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coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions/pdt/getters.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#ifndef FST_EXTENSIONS_PDT_GETTERS_H_
#define FST_EXTENSIONS_PDT_GETTERS_H_
#include <string>
#include <fst/extensions/pdt/compose.h>
#include <fst/extensions/pdt/replace.h>
namespace fst {
namespace script {
bool GetPdtComposeFilter(const string &str, PdtComposeFilter *cf);
bool GetPdtParserType(const string &str, PdtParserType *pt);
} // namespace script
} // namespace fst
#endif // FST_EXTENSIONS_PDT_GETTERS_H_
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/host-build-dbg.sh | #!/bin/bash
set -xe
runtime=$1
source $(dirname "$0")/tc-tests-utils.sh
source $(dirname "$0")/tf_tc-vars.sh
BAZEL_TARGETS="
//native_client:libstt.so
"
if [ "${runtime}" = "tflite" ]; then
BAZEL_BUILD_TFLITE="--define=runtime=tflite"
fi;
BAZEL_BUILD_FLAGS="${BAZEL_BUILD_TFLITE} ${BAZEL_OPT_FLAGS} ${BAZEL_EXTRA_FLAGS}"
BAZEL_ENV_FLAGS="TF_NEED_CUDA=0"
SYSTEM_TARGET=host
do_bazel_build "dbg"
export EXTRA_LOCAL_CFLAGS="-ggdb"
do_deepspeech_binary_build
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm | coqui_public_repos/inference-engine/third_party/kenlm/lm/value_build.cc | #include "lm/value_build.hh"
#include "lm/model.hh"
#include "lm/read_arpa.hh"
namespace lm {
namespace ngram {
template <class Model> LowerRestBuild<Model>::LowerRestBuild(const Config &config, unsigned int order, const typename Model::Vocabulary &vocab) {
UTIL_THROW_IF(config.rest_lower_files.size() != order - 1, ConfigException, "This model has order " << order << " so there should be " << (order - 1) << " lower-order models for rest cost purposes.");
Config for_lower = config;
for_lower.write_mmap = NULL;
for_lower.rest_lower_files.clear();
// Unigram models aren't supported, so this is a custom loader.
// TODO: optimize the unigram loading?
{
util::FilePiece uni(config.rest_lower_files[0].c_str());
std::vector<uint64_t> number;
ReadARPACounts(uni, number);
UTIL_THROW_IF(number.size() != 1, FormatLoadException, "Expected the unigram model to have order 1, not " << number.size());
ReadNGramHeader(uni, 1);
unigrams_.resize(number[0]);
unigrams_[0] = config.unknown_missing_logprob;
PositiveProbWarn warn;
for (uint64_t i = 0; i < number[0]; ++i) {
WordIndex w;
Prob entry;
ReadNGram(uni, 1, vocab, &w, entry, warn);
unigrams_[w] = entry.prob;
}
}
try {
for (unsigned int i = 2; i < order; ++i) {
models_.push_back(new Model(config.rest_lower_files[i - 1].c_str(), for_lower));
UTIL_THROW_IF(models_.back()->Order() != i, FormatLoadException, "Lower order file " << config.rest_lower_files[i-1] << " should have order " << i);
}
} catch (...) {
for (typename std::vector<const Model*>::const_iterator i = models_.begin(); i != models_.end(); ++i) {
delete *i;
}
models_.clear();
throw;
}
// TODO: force/check same vocab.
}
template <class Model> LowerRestBuild<Model>::~LowerRestBuild() {
for (typename std::vector<const Model*>::const_iterator i = models_.begin(); i != models_.end(); ++i) {
delete *i;
}
}
template class LowerRestBuild<ProbingModel>;
} // namespace ngram
} // namespace lm
| 0 |
coqui_public_repos/STT-models/estonian/itml | coqui_public_repos/STT-models/estonian/itml/v0.1.0/LICENSE | GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
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If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
| 0 |
coqui_public_repos/STT-models/dutch/acabunoc | coqui_public_repos/STT-models/dutch/acabunoc/v0.0.1/LICENSE | Mozilla Public License Version 2.0
==================================
1. Definitions
--------------
1.1. "Contributor"
means each individual or legal entity that creates, contributes to
the creation of, or owns Covered Software.
1.2. "Contributor Version"
means the combination of the Contributions of others (if any) used
by a Contributor and that particular Contributor's Contribution.
1.3. "Contribution"
means Covered Software of a particular Contributor.
1.4. "Covered Software"
means Source Code Form to which the initial Contributor has attached
the notice in Exhibit A, the Executable Form of such Source Code
Form, and Modifications of such Source Code Form, in each case
including portions thereof.
1.5. "Incompatible With Secondary Licenses"
means
(a) that the initial Contributor has attached the notice described
in Exhibit B to the Covered Software; or
(b) that the Covered Software was made available under the terms of
version 1.1 or earlier of the License, but not also under the
terms of a Secondary License.
1.6. "Executable Form"
means any form of the work other than Source Code Form.
1.7. "Larger Work"
means a work that combines Covered Software with other material, in
a separate file or files, that is not Covered Software.
1.8. "License"
means this document.
1.9. "Licensable"
means having the right to grant, to the maximum extent possible,
whether at the time of the initial grant or subsequently, any and
all of the rights conveyed by this License.
1.10. "Modifications"
means any of the following:
(a) any file in Source Code Form that results from an addition to,
deletion from, or modification of the contents of Covered
Software; or
(b) any new file in Source Code Form that contains any Covered
Software.
1.11. "Patent Claims" of a Contributor
means any patent claim(s), including without limitation, method,
process, and apparatus claims, in any patent Licensable by such
Contributor that would be infringed, but for the grant of the
License, by the making, using, selling, offering for sale, having
made, import, or transfer of either its Contributions or its
Contributor Version.
1.12. "Secondary License"
means either the GNU General Public License, Version 2.0, the GNU
Lesser General Public License, Version 2.1, the GNU Affero General
Public License, Version 3.0, or any later versions of those
licenses.
1.13. "Source Code Form"
means the form of the work preferred for making modifications.
1.14. "You" (or "Your")
means an individual or a legal entity exercising rights under this
License. For legal entities, "You" includes any entity that
controls, is controlled by, or is under common control with You. For
purposes of this definition, "control" means (a) the power, direct
or indirect, to cause the direction or management of such entity,
whether by contract or otherwise, or (b) ownership of more than
fifty percent (50%) of the outstanding shares or beneficial
ownership of such entity.
2. License Grants and Conditions
--------------------------------
2.1. Grants
Each Contributor hereby grants You a world-wide, royalty-free,
non-exclusive license:
(a) under intellectual property rights (other than patent or trademark)
Licensable by such Contributor to use, reproduce, make available,
modify, display, perform, distribute, and otherwise exploit its
Contributions, either on an unmodified basis, with Modifications, or
as part of a Larger Work; and
(b) under Patent Claims of such Contributor to make, use, sell, offer
for sale, have made, import, and otherwise transfer either its
Contributions or its Contributor Version.
2.2. Effective Date
The licenses granted in Section 2.1 with respect to any Contribution
become effective for each Contribution on the date the Contributor first
distributes such Contribution.
2.3. Limitations on Grant Scope
The licenses granted in this Section 2 are the only rights granted under
this License. No additional rights or licenses will be implied from the
distribution or licensing of Covered Software under this License.
Notwithstanding Section 2.1(b) above, no patent license is granted by a
Contributor:
(a) for any code that a Contributor has removed from Covered Software;
or
(b) for infringements caused by: (i) Your and any other third party's
modifications of Covered Software, or (ii) the combination of its
Contributions with other software (except as part of its Contributor
Version); or
(c) under Patent Claims infringed by Covered Software in the absence of
its Contributions.
This License does not grant any rights in the trademarks, service marks,
or logos of any Contributor (except as may be necessary to comply with
the notice requirements in Section 3.4).
2.4. Subsequent Licenses
No Contributor makes additional grants as a result of Your choice to
distribute the Covered Software under a subsequent version of this
License (see Section 10.2) or under the terms of a Secondary License (if
permitted under the terms of Section 3.3).
2.5. Representation
Each Contributor represents that the Contributor believes its
Contributions are its original creation(s) or it has sufficient rights
to grant the rights to its Contributions conveyed by this License.
2.6. Fair Use
This License is not intended to limit any rights You have under
applicable copyright doctrines of fair use, fair dealing, or other
equivalents.
2.7. Conditions
Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
in Section 2.1.
3. Responsibilities
-------------------
3.1. Distribution of Source Form
All distribution of Covered Software in Source Code Form, including any
Modifications that You create or to which You contribute, must be under
the terms of this License. You must inform recipients that the Source
Code Form of the Covered Software is governed by the terms of this
License, and how they can obtain a copy of this License. You may not
attempt to alter or restrict the recipients' rights in the Source Code
Form.
3.2. Distribution of Executable Form
If You distribute Covered Software in Executable Form then:
(a) such Covered Software must also be made available in Source Code
Form, as described in Section 3.1, and You must inform recipients of
the Executable Form how they can obtain a copy of such Source Code
Form by reasonable means in a timely manner, at a charge no more
than the cost of distribution to the recipient; and
(b) You may distribute such Executable Form under the terms of this
License, or sublicense it under different terms, provided that the
license for the Executable Form does not attempt to limit or alter
the recipients' rights in the Source Code Form under this License.
3.3. Distribution of a Larger Work
You may create and distribute a Larger Work under terms of Your choice,
provided that You also comply with the requirements of this License for
the Covered Software. If the Larger Work is a combination of Covered
Software with a work governed by one or more Secondary Licenses, and the
Covered Software is not Incompatible With Secondary Licenses, this
License permits You to additionally distribute such Covered Software
under the terms of such Secondary License(s), so that the recipient of
the Larger Work may, at their option, further distribute the Covered
Software under the terms of either this License or such Secondary
License(s).
3.4. Notices
You may not remove or alter the substance of any license notices
(including copyright notices, patent notices, disclaimers of warranty,
or limitations of liability) contained within the Source Code Form of
the Covered Software, except that You may alter any license notices to
the extent required to remedy known factual inaccuracies.
3.5. Application of Additional Terms
You may choose to offer, and to charge a fee for, warranty, support,
indemnity or liability obligations to one or more recipients of Covered
Software. However, You may do so only on Your own behalf, and not on
behalf of any Contributor. You must make it absolutely clear that any
such warranty, support, indemnity, or liability obligation is offered by
You alone, and You hereby agree to indemnify every Contributor for any
liability incurred by such Contributor as a result of warranty, support,
indemnity or liability terms You offer. You may include additional
disclaimers of warranty and limitations of liability specific to any
jurisdiction.
4. Inability to Comply Due to Statute or Regulation
---------------------------------------------------
If it is impossible for You to comply with any of the terms of this
License with respect to some or all of the Covered Software due to
statute, judicial order, or regulation then You must: (a) comply with
the terms of this License to the maximum extent possible; and (b)
describe the limitations and the code they affect. Such description must
be placed in a text file included with all distributions of the Covered
Software under this License. Except to the extent prohibited by statute
or regulation, such description must be sufficiently detailed for a
recipient of ordinary skill to be able to understand it.
5. Termination
--------------
5.1. The rights granted under this License will terminate automatically
if You fail to comply with any of its terms. However, if You become
compliant, then the rights granted under this License from a particular
Contributor are reinstated (a) provisionally, unless and until such
Contributor explicitly and finally terminates Your grants, and (b) on an
ongoing basis, if such Contributor fails to notify You of the
non-compliance by some reasonable means prior to 60 days after You have
come back into compliance. Moreover, Your grants from a particular
Contributor are reinstated on an ongoing basis if such Contributor
notifies You of the non-compliance by some reasonable means, this is the
first time You have received notice of non-compliance with this License
from such Contributor, and You become compliant prior to 30 days after
Your receipt of the notice.
5.2. If You initiate litigation against any entity by asserting a patent
infringement claim (excluding declaratory judgment actions,
counter-claims, and cross-claims) alleging that a Contributor Version
directly or indirectly infringes any patent, then the rights granted to
You by any and all Contributors for the Covered Software under Section
2.1 of this License shall terminate.
5.3. In the event of termination under Sections 5.1 or 5.2 above, all
end user license agreements (excluding distributors and resellers) which
have been validly granted by You or Your distributors under this License
prior to termination shall survive termination.
************************************************************************
* *
* 6. Disclaimer of Warranty *
* ------------------------- *
* *
* Covered Software is provided under this License on an "as is" *
* basis, without warranty of any kind, either expressed, implied, or *
* statutory, including, without limitation, warranties that the *
* Covered Software is free of defects, merchantable, fit for a *
* particular purpose or non-infringing. The entire risk as to the *
* quality and performance of the Covered Software is with You. *
* Should any Covered Software prove defective in any respect, You *
* (not any Contributor) assume the cost of any necessary servicing, *
* repair, or correction. This disclaimer of warranty constitutes an *
* essential part of this License. No use of any Covered Software is *
* authorized under this License except under this disclaimer. *
* *
************************************************************************
************************************************************************
* *
* 7. Limitation of Liability *
* -------------------------- *
* *
* Under no circumstances and under no legal theory, whether tort *
* (including negligence), contract, or otherwise, shall any *
* Contributor, or anyone who distributes Covered Software as *
* permitted above, be liable to You for any direct, indirect, *
* special, incidental, or consequential damages of any character *
* including, without limitation, damages for lost profits, loss of *
* goodwill, work stoppage, computer failure or malfunction, or any *
* and all other commercial damages or losses, even if such party *
* shall have been informed of the possibility of such damages. This *
* limitation of liability shall not apply to liability for death or *
* personal injury resulting from such party's negligence to the *
* extent applicable law prohibits such limitation. Some *
* jurisdictions do not allow the exclusion or limitation of *
* incidental or consequential damages, so this exclusion and *
* limitation may not apply to You. *
* *
************************************************************************
8. Litigation
-------------
Any litigation relating to this License may be brought only in the
courts of a jurisdiction where the defendant maintains its principal
place of business and such litigation shall be governed by laws of that
jurisdiction, without reference to its conflict-of-law provisions.
Nothing in this Section shall prevent a party's ability to bring
cross-claims or counter-claims.
9. Miscellaneous
----------------
This License represents the complete agreement concerning the subject
matter hereof. If any provision of this License is held to be
unenforceable, such provision shall be reformed only to the extent
necessary to make it enforceable. Any law or regulation which provides
that the language of a contract shall be construed against the drafter
shall not be used to construe this License against a Contributor.
10. Versions of the License
---------------------------
10.1. New Versions
Mozilla Foundation is the license steward. Except as provided in Section
10.3, no one other than the license steward has the right to modify or
publish new versions of this License. Each version will be given a
distinguishing version number.
10.2. Effect of New Versions
You may distribute the Covered Software under the terms of the version
of the License under which You originally received the Covered Software,
or under the terms of any subsequent version published by the license
steward.
10.3. Modified Versions
If you create software not governed by this License, and you want to
create a new license for such software, you may create and use a
modified version of this License if you rename the license and remove
any references to the name of the license steward (except to note that
such modified license differs from this License).
10.4. Distributing Source Code Form that is Incompatible With Secondary
Licenses
If You choose to distribute Source Code Form that is Incompatible With
Secondary Licenses under the terms of this version of the License, the
notice described in Exhibit B of this License must be attached.
Exhibit A - Source Code Form License Notice
-------------------------------------------
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
If it is not possible or desirable to put the notice in a particular
file, then You may include the notice in a location (such as a LICENSE
file in a relevant directory) where a recipient would be likely to look
for such a notice.
You may add additional accurate notices of copyright ownership.
Exhibit B - "Incompatible With Secondary Licenses" Notice
---------------------------------------------------------
This Source Code Form is "Incompatible With Secondary Licenses", as
defined by the Mozilla Public License, v. 2.0.
| 0 |
coqui_public_repos | coqui_public_repos/TTS-papers/LICENSE | Mozilla Public License Version 2.0
==================================
1. Definitions
--------------
1.1. "Contributor"
means each individual or legal entity that creates, contributes to
the creation of, or owns Covered Software.
1.2. "Contributor Version"
means the combination of the Contributions of others (if any) used
by a Contributor and that particular Contributor's Contribution.
1.3. "Contribution"
means Covered Software of a particular Contributor.
1.4. "Covered Software"
means Source Code Form to which the initial Contributor has attached
the notice in Exhibit A, the Executable Form of such Source Code
Form, and Modifications of such Source Code Form, in each case
including portions thereof.
1.5. "Incompatible With Secondary Licenses"
means
(a) that the initial Contributor has attached the notice described
in Exhibit B to the Covered Software; or
(b) that the Covered Software was made available under the terms of
version 1.1 or earlier of the License, but not also under the
terms of a Secondary License.
1.6. "Executable Form"
means any form of the work other than Source Code Form.
1.7. "Larger Work"
means a work that combines Covered Software with other material, in
a separate file or files, that is not Covered Software.
1.8. "License"
means this document.
1.9. "Licensable"
means having the right to grant, to the maximum extent possible,
whether at the time of the initial grant or subsequently, any and
all of the rights conveyed by this License.
1.10. "Modifications"
means any of the following:
(a) any file in Source Code Form that results from an addition to,
deletion from, or modification of the contents of Covered
Software; or
(b) any new file in Source Code Form that contains any Covered
Software.
1.11. "Patent Claims" of a Contributor
means any patent claim(s), including without limitation, method,
process, and apparatus claims, in any patent Licensable by such
Contributor that would be infringed, but for the grant of the
License, by the making, using, selling, offering for sale, having
made, import, or transfer of either its Contributions or its
Contributor Version.
1.12. "Secondary License"
means either the GNU General Public License, Version 2.0, the GNU
Lesser General Public License, Version 2.1, the GNU Affero General
Public License, Version 3.0, or any later versions of those
licenses.
1.13. "Source Code Form"
means the form of the work preferred for making modifications.
1.14. "You" (or "Your")
means an individual or a legal entity exercising rights under this
License. For legal entities, "You" includes any entity that
controls, is controlled by, or is under common control with You. For
purposes of this definition, "control" means (a) the power, direct
or indirect, to cause the direction or management of such entity,
whether by contract or otherwise, or (b) ownership of more than
fifty percent (50%) of the outstanding shares or beneficial
ownership of such entity.
2. License Grants and Conditions
--------------------------------
2.1. Grants
Each Contributor hereby grants You a world-wide, royalty-free,
non-exclusive license:
(a) under intellectual property rights (other than patent or trademark)
Licensable by such Contributor to use, reproduce, make available,
modify, display, perform, distribute, and otherwise exploit its
Contributions, either on an unmodified basis, with Modifications, or
as part of a Larger Work; and
(b) under Patent Claims of such Contributor to make, use, sell, offer
for sale, have made, import, and otherwise transfer either its
Contributions or its Contributor Version.
2.2. Effective Date
The licenses granted in Section 2.1 with respect to any Contribution
become effective for each Contribution on the date the Contributor first
distributes such Contribution.
2.3. Limitations on Grant Scope
The licenses granted in this Section 2 are the only rights granted under
this License. No additional rights or licenses will be implied from the
distribution or licensing of Covered Software under this License.
Notwithstanding Section 2.1(b) above, no patent license is granted by a
Contributor:
(a) for any code that a Contributor has removed from Covered Software;
or
(b) for infringements caused by: (i) Your and any other third party's
modifications of Covered Software, or (ii) the combination of its
Contributions with other software (except as part of its Contributor
Version); or
(c) under Patent Claims infringed by Covered Software in the absence of
its Contributions.
This License does not grant any rights in the trademarks, service marks,
or logos of any Contributor (except as may be necessary to comply with
the notice requirements in Section 3.4).
2.4. Subsequent Licenses
No Contributor makes additional grants as a result of Your choice to
distribute the Covered Software under a subsequent version of this
License (see Section 10.2) or under the terms of a Secondary License (if
permitted under the terms of Section 3.3).
2.5. Representation
Each Contributor represents that the Contributor believes its
Contributions are its original creation(s) or it has sufficient rights
to grant the rights to its Contributions conveyed by this License.
2.6. Fair Use
This License is not intended to limit any rights You have under
applicable copyright doctrines of fair use, fair dealing, or other
equivalents.
2.7. Conditions
Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
in Section 2.1.
3. Responsibilities
-------------------
3.1. Distribution of Source Form
All distribution of Covered Software in Source Code Form, including any
Modifications that You create or to which You contribute, must be under
the terms of this License. You must inform recipients that the Source
Code Form of the Covered Software is governed by the terms of this
License, and how they can obtain a copy of this License. You may not
attempt to alter or restrict the recipients' rights in the Source Code
Form.
3.2. Distribution of Executable Form
If You distribute Covered Software in Executable Form then:
(a) such Covered Software must also be made available in Source Code
Form, as described in Section 3.1, and You must inform recipients of
the Executable Form how they can obtain a copy of such Source Code
Form by reasonable means in a timely manner, at a charge no more
than the cost of distribution to the recipient; and
(b) You may distribute such Executable Form under the terms of this
License, or sublicense it under different terms, provided that the
license for the Executable Form does not attempt to limit or alter
the recipients' rights in the Source Code Form under this License.
3.3. Distribution of a Larger Work
You may create and distribute a Larger Work under terms of Your choice,
provided that You also comply with the requirements of this License for
the Covered Software. If the Larger Work is a combination of Covered
Software with a work governed by one or more Secondary Licenses, and the
Covered Software is not Incompatible With Secondary Licenses, this
License permits You to additionally distribute such Covered Software
under the terms of such Secondary License(s), so that the recipient of
the Larger Work may, at their option, further distribute the Covered
Software under the terms of either this License or such Secondary
License(s).
3.4. Notices
You may not remove or alter the substance of any license notices
(including copyright notices, patent notices, disclaimers of warranty,
or limitations of liability) contained within the Source Code Form of
the Covered Software, except that You may alter any license notices to
the extent required to remedy known factual inaccuracies.
3.5. Application of Additional Terms
You may choose to offer, and to charge a fee for, warranty, support,
indemnity or liability obligations to one or more recipients of Covered
Software. However, You may do so only on Your own behalf, and not on
behalf of any Contributor. You must make it absolutely clear that any
such warranty, support, indemnity, or liability obligation is offered by
You alone, and You hereby agree to indemnify every Contributor for any
liability incurred by such Contributor as a result of warranty, support,
indemnity or liability terms You offer. You may include additional
disclaimers of warranty and limitations of liability specific to any
jurisdiction.
4. Inability to Comply Due to Statute or Regulation
---------------------------------------------------
If it is impossible for You to comply with any of the terms of this
License with respect to some or all of the Covered Software due to
statute, judicial order, or regulation then You must: (a) comply with
the terms of this License to the maximum extent possible; and (b)
describe the limitations and the code they affect. Such description must
be placed in a text file included with all distributions of the Covered
Software under this License. Except to the extent prohibited by statute
or regulation, such description must be sufficiently detailed for a
recipient of ordinary skill to be able to understand it.
5. Termination
--------------
5.1. The rights granted under this License will terminate automatically
if You fail to comply with any of its terms. However, if You become
compliant, then the rights granted under this License from a particular
Contributor are reinstated (a) provisionally, unless and until such
Contributor explicitly and finally terminates Your grants, and (b) on an
ongoing basis, if such Contributor fails to notify You of the
non-compliance by some reasonable means prior to 60 days after You have
come back into compliance. Moreover, Your grants from a particular
Contributor are reinstated on an ongoing basis if such Contributor
notifies You of the non-compliance by some reasonable means, this is the
first time You have received notice of non-compliance with this License
from such Contributor, and You become compliant prior to 30 days after
Your receipt of the notice.
5.2. If You initiate litigation against any entity by asserting a patent
infringement claim (excluding declaratory judgment actions,
counter-claims, and cross-claims) alleging that a Contributor Version
directly or indirectly infringes any patent, then the rights granted to
You by any and all Contributors for the Covered Software under Section
2.1 of this License shall terminate.
5.3. In the event of termination under Sections 5.1 or 5.2 above, all
end user license agreements (excluding distributors and resellers) which
have been validly granted by You or Your distributors under this License
prior to termination shall survive termination.
************************************************************************
* *
* 6. Disclaimer of Warranty *
* ------------------------- *
* *
* Covered Software is provided under this License on an "as is" *
* basis, without warranty of any kind, either expressed, implied, or *
* statutory, including, without limitation, warranties that the *
* Covered Software is free of defects, merchantable, fit for a *
* particular purpose or non-infringing. The entire risk as to the *
* quality and performance of the Covered Software is with You. *
* Should any Covered Software prove defective in any respect, You *
* (not any Contributor) assume the cost of any necessary servicing, *
* repair, or correction. This disclaimer of warranty constitutes an *
* essential part of this License. No use of any Covered Software is *
* authorized under this License except under this disclaimer. *
* *
************************************************************************
************************************************************************
* *
* 7. Limitation of Liability *
* -------------------------- *
* *
* Under no circumstances and under no legal theory, whether tort *
* (including negligence), contract, or otherwise, shall any *
* Contributor, or anyone who distributes Covered Software as *
* permitted above, be liable to You for any direct, indirect, *
* special, incidental, or consequential damages of any character *
* including, without limitation, damages for lost profits, loss of *
* goodwill, work stoppage, computer failure or malfunction, or any *
* and all other commercial damages or losses, even if such party *
* shall have been informed of the possibility of such damages. This *
* limitation of liability shall not apply to liability for death or *
* personal injury resulting from such party's negligence to the *
* extent applicable law prohibits such limitation. Some *
* jurisdictions do not allow the exclusion or limitation of *
* incidental or consequential damages, so this exclusion and *
* limitation may not apply to You. *
* *
************************************************************************
8. Litigation
-------------
Any litigation relating to this License may be brought only in the
courts of a jurisdiction where the defendant maintains its principal
place of business and such litigation shall be governed by laws of that
jurisdiction, without reference to its conflict-of-law provisions.
Nothing in this Section shall prevent a party's ability to bring
cross-claims or counter-claims.
9. Miscellaneous
----------------
This License represents the complete agreement concerning the subject
matter hereof. If any provision of this License is held to be
unenforceable, such provision shall be reformed only to the extent
necessary to make it enforceable. Any law or regulation which provides
that the language of a contract shall be construed against the drafter
shall not be used to construe this License against a Contributor.
10. Versions of the License
---------------------------
10.1. New Versions
Mozilla Foundation is the license steward. Except as provided in Section
10.3, no one other than the license steward has the right to modify or
publish new versions of this License. Each version will be given a
distinguishing version number.
10.2. Effect of New Versions
You may distribute the Covered Software under the terms of the version
of the License under which You originally received the Covered Software,
or under the terms of any subsequent version published by the license
steward.
10.3. Modified Versions
If you create software not governed by this License, and you want to
create a new license for such software, you may create and use a
modified version of this License if you rename the license and remove
any references to the name of the license steward (except to note that
such modified license differs from this License).
10.4. Distributing Source Code Form that is Incompatible With Secondary
Licenses
If You choose to distribute Source Code Form that is Incompatible With
Secondary Licenses under the terms of this version of the License, the
notice described in Exhibit B of this License must be attached.
Exhibit A - Source Code Form License Notice
-------------------------------------------
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
If it is not possible or desirable to put the notice in a particular
file, then You may include the notice in a location (such as a LICENSE
file in a relevant directory) where a recipient would be likely to look
for such a notice.
You may add additional accurate notices of copyright ownership.
Exhibit B - "Incompatible With Secondary Licenses" Notice
---------------------------------------------------------
This Source Code Form is "Incompatible With Secondary Licenses", as
defined by the Mozilla Public License, v. 2.0.
| 0 |
coqui_public_repos/STT/native_client/kenlm | coqui_public_repos/STT/native_client/kenlm/lm/value_build.hh | #ifndef LM_VALUE_BUILD_H
#define LM_VALUE_BUILD_H
#include "weights.hh"
#include "word_index.hh"
#include "../util/bit_packing.hh"
#include <vector>
namespace lm {
namespace ngram {
struct Config;
struct BackoffValue;
struct RestValue;
class NoRestBuild {
public:
typedef BackoffValue Value;
NoRestBuild() {}
void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {}
void SetRest(const WordIndex *, unsigned int, const ProbBackoff &) const {}
template <class Second> bool MarkExtends(ProbBackoff &weights, const Second &) const {
util::UnsetSign(weights.prob);
return false;
}
// Probing doesn't need to go back to unigram.
const static bool kMarkEvenLower = false;
};
class MaxRestBuild {
public:
typedef RestValue Value;
MaxRestBuild() {}
void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {}
void SetRest(const WordIndex *, unsigned int, RestWeights &weights) const {
weights.rest = weights.prob;
util::SetSign(weights.rest);
}
bool MarkExtends(RestWeights &weights, const RestWeights &to) const {
util::UnsetSign(weights.prob);
if (weights.rest >= to.rest) return false;
weights.rest = to.rest;
return true;
}
bool MarkExtends(RestWeights &weights, const Prob &to) const {
util::UnsetSign(weights.prob);
if (weights.rest >= to.prob) return false;
weights.rest = to.prob;
return true;
}
// Probing does need to go back to unigram.
const static bool kMarkEvenLower = true;
};
template <class Model> class LowerRestBuild {
public:
typedef RestValue Value;
LowerRestBuild(const Config &config, unsigned int order, const typename Model::Vocabulary &vocab);
~LowerRestBuild();
void SetRest(const WordIndex *, unsigned int, const Prob &/*prob*/) const {}
void SetRest(const WordIndex *vocab_ids, unsigned int n, RestWeights &weights) const {
typename Model::State ignored;
if (n == 1) {
weights.rest = unigrams_[*vocab_ids];
} else {
weights.rest = models_[n-2]->FullScoreForgotState(vocab_ids + 1, vocab_ids + n, *vocab_ids, ignored).prob;
}
}
template <class Second> bool MarkExtends(RestWeights &weights, const Second &) const {
util::UnsetSign(weights.prob);
return false;
}
const static bool kMarkEvenLower = false;
std::vector<float> unigrams_;
std::vector<const Model*> models_;
};
} // namespace ngram
} // namespace lm
#endif // LM_VALUE_BUILD_H
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/extensions/mpdt/mpdtexpand.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Expands a (bounded-stack) MPDT as an FST.
#include <cstring>
#include <memory>
#include <string>
#include <vector>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/extensions/mpdt/mpdtscript.h>
#include <fst/extensions/mpdt/read_write_utils.h>
#include <fst/util.h>
DEFINE_string(mpdt_parentheses, "",
"MPDT parenthesis label pairs with assignments");
DEFINE_bool(connect, true, "Trim output?");
DEFINE_bool(keep_parentheses, false, "Keep PDT parentheses in result?");
int main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::VectorFstClass;
using fst::ReadLabelTriples;
using fst::MPdtExpandOptions;
string usage = "Expand a (bounded-stack) MPDT as an FST.\n\n Usage: ";
usage += argv[0];
usage += " in.pdt [out.fst]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name =
(argc > 1 && (strcmp(argv[1], "-") != 0)) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<FstClass> ifst(FstClass::Read(in_name));
if (!ifst) return 1;
if (FLAGS_mpdt_parentheses.empty()) {
LOG(ERROR) << argv[0] << ": No MPDT parenthesis label pairs provided";
return 1;
}
std::vector<s::LabelPair> parens;
std::vector<int64> assignments;
if (!ReadLabelTriples(FLAGS_mpdt_parentheses, &parens, &assignments, false))
return 1;
VectorFstClass ofst(ifst->ArcType());
const MPdtExpandOptions opts(FLAGS_connect, FLAGS_keep_parentheses);
s::MPdtExpand(*ifst, parens, assignments, &ofst, opts);
ofst.Write(out_name);
return 0;
}
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/extensions/mpdt/reverse.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Reverses an MPDT.
#ifndef FST_EXTENSIONS_MPDT_REVERSE_H_
#define FST_EXTENSIONS_MPDT_REVERSE_H_
#include <limits>
#include <vector>
#include <fst/mutable-fst.h>
#include <fst/relabel.h>
#include <fst/reverse.h>
namespace fst {
// Reverses a multi-stack pushdown transducer (MPDT) encoded as an FST.
template <class Arc, class RevArc>
void Reverse(
const Fst<Arc> &ifst,
const std::vector<std::pair<typename Arc::Label, typename Arc::Label>>
&parens,
std::vector<typename Arc::Label> *assignments, MutableFst<RevArc> *ofst) {
using Label = typename Arc::Label;
// Reverses FST component.
Reverse(ifst, ofst);
// Exchanges open and close parenthesis pairs.
std::vector<std::pair<Label, Label>> relabel_pairs;
relabel_pairs.reserve(2 * parens.size());
for (const auto &pair : parens) {
relabel_pairs.emplace_back(pair.first, pair.second);
relabel_pairs.emplace_back(pair.second, pair.first);
}
Relabel(ofst, relabel_pairs, relabel_pairs);
// Computes new bounds for the stack assignments.
Label max_level = -1;
Label min_level = std::numeric_limits<Label>::max();
for (const auto assignment : *assignments) {
if (assignment < min_level) {
min_level = assignment;
} else if (assignment > max_level) {
max_level = assignment;
}
}
// Actually reverses stack assignments.
for (auto &assignment : *assignments) {
assignment = (max_level - assignment) + min_level;
}
}
} // namespace fst
#endif // FST_EXTENSIONS_MPDT_REVERSE_H_
| 0 |
coqui_public_repos/STT/native_client/kenlm/util | coqui_public_repos/STT/native_client/kenlm/util/double-conversion/strtod.cc | // Copyright 2010 the V8 project authors. All rights reserved.
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above
// copyright notice, this list of conditions and the following
// disclaimer in the documentation and/or other materials provided
// with the distribution.
// * Neither the name of Google Inc. nor the names of its
// contributors may be used to endorse or promote products derived
// from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include <climits>
#include <cstdarg>
#include "bignum.h"
#include "cached-powers.h"
#include "ieee.h"
#include "strtod.h"
namespace kenlm_double_conversion {
#if defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS)
// 2^53 = 9007199254740992.
// Any integer with at most 15 decimal digits will hence fit into a double
// (which has a 53bit significand) without loss of precision.
static const int kMaxExactDoubleIntegerDecimalDigits = 15;
#endif // #if defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS)
// 2^64 = 18446744073709551616 > 10^19
static const int kMaxUint64DecimalDigits = 19;
// Max double: 1.7976931348623157 x 10^308
// Min non-zero double: 4.9406564584124654 x 10^-324
// Any x >= 10^309 is interpreted as +infinity.
// Any x <= 10^-324 is interpreted as 0.
// Note that 2.5e-324 (despite being smaller than the min double) will be read
// as non-zero (equal to the min non-zero double).
static const int kMaxDecimalPower = 309;
static const int kMinDecimalPower = -324;
// 2^64 = 18446744073709551616
static const uint64_t kMaxUint64 = DOUBLE_CONVERSION_UINT64_2PART_C(0xFFFFFFFF, FFFFFFFF);
#if defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS)
static const double exact_powers_of_ten[] = {
1.0, // 10^0
10.0,
100.0,
1000.0,
10000.0,
100000.0,
1000000.0,
10000000.0,
100000000.0,
1000000000.0,
10000000000.0, // 10^10
100000000000.0,
1000000000000.0,
10000000000000.0,
100000000000000.0,
1000000000000000.0,
10000000000000000.0,
100000000000000000.0,
1000000000000000000.0,
10000000000000000000.0,
100000000000000000000.0, // 10^20
1000000000000000000000.0,
// 10^22 = 0x21e19e0c9bab2400000 = 0x878678326eac9 * 2^22
10000000000000000000000.0
};
static const int kExactPowersOfTenSize = DOUBLE_CONVERSION_ARRAY_SIZE(exact_powers_of_ten);
#endif // #if defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS)
// Maximum number of significant digits in the decimal representation.
// In fact the value is 772 (see conversions.cc), but to give us some margin
// we round up to 780.
static const int kMaxSignificantDecimalDigits = 780;
static Vector<const char> TrimLeadingZeros(Vector<const char> buffer) {
for (int i = 0; i < buffer.length(); i++) {
if (buffer[i] != '0') {
return buffer.SubVector(i, buffer.length());
}
}
return Vector<const char>(buffer.start(), 0);
}
static void CutToMaxSignificantDigits(Vector<const char> buffer,
int exponent,
char* significant_buffer,
int* significant_exponent) {
for (int i = 0; i < kMaxSignificantDecimalDigits - 1; ++i) {
significant_buffer[i] = buffer[i];
}
// The input buffer has been trimmed. Therefore the last digit must be
// different from '0'.
DOUBLE_CONVERSION_ASSERT(buffer[buffer.length() - 1] != '0');
// Set the last digit to be non-zero. This is sufficient to guarantee
// correct rounding.
significant_buffer[kMaxSignificantDecimalDigits - 1] = '1';
*significant_exponent =
exponent + (buffer.length() - kMaxSignificantDecimalDigits);
}
// Trims the buffer and cuts it to at most kMaxSignificantDecimalDigits.
// If possible the input-buffer is reused, but if the buffer needs to be
// modified (due to cutting), then the input needs to be copied into the
// buffer_copy_space.
static void TrimAndCut(Vector<const char> buffer, int exponent,
char* buffer_copy_space, int space_size,
Vector<const char>* trimmed, int* updated_exponent) {
Vector<const char> left_trimmed = TrimLeadingZeros(buffer);
Vector<const char> right_trimmed = TrimTrailingZeros(left_trimmed);
exponent += left_trimmed.length() - right_trimmed.length();
if (right_trimmed.length() > kMaxSignificantDecimalDigits) {
(void) space_size; // Mark variable as used.
DOUBLE_CONVERSION_ASSERT(space_size >= kMaxSignificantDecimalDigits);
CutToMaxSignificantDigits(right_trimmed, exponent,
buffer_copy_space, updated_exponent);
*trimmed = Vector<const char>(buffer_copy_space,
kMaxSignificantDecimalDigits);
} else {
*trimmed = right_trimmed;
*updated_exponent = exponent;
}
}
// Reads digits from the buffer and converts them to a uint64.
// Reads in as many digits as fit into a uint64.
// When the string starts with "1844674407370955161" no further digit is read.
// Since 2^64 = 18446744073709551616 it would still be possible read another
// digit if it was less or equal than 6, but this would complicate the code.
static uint64_t ReadUint64(Vector<const char> buffer,
int* number_of_read_digits) {
uint64_t result = 0;
int i = 0;
while (i < buffer.length() && result <= (kMaxUint64 / 10 - 1)) {
int digit = buffer[i++] - '0';
DOUBLE_CONVERSION_ASSERT(0 <= digit && digit <= 9);
result = 10 * result + digit;
}
*number_of_read_digits = i;
return result;
}
// Reads a DiyFp from the buffer.
// The returned DiyFp is not necessarily normalized.
// If remaining_decimals is zero then the returned DiyFp is accurate.
// Otherwise it has been rounded and has error of at most 1/2 ulp.
static void ReadDiyFp(Vector<const char> buffer,
DiyFp* result,
int* remaining_decimals) {
int read_digits;
uint64_t significand = ReadUint64(buffer, &read_digits);
if (buffer.length() == read_digits) {
*result = DiyFp(significand, 0);
*remaining_decimals = 0;
} else {
// Round the significand.
if (buffer[read_digits] >= '5') {
significand++;
}
// Compute the binary exponent.
int exponent = 0;
*result = DiyFp(significand, exponent);
*remaining_decimals = buffer.length() - read_digits;
}
}
static bool DoubleStrtod(Vector<const char> trimmed,
int exponent,
double* result) {
#if !defined(DOUBLE_CONVERSION_CORRECT_DOUBLE_OPERATIONS)
// Avoid "unused parameter" warnings
(void) trimmed;
(void) exponent;
(void) result;
// On x86 the floating-point stack can be 64 or 80 bits wide. If it is
// 80 bits wide (as is the case on Linux) then double-rounding occurs and the
// result is not accurate.
// We know that Windows32 uses 64 bits and is therefore accurate.
return false;
#else
if (trimmed.length() <= kMaxExactDoubleIntegerDecimalDigits) {
int read_digits;
// The trimmed input fits into a double.
// If the 10^exponent (resp. 10^-exponent) fits into a double too then we
// can compute the result-double simply by multiplying (resp. dividing) the
// two numbers.
// This is possible because IEEE guarantees that floating-point operations
// return the best possible approximation.
if (exponent < 0 && -exponent < kExactPowersOfTenSize) {
// 10^-exponent fits into a double.
*result = static_cast<double>(ReadUint64(trimmed, &read_digits));
DOUBLE_CONVERSION_ASSERT(read_digits == trimmed.length());
*result /= exact_powers_of_ten[-exponent];
return true;
}
if (0 <= exponent && exponent < kExactPowersOfTenSize) {
// 10^exponent fits into a double.
*result = static_cast<double>(ReadUint64(trimmed, &read_digits));
DOUBLE_CONVERSION_ASSERT(read_digits == trimmed.length());
*result *= exact_powers_of_ten[exponent];
return true;
}
int remaining_digits =
kMaxExactDoubleIntegerDecimalDigits - trimmed.length();
if ((0 <= exponent) &&
(exponent - remaining_digits < kExactPowersOfTenSize)) {
// The trimmed string was short and we can multiply it with
// 10^remaining_digits. As a result the remaining exponent now fits
// into a double too.
*result = static_cast<double>(ReadUint64(trimmed, &read_digits));
DOUBLE_CONVERSION_ASSERT(read_digits == trimmed.length());
*result *= exact_powers_of_ten[remaining_digits];
*result *= exact_powers_of_ten[exponent - remaining_digits];
return true;
}
}
return false;
#endif
}
// Returns 10^exponent as an exact DiyFp.
// The given exponent must be in the range [1; kDecimalExponentDistance[.
static DiyFp AdjustmentPowerOfTen(int exponent) {
DOUBLE_CONVERSION_ASSERT(0 < exponent);
DOUBLE_CONVERSION_ASSERT(exponent < PowersOfTenCache::kDecimalExponentDistance);
// Simply hardcode the remaining powers for the given decimal exponent
// distance.
DOUBLE_CONVERSION_ASSERT(PowersOfTenCache::kDecimalExponentDistance == 8);
switch (exponent) {
case 1: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0xa0000000, 00000000), -60);
case 2: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0xc8000000, 00000000), -57);
case 3: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0xfa000000, 00000000), -54);
case 4: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0x9c400000, 00000000), -50);
case 5: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0xc3500000, 00000000), -47);
case 6: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0xf4240000, 00000000), -44);
case 7: return DiyFp(DOUBLE_CONVERSION_UINT64_2PART_C(0x98968000, 00000000), -40);
default:
DOUBLE_CONVERSION_UNREACHABLE();
}
}
// If the function returns true then the result is the correct double.
// Otherwise it is either the correct double or the double that is just below
// the correct double.
static bool DiyFpStrtod(Vector<const char> buffer,
int exponent,
double* result) {
DiyFp input;
int remaining_decimals;
ReadDiyFp(buffer, &input, &remaining_decimals);
// Since we may have dropped some digits the input is not accurate.
// If remaining_decimals is different than 0 than the error is at most
// .5 ulp (unit in the last place).
// We don't want to deal with fractions and therefore keep a common
// denominator.
const int kDenominatorLog = 3;
const int kDenominator = 1 << kDenominatorLog;
// Move the remaining decimals into the exponent.
exponent += remaining_decimals;
uint64_t error = (remaining_decimals == 0 ? 0 : kDenominator / 2);
int old_e = input.e();
input.Normalize();
error <<= old_e - input.e();
DOUBLE_CONVERSION_ASSERT(exponent <= PowersOfTenCache::kMaxDecimalExponent);
if (exponent < PowersOfTenCache::kMinDecimalExponent) {
*result = 0.0;
return true;
}
DiyFp cached_power;
int cached_decimal_exponent;
PowersOfTenCache::GetCachedPowerForDecimalExponent(exponent,
&cached_power,
&cached_decimal_exponent);
if (cached_decimal_exponent != exponent) {
int adjustment_exponent = exponent - cached_decimal_exponent;
DiyFp adjustment_power = AdjustmentPowerOfTen(adjustment_exponent);
input.Multiply(adjustment_power);
if (kMaxUint64DecimalDigits - buffer.length() >= adjustment_exponent) {
// The product of input with the adjustment power fits into a 64 bit
// integer.
DOUBLE_CONVERSION_ASSERT(DiyFp::kSignificandSize == 64);
} else {
// The adjustment power is exact. There is hence only an error of 0.5.
error += kDenominator / 2;
}
}
input.Multiply(cached_power);
// The error introduced by a multiplication of a*b equals
// error_a + error_b + error_a*error_b/2^64 + 0.5
// Substituting a with 'input' and b with 'cached_power' we have
// error_b = 0.5 (all cached powers have an error of less than 0.5 ulp),
// error_ab = 0 or 1 / kDenominator > error_a*error_b/ 2^64
int error_b = kDenominator / 2;
int error_ab = (error == 0 ? 0 : 1); // We round up to 1.
int fixed_error = kDenominator / 2;
error += error_b + error_ab + fixed_error;
old_e = input.e();
input.Normalize();
error <<= old_e - input.e();
// See if the double's significand changes if we add/subtract the error.
int order_of_magnitude = DiyFp::kSignificandSize + input.e();
int effective_significand_size =
Double::SignificandSizeForOrderOfMagnitude(order_of_magnitude);
int precision_digits_count =
DiyFp::kSignificandSize - effective_significand_size;
if (precision_digits_count + kDenominatorLog >= DiyFp::kSignificandSize) {
// This can only happen for very small denormals. In this case the
// half-way multiplied by the denominator exceeds the range of an uint64.
// Simply shift everything to the right.
int shift_amount = (precision_digits_count + kDenominatorLog) -
DiyFp::kSignificandSize + 1;
input.set_f(input.f() >> shift_amount);
input.set_e(input.e() + shift_amount);
// We add 1 for the lost precision of error, and kDenominator for
// the lost precision of input.f().
error = (error >> shift_amount) + 1 + kDenominator;
precision_digits_count -= shift_amount;
}
// We use uint64_ts now. This only works if the DiyFp uses uint64_ts too.
DOUBLE_CONVERSION_ASSERT(DiyFp::kSignificandSize == 64);
DOUBLE_CONVERSION_ASSERT(precision_digits_count < 64);
uint64_t one64 = 1;
uint64_t precision_bits_mask = (one64 << precision_digits_count) - 1;
uint64_t precision_bits = input.f() & precision_bits_mask;
uint64_t half_way = one64 << (precision_digits_count - 1);
precision_bits *= kDenominator;
half_way *= kDenominator;
DiyFp rounded_input(input.f() >> precision_digits_count,
input.e() + precision_digits_count);
if (precision_bits >= half_way + error) {
rounded_input.set_f(rounded_input.f() + 1);
}
// If the last_bits are too close to the half-way case than we are too
// inaccurate and round down. In this case we return false so that we can
// fall back to a more precise algorithm.
*result = Double(rounded_input).value();
if (half_way - error < precision_bits && precision_bits < half_way + error) {
// Too imprecise. The caller will have to fall back to a slower version.
// However the returned number is guaranteed to be either the correct
// double, or the next-lower double.
return false;
} else {
return true;
}
}
// Returns
// - -1 if buffer*10^exponent < diy_fp.
// - 0 if buffer*10^exponent == diy_fp.
// - +1 if buffer*10^exponent > diy_fp.
// Preconditions:
// buffer.length() + exponent <= kMaxDecimalPower + 1
// buffer.length() + exponent > kMinDecimalPower
// buffer.length() <= kMaxDecimalSignificantDigits
static int CompareBufferWithDiyFp(Vector<const char> buffer,
int exponent,
DiyFp diy_fp) {
DOUBLE_CONVERSION_ASSERT(buffer.length() + exponent <= kMaxDecimalPower + 1);
DOUBLE_CONVERSION_ASSERT(buffer.length() + exponent > kMinDecimalPower);
DOUBLE_CONVERSION_ASSERT(buffer.length() <= kMaxSignificantDecimalDigits);
// Make sure that the Bignum will be able to hold all our numbers.
// Our Bignum implementation has a separate field for exponents. Shifts will
// consume at most one bigit (< 64 bits).
// ln(10) == 3.3219...
DOUBLE_CONVERSION_ASSERT(((kMaxDecimalPower + 1) * 333 / 100) < Bignum::kMaxSignificantBits);
Bignum buffer_bignum;
Bignum diy_fp_bignum;
buffer_bignum.AssignDecimalString(buffer);
diy_fp_bignum.AssignUInt64(diy_fp.f());
if (exponent >= 0) {
buffer_bignum.MultiplyByPowerOfTen(exponent);
} else {
diy_fp_bignum.MultiplyByPowerOfTen(-exponent);
}
if (diy_fp.e() > 0) {
diy_fp_bignum.ShiftLeft(diy_fp.e());
} else {
buffer_bignum.ShiftLeft(-diy_fp.e());
}
return Bignum::Compare(buffer_bignum, diy_fp_bignum);
}
// Returns true if the guess is the correct double.
// Returns false, when guess is either correct or the next-lower double.
static bool ComputeGuess(Vector<const char> trimmed, int exponent,
double* guess) {
if (trimmed.length() == 0) {
*guess = 0.0;
return true;
}
if (exponent + trimmed.length() - 1 >= kMaxDecimalPower) {
*guess = Double::Infinity();
return true;
}
if (exponent + trimmed.length() <= kMinDecimalPower) {
*guess = 0.0;
return true;
}
if (DoubleStrtod(trimmed, exponent, guess) ||
DiyFpStrtod(trimmed, exponent, guess)) {
return true;
}
if (*guess == Double::Infinity()) {
return true;
}
return false;
}
static bool IsDigit(const char d) {
return ('0' <= d) && (d <= '9');
}
static bool IsNonZeroDigit(const char d) {
return ('1' <= d) && (d <= '9');
}
#ifdef __has_cpp_attribute
#if __has_cpp_attribute(maybe_unused)
[[maybe_unused]]
#endif
#endif
static bool AssertTrimmedDigits(const Vector<const char>& buffer) {
for(int i = 0; i < buffer.length(); ++i) {
if(!IsDigit(buffer[i])) {
return false;
}
}
return (buffer.length() == 0) || (IsNonZeroDigit(buffer[0]) && IsNonZeroDigit(buffer[buffer.length()-1]));
}
double StrtodTrimmed(Vector<const char> trimmed, int exponent) {
DOUBLE_CONVERSION_ASSERT(trimmed.length() <= kMaxSignificantDecimalDigits);
DOUBLE_CONVERSION_ASSERT(AssertTrimmedDigits(trimmed));
double guess;
const bool is_correct = ComputeGuess(trimmed, exponent, &guess);
if (is_correct) {
return guess;
}
DiyFp upper_boundary = Double(guess).UpperBoundary();
int comparison = CompareBufferWithDiyFp(trimmed, exponent, upper_boundary);
if (comparison < 0) {
return guess;
} else if (comparison > 0) {
return Double(guess).NextDouble();
} else if ((Double(guess).Significand() & 1) == 0) {
// Round towards even.
return guess;
} else {
return Double(guess).NextDouble();
}
}
double Strtod(Vector<const char> buffer, int exponent) {
char copy_buffer[kMaxSignificantDecimalDigits];
Vector<const char> trimmed;
int updated_exponent;
TrimAndCut(buffer, exponent, copy_buffer, kMaxSignificantDecimalDigits,
&trimmed, &updated_exponent);
return StrtodTrimmed(trimmed, updated_exponent);
}
static float SanitizedDoubletof(double d) {
DOUBLE_CONVERSION_ASSERT(d >= 0.0);
// ASAN has a sanitize check that disallows casting doubles to floats if
// they are too big.
// https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html#available-checks
// The behavior should be covered by IEEE 754, but some projects use this
// flag, so work around it.
float max_finite = 3.4028234663852885981170418348451692544e+38;
// The half-way point between the max-finite and infinity value.
// Since infinity has an even significand everything equal or greater than
// this value should become infinity.
double half_max_finite_infinity =
3.40282356779733661637539395458142568448e+38;
if (d >= max_finite) {
if (d >= half_max_finite_infinity) {
return Single::Infinity();
} else {
return max_finite;
}
} else {
return static_cast<float>(d);
}
}
float Strtof(Vector<const char> buffer, int exponent) {
char copy_buffer[kMaxSignificantDecimalDigits];
Vector<const char> trimmed;
int updated_exponent;
TrimAndCut(buffer, exponent, copy_buffer, kMaxSignificantDecimalDigits,
&trimmed, &updated_exponent);
exponent = updated_exponent;
return StrtofTrimmed(trimmed, exponent);
}
float StrtofTrimmed(Vector<const char> trimmed, int exponent) {
DOUBLE_CONVERSION_ASSERT(trimmed.length() <= kMaxSignificantDecimalDigits);
DOUBLE_CONVERSION_ASSERT(AssertTrimmedDigits(trimmed));
double double_guess;
bool is_correct = ComputeGuess(trimmed, exponent, &double_guess);
float float_guess = SanitizedDoubletof(double_guess);
if (float_guess == double_guess) {
// This shortcut triggers for integer values.
return float_guess;
}
// We must catch double-rounding. Say the double has been rounded up, and is
// now a boundary of a float, and rounds up again. This is why we have to
// look at previous too.
// Example (in decimal numbers):
// input: 12349
// high-precision (4 digits): 1235
// low-precision (3 digits):
// when read from input: 123
// when rounded from high precision: 124.
// To do this we simply look at the neighbors of the correct result and see
// if they would round to the same float. If the guess is not correct we have
// to look at four values (since two different doubles could be the correct
// double).
double double_next = Double(double_guess).NextDouble();
double double_previous = Double(double_guess).PreviousDouble();
float f1 = SanitizedDoubletof(double_previous);
float f2 = float_guess;
float f3 = SanitizedDoubletof(double_next);
float f4;
if (is_correct) {
f4 = f3;
} else {
double double_next2 = Double(double_next).NextDouble();
f4 = SanitizedDoubletof(double_next2);
}
(void) f2; // Mark variable as used.
DOUBLE_CONVERSION_ASSERT(f1 <= f2 && f2 <= f3 && f3 <= f4);
// If the guess doesn't lie near a single-precision boundary we can simply
// return its float-value.
if (f1 == f4) {
return float_guess;
}
DOUBLE_CONVERSION_ASSERT((f1 != f2 && f2 == f3 && f3 == f4) ||
(f1 == f2 && f2 != f3 && f3 == f4) ||
(f1 == f2 && f2 == f3 && f3 != f4));
// guess and next are the two possible candidates (in the same way that
// double_guess was the lower candidate for a double-precision guess).
float guess = f1;
float next = f4;
DiyFp upper_boundary;
if (guess == 0.0f) {
float min_float = 1e-45f;
upper_boundary = Double(static_cast<double>(min_float) / 2).AsDiyFp();
} else {
upper_boundary = Single(guess).UpperBoundary();
}
int comparison = CompareBufferWithDiyFp(trimmed, exponent, upper_boundary);
if (comparison < 0) {
return guess;
} else if (comparison > 0) {
return next;
} else if ((Single(guess).Significand() & 1) == 0) {
// Round towards even.
return guess;
} else {
return next;
}
}
} // namespace kenlm_double_conversion
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstmap-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Applies an operation to each arc of an FST.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/script/getters.h>
#include <fst/script/map.h>
DECLARE_double(delta);
DECLARE_string(map_type);
DECLARE_double(power);
DECLARE_string(weight);
int fstmap_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::WeightClass;
string usage = "Applies an operation to each arc of an FST.\n\n Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<FstClass> ifst(FstClass::Read(in_name));
if (!ifst) return 1;
s::MapType map_type;
if (!s::GetMapType(FLAGS_map_type, &map_type)) {
LOG(ERROR) << argv[0] << ": Unknown or unsupported map type "
<< FLAGS_map_type;
return 1;
}
const auto weight_param =
!FLAGS_weight.empty()
? WeightClass(ifst->WeightType(), FLAGS_weight)
: (FLAGS_map_type == "times" ? WeightClass::One(ifst->WeightType())
: WeightClass::Zero(ifst->WeightType()));
std::unique_ptr<FstClass> ofst(
s::Map(*ifst, map_type, FLAGS_delta, FLAGS_power, weight_param));
return !ofst->Write(out_name);
}
| 0 |
coqui_public_repos/TTS/tests | coqui_public_repos/TTS/tests/text_tests/test_belarusian_phonemizer.py | import os
import unittest
import warnings
from TTS.tts.utils.text.belarusian.phonemizer import belarusian_text_to_phonemes
_TEST_CASES = """
Фанетычны канвертар/fanʲɛˈtɨt͡ʂnɨ kanˈvʲɛrtar
Гэтак мы працавалі/ˈɣɛtak ˈmɨ prat͡saˈvalʲi
"""
class TestText(unittest.TestCase):
def test_belarusian_text_to_phonemes(self):
try:
os.environ["BEL_FANETYKA_JAR"]
except KeyError:
warnings.warn(
"You need to define 'BEL_FANETYKA_JAR' environment variable as path to the fanetyka.jar file to test Belarusian phonemizer",
Warning,
)
return
for line in _TEST_CASES.strip().split("\n"):
text, phonemes = line.split("/")
self.assertEqual(belarusian_text_to_phonemes(text), phonemes)
if __name__ == "__main__":
unittest.main()
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_14x_16k_multiarchpkg-linux-amd64-opt.yml | build:
template_file: test-linux-opt-base.tyml
docker_image: "ubuntu:16.04"
dependencies:
- "node-package-cpu"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${nodejs.packages_xenial.prep_14} && ${nodejs.packages_xenial.apt_pinning} && apt-get -qq update && apt-get -qq -y install ${nodejs.packages_xenial.apt}
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-node-tests.sh 14.x 16k"
workerType: "${docker.dsTests}"
metadata:
name: "DeepSpeech Linux AMD64 CPU NodeJS MultiArch Package 14.x tests (16kHz)"
description: "Testing DeepSpeech for Linux/AMD64 on NodeJS MultiArch Package v14.x, CPU only, optimized version (16kHz)"
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/extensions | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/extensions/far/sttable.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// A generic string-to-type table file format.
//
// This is not meant as a generalization of SSTable. This is more of a simple
// replacement for SSTable in order to provide an open-source implementation
// of the FAR format for the external version of the FST library.
#ifndef FST_EXTENSIONS_FAR_STTABLE_H_
#define FST_EXTENSIONS_FAR_STTABLE_H_
#include <algorithm>
#include <istream>
#include <memory>
#include <fstream>
#include <fst/util.h>
namespace fst {
static constexpr int32 kSTTableMagicNumber = 2125656924;
static constexpr int32 kSTTableFileVersion = 1;
// String-type table writing class for an object of type T using a functor
// Writer. The Writer functor must provide at least the following interface:
//
// struct Writer {
// void operator()(std::ostream &, const T &) const;
// };
template <class T, class Writer>
class STTableWriter {
public:
explicit STTableWriter(const string &filename)
: stream_(filename, std::ios_base::out | std::ios_base::binary),
error_(false) {
WriteType(stream_, kSTTableMagicNumber);
WriteType(stream_, kSTTableFileVersion);
if (stream_.fail()) {
FSTERROR() << "STTableWriter::STTableWriter: Error writing to file: "
<< filename;
error_ = true;
}
}
static STTableWriter<T, Writer> *Create(const string &filename) {
if (filename.empty()) {
LOG(ERROR) << "STTableWriter: Writing to standard out unsupported.";
return nullptr;
}
return new STTableWriter<T, Writer>(filename);
}
void Add(const string &key, const T &t) {
if (key == "") {
FSTERROR() << "STTableWriter::Add: Key empty: " << key;
error_ = true;
} else if (key < last_key_) {
FSTERROR() << "STTableWriter::Add: Key out of order: " << key;
error_ = true;
}
if (error_) return;
last_key_ = key;
positions_.push_back(stream_.tellp());
WriteType(stream_, key);
entry_writer_(stream_, t);
}
bool Error() const { return error_; }
~STTableWriter() {
WriteType(stream_, positions_);
WriteType(stream_, static_cast<int64>(positions_.size()));
}
private:
Writer entry_writer_;
std::ofstream stream_;
std::vector<int64> positions_; // Position in file of each key-entry pair.
string last_key_; // Last key.
bool error_;
STTableWriter(const STTableWriter &) = delete;
STTableWriter &operator=(const STTableWriter &) = delete;
};
// String-type table reading class for object of type T using a functor Reader.
// Reader must provide at least the following interface:
//
// struct Reader {
// T *operator()(std::istream &) const;
// };
//
template <class T, class Reader>
class STTableReader {
public:
explicit STTableReader(const std::vector<string> &filenames)
: sources_(filenames), error_(false) {
compare_.reset(new Compare(&keys_));
keys_.resize(filenames.size());
streams_.resize(filenames.size(), 0);
positions_.resize(filenames.size());
for (size_t i = 0; i < filenames.size(); ++i) {
streams_[i] = new std::ifstream(
filenames[i], std::ios_base::in | std::ios_base::binary);
int32 magic_number = 0;
ReadType(*streams_[i], &magic_number);
int32 file_version = 0;
ReadType(*streams_[i], &file_version);
if (magic_number != kSTTableMagicNumber) {
FSTERROR() << "STTableReader::STTableReader: Wrong file type: "
<< filenames[i];
error_ = true;
return;
}
if (file_version != kSTTableFileVersion) {
FSTERROR() << "STTableReader::STTableReader: Wrong file version: "
<< filenames[i];
error_ = true;
return;
}
int64 num_entries;
streams_[i]->seekg(-static_cast<int>(sizeof(int64)), std::ios_base::end);
ReadType(*streams_[i], &num_entries);
if (num_entries > 0) {
streams_[i]->seekg(-static_cast<int>(sizeof(int64)) * (num_entries + 1),
std::ios_base::end);
positions_[i].resize(num_entries);
for (size_t j = 0; (j < num_entries) && (!streams_[i]->fail()); ++j) {
ReadType(*streams_[i], &(positions_[i][j]));
}
streams_[i]->seekg(positions_[i][0]);
if (streams_[i]->fail()) {
FSTERROR() << "STTableReader::STTableReader: Error reading file: "
<< filenames[i];
error_ = true;
return;
}
}
}
MakeHeap();
}
~STTableReader() {
for (auto &stream : streams_) delete stream;
}
static STTableReader<T, Reader> *Open(const string &filename) {
if (filename.empty()) {
LOG(ERROR) << "STTableReader: Operation not supported on standard input";
return nullptr;
}
std::vector<string> filenames;
filenames.push_back(filename);
return new STTableReader<T, Reader>(filenames);
}
static STTableReader<T, Reader> *Open(const std::vector<string> &filenames) {
return new STTableReader<T, Reader>(filenames);
}
void Reset() {
if (error_) return;
for (size_t i = 0; i < streams_.size(); ++i)
streams_[i]->seekg(positions_[i].front());
MakeHeap();
}
bool Find(const string &key) {
if (error_) return false;
for (size_t i = 0; i < streams_.size(); ++i) LowerBound(i, key);
MakeHeap();
if (heap_.empty()) return false;
return keys_[current_] == key;
}
bool Done() const { return error_ || heap_.empty(); }
void Next() {
if (error_) return;
if (streams_[current_]->tellg() <= positions_[current_].back()) {
ReadType(*(streams_[current_]), &(keys_[current_]));
if (streams_[current_]->fail()) {
FSTERROR() << "STTableReader: Error reading file: "
<< sources_[current_];
error_ = true;
return;
}
std::push_heap(heap_.begin(), heap_.end(), *compare_);
} else {
heap_.pop_back();
}
if (!heap_.empty()) PopHeap();
}
const string &GetKey() const { return keys_[current_]; }
const T *GetEntry() const { return entry_.get(); }
bool Error() const { return error_; }
private:
// Comparison functor used to compare stream IDs in the heap.
struct Compare {
explicit Compare(const std::vector<string> *keys) : keys(keys) {}
bool operator()(size_t i, size_t j) const {
return (*keys)[i] > (*keys)[j];
};
private:
const std::vector<string> *keys;
};
// Positions the stream at the position corresponding to the lower bound for
// the specified key.
void LowerBound(size_t id, const string &find_key) {
auto *strm = streams_[id];
const auto &positions = positions_[id];
if (positions.empty()) return;
size_t low = 0;
size_t high = positions.size() - 1;
while (low < high) {
size_t mid = (low + high) / 2;
strm->seekg(positions[mid]);
string key;
ReadType(*strm, &key);
if (key > find_key) {
high = mid;
} else if (key < find_key) {
low = mid + 1;
} else {
for (size_t i = mid; i > low; --i) {
strm->seekg(positions[i - 1]);
ReadType(*strm, &key);
if (key != find_key) {
strm->seekg(positions[i]);
return;
}
}
strm->seekg(positions[low]);
return;
}
}
strm->seekg(positions[low]);
}
// Adds all streams to the heap.
void MakeHeap() {
heap_.clear();
for (size_t i = 0; i < streams_.size(); ++i) {
if (positions_[i].empty()) continue;
ReadType(*streams_[i], &(keys_[i]));
if (streams_[i]->fail()) {
FSTERROR() << "STTableReader: Error reading file: " << sources_[i];
error_ = true;
return;
}
heap_.push_back(i);
}
if (heap_.empty()) return;
std::make_heap(heap_.begin(), heap_.end(), *compare_);
PopHeap();
}
// Positions the stream with the lowest key at the top of the heap, sets
// current_ to the ID of that stream, and reads the current entry from that
// stream.
void PopHeap() {
std::pop_heap(heap_.begin(), heap_.end(), *compare_);
current_ = heap_.back();
entry_.reset(entry_reader_(*streams_[current_]));
if (!entry_) error_ = true;
if (streams_[current_]->fail()) {
FSTERROR() << "STTableReader: Error reading entry for key: "
<< keys_[current_] << ", file: " << sources_[current_];
error_ = true;
}
}
Reader entry_reader_;
std::vector<std::istream *> streams_; // Input streams.
std::vector<string> sources_; // Corresponding file names.
std::vector<std::vector<int64>> positions_; // Index of positions.
std::vector<string> keys_; // Lowest unread key for each stream.
std::vector<int64> heap_; // Heap containing ID of streams with unread keys.
int64 current_; // ID of current stream to be read.
std::unique_ptr<Compare> compare_; // Functor comparing stream IDs.
mutable std::unique_ptr<T> entry_; // The currently read entry.
bool error_;
};
// String-type table header reading function template on the entry header type.
// The Header type must provide at least the following interface:
//
// struct Header {
// void Read(std::istream &istrm, const string &filename);
// };
template <class Header>
bool ReadSTTableHeader(const string &filename, Header *header) {
if (filename.empty()) {
LOG(ERROR) << "ReadSTTable: Can't read header from standard input";
return false;
}
std::ifstream strm(filename, std::ios_base::in | std::ios_base::binary);
if (!strm) {
LOG(ERROR) << "ReadSTTableHeader: Could not open file: " << filename;
return false;
}
int32 magic_number = 0;
ReadType(strm, &magic_number);
int32 file_version = 0;
ReadType(strm, &file_version);
if (magic_number != kSTTableMagicNumber) {
LOG(ERROR) << "ReadSTTableHeader: Wrong file type: " << filename;
return false;
}
if (file_version != kSTTableFileVersion) {
LOG(ERROR) << "ReadSTTableHeader: Wrong file version: " << filename;
return false;
}
int64 i = -1;
strm.seekg(-static_cast<int>(sizeof(int64)), std::ios_base::end);
ReadType(strm, &i); // Reads number of entries
if (strm.fail()) {
LOG(ERROR) << "ReadSTTableHeader: Error reading file: " << filename;
return false;
}
if (i == 0) return true; // No entry header to read.
strm.seekg(-2 * static_cast<int>(sizeof(int64)), std::ios_base::end);
ReadType(strm, &i); // Reads position for last entry in file.
strm.seekg(i);
string key;
ReadType(strm, &key);
header->Read(strm, filename + ":" + key);
if (strm.fail()) {
LOG(ERROR) << "ReadSTTableHeader: Error reading file: " << filename;
return false;
}
return true;
}
bool IsSTTable(const string &filename);
} // namespace fst
#endif // FST_EXTENSIONS_FAR_STTABLE_H_
| 0 |
coqui_public_repos/inference-engine/third_party | coqui_public_repos/inference-engine/third_party/kenlm/setup.py | from setuptools import setup, Extension
import glob
import platform
import os
import sys
import re
#Does gcc compile with this header and library?
def compile_test(header, library):
dummy_path = os.path.join(os.path.dirname(__file__), "dummy")
command = "bash -c \"g++ -include " + header + " -l" + library + " -x c++ - <<<'int main() {}' -o " + dummy_path + " >/dev/null 2>/dev/null && rm " + dummy_path + " 2>/dev/null\""
return os.system(command) == 0
max_order = "6"
is_max_order = [s for s in sys.argv if "--max_order" in s]
for element in is_max_order:
max_order = re.split('[= ]',element)[1]
sys.argv.remove(element)
FILES = glob.glob('util/*.cc') + glob.glob('lm/*.cc') + glob.glob('util/double-conversion/*.cc') + glob.glob('python/*.cc')
FILES = [fn for fn in FILES if not (fn.endswith('main.cc') or fn.endswith('test.cc'))]
if platform.system() == 'Linux':
LIBS = ['stdc++', 'rt']
elif platform.system() == 'Darwin':
LIBS = ['c++']
else:
LIBS = []
#We don't need -std=c++11 but python seems to be compiled with it now. https://github.com/kpu/kenlm/issues/86
ARGS = ['-O3', '-DNDEBUG', '-DKENLM_MAX_ORDER='+max_order, '-std=c++11']
#Attempted fix to https://github.com/kpu/kenlm/issues/186 and https://github.com/kpu/kenlm/issues/197
if platform.system() == 'Darwin':
ARGS += ["-stdlib=libc++", "-mmacosx-version-min=10.7"]
if compile_test('zlib.h', 'z'):
ARGS.append('-DHAVE_ZLIB')
LIBS.append('z')
if compile_test('bzlib.h', 'bz2'):
ARGS.append('-DHAVE_BZLIB')
LIBS.append('bz2')
if compile_test('lzma.h', 'lzma'):
ARGS.append('-DHAVE_XZLIB')
LIBS.append('lzma')
ext_modules = [
Extension(name='kenlm',
sources=FILES + ['python/kenlm.cpp'],
language='C++',
include_dirs=['.'],
libraries=LIBS,
extra_compile_args=ARGS)
]
setup(
name='kenlm',
ext_modules=ext_modules,
include_package_data=True,
)
| 0 |
coqui_public_repos/STT-models/dhivehi/itml | coqui_public_repos/STT-models/dhivehi/itml/v0.1.0/alphabet.txt |
ހ
ށ
ނ
ރ
ބ
ޅ
ކ
އ
ވ
މ
ފ
ދ
ތ
ލ
ގ
ޏ
ސ
ޑ
ޒ
ޓ
ޔ
ޕ
ޖ
ޗ
ޘ
ޙ
ޚ
ޛ
ޜ
ޝ
ޞ
ޟ
ޠ
ޡ
ޢ
ޣ
ޤ
ޥ
ަ
ާ
ި
ީ
ު
ޫ
ެ
ޭ
ޮ
ޯ
ް
| 0 |
coqui_public_repos/TTS/TTS/tts/utils/text | coqui_public_repos/TTS/TTS/tts/utils/text/phonemizers/multi_phonemizer.py | from typing import Dict, List
from TTS.tts.utils.text.phonemizers import DEF_LANG_TO_PHONEMIZER, get_phonemizer_by_name
class MultiPhonemizer:
"""🐸TTS multi-phonemizer that operates phonemizers for multiple langugages
Args:
custom_lang_to_phonemizer (Dict):
Custom phonemizer mapping if you want to change the defaults. In the format of
`{"lang_code", "phonemizer_name"}`. When it is None, `DEF_LANG_TO_PHONEMIZER` is used. Defaults to `{}`.
TODO: find a way to pass custom kwargs to the phonemizers
"""
lang_to_phonemizer = {}
def __init__(self, lang_to_phonemizer_name: Dict = {}) -> None: # pylint: disable=dangerous-default-value
for k, v in lang_to_phonemizer_name.items():
if v == "" and k in DEF_LANG_TO_PHONEMIZER.keys():
lang_to_phonemizer_name[k] = DEF_LANG_TO_PHONEMIZER[k]
elif v == "":
raise ValueError(f"Phonemizer wasn't set for language {k} and doesn't have a default.")
self.lang_to_phonemizer_name = lang_to_phonemizer_name
self.lang_to_phonemizer = self.init_phonemizers(self.lang_to_phonemizer_name)
@staticmethod
def init_phonemizers(lang_to_phonemizer_name: Dict) -> Dict:
lang_to_phonemizer = {}
for k, v in lang_to_phonemizer_name.items():
lang_to_phonemizer[k] = get_phonemizer_by_name(v, language=k)
return lang_to_phonemizer
@staticmethod
def name():
return "multi-phonemizer"
def phonemize(self, text, separator="|", language=""):
if language == "":
raise ValueError("Language must be set for multi-phonemizer to phonemize.")
return self.lang_to_phonemizer[language].phonemize(text, separator)
def supported_languages(self) -> List:
return list(self.lang_to_phonemizer.keys())
def print_logs(self, level: int = 0):
indent = "\t" * level
print(f"{indent}| > phoneme language: {self.supported_languages()}")
print(f"{indent}| > phoneme backend: {self.name()}")
# if __name__ == "__main__":
# texts = {
# "tr": "Merhaba, bu Türkçe bit örnek!",
# "en-us": "Hello, this is English example!",
# "de": "Hallo, das ist ein Deutches Beipiel!",
# "zh-cn": "这是中国的例子",
# }
# phonemes = {}
# ph = MultiPhonemizer({"tr": "espeak", "en-us": "", "de": "gruut", "zh-cn": ""})
# for lang, text in texts.items():
# phoneme = ph.phonemize(text, lang)
# phonemes[lang] = phoneme
# print(phonemes)
| 0 |
coqui_public_repos/TTS/TTS/vocoder | coqui_public_repos/TTS/TTS/vocoder/configs/multiband_melgan_config.py | from dataclasses import dataclass, field
from TTS.vocoder.configs.shared_configs import BaseGANVocoderConfig
@dataclass
class MultibandMelganConfig(BaseGANVocoderConfig):
"""Defines parameters for MultiBandMelGAN vocoder.
Example:
>>> from TTS.vocoder.configs import MultibandMelganConfig
>>> config = MultibandMelganConfig()
Args:
model (str):
Model name used for selecting the right model at initialization. Defaults to `multiband_melgan`.
discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to
'melgan_multiscale_discriminator`.
discriminator_model_params (dict): The discriminator model parameters. Defaults to
'{
"base_channels": 16,
"max_channels": 512,
"downsample_factors": [4, 4, 4]
}`
generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is
considered as a generator too. Defaults to `melgan_generator`.
generator_model_param (dict):
The generator model parameters. Defaults to `{"upsample_factors": [8, 4, 2], "num_res_blocks": 4}`.
use_pqmf (bool):
enable / disable PQMF modulation for multi-band training. Defaults to True.
lr_gen (float):
Initial learning rate for the generator model. Defaults to 0.0001.
lr_disc (float):
Initial learning rate for the discriminator model. Defaults to 0.0001.
optimizer (torch.optim.Optimizer):
Optimizer used for the training. Defaults to `AdamW`.
optimizer_params (dict):
Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
lr_scheduler_gen (torch.optim.Scheduler):
Learning rate scheduler for the generator. Defaults to `MultiStepLR`.
lr_scheduler_gen_params (dict):
Parameters for the generator learning rate scheduler. Defaults to
`{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`.
lr_scheduler_disc (torch.optim.Scheduler):
Learning rate scheduler for the discriminator. Defaults to `MultiStepLR`.
lr_scheduler_dict_params (dict):
Parameters for the discriminator learning rate scheduler. Defaults to
`{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`.
batch_size (int):
Batch size used at training. Larger values use more memory. Defaults to 16.
seq_len (int):
Audio segment length used at training. Larger values use more memory. Defaults to 8192.
pad_short (int):
Additional padding applied to the audio samples shorter than `seq_len`. Defaults to 0.
use_noise_augment (bool):
enable / disable random noise added to the input waveform. The noise is added after computing the
features. Defaults to True.
use_cache (bool):
enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is
not large enough. Defaults to True.
steps_to_start_discriminator (int):
Number of steps required to start training the discriminator. Defaults to 0.
use_stft_loss (bool):`
enable / disable use of STFT loss originally used by ParallelWaveGAN model. Defaults to True.
use_subband_stft (bool):
enable / disable use of subband loss computation originally used by MultiBandMelgan model. Defaults to True.
use_mse_gan_loss (bool):
enable / disable using Mean Squeare Error GAN loss. Defaults to True.
use_hinge_gan_loss (bool):
enable / disable using Hinge GAN loss. You should choose either Hinge or MSE loss for training GAN models.
Defaults to False.
use_feat_match_loss (bool):
enable / disable using Feature Matching loss originally used by MelGAN model. Defaults to True.
use_l1_spec_loss (bool):
enable / disable using L1 spectrogram loss originally used by HifiGAN model. Defaults to False.
stft_loss_params (dict): STFT loss parameters. Default to
`{"n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240]}`
stft_loss_weight (float): STFT loss weight that multiplies the computed loss before summing up the total
model loss. Defaults to 0.5.
subband_stft_loss_weight (float):
Subband STFT loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
mse_G_loss_weight (float):
MSE generator loss weight that multiplies the computed loss before summing up the total loss. faults to 2.5.
hinge_G_loss_weight (float):
Hinge generator loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
feat_match_loss_weight (float):
Feature matching loss weight that multiplies the computed loss before summing up the total loss. faults to 108.
l1_spec_loss_weight (float):
L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
"""
model: str = "multiband_melgan"
# Model specific params
discriminator_model: str = "melgan_multiscale_discriminator"
discriminator_model_params: dict = field(
default_factory=lambda: {"base_channels": 16, "max_channels": 512, "downsample_factors": [4, 4, 4]}
)
generator_model: str = "multiband_melgan_generator"
generator_model_params: dict = field(default_factory=lambda: {"upsample_factors": [8, 4, 2], "num_res_blocks": 4})
use_pqmf: bool = True
# optimizer - overrides
lr_gen: float = 0.0001 # Initial learning rate.
lr_disc: float = 0.0001 # Initial learning rate.
optimizer: str = "AdamW"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "weight_decay": 0.0})
lr_scheduler_gen: str = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
lr_scheduler_gen_params: dict = field(
default_factory=lambda: {"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}
)
lr_scheduler_disc: str = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
lr_scheduler_disc_params: dict = field(
default_factory=lambda: {"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}
)
# Training - overrides
batch_size: int = 64
seq_len: int = 16384
pad_short: int = 2000
use_noise_augment: bool = False
use_cache: bool = True
steps_to_start_discriminator: bool = 200000
# LOSS PARAMETERS - overrides
use_stft_loss: bool = True
use_subband_stft_loss: bool = True
use_mse_gan_loss: bool = True
use_hinge_gan_loss: bool = False
use_feat_match_loss: bool = False # requires MelGAN Discriminators (MelGAN and HifiGAN)
use_l1_spec_loss: bool = False
subband_stft_loss_params: dict = field(
default_factory=lambda: {"n_ffts": [384, 683, 171], "hop_lengths": [30, 60, 10], "win_lengths": [150, 300, 60]}
)
# loss weights - overrides
stft_loss_weight: float = 0.5
subband_stft_loss_weight: float = 0
mse_G_loss_weight: float = 2.5
hinge_G_loss_weight: float = 0
feat_match_loss_weight: float = 108
l1_spec_loss_weight: float = 0
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/script/fst-class.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// These classes are only recommended for use in high-level scripting
// applications. Most users should use the lower-level templated versions
// corresponding to these classes.
#include <istream>
#include <fst/log.h>
#include <fst/equal.h>
#include <fst/fst-decl.h>
#include <fst/reverse.h>
#include <fst/union.h>
#include <fst/script/fst-class.h>
#include <fst/script/register.h>
namespace fst {
namespace script {
// Registration.
REGISTER_FST_CLASSES(StdArc);
REGISTER_FST_CLASSES(LogArc);
REGISTER_FST_CLASSES(Log64Arc);
// Helper functions.
namespace {
template <class F>
F *ReadFstClass(std::istream &istrm, const string &fname) {
if (!istrm) {
LOG(ERROR) << "ReadFstClass: Can't open file: " << fname;
return nullptr;
}
FstHeader hdr;
if (!hdr.Read(istrm, fname)) return nullptr;
const FstReadOptions read_options(fname, &hdr);
const auto &arc_type = hdr.ArcType();
static const auto *io_register = IORegistration<F>::Register::GetRegister();
const auto reader = io_register->GetReader(arc_type);
if (!reader) {
LOG(ERROR) << "ReadFstClass: Unknown arc type: " << arc_type;
return nullptr;
}
return reader(istrm, read_options);
}
template <class F>
FstClassImplBase *CreateFstClass(const string &arc_type) {
static const auto *io_register =
IORegistration<F>::Register::GetRegister();
auto creator = io_register->GetCreator(arc_type);
if (!creator) {
FSTERROR() << "CreateFstClass: Unknown arc type: " << arc_type;
return nullptr;
}
return creator();
}
template <class F>
FstClassImplBase *ConvertFstClass(const FstClass &other) {
static const auto *io_register =
IORegistration<F>::Register::GetRegister();
auto converter = io_register->GetConverter(other.ArcType());
if (!converter) {
FSTERROR() << "ConvertFstClass: Unknown arc type: " << other.ArcType();
return nullptr;
}
return converter(other);
}
} // namespace
// FstClass methods.
FstClass *FstClass::Read(const string &fname) {
if (!fname.empty()) {
std::ifstream istrm(fname, std::ios_base::in | std::ios_base::binary);
return ReadFstClass<FstClass>(istrm, fname);
} else {
return ReadFstClass<FstClass>(std::cin, "standard input");
}
}
FstClass *FstClass::Read(std::istream &istrm, const string &source) {
return ReadFstClass<FstClass>(istrm, source);
}
bool FstClass::WeightTypesMatch(const WeightClass &weight,
const string &op_name) const {
if (WeightType() != weight.Type()) {
FSTERROR() << "FST and weight with non-matching weight types passed to "
<< op_name << ": " << WeightType() << " and " << weight.Type();
return false;
}
return true;
}
// MutableFstClass methods.
MutableFstClass *MutableFstClass::Read(const string &fname, bool convert) {
if (convert == false) {
if (!fname.empty()) {
std::ifstream in(fname, std::ios_base::in | std::ios_base::binary);
return ReadFstClass<MutableFstClass>(in, fname);
} else {
return ReadFstClass<MutableFstClass>(std::cin, "standard input");
}
} else { // Converts to VectorFstClass if not mutable.
std::unique_ptr<FstClass> ifst(FstClass::Read(fname));
if (!ifst) return nullptr;
if (ifst->Properties(kMutable, false) == kMutable) {
return static_cast<MutableFstClass *>(ifst.release());
} else {
return new VectorFstClass(*ifst.release());
}
}
}
// VectorFstClass methods.
VectorFstClass *VectorFstClass::Read(const string &fname) {
if (!fname.empty()) {
std::ifstream in(fname, std::ios_base::in | std::ios_base::binary);
return ReadFstClass<VectorFstClass>(in, fname);
} else {
return ReadFstClass<VectorFstClass>(std::cin, "standard input");
}
}
VectorFstClass::VectorFstClass(const string &arc_type)
: MutableFstClass(CreateFstClass<VectorFstClass>(arc_type)) {}
VectorFstClass::VectorFstClass(const FstClass &other)
: MutableFstClass(ConvertFstClass<VectorFstClass>(other)) {}
} // namespace script
} // namespace fst
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/script/draw.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <ostream>
#include <string>
#include <fst/script/draw.h>
#include <fst/script/fst-class.h>
#include <fst/script/script-impl.h>
namespace fst {
namespace script {
void DrawFst(const FstClass &fst, const SymbolTable *isyms,
const SymbolTable *osyms, const SymbolTable *ssyms, bool accep,
const string &title, float width, float height, bool portrait,
bool vertical, float ranksep, float nodesep, int fontsize,
int precision, const string &float_format, bool show_weight_one,
std::ostream *ostrm, const string &dest) {
FstDrawerArgs args(fst, isyms, osyms, ssyms, accep, title, width, height,
portrait, vertical, ranksep, nodesep, fontsize, precision,
float_format, show_weight_one, ostrm, dest);
Apply<Operation<FstDrawerArgs>>("DrawFst", fst.ArcType(), &args);
}
REGISTER_FST_OPERATION(DrawFst, StdArc, FstDrawerArgs);
REGISTER_FST_OPERATION(DrawFst, LogArc, FstDrawerArgs);
REGISTER_FST_OPERATION(DrawFst, Log64Arc, FstDrawerArgs);
} // namespace script
} // namespace fst
| 0 |
coqui_public_repos/coqpit | coqui_public_repos/coqpit/tests/test_copying.py | import copy
from dataclasses import dataclass
from coqpit.coqpit import Coqpit
@dataclass
class SimpleConfig(Coqpit):
val_a: int = 10
def test_copying():
config = SimpleConfig()
config_new = config.copy()
config_new.val_a = 1234
assert config.val_a != config_new.val_a
config_new = copy.copy(config)
config_new.val_a = 4321
assert config.val_a != config_new.val_a
config_new = copy.deepcopy(config)
config_new.val_a = 4321
assert config.val_a != config_new.val_a
| 0 |
coqui_public_repos/TTS/docs/source | coqui_public_repos/TTS/docs/source/models/bark.md | # 🐶 Bark
Bark is a multi-lingual TTS model created by [Suno-AI](https://www.suno.ai/). It can generate conversational speech as well as music and sound effects.
It is architecturally very similar to Google's [AudioLM](https://arxiv.org/abs/2209.03143). For more information, please refer to the [Suno-AI's repo](https://github.com/suno-ai/bark).
## Acknowledgements
- 👑[Suno-AI](https://www.suno.ai/) for training and open-sourcing this model.
- 👑[gitmylo](https://github.com/gitmylo) for finding [the solution](https://github.com/gitmylo/bark-voice-cloning-HuBERT-quantizer/) to the semantic token generation for voice clones and finetunes.
- 👑[serp-ai](https://github.com/serp-ai/bark-with-voice-clone) for controlled voice cloning.
## Example Use
```python
text = "Hello, my name is Manmay , how are you?"
from TTS.tts.configs.bark_config import BarkConfig
from TTS.tts.models.bark import Bark
config = BarkConfig()
model = Bark.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="path/to/model/dir/", eval=True)
# with random speaker
output_dict = model.synthesize(text, config, speaker_id="random", voice_dirs=None)
# cloning a speaker.
# It assumes that you have a speaker file in `bark_voices/speaker_n/speaker.wav` or `bark_voices/speaker_n/speaker.npz`
output_dict = model.synthesize(text, config, speaker_id="ljspeech", voice_dirs="bark_voices/")
```
Using 🐸TTS API:
```python
from TTS.api import TTS
# Load the model to GPU
# Bark is really slow on CPU, so we recommend using GPU.
tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True)
# Cloning a new speaker
# This expects to find a mp3 or wav file like `bark_voices/new_speaker/speaker.wav`
# It computes the cloning values and stores in `bark_voices/new_speaker/speaker.npz`
tts.tts_to_file(text="Hello, my name is Manmay , how are you?",
file_path="output.wav",
voice_dir="bark_voices/",
speaker="ljspeech")
# When you run it again it uses the stored values to generate the voice.
tts.tts_to_file(text="Hello, my name is Manmay , how are you?",
file_path="output.wav",
voice_dir="bark_voices/",
speaker="ljspeech")
# random speaker
tts = TTS("tts_models/multilingual/multi-dataset/bark", gpu=True)
tts.tts_to_file("hello world", file_path="out.wav")
```
Using 🐸TTS Command line:
```console
# cloning the `ljspeech` voice
tts --model_name tts_models/multilingual/multi-dataset/bark \
--text "This is an example." \
--out_path "output.wav" \
--voice_dir bark_voices/ \
--speaker_idx "ljspeech" \
--progress_bar True
# Random voice generation
tts --model_name tts_models/multilingual/multi-dataset/bark \
--text "This is an example." \
--out_path "output.wav" \
--progress_bar True
```
## Important resources & papers
- Original Repo: https://github.com/suno-ai/bark
- Cloning implementation: https://github.com/serp-ai/bark-with-voice-clone
- AudioLM: https://arxiv.org/abs/2209.03143
## BarkConfig
```{eval-rst}
.. autoclass:: TTS.tts.configs.bark_config.BarkConfig
:members:
```
## Bark Model
```{eval-rst}
.. autoclass:: TTS.tts.models.bark.Bark
:members:
```
| 0 |
coqui_public_repos | coqui_public_repos/STT/requirements_eval_tflite.txt | absl-py==0.9.0
attrdict==2.0.1
stt
numpy==1.16.0
progressbar2==3.47.0
python-utils==2.3.0
six==1.13.0
pandas==0.25.3
| 0 |
coqui_public_repos/STT-examples | coqui_public_repos/STT-examples/electron/package.json | {
"name": "STT-electron",
"productName": "STT-electron",
"version": "1.0.0",
"description": "My Electron application description",
"main": "public/electron.js",
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject",
"dev": "concurrently \"BROWSER=none npm start\" \"wait-on http://localhost:3000 && electron --inspect=5858 .\"",
"dev-win": "concurrently \"npm start\" \"wait-on http://localhost:3000 && electron --inspect=5858 .\"",
"rebuild": "npm rebuild --runtime=electron --target=9.0.5 --disturl=https://atom.io/download/atom-shell --abi=75",
"pack": "npm run build && electron-builder --dir",
"dist": "npm run build && electron-builder",
"dist-win": "npm run build && electron-builder --x64",
"dev-test": "concurrently --kill-others --success first \"wait-on http://localhost:3000 && ./node_modules/.bin/electron public/electron.js STT_TEST\" \"BROWSER=none npm start\""
},
"postinstall": "electron-builder install-app-deps",
"homepage": "./",
"build": {
"appId": "STT-electron",
"productName": "STT-electron",
"files": [
"build/**/*",
"node_modules/**/*",
"package.json"
],
"buildDependenciesFromSource": true,
"artifactName": "STT-electron-${version}-${os}-${arch}.${ext}",
"dmg": {
"title": "${productName}"
},
"mac": {
"category": "public.app-category.utilities",
"target": [
{
"target": "dmg",
"arch": [
"x64"
]
},
{
"target": "zip",
"arch": [
"x64"
]
}
],
"identity": null
},
"win": {
"target": "nsis",
"artifactName": "STT-electron-${version}-${os}-${arch}.${ext}"
},
"linux": {
"target": [
{
"target": "AppImage"
}
],
"category": "Utility"
}
},
"keywords": [],
"license": "MIT",
"dependencies": {
"STT": "^1.0.0",
"electron-is-dev": "^1.1.0",
"lodash": "^4.17.15",
"node-abi": "^2.18.0",
"react": "^16.12.0",
"react-dom": "^16.12.0",
"react-scripts": "^3.4.1",
"request": "^2.88.2",
"wav": "^1.0.2"
},
"devDependencies": {
"concurrently": "^5.0.0",
"electron": "9.0.5",
"electron-builder": "^22.7.0",
"electron-rebuild": "^1.11.0",
"wait-on": "^3.3.0"
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
}
}
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm/lm | coqui_public_repos/inference-engine/third_party/kenlm/lm/common/size_option.hh | #include <boost/program_options.hpp>
#include <cstddef>
#include <string>
namespace lm {
// Create a boost program option for data sizes. This parses sizes like 1T and 10k.
boost::program_options::typed_value<std::string> *SizeOption(std::size_t &to, const char *default_value);
} // namespace lm
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm/lm/common | coqui_public_repos/inference-engine/third_party/kenlm/lm/common/test_data/toy1.arpa | \data\
ngram 1=6
ngram 2=7
ngram 3=6
\1-grams:
-1 <unk> 0
0 <s> -0.30103
-0.6146491 a -0.30103
-0.6146491 </s> 0
-0.7659168 c -0.30103
-0.6146491 b -0.30103
\2-grams:
-0.4301247 <s> a -0.30103
-0.4301247 a a -0.30103
-0.20660876 c </s> 0
-0.5404639 b </s> 0
-0.4740302 <s> c -0.30103
-0.4301247 a b -0.30103
-0.3422159 b b -0.47712123
\3-grams:
-0.1638568 <s> a a
-0.09113217 <s> c </s>
-0.7462621 b b </s>
-0.1638568 a a b
-0.13823806 a b b
-0.13375957 b b b
\end\
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-cpp-linux-amd64-prod_pbmodel-opt.yml | build:
template_file: test-linux-opt-base.tyml
dependencies:
- "linux-amd64-cpu-opt"
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-cpp-ds-tests-prod.sh 16k"
workerType: "${docker.dsTests}"
metadata:
name: "DeepSpeech Linux AMD64 CPU C++ prod tests"
description: "Testing DeepSpeech C++ for Linux/AMD64 on prod model, CPU only, optimized version"
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/concat.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Functions and classes to compute the concatenation of two FSTs.
#ifndef FST_CONCAT_H_
#define FST_CONCAT_H_
#include <algorithm>
#include <vector>
#include <fst/mutable-fst.h>
#include <fst/rational.h>
namespace fst {
// Computes the concatenation (product) of two FSTs. If FST1 transduces string
// x to y with weight a and FST2 transduces string w to v with weight b, then
// their concatenation transduces string xw to yv with weight Times(a, b).
//
// This version modifies its MutableFst argument (in first position).
//
// Complexity:
//
// Time: O(V1 + V2 + E2)
// Space: O(V1 + V2 + E2)
//
// where Vi is the number of states, and Ei is the number of arcs, of the ith
// FST.
template <class Arc>
void Concat(MutableFst<Arc> *fst1, const Fst<Arc> &fst2) {
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
// Checks that the symbol table are compatible.
if (!CompatSymbols(fst1->InputSymbols(), fst2.InputSymbols()) ||
!CompatSymbols(fst1->OutputSymbols(), fst2.OutputSymbols())) {
FSTERROR() << "Concat: Input/output symbol tables of 1st argument "
<< "does not match input/output symbol tables of 2nd argument";
fst1->SetProperties(kError, kError);
return;
}
const auto props1 = fst1->Properties(kFstProperties, false);
const auto props2 = fst2.Properties(kFstProperties, false);
const auto start1 = fst1->Start();
if (start1 == kNoStateId) {
if (props2 & kError) fst1->SetProperties(kError, kError);
return;
}
const auto numstates1 = fst1->NumStates();
if (fst2.Properties(kExpanded, false)) {
fst1->ReserveStates(numstates1 + CountStates(fst2));
}
for (StateIterator<Fst<Arc>> siter2(fst2); !siter2.Done(); siter2.Next()) {
const auto s1 = fst1->AddState();
const auto s2 = siter2.Value();
fst1->SetFinal(s1, fst2.Final(s2));
fst1->ReserveArcs(s1, fst2.NumArcs(s2));
for (ArcIterator<Fst<Arc>> aiter(fst2, s2); !aiter.Done(); aiter.Next()) {
auto arc = aiter.Value();
arc.nextstate += numstates1;
fst1->AddArc(s1, arc);
}
}
const auto start2 = fst2.Start();
for (StateId s1 = 0; s1 < numstates1; ++s1) {
const auto weight = fst1->Final(s1);
if (weight != Weight::Zero()) {
fst1->SetFinal(s1, Weight::Zero());
if (start2 != kNoStateId) {
fst1->AddArc(s1, Arc(0, 0, weight, start2 + numstates1));
}
}
}
if (start2 != kNoStateId) {
fst1->SetProperties(ConcatProperties(props1, props2), kFstProperties);
}
}
// Computes the concatentation of two FSTs. This version modifies its
// MutableFst argument (in second position).
//
// Complexity:
//
// Time: O(V1 + E1)
// Space: O(V1 + E1)
//
// where Vi is the number of states, and Ei is the number of arcs, of the ith
// FST.
template <class Arc>
void Concat(const Fst<Arc> &fst1, MutableFst<Arc> *fst2) {
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
// Checks that the symbol table are compatible.
if (!CompatSymbols(fst1.InputSymbols(), fst2->InputSymbols()) ||
!CompatSymbols(fst1.OutputSymbols(), fst2->OutputSymbols())) {
FSTERROR() << "Concat: Input/output symbol tables of 1st argument "
<< "does not match input/output symbol tables of 2nd argument";
fst2->SetProperties(kError, kError);
return;
}
const auto props1 = fst1.Properties(kFstProperties, false);
const auto props2 = fst2->Properties(kFstProperties, false);
const auto start2 = fst2->Start();
if (start2 == kNoStateId) {
if (props1 & kError) fst2->SetProperties(kError, kError);
return;
}
const auto numstates2 = fst2->NumStates();
if (fst1.Properties(kExpanded, false)) {
fst2->ReserveStates(numstates2 + CountStates(fst1));
}
for (StateIterator<Fst<Arc>> siter(fst1); !siter.Done(); siter.Next()) {
const auto s1 = siter.Value();
const auto s2 = fst2->AddState();
const auto weight = fst1.Final(s1);
if (weight != Weight::Zero()) {
fst2->ReserveArcs(s2, fst1.NumArcs(s1) + 1);
fst2->AddArc(s2, Arc(0, 0, weight, start2));
} else {
fst2->ReserveArcs(s2, fst1.NumArcs(s1));
}
for (ArcIterator<Fst<Arc>> aiter(fst1, s1); !aiter.Done(); aiter.Next()) {
auto arc = aiter.Value();
arc.nextstate += numstates2;
fst2->AddArc(s2, arc);
}
}
const auto start1 = fst1.Start();
if (start1 != kNoStateId) {
fst2->SetStart(start1 + numstates2);
fst2->SetProperties(ConcatProperties(props1, props2), kFstProperties);
} else {
fst2->SetStart(fst2->AddState());
}
}
// Computes the concatentation of two FSTs. This version modifies its
// RationalFst input (in first position).
template <class Arc>
void Concat(RationalFst<Arc> *fst1, const Fst<Arc> &fst2) {
fst1->GetMutableImpl()->AddConcat(fst2, true);
}
// Computes the concatentation of two FSTs. This version modifies its
// RationalFst input (in second position).
template <class Arc>
void Concat(const Fst<Arc> &fst1, RationalFst<Arc> *fst2) {
fst2->GetMutableImpl()->AddConcat(fst1, false);
}
using ConcatFstOptions = RationalFstOptions;
// Computes the concatenation (product) of two FSTs; this version is a delayed
// FST. If FST1 transduces string x to y with weight a and FST2 transduces
// string w to v with weight b, then their concatenation transduces string xw
// to yv with Times(a, b).
//
// Complexity:
//
// Time: O(v1 + e1 + v2 + e2),
// Space: O(v1 + v2)
//
// where vi is the number of states visited, and ei is the number of arcs
// visited, of the ith FST. Constant time and space to visit an input state or
// arc is assumed and exclusive of caching.
template <class A>
class ConcatFst : public RationalFst<A> {
public:
using Arc = A;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
ConcatFst(const Fst<Arc> &fst1, const Fst<Arc> &fst2) {
GetMutableImpl()->InitConcat(fst1, fst2);
}
ConcatFst(const Fst<Arc> &fst1, const Fst<Arc> &fst2,
const ConcatFstOptions &opts)
: RationalFst<Arc>(opts) {
GetMutableImpl()->InitConcat(fst1, fst2);
}
// See Fst<>::Copy() for doc.
ConcatFst(const ConcatFst<Arc> &fst, bool safe = false)
: RationalFst<Arc>(fst, safe) {}
// Get a copy of this ConcatFst. See Fst<>::Copy() for further doc.
ConcatFst<Arc> *Copy(bool safe = false) const override {
return new ConcatFst<Arc>(*this, safe);
}
private:
using ImplToFst<internal::RationalFstImpl<Arc>>::GetImpl;
using ImplToFst<internal::RationalFstImpl<Arc>>::GetMutableImpl;
};
// Specialization for ConcatFst.
template <class Arc>
class StateIterator<ConcatFst<Arc>> : public StateIterator<RationalFst<Arc>> {
public:
explicit StateIterator(const ConcatFst<Arc> &fst)
: StateIterator<RationalFst<Arc>>(fst) {}
};
// Specialization for ConcatFst.
template <class Arc>
class ArcIterator<ConcatFst<Arc>> : public ArcIterator<RationalFst<Arc>> {
public:
using StateId = typename Arc::StateId;
ArcIterator(const ConcatFst<Arc> &fst, StateId s)
: ArcIterator<RationalFst<Arc>>(fst, s) {}
};
// Useful alias when using StdArc.
using StdConcatFst = ConcatFst<StdArc>;
} // namespace fst
#endif // FST_CONCAT_H_
| 0 |
coqui_public_repos/TTS/TTS | coqui_public_repos/TTS/TTS/utils/download.py | # Adapted from https://github.com/pytorch/audio/
import hashlib
import logging
import os
import tarfile
import urllib
import urllib.request
import zipfile
from os.path import expanduser
from typing import Any, Iterable, List, Optional
from torch.utils.model_zoo import tqdm
def stream_url(
url: str, start_byte: Optional[int] = None, block_size: int = 32 * 1024, progress_bar: bool = True
) -> Iterable:
"""Stream url by chunk
Args:
url (str): Url.
start_byte (int or None, optional): Start streaming at that point (Default: ``None``).
block_size (int, optional): Size of chunks to stream (Default: ``32 * 1024``).
progress_bar (bool, optional): Display a progress bar (Default: ``True``).
"""
# If we already have the whole file, there is no need to download it again
req = urllib.request.Request(url, method="HEAD")
with urllib.request.urlopen(req) as response:
url_size = int(response.info().get("Content-Length", -1))
if url_size == start_byte:
return
req = urllib.request.Request(url)
if start_byte:
req.headers["Range"] = "bytes={}-".format(start_byte)
with urllib.request.urlopen(req) as upointer, tqdm(
unit="B",
unit_scale=True,
unit_divisor=1024,
total=url_size,
disable=not progress_bar,
) as pbar:
num_bytes = 0
while True:
chunk = upointer.read(block_size)
if not chunk:
break
yield chunk
num_bytes += len(chunk)
pbar.update(len(chunk))
def download_url(
url: str,
download_folder: str,
filename: Optional[str] = None,
hash_value: Optional[str] = None,
hash_type: str = "sha256",
progress_bar: bool = True,
resume: bool = False,
) -> None:
"""Download file to disk.
Args:
url (str): Url.
download_folder (str): Folder to download file.
filename (str or None, optional): Name of downloaded file. If None, it is inferred from the url
(Default: ``None``).
hash_value (str or None, optional): Hash for url (Default: ``None``).
hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).
progress_bar (bool, optional): Display a progress bar (Default: ``True``).
resume (bool, optional): Enable resuming download (Default: ``False``).
"""
req = urllib.request.Request(url, method="HEAD")
req_info = urllib.request.urlopen(req).info() # pylint: disable=consider-using-with
# Detect filename
filename = filename or req_info.get_filename() or os.path.basename(url)
filepath = os.path.join(download_folder, filename)
if resume and os.path.exists(filepath):
mode = "ab"
local_size: Optional[int] = os.path.getsize(filepath)
elif not resume and os.path.exists(filepath):
raise RuntimeError("{} already exists. Delete the file manually and retry.".format(filepath))
else:
mode = "wb"
local_size = None
if hash_value and local_size == int(req_info.get("Content-Length", -1)):
with open(filepath, "rb") as file_obj:
if validate_file(file_obj, hash_value, hash_type):
return
raise RuntimeError("The hash of {} does not match. Delete the file manually and retry.".format(filepath))
with open(filepath, mode) as fpointer:
for chunk in stream_url(url, start_byte=local_size, progress_bar=progress_bar):
fpointer.write(chunk)
with open(filepath, "rb") as file_obj:
if hash_value and not validate_file(file_obj, hash_value, hash_type):
raise RuntimeError("The hash of {} does not match. Delete the file manually and retry.".format(filepath))
def validate_file(file_obj: Any, hash_value: str, hash_type: str = "sha256") -> bool:
"""Validate a given file object with its hash.
Args:
file_obj: File object to read from.
hash_value (str): Hash for url.
hash_type (str, optional): Hash type, among "sha256" and "md5" (Default: ``"sha256"``).
Returns:
bool: return True if its a valid file, else False.
"""
if hash_type == "sha256":
hash_func = hashlib.sha256()
elif hash_type == "md5":
hash_func = hashlib.md5()
else:
raise ValueError
while True:
# Read by chunk to avoid filling memory
chunk = file_obj.read(1024**2)
if not chunk:
break
hash_func.update(chunk)
return hash_func.hexdigest() == hash_value
def extract_archive(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
"""Extract archive.
Args:
from_path (str): the path of the archive.
to_path (str or None, optional): the root path of the extraced files (directory of from_path)
(Default: ``None``)
overwrite (bool, optional): overwrite existing files (Default: ``False``)
Returns:
list: List of paths to extracted files even if not overwritten.
"""
if to_path is None:
to_path = os.path.dirname(from_path)
try:
with tarfile.open(from_path, "r") as tar:
logging.info("Opened tar file %s.", from_path)
files = []
for file_ in tar: # type: Any
file_path = os.path.join(to_path, file_.name)
if file_.isfile():
files.append(file_path)
if os.path.exists(file_path):
logging.info("%s already extracted.", file_path)
if not overwrite:
continue
tar.extract(file_, to_path)
return files
except tarfile.ReadError:
pass
try:
with zipfile.ZipFile(from_path, "r") as zfile:
logging.info("Opened zip file %s.", from_path)
files = zfile.namelist()
for file_ in files:
file_path = os.path.join(to_path, file_)
if os.path.exists(file_path):
logging.info("%s already extracted.", file_path)
if not overwrite:
continue
zfile.extract(file_, to_path)
return files
except zipfile.BadZipFile:
pass
raise NotImplementedError(" > [!] only supports tar.gz, tgz, and zip achives.")
def download_kaggle_dataset(dataset_path: str, dataset_name: str, output_path: str):
"""Download dataset from kaggle.
Args:
dataset_path (str):
This the kaggle link to the dataset. for example vctk is 'mfekadu/english-multispeaker-corpus-for-voice-cloning'
dataset_name (str): Name of the folder the dataset will be saved in.
output_path (str): Path of the location you want the dataset folder to be saved to.
"""
data_path = os.path.join(output_path, dataset_name)
try:
import kaggle # pylint: disable=import-outside-toplevel
kaggle.api.authenticate()
print(f"""\nDownloading {dataset_name}...""")
kaggle.api.dataset_download_files(dataset_path, path=data_path, unzip=True)
except OSError:
print(
f"""[!] in order to download kaggle datasets, you need to have a kaggle api token stored in your {os.path.join(expanduser('~'), '.kaggle/kaggle.json')}"""
)
| 0 |
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core | coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/session/experimental_onnxruntime_cxx_api.h | // Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Summary: The experimental Ort C++ API is a wrapper around the Ort C++ API.
//
// This C++ API further simplifies usage and provides support for modern C++ syntax/features
// at the cost of some overhead (i.e. using std::string over char *).
//
// Where applicable, default memory allocator options are used unless explicitly set.
//
// Experimental components are designed as drop-in replacements of the regular API, requiring
// minimal code modifications to use.
//
// Example: Ort::Session -> Ort::Experimental::Session
//
// NOTE: Experimental API components are subject to change based on feedback and provide no
// guarantee of backwards compatibility in future releases.
#pragma once
#include "onnxruntime_cxx_api.h"
namespace Ort {
namespace Experimental {
struct Session : Ort::Session {
Session(Env& env, std::basic_string<ORTCHAR_T>& model_path, SessionOptions& options)
: Ort::Session(env, model_path.data(), options){};
Session(Env& env, void* model_data, size_t model_data_length, SessionOptions& options)
: Ort::Session(env, model_data, model_data_length, options){};
// overloaded Run() with sensible defaults
std::vector<Ort::Value> Run(const std::vector<std::string>& input_names,
const std::vector<Ort::Value>& input_values,
const std::vector<std::string>& output_names,
const RunOptions& run_options = RunOptions());
void Run(const std::vector<std::string>& input_names,
const std::vector<Ort::Value>& input_values,
const std::vector<std::string>& output_names,
std::vector<Ort::Value>& output_values,
const RunOptions& run_options = RunOptions());
// convenience methods that simplify common lower-level API calls
std::vector<std::string> GetInputNames() const;
std::vector<std::string> GetOutputNames() const;
std::vector<std::string> GetOverridableInitializerNames() const;
// NOTE: shape dimensions may have a negative value to indicate a symbolic/unknown dimension.
std::vector<std::vector<int64_t> > GetInputShapes() const;
std::vector<std::vector<int64_t> > GetOutputShapes() const;
std::vector<std::vector<int64_t> > GetOverridableInitializerShapes() const;
};
struct Value : Ort::Value {
Value(OrtValue* p)
: Ort::Value(p){};
template <typename T>
static Ort::Value CreateTensor(T* p_data, size_t p_data_element_count, const std::vector<int64_t>& shape);
static Ort::Value CreateTensor(void* p_data, size_t p_data_byte_count, const std::vector<int64_t>& shape, ONNXTensorElementDataType type);
template <typename T>
static Ort::Value CreateTensor(const std::vector<int64_t>& shape);
static Ort::Value CreateTensor(const std::vector<int64_t>& shape, ONNXTensorElementDataType type);
};
}
}
#include "experimental_onnxruntime_cxx_inline.h"
| 0 |
coqui_public_repos/inference-engine/third_party | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/INSTALL | Installation Instructions
*************************
Copyright (C) 1994, 1995, 1996, 1999, 2000, 2001, 2002, 2004, 2005,
2006, 2007 Free Software Foundation, Inc.
This file is free documentation; the Free Software Foundation gives
unlimited permission to copy, distribute and modify it.
Basic Installation
==================
Briefly, the shell commands `./configure; make; make install' should
configure, build, and install this package. The following
more-detailed instructions are generic; see the `README' file for
instructions specific to this package.
The `configure' shell script attempts to guess correct values for
various system-dependent variables used during compilation. It uses
those values to create a `Makefile' in each directory of the package.
It may also create one or more `.h' files containing system-dependent
definitions. Finally, it creates a shell script `config.status' that
you can run in the future to recreate the current configuration, and a
file `config.log' containing compiler output (useful mainly for
debugging `configure').
It can also use an optional file (typically called `config.cache'
and enabled with `--cache-file=config.cache' or simply `-C') that saves
the results of its tests to speed up reconfiguring. Caching is
disabled by default to prevent problems with accidental use of stale
cache files.
If you need to do unusual things to compile the package, please try
to figure out how `configure' could check whether to do them, and mail
diffs or instructions to the address given in the `README' so they can
be considered for the next release. If you are using the cache, and at
some point `config.cache' contains results you don't want to keep, you
may remove or edit it.
The file `configure.ac' (or `configure.in') is used to create
`configure' by a program called `autoconf'. You need `configure.ac' if
you want to change it or regenerate `configure' using a newer version
of `autoconf'.
The simplest way to compile this package is:
1. `cd' to the directory containing the package's source code and type
`./configure' to configure the package for your system.
Running `configure' might take a while. While running, it prints
some messages telling which features it is checking for.
2. Type `make' to compile the package.
3. Optionally, type `make check' to run any self-tests that come with
the package.
4. Type `make install' to install the programs and any data files and
documentation.
5. You can remove the program binaries and object files from the
source code directory by typing `make clean'. To also remove the
files that `configure' created (so you can compile the package for
a different kind of computer), type `make distclean'. There is
also a `make maintainer-clean' target, but that is intended mainly
for the package's developers. If you use it, you may have to get
all sorts of other programs in order to regenerate files that came
with the distribution.
6. Often, you can also type `make uninstall' to remove the installed
files again.
Compilers and Options
=====================
Some systems require unusual options for compilation or linking that the
`configure' script does not know about. Run `./configure --help' for
details on some of the pertinent environment variables.
You can give `configure' initial values for configuration parameters
by setting variables in the command line or in the environment. Here
is an example:
./configure CC=c99 CFLAGS=-g LIBS=-lposix
*Note Defining Variables::, for more details.
Compiling For Multiple Architectures
====================================
You can compile the package for more than one kind of computer at the
same time, by placing the object files for each architecture in their
own directory. To do this, you can use GNU `make'. `cd' to the
directory where you want the object files and executables to go and run
the `configure' script. `configure' automatically checks for the
source code in the directory that `configure' is in and in `..'.
With a non-GNU `make', it is safer to compile the package for one
architecture at a time in the source code directory. After you have
installed the package for one architecture, use `make distclean' before
reconfiguring for another architecture.
Installation Names
==================
By default, `make install' installs the package's commands under
`/usr/local/bin', include files under `/usr/local/include', etc. You
can specify an installation prefix other than `/usr/local' by giving
`configure' the option `--prefix=PREFIX'.
You can specify separate installation prefixes for
architecture-specific files and architecture-independent files. If you
pass the option `--exec-prefix=PREFIX' to `configure', the package uses
PREFIX as the prefix for installing programs and libraries.
Documentation and other data files still use the regular prefix.
In addition, if you use an unusual directory layout you can give
options like `--bindir=DIR' to specify different values for particular
kinds of files. Run `configure --help' for a list of the directories
you can set and what kinds of files go in them.
If the package supports it, you can cause programs to be installed
with an extra prefix or suffix on their names by giving `configure' the
option `--program-prefix=PREFIX' or `--program-suffix=SUFFIX'.
Optional Features
=================
Some packages pay attention to `--enable-FEATURE' options to
`configure', where FEATURE indicates an optional part of the package.
They may also pay attention to `--with-PACKAGE' options, where PACKAGE
is something like `gnu-as' or `x' (for the X Window System). The
`README' should mention any `--enable-' and `--with-' options that the
package recognizes.
For packages that use the X Window System, `configure' can usually
find the X include and library files automatically, but if it doesn't,
you can use the `configure' options `--x-includes=DIR' and
`--x-libraries=DIR' to specify their locations.
Specifying the System Type
==========================
There may be some features `configure' cannot figure out automatically,
but needs to determine by the type of machine the package will run on.
Usually, assuming the package is built to be run on the _same_
architectures, `configure' can figure that out, but if it prints a
message saying it cannot guess the machine type, give it the
`--build=TYPE' option. TYPE can either be a short name for the system
type, such as `sun4', or a canonical name which has the form:
CPU-COMPANY-SYSTEM
where SYSTEM can have one of these forms:
OS KERNEL-OS
See the file `config.sub' for the possible values of each field. If
`config.sub' isn't included in this package, then this package doesn't
need to know the machine type.
If you are _building_ compiler tools for cross-compiling, you should
use the option `--target=TYPE' to select the type of system they will
produce code for.
If you want to _use_ a cross compiler, that generates code for a
platform different from the build platform, you should specify the
"host" platform (i.e., that on which the generated programs will
eventually be run) with `--host=TYPE'.
Sharing Defaults
================
If you want to set default values for `configure' scripts to share, you
can create a site shell script called `config.site' that gives default
values for variables like `CC', `cache_file', and `prefix'.
`configure' looks for `PREFIX/share/config.site' if it exists, then
`PREFIX/etc/config.site' if it exists. Or, you can set the
`CONFIG_SITE' environment variable to the location of the site script.
A warning: not all `configure' scripts look for a site script.
Defining Variables
==================
Variables not defined in a site shell script can be set in the
environment passed to `configure'. However, some packages may run
configure again during the build, and the customized values of these
variables may be lost. In order to avoid this problem, you should set
them in the `configure' command line, using `VAR=value'. For example:
./configure CC=/usr/local2/bin/gcc
causes the specified `gcc' to be used as the C compiler (unless it is
overridden in the site shell script).
Unfortunately, this technique does not work for `CONFIG_SHELL' due to
an Autoconf bug. Until the bug is fixed you can use this workaround:
CONFIG_SHELL=/bin/bash /bin/bash ./configure CONFIG_SHELL=/bin/bash
`configure' Invocation
======================
`configure' recognizes the following options to control how it operates.
`--help'
`-h'
Print a summary of the options to `configure', and exit.
`--version'
`-V'
Print the version of Autoconf used to generate the `configure'
script, and exit.
`--cache-file=FILE'
Enable the cache: use and save the results of the tests in FILE,
traditionally `config.cache'. FILE defaults to `/dev/null' to
disable caching.
`--config-cache'
`-C'
Alias for `--cache-file=config.cache'.
`--quiet'
`--silent'
`-q'
Do not print messages saying which checks are being made. To
suppress all normal output, redirect it to `/dev/null' (any error
messages will still be shown).
`--srcdir=DIR'
Look for the package's source code in directory DIR. Usually
`configure' can determine that directory automatically.
`configure' also accepts some other, not widely useful, options. Run
`configure --help' for more details.
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/project.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Functions and classes to project an FST on to its domain or range.
#ifndef FST_PROJECT_H_
#define FST_PROJECT_H_
#include <fst/arc-map.h>
#include <fst/mutable-fst.h>
namespace fst {
// This specifies whether to project on input or output.
enum ProjectType { PROJECT_INPUT = 1, PROJECT_OUTPUT = 2 };
// Mapper to implement projection per arc.
template <class A>
class ProjectMapper {
public:
using FromArc = A;
using ToArc = A;
explicit ProjectMapper(ProjectType project_type)
: project_type_(project_type) {}
ToArc operator()(const FromArc &arc) const {
const auto label = project_type_ == PROJECT_INPUT ? arc.ilabel : arc.olabel;
return ToArc(label, label, arc.weight, arc.nextstate);
}
constexpr MapFinalAction FinalAction() const {
return MAP_NO_SUPERFINAL;
}
MapSymbolsAction InputSymbolsAction() const {
return project_type_ == PROJECT_INPUT ? MAP_COPY_SYMBOLS
: MAP_CLEAR_SYMBOLS;
}
MapSymbolsAction OutputSymbolsAction() const {
return project_type_ == PROJECT_OUTPUT ? MAP_COPY_SYMBOLS
: MAP_CLEAR_SYMBOLS;
}
uint64_t Properties(uint64_t props) const {
return ProjectProperties(props, project_type_ == PROJECT_INPUT);
}
private:
const ProjectType project_type_;
};
// Projects an FST onto its domain or range by either copying each arcs' input
// label to the output label or vice versa.
//
// Complexity:
//
// Time: O(V + E)
// Space: O(1)
//
// where V is the number of states and E is the number of arcs.
template <class Arc>
inline void Project(const Fst<Arc> &ifst, MutableFst<Arc> *ofst,
ProjectType project_type) {
ArcMap(ifst, ofst, ProjectMapper<Arc>(project_type));
switch (project_type) {
case PROJECT_INPUT:
ofst->SetOutputSymbols(ifst.InputSymbols());
return;
case PROJECT_OUTPUT:
ofst->SetInputSymbols(ifst.OutputSymbols());
return;
}
}
// Destructive variant of the above.
template <class Arc>
inline void Project(MutableFst<Arc> *fst, ProjectType project_type) {
ArcMap(fst, ProjectMapper<Arc>(project_type));
switch (project_type) {
case PROJECT_INPUT:
fst->SetOutputSymbols(fst->InputSymbols());
return;
case PROJECT_OUTPUT:
fst->SetInputSymbols(fst->OutputSymbols());
return;
}
}
// Projects an FST onto its domain or range by either copying each arc's input
// label to the output label or vice versa. This version is a delayed FST.
//
// Complexity:
//
// Time: O(v + e)
// Space: O(1)
//
// where v is the number of states visited and e is the number of arcs visited.
// Constant time and to visit an input state or arc is assumed and exclusive of
// caching.
template <class A>
class ProjectFst : public ArcMapFst<A, A, ProjectMapper<A>> {
public:
using FromArc = A;
using ToArc = A;
using Impl = internal::ArcMapFstImpl<A, A, ProjectMapper<A>>;
ProjectFst(const Fst<A> &fst, ProjectType project_type)
: ArcMapFst<A, A, ProjectMapper<A>>(fst, ProjectMapper<A>(project_type)) {
if (project_type == PROJECT_INPUT) {
GetMutableImpl()->SetOutputSymbols(fst.InputSymbols());
}
if (project_type == PROJECT_OUTPUT) {
GetMutableImpl()->SetInputSymbols(fst.OutputSymbols());
}
}
// See Fst<>::Copy() for doc.
ProjectFst(const ProjectFst<A> &fst, bool safe = false)
: ArcMapFst<A, A, ProjectMapper<A>>(fst, safe) {}
// Gets a copy of this ProjectFst. See Fst<>::Copy() for further doc.
ProjectFst<A> *Copy(bool safe = false) const override {
return new ProjectFst(*this, safe);
}
private:
using ImplToFst<Impl>::GetMutableImpl;
};
// Specialization for ProjectFst.
template <class A>
class StateIterator<ProjectFst<A>>
: public StateIterator<ArcMapFst<A, A, ProjectMapper<A>>> {
public:
explicit StateIterator(const ProjectFst<A> &fst)
: StateIterator<ArcMapFst<A, A, ProjectMapper<A>>>(fst) {}
};
// Specialization for ProjectFst.
template <class A>
class ArcIterator<ProjectFst<A>>
: public ArcIterator<ArcMapFst<A, A, ProjectMapper<A>>> {
public:
using StateId = typename A::StateId;
ArcIterator(const ProjectFst<A> &fst, StateId s)
: ArcIterator<ArcMapFst<A, A, ProjectMapper<A>>>(fst, s) {}
};
// Useful alias when using StdArc.
using StdProjectFst = ProjectFst<StdArc>;
} // namespace fst
#endif // FST_PROJECT_H_
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstreverse.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/flags.h>
DEFINE_bool(require_superinitial, true, "Always create a superinitial state");
int fstreverse_main(int argc, char **argv);
int main(int argc, char **argv) { return fstreverse_main(argc, argv); }
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_15x-darwin-amd64-opt.yml | build:
template_file: test-darwin-opt-base.tyml
dependencies:
- "darwin-amd64-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
- "homebrew_tests-darwin-amd64"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${nodejs.brew.prep_15}
args:
tests_cmdline: "$TASKCLUSTER_TASK_DIR/DeepSpeech/ds/taskcluster/tc-node-tests.sh 15.x 16k"
metadata:
name: "DeepSpeech OSX AMD64 CPU NodeJS 15.x tests"
description: "Testing DeepSpeech for OSX/AMD64 on NodeJS v15.x, CPU only, optimized version"
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/android-arm64-cpu-opt.yml | build:
template_file: linux-opt-base.tyml
dependencies:
- "swig-linux-amd64"
- "node-gyp-cache"
- "pyenv-linux-amd64"
- "tf_android-arm64-opt"
routes:
- "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.android-arm64"
- "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.${event.head.sha}.android-arm64"
- "index.project.deepspeech.deepspeech.native_client.android-arm64.${event.head.sha}"
tensorflow: ${system.tensorflow.android_arm64.url}
scripts:
setup: "taskcluster/tc-true.sh"
build: "taskcluster/android-build.sh arm64-v8a"
package: "taskcluster/android-package.sh arm64-v8a"
nc_asset_name: "native_client.arm64.cpu.android.tar.xz"
workerType: "${docker.dsBuild}"
metadata:
name: "DeepSpeech Android ARM64"
description: "Building DeepSpeech for Android ARM64, optimized version"
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm/lm | coqui_public_repos/inference-engine/third_party/kenlm/lm/common/compare.hh | #ifndef LM_COMMON_COMPARE_H
#define LM_COMMON_COMPARE_H
#include "lm/common/ngram.hh"
#include "lm/word_index.hh"
#include <functional>
#include <string>
namespace lm {
/**
* Abstract parent class for defining custom n-gram comparators.
*/
template <class Child> class Comparator : public std::binary_function<const void *, const void *, bool> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit Comparator(std::size_t order) : order_(order) {}
/**
* Applies the comparator using the Compare method that must be defined in any class that inherits from this class.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*
* @see ContextOrder::Compare
* @see PrefixOrder::Compare
* @see SuffixOrder::Compare
*/
inline bool operator()(const void *lhs, const void *rhs) const {
return static_cast<const Child*>(this)->Compare(static_cast<const WordIndex*>(lhs), static_cast<const WordIndex*>(rhs));
}
/** Gets the n-gram order defined for this comparator. */
std::size_t Order() const { return order_; }
protected:
std::size_t order_;
};
/**
* N-gram comparator that compares n-grams according to their reverse (suffix) order.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the last word of each n-gram and ending with the first word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c > a d b
* - a b c > a b b
* - a b c > x a c
* - a b c < x y z
*/
class SuffixOrder : public Comparator<SuffixOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit SuffixOrder(std::size_t order) : Comparator<SuffixOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the last word of each n-gram and ending with the first word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (std::size_t i = order_ - 1; i != 0; --i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return lhs[0] < rhs[0];
}
static const unsigned kMatchOffset = 1;
};
/**
* N-gram comparator that compares n-grams according to the reverse (suffix) order of the n-gram context.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the penultimate word of each n-gram and ending with the first word of each n-gram;
* finally, this comparator compares the last word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c < a d b
* - a b c > a b b
* - a b c > x a c
* - a b c < x y z
*/
class ContextOrder : public Comparator<ContextOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit ContextOrder(std::size_t order) : Comparator<ContextOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the penultimate word of each n-gram and ending with the first word of each n-gram;
* finally, this comparator compares the last word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (int i = order_ - 2; i >= 0; --i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return lhs[order_ - 1] < rhs[order_ - 1];
}
};
/**
* N-gram comparator that compares n-grams according to their natural (prefix) order.
*
* This comparator compares n-grams lexicographically, one word at a time,
* beginning with the first word of each n-gram and ending with the last word of each n-gram.
*
* Some examples of n-gram comparisons as defined by this comparator:
* - a b c == a b c
* - a b c < a b d
* - a b c < a d b
* - a b c > a b b
* - a b c < x a c
* - a b c < x y z
*/
class PrefixOrder : public Comparator<PrefixOrder> {
public:
/**
* Constructs a comparator capable of comparing two n-grams.
*
* @param order Number of words in each n-gram
*/
explicit PrefixOrder(std::size_t order) : Comparator<PrefixOrder>(order) {}
/**
* Compares two n-grams lexicographically, one word at a time,
* beginning with the first word of each n-gram and ending with the last word of each n-gram.
*
* @param lhs A pointer to the n-gram on the left-hand side of the comparison
* @param rhs A pointer to the n-gram on the right-hand side of the comparison
*/
inline bool Compare(const WordIndex *lhs, const WordIndex *rhs) const {
for (std::size_t i = 0; i < order_; ++i) {
if (lhs[i] != rhs[i])
return lhs[i] < rhs[i];
}
return false;
}
static const unsigned kMatchOffset = 0;
};
template <class Range> struct SuffixLexicographicLess : public std::binary_function<const Range, const Range, bool> {
bool operator()(const Range first, const Range second) const {
for (const WordIndex *f = first.end() - 1, *s = second.end() - 1; f >= first.begin() && s >= second.begin(); --f, --s) {
if (*f < *s) return true;
if (*f > *s) return false;
}
return first.size() < second.size();
}
};
} // namespace lm
#endif // LM_COMMON_COMPARE_H
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/Makefile.am | if HAVE_COMPRESS
compress_include_headers = fst/extensions/compress/compress.h \
fst/extensions/compress/compress-script.h fst/extensions/compress/gzfile.h \
fst/extensions/compress/elias.h fst/extensions/compress/randmod.h
endif
if HAVE_FAR
far_include_headers = fst/extensions/far/compile-strings.h \
fst/extensions/far/create.h fst/extensions/far/equal.h \
fst/extensions/far/extract.h fst/extensions/far/far.h \
fst/extensions/far/far-class.h fst/extensions/far/farlib.h \
fst/extensions/far/farscript.h fst/extensions/far/getters.h \
fst/extensions/far/info.h fst/extensions/far/isomorphic.h \
fst/extensions/far/print-strings.h fst/extensions/far/script-impl.h \
fst/extensions/far/stlist.h fst/extensions/far/sttable.h
endif
if HAVE_LINEAR
linear_include_headers = fst/extensions/linear/linear-fst-data-builder.h \
fst/extensions/linear/linear-fst-data.h fst/extensions/linear/linear-fst.h \
fst/extensions/linear/linearscript.h fst/extensions/linear/loglinear-apply.h \
fst/extensions/linear/trie.h
endif
if HAVE_MPDT
mpdt_include_headers = fst/extensions/mpdt/compose.h \
fst/extensions/mpdt/expand.h fst/extensions/mpdt/info.h \
fst/extensions/mpdt/mpdt.h fst/extensions/mpdt/mpdtlib.h \
fst/extensions/mpdt/mpdtscript.h fst/extensions/mpdt/read_write_utils.h \
fst/extensions/mpdt/reverse.h
endif
if HAVE_NGRAM
ngram_include_headers = fst/extensions/ngram/bitmap-index.h \
fst/extensions/ngram/ngram-fst.h fst/extensions/ngram/nthbit.h
endif
if HAVE_PDT
pdt_include_headers = fst/extensions/pdt/collection.h \
fst/extensions/pdt/compose.h fst/extensions/pdt/expand.h \
fst/extensions/pdt/getters.h fst/extensions/pdt/info.h \
fst/extensions/pdt/paren.h fst/extensions/pdt/pdt.h \
fst/extensions/pdt/pdtlib.h fst/extensions/pdt/pdtscript.h \
fst/extensions/pdt/replace.h fst/extensions/pdt/reverse.h \
fst/extensions/pdt/shortest-path.h
endif
if HAVE_SPECIAL
special_include_headers = fst/extensions/special/phi-fst.h \
fst/extensions/special/rho-fst.h fst/extensions/special/sigma-fst.h
endif
if HAVE_GRM
far_include_headers = fst/extensions/far/compile-strings.h \
fst/extensions/far/create.h fst/extensions/far/equal.h \
fst/extensions/far/extract.h fst/extensions/far/far.h \
fst/extensions/far/far-class.h fst/extensions/far/farlib.h \
fst/extensions/far/farscript.h fst/extensions/far/getters.h \
fst/extensions/far/info.h fst/extensions/far/isomorphic.h \
fst/extensions/far/print-strings.h fst/extensions/far/script-impl.h \
fst/extensions/far/stlist.h fst/extensions/far/sttable.h
mpdt_include_headers = fst/extensions/mpdt/compose.h \
fst/extensions/mpdt/expand.h fst/extensions/mpdt/info.h \
fst/extensions/mpdt/mpdt.h fst/extensions/mpdt/mpdtlib.h \
fst/extensions/mpdt/mpdtscript.h fst/extensions/mpdt/read_write_utils.h \
fst/extensions/mpdt/reverse.h
pdt_include_headers = fst/extensions/pdt/collection.h \
fst/extensions/pdt/compose.h fst/extensions/pdt/expand.h \
fst/extensions/pdt/getters.h fst/extensions/pdt/info.h \
fst/extensions/pdt/paren.h fst/extensions/pdt/pdt.h \
fst/extensions/pdt/pdtlib.h fst/extensions/pdt/pdtscript.h \
fst/extensions/pdt/replace.h fst/extensions/pdt/reverse.h \
fst/extensions/pdt/shortest-path.h
endif
script_include_headers = fst/script/arc-class.h \
fst/script/arciterator-class.h fst/script/arcsort.h \
fst/script/arg-packs.h fst/script/closure.h fst/script/compile-impl.h \
fst/script/compile.h fst/script/compose.h fst/script/concat.h \
fst/script/connect.h fst/script/convert.h fst/script/decode.h \
fst/script/determinize.h fst/script/difference.h fst/script/disambiguate.h \
fst/script/draw-impl.h fst/script/draw.h fst/script/encode.h \
fst/script/encodemapper-class.h fst/script/epsnormalize.h fst/script/equal.h \
fst/script/equivalent.h fst/script/fst-class.h fst/script/fstscript.h \
fst/script/getters.h fst/script/info-impl.h fst/script/info.h \
fst/script/intersect.h fst/script/invert.h fst/script/isomorphic.h \
fst/script/map.h fst/script/minimize.h fst/script/print-impl.h \
fst/script/print.h fst/script/project.h fst/script/prune.h \
fst/script/push.h fst/script/randequivalent.h fst/script/randgen.h \
fst/script/register.h fst/script/relabel.h fst/script/replace.h \
fst/script/reverse.h fst/script/reweight.h fst/script/rmepsilon.h \
fst/script/script-impl.h fst/script/shortest-distance.h \
fst/script/shortest-path.h fst/script/stateiterator-class.h \
fst/script/synchronize.h fst/script/text-io.h fst/script/topsort.h \
fst/script/union.h fst/script/weight-class.h fst/script/fstscript-decl.h \
fst/script/verify.h
nobase_include_HEADERS = fst/accumulator.h fst/add-on.h fst/arc-arena.h \
fst/arc-map.h fst/arc.h fst/arcfilter.h fst/arcsort.h fst/bi-table.h \
fst/cache.h fst/closure.h fst/compact-fst.h fst/compat.h fst/complement.h \
fst/compose-filter.h fst/compose.h fst/concat.h fst/config.h fst/connect.h \
fst/const-fst.h fst/determinize.h fst/dfs-visit.h fst/difference.h \
fst/disambiguate.h fst/edit-fst.h fst/encode.h fst/epsnormalize.h fst/equal.h \
fst/equivalent.h fst/expanded-fst.h fst/expectation-weight.h \
fst/factor-weight.h fst/filter-state.h fst/flags.h fst/float-weight.h \
fst/fst-decl.h fst/fst.h fst/fstlib.h fst/generic-register.h fst/heap.h \
fst/icu.h fst/intersect.h fst/interval-set.h fst/invert.h fst/isomorphic.h \
fst/label-reachable.h fst/lexicographic-weight.h fst/lock.h fst/log.h \
fst/lookahead-filter.h fst/lookahead-matcher.h fst/map.h fst/mapped-file.h \
fst/matcher-fst.h fst/matcher.h fst/memory.h fst/minimize.h fst/mutable-fst.h \
fst/pair-weight.h fst/partition.h fst/power-weight.h fst/product-weight.h \
fst/project.h fst/properties.h fst/prune.h fst/push.h fst/queue.h \
fst/randequivalent.h fst/randgen.h fst/rational.h fst/register.h \
fst/relabel.h fst/replace-util.h fst/replace.h fst/reverse.h fst/reweight.h \
fst/rmepsilon.h fst/rmfinalepsilon.h fst/set-weight.h fst/shortest-distance.h \
fst/shortest-path.h fst/signed-log-weight.h fst/sparse-power-weight.h \
fst/sparse-tuple-weight.h fst/state-map.h fst/state-reachable.h \
fst/state-table.h fst/statesort.h fst/string-weight.h fst/string.h \
fst/symbol-table-ops.h fst/symbol-table.h fst/synchronize.h \
fst/test-properties.h fst/topsort.h fst/tuple-weight.h fst/types.h \
fst/union-find.h fst/union-weight.h fst/union.h fst/util.h fst/vector-fst.h \
fst/verify.h fst/visit.h fst/weight.h \
$(compress_include_headers) \
$(far_include_headers) \
$(linear_include_headers) \
$(mpdt_include_headers) \
$(ngram_include_headers) \
$(pdt_include_headers) \
$(script_include_headers) \
$(special_include_headers)
| 0 |
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core | coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/framework/provider_shutdown.h | // Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
namespace onnxruntime {
void UnloadSharedProviders();
}
| 0 |
coqui_public_repos/STT-models/amharic/itml | coqui_public_repos/STT-models/amharic/itml/v0.1.0/MODEL_CARD.md | # Model card for Amharic STT
Jump to section:
- [Model details](#model-details)
- [Intended use](#intended-use)
- [Performance Factors](#performance-factors)
- [Metrics](#metrics)
- [Training data](#training-data)
- [Evaluation data](#evaluation-data)
- [Ethical considerations](#ethical-considerations)
- [Caveats and recommendations](#caveats-and-recommendations)
## Model details
- Person or organization developing model: Originally trained by [Francis Tyers](https://scholar.google.fr/citations?user=o5HSM6cAAAAJ) and the [Inclusive Technology for Marginalised Languages](https://itml.cl.indiana.edu/) group.
- Model language: Amharic / አማርኛ / `am`
- Model date: April 26, 2021
- Model type: `Speech-to-Text`
- Model version: `v0.1.0`
- Compatible with 🐸 STT version: `v0.9.3`
- License: AGPL
- Citation details: `@techreport{amharic-stt, author = {Tyers,Francis}, title = {Amharic STT 0.1}, institution = {Coqui}, address = {\url{https://github.com/coqui-ai/STT-models}} year = {2021}, month = {April}, number = {STT-ALFFA-AM-0.1} }`
- Where to send questions or comments about the model: You can leave an issue on [`STT-model` issues](https://github.com/coqui-ai/STT-models/issues), open a new discussion on [`STT-model` discussions](https://github.com/coqui-ai/STT-models/discussions), or chat with us on [Gitter](https://gitter.im/coqui-ai/).
## Intended use
Speech-to-Text for the [Amharic Language](https://en.wikipedia.org/wiki/Amharic_language) on 16kHz, mono-channel audio.
## Performance Factors
Factors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise. Read more about STT performance factors [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
## Metrics
STT models are usually evaluated in terms of their transcription accuracy, deployment Real-Time Factor, and model size on disk.
#### Transcription Accuracy
The following Word Error Rates and Character Error Rates are reported on [omnilingo](https://tepozcatl.omnilingo.cc/am/).
|Test Corpus|WER|CER|
|-----------|---|---|
|ALFFA|75.1\%|29.4\%|
#### Real-Time Factor
Real-Time Factor (RTF) is defined as `processing-time / length-of-audio`. The exact real-time factor of an STT model will depend on the hardware setup, so you may experience a different RTF.
Recorded average RTF on laptop CPU: ``
#### Model Size
`model.pbmm`: 181M
`model.tflite`: 46M
### Approaches to uncertainty and variability
Confidence scores and multiple paths from the decoding beam can be used to measure model uncertainty and provide multiple, variable transcripts for any processed audio.
## Training data
This model was trained on the Amharic subset of the [ALFFA](http://openslr.org/25/) corpus.
## Evaluation data
The Model was evaluated on the Amharic subset of the [ALFFA](http://openslr.org/25/) corpus.
## Ethical considerations
Deploying a Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
### Demographic Bias
You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
### Surveillance
Speech-to-Text may be mis-used to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in may countries. You should not assume consent to record and analyze private speech.
## Caveats and recommendations
Machine learning models (like this STT model) perform best on data that is similar to the data on which they were trained. Read about what to expect from an STT model with regard to your data [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
In most applications, it is recommended that you [train your own language model](https://stt.readthedocs.io/en/latest/LANGUAGE_MODEL.html) to improve transcription accuracy on your speech data.
| 0 |
coqui_public_repos/Trainer | coqui_public_repos/Trainer/tests/__init__.py | import os
def run_cli(command):
exit_status = os.system(command)
assert exit_status == 0, f" [!] command `{command}` failed."
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/script/verify.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/script/fst-class.h>
#include <fst/script/script-impl.h>
#include <fst/script/verify.h>
namespace fst {
namespace script {
bool Verify(const FstClass &fst) {
VerifyArgs args(fst);
Apply<Operation<VerifyArgs>>("Verify", fst.ArcType(), &args);
return args.retval;
}
REGISTER_FST_OPERATION(Verify, StdArc, VerifyArgs);
REGISTER_FST_OPERATION(Verify, LogArc, VerifyArgs);
REGISTER_FST_OPERATION(Verify, Log64Arc, VerifyArgs);
} // namespace script
} // namespace fst
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/bin/fstrandgen-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Generates random paths through an FST.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/script/getters.h>
#include <fst/script/randgen.h>
DECLARE_int32(max_length);
DECLARE_int32(npath);
DECLARE_int32(seed);
DECLARE_string(select);
DECLARE_bool(weighted);
DECLARE_bool(remove_total_weight);
int fstrandgen_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::VectorFstClass;
string usage = "Generates random paths through an FST.\n\n Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
VLOG(1) << argv[0] << ": Seed = " << FLAGS_seed;
const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<FstClass> ifst(FstClass::Read(in_name));
if (!ifst) return 1;
VectorFstClass ofst(ifst->ArcType());
s::RandArcSelection ras;
if (!s::GetRandArcSelection(FLAGS_select, &ras)) {
LOG(ERROR) << argv[0] << ": Unknown or unsupported select type "
<< FLAGS_select;
return 1;
}
s::RandGen(*ifst, &ofst, FLAGS_seed,
fst::RandGenOptions<s::RandArcSelection>(
ras, FLAGS_max_length, FLAGS_npath, FLAGS_weighted,
FLAGS_remove_total_weight));
return !ofst.Write(out_name);
}
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm/lm | coqui_public_repos/inference-engine/third_party/kenlm/lm/interpolate/normalize_test.cc | #include "lm/interpolate/normalize.hh"
#include "lm/interpolate/interpolate_info.hh"
#include "lm/interpolate/merge_probabilities.hh"
#include "lm/common/ngram_stream.hh"
#include "util/stream/chain.hh"
#include "util/stream/multi_stream.hh"
#define BOOST_TEST_MODULE NormalizeTest
#include <boost/test/unit_test.hpp>
namespace lm { namespace interpolate { namespace {
// log without backoff
const float kInputs[] = {-0.3, 1.2, -9.8, 4.0, -7.0, 0.0};
class WriteInput {
public:
WriteInput() {}
void Run(const util::stream::ChainPosition &to) {
util::stream::Stream out(to);
for (WordIndex i = 0; i < sizeof(kInputs) / sizeof(float); ++i, ++out) {
memcpy(out.Get(), &i, sizeof(WordIndex));
memcpy((uint8_t*)out.Get() + sizeof(WordIndex), &kInputs[i], sizeof(float));
}
out.Poison();
}
};
void CheckOutput(const util::stream::ChainPosition &from) {
NGramStream<float> in(from);
float sum = 0.0;
for (WordIndex i = 0; i < sizeof(kInputs) / sizeof(float) - 1 /* <s> at the end */; ++i) {
sum += pow(10.0, kInputs[i]);
}
sum = log10(sum);
BOOST_REQUIRE(in);
BOOST_CHECK_CLOSE(kInputs[0] - sum, in->Value(), 0.0001);
BOOST_REQUIRE(++in);
BOOST_CHECK_CLOSE(kInputs[1] - sum, in->Value(), 0.0001);
BOOST_REQUIRE(++in);
BOOST_CHECK_CLOSE(kInputs[2] - sum, in->Value(), 0.0001);
BOOST_REQUIRE(++in);
BOOST_CHECK_CLOSE(kInputs[3] - sum, in->Value(), 0.0001);
BOOST_REQUIRE(++in);
BOOST_CHECK_CLOSE(kInputs[4] - sum, in->Value(), 0.0001);
BOOST_REQUIRE(++in);
BOOST_CHECK_CLOSE(kInputs[5] - sum, in->Value(), 0.0001);
BOOST_CHECK(!++in);
}
BOOST_AUTO_TEST_CASE(Unigrams) {
InterpolateInfo info;
info.lambdas.push_back(2.0);
info.lambdas.push_back(-0.1);
info.orders.push_back(1);
info.orders.push_back(1);
BOOST_CHECK_EQUAL(0, MakeEncoder(info, 1).EncodedLength());
// No backoffs.
util::stream::Chains blank(0);
util::FixedArray<util::stream::ChainPositions> models_by_order(2);
models_by_order.push_back(blank);
models_by_order.push_back(blank);
util::stream::Chains merged_probabilities(1);
util::stream::Chains probabilities_out(1);
util::stream::Chains backoffs_out(0);
merged_probabilities.push_back(util::stream::ChainConfig(sizeof(WordIndex) + sizeof(float) + sizeof(float), 2, 24));
probabilities_out.push_back(util::stream::ChainConfig(sizeof(WordIndex) + sizeof(float), 2, 100));
merged_probabilities[0] >> WriteInput();
Normalize(info, models_by_order, merged_probabilities, probabilities_out, backoffs_out);
util::stream::ChainPosition checker(probabilities_out[0].Add());
merged_probabilities >> util::stream::kRecycle;
probabilities_out >> util::stream::kRecycle;
CheckOutput(checker);
probabilities_out.Wait();
}
}}} // namespaces
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/bin/fstdifference.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/flags.h>
DEFINE_string(compose_filter, "auto",
"Composition filter, one of: \"alt_sequence\", \"auto\", "
"\"match\", \"null\", \"sequence\", \"trivial\"");
DEFINE_bool(connect, true, "Trim output");
int fstdifference_main(int argc, char **argv);
int main(int argc, char **argv) { return fstdifference_main(argc, argv); }
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/script/shortest-path.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/script/fst-class.h>
#include <fst/script/script-impl.h>
#include <fst/script/shortest-path.h>
namespace fst {
namespace script {
void ShortestPath(const FstClass &ifst, MutableFstClass *ofst,
const ShortestPathOptions &opts) {
if (!internal::ArcTypesMatch(ifst, *ofst, "ShortestPath")) {
ofst->SetProperties(kError, kError);
return;
}
ShortestPathArgs args(ifst, ofst, opts);
Apply<Operation<ShortestPathArgs>>("ShortestPath", ifst.ArcType(), &args);
}
REGISTER_FST_OPERATION(ShortestPath, StdArc, ShortestPathArgs);
REGISTER_FST_OPERATION(ShortestPath, LogArc, ShortestPathArgs);
REGISTER_FST_OPERATION(ShortestPath, Log64Arc, ShortestPathArgs);
} // namespace script
} // namespace fst
| 0 |
coqui_public_repos/STT-models/russian/jemeyer | coqui_public_repos/STT-models/russian/jemeyer/v0.1.0/alphabet.txt |
а
б
в
г
д
е
ж
з
и
й
к
л
м
н
о
п
р
с
т
у
ф
х
ц
ч
ш
щ
ъ
ы
ь
э
ю
я
ё
| 0 |
coqui_public_repos/TTS/docs/source | coqui_public_repos/TTS/docs/source/models/xtts.md | # ⓍTTS
ⓍTTS is a super cool Text-to-Speech model that lets you clone voices in different languages by using just a quick 3-second audio clip. Built on the 🐢Tortoise,
ⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy.
There is no need for an excessive amount of training data that spans countless hours.
This is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui API](https://docs.coqui.ai/docs), however we apply
a few tricks to make it faster and support streaming inference.
### Features
- Voice cloning.
- Cross-language voice cloning.
- Multi-lingual speech generation.
- 24khz sampling rate.
- Streaming inference with < 200ms latency. (See [Streaming inference](#streaming-inference))
- Fine-tuning support. (See [Training](#training))
### Updates with v2
- Improved voice cloning.
- Voices can be cloned with a single audio file or multiple audio files, without any effect on the runtime.
- 2 new languages: Hungarian and Korean.
- Across the board quality improvements.
### Code
Current implementation only supports inference and GPT encoder training.
### Languages
As of now, XTTS-v2 supports 16 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu) and Korean (ko).
Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out.
### License
This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml).
### Contact
Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai).
You can also mail us at info@coqui.ai.
### Inference
#### 🐸TTS Command line
You can check all supported languages with the following command:
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--list_language_idx
```
You can check all Coqui available speakers with the following command:
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--list_speaker_idx
```
##### Coqui speakers
You can do inference using one of the available speakers using the following command:
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--text "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent." \
--speaker_idx "Ana Florence" \
--language_idx en \
--use_cuda true
```
##### Clone a voice
You can clone a speaker voice using a single or multiple references:
###### Single reference
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--text "Bugün okula gitmek istemiyorum." \
--speaker_wav /path/to/target/speaker.wav \
--language_idx tr \
--use_cuda true
```
###### Multiple references
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--text "Bugün okula gitmek istemiyorum." \
--speaker_wav /path/to/target/speaker.wav /path/to/target/speaker_2.wav /path/to/target/speaker_3.wav \
--language_idx tr \
--use_cuda true
```
or for all wav files in a directory you can use:
```console
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
--text "Bugün okula gitmek istemiyorum." \
--speaker_wav /path/to/target/*.wav \
--language_idx tr \
--use_cuda true
```
#### 🐸TTS API
##### Clone a voice
You can clone a speaker voice using a single or multiple references:
###### Single reference
Splits the text into sentences and generates audio for each sentence. The audio files are then concatenated to produce the final audio.
You can optionally disable sentence splitting for better coherence but more VRAM and possibly hitting models context length limit.
```python
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker_wav=["/path/to/target/speaker.wav"],
language="en",
split_sentences=True
)
```
###### Multiple references
You can pass multiple audio files to the `speaker_wav` argument for better voice cloning.
```python
from TTS.api import TTS
# using the default version set in 🐸TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
# using a specific version
# 👀 see the branch names for versions on https://huggingface.co/coqui/XTTS-v2/tree/main
# ❗some versions might be incompatible with the API
tts = TTS("xtts_v2.0.2", gpu=True)
# getting the latest XTTS_v2
tts = TTS("xtts", gpu=True)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker_wav=["/path/to/target/speaker.wav", "/path/to/target/speaker_2.wav", "/path/to/target/speaker_3.wav"],
language="en")
```
##### Coqui speakers
You can do inference using one of the available speakers using the following code:
```python
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker="Ana Florence",
language="en",
split_sentences=True
)
```
#### 🐸TTS Model API
To use the model API, you need to download the model files and pass config and model file paths manually.
#### Manual Inference
If you want to be able to `load_checkpoint` with `use_deepspeed=True` and **enjoy the speedup**, you need to install deepspeed first.
```console
pip install deepspeed==0.10.3
```
##### inference parameters
- `text`: The text to be synthesized.
- `language`: The language of the text to be synthesized.
- `gpt_cond_latent`: The latent vector you get with get_conditioning_latents. (You can cache for faster inference with same speaker)
- `speaker_embedding`: The speaker embedding you get with get_conditioning_latents. (You can cache for faster inference with same speaker)
- `temperature`: The softmax temperature of the autoregressive model. Defaults to 0.65.
- `length_penalty`: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs. Defaults to 1.0.
- `repetition_penalty`: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence of long silences or "uhhhhhhs", etc. Defaults to 2.0.
- `top_k`: Lower values mean the decoder produces more "likely" (aka boring) outputs. Defaults to 50.
- `top_p`: Lower values mean the decoder produces more "likely" (aka boring) outputs. Defaults to 0.8.
- `speed`: The speed rate of the generated audio. Defaults to 1.0. (can produce artifacts if far from 1.0)
- `enable_text_splitting`: Whether to split the text into sentences and generate audio for each sentence. It allows you to have infinite input length but might loose important context between sentences. Defaults to True.
##### Inference
```python
import os
import torch
import torchaudio
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
print("Loading model...")
config = XttsConfig()
config.load_json("/path/to/xtts/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=True)
model.cuda()
print("Computing speaker latents...")
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
print("Inference...")
out = model.inference(
"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
"en",
gpt_cond_latent,
speaker_embedding,
temperature=0.7, # Add custom parameters here
)
torchaudio.save("xtts.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
```
##### Streaming manually
Here the goal is to stream the audio as it is being generated. This is useful for real-time applications.
Streaming inference is typically slower than regular inference, but it allows to get a first chunk of audio faster.
```python
import os
import time
import torch
import torchaudio
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
print("Loading model...")
config = XttsConfig()
config.load_json("/path/to/xtts/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=True)
model.cuda()
print("Computing speaker latents...")
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
print("Inference...")
t0 = time.time()
chunks = model.inference_stream(
"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
"en",
gpt_cond_latent,
speaker_embedding
)
wav_chuncks = []
for i, chunk in enumerate(chunks):
if i == 0:
print(f"Time to first chunck: {time.time() - t0}")
print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
wav_chuncks.append(chunk)
wav = torch.cat(wav_chuncks, dim=0)
torchaudio.save("xtts_streaming.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
```
### Training
#### Easy training
To make `XTTS_v2` GPT encoder training easier for beginner users we did a gradio demo that implements the whole fine-tuning pipeline. The gradio demo enables the user to easily do the following steps:
- Preprocessing of the uploaded audio or audio files in 🐸 TTS coqui formatter
- Train the XTTS GPT encoder with the processed data
- Inference support using the fine-tuned model
The user can run this gradio demo locally or remotely using a Colab Notebook.
##### Run demo on Colab
To make the `XTTS_v2` fine-tuning more accessible for users that do not have good GPUs available we did a Google Colab Notebook.
The Colab Notebook is available [here](https://colab.research.google.com/drive/1GiI4_X724M8q2W-zZ-jXo7cWTV7RfaH-?usp=sharing).
To learn how to use this Colab Notebook please check the [XTTS fine-tuning video]().
If you are not able to acess the video you need to follow the steps:
1. Open the Colab notebook and start the demo by runining the first two cells (ignore pip install errors in the first one).
2. Click on the link "Running on public URL:" on the second cell output.
3. On the first Tab (1 - Data processing) you need to select the audio file or files, wait for upload, and then click on the button "Step 1 - Create dataset" and then wait until the dataset processing is done.
4. Soon as the dataset processing is done you need to go to the second Tab (2 - Fine-tuning XTTS Encoder) and press the button "Step 2 - Run the training" and then wait until the training is finished. Note that it can take up to 40 minutes.
5. Soon the training is done you can go to the third Tab (3 - Inference) and then click on the button "Step 3 - Load Fine-tuned XTTS model" and wait until the fine-tuned model is loaded. Then you can do the inference on the model by clicking on the button "Step 4 - Inference".
##### Run demo locally
To run the demo locally you need to do the following steps:
1. Install 🐸 TTS following the instructions available [here](https://tts.readthedocs.io/en/dev/installation.html#installation).
2. Install the Gradio demo requirements with the command `python3 -m pip install -r TTS/demos/xtts_ft_demo/requirements.txt`
3. Run the Gradio demo using the command `python3 TTS/demos/xtts_ft_demo/xtts_demo.py`
4. Follow the steps presented in the [tutorial video](https://www.youtube.com/watch?v=8tpDiiouGxc&feature=youtu.be) to be able to fine-tune and test the fine-tuned model.
If you are not able to access the video, here is what you need to do:
1. On the first Tab (1 - Data processing) select the audio file or files, wait for upload
2. Click on the button "Step 1 - Create dataset" and then wait until the dataset processing is done.
3. Go to the second Tab (2 - Fine-tuning XTTS Encoder) and press the button "Step 2 - Run the training" and then wait until the training is finished. it will take some time.
4. Go to the third Tab (3 - Inference) and then click on the button "Step 3 - Load Fine-tuned XTTS model" and wait until the fine-tuned model is loaded.
5. Now you can run inference with the model by clicking on the button "Step 4 - Inference".
#### Advanced training
A recipe for `XTTS_v2` GPT encoder training using `LJSpeech` dataset is available at https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v1/train_gpt_xtts.py
You need to change the fields of the `BaseDatasetConfig` to match your dataset and then update `GPTArgs` and `GPTTrainerConfig` fields as you need. By default, it will use the same parameters that XTTS v1.1 model was trained with. To speed up the model convergence, as default, it will also download the XTTS v1.1 checkpoint and load it.
After training you can do inference following the code bellow.
```python
import os
import torch
import torchaudio
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
# Add here the xtts_config path
CONFIG_PATH = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_10+36AM-653f2e75/config.json"
# Add here the vocab file that you have used to train the model
TOKENIZER_PATH = "recipes/ljspeech/xtts_v1/run/training/XTTS_v2_original_model_files/vocab.json"
# Add here the checkpoint that you want to do inference with
XTTS_CHECKPOINT = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT/best_model.pth"
# Add here the speaker reference
SPEAKER_REFERENCE = "LjSpeech_reference.wav"
# output wav path
OUTPUT_WAV_PATH = "xtts-ft.wav"
print("Loading model...")
config = XttsConfig()
config.load_json(CONFIG_PATH)
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_path=XTTS_CHECKPOINT, vocab_path=TOKENIZER_PATH, use_deepspeed=False)
model.cuda()
print("Computing speaker latents...")
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=[SPEAKER_REFERENCE])
print("Inference...")
out = model.inference(
"It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
"en",
gpt_cond_latent,
speaker_embedding,
temperature=0.7, # Add custom parameters here
)
torchaudio.save(OUTPUT_WAV_PATH, torch.tensor(out["wav"]).unsqueeze(0), 24000)
```
## References and Acknowledgements
- VallE: https://arxiv.org/abs/2301.02111
- Tortoise Repo: https://github.com/neonbjb/tortoise-tts
- Faster implementation: https://github.com/152334H/tortoise-tts-fast
- Univnet: https://arxiv.org/abs/2106.07889
- Latent Diffusion:https://arxiv.org/abs/2112.10752
- DALL-E: https://arxiv.org/abs/2102.12092
- Perceiver: https://arxiv.org/abs/2103.03206
## XttsConfig
```{eval-rst}
.. autoclass:: TTS.tts.configs.xtts_config.XttsConfig
:members:
```
## XttsArgs
```{eval-rst}
.. autoclass:: TTS.tts.models.xtts.XttsArgs
:members:
```
## XTTS Model
```{eval-rst}
.. autoclass:: TTS.tts.models.xtts.XTTS
:members:
```
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coqui_public_repos/STT/native_client | coqui_public_repos/STT/native_client/kenlm/README.coqui | KenLM source downloaded from https://github.com/kpu/kenlm on 2020/01/15
commit fee7b058bf3f96d570c852340729c0d4d4df2c25
This corresponds to https://github.com/kpu/kenlm/commit/fee7b058bf3f96d570c852340729c0d4d4df2c25
The following procedure was run to remove unneeded files:
cd kenlm
rm -rf windows include lm/filter lm/builder util/stream util/getopt.* python
This was done in order to ensure uniqueness of kenlm_double_conversion:
git grep 'kenlm_double_conversion' | cut -d':' -f1 | sort | uniq | xargs sed -ri 's/kenlm_double_conversion/kenlm_kenlm_double_conversion/g'
Cherry-pick fix for MSVC:
curl -vsSL https://github.com/kpu/kenlm/commit/d70e28403f07e88b276c6bd9f162d2a428530f2e.patch | git am -p1 --directory=native_client/kenlm
Most of the KenLM code is licensed under the LGPL. There are exceptions that
have their own licenses, listed below. See comments in those files for more
details.
util/getopt.* is getopt for Windows
util/murmur_hash.cc
util/string_piece.hh and util/string_piece.cc
util/double-conversion/LICENSE covers util/double-conversion except the build files
util/file.cc contains a modified implementation of mkstemp under the LGPL
util/integer_to_string.* is BSD
For the rest:
KenLM is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published
by the Free Software Foundation, either version 2.1 of the License, or
(at your option) any later version.
KenLM is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License 2.1
along with KenLM code. If not, see <http://www.gnu.org/licenses/lgpl-2.1.html>.
util/double-conversion:
Copyright 2006-2011, the V8 project authors. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of Google Inc. nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
util/integer_to_string.*:
Copyright (C) 2014 Milo Yip
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
| 0 |
coqui_public_repos/STT-examples/net_framework/STTWPF | coqui_public_repos/STT-examples/net_framework/STTWPF/ViewModels/BindableBase.cs | using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Runtime.CompilerServices;
namespace STT.WPF.ViewModels
{
/// <summary>
/// Implementation of <see cref="INotifyPropertyChanged"/> to simplify models.
/// </summary>
public abstract class BindableBase : INotifyPropertyChanged
{
/// <summary>
/// Checks if a property already matches a desired value. Sets the property and
/// notifies listeners only when necessary.
/// </summary>
/// <typeparam name="T">Type of the property.</typeparam>
/// <param name="storage">Reference to a property with both getter and setter.</param>
/// <param name="value">Desired value for the property.</param>
/// <param name="propertyName">Name of the property used to notify listeners. This
/// value is optional and can be provided automatically when invoked from compilers that
/// support CallerMemberName.</param>
/// <returns>True if the value was changed, false if the existing value matched the
/// desired value.</returns>
protected bool SetProperty<T>(ref T backingStore, T value,
[CallerMemberName]string propertyName = "",
Action onChanged = null)
{
if (EqualityComparer<T>.Default.Equals(backingStore, value))
return false;
backingStore = value;
onChanged?.Invoke();
OnPropertyChanged(propertyName);
return true;
}
#region INotifyPropertyChanged
/// <summary>
/// Notifies listeners that a property value has changed.
/// </summary>
/// <param name="propertyName">Name of the property used to notify listeners. This
/// value is optional and can be provided automatically when invoked from compilers
/// that support <see cref="CallerMemberNameAttribute"/>.</param>
public event PropertyChangedEventHandler PropertyChanged;
protected void OnPropertyChanged([CallerMemberName] string propertyName = "")
=> PropertyChanged?.Invoke(this, new PropertyChangedEventArgs(propertyName));
#endregion
}
}
| 0 |
coqui_public_repos/TTS/TTS/vc/modules/freevc | coqui_public_repos/TTS/TTS/vc/modules/freevc/speaker_encoder/hparams.py | ## Mel-filterbank
mel_window_length = 25 # In milliseconds
mel_window_step = 10 # In milliseconds
mel_n_channels = 40
## Audio
sampling_rate = 16000
# Number of spectrogram frames in a partial utterance
partials_n_frames = 160 # 1600 ms
## Voice Activation Detection
# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
# This sets the granularity of the VAD. Should not need to be changed.
vad_window_length = 30 # In milliseconds
# Number of frames to average together when performing the moving average smoothing.
# The larger this value, the larger the VAD variations must be to not get smoothed out.
vad_moving_average_width = 8
# Maximum number of consecutive silent frames a segment can have.
vad_max_silence_length = 6
## Audio volume normalization
audio_norm_target_dBFS = -30
## Model parameters
model_hidden_size = 256
model_embedding_size = 256
model_num_layers = 3
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coqui_public_repos/TTS/TTS/tts/utils/text | coqui_public_repos/TTS/TTS/tts/utils/text/english/number_norm.py | """ from https://github.com/keithito/tacotron """
import re
from typing import Dict
import inflect
_inflect = inflect.engine()
_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
_currency_re = re.compile(r"(£|\$|¥)([0-9\,\.]*[0-9]+)")
_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
_number_re = re.compile(r"-?[0-9]+")
def _remove_commas(m):
return m.group(1).replace(",", "")
def _expand_decimal_point(m):
return m.group(1).replace(".", " point ")
def __expand_currency(value: str, inflection: Dict[float, str]) -> str:
parts = value.replace(",", "").split(".")
if len(parts) > 2:
return f"{value} {inflection[2]}" # Unexpected format
text = []
integer = int(parts[0]) if parts[0] else 0
if integer > 0:
integer_unit = inflection.get(integer, inflection[2])
text.append(f"{integer} {integer_unit}")
fraction = int(parts[1]) if len(parts) > 1 and parts[1] else 0
if fraction > 0:
fraction_unit = inflection.get(fraction / 100, inflection[0.02])
text.append(f"{fraction} {fraction_unit}")
if len(text) == 0:
return f"zero {inflection[2]}"
return " ".join(text)
def _expand_currency(m: "re.Match") -> str:
currencies = {
"$": {
0.01: "cent",
0.02: "cents",
1: "dollar",
2: "dollars",
},
"€": {
0.01: "cent",
0.02: "cents",
1: "euro",
2: "euros",
},
"£": {
0.01: "penny",
0.02: "pence",
1: "pound sterling",
2: "pounds sterling",
},
"¥": {
# TODO rin
0.02: "sen",
2: "yen",
},
}
unit = m.group(1)
currency = currencies[unit]
value = m.group(2)
return __expand_currency(value, currency)
def _expand_ordinal(m):
return _inflect.number_to_words(m.group(0))
def _expand_number(m):
num = int(m.group(0))
if 1000 < num < 3000:
if num == 2000:
return "two thousand"
if 2000 < num < 2010:
return "two thousand " + _inflect.number_to_words(num % 100)
if num % 100 == 0:
return _inflect.number_to_words(num // 100) + " hundred"
return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ")
return _inflect.number_to_words(num, andword="")
def normalize_numbers(text):
text = re.sub(_comma_number_re, _remove_commas, text)
text = re.sub(_currency_re, _expand_currency, text)
text = re.sub(_decimal_number_re, _expand_decimal_point, text)
text = re.sub(_ordinal_re, _expand_ordinal, text)
text = re.sub(_number_re, _expand_number, text)
return text
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coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstepsnormalize-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Epsilon-normalizes an FST.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/script/epsnormalize.h>
#include <fst/script/getters.h>
DECLARE_bool(eps_norm_output);
int fstepsnormalize_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::FstClass;
using fst::script::VectorFstClass;
string usage = "Epsilon normalizes an FST.\n\n Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<FstClass> ifst(FstClass::Read(in_name));
if (!ifst) return 1;
VectorFstClass ofst(ifst->ArcType());
s::EpsNormalize(*ifst, &ofst, s::GetEpsNormalizeType(FLAGS_eps_norm_output));
return !ofst.Write(out_name);
}
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm | coqui_public_repos/inference-engine/third_party/kenlm/util/pool.hh | #ifndef UTIL_POOL_H
#define UTIL_POOL_H
#include <cassert>
#include <cstring>
#include <vector>
#include <stdint.h>
namespace util {
/* Very simple pool. It can only allocate memory. And all of the memory it
* allocates must be freed at the same time.
*/
class Pool {
public:
Pool();
~Pool();
void *Allocate(std::size_t size) {
void *ret = current_;
current_ += size;
if (current_ > current_end_) {
ret = More(size);
}
#ifdef DEBUG
base_check_ = ret;
#endif
return ret;
}
/** Extend (or contract) the most recent allocation.
* @param base The base pointer of the allocation. This must must have been
* returned by the MOST RECENT call to Allocate or Continue.
* @param additional Change in the size.
*
* In most cases, more memory from the same page is used, in which case
* base is unchanged and the function returns false.
* If the page runs out, a new page is created and the memory (from base)
* is copied. The function returns true.
*
* @return Whether the base had to be changed due to allocating a page.
*/
bool Continue(void *&base, std::ptrdiff_t additional) {
#ifdef DEBUG
assert(base == base_check_);
#endif
current_ += additional;
if (current_ > current_end_) {
std::size_t new_total = current_ - static_cast<uint8_t*>(base);
void *new_base = More(new_total);
std::memcpy(new_base, base, new_total - additional);
base = new_base;
#ifdef DEBUG
base_check_ = base;
#endif
return true;
}
return false;
}
void FreeAll();
private:
void *More(std::size_t size);
std::vector<void *> free_list_;
uint8_t *current_, *current_end_;
#ifdef DEBUG
// For debugging, check that Continue came from the most recent call.
void *base_check_;
#endif // DEBUG
// no copying
Pool(const Pool &);
Pool &operator=(const Pool &);
};
/**
* Pool designed to allow limited freeing.
* Keeps a linked list of free elements in the free spaces.
* Will not reduce in size until FreeAll is called.
*/
class FreePool {
public:
explicit FreePool(std::size_t element_size)
: free_list_(NULL), element_size_(element_size) {}
void *Allocate() {
if (free_list_) {
void *ret = free_list_;
free_list_ = *reinterpret_cast<void**>(free_list_);
return ret;
} else {
return backing_.Allocate(element_size_);
}
}
void Free(void *ptr) {
*reinterpret_cast<void**>(ptr) = free_list_;
free_list_ = ptr;
}
std::size_t ElementSize() const { return element_size_; }
private:
void *free_list_;
Pool backing_;
const std::size_t element_size_;
};
} // namespace util
#endif // UTIL_POOL_H
| 0 |
coqui_public_repos/inference-engine/third_party/kenlm/lm | coqui_public_repos/inference-engine/third_party/kenlm/lm/interpolate/backoff_reunification.cc | #include "lm/interpolate/backoff_reunification.hh"
#include "lm/common/model_buffer.hh"
#include "lm/common/ngram_stream.hh"
#include "lm/common/ngram.hh"
#include "lm/common/compare.hh"
#include <algorithm>
#include <cassert>
namespace lm {
namespace interpolate {
namespace {
class MergeWorker {
public:
MergeWorker(std::size_t order, const util::stream::ChainPosition &prob_pos,
const util::stream::ChainPosition &boff_pos)
: order_(order), prob_pos_(prob_pos), boff_pos_(boff_pos) {
// nothing
}
void Run(const util::stream::ChainPosition &position) {
lm::NGramStream<ProbBackoff> stream(position);
lm::NGramStream<float> prob_input(prob_pos_);
util::stream::Stream boff_input(boff_pos_);
for (; prob_input && boff_input; ++prob_input, ++boff_input, ++stream) {
std::copy(prob_input->begin(), prob_input->end(), stream->begin());
stream->Value().prob = std::min(0.0f, prob_input->Value());
stream->Value().backoff = *reinterpret_cast<float *>(boff_input.Get());
}
UTIL_THROW_IF2(prob_input || boff_input,
"Streams were not the same size during merging");
stream.Poison();
}
private:
std::size_t order_;
util::stream::ChainPosition prob_pos_;
util::stream::ChainPosition boff_pos_;
};
}
// Since we are *adding* something to the output chain here, we pass in the
// chain itself so that we can safely add a new step to the chain without
// creating a deadlock situation (since creating a new ChainPosition will
// make a new input/output pair---we want that position to be created
// *here*, not before).
void ReunifyBackoff(util::stream::ChainPositions &prob_pos,
util::stream::ChainPositions &boff_pos,
util::stream::Chains &output_chains) {
assert(prob_pos.size() == boff_pos.size());
for (size_t i = 0; i < prob_pos.size(); ++i)
output_chains[i] >> MergeWorker(i + 1, prob_pos[i], boff_pos[i]);
}
}
}
| 0 |
coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/common | coqui_public_repos/inference-engine/third_party/onnxruntime/include/onnxruntime/core/common/logging/isink.h | // Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#include <string>
#include "core/common/logging/logging.h"
namespace onnxruntime {
namespace logging {
class ISink {
public:
ISink() = default;
/**
Sends the message to the sink.
@param timestamp The timestamp.
@param logger_id The logger identifier.
@param message The captured message.
*/
void Send(const Timestamp& timestamp, const std::string& logger_id, const Capture& message) {
SendImpl(timestamp, logger_id, message);
}
/**
Sends a Profiling Event Record to the sink.
@param Profiling Event Record
*/
virtual void SendProfileEvent(profiling::EventRecord&) const {};
virtual ~ISink() = default;
private:
// Make Code Analysis happy by disabling all for now. Enable as needed.
ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(ISink);
virtual void SendImpl(const Timestamp& timestamp, const std::string& logger_id, const Capture& message) = 0;
};
} // namespace logging
} // namespace onnxruntime
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/state-reachable.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Class to determine whether a given (final) state can be reached from some
// other given state.
#ifndef FST_STATE_REACHABLE_H_
#define FST_STATE_REACHABLE_H_
#include <vector>
#include <fst/log.h>
#include <fst/connect.h>
#include <fst/dfs-visit.h>
#include <fst/fst.h>
#include <fst/interval-set.h>
#include <fst/vector-fst.h>
namespace fst {
// Computes the (final) states reachable from a given state in an FST. After
// this visitor has been called, a final state f can be reached from a state
// s iff (*isets)[s].Member(state2index[f]) is true, where (*isets[s]) is a
// set of half-open inteval of final state indices and state2index[f] maps from
// a final state to its index. If state2index is empty, it is filled-in with
// suitable indices. If it is non-empty, those indices are used; in this case,
// the final states must have out-degree 0.
template <class Arc, class I = typename Arc::StateId, class S = IntervalSet<I>>
class IntervalReachVisitor {
public:
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
using Index = I;
using ISet = S;
using Interval = typename ISet::Interval;
IntervalReachVisitor(const Fst<Arc> &fst, std::vector<S> *isets,
std::vector<Index> *state2index)
: fst_(fst),
isets_(isets),
state2index_(state2index),
index_(state2index->empty() ? 1 : -1),
error_(false) {
isets_->clear();
}
void InitVisit(const Fst<Arc> &) { error_ = false; }
bool InitState(StateId s, StateId r) {
while (isets_->size() <= s) isets_->push_back(S());
while (state2index_->size() <= s) state2index_->push_back(-1);
if (fst_.Final(s) != Weight::Zero()) {
// Create tree interval.
auto *intervals = (*isets_)[s].MutableIntervals();
if (index_ < 0) { // Uses state2index_ map to set index.
if (fst_.NumArcs(s) > 0) {
FSTERROR() << "IntervalReachVisitor: state2index map must be empty "
<< "for this FST";
error_ = true;
return false;
}
const auto index = (*state2index_)[s];
if (index < 0) {
FSTERROR() << "IntervalReachVisitor: state2index map incomplete";
error_ = true;
return false;
}
intervals->push_back(Interval(index, index + 1));
} else { // Use pre-order index.
intervals->push_back(Interval(index_, index_ + 1));
(*state2index_)[s] = index_++;
}
}
return true;
}
constexpr bool TreeArc(StateId, const Arc &) const { return true; }
bool BackArc(StateId s, const Arc &arc) {
FSTERROR() << "IntervalReachVisitor: Cyclic input";
error_ = true;
return false;
}
bool ForwardOrCrossArc(StateId s, const Arc &arc) {
// Non-tree interval.
(*isets_)[s].Union((*isets_)[arc.nextstate]);
return true;
}
void FinishState(StateId s, StateId p, const Arc *) {
if (index_ >= 0 && fst_.Final(s) != Weight::Zero()) {
auto *intervals = (*isets_)[s].MutableIntervals();
(*intervals)[0].end = index_; // Updates tree interval end.
}
(*isets_)[s].Normalize();
if (p != kNoStateId) {
(*isets_)[p].Union((*isets_)[s]); // Propagates intervals to parent.
}
}
void FinishVisit() {}
bool Error() const { return error_; }
private:
const Fst<Arc> &fst_;
std::vector<ISet> *isets_;
std::vector<Index> *state2index_;
Index index_;
bool error_;
};
// Tests reachability of final states from a given state. To test for
// reachability from a state s, first do SetState(s). Then a final state f can
// be reached from state s of FST iff Reach(f) is true. The input can be cyclic,
// but no cycle may contain a final state.
template <class Arc, class I = typename Arc::StateId, class S = IntervalSet<I>>
class StateReachable {
public:
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
using Index = I;
using ISet = S;
using Interval = typename ISet::Interval;
explicit StateReachable(const Fst<Arc> &fst) : error_(false) {
if (fst.Properties(kAcyclic, true)) {
AcyclicStateReachable(fst);
} else {
CyclicStateReachable(fst);
}
}
explicit StateReachable(const StateReachable<Arc> &reachable) {
FSTERROR() << "Copy constructor for state reachable class "
<< "not implemented.";
error_ = true;
}
// Sets current state.
void SetState(StateId s) { s_ = s; }
// Can reach this final state from current state?
bool Reach(StateId s) {
if (s >= state2index_.size()) return false;
const auto i = state2index_[s];
if (i < 0) {
FSTERROR() << "StateReachable: State non-final: " << s;
error_ = true;
return false;
}
return isets_[s_].Member(i);
}
// Access to the state-to-index mapping. Unassigned states have index -1.
std::vector<Index> &State2Index() { return state2index_; }
// Access to the interval sets. These specify the reachability to the final
// states as intervals of the final state indices.
const std::vector<ISet> &IntervalSets() { return isets_; }
bool Error() const { return error_; }
private:
void AcyclicStateReachable(const Fst<Arc> &fst) {
IntervalReachVisitor<Arc, StateId, ISet> reach_visitor(fst, &isets_,
&state2index_);
DfsVisit(fst, &reach_visitor);
if (reach_visitor.Error()) error_ = true;
}
void CyclicStateReachable(const Fst<Arc> &fst) {
// Finds state reachability on the acyclic condensation FST.
VectorFst<Arc> cfst;
std::vector<StateId> scc;
Condense(fst, &cfst, &scc);
StateReachable reachable(cfst);
if (reachable.Error()) {
error_ = true;
return;
}
// Gets the number of states per SCC.
std::vector<size_t> nscc;
for (StateId s = 0; s < scc.size(); ++s) {
const auto c = scc[s];
while (c >= nscc.size()) nscc.push_back(0);
++nscc[c];
}
// Constructs the interval sets and state index mapping for the original
// FST from the condensation FST.
state2index_.resize(scc.size(), -1);
isets_.resize(scc.size());
for (StateId s = 0; s < scc.size(); ++s) {
const auto c = scc[s];
isets_[s] = reachable.IntervalSets()[c];
state2index_[s] = reachable.State2Index()[c];
// Checks that each final state in an input FST is not contained in a
// cycle (i.e., not in a non-trivial SCC).
if (cfst.Final(c) != Weight::Zero() && nscc[c] > 1) {
FSTERROR() << "StateReachable: Final state contained in a cycle";
error_ = true;
return;
}
}
}
StateId s_; // Current state.
std::vector<ISet> isets_; // Interval sets per state.
std::vector<Index> state2index_; // Finds index for a final state.
bool error_;
StateReachable &operator=(const StateReachable &) = delete;
};
} // namespace fst
#endif // FST_STATE_REACHABLE_H_
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-electronjs_v5.0_16k-linux-amd64-opt.yml | build:
template_file: test-linux-opt-base.tyml
docker_image: "ubuntu:16.04"
dependencies:
- "linux-amd64-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${nodejs.packages_xenial.prep_12} && ${nodejs.packages_xenial.apt_pinning} && apt-get -qq update && apt-get -qq -y install ${nodejs.packages_xenial.apt} ${electronjs.packages_xenial.apt}
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-electron-tests.sh 12.x 5.0.6 16k"
workerType: "${docker.dsTests}"
metadata:
name: "DeepSpeech Linux AMD64 CPU ElectronJS v5.0 tests (16kHz)"
description: "Testing DeepSpeech for Linux/AMD64 on ElectronJS v5.0, CPU only, optimized version (16kHz)"
| 0 |
coqui_public_repos/STT/native_client/kenlm | coqui_public_repos/STT/native_client/kenlm/lm/virtual_interface.cc | #include "virtual_interface.hh"
#include "lm_exception.hh"
namespace lm {
namespace base {
Vocabulary::~Vocabulary() {}
void Vocabulary::SetSpecial(WordIndex begin_sentence, WordIndex end_sentence, WordIndex not_found) {
begin_sentence_ = begin_sentence;
end_sentence_ = end_sentence;
not_found_ = not_found;
}
Model::~Model() {}
} // namespace base
} // namespace lm
| 0 |
coqui_public_repos/STT/native_client/kenlm | coqui_public_repos/STT/native_client/kenlm/lm/model_test.cc | #include "model.hh"
#include <cstdlib>
#include <cstring>
#define BOOST_TEST_MODULE ModelTest
#include <boost/test/unit_test.hpp>
#include <boost/test/floating_point_comparison.hpp>
// Apparently some Boost versions use templates and are pretty strict about types matching.
#define SLOPPY_CHECK_CLOSE(ref, value, tol) BOOST_CHECK_CLOSE(static_cast<double>(ref), static_cast<double>(value), static_cast<double>(tol));
namespace lm {
namespace ngram {
std::ostream &operator<<(std::ostream &o, const State &state) {
o << "State length " << static_cast<unsigned int>(state.length) << ':';
for (const WordIndex *i = state.words; i < state.words + state.length; ++i) {
o << ' ' << *i;
}
return o;
}
namespace {
// Stupid bjam reverses the command line arguments randomly.
const char *TestLocation() {
if (boost::unit_test::framework::master_test_suite().argc < 3) {
return "test.arpa";
}
char **argv = boost::unit_test::framework::master_test_suite().argv;
return argv[strstr(argv[1], "nounk") ? 2 : 1];
}
const char *TestNoUnkLocation() {
if (boost::unit_test::framework::master_test_suite().argc < 3) {
return "test_nounk.arpa";
}
char **argv = boost::unit_test::framework::master_test_suite().argv;
return argv[strstr(argv[1], "nounk") ? 1 : 2];
}
template <class Model> State GetState(const Model &model, const char *word, const State &in) {
WordIndex context[in.length + 1];
context[0] = model.GetVocabulary().Index(word);
std::copy(in.words, in.words + in.length, context + 1);
State ret;
model.GetState(context, context + in.length + 1, ret);
return ret;
}
#define StartTest(word, ngram, score, indep_left) \
ret = model.FullScore( \
state, \
model.GetVocabulary().Index(word), \
out);\
SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \
BOOST_CHECK_EQUAL(static_cast<unsigned int>(ngram), ret.ngram_length); \
BOOST_CHECK_GE(std::min<unsigned char>(ngram, 5 - 1), out.length); \
BOOST_CHECK_EQUAL(indep_left, ret.independent_left); \
BOOST_CHECK_EQUAL(out, GetState(model, word, state));
#define AppendTest(word, ngram, score, indep_left) \
StartTest(word, ngram, score, indep_left) \
state = out;
template <class M> void Starters(const M &model) {
FullScoreReturn ret;
Model::State state(model.BeginSentenceState());
Model::State out;
StartTest("looking", 2, -0.4846522, true);
// , probability plus <s> backoff
StartTest(",", 1, -1.383514 + -0.4149733, true);
// <unk> probability plus <s> backoff
StartTest("this_is_not_found", 1, -1.995635 + -0.4149733, true);
}
template <class M> void Continuation(const M &model) {
FullScoreReturn ret;
Model::State state(model.BeginSentenceState());
Model::State out;
AppendTest("looking", 2, -0.484652, true);
AppendTest("on", 3, -0.348837, true);
AppendTest("a", 4, -0.0155266, true);
AppendTest("little", 5, -0.00306122, true);
State preserve = state;
AppendTest("the", 1, -4.04005, true);
AppendTest("biarritz", 1, -1.9889, true);
AppendTest("not_found", 1, -2.29666, true);
AppendTest("more", 1, -1.20632 - 20.0, true);
AppendTest(".", 2, -0.51363, true);
AppendTest("</s>", 3, -0.0191651, true);
BOOST_CHECK_EQUAL(0, state.length);
state = preserve;
AppendTest("more", 5, -0.00181395, true);
BOOST_CHECK_EQUAL(4, state.length);
AppendTest("loin", 5, -0.0432557, true);
BOOST_CHECK_EQUAL(1, state.length);
}
template <class M> void Blanks(const M &model) {
FullScoreReturn ret;
State state(model.NullContextState());
State out;
AppendTest("also", 1, -1.687872, false);
AppendTest("would", 2, -2, true);
AppendTest("consider", 3, -3, true);
State preserve = state;
AppendTest("higher", 4, -4, true);
AppendTest("looking", 5, -5, true);
BOOST_CHECK_EQUAL(1, state.length);
state = preserve;
// also would consider not_found
AppendTest("not_found", 1, -1.995635 - 7.0 - 0.30103, true);
state = model.NullContextState();
// higher looking is a blank.
AppendTest("higher", 1, -1.509559, false);
AppendTest("looking", 2, -1.285941 - 0.30103, false);
State higher_looking = state;
BOOST_CHECK_EQUAL(1, state.length);
AppendTest("not_found", 1, -1.995635 - 0.4771212, true);
state = higher_looking;
// higher looking consider
AppendTest("consider", 1, -1.687872 - 0.4771212, true);
state = model.NullContextState();
AppendTest("would", 1, -1.687872, false);
BOOST_CHECK_EQUAL(1, state.length);
AppendTest("consider", 2, -1.687872 -0.30103, false);
BOOST_CHECK_EQUAL(2, state.length);
AppendTest("higher", 3, -1.509559 - 0.30103, false);
BOOST_CHECK_EQUAL(3, state.length);
AppendTest("looking", 4, -1.285941 - 0.30103, false);
}
template <class M> void Unknowns(const M &model) {
FullScoreReturn ret;
State state(model.NullContextState());
State out;
AppendTest("not_found", 1, -1.995635, false);
State preserve = state;
AppendTest("not_found2", 2, -15.0, true);
AppendTest("not_found3", 2, -15.0 - 2.0, true);
state = preserve;
AppendTest("however", 2, -4, true);
AppendTest("not_found3", 3, -6, true);
}
template <class M> void MinimalState(const M &model) {
FullScoreReturn ret;
State state(model.NullContextState());
State out;
AppendTest("baz", 1, -6.535897, true);
BOOST_CHECK_EQUAL(0, state.length);
state = model.NullContextState();
AppendTest("foo", 1, -3.141592, true);
BOOST_CHECK_EQUAL(1, state.length);
AppendTest("bar", 2, -6.0, true);
// Has to include the backoff weight.
BOOST_CHECK_EQUAL(1, state.length);
AppendTest("bar", 1, -2.718281 + 3.0, true);
BOOST_CHECK_EQUAL(1, state.length);
state = model.NullContextState();
AppendTest("to", 1, -1.687872, false);
AppendTest("look", 2, -0.2922095, true);
BOOST_CHECK_EQUAL(2, state.length);
AppendTest("a", 3, -7, true);
}
template <class M> void ExtendLeftTest(const M &model) {
State right;
FullScoreReturn little(model.FullScore(model.NullContextState(), model.GetVocabulary().Index("little"), right));
const float kLittleProb = -1.285941;
SLOPPY_CHECK_CLOSE(kLittleProb, little.prob, 0.001);
unsigned char next_use;
float backoff_out[4];
FullScoreReturn extend_none(model.ExtendLeft(NULL, NULL, NULL, little.extend_left, 1, NULL, next_use));
BOOST_CHECK_EQUAL(0, next_use);
BOOST_CHECK_EQUAL(little.extend_left, extend_none.extend_left);
SLOPPY_CHECK_CLOSE(little.prob - little.rest, extend_none.prob, 0.001);
BOOST_CHECK_EQUAL(1, extend_none.ngram_length);
const WordIndex a = model.GetVocabulary().Index("a");
float backoff_in = 3.14;
// a little
FullScoreReturn extend_a(model.ExtendLeft(&a, &a + 1, &backoff_in, little.extend_left, 1, backoff_out, next_use));
BOOST_CHECK_EQUAL(1, next_use);
SLOPPY_CHECK_CLOSE(-0.69897, backoff_out[0], 0.001);
SLOPPY_CHECK_CLOSE(-0.09132547 - little.rest, extend_a.prob, 0.001);
BOOST_CHECK_EQUAL(2, extend_a.ngram_length);
BOOST_CHECK(!extend_a.independent_left);
const WordIndex on = model.GetVocabulary().Index("on");
FullScoreReturn extend_on(model.ExtendLeft(&on, &on + 1, &backoff_in, extend_a.extend_left, 2, backoff_out, next_use));
BOOST_CHECK_EQUAL(1, next_use);
SLOPPY_CHECK_CLOSE(-0.4771212, backoff_out[0], 0.001);
SLOPPY_CHECK_CLOSE(-0.0283603 - (extend_a.rest + little.rest), extend_on.prob, 0.001);
BOOST_CHECK_EQUAL(3, extend_on.ngram_length);
BOOST_CHECK(!extend_on.independent_left);
const WordIndex both[2] = {a, on};
float backoff_in_arr[4];
FullScoreReturn extend_both(model.ExtendLeft(both, both + 2, backoff_in_arr, little.extend_left, 1, backoff_out, next_use));
BOOST_CHECK_EQUAL(2, next_use);
SLOPPY_CHECK_CLOSE(-0.69897, backoff_out[0], 0.001);
SLOPPY_CHECK_CLOSE(-0.4771212, backoff_out[1], 0.001);
SLOPPY_CHECK_CLOSE(-0.0283603 - little.rest, extend_both.prob, 0.001);
BOOST_CHECK_EQUAL(3, extend_both.ngram_length);
BOOST_CHECK(!extend_both.independent_left);
BOOST_CHECK_EQUAL(extend_on.extend_left, extend_both.extend_left);
}
#define StatelessTest(word, provide, ngram, score) \
ret = model.FullScoreForgotState(indices + num_words - word, indices + num_words - word + provide, indices[num_words - word - 1], state); \
SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \
BOOST_CHECK_EQUAL(static_cast<unsigned int>(ngram), ret.ngram_length); \
model.GetState(indices + num_words - word, indices + num_words - word + provide, before); \
ret = model.FullScore(before, indices[num_words - word - 1], out); \
BOOST_CHECK(state == out); \
SLOPPY_CHECK_CLOSE(score, ret.prob, 0.001); \
BOOST_CHECK_EQUAL(static_cast<unsigned int>(ngram), ret.ngram_length);
template <class M> void Stateless(const M &model) {
const char *words[] = {"<s>", "looking", "on", "a", "little", "the", "biarritz", "not_found", "more", ".", "</s>"};
const size_t num_words = sizeof(words) / sizeof(const char*);
// Silience "array subscript is above array bounds" when extracting end pointer.
WordIndex indices[num_words + 1];
for (unsigned int i = 0; i < num_words; ++i) {
indices[num_words - 1 - i] = model.GetVocabulary().Index(words[i]);
}
FullScoreReturn ret;
State state, out, before;
ret = model.FullScoreForgotState(indices + num_words - 1, indices + num_words, indices[num_words - 2], state);
SLOPPY_CHECK_CLOSE(-0.484652, ret.prob, 0.001);
StatelessTest(1, 1, 2, -0.484652);
// looking
StatelessTest(1, 2, 2, -0.484652);
// on
AppendTest("on", 3, -0.348837, true);
StatelessTest(2, 3, 3, -0.348837);
StatelessTest(2, 2, 3, -0.348837);
StatelessTest(2, 1, 2, -0.4638903);
// a
StatelessTest(3, 4, 4, -0.0155266);
// little
AppendTest("little", 5, -0.00306122, true);
StatelessTest(4, 5, 5, -0.00306122);
// the
AppendTest("the", 1, -4.04005, true);
StatelessTest(5, 5, 1, -4.04005);
// No context of the.
StatelessTest(5, 0, 1, -1.687872);
// biarritz
StatelessTest(6, 1, 1, -1.9889);
// not found
StatelessTest(7, 1, 1, -2.29666);
StatelessTest(7, 0, 1, -1.995635);
WordIndex unk[1];
unk[0] = 0;
model.GetState(unk, unk + 1, state);
BOOST_CHECK_EQUAL(1, state.length);
BOOST_CHECK_EQUAL(static_cast<WordIndex>(0), state.words[0]);
}
template <class M> void NoUnkCheck(const M &model) {
WordIndex unk_index = 0;
State state;
FullScoreReturn ret = model.FullScoreForgotState(&unk_index, &unk_index + 1, unk_index, state);
SLOPPY_CHECK_CLOSE(-100.0, ret.prob, 0.001);
}
template <class M> void Everything(const M &m) {
Starters(m);
Continuation(m);
Blanks(m);
Unknowns(m);
MinimalState(m);
ExtendLeftTest(m);
Stateless(m);
}
class ExpectEnumerateVocab : public EnumerateVocab {
public:
ExpectEnumerateVocab() {}
void Add(WordIndex index, const StringPiece &str) {
BOOST_CHECK_EQUAL(seen.size(), index);
seen.push_back(std::string(str.data(), str.length()));
}
void Check(const base::Vocabulary &vocab) {
BOOST_CHECK_EQUAL(37ULL, seen.size());
BOOST_REQUIRE(!seen.empty());
BOOST_CHECK_EQUAL("<unk>", seen[0]);
for (WordIndex i = 0; i < seen.size(); ++i) {
BOOST_CHECK_EQUAL(i, vocab.Index(seen[i]));
}
}
void Clear() {
seen.clear();
}
std::vector<std::string> seen;
};
template <class ModelT> void LoadingTest() {
Config config;
config.arpa_complain = Config::NONE;
config.messages = NULL;
config.probing_multiplier = 2.0;
{
ExpectEnumerateVocab enumerate;
config.enumerate_vocab = &enumerate;
ModelT m(TestLocation(), config);
enumerate.Check(m.GetVocabulary());
BOOST_CHECK_EQUAL((WordIndex)37, m.GetVocabulary().Bound());
Everything(m);
}
{
ExpectEnumerateVocab enumerate;
config.enumerate_vocab = &enumerate;
ModelT m(TestNoUnkLocation(), config);
enumerate.Check(m.GetVocabulary());
BOOST_CHECK_EQUAL((WordIndex)37, m.GetVocabulary().Bound());
NoUnkCheck(m);
}
}
BOOST_AUTO_TEST_CASE(probing) {
LoadingTest<Model>();
}
BOOST_AUTO_TEST_CASE(trie) {
LoadingTest<TrieModel>();
}
BOOST_AUTO_TEST_CASE(quant_trie) {
LoadingTest<QuantTrieModel>();
}
BOOST_AUTO_TEST_CASE(bhiksha_trie) {
LoadingTest<ArrayTrieModel>();
}
BOOST_AUTO_TEST_CASE(quant_bhiksha_trie) {
LoadingTest<QuantArrayTrieModel>();
}
template <class ModelT> void BinaryTest(Config::WriteMethod write_method) {
Config config;
config.write_mmap = "test.binary";
config.messages = NULL;
config.write_method = write_method;
ExpectEnumerateVocab enumerate;
config.enumerate_vocab = &enumerate;
{
ModelT copy_model(TestLocation(), config);
enumerate.Check(copy_model.GetVocabulary());
enumerate.Clear();
Everything(copy_model);
}
config.write_mmap = NULL;
ModelType type;
BOOST_REQUIRE(RecognizeBinary("test.binary", type));
BOOST_CHECK_EQUAL(ModelT::kModelType, type);
{
ModelT binary("test.binary", config);
enumerate.Check(binary.GetVocabulary());
Everything(binary);
}
unlink("test.binary");
// Now test without <unk>.
config.write_mmap = "test_nounk.binary";
config.messages = NULL;
enumerate.Clear();
{
ModelT copy_model(TestNoUnkLocation(), config);
enumerate.Check(copy_model.GetVocabulary());
enumerate.Clear();
NoUnkCheck(copy_model);
}
config.write_mmap = NULL;
{
ModelT binary(TestNoUnkLocation(), config);
enumerate.Check(binary.GetVocabulary());
NoUnkCheck(binary);
}
unlink("test_nounk.binary");
}
template <class ModelT> void BinaryTest() {
BinaryTest<ModelT>(Config::WRITE_MMAP);
BinaryTest<ModelT>(Config::WRITE_AFTER);
}
BOOST_AUTO_TEST_CASE(write_and_read_probing) {
BinaryTest<ProbingModel>();
}
BOOST_AUTO_TEST_CASE(write_and_read_rest_probing) {
BinaryTest<RestProbingModel>();
}
BOOST_AUTO_TEST_CASE(write_and_read_trie) {
BinaryTest<TrieModel>();
}
BOOST_AUTO_TEST_CASE(write_and_read_quant_trie) {
BinaryTest<QuantTrieModel>();
}
BOOST_AUTO_TEST_CASE(write_and_read_array_trie) {
BinaryTest<ArrayTrieModel>();
}
BOOST_AUTO_TEST_CASE(write_and_read_quant_array_trie) {
BinaryTest<QuantArrayTrieModel>();
}
BOOST_AUTO_TEST_CASE(rest_max) {
Config config;
config.arpa_complain = Config::NONE;
config.messages = NULL;
RestProbingModel model(TestLocation(), config);
State state, out;
FullScoreReturn ret(model.FullScore(model.NullContextState(), model.GetVocabulary().Index("."), state));
SLOPPY_CHECK_CLOSE(-0.2705918, ret.rest, 0.001);
SLOPPY_CHECK_CLOSE(-0.01916512, model.FullScore(state, model.GetVocabulary().EndSentence(), out).rest, 0.001);
}
} // namespace
} // namespace ngram
} // namespace lm
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/flashlight/flashlight/lib/text | coqui_public_repos/STT/native_client/ctcdecode/third_party/flashlight/flashlight/lib/text/decoder/LexiconDecoder.cpp | /*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <stdlib.h>
#include <algorithm>
#include <cmath>
#include <functional>
#include <numeric>
#include <unordered_map>
#include "flashlight/lib/text/decoder/LexiconDecoder.h"
namespace fl {
namespace lib {
namespace text {
void LexiconDecoder::decodeBegin() {
hyp_.clear();
hyp_.emplace(0, std::vector<LexiconDecoderState>());
/* note: the lm reset itself with :start() */
hyp_[0].emplace_back(
0.0, lm_->start(0), lexicon_->getRoot(), nullptr, sil_, -1);
nDecodedFrames_ = 0;
nPrunedFrames_ = 0;
}
void LexiconDecoder::decodeStep(const float* emissions, int T, int N) {
int startFrame = nDecodedFrames_ - nPrunedFrames_;
// Extend hyp_ buffer
if (hyp_.size() < startFrame + T + 2) {
for (int i = hyp_.size(); i < startFrame + T + 2; i++) {
hyp_.emplace(i, std::vector<LexiconDecoderState>());
}
}
std::vector<size_t> idx(N);
for (int t = 0; t < T; t++) {
std::iota(idx.begin(), idx.end(), 0);
if (N > opt_.beamSizeToken) {
std::partial_sort(
idx.begin(),
idx.begin() + opt_.beamSizeToken,
idx.end(),
[&t, &N, &emissions](const size_t& l, const size_t& r) {
return emissions[t * N + l] > emissions[t * N + r];
});
}
candidatesReset(candidatesBestScore_, candidates_, candidatePtrs_);
for (const LexiconDecoderState& prevHyp : hyp_[startFrame + t]) {
const TrieNode* prevLex = prevHyp.lex;
const int prevIdx = prevHyp.token;
const float lexMaxScore =
prevLex == lexicon_->getRoot() ? 0 : prevLex->maxScore;
/* (1) Try children */
for (int r = 0; r < std::min(opt_.beamSizeToken, N); ++r) {
int n = idx[r];
auto iter = prevLex->children.find(n);
if (iter == prevLex->children.end()) {
continue;
}
const TrieNodePtr& lex = iter->second;
double amScore = emissions[t * N + n];
if (nDecodedFrames_ + t > 0 &&
opt_.criterionType == CriterionType::ASG) {
amScore += transitions_[n * N + prevIdx];
}
double score = prevHyp.score + amScore;
if (n == sil_) {
score += opt_.silScore;
}
LMStatePtr lmState;
double lmScore = 0.;
if (isLmToken_) {
auto lmStateScorePair = lm_->score(prevHyp.lmState, n);
lmState = lmStateScorePair.first;
lmScore = lmStateScorePair.second;
}
// We eat-up a new token
if (opt_.criterionType != CriterionType::CTC || prevHyp.prevBlank ||
n != prevIdx) {
if (!lex->children.empty()) {
if (!isLmToken_) {
lmState = prevHyp.lmState;
lmScore = lex->maxScore - lexMaxScore;
}
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
score + opt_.lmWeight * lmScore,
lmState,
lex.get(),
&prevHyp,
n,
-1,
false, // prevBlank
prevHyp.amScore + amScore,
prevHyp.lmScore + lmScore);
}
}
// If we got a true word
for (auto label : lex->labels) {
if (prevLex == lexicon_->getRoot() && prevHyp.token == n) {
// This is to avoid an situation that, when there is word with
// single token spelling (e.g. X -> x) in the lexicon and token `x`
// is predicted in several consecutive frames, multiple word `X`
// will be emitted. This violates the property of CTC, where
// there must be an blank token in between to predict 2 identical
// tokens consecutively.
continue;
}
if (!isLmToken_) {
auto lmStateScorePair = lm_->score(prevHyp.lmState, label);
lmState = lmStateScorePair.first;
lmScore = lmStateScorePair.second - lexMaxScore;
}
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
score + opt_.lmWeight * lmScore + opt_.wordScore,
lmState,
lexicon_->getRoot(),
&prevHyp,
n,
label,
false, // prevBlank
prevHyp.amScore + amScore,
prevHyp.lmScore + lmScore);
}
// If we got an unknown word
if (lex->labels.empty() && (opt_.unkScore > kNegativeInfinity)) {
if (!isLmToken_) {
auto lmStateScorePair = lm_->score(prevHyp.lmState, unk_);
lmState = lmStateScorePair.first;
lmScore = lmStateScorePair.second - lexMaxScore;
}
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
score + opt_.lmWeight * lmScore + opt_.unkScore,
lmState,
lexicon_->getRoot(),
&prevHyp,
n,
unk_,
false, // prevBlank
prevHyp.amScore + amScore,
prevHyp.lmScore + lmScore);
}
}
/* (2) Try same lexicon node */
if (opt_.criterionType != CriterionType::CTC || !prevHyp.prevBlank ||
prevLex == lexicon_->getRoot()) {
int n = prevLex == lexicon_->getRoot() ? sil_ : prevIdx;
double amScore = emissions[t * N + n];
if (nDecodedFrames_ + t > 0 &&
opt_.criterionType == CriterionType::ASG) {
amScore += transitions_[n * N + prevIdx];
}
double score = prevHyp.score + amScore;
if (n == sil_) {
score += opt_.silScore;
}
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
score,
prevHyp.lmState,
prevLex,
&prevHyp,
n,
-1,
false, // prevBlank
prevHyp.amScore + amScore,
prevHyp.lmScore);
}
/* (3) CTC only, try blank */
if (opt_.criterionType == CriterionType::CTC) {
int n = blank_;
double amScore = emissions[t * N + n];
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
prevHyp.score + amScore,
prevHyp.lmState,
prevLex,
&prevHyp,
n,
-1,
true, // prevBlank
prevHyp.amScore + amScore,
prevHyp.lmScore);
}
// finish proposing
}
candidatesStore(
candidates_,
candidatePtrs_,
hyp_[startFrame + t + 1],
opt_.beamSize,
candidatesBestScore_ - opt_.beamThreshold,
opt_.logAdd,
false);
updateLMCache(lm_, hyp_[startFrame + t + 1]);
}
nDecodedFrames_ += T;
}
void LexiconDecoder::decodeEnd() {
candidatesReset(candidatesBestScore_, candidates_, candidatePtrs_);
bool hasNiceEnding = false;
for (const LexiconDecoderState& prevHyp :
hyp_[nDecodedFrames_ - nPrunedFrames_]) {
if (prevHyp.lex == lexicon_->getRoot()) {
hasNiceEnding = true;
break;
}
}
for (const LexiconDecoderState& prevHyp :
hyp_[nDecodedFrames_ - nPrunedFrames_]) {
const TrieNode* prevLex = prevHyp.lex;
const LMStatePtr& prevLmState = prevHyp.lmState;
if (!hasNiceEnding || prevHyp.lex == lexicon_->getRoot()) {
auto lmStateScorePair = lm_->finish(prevLmState);
auto lmScore = lmStateScorePair.second;
candidatesAdd(
candidates_,
candidatesBestScore_,
opt_.beamThreshold,
prevHyp.score + opt_.lmWeight * lmScore,
lmStateScorePair.first,
prevLex,
&prevHyp,
sil_,
-1,
false, // prevBlank
prevHyp.amScore,
prevHyp.lmScore + lmScore);
}
}
candidatesStore(
candidates_,
candidatePtrs_,
hyp_[nDecodedFrames_ - nPrunedFrames_ + 1],
opt_.beamSize,
candidatesBestScore_ - opt_.beamThreshold,
opt_.logAdd,
true);
++nDecodedFrames_;
}
std::vector<DecodeResult> LexiconDecoder::getAllFinalHypothesis() const {
int finalFrame = nDecodedFrames_ - nPrunedFrames_;
if (finalFrame < 1) {
return std::vector<DecodeResult>{};
}
return getAllHypothesis(hyp_.find(finalFrame)->second, finalFrame);
}
DecodeResult LexiconDecoder::getBestHypothesis(int lookBack) const {
if (nDecodedFrames_ - nPrunedFrames_ - lookBack < 1) {
return DecodeResult();
}
const LexiconDecoderState* bestNode = findBestAncestor(
hyp_.find(nDecodedFrames_ - nPrunedFrames_)->second, lookBack);
return getHypothesis(bestNode, nDecodedFrames_ - nPrunedFrames_ - lookBack);
}
int LexiconDecoder::nHypothesis() const {
int finalFrame = nDecodedFrames_ - nPrunedFrames_;
return hyp_.find(finalFrame)->second.size();
}
int LexiconDecoder::nDecodedFramesInBuffer() const {
return nDecodedFrames_ - nPrunedFrames_ + 1;
}
void LexiconDecoder::prune(int lookBack) {
if (nDecodedFrames_ - nPrunedFrames_ - lookBack < 1) {
return; // Not enough decoded frames to prune
}
/* (1) Find the last emitted word in the best path */
const LexiconDecoderState* bestNode = findBestAncestor(
hyp_.find(nDecodedFrames_ - nPrunedFrames_)->second, lookBack);
if (!bestNode) {
return; // Not enough decoded frames to prune
}
int startFrame = nDecodedFrames_ - nPrunedFrames_ - lookBack;
if (startFrame < 1) {
return; // Not enough decoded frames to prune
}
/* (2) Move things from back of hyp_ to front and normalize scores */
pruneAndNormalize(hyp_, startFrame, lookBack);
nPrunedFrames_ = nDecodedFrames_ - lookBack;
}
} // namespace text
} // namespace lib
} // namespace fl
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/native_client/getopt_win.h | #ifndef __GETOPT_H__
/**
* DISCLAIMER
* This file is part of the mingw-w64 runtime package.
*
* The mingw-w64 runtime package and its code is distributed in the hope that it
* will be useful but WITHOUT ANY WARRANTY. ALL WARRANTIES, EXPRESSED OR
* IMPLIED ARE HEREBY DISCLAIMED. This includes but is not limited to
* warranties of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
*/
/*
* Copyright (c) 2002 Todd C. Miller <Todd.Miller@courtesan.com>
*
* Permission to use, copy, modify, and distribute this software for any
* purpose with or without fee is hereby granted, provided that the above
* copyright notice and this permission notice appear in all copies.
*
* THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
* WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
* MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
* ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
* WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
* ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
* OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
*
* Sponsored in part by the Defense Advanced Research Projects
* Agency (DARPA) and Air Force Research Laboratory, Air Force
* Materiel Command, USAF, under agreement number F39502-99-1-0512.
*/
/*-
* Copyright (c) 2000 The NetBSD Foundation, Inc.
* All rights reserved.
*
* This code is derived from software contributed to The NetBSD Foundation
* by Dieter Baron and Thomas Klausner.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE NETBSD FOUNDATION, INC. AND CONTRIBUTORS
* ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
* TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR CONTRIBUTORS
* BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#pragma warning(disable:4996);
#define __GETOPT_H__
/* All the headers include this file. */
#include <crtdefs.h>
#include <errno.h>
#include <stdlib.h>
#include <string.h>
#include <stdarg.h>
#include <stdio.h>
#include <windows.h>
#ifdef __cplusplus
extern "C" {
#endif
#define REPLACE_GETOPT /* use this getopt as the system getopt(3) */
#ifdef REPLACE_GETOPT
int opterr = 1; /* if error message should be printed */
int optind = 1; /* index into parent argv vector */
int optopt = '?'; /* character checked for validity */
#undef optreset /* see getopt.h */
#define optreset __mingw_optreset
int optreset; /* reset getopt */
char *optarg; /* argument associated with option */
#endif
//extern int optind; /* index of first non-option in argv */
//extern int optopt; /* single option character, as parsed */
//extern int opterr; /* flag to enable built-in diagnostics... */
// /* (user may set to zero, to suppress) */
//
//extern char *optarg; /* pointer to argument of current option */
#define PRINT_ERROR ((opterr) && (*options != ':'))
#define FLAG_PERMUTE 0x01 /* permute non-options to the end of argv */
#define FLAG_ALLARGS 0x02 /* treat non-options as args to option "-1" */
#define FLAG_LONGONLY 0x04 /* operate as getopt_long_only */
/* return values */
#define BADCH (int)'?'
#define BADARG ((*options == ':') ? (int)':' : (int)'?')
#define INORDER (int)1
#ifndef __CYGWIN__
#define __progname __argv[0]
#else
extern char __declspec(dllimport) *__progname;
#endif
#ifdef __CYGWIN__
static char EMSG[] = "";
#else
#define EMSG ""
#endif
static int getopt_internal(int, char * const *, const char *,
const struct option *, int *, int);
static int parse_long_options(char * const *, const char *,
const struct option *, int *, int);
static int gcd(int, int);
static void permute_args(int, int, int, char * const *);
static char *place = EMSG; /* option letter processing */
/* XXX: set optreset to 1 rather than these two */
static int nonopt_start = -1; /* first non option argument (for permute) */
static int nonopt_end = -1; /* first option after non options (for permute) */
/* Error messages */
static const char recargchar[] = "option requires an argument -- %c";
static const char recargstring[] = "option requires an argument -- %s";
static const char ambig[] = "ambiguous option -- %.*s";
static const char noarg[] = "option doesn't take an argument -- %.*s";
static const char illoptchar[] = "unknown option -- %c";
static const char illoptstring[] = "unknown option -- %s";
static void
_vwarnx(const char *fmt,va_list ap)
{
(void)fprintf(stderr,"%s: ",__progname);
if (fmt != NULL)
(void)vfprintf(stderr,fmt,ap);
(void)fprintf(stderr,"\n");
}
static void
warnx(const char *fmt,...)
{
va_list ap;
va_start(ap,fmt);
_vwarnx(fmt,ap);
va_end(ap);
}
/*
* Compute the greatest common divisor of a and b.
*/
static int
gcd(int a, int b)
{
int c;
c = a % b;
while (c != 0) {
a = b;
b = c;
c = a % b;
}
return (b);
}
/*
* Exchange the block from nonopt_start to nonopt_end with the block
* from nonopt_end to opt_end (keeping the same order of arguments
* in each block).
*/
static void
permute_args(int panonopt_start, int panonopt_end, int opt_end,
char * const *nargv)
{
int cstart, cyclelen, i, j, ncycle, nnonopts, nopts, pos;
char *swap;
/*
* compute lengths of blocks and number and size of cycles
*/
nnonopts = panonopt_end - panonopt_start;
nopts = opt_end - panonopt_end;
ncycle = gcd(nnonopts, nopts);
cyclelen = (opt_end - panonopt_start) / ncycle;
for (i = 0; i < ncycle; i++) {
cstart = panonopt_end+i;
pos = cstart;
for (j = 0; j < cyclelen; j++) {
if (pos >= panonopt_end)
pos -= nnonopts;
else
pos += nopts;
swap = nargv[pos];
/* LINTED const cast */
((char **) nargv)[pos] = nargv[cstart];
/* LINTED const cast */
((char **)nargv)[cstart] = swap;
}
}
}
#ifdef REPLACE_GETOPT
/*
* getopt --
* Parse argc/argv argument vector.
*
* [eventually this will replace the BSD getopt]
*/
int
getopt(int nargc, char * const *nargv, const char *options)
{
/*
* We don't pass FLAG_PERMUTE to getopt_internal() since
* the BSD getopt(3) (unlike GNU) has never done this.
*
* Furthermore, since many privileged programs call getopt()
* before dropping privileges it makes sense to keep things
* as simple (and bug-free) as possible.
*/
return (getopt_internal(nargc, nargv, options, NULL, NULL, 0));
}
#endif /* REPLACE_GETOPT */
//extern int getopt(int nargc, char * const *nargv, const char *options);
#ifdef _BSD_SOURCE
/*
* BSD adds the non-standard `optreset' feature, for reinitialisation
* of `getopt' parsing. We support this feature, for applications which
* proclaim their BSD heritage, before including this header; however,
* to maintain portability, developers are advised to avoid it.
*/
# define optreset __mingw_optreset
extern int optreset;
#endif
#ifdef __cplusplus
}
#endif
/*
* POSIX requires the `getopt' API to be specified in `unistd.h';
* thus, `unistd.h' includes this header. However, we do not want
* to expose the `getopt_long' or `getopt_long_only' APIs, when
* included in this manner. Thus, close the standard __GETOPT_H__
* declarations block, and open an additional __GETOPT_LONG_H__
* specific block, only when *not* __UNISTD_H_SOURCED__, in which
* to declare the extended API.
*/
#endif /* !defined(__GETOPT_H__) */
#if !defined(__UNISTD_H_SOURCED__) && !defined(__GETOPT_LONG_H__)
#define __GETOPT_LONG_H__
#ifdef __cplusplus
extern "C" {
#endif
struct option /* specification for a long form option... */
{
const char *name; /* option name, without leading hyphens */
int has_arg; /* does it take an argument? */
int *flag; /* where to save its status, or NULL */
int val; /* its associated status value */
};
enum /* permitted values for its `has_arg' field... */
{
no_argument = 0, /* option never takes an argument */
required_argument, /* option always requires an argument */
optional_argument /* option may take an argument */
};
/*
* parse_long_options --
* Parse long options in argc/argv argument vector.
* Returns -1 if short_too is set and the option does not match long_options.
*/
static int
parse_long_options(char * const *nargv, const char *options,
const struct option *long_options, int *idx, int short_too)
{
char *current_argv, *has_equal;
size_t current_argv_len;
int i, ambiguous, match;
#define IDENTICAL_INTERPRETATION(_x, _y) \
(long_options[(_x)].has_arg == long_options[(_y)].has_arg && \
long_options[(_x)].flag == long_options[(_y)].flag && \
long_options[(_x)].val == long_options[(_y)].val)
current_argv = place;
match = -1;
ambiguous = 0;
optind++;
if ((has_equal = strchr(current_argv, '=')) != NULL) {
/* argument found (--option=arg) */
current_argv_len = has_equal - current_argv;
has_equal++;
} else
current_argv_len = strlen(current_argv);
for (i = 0; long_options[i].name; i++) {
/* find matching long option */
if (strncmp(current_argv, long_options[i].name,
current_argv_len))
continue;
if (strlen(long_options[i].name) == current_argv_len) {
/* exact match */
match = i;
ambiguous = 0;
break;
}
/*
* If this is a known short option, don't allow
* a partial match of a single character.
*/
if (short_too && current_argv_len == 1)
continue;
if (match == -1) /* partial match */
match = i;
else if (!IDENTICAL_INTERPRETATION(i, match))
ambiguous = 1;
}
if (ambiguous) {
/* ambiguous abbreviation */
if (PRINT_ERROR)
warnx(ambig, (int)current_argv_len,
current_argv);
optopt = 0;
return (BADCH);
}
if (match != -1) { /* option found */
if (long_options[match].has_arg == no_argument
&& has_equal) {
if (PRINT_ERROR)
warnx(noarg, (int)current_argv_len,
current_argv);
/*
* XXX: GNU sets optopt to val regardless of flag
*/
if (long_options[match].flag == NULL)
optopt = long_options[match].val;
else
optopt = 0;
return (BADARG);
}
if (long_options[match].has_arg == required_argument ||
long_options[match].has_arg == optional_argument) {
if (has_equal)
optarg = has_equal;
else if (long_options[match].has_arg ==
required_argument) {
/*
* optional argument doesn't use next nargv
*/
optarg = nargv[optind++];
}
}
if ((long_options[match].has_arg == required_argument)
&& (optarg == NULL)) {
/*
* Missing argument; leading ':' indicates no error
* should be generated.
*/
if (PRINT_ERROR)
warnx(recargstring,
current_argv);
/*
* XXX: GNU sets optopt to val regardless of flag
*/
if (long_options[match].flag == NULL)
optopt = long_options[match].val;
else
optopt = 0;
--optind;
return (BADARG);
}
} else { /* unknown option */
if (short_too) {
--optind;
return (-1);
}
if (PRINT_ERROR)
warnx(illoptstring, current_argv);
optopt = 0;
return (BADCH);
}
if (idx)
*idx = match;
if (long_options[match].flag) {
*long_options[match].flag = long_options[match].val;
return (0);
} else
return (long_options[match].val);
#undef IDENTICAL_INTERPRETATION
}
/*
* getopt_internal --
* Parse argc/argv argument vector. Called by user level routines.
*/
static int
getopt_internal(int nargc, char * const *nargv, const char *options,
const struct option *long_options, int *idx, int flags)
{
char *oli; /* option letter list index */
int optchar, short_too;
static int posixly_correct = -1;
if (options == NULL)
return (-1);
/*
* XXX Some GNU programs (like cvs) set optind to 0 instead of
* XXX using optreset. Work around this braindamage.
*/
if (optind == 0)
optind = optreset = 1;
/*
* Disable GNU extensions if POSIXLY_CORRECT is set or options
* string begins with a '+'.
*
* CV, 2009-12-14: Check POSIXLY_CORRECT anew if optind == 0 or
* optreset != 0 for GNU compatibility.
*/
if (posixly_correct == -1 || optreset != 0)
posixly_correct = (getenv("POSIXLY_CORRECT") != NULL);
if (*options == '-')
flags |= FLAG_ALLARGS;
else if (posixly_correct || *options == '+')
flags &= ~FLAG_PERMUTE;
if (*options == '+' || *options == '-')
options++;
optarg = NULL;
if (optreset)
nonopt_start = nonopt_end = -1;
start:
if (optreset || !*place) { /* update scanning pointer */
optreset = 0;
if (optind >= nargc) { /* end of argument vector */
place = EMSG;
if (nonopt_end != -1) {
/* do permutation, if we have to */
permute_args(nonopt_start, nonopt_end,
optind, nargv);
optind -= nonopt_end - nonopt_start;
}
else if (nonopt_start != -1) {
/*
* If we skipped non-options, set optind
* to the first of them.
*/
optind = nonopt_start;
}
nonopt_start = nonopt_end = -1;
return (-1);
}
if (*(place = nargv[optind]) != '-' ||
(place[1] == '\0' && strchr(options, '-') == NULL)) {
place = EMSG; /* found non-option */
if (flags & FLAG_ALLARGS) {
/*
* GNU extension:
* return non-option as argument to option 1
*/
optarg = nargv[optind++];
return (INORDER);
}
if (!(flags & FLAG_PERMUTE)) {
/*
* If no permutation wanted, stop parsing
* at first non-option.
*/
return (-1);
}
/* do permutation */
if (nonopt_start == -1)
nonopt_start = optind;
else if (nonopt_end != -1) {
permute_args(nonopt_start, nonopt_end,
optind, nargv);
nonopt_start = optind -
(nonopt_end - nonopt_start);
nonopt_end = -1;
}
optind++;
/* process next argument */
goto start;
}
if (nonopt_start != -1 && nonopt_end == -1)
nonopt_end = optind;
/*
* If we have "-" do nothing, if "--" we are done.
*/
if (place[1] != '\0' && *++place == '-' && place[1] == '\0') {
optind++;
place = EMSG;
/*
* We found an option (--), so if we skipped
* non-options, we have to permute.
*/
if (nonopt_end != -1) {
permute_args(nonopt_start, nonopt_end,
optind, nargv);
optind -= nonopt_end - nonopt_start;
}
nonopt_start = nonopt_end = -1;
return (-1);
}
}
/*
* Check long options if:
* 1) we were passed some
* 2) the arg is not just "-"
* 3) either the arg starts with -- we are getopt_long_only()
*/
if (long_options != NULL && place != nargv[optind] &&
(*place == '-' || (flags & FLAG_LONGONLY))) {
short_too = 0;
if (*place == '-')
place++; /* --foo long option */
else if (*place != ':' && strchr(options, *place) != NULL)
short_too = 1; /* could be short option too */
optchar = parse_long_options(nargv, options, long_options,
idx, short_too);
if (optchar != -1) {
place = EMSG;
return (optchar);
}
}
if ((optchar = (int)*place++) == (int)':' ||
(optchar == (int)'-' && *place != '\0') ||
(oli = (char*)strchr(options, optchar)) == NULL) {
/*
* If the user specified "-" and '-' isn't listed in
* options, return -1 (non-option) as per POSIX.
* Otherwise, it is an unknown option character (or ':').
*/
if (optchar == (int)'-' && *place == '\0')
return (-1);
if (!*place)
++optind;
if (PRINT_ERROR)
warnx(illoptchar, optchar);
optopt = optchar;
return (BADCH);
}
if (long_options != NULL && optchar == 'W' && oli[1] == ';') {
/* -W long-option */
if (*place) /* no space */
/* NOTHING */;
else if (++optind >= nargc) { /* no arg */
place = EMSG;
if (PRINT_ERROR)
warnx(recargchar, optchar);
optopt = optchar;
return (BADARG);
} else /* white space */
place = nargv[optind];
optchar = parse_long_options(nargv, options, long_options,
idx, 0);
place = EMSG;
return (optchar);
}
if (*++oli != ':') { /* doesn't take argument */
if (!*place)
++optind;
} else { /* takes (optional) argument */
optarg = NULL;
if (*place) /* no white space */
optarg = place;
else if (oli[1] != ':') { /* arg not optional */
if (++optind >= nargc) { /* no arg */
place = EMSG;
if (PRINT_ERROR)
warnx(recargchar, optchar);
optopt = optchar;
return (BADARG);
} else
optarg = nargv[optind];
}
place = EMSG;
++optind;
}
/* dump back option letter */
return (optchar);
}
/*
* getopt_long --
* Parse argc/argv argument vector.
*/
int
getopt_long(int nargc, char * const *nargv, const char *options,
const struct option *long_options, int *idx)
{
return (getopt_internal(nargc, nargv, options, long_options, idx,
FLAG_PERMUTE));
}
/*
* getopt_long_only --
* Parse argc/argv argument vector.
*/
int
getopt_long_only(int nargc, char * const *nargv, const char *options,
const struct option *long_options, int *idx)
{
return (getopt_internal(nargc, nargv, options, long_options, idx,
FLAG_PERMUTE|FLAG_LONGONLY));
}
//extern int getopt_long(int nargc, char * const *nargv, const char *options,
// const struct option *long_options, int *idx);
//extern int getopt_long_only(int nargc, char * const *nargv, const char *options,
// const struct option *long_options, int *idx);
/*
* Previous MinGW implementation had...
*/
#ifndef HAVE_DECL_GETOPT
/*
* ...for the long form API only; keep this for compatibility.
*/
# define HAVE_DECL_GETOPT 1
#endif
#ifdef __cplusplus
}
#endif
#endif /* !defined(__UNISTD_H_SOURCED__) && !defined(__GETOPT_LONG_H__) */
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party | coqui_public_repos/STT/native_client/ctcdecode/third_party/object_pool/unique_ptr.h | #ifndef GODEFV_MEMORY_ALLOCATED_UNIQUE_PTR_H
#define GODEFV_MEMORY_ALLOCATED_UNIQUE_PTR_H
#include <memory>
namespace godefv{
//! A deleter to deallocate memory which have been allocated by the given allocator.
template<class Allocator>
struct allocator_deleter_t
{
allocator_deleter_t(Allocator const& allocator) :
mAllocator{ allocator }
{}
void operator()(typename Allocator::value_type* ptr)
{
mAllocator.deallocate(ptr, 1);
}
private:
Allocator mAllocator;
};
//! A smart pointer like std::unique_ptr but templated on an allocator instead of a deleter.
//! The deleter is deduced from the given allocator.
template<class T, class Allocator = std::allocator<T>>
struct unique_ptr_t : public std::unique_ptr<T, allocator_deleter_t<Allocator>>
{
using base_t = std::unique_ptr<T, allocator_deleter_t<Allocator>>;
unique_ptr_t(Allocator allocator = Allocator{}) :
base_t{ allocator.allocate(1), allocator_deleter_t<Allocator>{ allocator } }
{}
};
} // namespace godefv
#endif // GODEFV_MEMORY_ALLOCATED_UNIQUE_PTR_H
| 0 |
coqui_public_repos/STT/native_client | coqui_public_repos/STT/native_client/java/gradle.properties | # Project-wide Gradle settings.
# IDE (e.g. Android Studio) users:
# Gradle settings configured through the IDE *will override*
# any settings specified in this file.
# For more details on how to configure your build environment visit
# http://www.gradle.org/docs/current/userguide/build_environment.html
# Specifies the JVM arguments used for the daemon process.
# The setting is particularly useful for tweaking memory settings.
org.gradle.jvmargs=-Xmx1536m
# When configured, Gradle will run in incubating parallel mode.
# This option should only be used with decoupled projects. More details, visit
# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
# org.gradle.parallel=true
| 0 |
coqui_public_repos/STT/native_client/java | coqui_public_repos/STT/native_client/java/.idea/misc.xml | <?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="NullableNotNullManager">
<option name="myDefaultNullable" value="android.support.annotation.Nullable" />
<option name="myDefaultNotNull" value="android.support.annotation.NonNull" />
<option name="myNullables">
<value>
<list size="7">
<item index="0" class="java.lang.String" itemvalue="org.jetbrains.annotations.Nullable" />
<item index="1" class="java.lang.String" itemvalue="javax.annotation.Nullable" />
<item index="2" class="java.lang.String" itemvalue="javax.annotation.CheckForNull" />
<item index="3" class="java.lang.String" itemvalue="edu.umd.cs.findbugs.annotations.Nullable" />
<item index="4" class="java.lang.String" itemvalue="android.support.annotation.Nullable" />
<item index="5" class="java.lang.String" itemvalue="androidx.annotation.Nullable" />
<item index="6" class="java.lang.String" itemvalue="androidx.annotation.RecentlyNullable" />
</list>
</value>
</option>
<option name="myNotNulls">
<value>
<list size="6">
<item index="0" class="java.lang.String" itemvalue="org.jetbrains.annotations.NotNull" />
<item index="1" class="java.lang.String" itemvalue="javax.annotation.Nonnull" />
<item index="2" class="java.lang.String" itemvalue="edu.umd.cs.findbugs.annotations.NonNull" />
<item index="3" class="java.lang.String" itemvalue="android.support.annotation.NonNull" />
<item index="4" class="java.lang.String" itemvalue="androidx.annotation.NonNull" />
<item index="5" class="java.lang.String" itemvalue="androidx.annotation.RecentlyNonNull" />
</list>
</value>
</option>
</component>
<component name="ProjectRootManager" version="2" languageLevel="JDK_1_8" project-jdk-name="1.8" project-jdk-type="JavaSDK">
<output url="file://$PROJECT_DIR$/build/classes" />
</component>
<component name="ProjectType">
<option name="id" value="Android" />
</component>
</project>
| 0 |
coqui_public_repos/STT/native_client/dotnet/STTWPF | coqui_public_repos/STT/native_client/dotnet/STTWPF/Properties/Settings.settings | <?xml version='1.0' encoding='utf-8'?>
<SettingsFile xmlns="uri:settings" CurrentProfile="(Default)">
<Profiles>
<Profile Name="(Default)" />
</Profiles>
<Settings />
</SettingsFile>
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/script/encode.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/script/fst-class.h>
#include <fst/encode.h>
#include <fst/script/encode.h>
#include <fst/script/script-impl.h>
namespace fst {
namespace script {
void Encode(MutableFstClass *fst, uint32 flags, bool reuse_encoder,
const string &coder_fname) {
EncodeArgs1 args(fst, flags, reuse_encoder, coder_fname);
Apply<Operation<EncodeArgs1>>("Encode", fst->ArcType(), &args);
}
void Encode(MutableFstClass *fst, EncodeMapperClass *encoder) {
if (!internal::ArcTypesMatch(*fst, *encoder, "Encode")) {
fst->SetProperties(kError, kError);
return;
}
EncodeArgs2 args(fst, encoder);
Apply<Operation<EncodeArgs2>>("Encode", fst->ArcType(), &args);
}
REGISTER_FST_OPERATION(Encode, StdArc, EncodeArgs1);
REGISTER_FST_OPERATION(Encode, LogArc, EncodeArgs1);
REGISTER_FST_OPERATION(Encode, Log64Arc, EncodeArgs1);
REGISTER_FST_OPERATION(Encode, StdArc, EncodeArgs2);
REGISTER_FST_OPERATION(Encode, LogArc, EncodeArgs2);
REGISTER_FST_OPERATION(Encode, Log64Arc, EncodeArgs2);
} // namespace script
} // namespace fst
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-transcribe_16k-linux-amd64-py36m-opt.yml | build:
template_file: test-linux-opt-base.tyml
dependencies:
- "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
apt-get -qq update && apt-get -qq -y install ${training.packages_xenial.apt} ${python.packages_xenial.apt}
args:
tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/taskcluster/tc-transcribe-tests.sh 3.6.10:m 16k"
workerType: "${docker.dsTests}"
metadata:
name: "DeepSpeech Linux AMD64 CPU 16kHz transcribe Py3.6"
description: "Transcribe a DeepSpeech LDC93S1 model for Linux/AMD64 16kHz Python 3.6, CPU only, optimized version"
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions/compact/compact64_string-fst.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/fst.h>
#include <fst/compact-fst.h>
namespace fst {
static FstRegisterer<CompactStringFst<StdArc, uint64>>
CompactStringFst_StdArc_uint64_registerer;
static FstRegisterer<CompactStringFst<LogArc, uint64>>
CompactStringFst_LogArc_uint64_registerer;
} // namespace fst
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/extensions | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/extensions/far/farequal.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Tests if two Far files contains the same (key,fst) pairs.
#include <string>
#include <fst/flags.h>
#include <fst/extensions/far/farscript.h>
#include <fst/extensions/far/getters.h>
DEFINE_string(begin_key, "",
"First key to extract (def: first key in archive)");
DEFINE_string(end_key, "", "Last key to extract (def: last key in archive)");
DEFINE_double(delta, fst::kDelta, "Comparison/quantization delta");
int main(int argc, char **argv) {
namespace s = fst::script;
string usage = "Compares the FSTs in two FST archives for equality.";
usage += "\n\n Usage:";
usage += argv[0];
usage += " in1.far in2.far";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
s::ExpandArgs(argc, argv, &argc, &argv);
if (argc != 3) {
ShowUsage();
return 1;
}
const auto arc_type = s::LoadArcTypeFromFar(argv[1]);
if (arc_type.empty()) return 1;
bool result = s::FarEqual(argv[1], argv[2], arc_type, FLAGS_delta,
FLAGS_begin_key, FLAGS_end_key);
if (!result) VLOG(1) << "FARs are not equal.";
return result ? 0 : 2;
}
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstcompile-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Creates binary FSTs from simple text format used by AT&T.
#include <cstring>
#include <fstream>
#include <istream>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/script/compile.h>
DECLARE_bool(acceptor);
DECLARE_string(arc_type);
DECLARE_string(fst_type);
DECLARE_string(isymbols);
DECLARE_string(osymbols);
DECLARE_string(ssymbols);
DECLARE_bool(keep_isymbols);
DECLARE_bool(keep_osymbols);
DECLARE_bool(keep_state_numbering);
DECLARE_bool(allow_negative_labels);
int fstcompile_main(int argc, char **argv) {
namespace s = fst::script;
using fst::SymbolTable;
using fst::SymbolTableTextOptions;
string usage = "Creates binary FSTs from simple text format.\n\n Usage: ";
usage += argv[0];
usage += " [text.fst [binary.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
string source = "standard input";
std::ifstream fstrm;
if (argc > 1 && strcmp(argv[1], "-") != 0) {
fstrm.open(argv[1]);
if (!fstrm) {
LOG(ERROR) << argv[0] << ": Open failed, file = " << argv[1];
return 1;
}
source = argv[1];
}
std::istream &istrm = fstrm.is_open() ? fstrm : std::cin;
const SymbolTableTextOptions opts(FLAGS_allow_negative_labels);
std::unique_ptr<const SymbolTable> isyms;
if (!FLAGS_isymbols.empty()) {
isyms.reset(SymbolTable::ReadText(FLAGS_isymbols, opts));
if (!isyms) return 1;
}
std::unique_ptr<const SymbolTable> osyms;
if (!FLAGS_osymbols.empty()) {
osyms.reset(SymbolTable::ReadText(FLAGS_osymbols, opts));
if (!osyms) return 1;
}
std::unique_ptr<const SymbolTable> ssyms;
if (!FLAGS_ssymbols.empty()) {
ssyms.reset(SymbolTable::ReadText(FLAGS_ssymbols));
if (!ssyms) return 1;
}
const string dest = argc > 2 ? argv[2] : "";
s::CompileFst(istrm, source, dest, FLAGS_fst_type, FLAGS_arc_type,
isyms.get(), osyms.get(), ssyms.get(), FLAGS_acceptor,
FLAGS_keep_isymbols, FLAGS_keep_osymbols,
FLAGS_keep_state_numbering, FLAGS_allow_negative_labels);
return 0;
}
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/include/fst/script/rmepsilon.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#ifndef FST_SCRIPT_RMEPSILON_H_
#define FST_SCRIPT_RMEPSILON_H_
#include <utility>
#include <vector>
#include <fst/queue.h>
#include <fst/rmepsilon.h>
#include <fst/script/fst-class.h>
#include <fst/script/shortest-distance.h>
#include <fst/script/weight-class.h>
namespace fst {
namespace script {
struct RmEpsilonOptions : public ShortestDistanceOptions {
const bool connect;
const WeightClass &weight_threshold;
const int64_t state_threshold;
RmEpsilonOptions(QueueType queue_type, bool connect,
const WeightClass &weight_threshold,
int64_t state_threshold = kNoStateId, float delta = kDelta)
: ShortestDistanceOptions(queue_type, EPSILON_ARC_FILTER, kNoStateId,
delta),
connect(connect),
weight_threshold(weight_threshold),
state_threshold(state_threshold) {}
};
namespace internal {
// Code to implement switching on queue types.
template <class Arc, class Queue>
void RmEpsilon(MutableFst<Arc> *fst,
std::vector<typename Arc::Weight> *distance,
const RmEpsilonOptions &opts, Queue *queue) {
using Weight = typename Arc::Weight;
const fst::RmEpsilonOptions<Arc, Queue> ropts(
queue, opts.delta, opts.connect,
*opts.weight_threshold.GetWeight<Weight>(), opts.state_threshold);
RmEpsilon(fst, distance, ropts);
}
template <class Arc>
void RmEpsilon(MutableFst<Arc> *fst, const RmEpsilonOptions &opts) {
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
std::vector<Weight> distance;
switch (opts.queue_type) {
case AUTO_QUEUE: {
AutoQueue<StateId> queue(*fst, &distance, EpsilonArcFilter<Arc>());
RmEpsilon(fst, &distance, opts, &queue);
return;
}
case FIFO_QUEUE: {
FifoQueue<StateId> queue;
RmEpsilon(fst, &distance, opts, &queue);
return;
}
case LIFO_QUEUE: {
LifoQueue<StateId> queue;
RmEpsilon(fst, &distance, opts, &queue);
return;
}
case SHORTEST_FIRST_QUEUE: {
NaturalShortestFirstQueue<StateId, Weight> queue(distance);
RmEpsilon(fst, &distance, opts, &queue);
return;
}
case STATE_ORDER_QUEUE: {
StateOrderQueue<StateId> queue;
RmEpsilon(fst, &distance, opts, &queue);
return;
}
case TOP_ORDER_QUEUE: {
TopOrderQueue<StateId> queue(*fst, EpsilonArcFilter<Arc>());
internal::RmEpsilon(fst, &distance, opts, &queue);
return;
}
default: {
FSTERROR() << "RmEpsilon: Unknown queue type: " << opts.queue_type;
fst->SetProperties(kError, kError);
return;
}
}
}
} // namespace internal
using RmEpsilonArgs = std::pair<MutableFstClass *, const RmEpsilonOptions &>;
template <class Arc>
void RmEpsilon(RmEpsilonArgs *args) {
MutableFst<Arc> *fst = std::get<0>(*args)->GetMutableFst<Arc>();
const auto &opts = std::get<1>(*args);
internal::RmEpsilon(fst, opts);
}
void RmEpsilon(MutableFstClass *fst, const RmEpsilonOptions &opts);
} // namespace script
} // namespace fst
#endif // FST_SCRIPT_RMEPSILON_H_
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions/pdt/info.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Prints information about a PDT.
#ifndef FST_EXTENSIONS_PDT_INFO_H_
#define FST_EXTENSIONS_PDT_INFO_H_
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <fst/extensions/pdt/pdt.h>
#include <fst/fst.h>
namespace fst {
// Compute various information about PDTs.
template <class Arc>
class PdtInfo {
public:
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using Weight = typename Arc::Weight;
PdtInfo(const Fst<Arc> &fst,
const std::vector<std::pair<Label, Label>> &parents);
const string &FstType() const { return fst_type_; }
const string &ArcType() const { return Arc::Type(); }
int64 NumStates() const { return nstates_; }
int64 NumArcs() const { return narcs_; }
int64 NumOpenParens() const { return nopen_parens_; }
int64 NumCloseParens() const { return nclose_parens_; }
int64 NumUniqueOpenParens() const { return nuniq_open_parens_; }
int64 NumUniqueCloseParens() const { return nuniq_close_parens_; }
int64 NumOpenParenStates() const { return nopen_paren_states_; }
int64 NumCloseParenStates() const { return nclose_paren_states_; }
private:
string fst_type_;
int64 nstates_;
int64 narcs_;
int64 nopen_parens_;
int64 nclose_parens_;
int64 nuniq_open_parens_;
int64 nuniq_close_parens_;
int64 nopen_paren_states_;
int64 nclose_paren_states_;
};
template <class Arc>
PdtInfo<Arc>::PdtInfo(
const Fst<Arc> &fst,
const std::vector<std::pair<typename Arc::Label, typename Arc::Label>>
&parens)
: fst_type_(fst.Type()),
nstates_(0),
narcs_(0),
nopen_parens_(0),
nclose_parens_(0),
nuniq_open_parens_(0),
nuniq_close_parens_(0),
nopen_paren_states_(0),
nclose_paren_states_(0) {
std::unordered_map<Label, size_t> paren_map;
std::unordered_set<Label> paren_set;
std::unordered_set<StateId> open_paren_state_set;
std::unordered_set<StateId> close_paren_state_set;
for (size_t i = 0; i < parens.size(); ++i) {
const auto &pair = parens[i];
paren_map[pair.first] = i;
paren_map[pair.second] = i;
}
for (StateIterator<Fst<Arc>> siter(fst); !siter.Done(); siter.Next()) {
++nstates_;
const auto s = siter.Value();
for (ArcIterator<Fst<Arc>> aiter(fst, s); !aiter.Done(); aiter.Next()) {
const auto &arc = aiter.Value();
++narcs_;
const auto it = paren_map.find(arc.ilabel);
if (it != paren_map.end()) {
const auto open_paren = parens[it->second].first;
const auto close_paren = parens[it->second].second;
if (arc.ilabel == open_paren) {
++nopen_parens_;
if (!paren_set.count(open_paren)) {
++nuniq_open_parens_;
paren_set.insert(open_paren);
}
if (!open_paren_state_set.count(arc.nextstate)) {
++nopen_paren_states_;
open_paren_state_set.insert(arc.nextstate);
}
} else {
++nclose_parens_;
if (!paren_set.count(close_paren)) {
++nuniq_close_parens_;
paren_set.insert(close_paren);
}
if (!close_paren_state_set.count(s)) {
++nclose_paren_states_;
close_paren_state_set.insert(s);
}
}
}
}
}
}
template <class Arc>
void PrintPdtInfo(const PdtInfo<Arc> &info) {
const auto old = std::cout.setf(std::ios::left);
std::cout.width(50);
std::cout << "fst type" << info.FstType() << std::endl;
std::cout.width(50);
std::cout << "arc type" << info.ArcType() << std::endl;
std::cout.width(50);
std::cout << "# of states" << info.NumStates() << std::endl;
std::cout.width(50);
std::cout << "# of arcs" << info.NumArcs() << std::endl;
std::cout.width(50);
std::cout << "# of open parentheses" << info.NumOpenParens() << std::endl;
std::cout.width(50);
std::cout << "# of close parentheses" << info.NumCloseParens() << std::endl;
std::cout.width(50);
std::cout << "# of unique open parentheses" << info.NumUniqueOpenParens()
<< std::endl;
std::cout.width(50);
std::cout << "# of unique close parentheses" << info.NumUniqueCloseParens()
<< std::endl;
std::cout.width(50);
std::cout << "# of open parenthesis dest. states" << info.NumOpenParenStates()
<< std::endl;
std::cout.width(50);
std::cout << "# of close parenthesis source states"
<< info.NumCloseParenStates() << std::endl;
std::cout.setf(old);
}
} // namespace fst
#endif // FST_EXTENSIONS_PDT_INFO_H_
| 0 |
coqui_public_repos/STT/native_client/kenlm/lm/common | coqui_public_repos/STT/native_client/kenlm/lm/common/test_data/toy1.arpa | \data\
ngram 1=6
ngram 2=7
ngram 3=6
\1-grams:
-1 <unk> 0
0 <s> -0.30103
-0.6146491 a -0.30103
-0.6146491 </s> 0
-0.7659168 c -0.30103
-0.6146491 b -0.30103
\2-grams:
-0.4301247 <s> a -0.30103
-0.4301247 a a -0.30103
-0.20660876 c </s> 0
-0.5404639 b </s> 0
-0.4740302 <s> c -0.30103
-0.4301247 a b -0.30103
-0.3422159 b b -0.47712123
\3-grams:
-0.1638568 <s> a a
-0.09113217 <s> c </s>
-0.7462621 b b </s>
-0.1638568 a a b
-0.13823806 a b b
-0.13375957 b b b
\end\
| 0 |
coqui_public_repos/TTS/TTS/vc/modules/freevc | coqui_public_repos/TTS/TTS/vc/modules/freevc/wavlm/wavlm.py | # --------------------------------------------------------
# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/pytorch/fairseq
# --------------------------------------------------------
import logging
import math
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from TTS.vc.modules.freevc.wavlm.modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GLU_Linear,
GradMultiply,
MultiheadAttention,
SamePad,
TransposeLast,
get_activation_fn,
init_bert_params,
)
logger = logging.getLogger(__name__)
def compute_mask_indices(
shape: Tuple[int, int],
padding_mask: Optional[torch.Tensor],
mask_prob: float,
mask_length: int,
mask_type: str = "static",
mask_other: float = 0.0,
min_masks: int = 0,
no_overlap: bool = False,
min_space: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape
Args:
shape: the the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_type: how to compute mask lengths
static = fixed size
uniform = sample from uniform distribution [mask_other, mask_length*2]
normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
poisson = sample from possion distribution with lambda = mask length
min_masks: minimum number of masked spans
no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
"""
bsz, all_sz = shape
mask = np.full((bsz, all_sz), False)
all_num_mask = int(
# add a random number for probabilistic rounding
mask_prob * all_sz / float(mask_length)
+ np.random.rand()
)
all_num_mask = max(min_masks, all_num_mask)
mask_idcs = []
for i in range(bsz):
if padding_mask is not None:
sz = all_sz - padding_mask[i].long().sum().item()
num_mask = int(
# add a random number for probabilistic rounding
mask_prob * sz / float(mask_length)
+ np.random.rand()
)
num_mask = max(min_masks, num_mask)
else:
sz = all_sz
num_mask = all_num_mask
if mask_type == "static":
lengths = np.full(num_mask, mask_length)
elif mask_type == "uniform":
lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
elif mask_type == "normal":
lengths = np.random.normal(mask_length, mask_other, size=num_mask)
lengths = [max(1, int(round(x))) for x in lengths]
elif mask_type == "poisson":
lengths = np.random.poisson(mask_length, size=num_mask)
lengths = [int(round(x)) for x in lengths]
else:
raise Exception("unknown mask selection " + mask_type)
if sum(lengths) == 0:
lengths[0] = min(mask_length, sz - 1)
if no_overlap:
mask_idc = []
def arrange(s, e, length, keep_length):
span_start = np.random.randint(s, e - length)
mask_idc.extend(span_start + i for i in range(length))
new_parts = []
if span_start - s - min_space >= keep_length:
new_parts.append((s, span_start - min_space + 1))
if e - span_start - keep_length - min_space > keep_length:
new_parts.append((span_start + length + min_space, e))
return new_parts
parts = [(0, sz)]
min_length = min(lengths)
for length in sorted(lengths, reverse=True):
lens = np.fromiter(
(e - s if e - s >= length + min_space else 0 for s, e in parts),
np.int,
)
l_sum = np.sum(lens)
if l_sum == 0:
break
probs = lens / np.sum(lens)
c = np.random.choice(len(parts), p=probs)
s, e = parts.pop(c)
parts.extend(arrange(s, e, length, min_length))
mask_idc = np.asarray(mask_idc)
else:
min_len = min(lengths)
if sz - min_len <= num_mask:
min_len = sz - num_mask - 1
mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])])
mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
mask[i, mask_idc] = True
return mask
class WavLMConfig:
def __init__(self, cfg=None):
self.extractor_mode: str = "default" # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
self.encoder_layers: int = 12 # num encoder layers in the transformer
self.encoder_embed_dim: int = 768 # encoder embedding dimension
self.encoder_ffn_embed_dim: int = 3072 # encoder embedding dimension for FFN
self.encoder_attention_heads: int = 12 # num encoder attention heads
self.activation_fn: str = "gelu" # activation function to use
self.layer_norm_first: bool = False # apply layernorm first in the transformer
self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2" # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
self.conv_bias: bool = False # include bias in conv encoder
self.feature_grad_mult: float = 1.0 # multiply feature extractor var grads by this
self.normalize: bool = False # normalize input to have 0 mean and unit variance during training
# dropouts
self.dropout: float = 0.1 # dropout probability for the transformer
self.attention_dropout: float = 0.1 # dropout probability for attention weights
self.activation_dropout: float = 0.0 # dropout probability after activation in FFN
self.encoder_layerdrop: float = 0.0 # probability of dropping a tarnsformer layer
self.dropout_input: float = 0.0 # dropout to apply to the input (after feat extr)
self.dropout_features: float = 0.0 # dropout to apply to the features (after feat extr)
# masking
self.mask_length: int = 10 # mask length
self.mask_prob: float = 0.65 # probability of replacing a token with mask
self.mask_selection: str = "static" # how to choose mask length
self.mask_other: float = (
0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh
)
self.no_mask_overlap: bool = False # whether to allow masks to overlap
self.mask_min_space: int = 1 # min space between spans (if no overlap is enabled)
# channel masking
self.mask_channel_length: int = 10 # length of the mask for features (channels)
self.mask_channel_prob: float = 0.0 # probability of replacing a feature with 0
self.mask_channel_selection: str = "static" # how to choose mask length for channel masking
self.mask_channel_other: float = (
0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indices
)
self.no_mask_channel_overlap: bool = False # whether to allow channel masks to overlap
self.mask_channel_min_space: int = 1 # min space between spans (if no overlap is enabled)
# positional embeddings
self.conv_pos: int = 128 # number of filters for convolutional positional embeddings
self.conv_pos_groups: int = 16 # number of groups for convolutional positional embedding
# relative position embedding
self.relative_position_embedding: bool = False # apply relative position embedding
self.num_buckets: int = 320 # number of buckets for relative position embedding
self.max_distance: int = 1280 # maximum distance for relative position embedding
self.gru_rel_pos: bool = False # apply gated relative position embedding
if cfg is not None:
self.update(cfg)
def update(self, cfg: dict):
self.__dict__.update(cfg)
class WavLM(nn.Module):
def __init__(
self,
cfg: WavLMConfig,
) -> None:
super().__init__()
logger.info(f"WavLM Config: {cfg.__dict__}")
self.cfg = cfg
feature_enc_layers = eval(cfg.conv_feature_layers)
self.embed = feature_enc_layers[-1][0]
self.feature_extractor = ConvFeatureExtractionModel(
conv_layers=feature_enc_layers,
dropout=0.0,
mode=cfg.extractor_mode,
conv_bias=cfg.conv_bias,
)
self.post_extract_proj = (
nn.Linear(self.embed, cfg.encoder_embed_dim) if self.embed != cfg.encoder_embed_dim else None
)
self.mask_prob = cfg.mask_prob
self.mask_selection = cfg.mask_selection
self.mask_other = cfg.mask_other
self.mask_length = cfg.mask_length
self.no_mask_overlap = cfg.no_mask_overlap
self.mask_min_space = cfg.mask_min_space
self.mask_channel_prob = cfg.mask_channel_prob
self.mask_channel_selection = cfg.mask_channel_selection
self.mask_channel_other = cfg.mask_channel_other
self.mask_channel_length = cfg.mask_channel_length
self.no_mask_channel_overlap = cfg.no_mask_channel_overlap
self.mask_channel_min_space = cfg.mask_channel_min_space
self.dropout_input = nn.Dropout(cfg.dropout_input)
self.dropout_features = nn.Dropout(cfg.dropout_features)
self.feature_grad_mult = cfg.feature_grad_mult
self.mask_emb = nn.Parameter(torch.FloatTensor(cfg.encoder_embed_dim).uniform_())
self.encoder = TransformerEncoder(cfg)
self.layer_norm = LayerNorm(self.embed)
def apply_mask(self, x, padding_mask):
B, T, C = x.shape
if self.mask_prob > 0:
mask_indices = compute_mask_indices(
(B, T),
padding_mask,
self.mask_prob,
self.mask_length,
self.mask_selection,
self.mask_other,
min_masks=2,
no_overlap=self.no_mask_overlap,
min_space=self.mask_min_space,
)
mask_indices = torch.from_numpy(mask_indices).to(x.device)
x[mask_indices] = self.mask_emb
else:
mask_indices = None
if self.mask_channel_prob > 0:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_channel_prob,
self.mask_channel_length,
self.mask_channel_selection,
self.mask_channel_other,
no_overlap=self.no_mask_channel_overlap,
min_space=self.mask_channel_min_space,
)
mask_channel_indices = torch.from_numpy(mask_channel_indices).to(x.device).unsqueeze(1).expand(-1, T, -1)
x[mask_channel_indices] = 0
return x, mask_indices
def forward_padding_mask(
self,
features: torch.Tensor,
padding_mask: torch.Tensor,
) -> torch.Tensor:
extra = padding_mask.size(1) % features.size(1)
if extra > 0:
padding_mask = padding_mask[:, :-extra]
padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1)
# padding_mask = padding_mask.all(-1)
padding_mask = padding_mask.any(-1)
return padding_mask
def extract_features(
self,
source: torch.Tensor,
padding_mask: Optional[torch.Tensor] = None,
mask: bool = False,
ret_conv: bool = False,
output_layer: Optional[int] = None,
ret_layer_results: bool = False,
):
if self.feature_grad_mult > 0:
features = self.feature_extractor(source)
if self.feature_grad_mult != 1.0:
features = GradMultiply.apply(features, self.feature_grad_mult)
else:
with torch.no_grad():
features = self.feature_extractor(source)
features = features.transpose(1, 2)
features = self.layer_norm(features)
if padding_mask is not None:
padding_mask = self.forward_padding_mask(features, padding_mask)
if self.post_extract_proj is not None:
features = self.post_extract_proj(features)
features = self.dropout_input(features)
if mask:
x, mask_indices = self.apply_mask(features, padding_mask)
else:
x = features
# feature: (B, T, D), float
# target: (B, T), long
# x: (B, T, D), float
# padding_mask: (B, T), bool
# mask_indices: (B, T), bool
x, layer_results = self.encoder(
x, padding_mask=padding_mask, layer=None if output_layer is None else output_layer - 1
)
res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results}
feature = res["features"] if ret_conv else res["x"]
if ret_layer_results:
feature = (feature, res["layer_results"])
return feature, res["padding_mask"]
class ConvFeatureExtractionModel(nn.Module):
def __init__(
self,
conv_layers: List[Tuple[int, int, int]],
dropout: float = 0.0,
mode: str = "default",
conv_bias: bool = False,
conv_type: str = "default",
):
super().__init__()
assert mode in {"default", "layer_norm"}
def block(
n_in,
n_out,
k,
stride,
is_layer_norm=False,
is_group_norm=False,
conv_bias=False,
):
def make_conv():
conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
nn.init.kaiming_normal_(conv.weight)
return conv
assert (is_layer_norm and is_group_norm) == False, "layer norm and group norm are exclusive"
if is_layer_norm:
return nn.Sequential(
make_conv(),
nn.Dropout(p=dropout),
nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=True),
TransposeLast(),
),
nn.GELU(),
)
elif is_group_norm:
return nn.Sequential(
make_conv(),
nn.Dropout(p=dropout),
Fp32GroupNorm(dim, dim, affine=True),
nn.GELU(),
)
else:
return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU())
self.conv_type = conv_type
if self.conv_type == "default":
in_d = 1
self.conv_layers = nn.ModuleList()
for i, cl in enumerate(conv_layers):
assert len(cl) == 3, "invalid conv definition: " + str(cl)
(dim, k, stride) = cl
self.conv_layers.append(
block(
in_d,
dim,
k,
stride,
is_layer_norm=mode == "layer_norm",
is_group_norm=mode == "default" and i == 0,
conv_bias=conv_bias,
)
)
in_d = dim
elif self.conv_type == "conv2d":
in_d = 1
self.conv_layers = nn.ModuleList()
for i, cl in enumerate(conv_layers):
assert len(cl) == 3
(dim, k, stride) = cl
self.conv_layers.append(torch.nn.Conv2d(in_d, dim, k, stride))
self.conv_layers.append(torch.nn.ReLU())
in_d = dim
elif self.conv_type == "custom":
in_d = 1
idim = 80
self.conv_layers = nn.ModuleList()
for i, cl in enumerate(conv_layers):
assert len(cl) == 3
(dim, k, stride) = cl
self.conv_layers.append(torch.nn.Conv2d(in_d, dim, k, stride, padding=1))
self.conv_layers.append(torch.nn.LayerNorm([dim, idim]))
self.conv_layers.append(torch.nn.ReLU())
in_d = dim
if (i + 1) % 2 == 0:
self.conv_layers.append(torch.nn.MaxPool2d(2, stride=2, ceil_mode=True))
idim = int(math.ceil(idim / 2))
else:
pass
def forward(self, x, mask=None):
# BxT -> BxCxT
x = x.unsqueeze(1)
if self.conv_type == "custom":
for conv in self.conv_layers:
if isinstance(conv, nn.LayerNorm):
x = x.transpose(1, 2)
x = conv(x).transpose(1, 2)
else:
x = conv(x)
x = x.transpose(2, 3).contiguous()
x = x.view(x.size(0), -1, x.size(-1))
else:
for conv in self.conv_layers:
x = conv(x)
if self.conv_type == "conv2d":
b, c, t, f = x.size()
x = x.transpose(2, 3).contiguous().view(b, c * f, t)
return x
class TransformerEncoder(nn.Module):
def __init__(self, args):
super().__init__()
self.dropout = args.dropout
self.embedding_dim = args.encoder_embed_dim
self.pos_conv = nn.Conv1d(
self.embedding_dim,
self.embedding_dim,
kernel_size=args.conv_pos,
padding=args.conv_pos // 2,
groups=args.conv_pos_groups,
)
dropout = 0
std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
nn.init.constant_(self.pos_conv.bias, 0)
self.pos_conv = nn.utils.parametrizations.weight_norm(self.pos_conv, name="weight", dim=2)
self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
if hasattr(args, "relative_position_embedding"):
self.relative_position_embedding = args.relative_position_embedding
self.num_buckets = args.num_buckets
self.max_distance = args.max_distance
else:
self.relative_position_embedding = False
self.num_buckets = 0
self.max_distance = 0
self.layers = nn.ModuleList(
[
TransformerSentenceEncoderLayer(
embedding_dim=self.embedding_dim,
ffn_embedding_dim=args.encoder_ffn_embed_dim,
num_attention_heads=args.encoder_attention_heads,
dropout=self.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.activation_dropout,
activation_fn=args.activation_fn,
layer_norm_first=args.layer_norm_first,
has_relative_attention_bias=(self.relative_position_embedding and i == 0),
num_buckets=self.num_buckets,
max_distance=self.max_distance,
gru_rel_pos=args.gru_rel_pos,
)
for i in range(args.encoder_layers)
]
)
self.layer_norm_first = args.layer_norm_first
self.layer_norm = LayerNorm(self.embedding_dim)
self.layerdrop = args.encoder_layerdrop
self.apply(init_bert_params)
def forward(self, x, padding_mask=None, streaming_mask=None, layer=None):
x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer)
if self.layer_norm_first and layer is None:
x = self.layer_norm(x)
return x, layer_results
def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None):
if padding_mask is not None:
x[padding_mask] = 0
x_conv = self.pos_conv(x.transpose(1, 2))
x_conv = x_conv.transpose(1, 2)
x += x_conv
if not self.layer_norm_first:
x = self.layer_norm(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
layer_results = []
z = None
if tgt_layer is not None:
layer_results.append((x, z))
r = None
pos_bias = None
for i, layer in enumerate(self.layers):
dropout_probability = np.random.random()
if not self.training or (dropout_probability > self.layerdrop):
x, z, pos_bias = layer(
x,
self_attn_padding_mask=padding_mask,
need_weights=False,
self_attn_mask=streaming_mask,
pos_bias=pos_bias,
)
if tgt_layer is not None:
layer_results.append((x, z))
if i == tgt_layer:
r = x
break
if r is not None:
x = r
# T x B x C -> B x T x C
x = x.transpose(0, 1)
return x, layer_results
class TransformerSentenceEncoderLayer(nn.Module):
"""
Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
models.
"""
def __init__(
self,
embedding_dim: float = 768,
ffn_embedding_dim: float = 3072,
num_attention_heads: float = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
activation_fn: str = "relu",
layer_norm_first: bool = False,
has_relative_attention_bias: bool = False,
num_buckets: int = 0,
max_distance: int = 0,
rescale_init: bool = False,
gru_rel_pos: bool = False,
) -> None:
super().__init__()
# Initialize parameters
self.embedding_dim = embedding_dim
self.dropout = dropout
self.activation_dropout = activation_dropout
# Initialize blocks
self.activation_name = activation_fn
self.activation_fn = get_activation_fn(activation_fn)
self.self_attn = MultiheadAttention(
self.embedding_dim,
num_attention_heads,
dropout=attention_dropout,
self_attention=True,
has_relative_attention_bias=has_relative_attention_bias,
num_buckets=num_buckets,
max_distance=max_distance,
rescale_init=rescale_init,
gru_rel_pos=gru_rel_pos,
)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(self.activation_dropout)
self.dropout3 = nn.Dropout(dropout)
self.layer_norm_first = layer_norm_first
# layer norm associated with the self attention layer
self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
if self.activation_name == "glu":
self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
else:
self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
# layer norm associated with the position wise feed-forward NN
self.final_layer_norm = LayerNorm(self.embedding_dim)
def forward(
self,
x: torch.Tensor,
self_attn_mask: torch.Tensor = None,
self_attn_padding_mask: torch.Tensor = None,
need_weights: bool = False,
pos_bias=None,
):
"""
LayerNorm is applied either before or after the self-attention/ffn
modules similar to the original Transformer imlementation.
"""
residual = x
if self.layer_norm_first:
x = self.self_attn_layer_norm(x)
x, attn, pos_bias = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
need_weights=False,
attn_mask=self_attn_mask,
position_bias=pos_bias,
)
x = self.dropout1(x)
x = residual + x
residual = x
x = self.final_layer_norm(x)
if self.activation_name == "glu":
x = self.fc1(x)
else:
x = self.activation_fn(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
x = self.dropout3(x)
x = residual + x
else:
x, attn, pos_bias = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
need_weights=need_weights,
attn_mask=self_attn_mask,
position_bias=pos_bias,
)
x = self.dropout1(x)
x = residual + x
x = self.self_attn_layer_norm(x)
residual = x
if self.activation_name == "glu":
x = self.fc1(x)
else:
x = self.activation_fn(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
x = self.dropout3(x)
x = residual + x
x = self.final_layer_norm(x)
return x, attn, pos_bias
| 0 |
coqui_public_repos/STT-examples/uwp | coqui_public_repos/STT-examples/uwp/STTUWP/App.xaml | <Application
x:Class="STTUWP.App"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
xmlns:local="using:STTUWP">
</Application>
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/test-nodejs_14x-win-amd64-opt.yml | build:
template_file: test-win-opt-base.tyml
dependencies:
- "win-amd64-cpu-opt"
- "test-training_16k-linux-amd64-py36m-opt"
test_model_task: "test-training_16k-linux-amd64-py36m-opt"
system_setup:
>
${system.sox_win} && ${nodejs.win.prep_14}
args:
tests_cmdline: "${system.homedir.win}/DeepSpeech/ds/taskcluster/tc-node-tests.sh 14.x 16k"
metadata:
name: "DeepSpeech Windows AMD64 CPU NodeJS 14.x tests"
description: "Testing DeepSpeech for Windows/AMD64 on NodeJS v14.x, CPU only, optimized version"
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/linux-amd64-cpu-dbg.yml | build:
template_file: linux-opt-base.tyml
dependencies:
- "swig-linux-amd64"
- "node-gyp-cache"
- "pyenv-linux-amd64"
- "tf_linux-amd64-cpu_gcc9"
routes:
- "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.cpu-dbg"
- "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.${event.head.sha}.cpu-dbg"
- "index.project.deepspeech.deepspeech.native_client.cpu-dbg.${event.head.sha}"
tensorflow: ${system.tensorflow_gcc9.linux_amd64_cpu.url}
docker_image: "ubuntu:20.04"
system_config:
>
${deepspeech.packages_bionic.apt}
scripts:
setup: "taskcluster/tc-true.sh"
build: "taskcluster/host-build-dbg.sh"
package: "taskcluster/package.sh"
nc_asset_name: "native_client.amd64.cpu.linux_dbg.tar.xz"
workerType: "${docker.tfBuild}"
metadata:
name: "DeepSpeech Linux AMD64 CPU Debug"
description: "Building DeepSpeech for Linux/AMD64, CPU only, debug version"
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/tc-python_tflite-tests-prod.sh | #!/bin/bash
set -xe
source $(dirname "$0")/tc-tests-utils.sh
extract_python_versions "$1" "pyver" "pyver_pkg" "py_unicode_type" "pyconf" "pyalias"
bitrate=$2
set_ldc_sample_filename "${bitrate}"
model_source=${DEEPSPEECH_PROD_MODEL//.pb/.tflite}
model_name=$(basename "${model_source}")
model_name_mmap=$(basename "${model_source}")
model_source_mmap=${DEEPSPEECH_PROD_MODEL_MMAP//.pbmm/.tflite}
download_data
maybe_setup_virtualenv_cross_arm "${pyalias}" "deepspeech"
virtualenv_activate "${pyalias}" "deepspeech"
pkg_name=$(get_tflite_python_pkg_name)
deepspeech_pkg_url=$(get_python_pkg_url "${pyver_pkg}" "${py_unicode_type}" "${pkg_name}")
LD_LIBRARY_PATH=${PY37_LDPATH}:$LD_LIBRARY_PATH pip install --verbose --only-binary :all: --upgrade ${deepspeech_pkg_url} | cat
which deepspeech
deepspeech --version
run_prodtflite_inference_tests "${bitrate}"
virtualenv_deactivate "${pyalias}" "deepspeech"
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions/compress/randmod.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Generates a random FST according to a class-specific transition model.
#ifndef FST_EXTENSIONS_COMPRESS_RANDMOD_H_
#define FST_EXTENSIONS_COMPRESS_RANDMOD_H_
#include <vector>
#include <fst/compat.h>
#include <fst/mutable-fst.h>
namespace fst {
template <class Arc, class G>
class RandMod {
public:
typedef typename Arc::StateId StateId;
typedef typename Arc::Label Label;
typedef typename Arc::Weight Weight;
// Generates random FST with 'nstates' with 'nclasses' in the probability
// generation model, and 'nlabels' in the alphabet. If 'trans' = true, then
// a transducer is generated; iff 'generate_' is non-null, the output is
// randomly weighted.
RandMod(StateId nstates, StateId nclasses, Label nlabels, bool trans,
const G *generate)
: nstates_(nstates),
nclasses_(nclasses),
nlabels_(nlabels),
trans_(trans),
generate_(generate) {
for (StateId s = 0; s < nstates; ++s) {
classes_.push_back(rand() % nclasses); // NOLINT
}
}
// Generates a random FST according to a class-specific transition model
void Generate(StdMutableFst *fst) {
StateId start = rand() % nstates_; // NOLINT
fst->DeleteStates();
for (StateId s = 0; s < nstates_; ++s) {
fst->AddState();
if (s == start) fst->SetStart(start);
for (StateId n = 0; n <= nstates_; ++n) {
Arc arc;
StateId d = n == nstates_ ? kNoStateId : n;
if (!RandArc(s, d, &arc)) continue;
if (d == kNoStateId) { // A super-final transition?
fst->SetFinal(s, arc.weight);
} else {
fst->AddArc(s, arc);
}
}
}
}
private:
// Generates a transition from s to d. If d == kNoStateId, a superfinal
// transition is generated. Returns false if no transition generated.
bool RandArc(StateId s, StateId d, Arc *arc) {
StateId sclass = classes_[s];
StateId dclass = d != kNoStateId ? classes_[d] : 0;
int r = sclass + dclass + 2;
if ((rand() % r) != 0) // NOLINT
return false;
arc->nextstate = d;
Label ilabel = kNoLabel;
Label olabel = kNoLabel;
if (d != kNoStateId) {
ilabel = (dclass % nlabels_) + 1;
if (trans_)
olabel = (sclass % nlabels_) + 1;
else
olabel = ilabel;
}
Weight weight = Weight::One();
if (generate_) weight = (*generate_)();
arc->ilabel = ilabel;
arc->olabel = olabel;
arc->weight = weight;
return true;
}
StateId nstates_;
StateId nclasses_;
Label nlabels_;
bool trans_;
const G *generate_;
std::vector<StateId> classes_;
};
} // namespace fst
#endif // FST_EXTENSIONS_COMPRESS_RANDMOD_H_
| 0 |
coqui_public_repos/STT-examples/uwp/STTUWP | coqui_public_repos/STT-examples/uwp/STTUWP/Properties/AssemblyInfo.cs | using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
// General Information about an assembly is controlled through the following
// set of attributes. Change these attribute values to modify the information
// associated with an assembly.
[assembly: AssemblyTitle("STTUWP")]
[assembly: AssemblyDescription("")]
[assembly: AssemblyConfiguration("")]
[assembly: AssemblyCompany("")]
[assembly: AssemblyProduct("STTUWP")]
[assembly: AssemblyCopyright("Copyright © 2020")]
[assembly: AssemblyTrademark("")]
[assembly: AssemblyCulture("")]
// Version information for an assembly consists of the following four values:
//
// Major Version
// Minor Version
// Build Number
// Revision
//
// You can specify all the values or you can default the Build and Revision Numbers
// by using the '*' as shown below:
// [assembly: AssemblyVersion("1.0.*")]
[assembly: AssemblyVersion("1.0.0.0")]
[assembly: AssemblyFileVersion("1.0.0.0")]
[assembly: ComVisible(false)] | 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/extensions/compact/compact16_unweighted-fst.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/fst.h>
#include <fst/compact-fst.h>
namespace fst {
static FstRegisterer<CompactUnweightedFst<StdArc, uint16>>
CompactUnweightedFst_StdArc_uint16_registerer;
static FstRegisterer<CompactUnweightedFst<LogArc, uint16>>
CompactUnweightedFst_LogArc_uint16_registerer;
} // namespace fst
| 0 |
coqui_public_repos/STT/native_client | coqui_public_repos/STT/native_client/javascript/tsconfig.json | {
"compilerOptions": {
"baseUrl": ".",
"target": "es6",
"module": "commonjs",
"moduleResolution": "node",
"esModuleInterop": true,
"noImplicitAny": true,
"noImplicitThis": true,
"strictFunctionTypes": true,
"strictNullChecks": true,
"forceConsistentCasingInFileNames": true,
"alwaysStrict": true,
"declaration": true,
"stripInternal": true
},
"files": [
"index.ts",
"client.ts",
"node-pre-gyp.d.ts"
]
}
| 0 |
coqui_public_repos/snakepit/src | coqui_public_repos/snakepit/src/routes/jobs.js | const fs = require('fs-extra')
const path = require('path')
const Tail = require('tail').Tail
const Parallel = require('async-parallel')
const Sequelize = require('sequelize')
const Router = require('express-promise-router')
const Pit = require('../models/Pit-model.js')
const Job = require('../models/Job-model.js')
const config = require('../config.js')
const scheduler = require('../scheduler.js')
const pitRunner = require('../pitRunner.js')
const reservations = require('../reservations.js')
const parseClusterRequest = require('../clusterParser.js').parse
const log = require('../utils/logger.js')
const simplefs = require('../utils/simplefs.js')
const clusterEvents = require('../utils/clusterEvents.js')
const { getDuration } = require('../utils/dateTime.js')
const { ensureSignedIn, ensureUpgrade, targetJob, targetInstance, targetGroup } = require('./mw.js')
const jobStates = Job.jobStates
var router = module.exports = new Router()
router.use(ensureSignedIn)
router.post('/', async (req, res) => {
let job = req.body
let clusterRequest
try {
clusterRequest = parseClusterRequest(job.clusterRequest)
} catch (ex) {
res.status(400).send({ message: 'Problem parsing allocation' })
return
}
if (!(await reservations.canAllocate(clusterRequest, req.user))) {
res.status(406).send({ message: 'Cluster cannot fulfill resource request' })
return
}
if (job.continueJob) {
let continueJob = await Job.findByPk(job.continueJob)
if (!continueJob) {
res.status(404).send({ message: 'The job to continue is not existing' })
return
}
if (!(await req.user.canAccessJob(continueJob))) {
res.status(403).send({ message: 'Continuing provided job not allowed for current user' })
return
}
}
let pit
let newJob
let archivePath
try {
let provisioning
if (job.origin) {
if (job.hash) {
provisioning = 'Git commit ' + job.hash + ' from ' + job.origin
} else {
provisioning = 'Git clone of ' + job.origin
}
} else if (job.archive) {
let basePath = req.user.getDir()
archivePath = path.resolve(basePath, job.archive)
if (!archivePath.startsWith(basePath)) {
res.status(403).send({ message: 'Archive outside user home' })
return
}
if (!(await fs.pathExists(archivePath))) {
res.status(404).send({ message: 'Archive not found' })
return
}
provisioning = 'Archive (' + (await fs.stat(basePath)).size + ' bytes)'
} else {
provisioning = 'Script'
}
if (job.diff) {
provisioning += ' with ' +
(job.diff + '').split('\n').length + ' LoC diff'
}
pit = await Pit.create()
newJob = await Job.create({
id: pit.id,
userId: req.user.id,
description: ('' + job.description).substring(0,40),
provisioning: provisioning,
request: job.clusterRequest,
continues: job.continueJob
})
if (!job.private) {
for(let autoshare of (await req.user.getAutoshares())) {
await Job.JobGroup.create({ jobId: newJob.id, groupId: autoshare.groupId })
}
}
let files = {}
files['script.sh'] = (job.script || 'if [ -f .compute ]; then bash .compute; fi') + '\n'
if (job.origin) {
files['origin'] = job.origin
if (job.hash) {
files['hash'] = job.hash
}
} else if (archivePath) {
await fs.copy(archivePath, path.join(newJob.getDir(), 'archive.tar.gz'))
}
if (job.diff) {
files['git.patch'] = job.diff + '\n'
}
let jobDir = Pit.getDir(pit.id)
await Parallel.each(Object.keys(files), filename => fs.writeFile(path.join(jobDir, filename), files[filename]))
await newJob.setState(jobStates.NEW)
res.status(200).send({ id: pit.id })
} catch (ex) {
if (newJob) {
await newJob.destroy()
}
if (pit) {
await pit.destroy()
}
res.status(500).send({ message: ex.toString() })
}
})
function getJobDescription(job) {
return {
id: job.id,
description: job.description,
user: job.userId,
resources: job.allocation || job.request,
state: job.state,
date: job.since,
since: getDuration(new Date(), job.since),
schedulePosition: job.rank,
utilComp: job.state == jobStates.RUNNING ? job.dataValues.curcompute :
(job.dataValues.aggcompute / (job.dataValues.samples || 1)),
utilMem: job.state == jobStates.RUNNING ? job.dataValues.curmemory :
(job.dataValues.aggmemory / (job.dataValues.samples || 1))
}
}
router.get('/', async (req, res) => {
const orderings = {
'date': 'since',
'user': 'user',
'title': 'description',
'state': 'state'
}
let query = { where: {}, order: [], limit: config.queryLimit }
const parseDate = v => { try { return new Date(v) } catch (ex) { return null } }
let parsers = {
since: v => parseDate(v) ? (query.where.since = { [Sequelize.Op.gte]: parseDate(v) }) : false,
till: v => parseDate(v) ? (query.where.since = { [Sequelize.Op.lte]: parseDate(v) }) : false,
user: v => query.where.userId = v,
title: v => query.where.description = { [Sequelize.Op.like]: v },
asc: v => orderings[v] ? query.order.push([orderings[v], 'ASC']) : false,
desc: v => orderings[v] ? query.order.push([orderings[v], 'DESC']) : false,
limit: v => !isNaN(parseInt(v)) && (query.limit = Math.min(v, query.limit)),
offset: v => !isNaN(parseInt(v)) && (query.offset = v)
}
for(let param of Object.keys(req.query)) {
let parser = parsers[param]
if (parser) {
if (!parser(req.query[param])) {
res.status(400).send({ message: 'Cannot parse query parameter ' + param })
return
}
} else {
res.status(400).send({ message: 'Unknown query parameter ' + param })
return
}
}
query.order.push(['since', 'DESC'])
let jobs = await Job.findAll(Job.infoQuery(query))
res.send(jobs.map(job => getJobDescription(job)))
})
router.get('/status', async (req, res) => {
let query = Job.infoQuery({
where: { state: { [Sequelize.Op.gte]: jobStates.NEW, [Sequelize.Op.lte]: jobStates.STOPPING } }
})
let jobs = await Job.findAll(query)
let running = jobs
.filter(j => j.state >= jobStates.STARTING && j.state <= jobStates.STOPPING)
.sort((a,b) => a.id - b.id)
let waiting = jobs
.filter(j => j.state == jobStates.WAITING)
.sort((a,b) => a.rank - b.rank)
waiting = waiting.concat(jobs.filter(j => j.state == jobStates.PREPARING))
waiting = waiting.concat(jobs.filter(j => j.state == jobStates.NEW))
let done = await Job.findAll(Job.infoQuery({
where: { state: { [Sequelize.Op.gt]: jobStates.STOPPING } },
order: [['since', 'DESC']],
limit: 20
}))
res.send({
running: running.map(job => getJobDescription(job)),
waiting: waiting.map(job => getJobDescription(job)),
done: done .map(job => getJobDescription(job))
})
})
router.get('/:job', async (req, res) => {
let query = Job.infoQuery({ where: { id: req.params.job } })
let job = await Job.findOne(query)
if (!job) {
return Promise.reject({ code: 404, message: 'Job not found' })
}
let description = getJobDescription(job)
description.allocation = job.allocation
description.clusterRequest = job.clusterRequest
if (job.continues) {
description.continueJob = job.continues
}
if(await req.user.canAccessJob(job)) {
let groups = (await job.getJobgroups()).map(jg => jg.groupId)
description.provisioning = job.provisioning
description.groups = groups.length > 0 && groups
description.stateChanges = (await job.getStates({ order: ['since'] })).map(s => ({
state: s.state,
since: s.since,
reason: s.reason
}))
let processes = []
for(let processGroup of await job.getProcessgroups()) {
for(let jobProcess of await processGroup.getProcesses()) {
processes.push({
groupIndex: processGroup.index,
processIndex: jobProcess.index,
status: (jobProcess.status === 0 || jobProcess.status > 0) ? jobProcess.status : '?',
result: jobProcess.result
})
}
}
if (processes.length > 0) {
description.processes = processes
}
}
res.send(description)
})
async function canAccess (req, res) {
return (await req.user.canAccessJob(req.targetJob)) ? Promise.resolve('next') : Promise.reject({ code: 403, message: 'Forbidden' })
}
router.put('/:job/groups/:group', targetJob, canAccess, targetGroup, async (req, res) => {
await Job.JobGroup.upsert({ jobId: req.targetJob.id, groupId: req.targetGroup.id })
res.send()
})
router.delete('/:job/groups/:group', targetJob, canAccess, targetGroup, async (req, res) => {
await Job.JobGroup.destroy({ where: { jobId: req.targetJob.id, groupId: req.targetGroup.id } })
res.send()
clusterEvents.emit('restricted')
})
router.all('/:job/simplefs/' + simplefs.pattern, targetJob, canAccess, async (req, res) => {
let baseDir = Pit.getDir(req.targetJob.id)
await simplefs.performCommand(baseDir, req, res)
})
router.get('/:job/log', targetJob, canAccess, async (req, res) => {
res.writeHead(200, {
'Connection': 'keep-alive',
'Content-Type': 'text/plain',
'Cache-Control': 'no-cache'
})
req.connection.setTimeout(60 * 60 * 1000)
let interval = config.pollInterval
let logPath = path.join(Pit.getDir(req.targetJob.id), 'pit.log')
if (req.targetJob.state < jobStates.DONE) {
let tail
let startTail = () => {
tail = new Tail(logPath, { fromBeginning: true })
tail.on("line", line => !res.finished && res.write(line + '\n'))
tail.on("error", stopTail)
res.on('close', stopTail)
res.on('end', stopTail)
}
let stopTail = () => {
if (tail) {
tail.unwatch()
tail = null
}
res.end()
}
let poll = () => {
if (tail) {
req.targetJob.reload().then(() => {
if (req.targetJob.state == jobStates.DONE) {
stopTail()
} else {
setTimeout(poll, interval)
}
}).catch(stopTail)
} else {
if (fs.existsSync(logPath)) {
startTail()
}
setTimeout(poll, interval)
}
}
poll()
} else if (fs.existsSync(logPath)) {
let stream = fs.createReadStream(logPath)
stream.on('data', chunk => res.write(chunk))
stream.on('end', res.end.bind(res))
} else {
res.status(404).send()
}
})
router.get('/:job/instances/:instance/exec', ensureUpgrade, targetJob, targetInstance, canAccess, async (req, res) => {
if (!req.query.context) {
throw { code: 400, message: 'No command' }
}
let context = JSON.parse(req.query.context)
let pitSockets = await pitRunner.exec(req.targetJob.id, req.targetInstance, context)
if (!pitSockets) {
throw { code: 404, message: 'Worker not active' }
}
res.openSocket(async client => {
let stdin = pitSockets['0']
let control = pitSockets.control
client.on('message', msg => {
if (msg[0] == 0 && control.readyState === control.OPEN) {
control.send(msg.slice(1))
} else if (msg[0] == 1 && stdin.readyState === stdin.OPEN) {
stdin.send(msg.slice(1))
}
})
let sendToClient = (buffer, n) => {
if (client.readyState === client.OPEN) {
client.send(Buffer.concat([
new Buffer([n]),
Buffer.isBuffer(buffer) ? buffer : Buffer.from(buffer)
]))
}
}
let sockets
if (context.interactive) {
sockets = [client, control, stdin]
stdin.on('message', msg => sendToClient(msg, 1))
} else {
let stdout = pitSockets['1']
let stderr = pitSockets['2']
sockets = [client, control, stdin, stdout, stderr]
stdout.on('message', msg => sendToClient(msg, 1))
stderr.on('message', msg => sendToClient(msg, 2))
}
control.on('message', msg => sendToClient(msg, 0))
let close = () => sockets.forEach(s => s && s.close())
sockets.forEach(s => s && s.on('close', close))
})
})
router.get('/:job/instances/:instance/forward', ensureUpgrade, targetJob, targetInstance, canAccess, async (req, res) => {
let pitSockets = await pitRunner.exec(req.targetJob.id, req.targetInstance, {
command: ['forwarder.sh'],
interactive: false
})
if (!pitSockets) {
throw { code: 404, message: 'Worker not active' }
}
res.openSocket(async client => {
let stdin = pitSockets['0']
let stdout = pitSockets['1']
let sockets = [client, stdin, stdout, pitSockets['2'], pitSockets['control']]
let connected = true
client.on('message', msg => connected && stdin .send(msg))
stdout.on('message', msg => connected && client.send(msg))
let close = () => { connected = false; sockets.forEach(s => s.close()) }
sockets.forEach(s => s.on('close', close))
})
})
router.post('/:job/stop', targetJob, canAccess, async (req, res) => {
if (req.targetJob.state <= jobStates.STOPPING) {
await scheduler.stopJob(req.targetJob, 'Stopped by user ' + req.user.id)
res.send()
} else {
res.status(412).send({ message: 'Only jobs before or in running state can be stopped' })
}
})
router.delete('/:job', targetJob, canAccess, async (req, res) => {
if (req.targetJob.state >= jobStates.DONE) {
await req.targetJob.destroy()
res.send()
} else {
res.status(412).send({ message: 'Only stopped jobs can be deleted' })
}
})
| 0 |
coqui_public_repos/TTS/TTS/tts/utils | coqui_public_repos/TTS/TTS/tts/utils/monotonic_align/setup.py | # from distutils.core import setup
# from Cython.Build import cythonize
# import numpy
# setup(name='monotonic_align',
# ext_modules=cythonize("core.pyx"),
# include_dirs=[numpy.get_include()])
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/include/fst/script/getters.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Getters for converting command-line arguments into the appropriate enums
// or bitmasks, with the simplest ones defined as inline.
#ifndef FST_SCRIPT_GETTERS_H_
#define FST_SCRIPT_GETTERS_H_
#include <string>
#include <fst/compose.h> // For ComposeFilter.
#include <fst/determinize.h> // For DeterminizeType.
#include <fst/encode.h> // For kEncodeLabels (etc.).
#include <fst/epsnormalize.h> // For EpsNormalizeType.
#include <fst/project.h> // For ProjectType.
#include <fst/push.h> // For kPushWeights (etc.).
#include <fst/queue.h> // For QueueType.
#include <fst/rational.h> // For ClosureType.
#include <fst/script/arcsort.h> // For ArcSortType.
#include <fst/script/map.h> // For MapType.
#include <fst/script/script-impl.h> // For RandArcSelection.
#include <fst/log.h>
namespace fst {
namespace script {
bool GetArcSortType(const string &str, ArcSortType *sort_type);
inline ClosureType GetClosureType(bool closure_plus) {
return closure_plus ? CLOSURE_PLUS : CLOSURE_STAR;
}
bool GetComposeFilter(const string &str, ComposeFilter *compose_filter);
bool GetDeterminizeType(const string &str, DeterminizeType *det_type);
inline uint32_t GetEncodeFlags(bool encode_labels, bool encode_weights) {
return (encode_labels ? kEncodeLabels : 0) |
(encode_weights ? kEncodeWeights : 0);
}
inline EpsNormalizeType GetEpsNormalizeType(bool eps_norm_output) {
return eps_norm_output ? EPS_NORM_OUTPUT : EPS_NORM_INPUT;
}
bool GetMapType(const string &str, MapType *map_type);
inline ProjectType GetProjectType(bool project_output) {
return project_output ? PROJECT_OUTPUT : PROJECT_INPUT;
}
inline uint32_t GetPushFlags(bool push_weights, bool push_labels,
bool remove_total_weight, bool remove_common_affix) {
return ((push_weights ? kPushWeights : 0) |
(push_labels ? kPushLabels : 0) |
(remove_total_weight ? kPushRemoveTotalWeight : 0) |
(remove_common_affix ? kPushRemoveCommonAffix : 0));
}
bool GetQueueType(const string &str, QueueType *queue_type);
bool GetRandArcSelection(const string &str, RandArcSelection *ras);
bool GetReplaceLabelType(const string &str, bool epsilon_on_replace,
ReplaceLabelType *rlt);
inline ReweightType GetReweightType(bool to_final) {
return to_final ? REWEIGHT_TO_FINAL : REWEIGHT_TO_INITIAL;
}
} // namespace script
} // namespace fst
#endif // FST_SCRIPT_GETTERS_H_
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstproject-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Projects a transduction onto its input or output language.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/script/getters.h>
#include <fst/script/project.h>
DECLARE_bool(project_output);
int fstproject_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::MutableFstClass;
string usage =
"Projects a transduction onto its input"
" or output language.\n\n Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name = (argc > 1 && strcmp(argv[1], "-") != 0) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<MutableFstClass> fst(MutableFstClass::Read(in_name, true));
if (!fst) return 1;
s::Project(fst.get(), s::GetProjectType(FLAGS_project_output));
return !fst->Write(out_name);
}
| 0 |
coqui_public_repos/STT-models/irish/itml | coqui_public_repos/STT-models/irish/itml/v0.1.1/LICENSE | GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
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TERMS AND CONDITIONS
0. Definitions.
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All rights granted under this License are granted for the term of
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sale, or importing the Program or any portion of it.
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A "contributor" is a copyright holder who authorizes use under this
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work thus licensed is called the contributor's "contributor version".
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in a country, would infringe one or more identifiable patents in that
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work and works based on it.
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the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
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parties who would receive the covered work from you, a discriminatory
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or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
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to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
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License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/taskcluster/node-package-gpu.yml | build:
template_file: node-package-opt-base.tyml
dependencies:
- "linux-amd64-gpu-opt"
- "win-amd64-gpu-opt"
system_setup:
>
${nodejs.packages_xenial.prep_12} && ${nodejs.packages_xenial.apt_pinning}
&& apt-get -qq update && apt-get -qq -y install nodejs python-yaml
scripts:
build: "taskcluster/node-build.sh --cuda"
package: "taskcluster/node-package.sh"
workerType: "${docker.smallTask}"
metadata:
name: "DeepSpeech NodeJS GPU package"
description: "Packaging DeepSpeech GPU for registry"
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/src/bin/fstarcsort-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Sorts arcs of an FST.
#include <cstring>
#include <memory>
#include <string>
#include <fst/flags.h>
#include <fst/log.h>
#include <fst/script/arcsort.h>
#include <fst/script/getters.h>
DECLARE_string(sort_type);
int fstarcsort_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::MutableFstClass;
string usage = "Sorts arcs of an FST.\n\n Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name =
(argc > 1 && (strcmp(argv[1], "-") != 0)) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<MutableFstClass> fst(MutableFstClass::Read(in_name, true));
if (!fst) return 1;
s::ArcSortType sort_type;
if (!s::GetArcSortType(FLAGS_sort_type, &sort_type)) {
LOG(ERROR) << argv[0] << ": Unknown or unsupported sort type: "
<< FLAGS_sort_type;
return 1;
}
s::ArcSort(fst.get(), sort_type);
return !fst->Write(out_name);
}
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.9-win/src/bin/fstrelabel-main.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Relabels input or output space of an FST.
#include <cstring>
#include <memory>
#include <string>
#include <vector>
#include <fst/flags.h>
#include <fst/util.h>
#include <fst/script/relabel.h>
#include <fst/script/weight-class.h>
DECLARE_string(isymbols);
DECLARE_string(osymbols);
DECLARE_string(relabel_isymbols);
DECLARE_string(relabel_osymbols);
DECLARE_string(relabel_ipairs);
DECLARE_string(relabel_opairs);
DECLARE_string(unknown_isymbol);
DECLARE_string(unknown_osymbol);
DECLARE_bool(allow_negative_labels);
int fstrelabel_main(int argc, char **argv) {
namespace s = fst::script;
using fst::script::MutableFstClass;
using fst::SymbolTable;
using fst::SymbolTableTextOptions;
string usage =
"Relabels the input and/or the output labels of the FST.\n\n"
" Usage: ";
usage += argv[0];
usage += " [in.fst [out.fst]]\n";
usage += "\n Using SymbolTables flags:\n";
usage += " --relabel_isymbols isyms.map\n";
usage += " --relabel_osymbols osyms.map\n";
usage += "\n Using numeric labels flags:\n";
usage += " --relabel_ipairs ipairs.txt\n";
usage += " --relabel_opairs opairs.txt\n";
std::set_new_handler(FailedNewHandler);
SET_FLAGS(usage.c_str(), &argc, &argv, true);
if (argc > 3) {
ShowUsage();
return 1;
}
const string in_name =
(argc > 1 && (strcmp(argv[1], "-") != 0)) ? argv[1] : "";
const string out_name = argc > 2 ? argv[2] : "";
std::unique_ptr<MutableFstClass> fst(MutableFstClass::Read(in_name, true));
if (!fst) return 1;
// Relabel with symbol tables.
const SymbolTableTextOptions opts(FLAGS_allow_negative_labels);
if (!FLAGS_relabel_isymbols.empty() || !FLAGS_relabel_osymbols.empty()) {
bool attach_new_isymbols = (fst->InputSymbols() != nullptr);
std::unique_ptr<const SymbolTable> old_isymbols(
FLAGS_isymbols.empty() ? nullptr
: SymbolTable::ReadText(FLAGS_isymbols, opts));
const std::unique_ptr<const SymbolTable> relabel_isymbols(
FLAGS_relabel_isymbols.empty()
? nullptr
: SymbolTable::ReadText(FLAGS_relabel_isymbols, opts));
bool attach_new_osymbols = (fst->OutputSymbols() != nullptr);
std::unique_ptr<const SymbolTable> old_osymbols(
FLAGS_osymbols.empty() ? nullptr
: SymbolTable::ReadText(FLAGS_osymbols, opts));
const std::unique_ptr<const SymbolTable> relabel_osymbols(
FLAGS_relabel_osymbols.empty()
? nullptr
: SymbolTable::ReadText(FLAGS_relabel_osymbols, opts));
s::Relabel(fst.get(),
old_isymbols ? old_isymbols.get() : fst->InputSymbols(),
relabel_isymbols.get(), FLAGS_unknown_isymbol,
attach_new_isymbols,
old_osymbols ? old_osymbols.get() : fst->OutputSymbols(),
relabel_osymbols.get(), FLAGS_unknown_osymbol,
attach_new_osymbols);
} else {
// Reads in relabeling pairs.
std::vector<s::LabelPair> ipairs;
std::vector<s::LabelPair> opairs;
if (!FLAGS_relabel_ipairs.empty()) {
if (!fst::ReadLabelPairs(FLAGS_relabel_ipairs, &ipairs,
FLAGS_allow_negative_labels))
return 1;
}
if (!FLAGS_relabel_opairs.empty()) {
if (!fst::ReadLabelPairs(FLAGS_relabel_opairs, &opairs,
FLAGS_allow_negative_labels))
return 1;
}
s::Relabel(fst.get(), ipairs, opairs);
}
return !fst->Write(out_name);
}
| 0 |
coqui_public_repos/TTS/TTS | coqui_public_repos/TTS/TTS/utils/capacitron_optimizer.py | from typing import Generator
from trainer.trainer_utils import get_optimizer
class CapacitronOptimizer:
"""Double optimizer class for the Capacitron model."""
def __init__(self, config: dict, model_params: Generator) -> None:
self.primary_params, self.secondary_params = self.split_model_parameters(model_params)
optimizer_names = list(config.optimizer_params.keys())
optimizer_parameters = list(config.optimizer_params.values())
self.primary_optimizer = get_optimizer(
optimizer_names[0],
optimizer_parameters[0],
config.lr,
parameters=self.primary_params,
)
self.secondary_optimizer = get_optimizer(
optimizer_names[1],
self.extract_optimizer_parameters(optimizer_parameters[1]),
optimizer_parameters[1]["lr"],
parameters=self.secondary_params,
)
self.param_groups = self.primary_optimizer.param_groups
def first_step(self):
self.secondary_optimizer.step()
self.secondary_optimizer.zero_grad()
self.primary_optimizer.zero_grad()
def step(self):
# Update param groups to display the correct learning rate
self.param_groups = self.primary_optimizer.param_groups
self.primary_optimizer.step()
def zero_grad(self, set_to_none=False):
self.primary_optimizer.zero_grad(set_to_none)
self.secondary_optimizer.zero_grad(set_to_none)
def load_state_dict(self, state_dict):
self.primary_optimizer.load_state_dict(state_dict[0])
self.secondary_optimizer.load_state_dict(state_dict[1])
def state_dict(self):
return [self.primary_optimizer.state_dict(), self.secondary_optimizer.state_dict()]
@staticmethod
def split_model_parameters(model_params: Generator) -> list:
primary_params = []
secondary_params = []
for name, param in model_params:
if param.requires_grad:
if name == "capacitron_vae_layer.beta":
secondary_params.append(param)
else:
primary_params.append(param)
return [iter(primary_params), iter(secondary_params)]
@staticmethod
def extract_optimizer_parameters(params: dict) -> dict:
"""Extract parameters that are not the learning rate"""
return {k: v for k, v in params.items() if k != "lr"}
| 0 |
coqui_public_repos/STT-models/breton/itml | coqui_public_repos/STT-models/breton/itml/v0.1.0/MODEL_CARD.md | # Model card for Breton STT
Jump to section:
- [Model details](#model-details)
- [Intended use](#intended-use)
- [Performance Factors](#performance-factors)
- [Metrics](#metrics)
- [Training data](#training-data)
- [Evaluation data](#evaluation-data)
- [Ethical considerations](#ethical-considerations)
- [Caveats and recommendations](#caveats-and-recommendations)
## Model details
- Person or organization developing model: Originally trained by [Francis Tyers](https://scholar.google.fr/citations?user=o5HSM6cAAAAJ) and the [Inclusive Technology for Marginalised Languages](https://itml.cl.indiana.edu/) group.
- Model language: Breton / Brezhoneg / `br`
- Model date: April 9, 2021
- Model type: `Speech-to-Text`
- Model version: `v0.1.0`
- Compatible with 🐸 STT version: `v0.9.3`
- License: AGPL
- Citation details: `@techreport{breton-stt, author = {Tyers,Francis}, title = {Breton STT 0.1}, institution = {Coqui}, address = {\url{https://github.com/coqui-ai/STT-models}} year = {2021}, month = {April}, number = {STT-CV6.1-BR-0.1} }`
- Where to send questions or comments about the model: You can leave an issue on [`STT-model` issues](https://github.com/coqui-ai/STT-models/issues), open a new discussion on [`STT-model` discussions](https://github.com/coqui-ai/STT-models/discussions), or chat with us on [Gitter](https://gitter.im/coqui-ai/).
## Intended use
Speech-to-Text for the [Breton Language](https://en.wikipedia.org/wiki/Breton_language) on 16kHz, mono-channel audio.
## Performance Factors
Factors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise. Read more about STT performance factors [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
## Metrics
STT models are usually evaluated in terms of their transcription accuracy, deployment Real-Time Factor, and model size on disk.
#### Transcription Accuracy
The following Word Error Rates and Character Error Rates are reported on [omnilingo](https://tepozcatl.omnilingo.cc/cv/).
|Test Corpus|WER|CER|
|-----------|---|---|
|Common Voice|94.9\%|41.6\%|
#### Real-Time Factor
Real-Time Factor (RTF) is defined as `processing-time / length-of-audio`. The exact real-time factor of an STT model will depend on the hardware setup, so you may experience a different RTF.
Recorded average RTF on laptop CPU: ``
#### Model Size
`model.pbmm`: 181M
`model.tflite`: 46M
### Approaches to uncertainty and variability
Confidence scores and multiple paths from the decoding beam can be used to measure model uncertainty and provide multiple, variable transcripts for any processed audio.
## Training data
This model was trained on Common Voice 6.1 train.
## Evaluation data
The Model was evaluated on Common Voice 6.1 test.
## Ethical considerations
Deploying a Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
### Demographic Bias
You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
### Surveillance
Speech-to-Text may be mis-used to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in may countries. You should not assume consent to record and analyze private speech.
## Caveats and recommendations
Machine learning models (like this STT model) perform best on data that is similar to the data on which they were trained. Read about what to expect from an STT model with regard to your data [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
In most applications, it is recommended that you [train your own language model](https://stt.readthedocs.io/en/latest/LANGUAGE_MODEL.html) to improve transcription accuracy on your speech data.
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/include/fst/script/determinize.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#ifndef FST_SCRIPT_DETERMINIZE_H_
#define FST_SCRIPT_DETERMINIZE_H_
#include <tuple>
#include <fst/determinize.h>
#include <fst/script/fst-class.h>
#include <fst/script/weight-class.h>
namespace fst {
namespace script {
struct DeterminizeOptions {
const float delta;
const WeightClass &weight_threshold;
const int64 state_threshold;
const int64 subsequential_label;
const DeterminizeType det_type;
const bool increment_subsequential_label;
DeterminizeOptions(float delta, const WeightClass &weight_threshold,
int64 state_threshold = kNoStateId,
int64 subsequential_label = 0,
DeterminizeType det_type = DETERMINIZE_FUNCTIONAL,
bool increment_subsequential_label = false)
: delta(delta),
weight_threshold(weight_threshold),
state_threshold(state_threshold),
subsequential_label(subsequential_label),
det_type(det_type),
increment_subsequential_label(increment_subsequential_label) {}
};
using DeterminizeArgs = std::tuple<const FstClass &, MutableFstClass *,
const DeterminizeOptions &>;
template <class Arc>
void Determinize(DeterminizeArgs *args) {
using Weight = typename Arc::Weight;
const Fst<Arc> &ifst = *(std::get<0>(*args).GetFst<Arc>());
MutableFst<Arc> *ofst = std::get<1>(*args)->GetMutableFst<Arc>();
const auto &opts = std::get<2>(*args);
const auto weight_threshold = *(opts.weight_threshold.GetWeight<Weight>());
const fst::DeterminizeOptions<Arc> detargs(opts.delta, weight_threshold,
opts.state_threshold, opts.subsequential_label, opts.det_type,
opts.increment_subsequential_label);
Determinize(ifst, ofst, detargs);
}
void Determinize(const FstClass &ifst, MutableFstClass *ofst,
const DeterminizeOptions &opts);
} // namespace script
} // namespace fst
#endif // FST_SCRIPT_DETERMINIZE_H_
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.7/src/include/fst/extensions/pdt/paren.h | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
//
// Common classes for PDT parentheses.
#ifndef FST_EXTENSIONS_PDT_PAREN_H_
#define FST_EXTENSIONS_PDT_PAREN_H_
#include <algorithm>
#include <set>
#include <unordered_map>
#include <unordered_set>
#include <fst/log.h>
#include <fst/extensions/pdt/collection.h>
#include <fst/extensions/pdt/pdt.h>
#include <fst/dfs-visit.h>
#include <fst/fst.h>
namespace fst {
namespace internal {
// ParenState: Pair of an open (close) parenthesis and its destination (source)
// state.
template <class Arc>
struct ParenState {
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
Label paren_id; // ID of open (close) paren.
StateId state_id; // Destination (source) state of open (close) paren.
explicit ParenState(Label paren_id = kNoLabel, StateId state_id = kNoStateId)
: paren_id(paren_id), state_id(state_id) {}
bool operator==(const ParenState<Arc> &other) const {
if (&other == this) return true;
return other.paren_id == paren_id && other.state_id == state_id;
}
bool operator!=(const ParenState<Arc> &other) const {
return !(other == *this);
}
struct Hash {
size_t operator()(const ParenState<Arc> &pstate) const {
static constexpr auto prime = 7853;
return pstate.paren_id + pstate.state_id * prime;
}
};
};
// Creates an FST-style const iterator from an STL-style map.
template <class Map>
class MapIterator {
public:
using StlIterator = typename Map::const_iterator;
using ValueType = typename Map::mapped_type;
MapIterator(const Map &map, StlIterator it)
: begin_(it), end_(map.end()), it_(it) {}
bool Done() const { return it_ == end_ || it_->first != begin_->first; }
ValueType Value() const { return it_->second; }
void Next() { ++it_; }
void Reset() { it_ = begin_; }
private:
const StlIterator begin_;
const StlIterator end_;
StlIterator it_;
};
// PdtParenReachable: Provides various parenthesis reachability information.
template <class Arc>
class PdtParenReachable {
public:
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using State = ParenState<Arc>;
using StateHash = typename State::Hash;
// Maps from state ID to reachable paren IDs from (to) that state.
using ParenMultimap = std::unordered_multimap<StateId, Label>;
// Maps from paren ID and state ID to reachable state set ID.
using StateSetMap = std::unordered_map<State, ssize_t, StateHash>;
// Maps from paren ID and state ID to arcs exiting that state with that
// Label.
using ParenArcMultimap = std::unordered_map<State, Arc, StateHash>;
using ParenIterator = MapIterator<ParenMultimap>;
using ParenArcIterator = MapIterator<ParenArcMultimap>;
using SetIterator = typename Collection<ssize_t, StateId>::SetIterator;
// Computes close (open) parenthesis reachability information for a PDT with
// bounded stack.
PdtParenReachable(const Fst<Arc> &fst,
const std::vector<std::pair<Label, Label>> &parens,
bool close)
: fst_(fst), parens_(parens), close_(close), error_(false) {
paren_map_.reserve(2 * parens.size());
for (size_t i = 0; i < parens.size(); ++i) {
const auto &pair = parens[i];
paren_map_[pair.first] = i;
paren_map_[pair.second] = i;
}
if (close_) {
const auto start = fst.Start();
if (start == kNoStateId) return;
if (!DFSearch(start)) {
FSTERROR() << "PdtReachable: Underlying cyclicity not supported";
error_ = true;
}
} else {
FSTERROR() << "PdtParenReachable: Open paren info not implemented";
error_ = true;
}
}
bool Error() const { return error_; }
// Given a state ID, returns an iterator over paren IDs for close (open)
// parens reachable from that state along balanced paths.
ParenIterator FindParens(StateId s) const {
return ParenIterator(paren_multimap_, paren_multimap_.find(s));
}
// Given a paren ID and a state ID s, returns an iterator over states that can
// be reached along balanced paths from (to) s that have have close (open)
// parentheses matching the paren ID exiting (entering) those states.
SetIterator FindStates(Label paren_id, StateId s) const {
const State paren_state(paren_id, s);
const auto it = set_map_.find(paren_state);
if (it == set_map_.end()) {
return state_sets_.FindSet(-1);
} else {
return state_sets_.FindSet(it->second);
}
}
// Given a paren ID and a state ID s, return an iterator over arcs that exit
// (enter) s and are labeled with a close (open) parenthesis matching the
// paren ID.
ParenArcIterator FindParenArcs(Label paren_id, StateId s) const {
const State paren_state(paren_id, s);
return ParenArcIterator(paren_arc_multimap_,
paren_arc_multimap_.find(paren_state));
}
private:
// Returns false when cycle detected during DFS gathering paren and state set
// information.
bool DFSearch(StateId s);
// Unions state sets together gathered by the DFS.
void ComputeStateSet(StateId s);
// Gathers state set(s) from state.
void UpdateStateSet(StateId nextstate, std::set<Label> *paren_set,
std::vector<std::set<StateId>> *state_sets) const;
const Fst<Arc> &fst_;
// Paren IDs to labels.
const std::vector<std::pair<Label, Label>> &parens_;
// Close/open paren info?
const bool close_;
// Labels to paren IDs.
std::unordered_map<Label, Label> paren_map_;
// Paren reachability.
ParenMultimap paren_multimap_;
// Paren arcs.
ParenArcMultimap paren_arc_multimap_;
// DFS states.
std::vector<uint8> state_color_;
// Reachable states to IDs.
mutable Collection<ssize_t, StateId> state_sets_;
// IDs to reachable states.
StateSetMap set_map_;
bool error_;
PdtParenReachable(const PdtParenReachable &) = delete;
PdtParenReachable &operator=(const PdtParenReachable &) = delete;
};
// Gathers paren and state set information.
template <class Arc>
bool PdtParenReachable<Arc>::DFSearch(StateId s) {
static constexpr uint8 kWhiteState = 0x01; // Undiscovered.
static constexpr uint8 kGreyState = 0x02; // Discovered & unfinished.
static constexpr uint8 kBlackState = 0x04; // Finished.
if (s >= state_color_.size()) state_color_.resize(s + 1, kWhiteState);
if (state_color_[s] == kBlackState) return true;
if (state_color_[s] == kGreyState) return false;
state_color_[s] = kGreyState;
for (ArcIterator<Fst<Arc>> aiter(fst_, s); !aiter.Done(); aiter.Next()) {
const auto &arc = aiter.Value();
const auto it = paren_map_.find(arc.ilabel);
if (it != paren_map_.end()) { // Paren?
const auto paren_id = it->second;
if (arc.ilabel == parens_[paren_id].first) { // Open paren?
if (!DFSearch(arc.nextstate)) return false;
for (auto set_iter = FindStates(paren_id, arc.nextstate);
!set_iter.Done(); set_iter.Next()) {
for (auto paren_arc_iter =
FindParenArcs(paren_id, set_iter.Element());
!paren_arc_iter.Done(); paren_arc_iter.Next()) {
const auto &cparc = paren_arc_iter.Value();
if (!DFSearch(cparc.nextstate)) return false;
}
}
}
} else if (!DFSearch(arc.nextstate)) { // Non-paren.
return false;
}
}
ComputeStateSet(s);
state_color_[s] = kBlackState;
return true;
}
// Unions state sets.
template <class Arc>
void PdtParenReachable<Arc>::ComputeStateSet(StateId s) {
std::set<Label> paren_set;
std::vector<std::set<StateId>> state_sets(parens_.size());
for (ArcIterator<Fst<Arc>> aiter(fst_, s); !aiter.Done(); aiter.Next()) {
const auto &arc = aiter.Value();
const auto it = paren_map_.find(arc.ilabel);
if (it != paren_map_.end()) { // Paren?
const auto paren_id = it->second;
if (arc.ilabel == parens_[paren_id].first) { // Open paren?
for (auto set_iter = FindStates(paren_id, arc.nextstate);
!set_iter.Done(); set_iter.Next()) {
for (auto paren_arc_iter =
FindParenArcs(paren_id, set_iter.Element());
!paren_arc_iter.Done(); paren_arc_iter.Next()) {
const auto &cparc = paren_arc_iter.Value();
UpdateStateSet(cparc.nextstate, &paren_set, &state_sets);
}
}
} else { // Close paren.
paren_set.insert(paren_id);
state_sets[paren_id].insert(s);
const State paren_state(paren_id, s);
paren_arc_multimap_.insert(std::make_pair(paren_state, arc));
}
} else { // Non-paren.
UpdateStateSet(arc.nextstate, &paren_set, &state_sets);
}
}
std::vector<StateId> state_set;
for (auto paren_iter = paren_set.begin(); paren_iter != paren_set.end();
++paren_iter) {
state_set.clear();
const auto paren_id = *paren_iter;
paren_multimap_.insert(std::make_pair(s, paren_id));
for (auto state_iter = state_sets[paren_id].begin();
state_iter != state_sets[paren_id].end(); ++state_iter) {
state_set.push_back(*state_iter);
}
const State paren_state(paren_id, s);
set_map_[paren_state] = state_sets_.FindId(state_set);
}
}
// Gathers state sets.
template <class Arc>
void PdtParenReachable<Arc>::UpdateStateSet(
StateId nextstate, std::set<Label> *paren_set,
std::vector<std::set<StateId>> *state_sets) const {
for (auto paren_iter = FindParens(nextstate); !paren_iter.Done();
paren_iter.Next()) {
const auto paren_id = paren_iter.Value();
paren_set->insert(paren_id);
for (auto set_iter = FindStates(paren_id, nextstate); !set_iter.Done();
set_iter.Next()) {
(*state_sets)[paren_id].insert(set_iter.Element());
}
}
}
// Stores balancing parenthesis data for a PDT. Unlike PdtParenReachable above
// this allows on-the-fly construction (e.g., in PdtShortestPath).
template <class Arc>
class PdtBalanceData {
public:
using Label = typename Arc::Label;
using StateId = typename Arc::StateId;
using State = ParenState<Arc>;
using StateHash = typename State::Hash;
// Set for open parens.
using OpenParenSet = std::unordered_set<State, StateHash>;
// Maps from open paren destination state to parenthesis ID.
using OpenParenMap = std::unordered_multimap<StateId, Label>;
// Maps from open paren state to source states of matching close parens
using CloseParenMap = std::unordered_multimap<State, StateId, StateHash>;
// Maps from open paren state to close source set ID.
using CloseSourceMap = std::unordered_map<State, ssize_t, StateHash>;
using SetIterator = typename Collection<ssize_t, StateId>::SetIterator;
PdtBalanceData() {}
void Clear() {
open_paren_map_.clear();
close_paren_map_.clear();
}
// Adds an open parenthesis with destination state open_dest.
void OpenInsert(Label paren_id, StateId open_dest) {
const State key(paren_id, open_dest);
if (!open_paren_set_.count(key)) {
open_paren_set_.insert(key);
open_paren_map_.emplace(open_dest, paren_id);
}
}
// Adds a matching closing parenthesis with source state close_source
// balancing an open_parenthesis with destination state open_dest if
// OpenInsert() previously called.
void CloseInsert(Label paren_id, StateId open_dest, StateId close_source) {
const State key(paren_id, open_dest);
if (open_paren_set_.count(key)) {
close_paren_map_.emplace(key, close_source);
}
}
// Finds close paren source states matching an open parenthesis. The following
// methods are then used to iterate through those matching states. Should be
// called only after FinishInsert(open_dest).
SetIterator Find(Label paren_id, StateId open_dest) {
const State key(paren_id, open_dest);
const auto it = close_source_map_.find(key);
if (it == close_source_map_.end()) {
return close_source_sets_.FindSet(-1);
} else {
return close_source_sets_.FindSet(it->second);
}
}
// Called when all open and close parenthesis insertions (w.r.t. open
// parentheses entering state open_dest) are finished. Must be called before
// Find(open_dest).
void FinishInsert(StateId open_dest) {
std::vector<StateId> close_sources;
for (auto oit = open_paren_map_.find(open_dest);
oit != open_paren_map_.end() && oit->first == open_dest;) {
const auto paren_id = oit->second;
close_sources.clear();
const State key(paren_id, open_dest);
open_paren_set_.erase(open_paren_set_.find(key));
for (auto cit = close_paren_map_.find(key);
cit != close_paren_map_.end() && cit->first == key;) {
close_sources.push_back(cit->second);
close_paren_map_.erase(cit++);
}
std::sort(close_sources.begin(), close_sources.end());
auto unique_end = std::unique(close_sources.begin(), close_sources.end());
close_sources.resize(unique_end - close_sources.begin());
if (!close_sources.empty()) {
close_source_map_[key] = close_source_sets_.FindId(close_sources);
}
open_paren_map_.erase(oit++);
}
}
// Returns a new balance data object representing the reversed balance
// information.
PdtBalanceData<Arc> *Reverse(StateId num_states, StateId num_split,
StateId state_id_shift) const;
private:
// Open paren at destintation state?
OpenParenSet open_paren_set_;
// Open parens per state.
OpenParenMap open_paren_map_;
// Current open destination state.
State open_dest_;
// Current open paren/state.
typename OpenParenMap::const_iterator open_iter_;
// Close states to (open paren, state).
CloseParenMap close_paren_map_;
// (Paren, state) to set ID.
CloseSourceMap close_source_map_;
mutable Collection<ssize_t, StateId> close_source_sets_;
};
// Return a new balance data object representing the reversed balance
// information.
template <class Arc>
PdtBalanceData<Arc> *PdtBalanceData<Arc>::Reverse(
StateId num_states, StateId num_split, StateId state_id_shift) const {
auto *bd = new PdtBalanceData<Arc>;
std::unordered_set<StateId> close_sources;
const auto split_size = num_states / num_split;
for (StateId i = 0; i < num_states; i += split_size) {
close_sources.clear();
for (auto it = close_source_map_.begin(); it != close_source_map_.end();
++it) {
const auto &okey = it->first;
const auto open_dest = okey.state_id;
const auto paren_id = okey.paren_id;
for (auto set_iter = close_source_sets_.FindSet(it->second);
!set_iter.Done(); set_iter.Next()) {
const auto close_source = set_iter.Element();
if ((close_source < i) || (close_source >= i + split_size)) continue;
close_sources.insert(close_source + state_id_shift);
bd->OpenInsert(paren_id, close_source + state_id_shift);
bd->CloseInsert(paren_id, close_source + state_id_shift,
open_dest + state_id_shift);
}
}
for (auto it = close_sources.begin(); it != close_sources.end(); ++it) {
bd->FinishInsert(*it);
}
}
return bd;
}
} // namespace internal
} // namespace fst
#endif // FST_EXTENSIONS_PDT_PAREN_H_
| 0 |
coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src | coqui_public_repos/inference-engine/third_party/openfst-1.6.7/src/bin/fstcompile.cc | // See www.openfst.org for extensive documentation on this weighted
// finite-state transducer library.
#include <fst/flags.h>
DEFINE_bool(acceptor, false, "Input in acceptor format");
DEFINE_string(arc_type, "standard", "Output arc type");
DEFINE_string(fst_type, "vector", "Output FST type");
DEFINE_string(isymbols, "", "Input label symbol table");
DEFINE_string(osymbols, "", "Output label symbol table");
DEFINE_string(ssymbols, "", "State label symbol table");
DEFINE_bool(keep_isymbols, false, "Store input label symbol table with FST");
DEFINE_bool(keep_osymbols, false, "Store output label symbol table with FST");
DEFINE_bool(keep_state_numbering, false, "Do not renumber input states");
DEFINE_bool(allow_negative_labels, false,
"Allow negative labels (not recommended; may cause conflicts)");
int fstcompile_main(int argc, char **argv);
int main(int argc, char **argv) { return fstcompile_main(argc, argv); }
| 0 |
coqui_public_repos/inference-engine/third_party/cereal/include/cereal/external | coqui_public_repos/inference-engine/third_party/cereal/include/cereal/external/rapidjson/reader.h | // Tencent is pleased to support the open source community by making RapidJSON available.
//
// Copyright (C) 2015 THL A29 Limited, a Tencent company, and Milo Yip. All rights reserved.
//
// Licensed under the MIT License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// http://opensource.org/licenses/MIT
//
// 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.
#ifndef CEREAL_RAPIDJSON_READER_H_
#define CEREAL_RAPIDJSON_READER_H_
/*! \file reader.h */
#include "allocators.h"
#include "stream.h"
#include "encodedstream.h"
#include "internal/meta.h"
#include "internal/stack.h"
#include "internal/strtod.h"
#include <limits>
#if defined(CEREAL_RAPIDJSON_SIMD) && defined(_MSC_VER)
#include <intrin.h>
#pragma intrinsic(_BitScanForward)
#endif
#ifdef CEREAL_RAPIDJSON_SSE42
#include <nmmintrin.h>
#elif defined(CEREAL_RAPIDJSON_SSE2)
#include <emmintrin.h>
#elif defined(CEREAL_RAPIDJSON_NEON)
#include <arm_neon.h>
#endif
#ifdef __clang__
CEREAL_RAPIDJSON_DIAG_PUSH
CEREAL_RAPIDJSON_DIAG_OFF(old-style-cast)
CEREAL_RAPIDJSON_DIAG_OFF(padded)
CEREAL_RAPIDJSON_DIAG_OFF(switch-enum)
#elif defined(_MSC_VER)
CEREAL_RAPIDJSON_DIAG_PUSH
CEREAL_RAPIDJSON_DIAG_OFF(4127) // conditional expression is constant
CEREAL_RAPIDJSON_DIAG_OFF(4702) // unreachable code
#endif
#ifdef __GNUC__
CEREAL_RAPIDJSON_DIAG_PUSH
CEREAL_RAPIDJSON_DIAG_OFF(effc++)
#endif
//!@cond CEREAL_RAPIDJSON_HIDDEN_FROM_DOXYGEN
#define CEREAL_RAPIDJSON_NOTHING /* deliberately empty */
#ifndef CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN
#define CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(value) \
CEREAL_RAPIDJSON_MULTILINEMACRO_BEGIN \
if (CEREAL_RAPIDJSON_UNLIKELY(HasParseError())) { return value; } \
CEREAL_RAPIDJSON_MULTILINEMACRO_END
#endif
#define CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID \
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(CEREAL_RAPIDJSON_NOTHING)
//!@endcond
/*! \def CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN
\ingroup CEREAL_RAPIDJSON_ERRORS
\brief Macro to indicate a parse error.
\param parseErrorCode \ref rapidjson::ParseErrorCode of the error
\param offset position of the error in JSON input (\c size_t)
This macros can be used as a customization point for the internal
error handling mechanism of RapidJSON.
A common usage model is to throw an exception instead of requiring the
caller to explicitly check the \ref rapidjson::GenericReader::Parse's
return value:
\code
#define CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(parseErrorCode,offset) \
throw ParseException(parseErrorCode, #parseErrorCode, offset)
#include <stdexcept> // std::runtime_error
#include "rapidjson/error/error.h" // rapidjson::ParseResult
struct ParseException : std::runtime_error, rapidjson::ParseResult {
ParseException(rapidjson::ParseErrorCode code, const char* msg, size_t offset)
: std::runtime_error(msg), ParseResult(code, offset) {}
};
#include "rapidjson/reader.h"
\endcode
\see CEREAL_RAPIDJSON_PARSE_ERROR, rapidjson::GenericReader::Parse
*/
#ifndef CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN
#define CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(parseErrorCode, offset) \
CEREAL_RAPIDJSON_MULTILINEMACRO_BEGIN \
CEREAL_RAPIDJSON_ASSERT(!HasParseError()); /* Error can only be assigned once */ \
SetParseError(parseErrorCode, offset); \
CEREAL_RAPIDJSON_MULTILINEMACRO_END
#endif
/*! \def CEREAL_RAPIDJSON_PARSE_ERROR
\ingroup CEREAL_RAPIDJSON_ERRORS
\brief (Internal) macro to indicate and handle a parse error.
\param parseErrorCode \ref rapidjson::ParseErrorCode of the error
\param offset position of the error in JSON input (\c size_t)
Invokes CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN and stops the parsing.
\see CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN
\hideinitializer
*/
#ifndef CEREAL_RAPIDJSON_PARSE_ERROR
#define CEREAL_RAPIDJSON_PARSE_ERROR(parseErrorCode, offset) \
CEREAL_RAPIDJSON_MULTILINEMACRO_BEGIN \
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(parseErrorCode, offset); \
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID; \
CEREAL_RAPIDJSON_MULTILINEMACRO_END
#endif
#include "error/error.h" // ParseErrorCode, ParseResult
CEREAL_RAPIDJSON_NAMESPACE_BEGIN
///////////////////////////////////////////////////////////////////////////////
// ParseFlag
/*! \def CEREAL_RAPIDJSON_PARSE_DEFAULT_FLAGS
\ingroup CEREAL_RAPIDJSON_CONFIG
\brief User-defined kParseDefaultFlags definition.
User can define this as any \c ParseFlag combinations.
*/
#ifndef CEREAL_RAPIDJSON_PARSE_DEFAULT_FLAGS
#define CEREAL_RAPIDJSON_PARSE_DEFAULT_FLAGS kParseNoFlags
#endif
//! Combination of parseFlags
/*! \see Reader::Parse, Document::Parse, Document::ParseInsitu, Document::ParseStream
*/
enum ParseFlag {
kParseNoFlags = 0, //!< No flags are set.
kParseInsituFlag = 1, //!< In-situ(destructive) parsing.
kParseValidateEncodingFlag = 2, //!< Validate encoding of JSON strings.
kParseIterativeFlag = 4, //!< Iterative(constant complexity in terms of function call stack size) parsing.
kParseStopWhenDoneFlag = 8, //!< After parsing a complete JSON root from stream, stop further processing the rest of stream. When this flag is used, parser will not generate kParseErrorDocumentRootNotSingular error.
kParseFullPrecisionFlag = 16, //!< Parse number in full precision (but slower).
kParseCommentsFlag = 32, //!< Allow one-line (//) and multi-line (/**/) comments.
kParseNumbersAsStringsFlag = 64, //!< Parse all numbers (ints/doubles) as strings.
kParseTrailingCommasFlag = 128, //!< Allow trailing commas at the end of objects and arrays.
kParseNanAndInfFlag = 256, //!< Allow parsing NaN, Inf, Infinity, -Inf and -Infinity as doubles.
kParseDefaultFlags = CEREAL_RAPIDJSON_PARSE_DEFAULT_FLAGS //!< Default parse flags. Can be customized by defining CEREAL_RAPIDJSON_PARSE_DEFAULT_FLAGS
};
///////////////////////////////////////////////////////////////////////////////
// Handler
/*! \class rapidjson::Handler
\brief Concept for receiving events from GenericReader upon parsing.
The functions return true if no error occurs. If they return false,
the event publisher should terminate the process.
\code
concept Handler {
typename Ch;
bool Null();
bool Bool(bool b);
bool Int(int i);
bool Uint(unsigned i);
bool Int64(int64_t i);
bool Uint64(uint64_t i);
bool Double(double d);
/// enabled via kParseNumbersAsStringsFlag, string is not null-terminated (use length)
bool RawNumber(const Ch* str, SizeType length, bool copy);
bool String(const Ch* str, SizeType length, bool copy);
bool StartObject();
bool Key(const Ch* str, SizeType length, bool copy);
bool EndObject(SizeType memberCount);
bool StartArray();
bool EndArray(SizeType elementCount);
};
\endcode
*/
///////////////////////////////////////////////////////////////////////////////
// BaseReaderHandler
//! Default implementation of Handler.
/*! This can be used as base class of any reader handler.
\note implements Handler concept
*/
template<typename Encoding = UTF8<>, typename Derived = void>
struct BaseReaderHandler {
typedef typename Encoding::Ch Ch;
typedef typename internal::SelectIf<internal::IsSame<Derived, void>, BaseReaderHandler, Derived>::Type Override;
bool Default() { return true; }
bool Null() { return static_cast<Override&>(*this).Default(); }
bool Bool(bool) { return static_cast<Override&>(*this).Default(); }
bool Int(int) { return static_cast<Override&>(*this).Default(); }
bool Uint(unsigned) { return static_cast<Override&>(*this).Default(); }
bool Int64(int64_t) { return static_cast<Override&>(*this).Default(); }
bool Uint64(uint64_t) { return static_cast<Override&>(*this).Default(); }
bool Double(double) { return static_cast<Override&>(*this).Default(); }
/// enabled via kParseNumbersAsStringsFlag, string is not null-terminated (use length)
bool RawNumber(const Ch* str, SizeType len, bool copy) { return static_cast<Override&>(*this).String(str, len, copy); }
bool String(const Ch*, SizeType, bool) { return static_cast<Override&>(*this).Default(); }
bool StartObject() { return static_cast<Override&>(*this).Default(); }
bool Key(const Ch* str, SizeType len, bool copy) { return static_cast<Override&>(*this).String(str, len, copy); }
bool EndObject(SizeType) { return static_cast<Override&>(*this).Default(); }
bool StartArray() { return static_cast<Override&>(*this).Default(); }
bool EndArray(SizeType) { return static_cast<Override&>(*this).Default(); }
};
///////////////////////////////////////////////////////////////////////////////
// StreamLocalCopy
namespace internal {
template<typename Stream, int = StreamTraits<Stream>::copyOptimization>
class StreamLocalCopy;
//! Do copy optimization.
template<typename Stream>
class StreamLocalCopy<Stream, 1> {
public:
StreamLocalCopy(Stream& original) : s(original), original_(original) {}
~StreamLocalCopy() { original_ = s; }
Stream s;
private:
StreamLocalCopy& operator=(const StreamLocalCopy&) /* = delete */;
Stream& original_;
};
//! Keep reference.
template<typename Stream>
class StreamLocalCopy<Stream, 0> {
public:
StreamLocalCopy(Stream& original) : s(original) {}
Stream& s;
private:
StreamLocalCopy& operator=(const StreamLocalCopy&) /* = delete */;
};
} // namespace internal
///////////////////////////////////////////////////////////////////////////////
// SkipWhitespace
//! Skip the JSON white spaces in a stream.
/*! \param is A input stream for skipping white spaces.
\note This function has SSE2/SSE4.2 specialization.
*/
template<typename InputStream>
void SkipWhitespace(InputStream& is) {
internal::StreamLocalCopy<InputStream> copy(is);
InputStream& s(copy.s);
typename InputStream::Ch c;
while ((c = s.Peek()) == ' ' || c == '\n' || c == '\r' || c == '\t')
s.Take();
}
inline const char* SkipWhitespace(const char* p, const char* end) {
while (p != end && (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t'))
++p;
return p;
}
#ifdef CEREAL_RAPIDJSON_SSE42
//! Skip whitespace with SSE 4.2 pcmpistrm instruction, testing 16 8-byte characters at once.
inline const char *SkipWhitespace_SIMD(const char* p) {
// Fast return for single non-whitespace
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
// 16-byte align to the next boundary
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
// The rest of string using SIMD
static const char whitespace[16] = " \n\r\t";
const __m128i w = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespace[0]));
for (;; p += 16) {
const __m128i s = _mm_load_si128(reinterpret_cast<const __m128i *>(p));
const int r = _mm_cmpistri(w, s, _SIDD_UBYTE_OPS | _SIDD_CMP_EQUAL_ANY | _SIDD_LEAST_SIGNIFICANT | _SIDD_NEGATIVE_POLARITY);
if (r != 16) // some of characters is non-whitespace
return p + r;
}
}
inline const char *SkipWhitespace_SIMD(const char* p, const char* end) {
// Fast return for single non-whitespace
if (p != end && (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t'))
++p;
else
return p;
// The middle of string using SIMD
static const char whitespace[16] = " \n\r\t";
const __m128i w = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespace[0]));
for (; p <= end - 16; p += 16) {
const __m128i s = _mm_loadu_si128(reinterpret_cast<const __m128i *>(p));
const int r = _mm_cmpistri(w, s, _SIDD_UBYTE_OPS | _SIDD_CMP_EQUAL_ANY | _SIDD_LEAST_SIGNIFICANT | _SIDD_NEGATIVE_POLARITY);
if (r != 16) // some of characters is non-whitespace
return p + r;
}
return SkipWhitespace(p, end);
}
#elif defined(CEREAL_RAPIDJSON_SSE2)
//! Skip whitespace with SSE2 instructions, testing 16 8-byte characters at once.
inline const char *SkipWhitespace_SIMD(const char* p) {
// Fast return for single non-whitespace
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
// 16-byte align to the next boundary
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
// The rest of string
#define C16(c) { c, c, c, c, c, c, c, c, c, c, c, c, c, c, c, c }
static const char whitespaces[4][16] = { C16(' '), C16('\n'), C16('\r'), C16('\t') };
#undef C16
const __m128i w0 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[0][0]));
const __m128i w1 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[1][0]));
const __m128i w2 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[2][0]));
const __m128i w3 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[3][0]));
for (;; p += 16) {
const __m128i s = _mm_load_si128(reinterpret_cast<const __m128i *>(p));
__m128i x = _mm_cmpeq_epi8(s, w0);
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w1));
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w2));
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w3));
unsigned short r = static_cast<unsigned short>(~_mm_movemask_epi8(x));
if (r != 0) { // some of characters may be non-whitespace
#ifdef _MSC_VER // Find the index of first non-whitespace
unsigned long offset;
_BitScanForward(&offset, r);
return p + offset;
#else
return p + __builtin_ffs(r) - 1;
#endif
}
}
}
inline const char *SkipWhitespace_SIMD(const char* p, const char* end) {
// Fast return for single non-whitespace
if (p != end && (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t'))
++p;
else
return p;
// The rest of string
#define C16(c) { c, c, c, c, c, c, c, c, c, c, c, c, c, c, c, c }
static const char whitespaces[4][16] = { C16(' '), C16('\n'), C16('\r'), C16('\t') };
#undef C16
const __m128i w0 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[0][0]));
const __m128i w1 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[1][0]));
const __m128i w2 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[2][0]));
const __m128i w3 = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&whitespaces[3][0]));
for (; p <= end - 16; p += 16) {
const __m128i s = _mm_loadu_si128(reinterpret_cast<const __m128i *>(p));
__m128i x = _mm_cmpeq_epi8(s, w0);
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w1));
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w2));
x = _mm_or_si128(x, _mm_cmpeq_epi8(s, w3));
unsigned short r = static_cast<unsigned short>(~_mm_movemask_epi8(x));
if (r != 0) { // some of characters may be non-whitespace
#ifdef _MSC_VER // Find the index of first non-whitespace
unsigned long offset;
_BitScanForward(&offset, r);
return p + offset;
#else
return p + __builtin_ffs(r) - 1;
#endif
}
}
return SkipWhitespace(p, end);
}
#elif defined(CEREAL_RAPIDJSON_NEON)
//! Skip whitespace with ARM Neon instructions, testing 16 8-byte characters at once.
inline const char *SkipWhitespace_SIMD(const char* p) {
// Fast return for single non-whitespace
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
// 16-byte align to the next boundary
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t')
++p;
else
return p;
const uint8x16_t w0 = vmovq_n_u8(' ');
const uint8x16_t w1 = vmovq_n_u8('\n');
const uint8x16_t w2 = vmovq_n_u8('\r');
const uint8x16_t w3 = vmovq_n_u8('\t');
for (;; p += 16) {
const uint8x16_t s = vld1q_u8(reinterpret_cast<const uint8_t *>(p));
uint8x16_t x = vceqq_u8(s, w0);
x = vorrq_u8(x, vceqq_u8(s, w1));
x = vorrq_u8(x, vceqq_u8(s, w2));
x = vorrq_u8(x, vceqq_u8(s, w3));
x = vmvnq_u8(x); // Negate
x = vrev64q_u8(x); // Rev in 64
uint64_t low = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 0); // extract
uint64_t high = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 1); // extract
if (low == 0) {
if (high != 0) {
int lz =__builtin_clzll(high);;
return p + 8 + (lz >> 3);
}
} else {
int lz = __builtin_clzll(low);;
return p + (lz >> 3);
}
}
}
inline const char *SkipWhitespace_SIMD(const char* p, const char* end) {
// Fast return for single non-whitespace
if (p != end && (*p == ' ' || *p == '\n' || *p == '\r' || *p == '\t'))
++p;
else
return p;
const uint8x16_t w0 = vmovq_n_u8(' ');
const uint8x16_t w1 = vmovq_n_u8('\n');
const uint8x16_t w2 = vmovq_n_u8('\r');
const uint8x16_t w3 = vmovq_n_u8('\t');
for (; p <= end - 16; p += 16) {
const uint8x16_t s = vld1q_u8(reinterpret_cast<const uint8_t *>(p));
uint8x16_t x = vceqq_u8(s, w0);
x = vorrq_u8(x, vceqq_u8(s, w1));
x = vorrq_u8(x, vceqq_u8(s, w2));
x = vorrq_u8(x, vceqq_u8(s, w3));
x = vmvnq_u8(x); // Negate
x = vrev64q_u8(x); // Rev in 64
uint64_t low = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 0); // extract
uint64_t high = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 1); // extract
if (low == 0) {
if (high != 0) {
int lz = __builtin_clzll(high);
return p + 8 + (lz >> 3);
}
} else {
int lz = __builtin_clzll(low);
return p + (lz >> 3);
}
}
return SkipWhitespace(p, end);
}
#endif // CEREAL_RAPIDJSON_NEON
#ifdef CEREAL_RAPIDJSON_SIMD
//! Template function specialization for InsituStringStream
template<> inline void SkipWhitespace(InsituStringStream& is) {
is.src_ = const_cast<char*>(SkipWhitespace_SIMD(is.src_));
}
//! Template function specialization for StringStream
template<> inline void SkipWhitespace(StringStream& is) {
is.src_ = SkipWhitespace_SIMD(is.src_);
}
template<> inline void SkipWhitespace(EncodedInputStream<UTF8<>, MemoryStream>& is) {
is.is_.src_ = SkipWhitespace_SIMD(is.is_.src_, is.is_.end_);
}
#endif // CEREAL_RAPIDJSON_SIMD
///////////////////////////////////////////////////////////////////////////////
// GenericReader
//! SAX-style JSON parser. Use \ref Reader for UTF8 encoding and default allocator.
/*! GenericReader parses JSON text from a stream, and send events synchronously to an
object implementing Handler concept.
It needs to allocate a stack for storing a single decoded string during
non-destructive parsing.
For in-situ parsing, the decoded string is directly written to the source
text string, no temporary buffer is required.
A GenericReader object can be reused for parsing multiple JSON text.
\tparam SourceEncoding Encoding of the input stream.
\tparam TargetEncoding Encoding of the parse output.
\tparam StackAllocator Allocator type for stack.
*/
template <typename SourceEncoding, typename TargetEncoding, typename StackAllocator = CrtAllocator>
class GenericReader {
public:
typedef typename SourceEncoding::Ch Ch; //!< SourceEncoding character type
//! Constructor.
/*! \param stackAllocator Optional allocator for allocating stack memory. (Only use for non-destructive parsing)
\param stackCapacity stack capacity in bytes for storing a single decoded string. (Only use for non-destructive parsing)
*/
GenericReader(StackAllocator* stackAllocator = 0, size_t stackCapacity = kDefaultStackCapacity) :
stack_(stackAllocator, stackCapacity), parseResult_(), state_(IterativeParsingStartState) {}
//! Parse JSON text.
/*! \tparam parseFlags Combination of \ref ParseFlag.
\tparam InputStream Type of input stream, implementing Stream concept.
\tparam Handler Type of handler, implementing Handler concept.
\param is Input stream to be parsed.
\param handler The handler to receive events.
\return Whether the parsing is successful.
*/
template <unsigned parseFlags, typename InputStream, typename Handler>
ParseResult Parse(InputStream& is, Handler& handler) {
if (parseFlags & kParseIterativeFlag)
return IterativeParse<parseFlags>(is, handler);
parseResult_.Clear();
ClearStackOnExit scope(*this);
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
if (CEREAL_RAPIDJSON_UNLIKELY(is.Peek() == '\0')) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorDocumentEmpty, is.Tell());
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
}
else {
ParseValue<parseFlags>(is, handler);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
if (!(parseFlags & kParseStopWhenDoneFlag)) {
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
if (CEREAL_RAPIDJSON_UNLIKELY(is.Peek() != '\0')) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorDocumentRootNotSingular, is.Tell());
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
}
}
}
return parseResult_;
}
//! Parse JSON text (with \ref kParseDefaultFlags)
/*! \tparam InputStream Type of input stream, implementing Stream concept
\tparam Handler Type of handler, implementing Handler concept.
\param is Input stream to be parsed.
\param handler The handler to receive events.
\return Whether the parsing is successful.
*/
template <typename InputStream, typename Handler>
ParseResult Parse(InputStream& is, Handler& handler) {
return Parse<kParseDefaultFlags>(is, handler);
}
//! Initialize JSON text token-by-token parsing
/*!
*/
void IterativeParseInit() {
parseResult_.Clear();
state_ = IterativeParsingStartState;
}
//! Parse one token from JSON text
/*! \tparam InputStream Type of input stream, implementing Stream concept
\tparam Handler Type of handler, implementing Handler concept.
\param is Input stream to be parsed.
\param handler The handler to receive events.
\return Whether the parsing is successful.
*/
template <unsigned parseFlags, typename InputStream, typename Handler>
bool IterativeParseNext(InputStream& is, Handler& handler) {
while (CEREAL_RAPIDJSON_LIKELY(is.Peek() != '\0')) {
SkipWhitespaceAndComments<parseFlags>(is);
Token t = Tokenize(is.Peek());
IterativeParsingState n = Predict(state_, t);
IterativeParsingState d = Transit<parseFlags>(state_, t, n, is, handler);
// If we've finished or hit an error...
if (CEREAL_RAPIDJSON_UNLIKELY(IsIterativeParsingCompleteState(d))) {
// Report errors.
if (d == IterativeParsingErrorState) {
HandleError(state_, is);
return false;
}
// Transition to the finish state.
CEREAL_RAPIDJSON_ASSERT(d == IterativeParsingFinishState);
state_ = d;
// If StopWhenDone is not set...
if (!(parseFlags & kParseStopWhenDoneFlag)) {
// ... and extra non-whitespace data is found...
SkipWhitespaceAndComments<parseFlags>(is);
if (is.Peek() != '\0') {
// ... this is considered an error.
HandleError(state_, is);
return false;
}
}
// Success! We are done!
return true;
}
// Transition to the new state.
state_ = d;
// If we parsed anything other than a delimiter, we invoked the handler, so we can return true now.
if (!IsIterativeParsingDelimiterState(n))
return true;
}
// We reached the end of file.
stack_.Clear();
if (state_ != IterativeParsingFinishState) {
HandleError(state_, is);
return false;
}
return true;
}
//! Check if token-by-token parsing JSON text is complete
/*! \return Whether the JSON has been fully decoded.
*/
CEREAL_RAPIDJSON_FORCEINLINE bool IterativeParseComplete() const {
return IsIterativeParsingCompleteState(state_);
}
//! Whether a parse error has occurred in the last parsing.
bool HasParseError() const { return parseResult_.IsError(); }
//! Get the \ref ParseErrorCode of last parsing.
ParseErrorCode GetParseErrorCode() const { return parseResult_.Code(); }
//! Get the position of last parsing error in input, 0 otherwise.
size_t GetErrorOffset() const { return parseResult_.Offset(); }
protected:
void SetParseError(ParseErrorCode code, size_t offset) { parseResult_.Set(code, offset); }
private:
// Prohibit copy constructor & assignment operator.
GenericReader(const GenericReader&);
GenericReader& operator=(const GenericReader&);
void ClearStack() { stack_.Clear(); }
// clear stack on any exit from ParseStream, e.g. due to exception
struct ClearStackOnExit {
explicit ClearStackOnExit(GenericReader& r) : r_(r) {}
~ClearStackOnExit() { r_.ClearStack(); }
private:
GenericReader& r_;
ClearStackOnExit(const ClearStackOnExit&);
ClearStackOnExit& operator=(const ClearStackOnExit&);
};
template<unsigned parseFlags, typename InputStream>
void SkipWhitespaceAndComments(InputStream& is) {
SkipWhitespace(is);
if (parseFlags & kParseCommentsFlag) {
while (CEREAL_RAPIDJSON_UNLIKELY(Consume(is, '/'))) {
if (Consume(is, '*')) {
while (true) {
if (CEREAL_RAPIDJSON_UNLIKELY(is.Peek() == '\0'))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorUnspecificSyntaxError, is.Tell());
else if (Consume(is, '*')) {
if (Consume(is, '/'))
break;
}
else
is.Take();
}
}
else if (CEREAL_RAPIDJSON_LIKELY(Consume(is, '/')))
while (is.Peek() != '\0' && is.Take() != '\n') {}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorUnspecificSyntaxError, is.Tell());
SkipWhitespace(is);
}
}
}
// Parse object: { string : value, ... }
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseObject(InputStream& is, Handler& handler) {
CEREAL_RAPIDJSON_ASSERT(is.Peek() == '{');
is.Take(); // Skip '{'
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.StartObject()))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (Consume(is, '}')) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndObject(0))) // empty object
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
return;
}
for (SizeType memberCount = 0;;) {
if (CEREAL_RAPIDJSON_UNLIKELY(is.Peek() != '"'))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissName, is.Tell());
ParseString<parseFlags>(is, handler, true);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (CEREAL_RAPIDJSON_UNLIKELY(!Consume(is, ':')))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissColon, is.Tell());
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
ParseValue<parseFlags>(is, handler);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
++memberCount;
switch (is.Peek()) {
case ',':
is.Take();
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
break;
case '}':
is.Take();
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndObject(memberCount)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
return;
default:
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissCommaOrCurlyBracket, is.Tell()); break; // This useless break is only for making warning and coverage happy
}
if (parseFlags & kParseTrailingCommasFlag) {
if (is.Peek() == '}') {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndObject(memberCount)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
is.Take();
return;
}
}
}
}
// Parse array: [ value, ... ]
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseArray(InputStream& is, Handler& handler) {
CEREAL_RAPIDJSON_ASSERT(is.Peek() == '[');
is.Take(); // Skip '['
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.StartArray()))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (Consume(is, ']')) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndArray(0))) // empty array
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
return;
}
for (SizeType elementCount = 0;;) {
ParseValue<parseFlags>(is, handler);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
++elementCount;
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (Consume(is, ',')) {
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
}
else if (Consume(is, ']')) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndArray(elementCount)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
return;
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorArrayMissCommaOrSquareBracket, is.Tell());
if (parseFlags & kParseTrailingCommasFlag) {
if (is.Peek() == ']') {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.EndArray(elementCount)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
is.Take();
return;
}
}
}
}
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseNull(InputStream& is, Handler& handler) {
CEREAL_RAPIDJSON_ASSERT(is.Peek() == 'n');
is.Take();
if (CEREAL_RAPIDJSON_LIKELY(Consume(is, 'u') && Consume(is, 'l') && Consume(is, 'l'))) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.Null()))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, is.Tell());
}
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseTrue(InputStream& is, Handler& handler) {
CEREAL_RAPIDJSON_ASSERT(is.Peek() == 't');
is.Take();
if (CEREAL_RAPIDJSON_LIKELY(Consume(is, 'r') && Consume(is, 'u') && Consume(is, 'e'))) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.Bool(true)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, is.Tell());
}
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseFalse(InputStream& is, Handler& handler) {
CEREAL_RAPIDJSON_ASSERT(is.Peek() == 'f');
is.Take();
if (CEREAL_RAPIDJSON_LIKELY(Consume(is, 'a') && Consume(is, 'l') && Consume(is, 's') && Consume(is, 'e'))) {
if (CEREAL_RAPIDJSON_UNLIKELY(!handler.Bool(false)))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, is.Tell());
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, is.Tell());
}
template<typename InputStream>
CEREAL_RAPIDJSON_FORCEINLINE static bool Consume(InputStream& is, typename InputStream::Ch expect) {
if (CEREAL_RAPIDJSON_LIKELY(is.Peek() == expect)) {
is.Take();
return true;
}
else
return false;
}
// Helper function to parse four hexadecimal digits in \uXXXX in ParseString().
template<typename InputStream>
unsigned ParseHex4(InputStream& is, size_t escapeOffset) {
unsigned codepoint = 0;
for (int i = 0; i < 4; i++) {
Ch c = is.Peek();
codepoint <<= 4;
codepoint += static_cast<unsigned>(c);
if (c >= '0' && c <= '9')
codepoint -= '0';
else if (c >= 'A' && c <= 'F')
codepoint -= 'A' - 10;
else if (c >= 'a' && c <= 'f')
codepoint -= 'a' - 10;
else {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorStringUnicodeEscapeInvalidHex, escapeOffset);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(0);
}
is.Take();
}
return codepoint;
}
template <typename CharType>
class StackStream {
public:
typedef CharType Ch;
StackStream(internal::Stack<StackAllocator>& stack) : stack_(stack), length_(0) {}
CEREAL_RAPIDJSON_FORCEINLINE void Put(Ch c) {
*stack_.template Push<Ch>() = c;
++length_;
}
CEREAL_RAPIDJSON_FORCEINLINE void* Push(SizeType count) {
length_ += count;
return stack_.template Push<Ch>(count);
}
size_t Length() const { return length_; }
Ch* Pop() {
return stack_.template Pop<Ch>(length_);
}
private:
StackStream(const StackStream&);
StackStream& operator=(const StackStream&);
internal::Stack<StackAllocator>& stack_;
SizeType length_;
};
// Parse string and generate String event. Different code paths for kParseInsituFlag.
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseString(InputStream& is, Handler& handler, bool isKey = false) {
internal::StreamLocalCopy<InputStream> copy(is);
InputStream& s(copy.s);
CEREAL_RAPIDJSON_ASSERT(s.Peek() == '\"');
s.Take(); // Skip '\"'
bool success = false;
if (parseFlags & kParseInsituFlag) {
typename InputStream::Ch *head = s.PutBegin();
ParseStringToStream<parseFlags, SourceEncoding, SourceEncoding>(s, s);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
size_t length = s.PutEnd(head) - 1;
CEREAL_RAPIDJSON_ASSERT(length <= 0xFFFFFFFF);
const typename TargetEncoding::Ch* const str = reinterpret_cast<typename TargetEncoding::Ch*>(head);
success = (isKey ? handler.Key(str, SizeType(length), false) : handler.String(str, SizeType(length), false));
}
else {
StackStream<typename TargetEncoding::Ch> stackStream(stack_);
ParseStringToStream<parseFlags, SourceEncoding, TargetEncoding>(s, stackStream);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
SizeType length = static_cast<SizeType>(stackStream.Length()) - 1;
const typename TargetEncoding::Ch* const str = stackStream.Pop();
success = (isKey ? handler.Key(str, length, true) : handler.String(str, length, true));
}
if (CEREAL_RAPIDJSON_UNLIKELY(!success))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, s.Tell());
}
// Parse string to an output is
// This function handles the prefix/suffix double quotes, escaping, and optional encoding validation.
template<unsigned parseFlags, typename SEncoding, typename TEncoding, typename InputStream, typename OutputStream>
CEREAL_RAPIDJSON_FORCEINLINE void ParseStringToStream(InputStream& is, OutputStream& os) {
//!@cond CEREAL_RAPIDJSON_HIDDEN_FROM_DOXYGEN
#define Z16 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
static const char escape[256] = {
Z16, Z16, 0, 0,'\"', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,'/',
Z16, Z16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,'\\', 0, 0, 0,
0, 0,'\b', 0, 0, 0,'\f', 0, 0, 0, 0, 0, 0, 0,'\n', 0,
0, 0,'\r', 0,'\t', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
Z16, Z16, Z16, Z16, Z16, Z16, Z16, Z16
};
#undef Z16
//!@endcond
for (;;) {
// Scan and copy string before "\\\"" or < 0x20. This is an optional optimzation.
if (!(parseFlags & kParseValidateEncodingFlag))
ScanCopyUnescapedString(is, os);
Ch c = is.Peek();
if (CEREAL_RAPIDJSON_UNLIKELY(c == '\\')) { // Escape
size_t escapeOffset = is.Tell(); // For invalid escaping, report the initial '\\' as error offset
is.Take();
Ch e = is.Peek();
if ((sizeof(Ch) == 1 || unsigned(e) < 256) && CEREAL_RAPIDJSON_LIKELY(escape[static_cast<unsigned char>(e)])) {
is.Take();
os.Put(static_cast<typename TEncoding::Ch>(escape[static_cast<unsigned char>(e)]));
}
else if (CEREAL_RAPIDJSON_LIKELY(e == 'u')) { // Unicode
is.Take();
unsigned codepoint = ParseHex4(is, escapeOffset);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (CEREAL_RAPIDJSON_UNLIKELY(codepoint >= 0xD800 && codepoint <= 0xDBFF)) {
// Handle UTF-16 surrogate pair
if (CEREAL_RAPIDJSON_UNLIKELY(!Consume(is, '\\') || !Consume(is, 'u')))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringUnicodeSurrogateInvalid, escapeOffset);
unsigned codepoint2 = ParseHex4(is, escapeOffset);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN_VOID;
if (CEREAL_RAPIDJSON_UNLIKELY(codepoint2 < 0xDC00 || codepoint2 > 0xDFFF))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringUnicodeSurrogateInvalid, escapeOffset);
codepoint = (((codepoint - 0xD800) << 10) | (codepoint2 - 0xDC00)) + 0x10000;
}
TEncoding::Encode(os, codepoint);
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringEscapeInvalid, escapeOffset);
}
else if (CEREAL_RAPIDJSON_UNLIKELY(c == '"')) { // Closing double quote
is.Take();
os.Put('\0'); // null-terminate the string
return;
}
else if (CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(c) < 0x20)) { // RFC 4627: unescaped = %x20-21 / %x23-5B / %x5D-10FFFF
if (c == '\0')
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringMissQuotationMark, is.Tell());
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringInvalidEncoding, is.Tell());
}
else {
size_t offset = is.Tell();
if (CEREAL_RAPIDJSON_UNLIKELY((parseFlags & kParseValidateEncodingFlag ?
!Transcoder<SEncoding, TEncoding>::Validate(is, os) :
!Transcoder<SEncoding, TEncoding>::Transcode(is, os))))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorStringInvalidEncoding, offset);
}
}
}
template<typename InputStream, typename OutputStream>
static CEREAL_RAPIDJSON_FORCEINLINE void ScanCopyUnescapedString(InputStream&, OutputStream&) {
// Do nothing for generic version
}
#if defined(CEREAL_RAPIDJSON_SSE2) || defined(CEREAL_RAPIDJSON_SSE42)
// StringStream -> StackStream<char>
static CEREAL_RAPIDJSON_FORCEINLINE void ScanCopyUnescapedString(StringStream& is, StackStream<char>& os) {
const char* p = is.src_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = p;
return;
}
else
os.Put(*p++);
// The rest of string using SIMD
static const char dquote[16] = { '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"' };
static const char bslash[16] = { '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\' };
static const char space[16] = { 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F };
const __m128i dq = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&dquote[0]));
const __m128i bs = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&bslash[0]));
const __m128i sp = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&space[0]));
for (;; p += 16) {
const __m128i s = _mm_load_si128(reinterpret_cast<const __m128i *>(p));
const __m128i t1 = _mm_cmpeq_epi8(s, dq);
const __m128i t2 = _mm_cmpeq_epi8(s, bs);
const __m128i t3 = _mm_cmpeq_epi8(_mm_max_epu8(s, sp), sp); // s < 0x20 <=> max(s, 0x1F) == 0x1F
const __m128i x = _mm_or_si128(_mm_or_si128(t1, t2), t3);
unsigned short r = static_cast<unsigned short>(_mm_movemask_epi8(x));
if (CEREAL_RAPIDJSON_UNLIKELY(r != 0)) { // some of characters is escaped
SizeType length;
#ifdef _MSC_VER // Find the index of first escaped
unsigned long offset;
_BitScanForward(&offset, r);
length = offset;
#else
length = static_cast<SizeType>(__builtin_ffs(r) - 1);
#endif
if (length != 0) {
char* q = reinterpret_cast<char*>(os.Push(length));
for (size_t i = 0; i < length; i++)
q[i] = p[i];
p += length;
}
break;
}
_mm_storeu_si128(reinterpret_cast<__m128i *>(os.Push(16)), s);
}
is.src_ = p;
}
// InsituStringStream -> InsituStringStream
static CEREAL_RAPIDJSON_FORCEINLINE void ScanCopyUnescapedString(InsituStringStream& is, InsituStringStream& os) {
CEREAL_RAPIDJSON_ASSERT(&is == &os);
(void)os;
if (is.src_ == is.dst_) {
SkipUnescapedString(is);
return;
}
char* p = is.src_;
char *q = is.dst_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = p;
is.dst_ = q;
return;
}
else
*q++ = *p++;
// The rest of string using SIMD
static const char dquote[16] = { '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"' };
static const char bslash[16] = { '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\' };
static const char space[16] = { 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F };
const __m128i dq = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&dquote[0]));
const __m128i bs = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&bslash[0]));
const __m128i sp = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&space[0]));
for (;; p += 16, q += 16) {
const __m128i s = _mm_load_si128(reinterpret_cast<const __m128i *>(p));
const __m128i t1 = _mm_cmpeq_epi8(s, dq);
const __m128i t2 = _mm_cmpeq_epi8(s, bs);
const __m128i t3 = _mm_cmpeq_epi8(_mm_max_epu8(s, sp), sp); // s < 0x20 <=> max(s, 0x1F) == 0x1F
const __m128i x = _mm_or_si128(_mm_or_si128(t1, t2), t3);
unsigned short r = static_cast<unsigned short>(_mm_movemask_epi8(x));
if (CEREAL_RAPIDJSON_UNLIKELY(r != 0)) { // some of characters is escaped
size_t length;
#ifdef _MSC_VER // Find the index of first escaped
unsigned long offset;
_BitScanForward(&offset, r);
length = offset;
#else
length = static_cast<size_t>(__builtin_ffs(r) - 1);
#endif
for (const char* pend = p + length; p != pend; )
*q++ = *p++;
break;
}
_mm_storeu_si128(reinterpret_cast<__m128i *>(q), s);
}
is.src_ = p;
is.dst_ = q;
}
// When read/write pointers are the same for insitu stream, just skip unescaped characters
static CEREAL_RAPIDJSON_FORCEINLINE void SkipUnescapedString(InsituStringStream& is) {
CEREAL_RAPIDJSON_ASSERT(is.src_ == is.dst_);
char* p = is.src_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
for (; p != nextAligned; p++)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = is.dst_ = p;
return;
}
// The rest of string using SIMD
static const char dquote[16] = { '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"', '\"' };
static const char bslash[16] = { '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\', '\\' };
static const char space[16] = { 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F, 0x1F };
const __m128i dq = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&dquote[0]));
const __m128i bs = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&bslash[0]));
const __m128i sp = _mm_loadu_si128(reinterpret_cast<const __m128i *>(&space[0]));
for (;; p += 16) {
const __m128i s = _mm_load_si128(reinterpret_cast<const __m128i *>(p));
const __m128i t1 = _mm_cmpeq_epi8(s, dq);
const __m128i t2 = _mm_cmpeq_epi8(s, bs);
const __m128i t3 = _mm_cmpeq_epi8(_mm_max_epu8(s, sp), sp); // s < 0x20 <=> max(s, 0x1F) == 0x1F
const __m128i x = _mm_or_si128(_mm_or_si128(t1, t2), t3);
unsigned short r = static_cast<unsigned short>(_mm_movemask_epi8(x));
if (CEREAL_RAPIDJSON_UNLIKELY(r != 0)) { // some of characters is escaped
size_t length;
#ifdef _MSC_VER // Find the index of first escaped
unsigned long offset;
_BitScanForward(&offset, r);
length = offset;
#else
length = static_cast<size_t>(__builtin_ffs(r) - 1);
#endif
p += length;
break;
}
}
is.src_ = is.dst_ = p;
}
#elif defined(CEREAL_RAPIDJSON_NEON)
// StringStream -> StackStream<char>
static CEREAL_RAPIDJSON_FORCEINLINE void ScanCopyUnescapedString(StringStream& is, StackStream<char>& os) {
const char* p = is.src_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = p;
return;
}
else
os.Put(*p++);
// The rest of string using SIMD
const uint8x16_t s0 = vmovq_n_u8('"');
const uint8x16_t s1 = vmovq_n_u8('\\');
const uint8x16_t s2 = vmovq_n_u8('\b');
const uint8x16_t s3 = vmovq_n_u8(32);
for (;; p += 16) {
const uint8x16_t s = vld1q_u8(reinterpret_cast<const uint8_t *>(p));
uint8x16_t x = vceqq_u8(s, s0);
x = vorrq_u8(x, vceqq_u8(s, s1));
x = vorrq_u8(x, vceqq_u8(s, s2));
x = vorrq_u8(x, vcltq_u8(s, s3));
x = vrev64q_u8(x); // Rev in 64
uint64_t low = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 0); // extract
uint64_t high = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 1); // extract
SizeType length = 0;
bool escaped = false;
if (low == 0) {
if (high != 0) {
unsigned lz = (unsigned)__builtin_clzll(high);;
length = 8 + (lz >> 3);
escaped = true;
}
} else {
unsigned lz = (unsigned)__builtin_clzll(low);;
length = lz >> 3;
escaped = true;
}
if (CEREAL_RAPIDJSON_UNLIKELY(escaped)) { // some of characters is escaped
if (length != 0) {
char* q = reinterpret_cast<char*>(os.Push(length));
for (size_t i = 0; i < length; i++)
q[i] = p[i];
p += length;
}
break;
}
vst1q_u8(reinterpret_cast<uint8_t *>(os.Push(16)), s);
}
is.src_ = p;
}
// InsituStringStream -> InsituStringStream
static CEREAL_RAPIDJSON_FORCEINLINE void ScanCopyUnescapedString(InsituStringStream& is, InsituStringStream& os) {
CEREAL_RAPIDJSON_ASSERT(&is == &os);
(void)os;
if (is.src_ == is.dst_) {
SkipUnescapedString(is);
return;
}
char* p = is.src_;
char *q = is.dst_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
while (p != nextAligned)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = p;
is.dst_ = q;
return;
}
else
*q++ = *p++;
// The rest of string using SIMD
const uint8x16_t s0 = vmovq_n_u8('"');
const uint8x16_t s1 = vmovq_n_u8('\\');
const uint8x16_t s2 = vmovq_n_u8('\b');
const uint8x16_t s3 = vmovq_n_u8(32);
for (;; p += 16, q += 16) {
const uint8x16_t s = vld1q_u8(reinterpret_cast<uint8_t *>(p));
uint8x16_t x = vceqq_u8(s, s0);
x = vorrq_u8(x, vceqq_u8(s, s1));
x = vorrq_u8(x, vceqq_u8(s, s2));
x = vorrq_u8(x, vcltq_u8(s, s3));
x = vrev64q_u8(x); // Rev in 64
uint64_t low = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 0); // extract
uint64_t high = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 1); // extract
SizeType length = 0;
bool escaped = false;
if (low == 0) {
if (high != 0) {
unsigned lz = (unsigned)__builtin_clzll(high);
length = 8 + (lz >> 3);
escaped = true;
}
} else {
unsigned lz = (unsigned)__builtin_clzll(low);
length = lz >> 3;
escaped = true;
}
if (CEREAL_RAPIDJSON_UNLIKELY(escaped)) { // some of characters is escaped
for (const char* pend = p + length; p != pend; ) {
*q++ = *p++;
}
break;
}
vst1q_u8(reinterpret_cast<uint8_t *>(q), s);
}
is.src_ = p;
is.dst_ = q;
}
// When read/write pointers are the same for insitu stream, just skip unescaped characters
static CEREAL_RAPIDJSON_FORCEINLINE void SkipUnescapedString(InsituStringStream& is) {
CEREAL_RAPIDJSON_ASSERT(is.src_ == is.dst_);
char* p = is.src_;
// Scan one by one until alignment (unaligned load may cross page boundary and cause crash)
const char* nextAligned = reinterpret_cast<const char*>((reinterpret_cast<size_t>(p) + 15) & static_cast<size_t>(~15));
for (; p != nextAligned; p++)
if (CEREAL_RAPIDJSON_UNLIKELY(*p == '\"') || CEREAL_RAPIDJSON_UNLIKELY(*p == '\\') || CEREAL_RAPIDJSON_UNLIKELY(static_cast<unsigned>(*p) < 0x20)) {
is.src_ = is.dst_ = p;
return;
}
// The rest of string using SIMD
const uint8x16_t s0 = vmovq_n_u8('"');
const uint8x16_t s1 = vmovq_n_u8('\\');
const uint8x16_t s2 = vmovq_n_u8('\b');
const uint8x16_t s3 = vmovq_n_u8(32);
for (;; p += 16) {
const uint8x16_t s = vld1q_u8(reinterpret_cast<uint8_t *>(p));
uint8x16_t x = vceqq_u8(s, s0);
x = vorrq_u8(x, vceqq_u8(s, s1));
x = vorrq_u8(x, vceqq_u8(s, s2));
x = vorrq_u8(x, vcltq_u8(s, s3));
x = vrev64q_u8(x); // Rev in 64
uint64_t low = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 0); // extract
uint64_t high = vgetq_lane_u64(reinterpret_cast<uint64x2_t>(x), 1); // extract
if (low == 0) {
if (high != 0) {
int lz = __builtin_clzll(high);
p += 8 + (lz >> 3);
break;
}
} else {
int lz = __builtin_clzll(low);
p += lz >> 3;
break;
}
}
is.src_ = is.dst_ = p;
}
#endif // CEREAL_RAPIDJSON_NEON
template<typename InputStream, bool backup, bool pushOnTake>
class NumberStream;
template<typename InputStream>
class NumberStream<InputStream, false, false> {
public:
typedef typename InputStream::Ch Ch;
NumberStream(GenericReader& reader, InputStream& s) : is(s) { (void)reader; }
CEREAL_RAPIDJSON_FORCEINLINE Ch Peek() const { return is.Peek(); }
CEREAL_RAPIDJSON_FORCEINLINE Ch TakePush() { return is.Take(); }
CEREAL_RAPIDJSON_FORCEINLINE Ch Take() { return is.Take(); }
CEREAL_RAPIDJSON_FORCEINLINE void Push(char) {}
size_t Tell() { return is.Tell(); }
size_t Length() { return 0; }
const char* Pop() { return 0; }
protected:
NumberStream& operator=(const NumberStream&);
InputStream& is;
};
template<typename InputStream>
class NumberStream<InputStream, true, false> : public NumberStream<InputStream, false, false> {
typedef NumberStream<InputStream, false, false> Base;
public:
NumberStream(GenericReader& reader, InputStream& s) : Base(reader, s), stackStream(reader.stack_) {}
CEREAL_RAPIDJSON_FORCEINLINE Ch TakePush() {
stackStream.Put(static_cast<char>(Base::is.Peek()));
return Base::is.Take();
}
CEREAL_RAPIDJSON_FORCEINLINE void Push(char c) {
stackStream.Put(c);
}
size_t Length() { return stackStream.Length(); }
const char* Pop() {
stackStream.Put('\0');
return stackStream.Pop();
}
private:
StackStream<char> stackStream;
};
template<typename InputStream>
class NumberStream<InputStream, true, true> : public NumberStream<InputStream, true, false> {
typedef NumberStream<InputStream, true, false> Base;
public:
NumberStream(GenericReader& reader, InputStream& is) : Base(reader, is) {}
CEREAL_RAPIDJSON_FORCEINLINE Ch Take() { return Base::TakePush(); }
};
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseNumber(InputStream& is, Handler& handler) {
internal::StreamLocalCopy<InputStream> copy(is);
NumberStream<InputStream,
((parseFlags & kParseNumbersAsStringsFlag) != 0) ?
((parseFlags & kParseInsituFlag) == 0) :
((parseFlags & kParseFullPrecisionFlag) != 0),
(parseFlags & kParseNumbersAsStringsFlag) != 0 &&
(parseFlags & kParseInsituFlag) == 0> s(*this, copy.s);
size_t startOffset = s.Tell();
double d = 0.0;
bool useNanOrInf = false;
// Parse minus
bool minus = Consume(s, '-');
// Parse int: zero / ( digit1-9 *DIGIT )
unsigned i = 0;
uint64_t i64 = 0;
bool use64bit = false;
int significandDigit = 0;
if (CEREAL_RAPIDJSON_UNLIKELY(s.Peek() == '0')) {
i = 0;
s.TakePush();
}
else if (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '1' && s.Peek() <= '9')) {
i = static_cast<unsigned>(s.TakePush() - '0');
if (minus)
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (CEREAL_RAPIDJSON_UNLIKELY(i >= 214748364)) { // 2^31 = 2147483648
if (CEREAL_RAPIDJSON_LIKELY(i != 214748364 || s.Peek() > '8')) {
i64 = i;
use64bit = true;
break;
}
}
i = i * 10 + static_cast<unsigned>(s.TakePush() - '0');
significandDigit++;
}
else
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (CEREAL_RAPIDJSON_UNLIKELY(i >= 429496729)) { // 2^32 - 1 = 4294967295
if (CEREAL_RAPIDJSON_LIKELY(i != 429496729 || s.Peek() > '5')) {
i64 = i;
use64bit = true;
break;
}
}
i = i * 10 + static_cast<unsigned>(s.TakePush() - '0');
significandDigit++;
}
}
// Parse NaN or Infinity here
else if ((parseFlags & kParseNanAndInfFlag) && CEREAL_RAPIDJSON_LIKELY((s.Peek() == 'I' || s.Peek() == 'N'))) {
if (Consume(s, 'N')) {
if (Consume(s, 'a') && Consume(s, 'N')) {
d = std::numeric_limits<double>::quiet_NaN();
useNanOrInf = true;
}
}
else if (CEREAL_RAPIDJSON_LIKELY(Consume(s, 'I'))) {
if (Consume(s, 'n') && Consume(s, 'f')) {
d = (minus ? -std::numeric_limits<double>::infinity() : std::numeric_limits<double>::infinity());
useNanOrInf = true;
if (CEREAL_RAPIDJSON_UNLIKELY(s.Peek() == 'i' && !(Consume(s, 'i') && Consume(s, 'n')
&& Consume(s, 'i') && Consume(s, 't') && Consume(s, 'y')))) {
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, s.Tell());
}
}
}
if (CEREAL_RAPIDJSON_UNLIKELY(!useNanOrInf)) {
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, s.Tell());
}
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, s.Tell());
// Parse 64bit int
bool useDouble = false;
if (use64bit) {
if (minus)
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (CEREAL_RAPIDJSON_UNLIKELY(i64 >= CEREAL_RAPIDJSON_UINT64_C2(0x0CCCCCCC, 0xCCCCCCCC))) // 2^63 = 9223372036854775808
if (CEREAL_RAPIDJSON_LIKELY(i64 != CEREAL_RAPIDJSON_UINT64_C2(0x0CCCCCCC, 0xCCCCCCCC) || s.Peek() > '8')) {
d = static_cast<double>(i64);
useDouble = true;
break;
}
i64 = i64 * 10 + static_cast<unsigned>(s.TakePush() - '0');
significandDigit++;
}
else
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (CEREAL_RAPIDJSON_UNLIKELY(i64 >= CEREAL_RAPIDJSON_UINT64_C2(0x19999999, 0x99999999))) // 2^64 - 1 = 18446744073709551615
if (CEREAL_RAPIDJSON_LIKELY(i64 != CEREAL_RAPIDJSON_UINT64_C2(0x19999999, 0x99999999) || s.Peek() > '5')) {
d = static_cast<double>(i64);
useDouble = true;
break;
}
i64 = i64 * 10 + static_cast<unsigned>(s.TakePush() - '0');
significandDigit++;
}
}
// Force double for big integer
if (useDouble) {
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
d = d * 10 + (s.TakePush() - '0');
}
}
// Parse frac = decimal-point 1*DIGIT
int expFrac = 0;
size_t decimalPosition;
if (Consume(s, '.')) {
decimalPosition = s.Length();
if (CEREAL_RAPIDJSON_UNLIKELY(!(s.Peek() >= '0' && s.Peek() <= '9')))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorNumberMissFraction, s.Tell());
if (!useDouble) {
#if CEREAL_RAPIDJSON_64BIT
// Use i64 to store significand in 64-bit architecture
if (!use64bit)
i64 = i;
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (i64 > CEREAL_RAPIDJSON_UINT64_C2(0x1FFFFF, 0xFFFFFFFF)) // 2^53 - 1 for fast path
break;
else {
i64 = i64 * 10 + static_cast<unsigned>(s.TakePush() - '0');
--expFrac;
if (i64 != 0)
significandDigit++;
}
}
d = static_cast<double>(i64);
#else
// Use double to store significand in 32-bit architecture
d = static_cast<double>(use64bit ? i64 : i);
#endif
useDouble = true;
}
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
if (significandDigit < 17) {
d = d * 10.0 + (s.TakePush() - '0');
--expFrac;
if (CEREAL_RAPIDJSON_LIKELY(d > 0.0))
significandDigit++;
}
else
s.TakePush();
}
}
else
decimalPosition = s.Length(); // decimal position at the end of integer.
// Parse exp = e [ minus / plus ] 1*DIGIT
int exp = 0;
if (Consume(s, 'e') || Consume(s, 'E')) {
if (!useDouble) {
d = static_cast<double>(use64bit ? i64 : i);
useDouble = true;
}
bool expMinus = false;
if (Consume(s, '+'))
;
else if (Consume(s, '-'))
expMinus = true;
if (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
exp = static_cast<int>(s.Take() - '0');
if (expMinus) {
// (exp + expFrac) must not underflow int => we're detecting when -exp gets
// dangerously close to INT_MIN (a pessimistic next digit 9 would push it into
// underflow territory):
//
// -(exp * 10 + 9) + expFrac >= INT_MIN
// <=> exp <= (expFrac - INT_MIN - 9) / 10
CEREAL_RAPIDJSON_ASSERT(expFrac <= 0);
int maxExp = (expFrac + 2147483639) / 10;
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
exp = exp * 10 + static_cast<int>(s.Take() - '0');
if (CEREAL_RAPIDJSON_UNLIKELY(exp > maxExp)) {
while (CEREAL_RAPIDJSON_UNLIKELY(s.Peek() >= '0' && s.Peek() <= '9')) // Consume the rest of exponent
s.Take();
}
}
}
else { // positive exp
int maxExp = 308 - expFrac;
while (CEREAL_RAPIDJSON_LIKELY(s.Peek() >= '0' && s.Peek() <= '9')) {
exp = exp * 10 + static_cast<int>(s.Take() - '0');
if (CEREAL_RAPIDJSON_UNLIKELY(exp > maxExp))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorNumberTooBig, startOffset);
}
}
}
else
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorNumberMissExponent, s.Tell());
if (expMinus)
exp = -exp;
}
// Finish parsing, call event according to the type of number.
bool cont = true;
if (parseFlags & kParseNumbersAsStringsFlag) {
if (parseFlags & kParseInsituFlag) {
s.Pop(); // Pop stack no matter if it will be used or not.
typename InputStream::Ch* head = is.PutBegin();
const size_t length = s.Tell() - startOffset;
CEREAL_RAPIDJSON_ASSERT(length <= 0xFFFFFFFF);
// unable to insert the \0 character here, it will erase the comma after this number
const typename TargetEncoding::Ch* const str = reinterpret_cast<typename TargetEncoding::Ch*>(head);
cont = handler.RawNumber(str, SizeType(length), false);
}
else {
SizeType numCharsToCopy = static_cast<SizeType>(s.Length());
StringStream srcStream(s.Pop());
StackStream<typename TargetEncoding::Ch> dstStream(stack_);
while (numCharsToCopy--) {
Transcoder<UTF8<>, TargetEncoding>::Transcode(srcStream, dstStream);
}
dstStream.Put('\0');
const typename TargetEncoding::Ch* str = dstStream.Pop();
const SizeType length = static_cast<SizeType>(dstStream.Length()) - 1;
cont = handler.RawNumber(str, SizeType(length), true);
}
}
else {
size_t length = s.Length();
const char* decimal = s.Pop(); // Pop stack no matter if it will be used or not.
if (useDouble) {
int p = exp + expFrac;
if (parseFlags & kParseFullPrecisionFlag)
d = internal::StrtodFullPrecision(d, p, decimal, length, decimalPosition, exp);
else
d = internal::StrtodNormalPrecision(d, p);
// Use > max, instead of == inf, to fix bogus warning -Wfloat-equal
if (d > (std::numeric_limits<double>::max)()) {
// Overflow
// TODO: internal::StrtodX should report overflow (or underflow)
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorNumberTooBig, startOffset);
}
cont = handler.Double(minus ? -d : d);
}
else if (useNanOrInf) {
cont = handler.Double(d);
}
else {
if (use64bit) {
if (minus)
cont = handler.Int64(static_cast<int64_t>(~i64 + 1));
else
cont = handler.Uint64(i64);
}
else {
if (minus)
cont = handler.Int(static_cast<int32_t>(~i + 1));
else
cont = handler.Uint(i);
}
}
}
if (CEREAL_RAPIDJSON_UNLIKELY(!cont))
CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorTermination, startOffset);
}
// Parse any JSON value
template<unsigned parseFlags, typename InputStream, typename Handler>
void ParseValue(InputStream& is, Handler& handler) {
switch (is.Peek()) {
case 'n': ParseNull <parseFlags>(is, handler); break;
case 't': ParseTrue <parseFlags>(is, handler); break;
case 'f': ParseFalse <parseFlags>(is, handler); break;
case '"': ParseString<parseFlags>(is, handler); break;
case '{': ParseObject<parseFlags>(is, handler); break;
case '[': ParseArray <parseFlags>(is, handler); break;
default :
ParseNumber<parseFlags>(is, handler);
break;
}
}
// Iterative Parsing
// States
enum IterativeParsingState {
IterativeParsingFinishState = 0, // sink states at top
IterativeParsingErrorState, // sink states at top
IterativeParsingStartState,
// Object states
IterativeParsingObjectInitialState,
IterativeParsingMemberKeyState,
IterativeParsingMemberValueState,
IterativeParsingObjectFinishState,
// Array states
IterativeParsingArrayInitialState,
IterativeParsingElementState,
IterativeParsingArrayFinishState,
// Single value state
IterativeParsingValueState,
// Delimiter states (at bottom)
IterativeParsingElementDelimiterState,
IterativeParsingMemberDelimiterState,
IterativeParsingKeyValueDelimiterState,
cIterativeParsingStateCount
};
// Tokens
enum Token {
LeftBracketToken = 0,
RightBracketToken,
LeftCurlyBracketToken,
RightCurlyBracketToken,
CommaToken,
ColonToken,
StringToken,
FalseToken,
TrueToken,
NullToken,
NumberToken,
kTokenCount
};
CEREAL_RAPIDJSON_FORCEINLINE Token Tokenize(Ch c) const {
//!@cond CEREAL_RAPIDJSON_HIDDEN_FROM_DOXYGEN
#define N NumberToken
#define N16 N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N
// Maps from ASCII to Token
static const unsigned char tokenMap[256] = {
N16, // 00~0F
N16, // 10~1F
N, N, StringToken, N, N, N, N, N, N, N, N, N, CommaToken, N, N, N, // 20~2F
N, N, N, N, N, N, N, N, N, N, ColonToken, N, N, N, N, N, // 30~3F
N16, // 40~4F
N, N, N, N, N, N, N, N, N, N, N, LeftBracketToken, N, RightBracketToken, N, N, // 50~5F
N, N, N, N, N, N, FalseToken, N, N, N, N, N, N, N, NullToken, N, // 60~6F
N, N, N, N, TrueToken, N, N, N, N, N, N, LeftCurlyBracketToken, N, RightCurlyBracketToken, N, N, // 70~7F
N16, N16, N16, N16, N16, N16, N16, N16 // 80~FF
};
#undef N
#undef N16
//!@endcond
if (sizeof(Ch) == 1 || static_cast<unsigned>(c) < 256)
return static_cast<Token>(tokenMap[static_cast<unsigned char>(c)]);
else
return NumberToken;
}
CEREAL_RAPIDJSON_FORCEINLINE IterativeParsingState Predict(IterativeParsingState state, Token token) const {
// current state x one lookahead token -> new state
static const char G[cIterativeParsingStateCount][kTokenCount] = {
// Finish(sink state)
{
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState
},
// Error(sink state)
{
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState
},
// Start
{
IterativeParsingArrayInitialState, // Left bracket
IterativeParsingErrorState, // Right bracket
IterativeParsingObjectInitialState, // Left curly bracket
IterativeParsingErrorState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingValueState, // String
IterativeParsingValueState, // False
IterativeParsingValueState, // True
IterativeParsingValueState, // Null
IterativeParsingValueState // Number
},
// ObjectInitial
{
IterativeParsingErrorState, // Left bracket
IterativeParsingErrorState, // Right bracket
IterativeParsingErrorState, // Left curly bracket
IterativeParsingObjectFinishState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingMemberKeyState, // String
IterativeParsingErrorState, // False
IterativeParsingErrorState, // True
IterativeParsingErrorState, // Null
IterativeParsingErrorState // Number
},
// MemberKey
{
IterativeParsingErrorState, // Left bracket
IterativeParsingErrorState, // Right bracket
IterativeParsingErrorState, // Left curly bracket
IterativeParsingErrorState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingKeyValueDelimiterState, // Colon
IterativeParsingErrorState, // String
IterativeParsingErrorState, // False
IterativeParsingErrorState, // True
IterativeParsingErrorState, // Null
IterativeParsingErrorState // Number
},
// MemberValue
{
IterativeParsingErrorState, // Left bracket
IterativeParsingErrorState, // Right bracket
IterativeParsingErrorState, // Left curly bracket
IterativeParsingObjectFinishState, // Right curly bracket
IterativeParsingMemberDelimiterState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingErrorState, // String
IterativeParsingErrorState, // False
IterativeParsingErrorState, // True
IterativeParsingErrorState, // Null
IterativeParsingErrorState // Number
},
// ObjectFinish(sink state)
{
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState
},
// ArrayInitial
{
IterativeParsingArrayInitialState, // Left bracket(push Element state)
IterativeParsingArrayFinishState, // Right bracket
IterativeParsingObjectInitialState, // Left curly bracket(push Element state)
IterativeParsingErrorState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingElementState, // String
IterativeParsingElementState, // False
IterativeParsingElementState, // True
IterativeParsingElementState, // Null
IterativeParsingElementState // Number
},
// Element
{
IterativeParsingErrorState, // Left bracket
IterativeParsingArrayFinishState, // Right bracket
IterativeParsingErrorState, // Left curly bracket
IterativeParsingErrorState, // Right curly bracket
IterativeParsingElementDelimiterState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingErrorState, // String
IterativeParsingErrorState, // False
IterativeParsingErrorState, // True
IterativeParsingErrorState, // Null
IterativeParsingErrorState // Number
},
// ArrayFinish(sink state)
{
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState
},
// Single Value (sink state)
{
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState, IterativeParsingErrorState,
IterativeParsingErrorState
},
// ElementDelimiter
{
IterativeParsingArrayInitialState, // Left bracket(push Element state)
IterativeParsingArrayFinishState, // Right bracket
IterativeParsingObjectInitialState, // Left curly bracket(push Element state)
IterativeParsingErrorState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingElementState, // String
IterativeParsingElementState, // False
IterativeParsingElementState, // True
IterativeParsingElementState, // Null
IterativeParsingElementState // Number
},
// MemberDelimiter
{
IterativeParsingErrorState, // Left bracket
IterativeParsingErrorState, // Right bracket
IterativeParsingErrorState, // Left curly bracket
IterativeParsingObjectFinishState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingMemberKeyState, // String
IterativeParsingErrorState, // False
IterativeParsingErrorState, // True
IterativeParsingErrorState, // Null
IterativeParsingErrorState // Number
},
// KeyValueDelimiter
{
IterativeParsingArrayInitialState, // Left bracket(push MemberValue state)
IterativeParsingErrorState, // Right bracket
IterativeParsingObjectInitialState, // Left curly bracket(push MemberValue state)
IterativeParsingErrorState, // Right curly bracket
IterativeParsingErrorState, // Comma
IterativeParsingErrorState, // Colon
IterativeParsingMemberValueState, // String
IterativeParsingMemberValueState, // False
IterativeParsingMemberValueState, // True
IterativeParsingMemberValueState, // Null
IterativeParsingMemberValueState // Number
},
}; // End of G
return static_cast<IterativeParsingState>(G[state][token]);
}
// Make an advance in the token stream and state based on the candidate destination state which was returned by Transit().
// May return a new state on state pop.
template <unsigned parseFlags, typename InputStream, typename Handler>
CEREAL_RAPIDJSON_FORCEINLINE IterativeParsingState Transit(IterativeParsingState src, Token token, IterativeParsingState dst, InputStream& is, Handler& handler) {
(void)token;
switch (dst) {
case IterativeParsingErrorState:
return dst;
case IterativeParsingObjectInitialState:
case IterativeParsingArrayInitialState:
{
// Push the state(Element or MemeberValue) if we are nested in another array or value of member.
// In this way we can get the correct state on ObjectFinish or ArrayFinish by frame pop.
IterativeParsingState n = src;
if (src == IterativeParsingArrayInitialState || src == IterativeParsingElementDelimiterState)
n = IterativeParsingElementState;
else if (src == IterativeParsingKeyValueDelimiterState)
n = IterativeParsingMemberValueState;
// Push current state.
*stack_.template Push<SizeType>(1) = n;
// Initialize and push the member/element count.
*stack_.template Push<SizeType>(1) = 0;
// Call handler
bool hr = (dst == IterativeParsingObjectInitialState) ? handler.StartObject() : handler.StartArray();
// On handler short circuits the parsing.
if (!hr) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorTermination, is.Tell());
return IterativeParsingErrorState;
}
else {
is.Take();
return dst;
}
}
case IterativeParsingMemberKeyState:
ParseString<parseFlags>(is, handler, true);
if (HasParseError())
return IterativeParsingErrorState;
else
return dst;
case IterativeParsingKeyValueDelimiterState:
CEREAL_RAPIDJSON_ASSERT(token == ColonToken);
is.Take();
return dst;
case IterativeParsingMemberValueState:
// Must be non-compound value. Or it would be ObjectInitial or ArrayInitial state.
ParseValue<parseFlags>(is, handler);
if (HasParseError()) {
return IterativeParsingErrorState;
}
return dst;
case IterativeParsingElementState:
// Must be non-compound value. Or it would be ObjectInitial or ArrayInitial state.
ParseValue<parseFlags>(is, handler);
if (HasParseError()) {
return IterativeParsingErrorState;
}
return dst;
case IterativeParsingMemberDelimiterState:
case IterativeParsingElementDelimiterState:
is.Take();
// Update member/element count.
*stack_.template Top<SizeType>() = *stack_.template Top<SizeType>() + 1;
return dst;
case IterativeParsingObjectFinishState:
{
// Transit from delimiter is only allowed when trailing commas are enabled
if (!(parseFlags & kParseTrailingCommasFlag) && src == IterativeParsingMemberDelimiterState) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorObjectMissName, is.Tell());
return IterativeParsingErrorState;
}
// Get member count.
SizeType c = *stack_.template Pop<SizeType>(1);
// If the object is not empty, count the last member.
if (src == IterativeParsingMemberValueState)
++c;
// Restore the state.
IterativeParsingState n = static_cast<IterativeParsingState>(*stack_.template Pop<SizeType>(1));
// Transit to Finish state if this is the topmost scope.
if (n == IterativeParsingStartState)
n = IterativeParsingFinishState;
// Call handler
bool hr = handler.EndObject(c);
// On handler short circuits the parsing.
if (!hr) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorTermination, is.Tell());
return IterativeParsingErrorState;
}
else {
is.Take();
return n;
}
}
case IterativeParsingArrayFinishState:
{
// Transit from delimiter is only allowed when trailing commas are enabled
if (!(parseFlags & kParseTrailingCommasFlag) && src == IterativeParsingElementDelimiterState) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorValueInvalid, is.Tell());
return IterativeParsingErrorState;
}
// Get element count.
SizeType c = *stack_.template Pop<SizeType>(1);
// If the array is not empty, count the last element.
if (src == IterativeParsingElementState)
++c;
// Restore the state.
IterativeParsingState n = static_cast<IterativeParsingState>(*stack_.template Pop<SizeType>(1));
// Transit to Finish state if this is the topmost scope.
if (n == IterativeParsingStartState)
n = IterativeParsingFinishState;
// Call handler
bool hr = handler.EndArray(c);
// On handler short circuits the parsing.
if (!hr) {
CEREAL_RAPIDJSON_PARSE_ERROR_NORETURN(kParseErrorTermination, is.Tell());
return IterativeParsingErrorState;
}
else {
is.Take();
return n;
}
}
default:
// This branch is for IterativeParsingValueState actually.
// Use `default:` rather than
// `case IterativeParsingValueState:` is for code coverage.
// The IterativeParsingStartState is not enumerated in this switch-case.
// It is impossible for that case. And it can be caught by following assertion.
// The IterativeParsingFinishState is not enumerated in this switch-case either.
// It is a "derivative" state which cannot triggered from Predict() directly.
// Therefore it cannot happen here. And it can be caught by following assertion.
CEREAL_RAPIDJSON_ASSERT(dst == IterativeParsingValueState);
// Must be non-compound value. Or it would be ObjectInitial or ArrayInitial state.
ParseValue<parseFlags>(is, handler);
if (HasParseError()) {
return IterativeParsingErrorState;
}
return IterativeParsingFinishState;
}
}
template <typename InputStream>
void HandleError(IterativeParsingState src, InputStream& is) {
if (HasParseError()) {
// Error flag has been set.
return;
}
switch (src) {
case IterativeParsingStartState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorDocumentEmpty, is.Tell()); return;
case IterativeParsingFinishState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorDocumentRootNotSingular, is.Tell()); return;
case IterativeParsingObjectInitialState:
case IterativeParsingMemberDelimiterState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissName, is.Tell()); return;
case IterativeParsingMemberKeyState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissColon, is.Tell()); return;
case IterativeParsingMemberValueState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorObjectMissCommaOrCurlyBracket, is.Tell()); return;
case IterativeParsingKeyValueDelimiterState:
case IterativeParsingArrayInitialState:
case IterativeParsingElementDelimiterState: CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorValueInvalid, is.Tell()); return;
default: CEREAL_RAPIDJSON_ASSERT(src == IterativeParsingElementState); CEREAL_RAPIDJSON_PARSE_ERROR(kParseErrorArrayMissCommaOrSquareBracket, is.Tell()); return;
}
}
CEREAL_RAPIDJSON_FORCEINLINE bool IsIterativeParsingDelimiterState(IterativeParsingState s) const {
return s >= IterativeParsingElementDelimiterState;
}
CEREAL_RAPIDJSON_FORCEINLINE bool IsIterativeParsingCompleteState(IterativeParsingState s) const {
return s <= IterativeParsingErrorState;
}
template <unsigned parseFlags, typename InputStream, typename Handler>
ParseResult IterativeParse(InputStream& is, Handler& handler) {
parseResult_.Clear();
ClearStackOnExit scope(*this);
IterativeParsingState state = IterativeParsingStartState;
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
while (is.Peek() != '\0') {
Token t = Tokenize(is.Peek());
IterativeParsingState n = Predict(state, t);
IterativeParsingState d = Transit<parseFlags>(state, t, n, is, handler);
if (d == IterativeParsingErrorState) {
HandleError(state, is);
break;
}
state = d;
// Do not further consume streams if a root JSON has been parsed.
if ((parseFlags & kParseStopWhenDoneFlag) && state == IterativeParsingFinishState)
break;
SkipWhitespaceAndComments<parseFlags>(is);
CEREAL_RAPIDJSON_PARSE_ERROR_EARLY_RETURN(parseResult_);
}
// Handle the end of file.
if (state != IterativeParsingFinishState)
HandleError(state, is);
return parseResult_;
}
static const size_t kDefaultStackCapacity = 256; //!< Default stack capacity in bytes for storing a single decoded string.
internal::Stack<StackAllocator> stack_; //!< A stack for storing decoded string temporarily during non-destructive parsing.
ParseResult parseResult_;
IterativeParsingState state_;
}; // class GenericReader
//! Reader with UTF8 encoding and default allocator.
typedef GenericReader<UTF8<>, UTF8<> > Reader;
CEREAL_RAPIDJSON_NAMESPACE_END
#if defined(__clang__) || defined(_MSC_VER)
CEREAL_RAPIDJSON_DIAG_POP
#endif
#ifdef __GNUC__
CEREAL_RAPIDJSON_DIAG_POP
#endif
#endif // CEREAL_RAPIDJSON_READER_H_
| 0 |
coqui_public_repos/STT | coqui_public_repos/STT/bin/run-ci-ldc93s1_new_sdb_csv.sh | #!/bin/sh
set -xe
ldc93s1_dir="./data/smoke_test"
ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
ldc93s1_sdb="${ldc93s1_dir}/ldc93s1.sdb"
epoch_count=$1
audio_sample_rate=$2
if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
python -u bin/import_ldc93s1.py ${ldc93s1_dir}
fi;
if [ ! -f "${ldc93s1_dir}/ldc93s1.sdb" ]; then
echo "Converting LDC93S1 example data, saving to ${ldc93s1_sdb}."
python -u bin/data_set_tool.py --sources ${ldc93s1_csv} --target ${ldc93s1_sdb}
fi;
# Force only one visible device because we have a single-sample dataset
# and when trying to run on multiple devices (like GPUs), this will break
export CUDA_VISIBLE_DEVICES=0
python -u train.py --alphabet_config_path "data/alphabet.txt" \
--show_progressbar false --early_stop false \
--train_files ${ldc93s1_sdb} ${ldc93s1_csv} --train_batch_size 1 \
--feature_cache '/tmp/ldc93s1_cache_sdb_csv' \
--dev_files ${ldc93s1_sdb} ${ldc93s1_csv} --dev_batch_size 1 \
--test_files ${ldc93s1_sdb} ${ldc93s1_csv} --test_batch_size 1 \
--n_hidden 100 --epochs $epoch_count \
--max_to_keep 1 --checkpoint_dir '/tmp/ckpt_sdb_csv' \
--learning_rate 0.001 --dropout_rate 0.05 --export_dir '/tmp/train_sdb_csv' \
--scorer_path 'data/smoke_test/pruned_lm.scorer' \
--audio_sample_rate ${audio_sample_rate}
| 0 |
coqui_public_repos/coqui-py | coqui_public_repos/coqui-py/test/test_something.py | import pytest
# pytest forces us to redefine the `mock` global function as a parameter of
# the test cases as that's how you request a fixture, so we disable this lint
# globally in this file.
# pylint: disable=redefined-outer-name
@pytest.fixture
def mock():
# Setup
my_obj = {"what": "fixture?!?"}
# Yield the ready to be used fixture object
yield my_obj
# Cleanup
# my_obj.clean_resources()
def test_something(mock):
# response = mock.do_something()
# assert response == "expectation"
assert mock["what"] == "fixture?!?"
| 0 |
coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win | coqui_public_repos/STT/native_client/ctcdecode/third_party/openfst-1.6.9-win/m4/ltsugar.m4 | # ltsugar.m4 -- libtool m4 base layer. -*-Autoconf-*-
#
# Copyright (C) 2004-2005, 2007-2008, 2011-2015 Free Software
# Foundation, Inc.
# Written by Gary V. Vaughan, 2004
#
# This file is free software; the Free Software Foundation gives
# unlimited permission to copy and/or distribute it, with or without
# modifications, as long as this notice is preserved.
# serial 6 ltsugar.m4
# This is to help aclocal find these macros, as it can't see m4_define.
AC_DEFUN([LTSUGAR_VERSION], [m4_if([0.1])])
# lt_join(SEP, ARG1, [ARG2...])
# -----------------------------
# Produce ARG1SEPARG2...SEPARGn, omitting [] arguments and their
# associated separator.
# Needed until we can rely on m4_join from Autoconf 2.62, since all earlier
# versions in m4sugar had bugs.
m4_define([lt_join],
[m4_if([$#], [1], [],
[$#], [2], [[$2]],
[m4_if([$2], [], [], [[$2]_])$0([$1], m4_shift(m4_shift($@)))])])
m4_define([_lt_join],
[m4_if([$#$2], [2], [],
[m4_if([$2], [], [], [[$1$2]])$0([$1], m4_shift(m4_shift($@)))])])
# lt_car(LIST)
# lt_cdr(LIST)
# ------------
# Manipulate m4 lists.
# These macros are necessary as long as will still need to support
# Autoconf-2.59, which quotes differently.
m4_define([lt_car], [[$1]])
m4_define([lt_cdr],
[m4_if([$#], 0, [m4_fatal([$0: cannot be called without arguments])],
[$#], 1, [],
[m4_dquote(m4_shift($@))])])
m4_define([lt_unquote], $1)
# lt_append(MACRO-NAME, STRING, [SEPARATOR])
# ------------------------------------------
# Redefine MACRO-NAME to hold its former content plus 'SEPARATOR''STRING'.
# Note that neither SEPARATOR nor STRING are expanded; they are appended
# to MACRO-NAME as is (leaving the expansion for when MACRO-NAME is invoked).
# No SEPARATOR is output if MACRO-NAME was previously undefined (different
# than defined and empty).
#
# This macro is needed until we can rely on Autoconf 2.62, since earlier
# versions of m4sugar mistakenly expanded SEPARATOR but not STRING.
m4_define([lt_append],
[m4_define([$1],
m4_ifdef([$1], [m4_defn([$1])[$3]])[$2])])
# lt_combine(SEP, PREFIX-LIST, INFIX, SUFFIX1, [SUFFIX2...])
# ----------------------------------------------------------
# Produce a SEP delimited list of all paired combinations of elements of
# PREFIX-LIST with SUFFIX1 through SUFFIXn. Each element of the list
# has the form PREFIXmINFIXSUFFIXn.
# Needed until we can rely on m4_combine added in Autoconf 2.62.
m4_define([lt_combine],
[m4_if(m4_eval([$# > 3]), [1],
[m4_pushdef([_Lt_sep], [m4_define([_Lt_sep], m4_defn([lt_car]))])]]dnl
[[m4_foreach([_Lt_prefix], [$2],
[m4_foreach([_Lt_suffix],
]m4_dquote(m4_dquote(m4_shift(m4_shift(m4_shift($@)))))[,
[_Lt_sep([$1])[]m4_defn([_Lt_prefix])[$3]m4_defn([_Lt_suffix])])])])])
# lt_if_append_uniq(MACRO-NAME, VARNAME, [SEPARATOR], [UNIQ], [NOT-UNIQ])
# -----------------------------------------------------------------------
# Iff MACRO-NAME does not yet contain VARNAME, then append it (delimited
# by SEPARATOR if supplied) and expand UNIQ, else NOT-UNIQ.
m4_define([lt_if_append_uniq],
[m4_ifdef([$1],
[m4_if(m4_index([$3]m4_defn([$1])[$3], [$3$2$3]), [-1],
[lt_append([$1], [$2], [$3])$4],
[$5])],
[lt_append([$1], [$2], [$3])$4])])
# lt_dict_add(DICT, KEY, VALUE)
# -----------------------------
m4_define([lt_dict_add],
[m4_define([$1($2)], [$3])])
# lt_dict_add_subkey(DICT, KEY, SUBKEY, VALUE)
# --------------------------------------------
m4_define([lt_dict_add_subkey],
[m4_define([$1($2:$3)], [$4])])
# lt_dict_fetch(DICT, KEY, [SUBKEY])
# ----------------------------------
m4_define([lt_dict_fetch],
[m4_ifval([$3],
m4_ifdef([$1($2:$3)], [m4_defn([$1($2:$3)])]),
m4_ifdef([$1($2)], [m4_defn([$1($2)])]))])
# lt_if_dict_fetch(DICT, KEY, [SUBKEY], VALUE, IF-TRUE, [IF-FALSE])
# -----------------------------------------------------------------
m4_define([lt_if_dict_fetch],
[m4_if(lt_dict_fetch([$1], [$2], [$3]), [$4],
[$5],
[$6])])
# lt_dict_filter(DICT, [SUBKEY], VALUE, [SEPARATOR], KEY, [...])
# --------------------------------------------------------------
m4_define([lt_dict_filter],
[m4_if([$5], [], [],
[lt_join(m4_quote(m4_default([$4], [[, ]])),
lt_unquote(m4_split(m4_normalize(m4_foreach(_Lt_key, lt_car([m4_shiftn(4, $@)]),
[lt_if_dict_fetch([$1], _Lt_key, [$2], [$3], [_Lt_key ])])))))])[]dnl
])
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coqui_public_repos/TTS/TTS/tts/utils | coqui_public_repos/TTS/TTS/tts/utils/text/punctuation.py | import collections
import re
from enum import Enum
import six
_DEF_PUNCS = ';:,.!?¡¿—…"«»“”'
_PUNC_IDX = collections.namedtuple("_punc_index", ["punc", "position"])
class PuncPosition(Enum):
"""Enum for the punctuations positions"""
BEGIN = 0
END = 1
MIDDLE = 2
class Punctuation:
"""Handle punctuations in text.
Just strip punctuations from text or strip and restore them later.
Args:
puncs (str): The punctuations to be processed. Defaults to `_DEF_PUNCS`.
Example:
>>> punc = Punctuation()
>>> punc.strip("This is. example !")
'This is example'
>>> text_striped, punc_map = punc.strip_to_restore("This is. example !")
>>> ' '.join(text_striped)
'This is example'
>>> text_restored = punc.restore(text_striped, punc_map)
>>> text_restored[0]
'This is. example !'
"""
def __init__(self, puncs: str = _DEF_PUNCS):
self.puncs = puncs
@staticmethod
def default_puncs():
"""Return default set of punctuations."""
return _DEF_PUNCS
@property
def puncs(self):
return self._puncs
@puncs.setter
def puncs(self, value):
if not isinstance(value, six.string_types):
raise ValueError("[!] Punctuations must be of type str.")
self._puncs = "".join(list(dict.fromkeys(list(value)))) # remove duplicates without changing the oreder
self.puncs_regular_exp = re.compile(rf"(\s*[{re.escape(self._puncs)}]+\s*)+")
def strip(self, text):
"""Remove all the punctuations by replacing with `space`.
Args:
text (str): The text to be processed.
Example::
"This is. example !" -> "This is example "
"""
return re.sub(self.puncs_regular_exp, " ", text).rstrip().lstrip()
def strip_to_restore(self, text):
"""Remove punctuations from text to restore them later.
Args:
text (str): The text to be processed.
Examples ::
"This is. example !" -> [["This is", "example"], [".", "!"]]
"""
text, puncs = self._strip_to_restore(text)
return text, puncs
def _strip_to_restore(self, text):
"""Auxiliary method for Punctuation.preserve()"""
matches = list(re.finditer(self.puncs_regular_exp, text))
if not matches:
return [text], []
# the text is only punctuations
if len(matches) == 1 and matches[0].group() == text:
return [], [_PUNC_IDX(text, PuncPosition.BEGIN)]
# build a punctuation map to be used later to restore punctuations
puncs = []
for match in matches:
position = PuncPosition.MIDDLE
if match == matches[0] and text.startswith(match.group()):
position = PuncPosition.BEGIN
elif match == matches[-1] and text.endswith(match.group()):
position = PuncPosition.END
puncs.append(_PUNC_IDX(match.group(), position))
# convert str text to a List[str], each item is separated by a punctuation
splitted_text = []
for idx, punc in enumerate(puncs):
split = text.split(punc.punc)
prefix, suffix = split[0], punc.punc.join(split[1:])
text = suffix
if prefix == "":
# We don't want to insert an empty string in case of initial punctuation
continue
splitted_text.append(prefix)
# if the text does not end with a punctuation, add it to the last item
if idx == len(puncs) - 1 and len(suffix) > 0:
splitted_text.append(suffix)
return splitted_text, puncs
@classmethod
def restore(cls, text, puncs):
"""Restore punctuation in a text.
Args:
text (str): The text to be processed.
puncs (List[str]): The list of punctuations map to be used for restoring.
Examples ::
['This is', 'example'], ['.', '!'] -> "This is. example!"
"""
return cls._restore(text, puncs)
@classmethod
def _restore(cls, text, puncs): # pylint: disable=too-many-return-statements
"""Auxiliary method for Punctuation.restore()"""
if not puncs:
return text
# nothing have been phonemized, returns the puncs alone
if not text:
return ["".join(m.punc for m in puncs)]
current = puncs[0]
if current.position == PuncPosition.BEGIN:
return cls._restore([current.punc + text[0]] + text[1:], puncs[1:])
if current.position == PuncPosition.END:
return [text[0] + current.punc] + cls._restore(text[1:], puncs[1:])
# POSITION == MIDDLE
if len(text) == 1: # pragma: nocover
# a corner case where the final part of an intermediate
# mark (I) has not been phonemized
return cls._restore([text[0] + current.punc], puncs[1:])
return cls._restore([text[0] + current.punc + text[1]] + text[2:], puncs[1:])
# if __name__ == "__main__":
# punc = Punctuation()
# text = "This is. This is, example!"
# print(punc.strip(text))
# split_text, puncs = punc.strip_to_restore(text)
# print(split_text, " ---- ", puncs)
# restored_text = punc.restore(split_text, puncs)
# print(restored_text)
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