
|
)
|
frontend = None
|
if vad_infer_args.frontend is not None:
|
frontend = WavFrontend(cmvn_file=vad_cmvn_file, **vad_infer_args.frontend_conf)
|
|
logging.info("vad_model: {}".format(vad_model))
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logging.info("vad_infer_args: {}".format(vad_infer_args))
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vad_model.to(dtype=getattr(torch, dtype)).eval()
|
|
self.vad_model = vad_model
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self.vad_infer_args = vad_infer_args
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self.device = device
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self.dtype = dtype
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self.frontend = frontend
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self.batch_size = batch_size
|
|
@torch.no_grad()
|
def __call__(
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self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
|
in_cache: Dict[str, torch.Tensor] = dict()
|
) -> Tuple[List[List[int]], Dict[str, torch.Tensor]]:
|
"""Inference
|
|
Args:
|
speech: Input speech data
|
Returns:
|
text, token, token_int, hyp
|
|
"""
|
assert check_argument_types()
|
|
# Input as audio signal
|
if isinstance(speech, np.ndarray):
|
speech = torch.tensor(speech)
|
|
if self.frontend is not None:
|
self.frontend.filter_length_max = math.inf
|
fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
|
feats, feats_len = self.frontend.forward_lfr_cmvn(fbanks, fbanks_len)
|
fbanks = to_device(fbanks, device=self.device)
|
feats = to_device(feats, device=self.device)
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feats_len = feats_len.int()
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else:
|
raise Exception("Need to extract feats first, please configure frontend configuration")
|
|
# b. Forward Encoder streaming
|
t_offset = 0
|
step = min(feats_len.max(), 6000)
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segments = [[]] * self.batch_size
|
for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
|
if t_offset + step >= feats_len - 1:
|
step = feats_len - t_offset
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is_final = True
|
else:
|
is_final = False
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batch = {
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"feats": feats[:, t_offset:t_offset + step, :],
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"waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
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"is_final": is_final,
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"in_cache": in_cache
|
}
|
# a. To device
|
#batch = to_device(batch, device=self.device)
|
segments_part, in_cache = self.vad_model(**batch)
|
if segments_part:
|
for batch_num in range(0, self.batch_size):
|
segments[batch_num] += segments_part[batch_num]
|
return fbanks, segments
|
|
class Speech2VadSegmentOnline(Speech2VadSegment):
|
"""Speech2VadSegmentOnline class
|
|
Examples:
|
>>> import soundfile
|
>>> speech2segment = Speech2VadSegmentOnline("vad_config.yml", "vad.pt")
|
>>> audio, rate = soundfile.read("speech.wav")
|
>>> speech2segment(audio)
|
[[10, 230], [245, 450], ...]
|
|
"""
|
def __init__(self, **kwargs):
|
super(Speech2VadSegmentOnline, self).__init__(**kwargs)
|
vad_cmvn_file = kwargs.get('vad_cmvn_file', None)
|
self.frontend = None
|
if self.vad_infer_args.frontend is not None:
|
self.frontend = WavFrontendOnline(cmvn_file=vad_cmvn_file, **self.vad_infer_args.frontend_conf)
|
|
|
@torch.no_grad()
|
def __call__(
|
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
|
in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False, max_end_sil: int = 800
|
) -> Tuple[torch.Tensor, List[List[int]], torch.Tensor]:
|
"""Inference
|
|
Args:
|
speech: Input speech data
|
Returns:
|
text, token, token_int, hyp
|
|
"""
|
assert check_argument_types()
|
|
# Input as audio signal
|
if isinstance(speech, np.ndarray):
|
speech = torch.tensor(speech)
|
batch_size = speech.shape[0]
|
segments = [[]] * batch_size
|
if self.frontend is not None:
|
feats, feats_len = self.frontend.forward(speech, speech_lengths, is_final)
|
fbanks, _ = self.frontend.get_fbank()
|
else:
|
raise Exception("Need to extract feats first, please configure frontend configuration")
|
if feats.shape[0]:
|
feats = to_device(feats, device=self.device)
|
feats_len = feats_len.int()
|
waveforms = self.frontend.get_waveforms()
|
|
batch = {
|
"feats": feats,
|
"waveform": waveforms,
|
"in_cache": in_cache,
|
"is_final": is_final,
|
"max_end_sil": max_end_sil
|
}
|
# a. To device
|
batch = to_device(batch, device=self.device)
|
segments, in_cache = self.vad_model.forward_online(**batch)
|
# in_cache.update(batch['in_cache'])
|
# in_cache = {key: value for key, value in batch['in_cache'].items()}
|
return fbanks, segments, in_cache
|
|
|
def inference(
|
batch_size: int,
|
ngpu: int,
|
log_level: Union[int, str],
|
data_path_and_name_and_type,
|
vad_infer_config: Optional[str],
|
vad_model_file: Optional[str],
|
vad_cmvn_file: Optional[str] = None,
|
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
key_file: Optional[str] = None,
|
allow_variable_data_keys: bool = False,
|
output_dir: Optional[str] = None,
|
dtype: str = "float32",
|
seed: int = 0,
|
num_workers: int = 1,
|
online: bool = False,
|
**kwargs,
|
):
|
if not online:
|
inference_pipeline = inference_modelscope(
|
batch_size=batch_size,
|
ngpu=ngpu,
|
log_level=log_level,
|
vad_infer_config=vad_infer_config,
|
vad_model_file=vad_model_file,
|
vad_cmvn_file=vad_cmvn_file,
|
key_file=key_file,
|
allow_variable_data_keys=allow_variable_data_keys,
|
output_dir=output_dir,
|
dtype=dtype,
|
seed=seed,
|
num_workers=num_workers,
|
**kwargs,
|
)
|
else:
|
inference_pipeline = inference_modelscope_online(
|
batch_size=batch_size,
|
ngpu=ngpu,
|
log_level=log_level,
|
vad_infer_config=vad_infer_config,
|
vad_model_file=vad_model_file,
|
vad_cmvn_file=vad_cmvn_file,
|
key_file=key_file,
|
allow_variable_data_keys=allow_variable_data_keys,
|
output_dir=output_dir,
|
dtype=dtype,
|
seed=seed,
|
num_workers=num_workers,
|
**kwargs,
|
)
|
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
|
|
def inference_modelscope(
|
batch_size: int,
|
ngpu: int,
|
log_level: Union[int, str],
|
# data_path_and_name_and_type,
|
vad_infer_config: Optional[str],
|
vad_model_file: Optional[str],
|
vad_cmvn_file: Optional[str] = None,
|
# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
key_file: Optional[str] = None,
|
allow_variable_data_keys: bool = False,
|
output_dir: Optional[str] = None,
|
dtype: str = "float32",
|
seed: int = 0,
|
num_workers: int = 1,
|
**kwargs,
|
):
|
assert check_argument_types()
|
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 and torch.cuda.is_available():
|
device = "cuda"
|
else:
|
device = "cpu"
|
|
# 1. Set random-seed
|
set_all_random_seed(seed)
|
|
# 2. Build speech2vadsegment
|
speech2vadsegment_kwargs = dict(
|
vad_infer_config=vad_infer_config,
|
vad_model_file=vad_model_file,
|
vad_cmvn_file=vad_cmvn_file,
|
device=device,
|
dtype=dtype,
|
)
|
logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
|
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
|
|
def _forward(
|
data_path_and_name_and_type,
|
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
output_dir_v2: Optional[str] = None,
|
fs: dict = None,
|
param_dict: dict = None
|
):
|
# 3. Build data-iterator
|
if data_path_and_name_and_type is None and raw_inputs is not None:
|
if isinstance(raw_inputs, torch.Tensor):
|
raw_inputs = raw_inputs.numpy()
|
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
|
loader = VADTask.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=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
|
collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
|
allow_variable_data_keys=allow_variable_data_keys,
|
inference=True,
|
)
|
|
finish_count = 0
|
file_count = 1
|
# 7 .Start for-loop
|
# FIXME(kamo): The output format should be discussed about
|
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
|
if output_path is not None:
|
writer = DatadirWriter(output_path)
|
ibest_writer = writer[f"1best_recog"]
|
else:
|
writer = None
|
ibest_writer = None
|
|
vad_results = []
|
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}"
|
|
# do vad segment
|
_, results = speech2vadsegment(**batch)
|
for i, _ in enumerate(keys):
|
if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
|
results[i] = json.dumps(results[i])
|
item = {'key': keys[i], 'value': results[i]}
|
vad_results.append(item)
|
if writer is not None:
|
ibest_writer["text"][keys[i]] = "{}".format(results[i])
|
|
return vad_results
|
|
return _forward
|
|
def inference_modelscope_online(
|
batch_size: int,
|
ngpu: int,
|
log_level: Union[int, str],
|
# data_path_and_name_and_type,
|
vad_infer_config: Optional[str],
|
vad_model_file: Optional[str],
|
vad_cmvn_file: Optional[str] = None,
|
# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
key_file: Optional[str] = None,
|
allow_variable_data_keys: bool = False,
|
output_dir: Optional[str] = None,
|
dtype: str = "float32",
|
seed: int = 0,
|
num_workers: int = 1,
|
**kwargs,
|
):
|
assert check_argument_types()
|
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 and torch.cuda.is_available():
|
device = "cuda"
|
else:
|
device = "cpu"
|
|
# 1. Set random-seed
|
set_all_random_seed(seed)
|
|
# 2. Build speech2vadsegment
|
speech2vadsegment_kwargs = dict(
|
vad_infer_config=vad_infer_config,
|
vad_model_file=vad_model_file,
|
vad_cmvn_file=vad_cmvn_file,
|
device=device,
|
dtype=dtype,
|
)
|
logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
|
speech2vadsegment = Speech2VadSegmentOnline(**speech2vadsegment_kwargs)
|
|
def _forward(
|
data_path_and_name_and_type,
|
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
|
output_dir_v2: Optional[str] = None,
|
fs: dict = None,
|
param_dict: dict = None,
|
):
|
# 3. Build data-iterator
|
if data_path_and_name_and_type is None and raw_inputs is not None:
|
if isinstance(raw_inputs, torch.Tensor):
|
raw_inputs = raw_inputs.numpy()
|
data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
|
loader = VADTask.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=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
|
collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
|
allow_variable_data_keys=allow_variable_data_keys,
|
inference=True,
|
)
|
|
finish_count = 0
|
file_count = 1
|
# 7 .Start for-loop
|
# FIXME(kamo): The output format should be discussed about
|
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
|
if output_path is not None:
|
writer = DatadirWriter(output_path)
|
ibest_writer = writer[f"1best_recog"]
|
else:
|
writer = None
|
ibest_writer = None
|
|
vad_results = []
|
batch_in_cache = param_dict['in_cache'] if param_dict is not None else dict()
|
is_final = param_dict.get('is_final', False) if param_dict is not None else False
|
max_end_sil = param_dict.get('max_end_sil', 800) if param_dict is not None else 800
|
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['in_cache'] = batch_in_cache
|
batch['is_final'] = is_final
|
batch['max_end_sil'] = max_end_sil
|
|
# do vad segment
|
_, results, param_dict['in_cache'] = speech2vadsegment(**batch)
|
# param_dict['in_cache'] = batch['in_cache']
|
if results:
|
for i, _ in enumerate(keys):
|
if results[i]:
|
if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
|
results[i] = json.dumps(results[i])
|
item = {'key': keys[i], 'value': results[i]}
|
vad_results.append(item)
|
if writer is not None:
|
ibest_writer["text"][keys[i]] = "{}".format(results[i])
|
|
return vad_results
|
|
return _forward
|
|
def get_parser():
|
parser = config_argparse.ArgumentParser(
|
description="VAD 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=False)
|
parser.add_argument(
|
"--ngpu",
|
type=int,
|
default=0,
|
help="The number of gpus. 0 indicates CPU mode",
|
)
|
parser.add_argument(
|
"--gpuid_list",
|
type=str,
|
default="",
|
help="The visible gpus",
|
)
|
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=False,
|
action="append",
|
)
|
group.add_argument("--raw_inputs", type=list, default=None)
|
# example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])
|
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(
|
"--vad_infer_config",
|
type=str,
|
help="VAD infer configuration",
|
)
|
group.add_argument(
|
"--vad_model_file",
|
type=str,
|
help="VAD model parameter file",
|
)
|
group.add_argument(
|
"--vad_cmvn_file",
|
type=str,
|
help="Global cmvn file",
|
)
|
group.add_argument(
|
"--online",
|
type=str,
|
help="decoding mode",
|
)
|
|
group = parser.add_argument_group("infer related")
|
group.add_argument(
|
"--batch_size",
|
type=int,
|
default=1,
|
help="The batch size for inference",
|
)
|
|
return parser
|
|
|
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()
|
