diff --git a/paddlespeech/cli/asr/infer.py b/paddlespeech/cli/asr/infer.py index b12b9f6fce89a44564ed66a4346b10032100a4af..4b63e1e398c9152d6df41f5436f22995d5fc7e00 100644 --- a/paddlespeech/cli/asr/infer.py +++ b/paddlespeech/cli/asr/infer.py @@ -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 +40,14 @@ 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 +57,9 @@ 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 +115,6 @@ 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', @@ -228,10 +134,11 @@ 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) @@ -255,8 +162,8 @@ 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) @@ -274,7 +181,7 @@ class ASRExecutor(BaseExecutor): 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 diff --git a/paddlespeech/cli/asr/pretrained_models.py b/paddlespeech/cli/asr/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..a16c4750d3a6a5aa641192838a9c99ec64a72df2 --- /dev/null +++ b/paddlespeech/cli/asr/pretrained_models.py @@ -0,0 +1,95 @@ +# 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", + "transformer": + "paddlespeech.s2t.models.u2:U2Model", + "wenetspeech": + "paddlespeech.s2t.models.u2:U2Model", +} diff --git a/paddlespeech/cli/cls/infer.py b/paddlespeech/cli/cls/infer.py index f56d8a579c5d85a9376748b482897483e5886115..1f637a8fee69179f0ca5a538ce9c360d1c6cc935 100644 --- a/paddlespeech/cli/cls/infer.py +++ b/paddlespeech/cli/cls/infer.py @@ -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) diff --git a/paddlespeech/cli/cls/pretrained_models.py b/paddlespeech/cli/cls/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..1d66850aa7fa55733c8a0680889906894e235126 --- /dev/null +++ b/paddlespeech/cli/cls/pretrained_models.py @@ -0,0 +1,47 @@ +# 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", +} diff --git a/paddlespeech/cli/executor.py b/paddlespeech/cli/executor.py index 064939a85da7a87ac0de8d68c8729d78a5c2125c..df0b6783823b7ac0e23b373f0bb898e0d103be64 100644 --- a/paddlespeech/cli/executor.py +++ b/paddlespeech/cli/executor.py @@ -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 diff --git a/paddlespeech/cli/st/infer.py b/paddlespeech/cli/st/infer.py index e64fc57d1bf2574a016e2655aa021a919e59ab98..29d95f79914e09d7dbf6e258d7ceaba9ae2228c6 100644 --- a/paddlespeech/cli/st/infer.py +++ b/paddlespeech/cli/st/infer.py @@ -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() diff --git a/paddlespeech/cli/st/pretrained_models.py b/paddlespeech/cli/st/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7410d253f34109424e49ea0d2622e12ce93ea5 --- /dev/null +++ b/paddlespeech/cli/st/pretrained_models.py @@ -0,0 +1,35 @@ +# 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", +} diff --git a/paddlespeech/cli/stats/infer.py b/paddlespeech/cli/stats/infer.py index 4ef50449c37e08c1a3c5f9b8894a5b4141e1c33f..7cf4f2368cbced90bac54cb61bdc1bd8fc3d07f8 100644 --- a/paddlespeech/cli/stats/infer.py +++ b/paddlespeech/cli/stats/infer.py @@ -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." + ) diff --git a/paddlespeech/cli/text/infer.py b/paddlespeech/cli/text/infer.py index dcf306c69f850bbcf13c08b81c1bb906141c71ea..69e62e4b448ca24054bf8e07b22b704f2e78eb32 100644 --- a/paddlespeech/cli/text/infer.py +++ b/paddlespeech/cli/text/infer.py @@ -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') diff --git a/paddlespeech/cli/text/pretrained_models.py b/paddlespeech/cli/text/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..817d3caa3cdc634a202703d4885796b21eee4f56 --- /dev/null +++ b/paddlespeech/cli/text/pretrained_models.py @@ -0,0 +1,54 @@ +# 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", +} diff --git a/paddlespeech/cli/tts/infer.py b/paddlespeech/cli/tts/infer.py index 1c3fb29f413b8f8a5fff24ac86f013f59912e802..1c7199306c7bf5aae39781b510a6145bd42b9894 100644 --- a/paddlespeech/cli/tts/infer.py +++ b/paddlespeech/cli/tts/infer.py @@ -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 diff --git a/paddlespeech/cli/tts/pretrained_models.py b/paddlespeech/cli/tts/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..65254a9353fc997038d84368d3918f055d2ccee0 --- /dev/null +++ b/paddlespeech/cli/tts/pretrained_models.py @@ -0,0 +1,300 @@ +# 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", +} diff --git a/paddlespeech/cli/vector/infer.py b/paddlespeech/cli/vector/infer.py index 68e832ac74d4dda805a4185ab09a72f2eb7d6413..1dff6edb42d7e0519375156403d40180647b9b13 100644 --- a/paddlespeech/cli/vector/infer.py +++ b/paddlespeech/cli/vector/infer.py @@ -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( diff --git a/paddlespeech/cli/vector/pretrained_models.py b/paddlespeech/cli/vector/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..686a22d8fb025007738a986a18a8eea236c1b3a9 --- /dev/null +++ b/paddlespeech/cli/vector/pretrained_models.py @@ -0,0 +1,36 @@ +# 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", +} diff --git a/tests/unit/cli/test_cli.sh b/tests/unit/cli/test_cli.sh index 96ab84d65312d4ea7d4974fa86ab85e991108f27..87c24b099ce01f9bd1b319809e3860137faec24b 100755 --- a/tests/unit/cli/test_cli.sh +++ b/tests/unit/cli/test_cli.sh @@ -1,5 +1,6 @@ #!/bin/bash set -e + # Audio classification wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/dog.wav paddlespeech cls --input ./cat.wav --topk 10 @@ -28,26 +29,16 @@ paddlespeech tts --am tacotron2_csmsc --input "你好，欢迎使用百度飞桨 paddlespeech tts --am tacotron2_csmsc --voc wavernn_csmsc --input "你好，欢迎使用百度飞桨深度学习框架！" paddlespeech tts --am tacotron2_ljspeech --voc pwgan_ljspeech --lang en --input "Life was like a box of chocolates, you never know what you're gonna get." - # Speech Translation (only support linux) paddlespeech st --input ./en.wav - -# batch process -echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts - -# shell pipeline -paddlespeech asr --input ./zh.wav | paddlespeech text --task punc - -# stats -paddlespeech stats --task asr -paddlespeech stats --task tts -paddlespeech stats --task cls - # Speaker Verification wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav paddlespeech vector --task spk --input 85236145389.wav +# batch process +echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts + echo -e "demo1 85236145389.wav \n demo2 85236145389.wav" > vec.job paddlespeech vector --task spk --input vec.job @@ -55,4 +46,13 @@ echo -e "demo3 85236145389.wav \n demo4 85236145389.wav" | paddlespeech vector - rm 85236145389.wav rm vec.job +# shell pipeline +paddlespeech asr --input ./zh.wav | paddlespeech text --task punc +# stats +paddlespeech stats --task asr +paddlespeech stats --task tts +paddlespeech stats --task cls +paddlespeech stats --task text +paddlespeech stats --task vector +paddlespeech stats --task st