qingen
2 years ago
committed by
GitHub
commit
26d5dded7c
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
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||||
# http://www.apache.org/licenses/LICENSE-2.0
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||||
#
|
||||
# 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 = {
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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"conformer_wenetspeech-zh-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
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'md5':
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'76cb19ed857e6623856b7cd7ebbfeda4',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/conformer/checkpoints/wenetspeech',
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},
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"transformer_librispeech-en-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz',
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'md5':
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'2c667da24922aad391eacafe37bc1660',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/transformer/checkpoints/avg_10',
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},
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"deepspeech2offline_aishell-zh-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz',
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'md5':
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'932c3593d62fe5c741b59b31318aa314',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/deepspeech2/checkpoints/avg_1',
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'lm_url':
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'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
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'lm_md5':
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'29e02312deb2e59b3c8686c7966d4fe3'
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},
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"deepspeech2online_aishell-zh-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_online_aishell_ckpt_0.2.0.model.tar.gz',
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'md5':
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'23e16c69730a1cb5d735c98c83c21e16',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/deepspeech2_online/checkpoints/avg_1',
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'lm_url':
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'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
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'lm_md5':
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'29e02312deb2e59b3c8686c7966d4fe3'
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},
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"deepspeech2offline_librispeech-en-16k": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz',
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'md5':
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'f5666c81ad015c8de03aac2bc92e5762',
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'cfg_path':
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'model.yaml',
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'ckpt_path':
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'exp/deepspeech2/checkpoints/avg_1',
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'lm_url':
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'https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm',
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'lm_md5':
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'099a601759d467cd0a8523ff939819c5'
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},
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}
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model_alias = {
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"deepspeech2offline":
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"paddlespeech.s2t.models.ds2:DeepSpeech2Model",
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"deepspeech2online":
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"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
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"conformer":
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"paddlespeech.s2t.models.u2:U2Model",
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"conformer_online":
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"paddlespeech.s2t.models.u2:U2Model",
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"transformer":
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"paddlespeech.s2t.models.u2:U2Model",
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"wenetspeech":
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"paddlespeech.s2t.models.u2:U2Model",
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}
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@ -0,0 +1,47 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# 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.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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||||
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pretrained_models = {
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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"panns_cnn6-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
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'md5': '4cf09194a95df024fd12f84712cf0f9c',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn6.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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"panns_cnn10-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
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'md5': 'cb8427b22176cc2116367d14847f5413',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn10.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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"panns_cnn14-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
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'md5': 'e3b9b5614a1595001161d0ab95edee97',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn14.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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}
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model_alias = {
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"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
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"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
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"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
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}
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@ -0,0 +1,35 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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pretrained_models = {
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"fat_st_ted-en-zh": {
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"url":
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"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/st1_transformer_mtl_noam_ted-en-zh_ckpt_0.1.1.model.tar.gz",
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"md5":
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"d62063f35a16d91210a71081bd2dd557",
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"cfg_path":
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"model.yaml",
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"ckpt_path":
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"exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
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}
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}
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model_alias = {"fat_st": "paddlespeech.s2t.models.u2_st:U2STModel"}
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kaldi_bins = {
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"url":
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"https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
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"md5":
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"c0682303b3f3393dbf6ed4c4e35a53eb",
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}
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@ -0,0 +1,54 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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||||
# 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.
|
||||
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pretrained_models = {
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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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||||
# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"ernie_linear_p7_wudao-punc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p7_wudao-punc-zh.tar.gz',
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'md5':
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'12283e2ddde1797c5d1e57036b512746',
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'cfg_path':
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'ckpt/model_config.json',
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'ckpt_path':
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'ckpt/model_state.pdparams',
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'vocab_file':
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'punc_vocab.txt',
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},
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"ernie_linear_p3_wudao-punc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_wudao-punc-zh.tar.gz',
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'md5':
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'448eb2fdf85b6a997e7e652e80c51dd2',
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'cfg_path':
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'ckpt/model_config.json',
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'ckpt_path':
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'ckpt/model_state.pdparams',
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'vocab_file':
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'punc_vocab.txt',
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},
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}
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model_alias = {
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"ernie_linear_p7": "paddlespeech.text.models:ErnieLinear",
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"ernie_linear_p3": "paddlespeech.text.models:ErnieLinear",
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}
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tokenizer_alias = {
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"ernie_linear_p7": "paddlenlp.transformers:ErnieTokenizer",
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"ernie_linear_p3": "paddlenlp.transformers:ErnieTokenizer",
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}
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@ -0,0 +1,300 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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||||
# Licensed under the Apache License, Version 2.0 (the "License");
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||||
# 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
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||||
#
|
||||
# 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.
|
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pretrained_models = {
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# speedyspeech
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"speedyspeech_csmsc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/speedyspeech/speedyspeech_csmsc_ckpt_0.2.0.zip',
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'md5':
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'6f6fa967b408454b6662c8c00c0027cb',
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'config':
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'default.yaml',
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'ckpt':
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'snapshot_iter_30600.pdz',
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'speech_stats':
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'feats_stats.npy',
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'phones_dict':
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'phone_id_map.txt',
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'tones_dict':
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'tone_id_map.txt',
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},
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# fastspeech2
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"fastspeech2_csmsc-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip',
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'md5':
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'637d28a5e53aa60275612ba4393d5f22',
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'config':
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'default.yaml',
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'ckpt':
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'snapshot_iter_76000.pdz',
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'speech_stats':
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'speech_stats.npy',
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'phones_dict':
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'phone_id_map.txt',
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},
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"fastspeech2_ljspeech-en": {
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'url':
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'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_ljspeech_ckpt_0.5.zip',
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'md5':
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'ffed800c93deaf16ca9b3af89bfcd747',
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'config':
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'default.yaml',
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'ckpt':
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'snapshot_iter_100000.pdz',
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'speech_stats':
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'speech_stats.npy',
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'phones_dict':
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'phone_id_map.txt',
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},
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"fastspeech2_aishell3-zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_ckpt_0.4.zip',
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'md5':
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'f4dd4a5f49a4552b77981f544ab3392e',
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'config':
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'default.yaml',
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'ckpt':
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'snapshot_iter_96400.pdz',
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'speech_stats':
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'speech_stats.npy',
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'phones_dict':
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'phone_id_map.txt',
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'speaker_dict':
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'speaker_id_map.txt',
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},
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"fastspeech2_vctk-en": {
|
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'url':
|
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'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':
|
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'speech_stats.npy',
|
||||
'phones_dict':
|
||||
'phone_id_map.txt',
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'speaker_dict':
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'speaker_id_map.txt',
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},
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# tacotron2
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"tacotron2_csmsc-zh": {
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'url':
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'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',
|
||||
},
|
||||
|
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# pwgan
|
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"pwgan_csmsc-zh": {
|
||||
'url':
|
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'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",
|
||||
}
|
||||
@ -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",
|
||||
}
|
||||
@ -0,0 +1,45 @@
|
||||
# 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
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,128 @@
|
||||
# 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
|
||||
@ -0,0 +1,13 @@
|
||||
# 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.
|
||||
@ -0,0 +1,13 @@
|
||||
# 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.
|
||||
@ -0,0 +1,13 @@
|
||||
# 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.
|
||||
@ -0,0 +1,13 @@
|
||||
# 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.
|
||||
@ -0,0 +1,37 @@
|
||||
#!/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
|
||||
@ -0,0 +1,66 @@
|
||||
#!/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
|
||||
@ -0,0 +1,85 @@
|
||||
// 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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,10 @@
|
||||
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})
|
||||
|
||||
@ -0,0 +1,82 @@
|
||||
// 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;
|
||||
}
|
||||
@ -0,0 +1,30 @@
|
||||
// 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;
|
||||
}
|
||||
@ -0,0 +1,94 @@
|
||||
// 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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,60 @@
|
||||
// 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
|
||||
@ -0,0 +1,59 @@
|
||||
// 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
|
||||
@ -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.
|
||||
|
||||
#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
|
||||
@ -0,0 +1,57 @@
|
||||
// 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 "frontend/audio/audio_cache.h"
|
||||
#include "frontend/audio/data_cache.h"
|
||||
#include "frontend/audio/feature_cache.h"
|
||||
#include "frontend/audio/frontend_itf.h"
|
||||
#include "frontend/audio/linear_spectrogram.h"
|
||||
#include "frontend/audio/normalizer.h"
|
||||
|
||||
namespace ppspeech {
|
||||
|
||||
struct FeaturePipelineOptions {
|
||||
std::string cmvn_file;
|
||||
bool convert2PCM32;
|
||||
LinearSpectrogramOptions linear_spectrogram_opts;
|
||||
FeatureCacheOptions feature_cache_opts;
|
||||
FeaturePipelineOptions()
|
||||
: cmvn_file(""),
|
||||
convert2PCM32(false),
|
||||
linear_spectrogram_opts(),
|
||||
feature_cache_opts() {}
|
||||
};
|
||||
|
||||
class FeaturePipeline : public FrontendInterface {
|
||||
public:
|
||||
explicit FeaturePipeline(const FeaturePipelineOptions& opts);
|
||||
virtual void Accept(const kaldi::VectorBase<kaldi::BaseFloat>& waves) {
|
||||
base_extractor_->Accept(waves);
|
||||
}
|
||||
virtual bool Read(kaldi::Vector<kaldi::BaseFloat>* feats) {
|
||||
return base_extractor_->Read(feats);
|
||||
}
|
||||
virtual size_t Dim() const { return base_extractor_->Dim(); }
|
||||
virtual void SetFinished() { base_extractor_->SetFinished(); }
|
||||
virtual bool IsFinished() const { return base_extractor_->IsFinished(); }
|
||||
virtual void Reset() { base_extractor_->Reset(); }
|
||||
|
||||
private:
|
||||
std::unique_ptr<FrontendInterface> base_extractor_;
|
||||
};
|
||||
}
|
||||
Loading…
Reference in new issue