gongel
3 years ago
parent
8e685a1a43
commit
f5c61ced28
@ -0,0 +1,14 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .infer import STExecutor
|
||||
@ -0,0 +1,318 @@
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
from typing import List
|
||||
from typing import Optional
|
||||
from typing import Union
|
||||
|
||||
import kaldi_io
|
||||
import numpy as np
|
||||
import paddle
|
||||
from kaldiio import WriteHelper
|
||||
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
|
||||
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
|
||||
from paddlespeech.s2t.utils.utility import UpdateConfig
|
||||
from yacs.config import CfgNode
|
||||
|
||||
from ..executor import BaseExecutor
|
||||
from ..utils import cli_register
|
||||
from ..utils import download_and_decompress
|
||||
from ..utils import logger
|
||||
from ..utils import MODEL_HOME
|
||||
|
||||
__all__ = ["STExecutor"]
|
||||
|
||||
pretrained_models = {
|
||||
"fat_st_ted_en_zh": {
|
||||
"url":
|
||||
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_mtl.model.tar.gz",
|
||||
"md5":
|
||||
"210b8eacc390d9965334fa8e96c49a13",
|
||||
"cfg_path":
|
||||
"conf/transformer_mtl_noam.yaml",
|
||||
"ckpt_path":
|
||||
"exp/transformer_mtl_noam/checkpoints/fat_st_ted_en_zh",
|
||||
}
|
||||
}
|
||||
|
||||
model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
|
||||
|
||||
kaldi_bins = {
|
||||
"url":
|
||||
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
|
||||
"md5":
|
||||
"c0682303b3f3393dbf6ed4c4e35a53eb",
|
||||
}
|
||||
|
||||
|
||||
@cli_register(
|
||||
name="paddlespeech.st", description="Speech translation infer command.")
|
||||
class STExecutor(BaseExecutor):
|
||||
def __init__(self):
|
||||
super(STExecutor, self).__init__()
|
||||
|
||||
self.parser = argparse.ArgumentParser(
|
||||
prog="paddlespeech.st", add_help=True)
|
||||
self.parser.add_argument(
|
||||
"--input", type=str, required=True, help="Audio file to translate.")
|
||||
self.parser.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
default="fat_st",
|
||||
help="Choose model type of st task.")
|
||||
self.parser.add_argument(
|
||||
"--lang",
|
||||
type=str,
|
||||
default="ted_en_zh",
|
||||
help="Choose model language.")
|
||||
self.parser.add_argument(
|
||||
"--config",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Config of st task. Use deault config when it is None.")
|
||||
self.parser.add_argument(
|
||||
"--ckpt_path",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Checkpoint file of model.")
|
||||
self.parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default=paddle.get_device(),
|
||||
help="Choose device to execute model inference.")
|
||||
|
||||
def _get_pretrained_path(self, tag: str) -> os.PathLike:
|
||||
"""
|
||||
Download and returns pretrained resources path of current task.
|
||||
"""
|
||||
assert tag in pretrained_models, "Can not find pretrained resources of {}.".format(
|
||||
tag)
|
||||
|
||||
res_path = os.path.join(MODEL_HOME, tag)
|
||||
decompressed_path = download_and_decompress(pretrained_models[tag],
|
||||
res_path)
|
||||
decompressed_path = os.path.abspath(decompressed_path)
|
||||
logger.info(
|
||||
"Use pretrained model stored in: {}".format(decompressed_path))
|
||||
|
||||
return decompressed_path
|
||||
|
||||
def _set_kaldi_bins(self) -> os.PathLike:
|
||||
"""
|
||||
Download and returns kaldi_bins resources path of current task.
|
||||
"""
|
||||
decompressed_path = download_and_decompress(kaldi_bins, MODEL_HOME)
|
||||
decompressed_path = os.path.abspath(decompressed_path)
|
||||
logger.info("Kaldi_bins stored in: {}".format(decompressed_path))
|
||||
os.environ['LD_LIBRARY_PATH'] += f':{decompressed_path}'
|
||||
os.environ["PATH"] += f":{decompressed_path}"
|
||||
return decompressed_path
|
||||
|
||||
def _init_from_path(self,
|
||||
model_type: str="fat_st",
|
||||
lang: str="zh",
|
||||
cfg_path: Optional[os.PathLike]=None,
|
||||
ckpt_path: Optional[os.PathLike]=None):
|
||||
"""
|
||||
Init model and other resources from a specific path.
|
||||
"""
|
||||
if cfg_path is None or ckpt_path is None:
|
||||
tag = model_type + "_" + lang
|
||||
res_path = self._get_pretrained_path(tag)
|
||||
self.cfg_path = os.path.join(res_path,
|
||||
pretrained_models[tag]["cfg_path"])
|
||||
self.ckpt_path = os.path.join(res_path,
|
||||
pretrained_models[tag]["ckpt_path"])
|
||||
logger.info(res_path)
|
||||
logger.info(self.cfg_path)
|
||||
logger.info(self.ckpt_path)
|
||||
else:
|
||||
self.cfg_path = os.path.abspath(cfg_path)
|
||||
self.ckpt_path = os.path.abspath(ckpt_path)
|
||||
res_path = os.path.dirname(
|
||||
os.path.dirname(os.path.abspath(self.cfg_path)))
|
||||
|
||||
#Init body.
|
||||
self.config = CfgNode(new_allowed=True)
|
||||
self.config.merge_from_file(self.cfg_path)
|
||||
self.config.decoding.decoding_method = "fullsentence"
|
||||
|
||||
with UpdateConfig(self.config):
|
||||
self.config.collator.vocab_filepath = os.path.join(
|
||||
res_path, self.config.collator.vocab_filepath)
|
||||
self.config.collator.cmvn_path = os.path.join(
|
||||
res_path, self.config.collator.cmvn_path)
|
||||
self.config.collator.spm_model_prefix = os.path.join(
|
||||
res_path, self.config.collator.spm_model_prefix)
|
||||
self.text_feature = TextFeaturizer(
|
||||
unit_type=self.config.collator.unit_type,
|
||||
vocab_filepath=self.config.collator.vocab_filepath,
|
||||
spm_model_prefix=self.config.collator.spm_model_prefix)
|
||||
self.config.model.input_dim = self.config.collator.feat_dim
|
||||
self.config.model.output_dim = self.text_feature.vocab_size
|
||||
|
||||
model_conf = self.config.model
|
||||
logger.info(model_conf)
|
||||
model_class = dynamic_import(model_type, model_alias)
|
||||
self.model = model_class.from_config(model_conf)
|
||||
self.model.eval()
|
||||
|
||||
# load model
|
||||
params_path = self.ckpt_path + ".pdparams"
|
||||
model_dict = paddle.load(params_path)
|
||||
self.model.set_state_dict(model_dict)
|
||||
|
||||
# set kaldi bins
|
||||
self._set_kaldi_bins()
|
||||
|
||||
def preprocess(self, wav_file: Union[str, os.PathLike], model_type: str):
|
||||
"""
|
||||
Input preprocess and return paddle.Tensor stored in self.input.
|
||||
Input content can be a file(wav).
|
||||
"""
|
||||
audio_file = os.path.abspath(wav_file)
|
||||
logger.info("Preprocess audio_file:" + audio_file)
|
||||
|
||||
if model_type == "fat_st":
|
||||
cmvn = self.config.collator.cmvn_path
|
||||
utt_name = "_tmp"
|
||||
|
||||
# Get the object for feature extraction
|
||||
fbank_extract_command = [
|
||||
"compute-fbank-feats", "--num-mel-bins=80", "--verbose=2",
|
||||
"--sample-frequency=16000", "scp:-", "ark:-"
|
||||
]
|
||||
fbank_extract_process = subprocess.Popen(
|
||||
fbank_extract_command,
|
||||
stdin=subprocess.PIPE,
|
||||
stdout=subprocess.PIPE)
|
||||
fbank_extract_process.stdin.write(
|
||||
f"{utt_name} {wav_file}".encode("utf8"))
|
||||
fbank_extract_process.stdin.close()
|
||||
fbank_feat = dict(
|
||||
kaldi_io.read_mat_ark(fbank_extract_process.stdout))[utt_name]
|
||||
|
||||
extract_command = ["compute-kaldi-pitch-feats", "scp:-", "ark:-"]
|
||||
pitch_extract_process = subprocess.Popen(
|
||||
extract_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
|
||||
pitch_extract_process.stdin.write(
|
||||
f"{utt_name} {wav_file}".encode("utf8"))
|
||||
process_command = ["process-kaldi-pitch-feats", "ark:", "ark:-"]
|
||||
pitch_process = subprocess.Popen(
|
||||
process_command,
|
||||
stdin=pitch_extract_process.stdout,
|
||||
stdout=subprocess.PIPE)
|
||||
pitch_extract_process.stdin.close()
|
||||
pitch_feat = dict(
|
||||
kaldi_io.read_mat_ark(pitch_process.stdout))[utt_name]
|
||||
concated_feat = np.concatenate((fbank_feat, pitch_feat), axis=1)
|
||||
raw_feat = f"{utt_name}.raw"
|
||||
with WriteHelper(
|
||||
f"ark,scp:{raw_feat}.ark,{raw_feat}.scp") as writer:
|
||||
writer(utt_name, concated_feat)
|
||||
cmvn_command = [
|
||||
"apply-cmvn", "--norm-vars=true", cmvn, f"scp:{raw_feat}.scp",
|
||||
"ark:-"
|
||||
]
|
||||
cmvn_process = subprocess.Popen(
|
||||
cmvn_command, stdout=subprocess.PIPE)
|
||||
process_command = [
|
||||
"copy-feats", "--compress=true", "ark:-", "ark:-"
|
||||
]
|
||||
process = subprocess.Popen(
|
||||
process_command,
|
||||
stdin=cmvn_process.stdout,
|
||||
stdout=subprocess.PIPE)
|
||||
norm_feat = dict(kaldi_io.read_mat_ark(process.stdout))[utt_name]
|
||||
self.audio = paddle.to_tensor(norm_feat).unsqueeze(0)
|
||||
self.audio_len = paddle.to_tensor(
|
||||
self.audio.shape[1], dtype="int64")
|
||||
logger.info(f"audio feat shape: {self.audio.shape}")
|
||||
else:
|
||||
raise ValueError("Wrong model type.")
|
||||
|
||||
@paddle.no_grad()
|
||||
def infer(self, model_type: str):
|
||||
"""
|
||||
Model inference and result stored in self.output.
|
||||
"""
|
||||
cfg = self.config.decoding
|
||||
audio = self.audio
|
||||
audio_len = self.audio_len
|
||||
if model_type == "fat_st":
|
||||
hyps = self.model.decode(
|
||||
audio,
|
||||
audio_len,
|
||||
text_feature=self.text_feature,
|
||||
decoding_method=cfg.decoding_method,
|
||||
lang_model_path=None,
|
||||
beam_alpha=cfg.alpha,
|
||||
beam_beta=cfg.beta,
|
||||
beam_size=cfg.beam_size,
|
||||
cutoff_prob=cfg.cutoff_prob,
|
||||
cutoff_top_n=cfg.cutoff_top_n,
|
||||
num_processes=cfg.num_proc_bsearch,
|
||||
ctc_weight=cfg.ctc_weight,
|
||||
word_reward=cfg.word_reward,
|
||||
decoding_chunk_size=cfg.decoding_chunk_size,
|
||||
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
|
||||
simulate_streaming=cfg.simulate_streaming)
|
||||
self.result_transcripts = hyps
|
||||
else:
|
||||
raise ValueError("Wrong model type.")
|
||||
|
||||
def postprocess(self, model_type: str) -> Union[str, os.PathLike]:
|
||||
"""
|
||||
Output postprocess and return human-readable results such as texts and audio files.
|
||||
"""
|
||||
if model_type == "fat_st":
|
||||
return self.result_transcripts
|
||||
else:
|
||||
raise ValueError("Wrong model type.")
|
||||
|
||||
def execute(self, argv: List[str]) -> bool:
|
||||
"""
|
||||
Command line entry.
|
||||
"""
|
||||
parser_args = self.parser.parse_args(argv)
|
||||
|
||||
model = parser_args.model
|
||||
lang = parser_args.lang
|
||||
config = parser_args.config
|
||||
ckpt_path = parser_args.ckpt_path
|
||||
audio_file = parser_args.input
|
||||
device = parser_args.device
|
||||
|
||||
try:
|
||||
res = self(model, lang, config, ckpt_path, audio_file, device)
|
||||
logger.info('ST Result: {}'.format(res))
|
||||
return True
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return False
|
||||
|
||||
def __call__(self, model, lang, config, ckpt_path, audio_file, device):
|
||||
"""
|
||||
Python API to call an executor.
|
||||
"""
|
||||
audio_file = os.path.abspath(audio_file)
|
||||
paddle.set_device(device)
|
||||
self._init_from_path(model, lang, config, ckpt_path)
|
||||
self.preprocess(audio_file, model)
|
||||
self.infer(model)
|
||||
res = self.postprocess(model)
|
||||
|
||||
return res
|
||||
Loading…
Reference in new issue