You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
358 lines
14 KiB
358 lines
14 KiB
# 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 kaldiio
|
|
import numpy as np
|
|
import paddle
|
|
import soundfile
|
|
from kaldiio import WriteHelper
|
|
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
|
|
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
|
|
|
|
__all__ = ["STExecutor"]
|
|
|
|
pretrained_models = {
|
|
"fat_st_ted-en-zh": {
|
|
"url":
|
|
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz",
|
|
"md5":
|
|
"fa0a7425b91b4f8d259c70b2aca5ae67",
|
|
"cfg_path":
|
|
"conf/transformer_mtl_noam.yaml",
|
|
"ckpt_path":
|
|
"exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
|
|
}
|
|
}
|
|
|
|
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_ted",
|
|
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
|
|
help="Choose model type of st task.")
|
|
self.parser.add_argument(
|
|
"--src_lang",
|
|
type=str,
|
|
default="en",
|
|
help="Choose model source language.")
|
|
self.parser.add_argument(
|
|
"--tgt_lang",
|
|
type=str,
|
|
default="zh",
|
|
help="Choose model target language.")
|
|
self.parser.add_argument(
|
|
"--sample_rate",
|
|
type=int,
|
|
default=16000,
|
|
choices=[16000],
|
|
help='Choose the audio sample rate of the model. 8000 or 16000')
|
|
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))
|
|
if "LD_LIBRARY_PATH" in os.environ:
|
|
os.environ["LD_LIBRARY_PATH"] += f":{decompressed_path}"
|
|
else:
|
|
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_ted",
|
|
src_lang: str="en",
|
|
tgt_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 hasattr(self, 'model'):
|
|
logger.info('Model had been initialized.')
|
|
return
|
|
|
|
if cfg_path is None or ckpt_path is None:
|
|
tag = model_type + "-" + src_lang + "-" + tgt_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_name = model_type[:model_type.rindex(
|
|
'_')] # model_type: {model_name}_{dataset}
|
|
model_class = dynamic_import(model_name, model_alias)
|
|
self.model = model_class.from_config(model_conf)
|
|
self.model.eval()
|
|
|
|
# load model
|
|
params_path = self.ckpt_path
|
|
model_dict = paddle.load(params_path)
|
|
self.model.set_state_dict(model_dict)
|
|
|
|
# set kaldi bins
|
|
self._set_kaldi_bins()
|
|
|
|
def _check(self, audio_file: str, sample_rate: int):
|
|
_, audio_sample_rate = soundfile.read(
|
|
audio_file, dtype="int16", always_2d=True)
|
|
if audio_sample_rate != sample_rate:
|
|
raise Exception("invalid sample rate")
|
|
sys.exit(-1)
|
|
|
|
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 "fat_st" in model_type:
|
|
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,
|
|
stderr=subprocess.PIPE)
|
|
fbank_extract_process.stdin.write(
|
|
f"{utt_name} {wav_file}".encode("utf8"))
|
|
fbank_extract_process.stdin.close()
|
|
fbank_feat = dict(
|
|
kaldiio.load_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,
|
|
stderr=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,
|
|
stderr=subprocess.PIPE)
|
|
pitch_extract_process.stdin.close()
|
|
pitch_feat = dict(kaldiio.load_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, stderr=subprocess.PIPE)
|
|
process_command = [
|
|
"copy-feats", "--compress=true", "ark:-", "ark:-"
|
|
]
|
|
process = subprocess.Popen(
|
|
process_command,
|
|
stdin=cmvn_process.stdout,
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.PIPE)
|
|
norm_feat = dict(kaldiio.load_ark(process.stdout))[utt_name]
|
|
self._inputs["audio"] = paddle.to_tensor(norm_feat).unsqueeze(0)
|
|
self._inputs["audio_len"] = paddle.to_tensor(
|
|
self._inputs["audio"].shape[1], dtype="int64")
|
|
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._inputs["audio"]
|
|
audio_len = self._inputs["audio_len"]
|
|
if model_type == "fat_st_ted":
|
|
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._outputs["result"] = 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_ted":
|
|
return self._outputs["result"]
|
|
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
|
|
src_lang = parser_args.src_lang
|
|
tgt_lang = parser_args.tgt_lang
|
|
sample_rate = parser_args.sample_rate
|
|
config = parser_args.config
|
|
ckpt_path = parser_args.ckpt_path
|
|
audio_file = parser_args.input
|
|
device = parser_args.device
|
|
|
|
try:
|
|
res = self(model, src_lang, tgt_lang, sample_rate, config,
|
|
ckpt_path, audio_file, device)
|
|
logger.info("ST Result: {}".format(res))
|
|
return True
|
|
except Exception as e:
|
|
logger.exception(e)
|
|
return False
|
|
|
|
def __call__(self, model, src_lang, tgt_lang, sample_rate, config,
|
|
ckpt_path, audio_file, device):
|
|
"""
|
|
Python API to call an executor.
|
|
"""
|
|
audio_file = os.path.abspath(audio_file)
|
|
self._check(audio_file, sample_rate)
|
|
paddle.set_device(device)
|
|
self._init_from_path(model, src_lang, tgt_lang, config, ckpt_path)
|
|
self.preprocess(audio_file, model)
|
|
self.infer(model)
|
|
res = self.postprocess(model)
|
|
|
|
return res
|