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D
DeepSpeech
未验证 提交 26d5dded 编写于 作者: Q qingen 提交者： GitHub
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Merge branch 'PaddlePaddle:develop' into cluster

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paddlespeech/cli/asr/infer.py
浏览文件 @ 26d5dded
......@@ -29,9 +29,10 @@ from ..download import get_path_from_url
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
from paddlespeech.s2t.transform.transformation import Transformation
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
......@@ -39,94 +40,13 @@ from paddlespeech.s2t.utils.utility import UpdateConfig
__all__ = ['ASRExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"conformer_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
'md5':
'76cb19ed857e6623856b7cd7ebbfeda4',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/wenetspeech',
},
"transformer_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'2c667da24922aad391eacafe37bc1660',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/transformer/checkpoints/avg_10',
},
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'23e16c69730a1cb5d735c98c83c21e16',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2offline_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'f5666c81ad015c8de03aac2bc92e5762',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
'lm_md5':
'099a601759d467cd0a8523ff939819c5'
},
}
model_alias = {
"deepspeech2offline":
"paddlespeech.s2t.models.ds2:DeepSpeech2Model",
"deepspeech2online":
"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
"conformer":
"paddlespeech.s2t.models.u2:U2Model",
"transformer":
"paddlespeech.s2t.models.u2:U2Model",
"wenetspeech":
"paddlespeech.s2t.models.u2:U2Model",
}
@cli_register(
name='paddlespeech.asr', description='Speech to text infer command.')
class ASRExecutor(BaseExecutor):
def __init__(self):
super(ASRExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.asr', add_help=True)
......@@ -136,7 +56,9 @@ class ASRExecutor(BaseExecutor):
'--model',
type=str,
default='conformer_wenetspeech',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of asr task.')
self.parser.add_argument(
'--lang',
......@@ -192,23 +114,6 @@ class ASRExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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 _init_from_path(self,
model_type: str='wenetspeech',
lang: str='zh',
......@@ -219,6 +124,7 @@ class ASRExecutor(BaseExecutor):
"""
Init model and other resources from a specific path.
"""
logger.info("start to init the model")
if hasattr(self, 'model'):
logger.info('Model had been initialized.')
return
......@@ -228,19 +134,21 @@ class ASRExecutor(BaseExecutor):
tag = model_type + '-' + lang + '-' + sample_rate_str
res_path = self._get_pretrained_path(tag) # wenetspeech_zh
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
res_path, pretrained_models[tag]['ckpt_path'] + ".pdparams")
res_path,
self.pretrained_models[tag]['ckpt_path'] + ".pdparams")
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 + ".pdparams")
self.res_path = os.path.dirname(
os.path.dirname(os.path.abspath(self.cfg_path)))
logger.info(self.cfg_path)
logger.info(self.ckpt_path)
#Init body.
self.config = CfgNode(new_allowed=True)
self.config.merge_from_file(self.cfg_path)
......@@ -255,8 +163,8 @@ class ASRExecutor(BaseExecutor):
self.collate_fn_test = SpeechCollator.from_config(self.config)
self.text_feature = TextFeaturizer(
unit_type=self.config.unit_type, vocab=self.vocab)
lm_url = pretrained_models[tag]['lm_url']
lm_md5 = pretrained_models[tag]['lm_md5']
lm_url = self.pretrained_models[tag]['lm_url']
lm_md5 = self.pretrained_models[tag]['lm_md5']
self.download_lm(
lm_url,
os.path.dirname(self.config.decode.lang_model_path), lm_md5)
......@@ -269,12 +177,11 @@ class ASRExecutor(BaseExecutor):
vocab=self.config.vocab_filepath,
spm_model_prefix=self.config.spm_model_prefix)
self.config.decode.decoding_method = decode_method
else:
raise Exception("wrong type")
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config
model = model_class.from_config(model_conf)
self.model = model
......@@ -347,12 +254,14 @@ class ASRExecutor(BaseExecutor):
else:
raise Exception("wrong type")
logger.info("audio feat process success")
@paddle.no_grad()
def infer(self, model_type: str):
"""
Model inference and result stored in self.output.
"""
logger.info("start to infer the model to get the output")
cfg = self.config.decode
audio = self._inputs["audio"]
audio_len = self._inputs["audio_len"]
......@@ -369,17 +278,22 @@ class ASRExecutor(BaseExecutor):
self._outputs["result"] = result_transcripts[0]
elif "conformer" in model_type or "transformer" in model_type:
result_transcripts = self.model.decode(
audio,
audio_len,
text_feature=self.text_feature,
decoding_method=cfg.decoding_method,
beam_size=cfg.beam_size,
ctc_weight=cfg.ctc_weight,
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
self._outputs["result"] = result_transcripts[0][0]
logger.info(f"we will use the transformer like model : {model_type}")
try:
result_transcripts = self.model.decode(
audio,
audio_len,
text_feature=self.text_feature,
decoding_method=cfg.decoding_method,
beam_size=cfg.beam_size,
ctc_weight=cfg.ctc_weight,
decoding_chunk_size=cfg.decoding_chunk_size,
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
simulate_streaming=cfg.simulate_streaming)
self._outputs["result"] = result_transcripts[0][0]
except Exception as e:
logger.exception(e)
else:
raise Exception("invalid model name")
......
paddlespeech/cli/asr/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"conformer_wenetspeech-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
'md5':
'76cb19ed857e6623856b7cd7ebbfeda4',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/conformer/checkpoints/wenetspeech',
},
"transformer_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'2c667da24922aad391eacafe37bc1660',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/transformer/checkpoints/avg_10',
},
"deepspeech2offline_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
'md5':
'932c3593d62fe5c741b59b31318aa314',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2online_aishell-zh-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz',
'md5':
'23e16c69730a1cb5d735c98c83c21e16',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2_online/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
'lm_md5':
'29e02312deb2e59b3c8686c7966d4fe3'
},
"deepspeech2offline_librispeech-en-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
'md5':
'f5666c81ad015c8de03aac2bc92e5762',
'cfg_path':
'model.yaml',
'ckpt_path':
'exp/deepspeech2/checkpoints/avg_1',
'lm_url':
'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
'lm_md5':
'099a601759d467cd0a8523ff939819c5'
},
}
model_alias = {
"deepspeech2offline":
"paddlespeech.s2t.models.ds2:DeepSpeech2Model",
"deepspeech2online":
"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
"conformer":
"paddlespeech.s2t.models.u2:U2Model",
"conformer_online":
"paddlespeech.s2t.models.u2:U2Model",
"transformer":
"paddlespeech.s2t.models.u2:U2Model",
"wenetspeech":
"paddlespeech.s2t.models.u2:U2Model",
}
paddlespeech/cli/cls/infer.py
浏览文件 @ 26d5dded
......@@ -25,55 +25,23 @@ import yaml
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddleaudio import load
from paddleaudio.features import LogMelSpectrogram
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
__all__ = ['CLSExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"panns_cnn6-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
'md5': '4cf09194a95df024fd12f84712cf0f9c',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn6.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
'md5': 'cb8427b22176cc2116367d14847f5413',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn10.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
'md5': 'e3b9b5614a1595001161d0ab95edee97',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn14.pdparams',
'label_file': 'audioset_labels.txt',
},
}
model_alias = {
"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
}
@cli_register(
name='paddlespeech.cls', description='Audio classification infer command.')
class CLSExecutor(BaseExecutor):
def __init__(self):
super(CLSExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.cls', add_help=True)
......@@ -83,7 +51,9 @@ class CLSExecutor(BaseExecutor):
'--model',
type=str,
default='panns_cnn14',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of cls task.')
self.parser.add_argument(
'--config',
......@@ -121,23 +91,6 @@ class CLSExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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 _init_from_path(self,
model_type: str='panns_cnn14',
cfg_path: Optional[os.PathLike]=None,
......@@ -153,12 +106,12 @@ class CLSExecutor(BaseExecutor):
if label_file is None or ckpt_path is None:
tag = model_type + '-' + '32k' # panns_cnn14-32k
self.res_path = self._get_pretrained_path(tag)
self.cfg_path = os.path.join(self.res_path,
pretrained_models[tag]['cfg_path'])
self.label_file = os.path.join(self.res_path,
pretrained_models[tag]['label_file'])
self.ckpt_path = os.path.join(self.res_path,
pretrained_models[tag]['ckpt_path'])
self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path'])
self.label_file = os.path.join(
self.res_path, self.pretrained_models[tag]['label_file'])
self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.label_file = os.path.abspath(label_file)
......@@ -175,7 +128,7 @@ class CLSExecutor(BaseExecutor):
self._label_list.append(line.strip())
# model
model_class = dynamic_import(model_type, model_alias)
model_class = dynamic_import(model_type, self.model_alias)
model_dict = paddle.load(self.ckpt_path)
self.model = model_class(extract_embedding=False)
self.model.set_state_dict(model_dict)
......
paddlespeech/cli/cls/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"panns_cnn6-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
'md5': '4cf09194a95df024fd12f84712cf0f9c',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn6.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn10-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
'md5': 'cb8427b22176cc2116367d14847f5413',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn10.pdparams',
'label_file': 'audioset_labels.txt',
},
"panns_cnn14-32k": {
'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
'md5': 'e3b9b5614a1595001161d0ab95edee97',
'cfg_path': 'panns.yaml',
'ckpt_path': 'cnn14.pdparams',
'label_file': 'audioset_labels.txt',
},
}
model_alias = {
"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
}
paddlespeech/cli/executor.py
浏览文件 @ 26d5dded
......@@ -25,6 +25,8 @@ from typing import Union
import paddle
from .log import logger
from .utils import download_and_decompress
from .utils import MODEL_HOME
class BaseExecutor(ABC):
......@@ -35,19 +37,8 @@ class BaseExecutor(ABC):
def __init__(self):
self._inputs = OrderedDict()
self._outputs = OrderedDict()
@abstractmethod
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
Args:
tag (str): A tag of pretrained model.
Returns:
os.PathLike: The path on which resources of pretrained model locate.
"""
pass
self.pretrained_models = OrderedDict()
self.model_alias = OrderedDict()
@abstractmethod
def _init_from_path(self, *args, **kwargs):
......@@ -227,3 +218,20 @@ class BaseExecutor(ABC):
]
for l in loggers:
l.disabled = True
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(self.pretrained_models.keys())
assert tag in self.pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
res_path = os.path.join(MODEL_HOME, tag)
decompressed_path = download_and_decompress(self.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
paddlespeech/cli/st/infer.py
浏览文件 @ 26d5dded
......@@ -32,40 +32,24 @@ from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import kaldi_bins
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
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/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
"md5":
"d62063f35a16d91210a71081bd2dd557",
"cfg_path":
"model.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__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.kaldi_bins = kaldi_bins
self.parser = argparse.ArgumentParser(
prog="paddlespeech.st", add_help=True)
......@@ -75,7 +59,9 @@ class STExecutor(BaseExecutor):
"--model",
type=str,
default="fat_st_ted",
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help="Choose model type of st task.")
self.parser.add_argument(
"--src_lang",
......@@ -119,28 +105,11 @@ class STExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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 = download_and_decompress(self.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:
......@@ -197,7 +166,7 @@ class STExecutor(BaseExecutor):
model_conf = self.config
model_name = model_type[:model_type.rindex(
'_')] # model_type: {model_name}_{dataset}
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
self.model = model_class.from_config(model_conf)
self.model.eval()
......
paddlespeech/cli/st/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
"fat_st_ted-en-zh": {
"url":
"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
"md5":
"d62063f35a16d91210a71081bd2dd557",
"cfg_path":
"model.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",
}
paddlespeech/cli/stats/infer.py
浏览文件 @ 26d5dded
......@@ -16,7 +16,6 @@ from typing import List
from prettytable import PrettyTable
from ..log import logger
from ..utils import cli_register
from ..utils import stats_wrapper
......@@ -27,7 +26,8 @@ model_name_format = {
'cls': 'Model-Sample Rate',
'st': 'Model-Source language-Target language',
'text': 'Model-Task-Language',
'tts': 'Model-Language'
'tts': 'Model-Language',
'vector': 'Model-Sample Rate'
}
......@@ -36,18 +36,18 @@ model_name_format = {
description='Get speech tasks support models list.')
class StatsExecutor():
def __init__(self):
super(StatsExecutor, self).__init__()
super().__init__()
self.parser = argparse.ArgumentParser(
prog='paddlespeech.stats', add_help=True)
self.task_choices = ['asr', 'cls', 'st', 'text', 'tts', 'vector']
self.parser.add_argument(
'--task',
type=str,
default='asr',
choices=['asr', 'cls', 'st', 'text', 'tts'],
choices=self.task_choices,
help='Choose speech task.',
required=True)
self.task_choices = ['asr', 'cls', 'st', 'text', 'tts']
def show_support_models(self, pretrained_models: dict):
fields = model_name_format[self.task].split("-")
......@@ -61,73 +61,15 @@ class StatsExecutor():
Command line entry.
"""
parser_args = self.parser.parse_args(argv)
self.task = parser_args.task
if self.task not in self.task_choices:
logger.error(
"Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']"
)
has_exceptions = False
try:
self(parser_args.task)
except Exception as e:
has_exceptions = True
if has_exceptions:
return False
elif self.task == 'asr':
try:
from ..asr.infer import pretrained_models
logger.info(
"Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of ASR pretrained models.")
return False
elif self.task == 'cls':
try:
from ..cls.infer import pretrained_models
logger.info(
"Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of CLS pretrained models.")
return False
elif self.task == 'st':
try:
from ..st.infer import pretrained_models
logger.info(
"Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of ST pretrained models.")
return False
elif self.task == 'text':
try:
from ..text.infer import pretrained_models
logger.info(
"Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error(
"Failed to get the list of TEXT pretrained models.")
return False
elif self.task == 'tts':
try:
from ..tts.infer import pretrained_models
logger.info(
"Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
return True
except BaseException:
logger.error("Failed to get the list of TTS pretrained models.")
return False
else:
return True
@stats_wrapper
def __call__(
......@@ -138,13 +80,12 @@ class StatsExecutor():
"""
self.task = task
if self.task not in self.task_choices:
print(
"Please input correct speech task, choices = ['asr', 'cls', 'st', 'text', 'tts']"
)
print("Please input correct speech task, choices = " + str(
self.task_choices))
elif self.task == 'asr':
try:
from ..asr.infer import pretrained_models
from ..asr.pretrained_models import pretrained_models
print(
"Here is the list of ASR pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -154,7 +95,7 @@ class StatsExecutor():
elif self.task == 'cls':
try:
from ..cls.infer import pretrained_models
from ..cls.pretrained_models import pretrained_models
print(
"Here is the list of CLS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -164,7 +105,7 @@ class StatsExecutor():
elif self.task == 'st':
try:
from ..st.infer import pretrained_models
from ..st.pretrained_models import pretrained_models
print(
"Here is the list of ST pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -174,7 +115,7 @@ class StatsExecutor():
elif self.task == 'text':
try:
from ..text.infer import pretrained_models
from ..text.pretrained_models import pretrained_models
print(
"Here is the list of TEXT pretrained models released by PaddleSpeech that can be used by command line and python API"
)
......@@ -184,10 +125,22 @@ class StatsExecutor():
elif self.task == 'tts':
try:
from ..tts.infer import pretrained_models
from ..tts.pretrained_models import pretrained_models
print(
"Here is the list of TTS pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
except BaseException:
print("Failed to get the list of TTS pretrained models.")
elif self.task == 'vector':
try:
from ..vector.pretrained_models import pretrained_models
print(
"Here is the list of Speaker Recognition pretrained models released by PaddleSpeech that can be used by command line and python API"
)
self.show_support_models(pretrained_models)
except BaseException:
print(
"Failed to get the list of Speaker Recognition pretrained models."
)
paddlespeech/cli/text/infer.py
浏览文件 @ 26d5dded
......@@ -25,58 +25,21 @@ from ...s2t.utils.dynamic_import import dynamic_import
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from .pretrained_models import tokenizer_alias
__all__ = ['TextExecutor']
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"ernie_linear_p7_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
'md5':
'12283e2ddde1797c5d1e57036b512746',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
"ernie_linear_p3_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
'md5':
'448eb2fdf85b6a997e7e652e80c51dd2',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
}
model_alias = {
"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
}
tokenizer_alias = {
"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
}
@cli_register(name='paddlespeech.text', description='Text infer command.')
class TextExecutor(BaseExecutor):
def __init__(self):
super(TextExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.tokenizer_alias = tokenizer_alias
self.parser = argparse.ArgumentParser(
prog='paddlespeech.text', add_help=True)
......@@ -92,7 +55,9 @@ class TextExecutor(BaseExecutor):
'--model',
type=str,
default='ernie_linear_p7_wudao',
choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
choices=[
tag[:tag.index('-')] for tag in self.pretrained_models.keys()
],
help='Choose model type of text task.')
self.parser.add_argument(
'--lang',
......@@ -131,23 +96,6 @@ class TextExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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 _init_from_path(self,
task: str='punc',
model_type: str='ernie_linear_p7_wudao',
......@@ -167,12 +115,12 @@ class TextExecutor(BaseExecutor):
if cfg_path is None or ckpt_path is None or vocab_file is None:
tag = '-'.join([model_type, task, lang])
self.res_path = self._get_pretrained_path(tag)
self.cfg_path = os.path.join(self.res_path,
pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(self.res_path,
pretrained_models[tag]['ckpt_path'])
self.vocab_file = os.path.join(self.res_path,
pretrained_models[tag]['vocab_file'])
self.cfg_path = os.path.join(
self.res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
self.res_path, self.pretrained_models[tag]['ckpt_path'])
self.vocab_file = os.path.join(
self.res_path, self.pretrained_models[tag]['vocab_file'])
else:
self.cfg_path = os.path.abspath(cfg_path)
self.ckpt_path = os.path.abspath(ckpt_path)
......@@ -187,8 +135,8 @@ class TextExecutor(BaseExecutor):
self._punc_list.append(line.strip())
# model
model_class = dynamic_import(model_name, model_alias)
tokenizer_class = dynamic_import(model_name, tokenizer_alias)
model_class = dynamic_import(model_name, self.model_alias)
tokenizer_class = dynamic_import(model_name, self.tokenizer_alias)
self.model = model_class(
cfg_path=self.cfg_path, ckpt_path=self.ckpt_path)
self.tokenizer = tokenizer_class.from_pretrained('ernie-1.0')
......
paddlespeech/cli/text/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
"ernie_linear_p7_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
'md5':
'12283e2ddde1797c5d1e57036b512746',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
"ernie_linear_p3_wudao-punc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
'md5':
'448eb2fdf85b6a997e7e652e80c51dd2',
'cfg_path':
'ckpt/model_config.json',
'ckpt_path':
'ckpt/model_state.pdparams',
'vocab_file':
'punc_vocab.txt',
},
}
model_alias = {
"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
}
tokenizer_alias = {
"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
}
paddlespeech/cli/tts/infer.py
浏览文件 @ 26d5dded
......@@ -29,9 +29,9 @@ from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
......@@ -39,299 +39,14 @@ from paddlespeech.t2s.modules.normalizer import ZScore
__all__ = ['TTSExecutor']
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
'md5':
'6f6fa967b408454b6662c8c00c0027cb',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'feats_stats.npy',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
'md5':
'ffed800c93deaf16ca9b3af89bfcd747',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_100000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
'md5':
'f4dd4a5f49a4552b77981f544ab3392e',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_96400.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
"fastspeech2_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip',
'md5':
'743e5024ca1e17a88c5c271db9779ba4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_66200.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
# tacotron2
"tacotron2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip',
'md5':
'0df4b6f0bcbe0d73c5ed6df8867ab91a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"tacotron2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip',
'md5':
'6a5eddd81ae0e81d16959b97481135f3',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_60300.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip',
'md5':
'2e481633325b5bdf0a3823c714d2c117',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip',
'md5':
'53610ba9708fd3008ccaf8e99dacbaf0',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip',
'md5':
'd7598fa41ad362d62f85ffc0f07e3d84',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
"pwgan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
'md5':
'b3da1defcde3e578be71eb284cb89f2c',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# style_melgan
"style_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'5de2d5348f396de0c966926b8c462755',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip',
'md5':
'70e9131695decbca06a65fe51ed38a72',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip',
'md5':
'3bb49bc75032ed12f79c00c8cc79a09a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip',
'md5':
'7da8f88359bca2457e705d924cf27bd4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# wavernn
"wavernn_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip',
'md5':
'ee37b752f09bcba8f2af3b777ca38e13',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_400000.pdz',
'speech_stats':
'feats_stats.npy',
}
}
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
"tacotron2":
"paddlespeech.t2s.models.tacotron2:Tacotron2",
"tacotron2_inference":
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"style_melgan":
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
"style_melgan_inference":
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
"wavernn":
"paddlespeech.t2s.models.wavernn:WaveRNN",
"wavernn_inference":
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
}
@cli_register(
name='paddlespeech.tts', description='Text to Speech infer command.')
class TTSExecutor(BaseExecutor):
def __init__(self):
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog='paddlespeech.tts', add_help=True)
......@@ -449,22 +164,6 @@ class TTSExecutor(BaseExecutor):
action='store_true',
help='Increase logger verbosity of current task.')
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""
Download and returns pretrained resources path of current task.
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
tag, '\n\t\t'.join(support_models))
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 _init_from_path(
self,
am: str='fastspeech2_csmsc',
......@@ -490,16 +189,15 @@ class TTSExecutor(BaseExecutor):
if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
am_res_path = self._get_pretrained_path(am_tag)
self.am_res_path = am_res_path
self.am_config = os.path.join(am_res_path,
pretrained_models[am_tag]['config'])
self.am_config = os.path.join(
am_res_path, self.pretrained_models[am_tag]['config'])
self.am_ckpt = os.path.join(am_res_path,
pretrained_models[am_tag]['ckpt'])
self.pretrained_models[am_tag]['ckpt'])
self.am_stat = os.path.join(
am_res_path, pretrained_models[am_tag]['speech_stats'])
am_res_path, self.pretrained_models[am_tag]['speech_stats'])
# must have phones_dict in acoustic
self.phones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['phones_dict'])
print("self.phones_dict:", self.phones_dict)
am_res_path, self.pretrained_models[am_tag]['phones_dict'])
logger.info(am_res_path)
logger.info(self.am_config)
logger.info(self.am_ckpt)
......@@ -509,21 +207,20 @@ class TTSExecutor(BaseExecutor):
self.am_stat = os.path.abspath(am_stat)
self.phones_dict = os.path.abspath(phones_dict)
self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
print("self.phones_dict:", self.phones_dict)
# for speedyspeech
self.tones_dict = None
if 'tones_dict' in pretrained_models[am_tag]:
if 'tones_dict' in self.pretrained_models[am_tag]:
self.tones_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['tones_dict'])
am_res_path, self.pretrained_models[am_tag]['tones_dict'])
if tones_dict:
self.tones_dict = tones_dict
# for multi speaker fastspeech2
self.speaker_dict = None
if 'speaker_dict' in pretrained_models[am_tag]:
if 'speaker_dict' in self.pretrained_models[am_tag]:
self.speaker_dict = os.path.join(
am_res_path, pretrained_models[am_tag]['speaker_dict'])
am_res_path, self.pretrained_models[am_tag]['speaker_dict'])
if speaker_dict:
self.speaker_dict = speaker_dict
......@@ -532,12 +229,12 @@ class TTSExecutor(BaseExecutor):
if voc_ckpt is None or voc_config is None or voc_stat is None:
voc_res_path = self._get_pretrained_path(voc_tag)
self.voc_res_path = voc_res_path
self.voc_config = os.path.join(voc_res_path,
pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(voc_res_path,
pretrained_models[voc_tag]['ckpt'])
self.voc_config = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['config'])
self.voc_ckpt = os.path.join(
voc_res_path, self.pretrained_models[voc_tag]['ckpt'])
self.voc_stat = os.path.join(
voc_res_path, pretrained_models[voc_tag]['speech_stats'])
voc_res_path, self.pretrained_models[voc_tag]['speech_stats'])
logger.info(voc_res_path)
logger.info(self.voc_config)
logger.info(self.voc_ckpt)
......@@ -588,8 +285,9 @@ class TTSExecutor(BaseExecutor):
# model: {model_name}_{dataset}
am_name = am[:am.rindex('_')]
am_class = dynamic_import(am_name, model_alias)
am_inference_class = dynamic_import(am_name + '_inference', model_alias)
am_class = dynamic_import(am_name, self.model_alias)
am_inference_class = dynamic_import(am_name + '_inference',
self.model_alias)
if am_name == 'fastspeech2':
am = am_class(
......@@ -618,9 +316,9 @@ class TTSExecutor(BaseExecutor):
# vocoder
# model: {model_name}_{dataset}
voc_name = voc[:voc.rindex('_')]
voc_class = dynamic_import(voc_name, model_alias)
voc_class = dynamic_import(voc_name, self.model_alias)
voc_inference_class = dynamic_import(voc_name + '_inference',
model_alias)
self.model_alias)
if voc_name != 'wavernn':
voc = voc_class(**self.voc_config["generator_params"])
voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
......@@ -735,7 +433,6 @@ class TTSExecutor(BaseExecutor):
am_ckpt = args.am_ckpt
am_stat = args.am_stat
phones_dict = args.phones_dict
print("phones_dict:", phones_dict)
tones_dict = args.tones_dict
speaker_dict = args.speaker_dict
voc = args.voc
......
paddlespeech/cli/tts/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
# speedyspeech
"speedyspeech_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
'md5':
'6f6fa967b408454b6662c8c00c0027cb',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'feats_stats.npy',
'phones_dict':
'phone_id_map.txt',
'tones_dict':
'tone_id_map.txt',
},
# fastspeech2
"fastspeech2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
'md5':
'637d28a5e53aa60275612ba4393d5f22',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_76000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
'md5':
'ffed800c93deaf16ca9b3af89bfcd747',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_100000.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"fastspeech2_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
'md5':
'f4dd4a5f49a4552b77981f544ab3392e',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_96400.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
"fastspeech2_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip',
'md5':
'743e5024ca1e17a88c5c271db9779ba4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_66200.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
'speaker_dict':
'speaker_id_map.txt',
},
# tacotron2
"tacotron2_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_csmsc_ckpt_0.2.0.zip',
'md5':
'0df4b6f0bcbe0d73c5ed6df8867ab91a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_30600.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
"tacotron2_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.2.0.zip',
'md5':
'6a5eddd81ae0e81d16959b97481135f3',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_60300.pdz',
'speech_stats':
'speech_stats.npy',
'phones_dict':
'phone_id_map.txt',
},
# pwgan
"pwgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip',
'md5':
'2e481633325b5bdf0a3823c714d2c117',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip',
'md5':
'53610ba9708fd3008ccaf8e99dacbaf0',
'config':
'pwg_default.yaml',
'ckpt':
'pwg_snapshot_iter_400000.pdz',
'speech_stats':
'pwg_stats.npy',
},
"pwgan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip',
'md5':
'd7598fa41ad362d62f85ffc0f07e3d84',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
"pwgan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
'md5':
'b3da1defcde3e578be71eb284cb89f2c',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# mb_melgan
"mb_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'ee5f0604e20091f0d495b6ec4618b90d',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1000000.pdz',
'speech_stats':
'feats_stats.npy',
},
# style_melgan
"style_melgan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/style_melgan/style_melgan_csmsc_ckpt_0.1.1.zip',
'md5':
'5de2d5348f396de0c966926b8c462755',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_1500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# hifigan
"hifigan_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_csmsc_ckpt_0.1.1.zip',
'md5':
'dd40a3d88dfcf64513fba2f0f961ada6',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_ljspeech-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_ljspeech_ckpt_0.2.0.zip',
'md5':
'70e9131695decbca06a65fe51ed38a72',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_aishell3-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip',
'md5':
'3bb49bc75032ed12f79c00c8cc79a09a',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
"hifigan_vctk-en": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip',
'md5':
'7da8f88359bca2457e705d924cf27bd4',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_2500000.pdz',
'speech_stats':
'feats_stats.npy',
},
# wavernn
"wavernn_csmsc-zh": {
'url':
'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_ckpt_0.2.0.zip',
'md5':
'ee37b752f09bcba8f2af3b777ca38e13',
'config':
'default.yaml',
'ckpt':
'snapshot_iter_400000.pdz',
'speech_stats':
'feats_stats.npy',
}
}
model_alias = {
# acoustic model
"speedyspeech":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
"speedyspeech_inference":
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
"fastspeech2":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
"fastspeech2_inference":
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
"tacotron2":
"paddlespeech.t2s.models.tacotron2:Tacotron2",
"tacotron2_inference":
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
# voc
"pwgan":
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
"pwgan_inference":
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
"mb_melgan":
"paddlespeech.t2s.models.melgan:MelGANGenerator",
"mb_melgan_inference":
"paddlespeech.t2s.models.melgan:MelGANInference",
"style_melgan":
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
"style_melgan_inference":
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
"hifigan":
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
"hifigan_inference":
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
"wavernn":
"paddlespeech.t2s.models.wavernn:WaveRNN",
"wavernn_inference":
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
}
paddlespeech/cli/vector/infer.py
浏览文件 @ 26d5dded
......@@ -27,45 +27,24 @@ from yacs.config import CfgNode
from ..executor import BaseExecutor
from ..log import logger
from ..utils import cli_register
from ..utils import download_and_decompress
from ..utils import MODEL_HOME
from ..utils import stats_wrapper
from .pretrained_models import model_alias
from .pretrained_models import pretrained_models
from paddleaudio.backends import load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
from paddlespeech.vector.io.batch import feature_normalize
from paddlespeech.vector.modules.sid_model import SpeakerIdetification
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]".
# e.g. "ecapatdnn_voxceleb12-16k".
# Command line and python api use "{model_name}[-{dataset}]" as --model, usage:
# "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav"
"ecapatdnn_voxceleb12-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz',
'md5':
'cc33023c54ab346cd318408f43fcaf95',
'cfg_path':
'conf/model.yaml', # the yaml config path
'ckpt_path':
'model/model', # the format is ${dir}/{model_name},
# so the first 'model' is dir, the second 'model' is the name
# this means we have a model stored as model/model.pdparams
},
}
model_alias = {
"ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn",
}
@cli_register(
name="paddlespeech.vector",
description="Speech to vector embedding infer command.")
class VectorExecutor(BaseExecutor):
def __init__(self):
super(VectorExecutor, self).__init__()
super().__init__()
self.model_alias = model_alias
self.pretrained_models = pretrained_models
self.parser = argparse.ArgumentParser(
prog="paddlespeech.vector", add_help=True)
......@@ -128,8 +107,8 @@ class VectorExecutor(BaseExecutor):
Returns:
bool:
False: some audio occurs error
True: all audio process success
False: some audio occurs error
True: all audio process success
"""
# stage 0: parse the args and get the required args
parser_args = self.parser.parse_args(argv)
......@@ -289,32 +268,6 @@ class VectorExecutor(BaseExecutor):
return res
def _get_pretrained_path(self, tag: str) -> os.PathLike:
"""get the neural network path from the pretrained model list
we stored all the pretained mode in the variable `pretrained_models`
Args:
tag (str): model tag in the pretrained model list
Returns:
os.PathLike: the downloaded pretrained model path in the disk
"""
support_models = list(pretrained_models.keys())
assert tag in pretrained_models, \
'The model "{}" you want to use has not been supported,'\
'please choose other models.\n' \
'The support models includes\n\t\t{}'.format(tag, "\n\t\t".join(support_models))
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 _init_from_path(self,
model_type: str='ecapatdnn_voxceleb12',
sample_rate: int=16000,
......@@ -350,10 +303,11 @@ class VectorExecutor(BaseExecutor):
res_path = self._get_pretrained_path(tag)
self.res_path = res_path
self.cfg_path = os.path.join(res_path,
pretrained_models[tag]['cfg_path'])
self.cfg_path = os.path.join(
res_path, self.pretrained_models[tag]['cfg_path'])
self.ckpt_path = os.path.join(
res_path, pretrained_models[tag]['ckpt_path'] + '.pdparams')
res_path,
self.pretrained_models[tag]['ckpt_path'] + '.pdparams')
else:
# get the model from disk
self.cfg_path = os.path.abspath(cfg_path)
......@@ -373,7 +327,7 @@ class VectorExecutor(BaseExecutor):
logger.info("start to dynamic import the model class")
model_name = model_type[:model_type.rindex('_')]
logger.info(f"model name {model_name}")
model_class = dynamic_import(model_name, model_alias)
model_class = dynamic_import(model_name, self.model_alias)
model_conf = self.config.model
backbone = model_class(**model_conf)
model = SpeakerIdetification(
......
paddlespeech/cli/vector/pretrained_models.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
pretrained_models = {
# The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]".
# e.g. "ecapatdnn_voxceleb12-16k".
# Command line and python api use "{model_name}[-{dataset}]" as --model, usage:
# "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav"
"ecapatdnn_voxceleb12-16k": {
'url':
'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz',
'md5':
'cc33023c54ab346cd318408f43fcaf95',
'cfg_path':
'conf/model.yaml', # the yaml config path
'ckpt_path':
'model/model', # the format is ${dir}/{model_name},
# so the first 'model' is dir, the second 'model' is the name
# this means we have a model stored as model/model.pdparams
},
}
model_alias = {
"ecapatdnn": "paddlespeech.vector.models.ecapa_tdnn:EcapaTdnn",
}
paddlespeech/s2t/models/u2/u2.py
浏览文件 @ 26d5dded
......@@ -279,14 +279,13 @@ class U2BaseModel(ASRInterface, nn.Layer):
# TODO(Hui Zhang): if end_flag.sum() == running_size:
if end_flag.cast(paddle.int64).sum() == running_size:
break
# 2.1 Forward decoder step
hyps_mask = subsequent_mask(i).unsqueeze(0).repeat(
running_size, 1, 1).to(device) # (B*N, i, i)
# logp: (B*N, vocab)
logp, cache = self.decoder.forward_one_step(
encoder_out, encoder_mask, hyps, hyps_mask, cache)
# 2.2 First beam prune: select topk best prob at current time
top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N)
top_k_logp = mask_finished_scores(top_k_logp, end_flag)
......@@ -708,11 +707,11 @@ class U2BaseModel(ASRInterface, nn.Layer):
batch_size = feats.shape[0]
if decoding_method in ['ctc_prefix_beam_search',
'attention_rescoring'] and batch_size > 1:
logger.fatal(
logger.error(
f'decoding mode {decoding_method} must be running with batch_size == 1'
)
logger.error(f"current batch_size is {batch_size}")
sys.exit(1)
if decoding_method == 'attention':
hyps = self.recognize(
feats,
......
paddlespeech/s2t/modules/ctc.py
浏览文件 @ 26d5dded
......@@ -180,7 +180,7 @@ class CTCDecoder(CTCDecoderBase):
# init once
if self._ext_scorer is not None:
return
if language_model_path != '':
logger.info("begin to initialize the external scorer "
"for decoding")
......
paddlespeech/server/README.md
浏览文件 @ 26d5dded
......@@ -35,3 +35,16 @@
```bash
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav
```
## Online ASR Server
### Lanuch online asr server
```
paddlespeech_server start --config_file conf/ws_conformer_application.yaml
```
### Access online asr server
```
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
```
\ No newline at end of file
paddlespeech/server/README_cn.md
浏览文件 @ 26d5dded
......@@ -35,3 +35,17 @@
```bash
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav
```
## 流式ASR
### 启动流式语音识别服务
```
paddlespeech_server start --config_file conf/ws_conformer_application.yaml
```
### 访问流式语音识别服务
```
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input zh.wav
```
\ No newline at end of file
paddlespeech/server/bin/paddlespeech_client.py
浏览文件 @ 26d5dded
......@@ -277,11 +277,12 @@ class ASRClientExecutor(BaseExecutor):
lang=lang,
audio_format=audio_format)
time_end = time.time()
logger.info(res.json())
logger.info(res)
logger.info("Response time %f s." % (time_end - time_start))
return True
except Exception as e:
logger.error("Failed to speech recognition.")
logger.error(e)
return False
@stats_wrapper
......@@ -299,9 +300,10 @@ class ASRClientExecutor(BaseExecutor):
logging.info("asr websocket client start")
handler = ASRAudioHandler(server_ip, port)
loop = asyncio.get_event_loop()
loop.run_until_complete(handler.run(input))
res = loop.run_until_complete(handler.run(input))
logging.info("asr websocket client finished")
return res['asr_results']
@cli_client_register(
name='paddlespeech_client.cls', description='visit cls service')
......
paddlespeech/server/conf/ws_application.yaml
浏览文件 @ 26d5dded
......@@ -41,11 +41,7 @@ asr_online:
shift_ms: 40
sample_rate: 16000
sample_width: 2
vad_conf:
aggressiveness: 2
sample_rate: 16000
frame_duration_ms: 20
sample_width: 2
padding_ms: 200
padding_ratio: 0.9
window_n: 7 # frame
shift_n: 4 # frame
window_ms: 20 # ms
shift_ms: 10 # ms
paddlespeech/server/conf/ws_conformer_application.yaml 0 → 100644
浏览文件 @ 26d5dded
# This is the parameter configuration file for PaddleSpeech Serving.
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_online', 'tts_online']
# protocol = ['websocket', 'http'] (only one can be selected).
# websocket only support online engine type.
protocol: 'websocket'
engine_list: ['asr_online']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: online #######################
asr_online:
model_type: 'conformer_online_multicn'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method:
force_yes: True
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
chunk_buffer_conf:
window_n: 7 # frame
shift_n: 4 # frame
window_ms: 25 # ms
shift_ms: 10 # ms
sample_rate: 16000
sample_width: 2
\ No newline at end of file
paddlespeech/server/engine/asr/online/ctc_search.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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 collections import defaultdict
import paddle
from paddlespeech.cli.log import logger
from paddlespeech.s2t.utils.utility import log_add
__all__ = ['CTCPrefixBeamSearch']
class CTCPrefixBeamSearch:
def __init__(self, config):
"""Implement the ctc prefix beam search
Args:
config (yacs.config.CfgNode): _description_
"""
self.config = config
self.reset()
@paddle.no_grad()
def search(self, ctc_probs, device, blank_id=0):
"""ctc prefix beam search method decode a chunk feature
Args:
xs (paddle.Tensor): feature data
ctc_probs (paddle.Tensor): the ctc probability of all the tokens
device (paddle.fluid.core_avx.Place): the feature host device, such as CUDAPlace(0).
blank_id (int, optional): the blank id in the vocab. Defaults to 0.
Returns:
list: the search result
"""
# decode
logger.info("start to ctc prefix search")
batch_size = 1
beam_size = self.config.beam_size
maxlen = ctc_probs.shape[0]
assert len(ctc_probs.shape) == 2
# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
# blank_ending_score and none_blank_ending_score in ln domain
if self.cur_hyps is None:
self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
# 2. CTC beam search step by step
for t in range(0, maxlen):
logp = ctc_probs[t] # (vocab_size,)
# key: prefix, value (pb, pnb), default value(-inf, -inf)
next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
# 2.1 First beam prune: select topk best
# do token passing process
top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,)
for s in top_k_index:
s = s.item()
ps = logp[s].item()
for prefix, (pb, pnb) in self.cur_hyps:
last = prefix[-1] if len(prefix) > 0 else None
if s == blank_id: # blank
n_pb, n_pnb = next_hyps[prefix]
n_pb = log_add([n_pb, pb + ps, pnb + ps])
next_hyps[prefix] = (n_pb, n_pnb)
elif s == last:
# Update *ss -> *s;
n_pb, n_pnb = next_hyps[prefix]
n_pnb = log_add([n_pnb, pnb + ps])
next_hyps[prefix] = (n_pb, n_pnb)
# Update *s-s -> *ss, - is for blank
n_prefix = prefix + (s, )
n_pb, n_pnb = next_hyps[n_prefix]
n_pnb = log_add([n_pnb, pb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb)
else:
n_prefix = prefix + (s, )
n_pb, n_pnb = next_hyps[n_prefix]
n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb)
# 2.2 Second beam prune
next_hyps = sorted(
next_hyps.items(),
key=lambda x: log_add(list(x[1])),
reverse=True)
self.cur_hyps = next_hyps[:beam_size]
self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
logger.info("ctc prefix search success")
return self.hyps
def get_one_best_hyps(self):
"""Return the one best result
Returns:
list: the one best result
"""
return [self.hyps[0][0]]
def get_hyps(self):
"""Return the search hyps
Returns:
list: return the search hyps
"""
return self.hyps
def reset(self):
"""Rest the search cache value
"""
self.cur_hyps = None
self.hyps = None
def finalize_search(self):
"""do nothing in ctc_prefix_beam_search
"""
pass
paddlespeech/server/tests/__init__.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
paddlespeech/server/tests/asr/__init__.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
paddlespeech/server/tests/asr/offline/__init__.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
paddlespeech/server/tests/asr/online/__init__.py 0 → 100644
浏览文件 @ 26d5dded
# Copyright (c) 2022 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.
paddlespeech/server/tests/asr/online/websocket_client.py
浏览文件 @ 26d5dded
......@@ -34,10 +34,9 @@ class ASRAudioHandler:
def read_wave(self, wavfile_path: str):
samples, sample_rate = soundfile.read(wavfile_path, dtype='int16')
x_len = len(samples)
# chunk_stride = 40 * 16 #40ms, sample_rate = 16kHz
chunk_size = 80 * 16 #80ms, sample_rate = 16kHz
if x_len % chunk_size != 0:
chunk_size = 85 * 16 #80ms, sample_rate = 16kHz
if x_len % chunk_size!= 0:
padding_len_x = chunk_size - x_len % chunk_size
else:
padding_len_x = 0
......@@ -48,7 +47,6 @@ class ASRAudioHandler:
assert (x_len + padding_len_x) % chunk_size == 0
num_chunk = (x_len + padding_len_x) / chunk_size
num_chunk = int(num_chunk)
for i in range(0, num_chunk):
start = i * chunk_size
end = start + chunk_size
......@@ -57,7 +55,11 @@ class ASRAudioHandler:
async def run(self, wavfile_path: str):
logging.info("send a message to the server")
# self.read_wave()
# send websocket handshake protocal
async with websockets.connect(self.url) as ws:
# server has already received handshake protocal
# client start to send the command
audio_info = json.dumps(
{
"name": "test.wav",
......@@ -78,7 +80,6 @@ class ASRAudioHandler:
msg = json.loads(msg)
logging.info("receive msg={}".format(msg))
result = msg
# finished
audio_info = json.dumps(
{
......@@ -91,10 +92,12 @@ class ASRAudioHandler:
separators=(',', ': '))
await ws.send(audio_info)
msg = await ws.recv()
# decode the bytes to str
msg = json.loads(msg)
logging.info("receive msg={}".format(msg))
return result
logging.info("final receive msg={}".format(msg))
result = msg
return result
def main(args):
......
paddlespeech/server/utils/buffer.py
浏览文件 @ 26d5dded
......@@ -63,12 +63,12 @@ class ChunkBuffer(object):
the sample rate.
Yields Frames of the requested duration.
"""
audio = self.remained_audio + audio
self.remained_audio = b''
offset = 0
timestamp = 0.0
while offset + self.window_bytes <= len(audio):
yield Frame(audio[offset:offset + self.window_bytes], timestamp,
self.window_sec)
......
paddlespeech/server/utils/util.py
浏览文件 @ 26d5dded
......@@ -52,6 +52,10 @@ def get_chunks(data, block_size, pad_size, step):
Returns:
list: chunks list
"""
if block_size == -1:
return [data]
if step == "am":
data_len = data.shape[1]
elif step == "voc":
......
paddlespeech/server/ws/asr_socket.py
浏览文件 @ 26d5dded
......@@ -13,12 +13,12 @@
# limitations under the License.
import json
import numpy as np
from fastapi import APIRouter
from fastapi import WebSocket
from fastapi import WebSocketDisconnect
from starlette.websockets import WebSocketState as WebSocketState
from paddlespeech.server.engine.asr.online.asr_engine import PaddleASRConnectionHanddler
from paddlespeech.server.engine.engine_pool import get_engine_pool
from paddlespeech.server.utils.buffer import ChunkBuffer
from paddlespeech.server.utils.vad import VADAudio
......@@ -28,26 +28,29 @@ router = APIRouter()
@router.websocket('/ws/asr')
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
engine_pool = get_engine_pool()
asr_engine = engine_pool['asr']
connection_handler = None
# init buffer
# each websocekt connection has its own chunk buffer
chunk_buffer_conf = asr_engine.config.chunk_buffer_conf
chunk_buffer = ChunkBuffer(
window_n=7,
shift_n=4,
window_ms=20,
shift_ms=10,
sample_rate=chunk_buffer_conf['sample_rate'],
sample_width=chunk_buffer_conf['sample_width'])
window_n=chunk_buffer_conf.window_n,
shift_n=chunk_buffer_conf.shift_n,
window_ms=chunk_buffer_conf.window_ms,
shift_ms=chunk_buffer_conf.shift_ms,
sample_rate=chunk_buffer_conf.sample_rate,
sample_width=chunk_buffer_conf.sample_width)
# init vad
vad_conf = asr_engine.config.vad_conf
vad = VADAudio(
aggressiveness=vad_conf['aggressiveness'],
rate=vad_conf['sample_rate'],
frame_duration_ms=vad_conf['frame_duration_ms'])
vad_conf = asr_engine.config.get('vad_conf', None)
if vad_conf:
vad = VADAudio(
aggressiveness=vad_conf['aggressiveness'],
rate=vad_conf['sample_rate'],
frame_duration_ms=vad_conf['frame_duration_ms'])
try:
while True:
......@@ -64,13 +67,21 @@ async def websocket_endpoint(websocket: WebSocket):
if message['signal'] == 'start':
resp = {"status": "ok", "signal": "server_ready"}
# do something at begining here
# create the instance to process the audio
connection_handler = PaddleASRConnectionHanddler(asr_engine)
await websocket.send_json(resp)
elif message['signal'] == 'end':
engine_pool = get_engine_pool()
asr_engine = engine_pool['asr']
# reset single engine for an new connection
asr_engine.reset()
resp = {"status": "ok", "signal": "finished"}
connection_handler.decode(is_finished=True)
connection_handler.rescoring()
asr_results = connection_handler.get_result()
connection_handler.reset()
resp = {
"status": "ok",
"signal": "finished",
'asr_results': asr_results
}
await websocket.send_json(resp)
break
else:
......@@ -79,21 +90,11 @@ async def websocket_endpoint(websocket: WebSocket):
elif "bytes" in message:
message = message["bytes"]
engine_pool = get_engine_pool()
asr_engine = engine_pool['asr']
asr_results = ""
frames = chunk_buffer.frame_generator(message)
for frame in frames:
samples = np.frombuffer(frame.bytes, dtype=np.int16)
sample_rate = asr_engine.config.sample_rate
x_chunk, x_chunk_lens = asr_engine.preprocess(samples,
sample_rate)
asr_engine.run(x_chunk, x_chunk_lens)
asr_results = asr_engine.postprocess()
connection_handler.extract_feat(message)
connection_handler.decode(is_finished=False)
asr_results = connection_handler.get_result()
asr_results = asr_engine.postprocess()
resp = {'asr_results': asr_results}
await websocket.send_json(resp)
except WebSocketDisconnect:
pass
speechx/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -63,7 +63,8 @@ include(libsndfile)
# include(boost) # not work
set(boost_SOURCE_DIR ${fc_patch}/boost-src)
set(BOOST_ROOT ${boost_SOURCE_DIR})
# #find_package(boost REQUIRED PATHS ${BOOST_ROOT})
include_directories(${boost_SOURCE_DIR})
link_directories(${boost_SOURCE_DIR}/stage/lib)
# Eigen
include(eigen)
......@@ -141,4 +142,4 @@ set(DEPS ${DEPS}
set(SPEECHX_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/speechx)
add_subdirectory(speechx)
add_subdirectory(examples)
\ No newline at end of file
add_subdirectory(examples)
speechx/examples/ds2_ol/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -2,4 +2,5 @@ cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
add_subdirectory(feat)
add_subdirectory(nnet)
add_subdirectory(decoder)
\ No newline at end of file
add_subdirectory(decoder)
add_subdirectory(websocket)
speechx/examples/ds2_ol/aishell/path.sh
浏览文件 @ 26d5dded
# This contains the locations of binarys build required for running the examples.
SPEECHX_ROOT=$PWD/../../../
SPEECHX_ROOT=$PWD/../../..
SPEECHX_EXAMPLES=$SPEECHX_ROOT/build/examples
SPEECHX_TOOLS=$SPEECHX_ROOT/tools
......@@ -10,5 +10,5 @@ TOOLS_BIN=$SPEECHX_TOOLS/valgrind/install/bin
export LC_AL=C
SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat
export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
\ No newline at end of file
SPEECHX_BIN=$SPEECHX_EXAMPLES/ds2_ol/decoder:$SPEECHX_EXAMPLES/ds2_ol/feat:$SPEECHX_EXAMPLES/ds2_ol/websocket
export PATH=$PATH:$SPEECHX_BIN:$TOOLS_BIN
speechx/examples/ds2_ol/aishell/run.sh
浏览文件 @ 26d5dded
......@@ -86,7 +86,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--result_wspecifier=ark,t:$data/split${nj}/JOB/result
......@@ -101,7 +101,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
ctc-prefix-beam-search-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--params_path=$model_dir/avg_1.jit.pdiparams \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--dict_file=$vocb_dir/vocab.txt \
--lm_path=$lm \
......@@ -128,7 +128,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
wfst-decoder-ol \
--feature_rspecifier=scp:$data/split${nj}/JOB/feat.scp \
--model_path=$model_dir/avg_1.jit.pdmodel \
--param_path=$model_dir/avg_1.jit.pdiparams \
--params_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$graph_dir/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
......@@ -137,4 +137,4 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
cat $data/split${nj}/*/result_tlg > $exp/${label_file}_tlg
utils/compute-wer.py --char=1 --v=1 $exp/${label_file}_tlg $text > $exp/${wer}_tlg
fi
\ No newline at end of file
fi
speechx/examples/ds2_ol/aishell/websocket_client.sh 0 → 100644
浏览文件 @ 26d5dded
#!/bin/bash
set +x
set -e
. path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# input
mkdir -p data
data=$PWD/data
ckpt_dir=$data/model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
vocb_dir=$ckpt_dir/data/lang_char
# output
aishell_wav_scp=aishell_test.scp
if [ ! -d $data/test ]; then
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
popd
realpath $data/test/*/*.wav > $data/wavlist
awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
fi
export GLOG_logtostderr=1
# websocket client
websocket_client_main \
--wav_rspecifier=scp:$data/$aishell_wav_scp --streaming_chunk=0.36
speechx/examples/ds2_ol/aishell/websocket_server.sh 0 → 100644
浏览文件 @ 26d5dded
#!/bin/bash
set +x
set -e
. path.sh
# 1. compile
if [ ! -d ${SPEECHX_EXAMPLES} ]; then
pushd ${SPEECHX_ROOT}
bash build.sh
popd
fi
# input
mkdir -p data
data=$PWD/data
ckpt_dir=$data/model
model_dir=$ckpt_dir/exp/deepspeech2_online/checkpoints/
vocb_dir=$ckpt_dir/data/lang_char/
# output
aishell_wav_scp=aishell_test.scp
if [ ! -d $data/test ]; then
pushd $data
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_test.zip
unzip aishell_test.zip
popd
realpath $data/test/*/*.wav > $data/wavlist
awk -F '/' '{ print $(NF) }' $data/wavlist | awk -F '.' '{ print $1 }' > $data/utt_id
paste $data/utt_id $data/wavlist > $data/$aishell_wav_scp
fi
if [ ! -d $ckpt_dir ]; then
mkdir -p $ckpt_dir
wget -P $ckpt_dir -c https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz
tar xzfv $ckpt_dir/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz -C $ckpt_dir
fi
export GLOG_logtostderr=1
# 3. gen cmvn
cmvn=$PWD/cmvn.ark
cmvn-json2kaldi --json_file=$ckpt_dir/data/mean_std.json --cmvn_write_path=$cmvn
text=$data/test/text
graph_dir=./aishell_graph
if [ ! -d $graph_dir ]; then
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/aishell_graph.zip
unzip aishell_graph.zip
fi
# 5. test websocket server
websocket_server_main \
--cmvn_file=$cmvn \
--model_path=$model_dir/avg_1.jit.pdmodel \
--streaming_chunk=0.1 \
--convert2PCM32=true \
--params_path=$model_dir/avg_1.jit.pdiparams \
--word_symbol_table=$graph_dir/words.txt \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
--acoustic_scale=1.2
speechx/examples/ds2_ol/decoder/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -17,3 +17,6 @@ add_executable(${bin_name} ${CMAKE_CURRENT_SOURCE_DIR}/${bin_name}.cc)
target_include_directories(${bin_name} PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(${bin_name} PUBLIC nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util ${DEPS})
add_executable(recognizer_test_main ${CMAKE_CURRENT_SOURCE_DIR}/recognizer_test_main.cc)
target_include_directories(recognizer_test_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(recognizer_test_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder ${DEPS})
speechx/examples/ds2_ol/decoder/ctc-prefix-beam-search-decoder-ol.cc
浏览文件 @ 26d5dded
......@@ -34,12 +34,10 @@ DEFINE_int32(receptive_field_length,
DEFINE_int32(downsampling_rate,
4,
"two CNN(kernel=5) module downsampling rate.");
DEFINE_string(
model_input_names,
"audio_chunk,audio_chunk_lens,chunk_state_h_box,chunk_state_c_box",
"model input names");
DEFINE_string(model_output_names,
"softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0",
"save_infer_model/scale_0.tmp_1,save_infer_model/"
"scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
"scale_3.tmp_1",
"model output names");
DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
......@@ -58,12 +56,11 @@ int main(int argc, char* argv[]) {
kaldi::SequentialBaseFloatMatrixReader feature_reader(
FLAGS_feature_rspecifier);
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
std::string model_graph = FLAGS_model_path;
std::string model_path = FLAGS_model_path;
std::string model_params = FLAGS_param_path;
std::string dict_file = FLAGS_dict_file;
std::string lm_path = FLAGS_lm_path;
LOG(INFO) << "model path: " << model_graph;
LOG(INFO) << "model path: " << model_path;
LOG(INFO) << "model param: " << model_params;
LOG(INFO) << "dict path: " << dict_file;
LOG(INFO) << "lm path: " << lm_path;
......@@ -76,10 +73,9 @@ int main(int argc, char* argv[]) {
ppspeech::CTCBeamSearch decoder(opts);
ppspeech::ModelOptions model_opts;
model_opts.model_path = model_graph;
model_opts.model_path = model_path;
model_opts.params_path = model_params;
model_opts.cache_shape = FLAGS_model_cache_names;
model_opts.input_names = FLAGS_model_input_names;
model_opts.output_names = FLAGS_model_output_names;
std::shared_ptr<ppspeech::PaddleNnet> nnet(
new ppspeech::PaddleNnet(model_opts));
......@@ -125,7 +121,6 @@ int main(int argc, char* argv[]) {
if (feature_chunk_size < receptive_field_length) break;
int32 start = chunk_idx * chunk_stride;
int32 end = start + chunk_size;
for (int row_id = 0; row_id < chunk_size; ++row_id) {
kaldi::SubVector<kaldi::BaseFloat> tmp(feature, start);
......
speechx/examples/ds2_ol/decoder/recognizer_test_main.cc 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#include "decoder/recognizer.h"
#include "decoder/param.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/table-types.h"
DEFINE_string(wav_rspecifier, "", "test feature rspecifier");
DEFINE_string(result_wspecifier, "", "test result wspecifier");
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure();
ppspeech::Recognizer recognizer(resource);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
FLAGS_wav_rspecifier);
kaldi::TokenWriter result_writer(FLAGS_result_wspecifier);
int sample_rate = 16000;
float streaming_chunk = FLAGS_streaming_chunk;
int chunk_sample_size = streaming_chunk * sample_rate;
LOG(INFO) << "sr: " << sample_rate;
LOG(INFO) << "chunk size (s): " << streaming_chunk;
LOG(INFO) << "chunk size (sample): " << chunk_sample_size;
int32 num_done = 0, num_err = 0;
for (; !wav_reader.Done(); wav_reader.Next()) {
std::string utt = wav_reader.Key();
const kaldi::WaveData& wave_data = wav_reader.Value();
int32 this_channel = 0;
kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
this_channel);
int tot_samples = waveform.Dim();
LOG(INFO) << "wav len (sample): " << tot_samples;
int sample_offset = 0;
std::vector<kaldi::Vector<BaseFloat>> feats;
int feature_rows = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size =
std::min(chunk_sample_size, tot_samples - sample_offset);
kaldi::Vector<kaldi::BaseFloat> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk(i) = waveform(sample_offset + i);
}
recognizer.Accept(wav_chunk);
if (cur_chunk_size < chunk_sample_size) {
recognizer.SetFinished();
}
recognizer.Decode();
sample_offset += cur_chunk_size;
}
std::string result;
result = recognizer.GetFinalResult();
recognizer.Reset();
if (result.empty()) {
// the TokenWriter can not write empty string.
++num_err;
KALDI_LOG << " the result of " << utt << " is empty";
continue;
}
KALDI_LOG << " the result of " << utt << " is " << result;
result_writer.Write(utt, result);
++num_done;
}
}
\ No newline at end of file
speechx/examples/ds2_ol/feat/cmvn-json2kaldi.cc
浏览文件 @ 26d5dded
......@@ -73,9 +73,9 @@ int main(int argc, char* argv[]) {
LOG(INFO) << "cmvn stats have write into: " << FLAGS_cmvn_write_path;
LOG(INFO) << "Binary: " << FLAGS_binary;
} catch (simdjson::simdjson_error& err) {
LOG(ERR) << err.what();
LOG(ERROR) << err.what();
}
return 0;
}
\ No newline at end of file
}
speechx/examples/ds2_ol/feat/linear-spectrogram-wo-db-norm-ol.cc
浏览文件 @ 26d5dded
......@@ -32,7 +32,6 @@ DEFINE_string(feature_wspecifier, "", "output feats wspecifier");
DEFINE_string(cmvn_file, "./cmvn.ark", "read cmvn");
DEFINE_double(streaming_chunk, 0.36, "streaming feature chunk size");
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
......@@ -66,7 +65,8 @@ int main(int argc, char* argv[]) {
std::unique_ptr<ppspeech::FrontendInterface> cmvn(
new ppspeech::CMVN(FLAGS_cmvn_file, std::move(linear_spectrogram)));
ppspeech::FeatureCache feature_cache(kint16max, std::move(cmvn));
ppspeech::FeatureCacheOptions feat_cache_opts;
ppspeech::FeatureCache feature_cache(feat_cache_opts, std::move(cmvn));
LOG(INFO) << "feat dim: " << feature_cache.Dim();
int sample_rate = 16000;
......
speechx/examples/ds2_ol/websocket/CMakeLists.txt 0 → 100644
浏览文件 @ 26d5dded
cmake_minimum_required(VERSION 3.14 FATAL_ERROR)
add_executable(websocket_server_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_server_main.cc)
target_include_directories(websocket_server_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(websocket_server_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS})
add_executable(websocket_client_main ${CMAKE_CURRENT_SOURCE_DIR}/websocket_client_main.cc)
target_include_directories(websocket_client_main PRIVATE ${SPEECHX_ROOT} ${SPEECHX_ROOT}/kaldi)
target_link_libraries(websocket_client_main PUBLIC frontend kaldi-feat-common nnet decoder fst utils gflags glog kaldi-base kaldi-matrix kaldi-util kaldi-decoder websocket ${DEPS})
speechx/examples/ds2_ol/websocket/websocket_client_main.cc 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#include "websocket/websocket_client.h"
#include "kaldi/feat/wave-reader.h"
#include "kaldi/util/kaldi-io.h"
#include "kaldi/util/table-types.h"
DEFINE_string(host, "127.0.0.1", "host of websocket server");
DEFINE_int32(port, 201314, "port of websocket server");
DEFINE_string(wav_rspecifier, "", "test wav scp path");
DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size");
using kaldi::int16;
int main(int argc, char* argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
ppspeech::WebSocketClient client(FLAGS_host, FLAGS_port);
kaldi::SequentialTableReader<kaldi::WaveHolder> wav_reader(
FLAGS_wav_rspecifier);
const int sample_rate = 16000;
const float streaming_chunk = FLAGS_streaming_chunk;
const int chunk_sample_size = streaming_chunk * sample_rate;
for (; !wav_reader.Done(); wav_reader.Next()) {
client.SendStartSignal();
std::string utt = wav_reader.Key();
const kaldi::WaveData& wave_data = wav_reader.Value();
CHECK_EQ(wave_data.SampFreq(), sample_rate);
int32 this_channel = 0;
kaldi::SubVector<kaldi::BaseFloat> waveform(wave_data.Data(),
this_channel);
const int tot_samples = waveform.Dim();
int sample_offset = 0;
while (sample_offset < tot_samples) {
int cur_chunk_size =
std::min(chunk_sample_size, tot_samples - sample_offset);
std::vector<int16> wav_chunk(cur_chunk_size);
for (int i = 0; i < cur_chunk_size; ++i) {
wav_chunk[i] = static_cast<int16>(waveform(sample_offset + i));
}
client.SendBinaryData(wav_chunk.data(),
wav_chunk.size() * sizeof(int16));
sample_offset += cur_chunk_size;
LOG(INFO) << "Send " << cur_chunk_size << " samples";
std::this_thread::sleep_for(
std::chrono::milliseconds(static_cast<int>(1 * 1000)));
if (cur_chunk_size < chunk_sample_size) {
client.SendEndSignal();
}
}
while (!client.Done()) {
}
std::string result = client.GetResult();
LOG(INFO) << "utt: " << utt << " " << result;
client.Join();
return 0;
}
return 0;
}
speechx/examples/ds2_ol/websocket/websocket_server_main.cc 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#include "websocket/websocket_server.h"
#include "decoder/param.h"
DEFINE_int32(port, 201314, "websocket listening port");
int main(int argc, char *argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, false);
google::InitGoogleLogging(argv[0]);
ppspeech::RecognizerResource resource = ppspeech::InitRecognizerResoure();
ppspeech::WebSocketServer server(FLAGS_port, resource);
LOG(INFO) << "Listening at port " << FLAGS_port;
server.Start();
return 0;
}
speechx/speechx/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -30,4 +30,10 @@ include_directories(
${CMAKE_CURRENT_SOURCE_DIR}
${CMAKE_CURRENT_SOURCE_DIR}/decoder
)
add_subdirectory(decoder)
\ No newline at end of file
add_subdirectory(decoder)
include_directories(
${CMAKE_CURRENT_SOURCE_DIR}
${CMAKE_CURRENT_SOURCE_DIR}/websocket
)
add_subdirectory(websocket)
speechx/speechx/base/common.h
浏览文件 @ 26d5dded
......@@ -28,8 +28,10 @@
#include <sstream>
#include <stack>
#include <string>
#include <thread>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "base/basic_types.h"
......
speechx/speechx/decoder/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -7,5 +7,6 @@ add_library(decoder STATIC
ctc_decoders/path_trie.cpp
ctc_decoders/scorer.cpp
ctc_tlg_decoder.cc
recognizer.cc
)
target_link_libraries(decoder PUBLIC kenlm utils fst)
target_link_libraries(decoder PUBLIC kenlm utils fst frontend nnet kaldi-decoder)
speechx/speechx/decoder/ctc_tlg_decoder.cc
浏览文件 @ 26d5dded
......@@ -33,7 +33,6 @@ void TLGDecoder::InitDecoder() {
void TLGDecoder::AdvanceDecode(
const std::shared_ptr<kaldi::DecodableInterface>& decodable) {
while (!decodable->IsLastFrame(frame_decoded_size_)) {
LOG(INFO) << "num frame decode: " << frame_decoded_size_;
AdvanceDecoding(decodable.get());
}
}
......@@ -63,4 +62,4 @@ std::string TLGDecoder::GetFinalBestPath() {
}
return words;
}
}
\ No newline at end of file
}
speechx/speechx/decoder/param.h 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#pragma once
#include "base/common.h"
#include "decoder/ctc_beam_search_decoder.h"
#include "decoder/ctc_tlg_decoder.h"
#include "frontend/audio/feature_pipeline.h"
DEFINE_string(cmvn_file, "", "read cmvn");
DEFINE_double(streaming_chunk, 0.1, "streaming feature chunk size");
DEFINE_bool(convert2PCM32, true, "audio convert to pcm32");
DEFINE_string(model_path, "avg_1.jit.pdmodel", "paddle nnet model");
DEFINE_string(params_path, "avg_1.jit.pdiparams", "paddle nnet model param");
DEFINE_string(word_symbol_table, "words.txt", "word symbol table");
DEFINE_string(graph_path, "TLG", "decoder graph");
DEFINE_double(acoustic_scale, 1.0, "acoustic scale");
DEFINE_int32(max_active, 7500, "max active");
DEFINE_double(beam, 15.0, "decoder beam");
DEFINE_double(lattice_beam, 7.5, "decoder beam");
DEFINE_int32(receptive_field_length,
7,
"receptive field of two CNN(kernel=5) downsampling module.");
DEFINE_int32(downsampling_rate,
4,
"two CNN(kernel=5) module downsampling rate.");
DEFINE_string(model_output_names,
"save_infer_model/scale_0.tmp_1,save_infer_model/"
"scale_1.tmp_1,save_infer_model/scale_2.tmp_1,save_infer_model/"
"scale_3.tmp_1",
"model output names");
DEFINE_string(model_cache_names, "5-1-1024,5-1-1024", "model cache names");
namespace ppspeech {
// todo refactor later
FeaturePipelineOptions InitFeaturePipelineOptions() {
FeaturePipelineOptions opts;
opts.cmvn_file = FLAGS_cmvn_file;
opts.linear_spectrogram_opts.streaming_chunk = FLAGS_streaming_chunk;
opts.convert2PCM32 = FLAGS_convert2PCM32;
kaldi::FrameExtractionOptions frame_opts;
frame_opts.frame_length_ms = 20;
frame_opts.frame_shift_ms = 10;
frame_opts.remove_dc_offset = false;
frame_opts.window_type = "hanning";
frame_opts.preemph_coeff = 0.0;
frame_opts.dither = 0.0;
opts.linear_spectrogram_opts.frame_opts = frame_opts;
opts.feature_cache_opts.frame_chunk_size = FLAGS_receptive_field_length;
opts.feature_cache_opts.frame_chunk_stride = FLAGS_downsampling_rate;
return opts;
}
ModelOptions InitModelOptions() {
ModelOptions model_opts;
model_opts.model_path = FLAGS_model_path;
model_opts.params_path = FLAGS_params_path;
model_opts.cache_shape = FLAGS_model_cache_names;
model_opts.output_names = FLAGS_model_output_names;
return model_opts;
}
TLGDecoderOptions InitDecoderOptions() {
TLGDecoderOptions decoder_opts;
decoder_opts.word_symbol_table = FLAGS_word_symbol_table;
decoder_opts.fst_path = FLAGS_graph_path;
decoder_opts.opts.max_active = FLAGS_max_active;
decoder_opts.opts.beam = FLAGS_beam;
decoder_opts.opts.lattice_beam = FLAGS_lattice_beam;
return decoder_opts;
}
RecognizerResource InitRecognizerResoure() {
RecognizerResource resource;
resource.acoustic_scale = FLAGS_acoustic_scale;
resource.feature_pipeline_opts = InitFeaturePipelineOptions();
resource.model_opts = InitModelOptions();
resource.tlg_opts = InitDecoderOptions();
return resource;
}
}
\ No newline at end of file
speechx/speechx/decoder/recognizer.cc 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#include "decoder/recognizer.h"
namespace ppspeech {
using kaldi::Vector;
using kaldi::VectorBase;
using kaldi::BaseFloat;
using std::vector;
using kaldi::SubVector;
using std::unique_ptr;
Recognizer::Recognizer(const RecognizerResource& resource) {
// resource_ = resource;
const FeaturePipelineOptions& feature_opts = resource.feature_pipeline_opts;
feature_pipeline_.reset(new FeaturePipeline(feature_opts));
std::shared_ptr<PaddleNnet> nnet(new PaddleNnet(resource.model_opts));
BaseFloat ac_scale = resource.acoustic_scale;
decodable_.reset(new Decodable(nnet, feature_pipeline_, ac_scale));
decoder_.reset(new TLGDecoder(resource.tlg_opts));
input_finished_ = false;
}
void Recognizer::Accept(const Vector<BaseFloat>& waves) {
feature_pipeline_->Accept(waves);
}
void Recognizer::Decode() { decoder_->AdvanceDecode(decodable_); }
std::string Recognizer::GetFinalResult() {
return decoder_->GetFinalBestPath();
}
void Recognizer::SetFinished() {
feature_pipeline_->SetFinished();
input_finished_ = true;
}
bool Recognizer::IsFinished() { return input_finished_; }
void Recognizer::Reset() {
feature_pipeline_->Reset();
decodable_->Reset();
decoder_->Reset();
}
} // namespace ppspeech
\ No newline at end of file
speechx/speechx/decoder/recognizer.h 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
// todo refactor later (SGoat)
#pragma once
#include "decoder/ctc_beam_search_decoder.h"
#include "decoder/ctc_tlg_decoder.h"
#include "frontend/audio/feature_pipeline.h"
#include "nnet/decodable.h"
#include "nnet/paddle_nnet.h"
namespace ppspeech {
struct RecognizerResource {
FeaturePipelineOptions feature_pipeline_opts;
ModelOptions model_opts;
TLGDecoderOptions tlg_opts;
// CTCBeamSearchOptions beam_search_opts;
kaldi::BaseFloat acoustic_scale;
RecognizerResource()
: acoustic_scale(1.0),
feature_pipeline_opts(),
model_opts(),
tlg_opts() {}
};
class Recognizer {
public:
explicit Recognizer(const RecognizerResource& resouce);
void Accept(const kaldi::Vector<kaldi::BaseFloat>& waves);
void Decode();
std::string GetFinalResult();
void SetFinished();
bool IsFinished();
void Reset();
private:
// std::shared_ptr<RecognizerResource> resource_;
// RecognizerResource resource_;
std::shared_ptr<FeaturePipeline> feature_pipeline_;
std::shared_ptr<Decodable> decodable_;
std::unique_ptr<TLGDecoder> decoder_;
bool input_finished_;
};
} // namespace ppspeech
\ No newline at end of file
speechx/speechx/frontend/audio/CMakeLists.txt
浏览文件 @ 26d5dded
......@@ -6,6 +6,7 @@ add_library(frontend STATIC
linear_spectrogram.cc
audio_cache.cc
feature_cache.cc
feature_pipeline.cc
)
target_link_libraries(frontend PUBLIC kaldi-matrix)
\ No newline at end of file
target_link_libraries(frontend PUBLIC kaldi-matrix kaldi-feat-common)
speechx/speechx/frontend/audio/audio_cache.cc
浏览文件 @ 26d5dded
......@@ -41,7 +41,7 @@ void AudioCache::Accept(const VectorBase<BaseFloat>& waves) {
ready_feed_condition_.wait(lock);
}
for (size_t idx = 0; idx < waves.Dim(); ++idx) {
int32 buffer_idx = (idx + offset_) % ring_buffer_.size();
int32 buffer_idx = (idx + offset_ + size_) % ring_buffer_.size();
ring_buffer_[buffer_idx] = waves(idx);
if (convert2PCM32_)
ring_buffer_[buffer_idx] = Convert2PCM32(waves(idx));
......
speechx/speechx/frontend/audio/audio_cache.h
浏览文件 @ 26d5dded
......@@ -24,7 +24,7 @@ namespace ppspeech {
class AudioCache : public FrontendInterface {
public:
explicit AudioCache(int buffer_size = 1000 * kint16max,
bool convert2PCM32 = false);
bool convert2PCM32 = true);
virtual void Accept(const kaldi::VectorBase<BaseFloat>& waves);
......
speechx/speechx/frontend/audio/feature_cache.cc
浏览文件 @ 26d5dded
......@@ -23,10 +23,13 @@ using std::vector;
using kaldi::SubVector;
using std::unique_ptr;
FeatureCache::FeatureCache(int max_size,
FeatureCache::FeatureCache(FeatureCacheOptions opts,
unique_ptr<FrontendInterface> base_extractor) {
max_size_ = max_size;
max_size_ = opts.max_size;
frame_chunk_stride_ = opts.frame_chunk_stride;
frame_chunk_size_ = opts.frame_chunk_size;
base_extractor_ = std::move(base_extractor);
dim_ = base_extractor_->Dim();
}
void FeatureCache::Accept(const kaldi::VectorBase<kaldi::BaseFloat>& inputs) {
......@@ -44,13 +47,14 @@ bool FeatureCache::Read(kaldi::Vector<kaldi::BaseFloat>* feats) {
std::unique_lock<std::mutex> lock(mutex_);
while (cache_.empty() && base_extractor_->IsFinished() == false) {
ready_read_condition_.wait(lock);
BaseFloat elapsed = timer.Elapsed() * 1000;
// todo replace 1.0 with timeout_
if (elapsed > 1.0) {
// todo refactor: wait
// ready_read_condition_.wait(lock);
int32 elapsed = static_cast<int32>(timer.Elapsed() * 1000);
// todo replace 1 with timeout_, 1 ms
if (elapsed > 1) {
return false;
}
usleep(1000); // sleep 1 ms
usleep(100); // sleep 0.1 ms
}
if (cache_.empty()) return false;
feats->Resize(cache_.front().Dim());
......@@ -63,25 +67,41 @@ bool FeatureCache::Read(kaldi::Vector<kaldi::BaseFloat>* feats) {
// read all data from base_feature_extractor_ into cache_
bool FeatureCache::Compute() {
// compute and feed
Vector<BaseFloat> feature_chunk;
bool result = base_extractor_->Read(&feature_chunk);
Vector<BaseFloat> feature;
bool result = base_extractor_->Read(&feature);
if (result == false || feature.Dim() == 0) return false;
int32 joint_len = feature.Dim() + remained_feature_.Dim();
int32 num_chunk =
((joint_len / dim_) - frame_chunk_size_) / frame_chunk_stride_ + 1;
std::unique_lock<std::mutex> lock(mutex_);
while (cache_.size() >= max_size_) {
ready_feed_condition_.wait(lock);
}
Vector<BaseFloat> joint_feature(joint_len);
joint_feature.Range(0, remained_feature_.Dim())
.CopyFromVec(remained_feature_);
joint_feature.Range(remained_feature_.Dim(), feature.Dim())
.CopyFromVec(feature);
// feed cache
if (feature_chunk.Dim() != 0) {
for (int chunk_idx = 0; chunk_idx < num_chunk; ++chunk_idx) {
int32 start = chunk_idx * frame_chunk_stride_ * dim_;
Vector<BaseFloat> feature_chunk(frame_chunk_size_ * dim_);
SubVector<BaseFloat> tmp(joint_feature.Data() + start,
frame_chunk_size_ * dim_);
feature_chunk.CopyFromVec(tmp);
std::unique_lock<std::mutex> lock(mutex_);
while (cache_.size() >= max_size_) {
ready_feed_condition_.wait(lock);
}
// feed cache
cache_.push(feature_chunk);
ready_read_condition_.notify_one();
}
ready_read_condition_.notify_one();
int32 remained_feature_len =
joint_len - num_chunk * frame_chunk_stride_ * dim_;
remained_feature_.Resize(remained_feature_len);
remained_feature_.CopyFromVec(joint_feature.Range(
frame_chunk_stride_ * num_chunk * dim_, remained_feature_len));
return result;
}
void Reset() {
// std::lock_guard<std::mutex> lock(mutex_);
return;
}
} // namespace ppspeech
\ No newline at end of file
speechx/speechx/frontend/audio/feature_cache.h
浏览文件 @ 26d5dded
......@@ -19,10 +19,18 @@
namespace ppspeech {
struct FeatureCacheOptions {
int32 max_size;
int32 frame_chunk_size;
int32 frame_chunk_stride;
FeatureCacheOptions()
: max_size(kint16max), frame_chunk_size(1), frame_chunk_stride(1) {}
};
class FeatureCache : public FrontendInterface {
public:
explicit FeatureCache(
int32 max_size = kint16max,
FeatureCacheOptions opts,
std::unique_ptr<FrontendInterface> base_extractor = NULL);
// Feed feats or waves
......@@ -32,12 +40,15 @@ class FeatureCache : public FrontendInterface {
virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* feats);
// feat dim
virtual size_t Dim() const { return base_extractor_->Dim(); }
virtual size_t Dim() const { return dim_; }
virtual void SetFinished() {
// std::unique_lock<std::mutex> lock(mutex_);
base_extractor_->SetFinished();
LOG(INFO) << "set finished";
// read the last chunk data
Compute();
// ready_feed_condition_.notify_one();
}
virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
......@@ -52,9 +63,13 @@ class FeatureCache : public FrontendInterface {
private:
bool Compute();
int32 dim_;
size_t max_size_;
std::unique_ptr<FrontendInterface> base_extractor_;
int32 frame_chunk_size_;
int32 frame_chunk_stride_;
kaldi::Vector<kaldi::BaseFloat> remained_feature_;
std::unique_ptr<FrontendInterface> base_extractor_;
std::mutex mutex_;
std::queue<kaldi::Vector<BaseFloat>> cache_;
std::condition_variable ready_feed_condition_;
......
speechx/speechx/frontend/audio/feature_pipeline.cc 0 → 100644
浏览文件 @ 26d5dded
// Copyright (c) 2022 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.
#include "frontend/audio/feature_pipeline.h"
namespace ppspeech {
using std::unique_ptr;
FeaturePipeline::FeaturePipeline(const FeaturePipelineOptions& opts) {
unique_ptr<FrontendInterface> data_source(
new ppspeech::AudioCache(1000 * kint16max, opts.convert2PCM32));
unique_ptr<FrontendInterface> linear_spectrogram(
new ppspeech::LinearSpectrogram(opts.linear_spectrogram_opts,
std::move(data_source)));
unique_ptr<FrontendInterface> cmvn(
new ppspeech::CMVN(opts.cmvn_file, std::move(linear_spectrogram)));
base_extractor_.reset(
new ppspeech::FeatureCache(opts.feature_cache_opts, std::move(cmvn)));
}
} // ppspeech
\ No newline at end of file
speechx/speechx/frontend/audio/feature_pipeline.h 0 → 100644
浏览文件 @ 26d5dded
此差异已折叠。
speechx/speechx/nnet/decodable.cc
浏览文件 @ 26d5dded
......@@ -78,7 +78,6 @@ bool Decodable::AdvanceChunk() {
}
int32 nnet_dim = 0;
Vector<BaseFloat> inferences;
Matrix<BaseFloat> nnet_cache_tmp;
nnet_->FeedForward(features, frontend_->Dim(), &inferences, &nnet_dim);
nnet_cache_.Resize(inferences.Dim() / nnet_dim, nnet_dim);
nnet_cache_.CopyRowsFromVec(inferences);
......
tests/unit/cli/test_cli.sh
浏览文件 @ 26d5dded
此差异已折叠。
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