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PaddleSpeech/paddlespeech/cli/st/infer.py

358 lines
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# 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
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import numpy as np
import paddle
import soundfile
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from kaldiio import WriteHelper
from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
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from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
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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
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__all__ = ["STExecutor"]
pretrained_models = {
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"fat_st_ted-en-zh": {
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"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz",
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"md5":
"fa0a7425b91b4f8d259c70b2aca5ae67",
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"cfg_path":
"conf/transformer_mtl_noam.yaml",
"ckpt_path":
"exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
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}
}
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model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
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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(
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"--model",
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type=str,
default="fat_st_ted",
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choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
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help="Choose model type of st task.")
self.parser.add_argument(
"--src_lang",
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type=str,
default="en",
help="Choose model source language.")
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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(
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"--config",
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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}"
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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",
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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
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if cfg_path is None or ckpt_path is None:
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tag = model_type + "-" + src_lang + "-" + tgt_lang
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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=self.config.collator.vocab_filepath,
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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)
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model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, model_alias)
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self.model = model_class.from_config(model_conf)
self.model.eval()
# load model
params_path = self.ckpt_path
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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)
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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)
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if "fat_st" in model_type:
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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)
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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]
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extract_command = ["compute-kaldi-pitch-feats", "scp:-", "ark:-"]
pitch_extract_process = subprocess.Popen(
extract_command,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
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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)
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pitch_extract_process.stdin.close()
pitch_feat = dict(kaldiio.load_ark(pitch_process.stdout))[utt_name]
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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)
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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")
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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":
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hyps = self.model.decode(
audio,
audio_len,
text_feature=self.text_feature,
decoding_method=cfg.decoding_method,
beam_size=cfg.beam_size,
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
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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"]
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else:
raise ValueError("Wrong model type.")
def execute(self, argv: List[str]) -> bool:
"""
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
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model = parser_args.model
src_lang = parser_args.src_lang
tgt_lang = parser_args.tgt_lang
sample_rate = parser_args.sample_rate
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config = parser_args.config
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ckpt_path = parser_args.ckpt_path
audio_file = parser_args.input
device = parser_args.device
try:
res = self(audio_file, model, src_lang, tgt_lang, sample_rate,
config, ckpt_path, device)
logger.info("ST Result: {}".format(res))
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return True
except Exception as e:
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logger.exception(e)
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return False
def __call__(self,
audio_file: os.PathLike,
model: str='fat_st_ted',
src_lang: str='en',
tgt_lang: str='zh',
sample_rate: int=16000,
config: Optional[os.PathLike]=None,
ckpt_path: Optional[os.PathLike]=None,
device: str=paddle.get_device()):
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"""
Python API to call an executor.
"""
audio_file = os.path.abspath(audio_file)
self._check(audio_file, sample_rate)
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paddle.set_device(device)
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self._init_from_path(model, src_lang, tgt_lang, config, ckpt_path)
self.preprocess(audio_file, model)
self.infer(model)
res = self.postprocess(model)
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return res