├── espnet_model_zoo
    ├── __init__.py
    ├── zenodo_upload.py
    ├── downloader.py
    └── table.csv
├── ci
    ├── test_python.sh
    └── test_model.py
├── .gitignore
├── setup.cfg
├── .github
    ├── stale.yml
    └── workflows
    │   ├── pythonpublish.yml
    │   ├── unittest.yaml
    │   └── model_test.yaml
├── test
    └── test_downloader.py
├── setup.py
├── README.md
└── LICENSE


/espnet_model_zoo/__init__.py:
--------------------------------------------------------------------------------
1 | 


--------------------------------------------------------------------------------
/ci/test_python.sh:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env bash
 2 | 
 3 | set -euo pipefail
 4 | modules="test espnet_model_zoo setup.py ci"
 5 | # black
 6 | if ! black --check ${modules}; then
 7 |     printf 'Please apply:\n    $ black %s\n' "${modules}"
 8 |     exit 1
 9 | fi
10 | # flake8
11 | flake8 --show-source $modules
12 | # pycodestyle
13 | pycodestyle -r $modules --show-source --show-pep8
14 | pytest -q
15 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | # general
 2 | *~
 3 | *.pyc
 4 | \#*\#
 5 | .\#*
 6 | *DS_Store
 7 | out.txt
 8 | espnet.egg-info/
 9 | doc/_build
10 | slurm-*.out
11 | tmp*
12 | .eggs/
13 | .hypothesis/
14 | .idea
15 | .pytest_cache/
16 | __pycache__/
17 | check_autopep8
18 | .coverage
19 | htmlcov
20 | coverage.xml*
21 | bats-core/
22 | shellcheck*
23 | check_shellcheck*
24 | test_spm.vocab
25 | test_spm.model
26 | .vscode*
27 | *.vim
28 | *.swp
29 | 
30 | # packaging related
31 | dist/
32 | *.egg-info
33 | 
34 | espnet_model_zoo/*.zip
35 | espnet_model_zoo/*.tgz
36 | espnet_model_zoo/*.tar
37 | espnet_model_zoo/*/
38 | 


--------------------------------------------------------------------------------
/setup.cfg:
--------------------------------------------------------------------------------
 1 | [aliases]
 2 | test=pytest
 3 | 
 4 | [tool:pytest]
 5 | addopts = --cov-config=.coveragerc --verbose --durations=0 --cov=espnet_model_zoo
 6 | testpaths = test
 7 | 
 8 | 
 9 | # [H238] old style class declaration, use new style (inherit from `object`)
10 | # [H102 H103] Newly contributed Source Code should be licensed under the Apache 2.0 license. All source files should have the following header::
11 | # [W504] Line break occurred after a binary operator
12 | 
13 | # Black says "W503, E203 is incompatible with PEP 8"
14 | # [W503] Line break occurred before a binary operator 
15 | # [E203] whitespace before :
16 | 
17 | [flake8]
18 | ignore = H102,H103,W503,H238,E203
19 | max-line-length = 88
20 | [pycodestyle]
21 | ignore = H102,H103,W503,H238,E203
22 | max-line-length = 88
23 | 


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/.github/stale.yml:
--------------------------------------------------------------------------------
 1 | # Number of days of inactivity before an issue becomes stale
 2 | daysUntilStale: 45
 3 | # Number of days of inactivity before a stale issue is closed
 4 | daysUntilClose: 30
 5 | # Issues with these labels will never be considered stale
 6 | exemptLabels:
 7 |   - Roadmap
 8 |   - Bug
 9 | # Label to use when marking an issue as stale
10 | staleLabel: Stale
11 | # Comment to post when marking an issue as stale. Set to `false` to disable
12 | markComment: >
13 |   This issue has been automatically marked as stale because it has not had
14 |   recent activity. It will be closed if no further activity occurs. Thank you
15 |   for your contributions.
16 | unmarkComment: false
17 | # Comment to post when closing a stale issue. Set to `false` to disable
18 | closeComment: >
19 |   This issue is closed. Please re-open if needed.
20 | 


--------------------------------------------------------------------------------
/.github/workflows/pythonpublish.yml:
--------------------------------------------------------------------------------
 1 | # This workflows will upload a Python Package using Twine when a release is created
 2 | # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 3 | 
 4 | name: Upload Python Package
 5 | 
 6 | on:
 7 |   push:
 8 |     tags:
 9 |       - 'v*'
10 | 
11 | jobs:
12 |   deploy:
13 | 
14 |     runs-on: ubuntu-latest
15 | 
16 |     steps:
17 |     - uses: actions/checkout@v2
18 |     - name: Set up Python
19 |       uses: actions/setup-python@v1
20 |       with:
21 |         python-version: '3.8'
22 |     - name: Install dependencies
23 |       run: |
24 |         python -m pip install --upgrade pip
25 |         pip install setuptools wheel twine
26 |     - name: Build and publish
27 |       env:
28 |         TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
29 |         TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
30 |       run: |
31 |         python setup.py sdist bdist_wheel
32 |         twine upload dist/*
33 | 


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/.github/workflows/unittest.yaml:
--------------------------------------------------------------------------------
 1 | name: Unitest
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 |   pull_request:
 8 |     branches:
 9 |       - master
10 | 
11 | jobs:
12 |   linter_and_test:
13 |     runs-on: ${{ matrix.os }}
14 |     strategy:
15 |       max-parallel: 20
16 |       matrix:
17 |         # os: [ubuntu-16.04, ubuntu-18.04]
18 |         os: [ubuntu-20.04]
19 |         python-version: [3.9]
20 |         espnet-version: ["espnet", "git+https://github.com/espnet/espnet.git"]
21 |     steps:
22 |       - uses: actions/checkout@master
23 |       - uses: actions/cache@v1
24 |         with:
25 |           path: ~/.cache/pip
26 |           key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ matrix.espnet-version }}-${{ hashFiles('**/setup.py') }}
27 |       - uses: actions/setup-python@v1
28 |         with:
29 |           python-version: ${{ matrix.python-version }}
30 |           architecture: 'x64'
31 |       - name: install dependencies
32 |         run: |
33 |           sudo apt-get update -qq
34 |           sudo apt-get install -qq -y cmake g++-7 libsndfile1-dev bc
35 |       - name: install
36 |         env:
37 |           ESPNET_VERSION: ${{ matrix.espnet-version }}
38 |         run: |
39 |           pip install -U pip setuptools wheel
40 |           pip install "${ESPNET_VERSION}"
41 |           pip install ".[test]"
42 |       - name: test python
43 |         run: |
44 |           ./ci/test_python.sh
45 |       - name: codecov
46 |         run: |
47 |           bash <(curl -s https://codecov.io/bash)
48 | 


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/.github/workflows/model_test.yaml:
--------------------------------------------------------------------------------
 1 | name: Model test
 2 | 
 3 | on:
 4 |   push:
 5 |     branches:
 6 |       - master
 7 |   pull_request:
 8 |     branches:
 9 |       - master
10 | 
11 | jobs:
12 |   test:
13 |     runs-on: ${{ matrix.os }}
14 |     strategy:
15 |       max-parallel: 20
16 |       matrix:
17 |         # os: [ubuntu-16.04, ubuntu-18.04]
18 |         os: [ubuntu-18.04]
19 |         python-version: [3.8]
20 |         # espnet-version: ["espnet", "git+https://github.com/espnet/espnet.git"]
21 |         espnet-version: ["git+https://github.com/espnet/espnet.git"]
22 |     steps:
23 |       - uses: actions/checkout@master
24 |       - uses: actions/cache@v1
25 |         with:
26 |           path: ~/.cache/pip
27 |           key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ matrix.espnet-version }}-${{ hashFiles('**/setup.py') }}
28 |       - uses: actions/setup-python@v1
29 |         with:
30 |           python-version: ${{ matrix.python-version }}
31 |           architecture: 'x64'
32 |       - name: install dependencies
33 |         run: |
34 |           sudo apt-get update -qq
35 |           sudo apt-get install -qq -y cmake g++-7 libsndfile1-dev bc
36 |       - name: install espnet
37 |         env:
38 |           ESPNET_VERSION: ${{ matrix.espnet-version }}
39 |         run: |
40 |           pip install -U pip setuptools wheel
41 |           pip install "${ESPNET_VERSION}"
42 |           pip install torch
43 |           pip install ".[test]"
44 |       - name: install pyopenjtalk
45 |         run: |
46 |           mkdir pyopenjtalk
47 |           cd pyopenjtalk
48 |           wget https://raw.githubusercontent.com/espnet/espnet/master/tools/installers/install_pyopenjtalk.sh
49 |           chmod +x install_pyopenjtalk.sh
50 |           ./install_pyopenjtalk.sh
51 |       - name: test model
52 |         run: |
53 |           export LD_LIBRARY_PATH=$(pwd)/pyopenjtalk/lib:${LD_LIBRARY_PATH}
54 |           pytest ./ci/test_model.py
55 | 


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/test/test_downloader.py:
--------------------------------------------------------------------------------
 1 | from pathlib import Path
 2 | import pytest
 3 | 
 4 | from espnet_model_zoo.downloader import cmd_download
 5 | from espnet_model_zoo.downloader import cmd_query
 6 | from espnet_model_zoo.downloader import download
 7 | from espnet_model_zoo.downloader import ModelDownloader
 8 | 
 9 | 
10 | def test_download():
11 |     download("http://example.com", "index.html")
12 | 
13 | 
14 | def test_update_model_table(tmp_path):
15 |     d = ModelDownloader(tmp_path)
16 |     d.update_model_table()
17 | 
18 | 
19 | def test_get_data_frame():
20 |     d = ModelDownloader()
21 |     d.get_data_frame()
22 | 
23 | 
24 | def test_new_cachedir(tmp_path):
25 |     ModelDownloader(tmp_path)
26 | 
27 | 
28 | def test_download_and_unpack_names_with_condition():
29 |     d = ModelDownloader()
30 |     d.query("name", task="asr")
31 | 
32 | 
33 | def test_get_model_names_and_urls():
34 |     d = ModelDownloader()
35 |     d.query(["name", "url"], task="asr")
36 | 
37 | 
38 | def test_get_model_names_non_matching():
39 |     d = ModelDownloader()
40 |     assert d.query("name", task="dummy") == []
41 | 
42 | 
43 | def test_download_and_unpack_with_url():
44 |     d = ModelDownloader()
45 |     d.download_and_unpack("https://zenodo.org/record/3951842/files/test.zip?download=1")
46 | 
47 | 
48 | def test_download_and_unpack_with_name():
49 |     d = ModelDownloader()
50 |     d.download_and_unpack("test")
51 | 
52 | 
53 | def test_download_and_unpack_no_inputting():
54 |     d = ModelDownloader()
55 |     with pytest.raises(TypeError):
56 |         d.download_and_unpack()
57 | 
58 | 
59 | def test_download_and_unpack_non_matching():
60 |     d = ModelDownloader()
61 |     with pytest.raises(RuntimeError):
62 |         d.download_and_unpack(task="dummy")
63 | 
64 | 
65 | def test_download_and_unpack_local_file():
66 |     d = ModelDownloader()
67 |     path = d.download("test")
68 |     d.download_and_unpack(path)
69 | 
70 | 
71 | def test_download_and_clean_cache():
72 |     d = ModelDownloader()
73 |     d.download_and_unpack("test")
74 |     p = d.download("test")
75 |     d.clean_cache("test")
76 |     assert not Path(p).exists()
77 | 
78 | 
79 | def test_cmd_download():
80 |     cmd_download(["test"])
81 | 
82 | 
83 | def test_query():
84 |     cmd_query([])
85 | 


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/ci/test_model.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import shutil
 3 | 
 4 | import numpy as np
 5 | 
 6 | from espnet2.bin.asr_inference import Speech2Text
 7 | from espnet2.bin.asr_inference_streaming import Speech2TextStreaming
 8 | from espnet2.bin.tts_inference import Text2Speech
 9 | from espnet_model_zoo.downloader import ModelDownloader
10 | 
11 | 
12 | def _asr(model_name):
13 |     d = ModelDownloader("downloads")
14 |     speech2text = Speech2Text(**d.download_and_unpack(model_name, quiet=True))
15 |     speech = np.zeros((10000,), dtype=np.float32)
16 |     nbests = speech2text(speech)
17 |     text, *_ = nbests[0]
18 |     assert isinstance(text, str)
19 | 
20 | 
21 | def _asr_streaming(model_name):
22 |     d = ModelDownloader("downloads")
23 |     speech2text = Speech2TextStreaming(**d.download_and_unpack(model_name, quiet=True))
24 |     speech = np.zeros((10000,), dtype=np.float32)
25 |     nbests = speech2text(speech)
26 |     text, *_ = nbests[0]
27 |     assert isinstance(text, str)
28 | 
29 | 
30 | def _tts(model_name):
31 |     d = ModelDownloader("downloads")
32 |     text2speech = Text2Speech(**d.download_and_unpack(model_name, quiet=True))
33 |     inputs = {"text": "foo"}
34 |     if text2speech.use_speech:
35 |         inputs["speech"] = np.zeros((10000,), dtype=np.float32)
36 |     if text2speech.use_spembs:
37 |         inputs["spembs"] = np.zeros((text2speech.tts.spk_embed_dim,), dtype=np.float32)
38 |     if text2speech.use_sids:
39 |         inputs["sids"] = np.ones((1,), dtype=np.int64)
40 |     if text2speech.use_lids:
41 |         inputs["lids"] = np.ones((1,), dtype=np.int64)
42 |     text2speech(**inputs)
43 | 
44 | 
45 | def test_model():
46 |     d = ModelDownloader()
47 |     tasks = ["asr", "asr_stream", "tts"]
48 | 
49 |     for task in tasks:
50 |         for corpus in list(set(d.query("corpus", task=task))):
51 |             for model_name in d.query(task=task, corpus=corpus):
52 |                 if d.query("valid", name=model_name)[0] == "false":
53 |                     continue
54 |                 print(f"#### Test {model_name} ####")
55 | 
56 |                 if task == "asr":
57 |                     _asr(model_name)
58 |                 elif task == "asr_stream":
59 |                     _asr_streaming(model_name)
60 |                 elif task == "tts":
61 |                     _tts(model_name)
62 |                 else:
63 |                     raise NotImplementedError(f"task={task}")
64 | 
65 |             # NOTE(kan-bayashi): remove and recreate cache dir to reduce the disk usage.
66 |             shutil.rmtree("downloads")
67 |             os.makedirs("downloads")
68 | 


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python3
 2 | import os
 3 | from setuptools import find_packages
 4 | from setuptools import setup
 5 | 
 6 | 
 7 | requirements = {
 8 |     "install": [
 9 |         "pandas",
10 |         "requests",
11 |         "tqdm",
12 |         "numpy",
13 |         "espnet",
14 |         "huggingface_hub",
15 |         "filelock",
16 |         "torchaudio",
17 |     ],
18 |     "setup": ["pytest-runner"],
19 |     "test": [
20 |         "pytest>=3.3.0",
21 |         "pytest-pythonpath>=0.7.3",
22 |         "pytest-cov>=2.7.1",
23 |         "hacking>=1.1.0",
24 |         "mock>=2.0.0",
25 |         "pycodestyle",
26 |         "flake8>=3.7.8",
27 |         "black",
28 |     ],
29 | }
30 | 
31 | install_requires = requirements["install"]
32 | setup_requires = requirements["setup"]
33 | tests_require = requirements["test"]
34 | extras_require = {
35 |     k: v for k, v in requirements.items() if k not in ["install", "setup"]
36 | }
37 | 
38 | dirname = os.path.dirname(__file__)
39 | setup(
40 |     name="espnet_model_zoo",
41 |     version="0.1.8",
42 |     url="http://github.com/espnet/espnet_model_zoo",
43 |     description="ESPnet Model Zoo",
44 |     long_description=open(os.path.join(dirname, "README.md"), encoding="utf-8").read(),
45 |     long_description_content_type="text/markdown",
46 |     license="Apache Software License",
47 |     packages=find_packages(include=["espnet_model_zoo*"]),
48 |     package_data={"espnet_model_zoo": ["table.csv"]},
49 |     entry_points={
50 |         "console_scripts": [
51 |             "espnet_model_zoo_upload = espnet_model_zoo.zenodo_upload:main",
52 |             "espnet_model_zoo_download = espnet_model_zoo.downloader:cmd_download",
53 |             "espnet_model_zoo_query = espnet_model_zoo.downloader:cmd_query",
54 |         ],
55 |     },
56 |     install_requires=install_requires,
57 |     setup_requires=setup_requires,
58 |     tests_require=tests_require,
59 |     extras_require=extras_require,
60 |     python_requires=">=3.6.0",
61 |     classifiers=[
62 |         "Programming Language :: Python",
63 |         "Programming Language :: Python :: 3",
64 |         "Programming Language :: Python :: 3.6",
65 |         "Programming Language :: Python :: 3.7",
66 |         "Programming Language :: Python :: 3.8",
67 |         "Programming Language :: Python :: 3.9",
68 |         "Development Status :: 5 - Production/Stable",
69 |         "Intended Audience :: Science/Research",
70 |         "Operating System :: POSIX :: Linux",
71 |         "License :: OSI Approved :: Apache Software License",
72 |         "Topic :: Software Development :: Libraries :: Python Modules",
73 |     ],
74 | )
75 | 


--------------------------------------------------------------------------------
/espnet_model_zoo/zenodo_upload.py:
--------------------------------------------------------------------------------
  1 | """Upload files to Zenodo.
  2 | 
  3 | You need to do as follows in order to access zenodo:
  4 | 
  5 | 1. Sign up to Zenodo: https://zenodo.org/
  6 | 2. Create access_token: https://zenodo.org/account/settings/applications/tokens/new/
  7 | """
  8 | 
  9 | import argparse
 10 | from datetime import datetime
 11 | from getpass import getpass
 12 | import json
 13 | import os
 14 | from pathlib import Path
 15 | import requests
 16 | from typing import Collection
 17 | from typing import Union
 18 | 
 19 | from espnet2.utils import config_argparse
 20 | from espnet2.utils.types import str2bool
 21 | 
 22 | 
 23 | class Zenodo:
 24 |     """Helper class to invoke Zenodo API
 25 | 
 26 |     REST API of zenodo: https://developers.zenodo.org/
 27 | 
 28 |     """
 29 | 
 30 |     def __init__(self, access_token: str, use_sandbox: bool = False):
 31 |         if use_sandbox:
 32 |             self.zenodo_url = "https://sandbox.zenodo.org"
 33 |         else:
 34 |             self.zenodo_url = "https://zenodo.org"
 35 | 
 36 |         self.params = {"access_token": access_token}
 37 |         self.headers = {"Content-Type": "application/json"}
 38 | 
 39 |     def create_deposition(self) -> requests.models.Response:
 40 |         r = requests.post(
 41 |             f"{self.zenodo_url}/api/deposit/depositions",
 42 |             params=self.params,
 43 |             json={},
 44 |             headers=self.headers,
 45 |         )
 46 |         if r.status_code != 201:
 47 |             raise RuntimeError(r.json()["message"])
 48 |         return r
 49 | 
 50 |     def get_deposition(self, r: Union[requests.models.Response, int]):
 51 |         if isinstance(r, requests.models.Response):
 52 |             deposition_id = r.json()["id"]
 53 |         else:
 54 |             deposition_id = r
 55 |         r = requests.get(
 56 |             f"{self.zenodo_url}/api/deposit/depositions/{deposition_id}",
 57 |             params=self.params,
 58 |             json={},
 59 |             headers=self.headers,
 60 |         )
 61 |         if r.status_code != 200:
 62 |             raise RuntimeError(r.json()["message"])
 63 |         return r
 64 | 
 65 |     def update_metadata(
 66 |         self, r: Union[requests.models.Response, int], data
 67 |     ) -> requests.models.Response:
 68 |         if isinstance(r, requests.models.Response):
 69 |             deposition_id = r.json()["id"]
 70 |         else:
 71 |             deposition_id = r
 72 | 
 73 |         r = requests.put(
 74 |             f"{self.zenodo_url}/api/deposit/depositions/{deposition_id}",
 75 |             params=self.params,
 76 |             data=json.dumps(data),
 77 |             headers=self.headers,
 78 |         )
 79 |         if r.status_code != 200:
 80 |             raise RuntimeError(r.json()["message"])
 81 |         return r
 82 | 
 83 |     def upload_file(
 84 |         self, r: Union[requests.models.Response, int], filename: Union[Path, str]
 85 |     ) -> requests.models.Response:
 86 |         if isinstance(r, int):
 87 |             r = requests.get(
 88 |                 f"{self.zenodo_url}/api/deposit/depositions/{r}", headers=self.headers
 89 |             )
 90 | 
 91 |         bucket_url = r.json()["links"]["bucket"]
 92 |         name = Path(filename).name
 93 |         with open(filename, "rb") as fp:
 94 |             r = requests.put(
 95 |                 f"{bucket_url}/{name}",
 96 |                 data=fp,
 97 |                 # No headers included since it's a raw byte request
 98 |                 params=self.params,
 99 |             )
100 |             if r.status_code != 200:
101 |                 raise RuntimeError(r.json()["message"])
102 |         return r
103 | 
104 |     def publish(
105 |         self, r: Union[requests.models.Response, int]
106 |     ) -> requests.models.Response:
107 |         if isinstance(r, requests.models.Response):
108 |             deposition_id = r.json()["id"]
109 |         else:
110 |             deposition_id = r
111 | 
112 |         r = requests.post(
113 |             f"{self.zenodo_url}/api/deposit/depositions/"
114 |             f"{deposition_id}/actions/publish",
115 |             params=self.params,
116 |         )
117 |         if r.status_code != 202:
118 |             raise RuntimeError(r.json()["message"])
119 |         return r
120 | 
121 | 
122 | def upload(
123 |     access_token: str,
124 |     title: str,
125 |     creator_name: str,
126 |     description: str = "",
127 |     files: Collection[Union[Path, str]] = (),
128 |     affiliation: str = None,
129 |     orcid: str = None,
130 |     gnd: str = None,
131 |     upload_type: str = "other",
132 |     license: str = "CC-BY-4.0",
133 |     keywords: Collection[str] = (),
134 |     related_identifiers: Collection[dict] = (),
135 |     community_identifer: str = None,
136 |     use_sandbox: bool = True,
137 |     publish: bool = False,
138 | ):
139 |     zenodo = Zenodo(access_token, use_sandbox=use_sandbox)
140 |     r = zenodo.create_deposition()
141 | 
142 |     # Update metatdata using old API
143 |     creator = {"name": creator_name}
144 |     if affiliation is not None:
145 |         creator["affiliation"] = affiliation
146 |     if orcid is not None:
147 |         creator["orcid"] = orcid
148 |     if gnd is not None:
149 |         creator["gnd"] = gnd
150 |     data = {
151 |         "metadata": {
152 |             "upload_type": upload_type,
153 |             "publication_date": datetime.now().strftime("%Y-%m-%d"),
154 |             "title": title,
155 |             "description": description,
156 |             "creators": [creator],
157 |             "license": license,
158 |             "keywords": list(keywords),
159 |             "related_identifiers": list(related_identifiers),
160 |         }
161 |     }
162 |     if community_identifer is not None:
163 |         data["communities"] = [{"identifier": community_identifer}]
164 |     zenodo.update_metadata(r, data)
165 | 
166 |     # Upload files using new API
167 |     for f in files:
168 |         # Check file existing
169 |         if not Path(f).exists():
170 |             raise FileNotFoundError(f"{f} is not found")
171 |     for f in files:
172 |         print(f"Now uploading {f}...")
173 |         zenodo.upload_file(r, f)
174 | 
175 |     if publish:
176 |         r = zenodo.publish(r)
177 |         url = r.json()["links"]["latest_html"]
178 |         print(f"Successfully published. Go to {url}")
179 |     else:
180 |         url = r.json()["links"]["html"]
181 |         print(f"Successfully uploaded, but not published yet. Go to {url}")
182 | 
183 | 
184 | def upload_espnet_model(
185 |     access_token: str,
186 |     title: str,
187 |     creator_name: str,
188 |     file: Collection[Union[Path, str]] = (),
189 |     description: str = "",
190 |     description_file: str = None,
191 |     affiliation: str = None,
192 |     license: str = "CC-BY-4.0",
193 |     orcid: str = None,
194 |     gnd: str = None,
195 |     use_sandbox: bool = False,
196 |     publish: bool = False,
197 | ):
198 |     if description_file is not None:
199 |         with open(description_file, "r", encoding="utf-8") as f:
200 |             description = f.read()
201 | 
202 |     upload(
203 |         access_token=access_token,
204 |         title=title,
205 |         description=description,
206 |         creator_name=creator_name,
207 |         files=file,
208 |         keywords=[
209 |             "ESPnet",
210 |             "deep-learning",
211 |             "python",
212 |             "pytorch",
213 |             "speech-recognition",
214 |             "speech-synthesis",
215 |             "speech-translation",
216 |             "machine-translation",
217 |         ],
218 |         related_identifiers=[
219 |             {
220 |                 "relation": "isSupplementTo",
221 |                 "identifier": "https://github.com/espnet/espnet",
222 |             }
223 |         ],
224 |         affiliation=affiliation,
225 |         license=license,
226 |         orcid=orcid,
227 |         gnd=gnd,
228 |         use_sandbox=use_sandbox,
229 |         publish=publish,
230 |     )
231 | 
232 | 
233 | def get_parser():
234 |     parser = config_argparse.ArgumentParser(
235 |         description="Upload files to Zenodo",
236 |         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
237 |     )
238 |     parser.add_argument(
239 |         "--access_token",
240 |         help="Get your access_token from "
241 |         "https://zenodo.org/account/settings/applications/ or  "
242 |         "https://sandbox.zenodo.org/account/settings/applications/ . "
243 |         "You can also give it from an environment variable 'ACCESS_TOKEN'",
244 |     )
245 |     parser.add_argument(
246 |         "--title",
247 |         required=True,
248 |         help="e.g. ESPnet pretrained model, MT, "
249 |         "Fisher-CallHome Spanish (Es->En), Transformer",
250 |     )
251 |     parser.add_argument("--creator_name", required=True, help="Your name")
252 |     parser.add_argument("--file", nargs="+", required=True)
253 |     group = parser.add_mutually_exclusive_group(required=True)
254 |     group.add_argument("--description", help="Give the description")
255 |     group.add_argument("--description_file", help="Give the description from file")
256 |     parser.add_argument(
257 |         "--use_sandbox",
258 |         type=str2bool,
259 |         default=False,
260 |         help="Use zenodo sandbox for testing",
261 |     )
262 |     parser.add_argument(
263 |         "--publish", type=str2bool, default=False, help="Publish after uploading"
264 |     )
265 |     parser.add_argument("--license", default="CC-BY-4.0")
266 |     parser.add_argument("--affiliation")
267 |     parser.add_argument("--orcid")
268 |     parser.add_argument("--gnd")
269 |     return parser
270 | 
271 | 
272 | def main(cmd=None):
273 |     # espnet_model_zoo_upload
274 | 
275 |     parser = get_parser()
276 |     args = parser.parse_args(cmd)
277 | 
278 |     # If --access_token is not given, get from "ACCESS_TOKEN"
279 |     if args.access_token is None:
280 |         args.access_token = os.environ.get("ACCESS_TOKEN")
281 | 
282 |     # If neither is given, input from stdin
283 |     if args.access_token is None:
284 |         if args.use_sandbox:
285 |             zenodo_url = "https://sandbox.zenodo.org"
286 |         else:
287 |             zenodo_url = "https://zenodo.org"
288 |         args.access_token = getpass(
289 |             "Input Zenodo API Token\n"
290 |             "(You can create it from "
291 |             f"{zenodo_url}/account/settings/applications/tokens/new/): "
292 |         )
293 | 
294 |     kwargs = vars(args)
295 |     kwargs.pop("config")
296 |     upload_espnet_model(**kwargs)
297 | 
298 | 
299 | if __name__ == "__main__":
300 |     main()
301 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # ESPnet Model Zoo
  2 | 
  3 | [![PyPI version](https://badge.fury.io/py/espnet-model-zoo.svg)](https://badge.fury.io/py/espnet-model-zoo)
  4 | [![Python Versions](https://img.shields.io/pypi/pyversions/espnet_model_zoo.svg)](https://pypi.org/project/espnet_model_zoo/)
  5 | [![Downloads](https://pepy.tech/badge/espnet_model_zoo)](https://pepy.tech/project/espnet_model_zoo)
  6 | [![GitHub license](https://img.shields.io/github/license/espnet/espnet_model_zoo.svg)](https://github.com/espnet/espnet_model_zoo)
  7 | [![Unitest](https://github.com/espnet/espnet_model_zoo/workflows/Unitest/badge.svg)](https://github.com/espnet/espnet_model_zoo/actions?query=workflow%3AUnitest)
  8 | [![Model test](https://github.com/espnet/espnet_model_zoo/workflows/Model%20test/badge.svg)](https://github.com/espnet/espnet_model_zoo/actions?query=workflow%3A%22Model+test%22)
  9 | [![codecov](https://codecov.io/gh/espnet/espnet_model_zoo/branch/master/graph/badge.svg)](https://codecov.io/gh/espnet/espnet_model_zoo)
 10 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 11 | 
 12 | Utilities managing the pretrained models created by [ESPnet](https://github.com/espnet/espnet). This function is inspired by the [Asteroid pretrained model function](https://github.com/mpariente/asteroid/blob/master/docs/source/readmes/pretrained_models.md).
 13 | 
 14 | - **From version 0.1.0, the huggingface models can be also used**: https://huggingface.co/models?filter=espnet
 15 | - Zenodo community: https://zenodo.org/communities/espnet/
 16 | - Registered models: [table.csv](espnet_model_zoo/table.csv)
 17 | 
 18 | ## Install
 19 | 
 20 | ```
 21 | pip install torch
 22 | pip install espnet_model_zoo
 23 | ```
 24 | 
 25 | ## Python API for inference
 26 | `model_name` in the following section should be `huggingface_id` or one of the tags in the [table.csv](espnet_model_zoo/table.csv).
 27 | Or you can directly provide zenodo URL (e.g., `https://zenodo.org/record/xxxxxxx/files/hogehoge.zip?download=1`).
 28 | 
 29 | ### ASR
 30 | 
 31 | ```python
 32 | import soundfile
 33 | from espnet2.bin.asr_inference import Speech2Text
 34 | speech2text = Speech2Text.from_pretrained(
 35 |     "model_name",
 36 |     # Decoding parameters are not included in the model file
 37 |     maxlenratio=0.0,
 38 |     minlenratio=0.0,
 39 |     beam_size=20,
 40 |     ctc_weight=0.3,
 41 |     lm_weight=0.5,
 42 |     penalty=0.0,
 43 |     nbest=1
 44 | )
 45 | # Confirm the sampling rate is equal to that of the training corpus.
 46 | # If not, you need to resample the audio data before inputting to speech2text
 47 | speech, rate = soundfile.read("speech.wav")
 48 | nbests = speech2text(speech)
 49 | 
 50 | text, *_ = nbests[0]
 51 | print(text)
 52 | ```
 53 | 
 54 | ### TTS
 55 | 
 56 | ```python
 57 | import soundfile
 58 | from espnet2.bin.tts_inference import Text2Speech
 59 | text2speech = Text2Speech.from_pretrained("model_name")
 60 | speech = text2speech("foobar")["wav"]
 61 | soundfile.write("out.wav", speech.numpy(), text2speech.fs, "PCM_16")
 62 | ```
 63 | 
 64 | ### Speech separation
 65 | 
 66 | ```python
 67 | import soundfile
 68 | from espnet2.bin.enh_inference import SeparateSpeech
 69 | separate_speech = SeparateSpeech.from_pretrained(
 70 |     "model_name",
 71 |     # for segment-wise process on long speech
 72 |     segment_size=2.4,
 73 |     hop_size=0.8,
 74 |     normalize_segment_scale=False,
 75 |     show_progressbar=True,
 76 |     ref_channel=None,
 77 |     normalize_output_wav=True,
 78 | )
 79 | # Confirm the sampling rate is equal to that of the training corpus.
 80 | # If not, you need to resample the audio data before inputting to speech2text
 81 | speech, rate = soundfile.read("long_speech.wav")
 82 | waves = separate_speech(speech[None, ...], fs=rate)
 83 | ```
 84 | 
 85 | This API allows processing both short audio samples and long audio samples. For long audio samples, you can set the value of arguments segment_size, hop_size (optionally normalize_segment_scale and show_progressbar) to perform segment-wise speech enhancement/separation on the input speech. Note that the segment-wise processing is disabled by default.
 86 | 
 87 | 
 88 | <details><summary>For old ESPnet (<=10.1) </summary><div>
 89 | 
 90 | ### ASR
 91 | 
 92 | ```python
 93 | import soundfile
 94 | from espnet_model_zoo.downloader import ModelDownloader
 95 | from espnet2.bin.asr_inference import Speech2Text
 96 | d = ModelDownloader()
 97 | speech2text = Speech2Text(
 98 |     **d.download_and_unpack("model_name"),
 99 |     # Decoding parameters are not included in the model file
100 |     maxlenratio=0.0,
101 |     minlenratio=0.0,
102 |     beam_size=20,
103 |     ctc_weight=0.3,
104 |     lm_weight=0.5,
105 |     penalty=0.0,
106 |     nbest=1
107 | )
108 | ```
109 | 
110 | ### TTS
111 | 
112 | ```python
113 | import soundfile
114 | from espnet_model_zoo.downloader import ModelDownloader
115 | from espnet2.bin.tts_inference import Text2Speech
116 | d = ModelDownloader()
117 | text2speech = Text2Speech(**d.download_and_unpack("model_name"))
118 | ```
119 | 
120 | ### Speech separation
121 | 
122 | ```python
123 | import soundfile
124 | from espnet_model_zoo.downloader import ModelDownloader
125 | from espnet2.bin.enh_inference import SeparateSpeech
126 | d = ModelDownloader()
127 | separate_speech = SeparateSpeech(
128 |     **d.download_and_unpack("model_name"),
129 |     # for segment-wise process on long speech
130 |     segment_size=2.4,
131 |     hop_size=0.8,
132 |     normalize_segment_scale=False,
133 |     show_progressbar=True,
134 |     ref_channel=None,
135 |     normalize_output_wav=True,
136 | )
137 | ```
138 | </div></details>
139 | 
140 | 
141 | ## Instruction for ModelDownloader
142 | 
143 | ```python
144 | from espnet_model_zoo.downloader import ModelDownloader
145 | d = ModelDownloader("~/.cache/espnet")  # Specify cachedir
146 | d = ModelDownloader()  # <module_dir> is used as cachedir by default
147 | ```
148 | 
149 | To obtain a model, you need to give a `huggingface_id`model` or a tag , which is listed in [table.csv](espnet_model_zoo/table.csv).
150 | 
151 | ```python
152 | >>> d.download_and_unpack("kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best")
153 | {"asr_train_config": <config path>, "asr_model_file": <model path>, ...}
154 | ```
155 | 
156 | You can specify the revision if it's huggingface_id giving with `@`:
157 | 
158 | ```python
159 | >>> d.download_and_unpack("kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best@<revision>")
160 | {"asr_train_config": <config path>, "asr_model_file": <model path>, ...}
161 | ```
162 | 
163 | Note that if the model already exists, you can skip downloading and unpacking.
164 | 
165 | You can also get a model with certain conditions.
166 | 
167 | ```python
168 | d.download_and_unpack(task="asr", corpus="wsj")
169 | ```
170 | 
171 | If multiple models are found with the condition, the last model is selected.
172 | You can also specify the condition using "version" option.
173 | 
174 | ```python
175 | d.download_and_unpack(task="asr", corpus="wsj", version=-1)  # Get the last model
176 | d.download_and_unpack(task="asr", corpus="wsj", version=-2)  # Get previous model
177 | ```
178 | 
179 | You can also obtain it from the URL directly.
180 | 
181 | ```python
182 | d.download_and_unpack("https://zenodo.org/record/...")
183 | ```
184 | 
185 | If you need to use a local model file using this API, you can also give it.
186 | 
187 | ```python
188 | d.download_and_unpack("./some/where/model.zip")
189 | ```
190 | 
191 | In this case, the contents are also expanded in the cache directory,
192 | but the model is identified by the file path,
193 | so if you move the model to somewhere and unpack again,
194 | it's treated as another model,
195 | thus the contents are expanded again at another place.
196 | 
197 | ## Query model names
198 | 
199 | You can view the model names from our Zenodo community, https://zenodo.org/communities/espnet/,
200 | or using `query()`.  All information are written in [table.csv](espnet_model_zoo/table.csv).
201 | 
202 | ```python
203 | d.query("name")
204 | ```
205 | 
206 | You can also show them with specifying certain conditions.
207 | 
208 | ```python
209 | d.query("name", task="asr")
210 | ```
211 | 
212 | ## Command line tools
213 | 
214 | - `espnet_model_zoo_query`
215 | 
216 |     ```sh
217 |     # Query model name
218 |     espnet_model_zoo_query task=asr corpus=wsj
219 |     # Show all model name
220 |     espnet_model_zoo_query
221 |     # Query the other key
222 |     espnet_model_zoo_query --key url task=asr corpus=wsj
223 |     ```
224 | - `espnet_model_zoo_download`
225 | 
226 |     ```sh
227 |     espnet_model_zoo_download <model_name>  # Print the path of the downloaded file
228 |     espnet_model_zoo_download --unpack true <model_name>   # Print the path of unpacked files
229 |     ```
230 | - `espnet_model_zoo_upload`
231 | 
232 |     ```sh
233 |     export ACCESS_TOKEN=<access_token>
234 |     espnet_zenodo_upload \
235 |         --file <packed_model> \
236 |         --title <title> \
237 |         --description <description> \
238 |         --creator_name <your-git-account>
239 |     ```
240 | 
241 | ## Use pretrained model in ESPnet recipe
242 | 
243 | ```sh
244 | # e.g. ASR WSJ task
245 | git clone https://github.com/espnet/espnet
246 | pip install -e .
247 | cd egs2/wsj/asr1
248 | ./run.sh --skip_data_prep false --skip_train true --download_model kamo-naoyuki/wsj
249 | ```
250 | 
251 | ## Register your model
252 | 
253 | ### Huggingface
254 | 1. Upload your model using huggingface API
255 | 
256 |     1. (if you do not have an HF hub account) Go to https://huggingface.co and create an HF account by clicking a `sign up` button below.
257 |     ![image](https://user-images.githubusercontent.com/11741550/147585941-af1a7e88-934e-4e24-b30e-4b120dbc023a.png)
258 |     2. From a [new model](https://huggingface.co/new) link in the profile, create a new model repository. Please include a recipe name (e.g., aidatatang_200zh) and model info (e.g., conformer) in the repository name
259 |     ![image](https://user-images.githubusercontent.com/11741550/147586093-51c98c53-6d23-45a0-b359-14a4489cc970.png)
260 |     3. In the espnet recipe, execute the following command:
261 |     ```
262 |     ./run.sh --stage 15 --skip_upload_hf false --hf_repo sw005320/aidatatang_200zh_conformer
263 |     ```
264 |     4. Please follow the instruction (e.g., type the HF Username/Password)
265 |     5. If it works successfully, you can get the following messages
266 |     ![image](https://user-images.githubusercontent.com/11741550/147586699-a3bb5a49-8b59-417d-b376-4d1ec270fb71.png)
267 | 
268 | 1. Create a Pull Request to modify [table.csv](espnet_model_zoo/table.csv)
269 | 
270 |     The model can be registered in [table.csv](https://github.com/espnet/espnet_model_zoo/blob/master/espnet_model_zoo/table.csv).
271 |     Then, the model will be tested in the CI.
272 |     Note that, unlike the zenodo case, you don't need to add the URL because huggingface_id itself can specify the model file, so please fill the value as `https://huggingface.co/`.
273 | 
274 |     e.g. `table.csv`
275 | 
276 |     ```
277 |     ...
278 |     aidatatang_200zh,asr,sw005320/aidatatang_200zh_conformer,https://huggingface.co/,16000,zh,,,,,true
279 |     ```
280 | 1. (Administrator does) Increment the third version number of [setup.py](setup.py), e.g. 0.0.3 -> 0.0.4
281 | 1. (Administrator does) Release new version
282 | 
283 | 
284 | ### Zenodo (Obsolete)
285 | 
286 | 1. Upload your model to Zenodo
287 | 
288 |     You need to [signup to Zenodo](https://zenodo.org/) and [create an access token](https://zenodo.org/account/settings/applications/tokens/new/) to upload models.
289 |     You can upload your own model by using `espnet_model_zoo_upload` command freely,
290 |     but we normally upload a model using [recipes](https://github.com/espnet/espnet/blob/master/egs2/TEMPLATE).
291 | 
292 | 1. Create a Pull Request to modify [table.csv](espnet_model_zoo/table.csv)
293 | 
294 |     You need to append your record at the last line.
295 | 1. (Administrator does) Increment the third version number of [setup.py](setup.py), e.g. 0.0.3 -> 0.0.4
296 | 1. (Administrator does) Release new version
297 | 


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189 |    Copyright 2017 Johns Hopkins University (Shinji Watanabe)
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202 | 


--------------------------------------------------------------------------------
/espnet_model_zoo/downloader.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | from distutils.util import strtobool
  3 | import hashlib
  4 | import os
  5 | from pathlib import Path
  6 | import re
  7 | import shutil
  8 | import tempfile
  9 | from typing import Dict
 10 | from typing import List
 11 | from typing import Sequence
 12 | from typing import Tuple
 13 | from typing import Union
 14 | import warnings
 15 | 
 16 | from filelock import FileLock
 17 | from huggingface_hub import snapshot_download
 18 | import pandas as pd
 19 | import requests
 20 | from tqdm import tqdm
 21 | import yaml
 22 | 
 23 | from espnet2.main_funcs.pack_funcs import find_path_and_change_it_recursive
 24 | from espnet2.main_funcs.pack_funcs import get_dict_from_cache
 25 | from espnet2.main_funcs.pack_funcs import unpack
 26 | 
 27 | 
 28 | MODELS_URL = (
 29 |     "https://raw.githubusercontent.com/espnet/espnet_model_zoo/master/"
 30 |     "espnet_model_zoo/table.csv"
 31 | )
 32 | 
 33 | 
 34 | URL_REGEX = re.compile(
 35 |     r"^(?:http|ftp)s?://"  # http:// or https://
 36 |     r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)"
 37 |     r"+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|"  # domain...
 38 |     r"localhost|"  # localhost...
 39 |     r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"  # ...or ip
 40 |     r"(?::\d+)?"  # optional port
 41 |     r"(?:/?|[/?]\S+)$",
 42 |     re.IGNORECASE,
 43 | )
 44 | 
 45 | 
 46 | def is_url(url: str) -> bool:
 47 |     return re.match(URL_REGEX, url) is not None
 48 | 
 49 | 
 50 | def str_to_hash(string: Union[str, Path]) -> str:
 51 |     return hashlib.md5(str(string).encode("utf-8")).hexdigest()
 52 | 
 53 | 
 54 | def download(
 55 |     url, output_path, retry: int = 3, chunk_size: int = 8192, quiet: bool = False
 56 | ):
 57 |     # Set retry
 58 |     session = requests.Session()
 59 |     session.mount("http://", requests.adapters.HTTPAdapter(max_retries=retry))
 60 |     session.mount("https://", requests.adapters.HTTPAdapter(max_retries=retry))
 61 | 
 62 |     # Timeout
 63 |     response = session.get(url=url, stream=True, timeout=(10.0, 30.0))
 64 |     file_size = int(response.headers["content-length"])
 65 | 
 66 |     # Raise error when connection error
 67 |     response.raise_for_status()
 68 | 
 69 |     # Write in temporary file
 70 |     with tempfile.TemporaryDirectory() as d:
 71 |         with (Path(d) / "tmp").open("wb") as f:
 72 |             if quiet:
 73 |                 for chunk in response.iter_content(chunk_size=chunk_size):
 74 |                     if chunk:
 75 |                         f.write(chunk)
 76 |             else:
 77 |                 with tqdm(
 78 |                     desc=url,
 79 |                     total=file_size,
 80 |                     unit="B",
 81 |                     unit_scale=True,
 82 |                     unit_divisor=1024,
 83 |                 ) as pbar:
 84 |                     for chunk in response.iter_content(chunk_size=chunk_size):
 85 |                         if chunk:
 86 |                             f.write(chunk)
 87 |                             pbar.update(len(chunk))
 88 | 
 89 |         Path(output_path).parent.mkdir(parents=True, exist_ok=True)
 90 |         shutil.move(Path(d) / "tmp", output_path)
 91 | 
 92 | 
 93 | class ModelDownloader:
 94 |     """Download model from zenodo and unpack."""
 95 | 
 96 |     def __init__(self, cachedir: Union[Path, str] = None):
 97 |         if cachedir is None:
 98 |             # The default path is the directory of this module
 99 |             cachedir = Path(__file__).parent
100 |             # If not having write permission, fallback to homedir
101 |             if not os.access(cachedir, os.W_OK):
102 |                 cachedir = Path.home() / ".cache" / "espnet_model_zoo"
103 |         else:
104 |             cachedir = Path(cachedir).expanduser().absolute()
105 |         cachedir.mkdir(parents=True, exist_ok=True)
106 | 
107 |         csv = Path(__file__).parent / "table.csv"
108 |         if not csv.exists():
109 |             download(MODELS_URL, csv)
110 | 
111 |         self.cachedir = cachedir
112 |         self.csv = csv
113 |         self.data_frame = pd.read_csv(csv, dtype=str)
114 | 
115 |     def get_data_frame(self):
116 |         return self.data_frame
117 | 
118 |     def update_model_table(self):
119 |         lock_file = str(self.csv) + ".lock"
120 |         Path(lock_file).parent.mkdir(parents=True, exist_ok=True)
121 |         with FileLock(lock_file):
122 |             download(MODELS_URL, self.csv)
123 | 
124 |     def clean_cache(self, name: str = None, version: int = -1, **kwargs: str):
125 |         url = self.get_url(name=name, version=version, **kwargs)
126 |         outdir = self.cachedir / str_to_hash(url)
127 |         shutil.rmtree(outdir)
128 | 
129 |     def query(
130 |         self, key: Union[Sequence[str]] = "name", **kwargs
131 |     ) -> List[Union[str, Tuple[str]]]:
132 |         conditions = None
133 |         for k, v in kwargs.items():
134 |             if k not in self.data_frame:
135 |                 warnings.warn(
136 |                     f"Invalid key: {k}: Available keys:\n"
137 |                     f"{list(self.data_frame.keys())}"
138 |                 )
139 |                 continue
140 |             condition = self.data_frame[k] == v
141 |             if conditions is None:
142 |                 conditions = condition
143 |             else:
144 |                 conditions &= condition
145 | 
146 |         if conditions is not None:
147 |             df = self.data_frame[conditions]
148 |         else:
149 |             df = self.data_frame
150 | 
151 |         if len(df) == 0:
152 |             return []
153 |         else:
154 |             if isinstance(key, (tuple, list)):
155 |                 return list(zip(*[df[k] for k in key]))
156 |             else:
157 |                 return list(df[key])
158 | 
159 |     def get_url(self, name: str = None, version: int = -1, **kwargs: str) -> str:
160 |         if name is None and len(kwargs) == 0:
161 |             raise TypeError("No arguments are given")
162 | 
163 |         if name is not None and is_url(name):
164 |             # Specify the downloading link directly. "kwargs" are ignored in this case.
165 |             url = name
166 | 
167 |         else:
168 |             if name is not None:
169 |                 kwargs["name"] = name
170 | 
171 |             conditions = None
172 |             for key, value in kwargs.items():
173 |                 condition = self.data_frame[key] == value
174 |                 if conditions is None:
175 |                     conditions = condition
176 |                 else:
177 |                     conditions &= condition
178 | 
179 |             if len(self.data_frame[conditions]) == 0:
180 |                 # If Specifying local file path
181 |                 if name is not None and Path(name).exists() and len(kwargs) == 1:
182 |                     url = str(Path(name).absolute())
183 | 
184 |                 else:
185 |                     return "huggingface.co"
186 |             else:
187 |                 urls = self.data_frame[conditions]["url"]
188 |                 if version < 0:
189 |                     version = len(urls) + version
190 |                 url = list(urls)[version]
191 |         return url
192 | 
193 |     @staticmethod
194 |     def _get_file_name(url):
195 |         ma = re.match(r"https://.*/([^/]*)\?download=[0-9]*$", url)
196 |         if ma is not None:
197 |             # URL e.g.
198 |             # https://sandbox.zenodo.org/record/646767/files/asr_train_raw_bpe_valid.acc.best.zip?download=1
199 |             a = ma.groups()[0]
200 |             return a
201 |         else:
202 |             # If not Zenodo
203 |             r = requests.head(url)
204 |             if "Content-Disposition" in r.headers:
205 |                 # e.g. attachment; filename=asr_train_raw_bpe_valid.acc.best.zip
206 |                 for v in r.headers["Content-Disposition"].split(";"):
207 |                     if "filename=" in v:
208 |                         return v.split("filename=")[1].strip()
209 | 
210 |             # if not specified or some error happens
211 |             return Path(url).name
212 | 
213 |     def unpack_local_file(self, name: str = None) -> Dict[str, Union[str, List[str]]]:
214 |         if not Path(name).exists():
215 |             raise FileNotFoundError(f"No such file or directory: {name}")
216 | 
217 |         warnings.warn(
218 |             "Expanding a local model to the cachedir. "
219 |             "If you'll move the file to another path, "
220 |             "it's treated as a different model."
221 |         )
222 |         name = Path(name).absolute()
223 | 
224 |         outdir = self.cachedir / str_to_hash(name)
225 |         filename = outdir / name.name
226 |         outdir.mkdir(parents=True, exist_ok=True)
227 | 
228 |         if not filename.exists():
229 |             if filename.is_symlink():
230 |                 filename.unlink()
231 |             filename.symlink_to(name)
232 | 
233 |         # Skip unpacking if the cache exists
234 |         meta_yaml = outdir / "meta.yaml"
235 |         outdir.mkdir(parents=True, exist_ok=True)
236 |         lock_file = str(meta_yaml) + ".lock"
237 |         with FileLock(lock_file):
238 |             if meta_yaml.exists():
239 |                 info = get_dict_from_cache(meta_yaml)
240 |                 if info is not None:
241 |                     return info
242 | 
243 |             # Extract files from archived file
244 |             return unpack(filename, outdir)
245 | 
246 |     def huggingface_download(
247 |         self, name: str = None, version: int = -1, quiet: bool = False, **kwargs: str
248 |     ) -> str:
249 |         # Get huggingface_id from table.csv
250 |         if name is None:
251 |             names = self.query(key="name", **kwargs)
252 |             if len(names) == 0:
253 |                 message = "Not found models:"
254 |                 for key, value in kwargs.items():
255 |                     message += f" {key}={value}"
256 |                 raise RuntimeError(message)
257 |             if version < 0:
258 |                 version = len(names) + version
259 |             name = list(names)[version]
260 | 
261 |         if "@" in name:
262 |             huggingface_id, revision = name.split("@", 1)
263 |         else:
264 |             huggingface_id = name
265 |             revision = None
266 | 
267 |         return snapshot_download(
268 |             huggingface_id,
269 |             revision=revision,
270 |             library_name="espnet",
271 |             cache_dir=self.cachedir,
272 |         )
273 | 
274 |     @staticmethod
275 |     def _unpack_cache_dir_for_huggingface(cache_dir: str):
276 |         meta_yaml = Path(cache_dir) / "meta.yaml"
277 |         lock_file = Path(cache_dir) / ".lock"
278 |         flag_file = Path(cache_dir) / ".done"
279 | 
280 |         with meta_yaml.open("r", encoding="utf-8") as f:
281 |             d = yaml.safe_load(f)
282 |             assert isinstance(d, dict), type(d)
283 | 
284 |             yaml_files = d["yaml_files"]
285 |             files = d["files"]
286 |             assert isinstance(yaml_files, dict), type(yaml_files)
287 |             assert isinstance(files, dict), type(files)
288 | 
289 |         # Rewrite yaml_files for first case
290 |         with FileLock(lock_file):
291 |             if not flag_file.exists():
292 |                 for key, value in yaml_files.items():
293 |                     yaml_file = Path(cache_dir) / value
294 |                     with yaml_file.open("r", encoding="utf-8") as f:
295 |                         d = yaml.safe_load(f)
296 |                         assert isinstance(d, dict), type(d)
297 |                         for name in Path(cache_dir).glob("**/*"):
298 |                             name = name.relative_to(Path(cache_dir))
299 |                             d = find_path_and_change_it_recursive(
300 |                                 d, name, str(Path(cache_dir) / name)
301 |                             )
302 | 
303 |                     with yaml_file.open("w", encoding="utf-8") as f:
304 |                         yaml.safe_dump(d, f)
305 | 
306 |                 with flag_file.open("w"):
307 |                     pass
308 | 
309 |         retval = {}
310 |         for key, value in list(yaml_files.items()) + list(files.items()):
311 |             retval[key] = str(Path(cache_dir) / value)
312 |         return retval
313 | 
314 |     def download(
315 |         self, name: str = None, version: int = -1, quiet: bool = False, **kwargs: str
316 |     ) -> str:
317 |         url = self.get_url(name=name, version=version, **kwargs)
318 | 
319 |         # Support direct huggingface url specification
320 |         if name is not None and name.startswith("https://huggingface.co/"):
321 |             url = "https://huggingface.co/"
322 |             name = name.replace("https://huggingface.co/", "")
323 | 
324 |         # For huggingface compatibility
325 |         if url in [
326 |             "https://huggingface.co/",
327 |             "https://huggingface.co",
328 |             "huggingface.co",
329 |         ]:
330 |             # TODO(kamo): Support quiet
331 |             cache_dir = self.huggingface_download(name=name, version=version, **kwargs)
332 |             self._unpack_cache_dir_for_huggingface(cache_dir)
333 |             return cache_dir
334 | 
335 |         if not is_url(url) and Path(url).exists():
336 |             return url
337 | 
338 |         outdir = self.cachedir / str_to_hash(url)
339 |         filename = self._get_file_name(url)
340 |         # Download the model file if not existing
341 |         outdir.mkdir(parents=True, exist_ok=True)
342 |         lock_file = str(outdir / filename) + ".lock"
343 |         with FileLock(lock_file):
344 |             if not (outdir / filename).exists():
345 |                 download(url, outdir / filename, quiet=quiet)
346 | 
347 |                 # Write the url for debugging
348 |                 with (outdir / "url").open("w", encoding="utf-8") as f:
349 |                     f.write(url)
350 | 
351 |                 r = requests.head(url)
352 |                 if "Content-MD5" in r.headers:
353 |                     checksum = r.headers["Content-MD5"]
354 | 
355 |                     # MD5 checksum
356 |                     sig = hashlib.md5()
357 |                     chunk_size = 8192
358 |                     with open(outdir / filename, "rb") as f:
359 |                         while True:
360 |                             chunk = f.read(chunk_size)
361 |                             if len(chunk) == 0:
362 |                                 break
363 |                             sig.update(chunk)
364 | 
365 |                     if sig.hexdigest() != checksum:
366 |                         Path(outdir / filename).unlink()
367 |                         raise RuntimeError(f"Failed to download file: {url}")
368 |                 else:
369 |                     warnings.warn("Not validating checksum")
370 |         return str(outdir / filename)
371 | 
372 |     def download_and_unpack(
373 |         self, name: str = None, version: int = -1, quiet: bool = False, **kwargs: str
374 |     ) -> Dict[str, Union[str, List[str]]]:
375 |         url = self.get_url(name=name, version=version, **kwargs)
376 |         if not is_url(url) and Path(url).exists():
377 |             return self.unpack_local_file(url)
378 | 
379 |         # Support direct huggingface url specification
380 |         if name is not None and name.startswith("https://huggingface.co/"):
381 |             url = "https://huggingface.co/"
382 |             name = name.replace("https://huggingface.co/", "")
383 | 
384 |         # For huggingface compatibility
385 |         if url in [
386 |             "https://huggingface.co/",
387 |             "https://huggingface.co",
388 |             "huggingface.co",
389 |         ]:
390 |             # download_and_unpack and download are same if huggingface case
391 |             # TODO(kamo): Support quiet
392 |             cache_dir = self.huggingface_download(name=name, version=version, **kwargs)
393 |             return self._unpack_cache_dir_for_huggingface(cache_dir)
394 | 
395 |         # Unpack to <cachedir>/<hash> in order to give an unique name
396 |         outdir = self.cachedir / str_to_hash(url)
397 | 
398 |         # Skip downloading and unpacking if the cache exists
399 |         meta_yaml = outdir / "meta.yaml"
400 |         outdir.mkdir(parents=True, exist_ok=True)
401 |         lock_file = str(meta_yaml) + ".lock"
402 |         with FileLock(lock_file):
403 |             if meta_yaml.exists():
404 |                 info = get_dict_from_cache(meta_yaml)
405 |                 if info is not None:
406 |                     return info
407 | 
408 |             # Download the file to an unique path
409 |             filename = self.download(url, quiet=quiet)
410 | 
411 |             # Extract files from archived file
412 |             return unpack(filename, outdir)
413 | 
414 | 
415 | def str2bool(v) -> bool:
416 |     return bool(strtobool(v))
417 | 
418 | 
419 | def cmd_download(cmd=None):
420 |     # espnet_model_zoo_download
421 | 
422 |     parser = argparse.ArgumentParser("Download file from Zenodo")
423 |     parser.add_argument(
424 |         "name",
425 |         help="URL or model name in the form of <username>/<model name>. "
426 |         "e.g. kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best",
427 |     )
428 |     parser.add_argument(
429 |         "--cachedir",
430 |         help="Specify cache dir. By default, download to module root.",
431 |     )
432 |     parser.add_argument(
433 |         "--unpack",
434 |         type=str2bool,
435 |         default=False,
436 |         help="Unpack the archived file after downloading.",
437 |     )
438 |     args = parser.parse_args(cmd)
439 | 
440 |     d = ModelDownloader(args.cachedir)
441 |     if args.unpack:
442 |         print(d.download_and_unpack(args.name))
443 |     else:
444 |         print(d.download(args.name))
445 | 
446 | 
447 | def cmd_query(cmd=None):
448 |     # espnet_model_zoo_query
449 | 
450 |     parser = argparse.ArgumentParser("Download file from Zenodo")
451 | 
452 |     parser.add_argument(
453 |         "condition",
454 |         nargs="*",
455 |         default=[],
456 |         help="Given desired condition in form of <key>=<value>. "
457 |         "e.g. fs=16000. "
458 |         "If no condition is given, you can view all available models",
459 |     )
460 |     parser.add_argument(
461 |         "--key",
462 |         default="name",
463 |         help="The key name you want",
464 |     )
465 |     parser.add_argument(
466 |         "--cachedir",
467 |         help="Specify cache dir. By default, download to module root.",
468 |     )
469 |     args = parser.parse_args(cmd)
470 | 
471 |     conditions = dict(s.split("=") for s in args.condition)
472 |     d = ModelDownloader(args.cachedir)
473 |     for v in d.query(args.key, **conditions):
474 |         print(v)
475 | 


--------------------------------------------------------------------------------
/espnet_model_zoo/table.csv:
--------------------------------------------------------------------------------
  1 | corpus,task,name,url,fs,lang,gender,pytorch,espnet,commit,valid
  2 | ,,test,https://zenodo.org/record/3951842/files/test.zip?download=1,,,,,,,true
  3 | mini_an4,asr,kamo-naoyuki/mini_an4_asr_train_raw_bpe_valid.acc.best,https://zenodo.org/record/3957940/files/asr_train_raw_bpe_valid.acc.best.zip?download=1,16000,en,,,,,true
  4 | librispeech,asr,Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best,https://zenodo.org/record/4030677/files/asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best.zip?download=1,16000,en,,,,,true
  5 | jsut,tts,kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best,https://zenodo.org/record/3963886/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best.zip?download=1,24000,jp,female,,,,true
  6 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best,https://zenodo.org/record/3986225/files/tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best.zip?download=1,24000,jp,female,,,,true
  7 | csmsc,tts,kan-bayashi/csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best,https://zenodo.org/record/3969118/files/tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best.zip?download=1,24000,zh,female,,,,true
  8 | csmsc,tts,kan-bayashi/csmsc_tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best,https://zenodo.org/record/3986227/files/tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best.zip?download=1,24000,zh,female,,,,true
  9 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best,https://zenodo.org/record/3989498/files/tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,22050,en,female,,,,true
 10 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best,https://zenodo.org/record/3986231/files/tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,22050,en,female,,,,true
 11 | vctk,tts,kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best,https://zenodo.org/record/3986237/files/tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,24000,en,,,,,true
 12 | vctk,tts,kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best,https://zenodo.org/record/3986241/files/tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,24000,en,,,,,false
 13 | jsut,tts,kan-bayashi/jsut_tacotron2,https://zenodo.org/record/3963886/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best.zip?download=1,24000,jp,female,,,,true
 14 | jsut,tts,kan-bayashi/jsut_fastspeech,https://zenodo.org/record/3986225/files/tts_train_fastspeech_raw_phn_jaconv_pyopenjtalk_train.loss.best.zip?download=1,24000,jp,female,,,,true
 15 | csmsc,tts,kan-bayashi/csmsc_tacotron2,https://zenodo.org/record/3969118/files/tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best.zip?download=1,24000,zh,female,,,,true
 16 | csmsc,tts,kan-bayashi/csmsc_fastspeech,https://zenodo.org/record/3986227/files/tts_train_fastspeech_raw_phn_pypinyin_g2p_phone_train.loss.best.zip?download=1,24000,zh,female,,,,true
 17 | ljspeech,tts,kan-bayashi/ljspeech_tacotron2,https://zenodo.org/record/3989498/files/tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,22050,en,female,,,,true
 18 | ljspeech,tts,kan-bayashi/ljspeech_fastspeech,https://zenodo.org/record/3986231/files/tts_train_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,22050,en,female,,,,true
 19 | vctk,tts,kan-bayashi/vctk_gst_tacotron2,https://zenodo.org/record/3986237/files/tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,24000,en,,,,,true
 20 | vctk,tts,kan-bayashi/vctk_gst_fastspeech,https://zenodo.org/record/3986241/files/tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best.zip?download=1,24000,en,,,,,false
 21 | wsj,asr,kamo-naoyuki/wsj,https://zenodo.org/record/4003381/files/asr_train_asr_transformer_raw_char_valid.acc.ave.zip?download=1,16000,en,,1.6.0,0.9.1,e67a1ad,true
 22 | zeroth_korean,asr,Hoon Chung/zeroth_korean_asr_train_asr_transformer5_raw_bpe_valid.acc.ave,https://zenodo.org/record/4014588/files/asr_train_asr_transformer5_raw_bpe_valid.acc.ave.zip?download=1,16000,kr,,1.2.0,0.7.0,de119e7,true
 23 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave,https://zenodo.org/record/4032224/files/tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.6.0,0.9.3,f366560,true
 24 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave,https://zenodo.org/record/4032246/files/tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.6.0,0.9.3,f366560,true
 25 | csmsc,tts,kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave,https://zenodo.org/record/4031953/files/tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.6.0,0.9.3,f366560,true
 26 | csmsc,tts,kan-bayashi/csmsc_tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave,https://zenodo.org/record/4031955/files/tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.6.0,0.9.3,f366560,true
 27 | jsut,tts,kan-bayashi/jsut_fastspeech2,https://zenodo.org/record/4032224/files/tts_train_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.6.0,0.9.3,f366560,true
 28 | jsut,tts,kan-bayashi/jsut_conformer_fastspeech2,https://zenodo.org/record/4032246/files/tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.6.0,0.9.3,f366560,true
 29 | csmsc,tts,kan-bayashi/csmsc_fastspeech2,https://zenodo.org/record/4031953/files/tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.6.0,0.9.3,f366560,true
 30 | csmsc,tts,kan-bayashi/csmsc_conformer_fastspeech2,https://zenodo.org/record/4031955/files/tts_train_conformer_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.6.0,0.9.3,f366560,true
 31 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4036272/files/tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.6.0,0.9.3,322a5cf,true
 32 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4036268/files/tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.6.0,0.9.3,322a5cf,true
 33 | jsut,tts,kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave,https://zenodo.org/record/4034121/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.3,67ca53d,true
 34 | csmsc,tts,kan-bayashi/csmsc_tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave,https://zenodo.org/record/4034125/files/tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.5.1,0.9.3,67ca53d,true
 35 | vctk,tts,kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4037456/files/tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.3,67ca53d,true
 36 | vctk,tts,kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4036266/files/tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.6.0,0.9.3,322a5cf,true
 37 | vctk,tts,kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4036264/files/tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.6.0,0.9.3,322a5cf,true
 38 | ljspeech,tts,kan-bayashi/ljspeech_fastspeech2,https://zenodo.org/record/4036272/files/tts_train_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.6.0,0.9.3,322a5cf,true
 39 | ljspeech,tts,kan-bayashi/ljspeech_conformer_fastspeech2,https://zenodo.org/record/4036268/files/tts_train_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.6.0,0.9.3,322a5cf,true
 40 | jsut,tts,kan-bayashi/jsut_transformer,https://zenodo.org/record/4034121/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.3,67ca53d,true
 41 | csmsc,tts,kan-bayashi/csmsc_transformer,https://zenodo.org/record/4034125/files/tts_train_transformer_raw_phn_pypinyin_g2p_phone_train.loss.ave.zip?download=1,24000,zh,female,1.5.1,0.9.3,67ca53d,true
 42 | vctk,tts,kan-bayashi/vctk_gst_transformer,https://zenodo.org/record/4037456/files/tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.3,67ca53d,true
 43 | vctk,tts,kan-bayashi/vctk_gst_fastspeech2,https://zenodo.org/record/4036266/files/tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.6.0,0.9.3,322a5cf,true
 44 | vctk,tts,kan-bayashi/vctk_gst_conformer_fastspeech2,https://zenodo.org/record/4036264/files/tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.6.0,0.9.3,322a5cf,true
 45 | csj,asr,kan-bayashi/csj_asr_train_asr_transformer_raw_char_sp_valid.acc.ave,https://zenodo.org/record/4037458/files/asr_train_asr_transformer_raw_char_sp_valid.acc.ave.zip?download=1,16000,jp,,1.5.1,0.9.3,67ca53d,true
 46 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4039194/files/tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.5.1,0.9.3,67ca53d,true
 47 | ljspeech,tts,kan-bayashi/ljspeech_transformer,https://zenodo.org/record/4039194/files/tts_train_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,22050,en,female,1.5.1,0.9.3,67ca53d,true
 48 | aishell,asr,kamo-naoyuki/aishell_conformer,https://zenodo.org/record/4105763/files/asr_train_asr_conformer3_raw_char_batch_bins4000000_accum_grad4_sp_valid.acc.ave.zip?download=1,16000,zh,,1.6.0,0.9.0,20b0c89,true
 49 | wsj,asr,kamo-naoyuki/wsj_transformer2,https://zenodo.org/record/4243201/files/asr_train_asr_transformer_raw_char_valid.acc.ave.zip?download=1,16000,en,,1.6.0,0.9.0,e7d278a,true
 50 | reverb,asr,kamo-naoyuki/reverb_asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.acc.ave,https://zenodo.org/record/4278363/files/asr_train_asr_transformer4_raw_char_batch_bins16000000_accum_grad1_sp_valid.acc.ave.zip?download=1,16000,en,,1.5.1,0.9.5,7aad824,true
 51 | timit,asr,kamo-naoyuki/timit_asr_train_asr_raw_word_valid.acc.ave,https://zenodo.org/record/4284058/files/asr_train_asr_raw_word_valid.acc.ave.zip?download=1,16000,en,,1.6.0,0.9.5,f157fcd,true
 52 | jsut,asr,Hoon Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave,https://zenodo.org/record/4292742/files/asr_train_asr_conformer8_raw_char_sp_valid.acc.ave.zip?download=1,16000,ja,,1.2.0,0.9.0,6448047,true
 53 | jsut,tts,kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4381098/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 54 | jsut,tts,kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4381096/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 55 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4381100/files/tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 56 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4381102/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 57 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4391405/files/tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 58 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave,https://zenodo.org/record/4391409/files/tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 59 | jsut,tts,kan-bayashi/jsut_tacotron2_accent,https://zenodo.org/record/4381098/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 60 | jsut,tts,kan-bayashi/jsut_transformer_accent,https://zenodo.org/record/4381096/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 61 | jsut,tts,kan-bayashi/jsut_fastspeech2_accent,https://zenodo.org/record/4381100/files/tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 62 | jsut,tts,kan-bayashi/jsut_conformer_fastspeech2_accent,https://zenodo.org/record/4381102/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,acd6957,true
 63 | chime4,asr,kamo-naoyuki/chime4_asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave,https://zenodo.org/record/4414883/files/asr_train_asr_transformer3_raw_en_char_sp_valid.acc.ave.zip?download=1,16000,en,,1.4.0,0.9.6,d5ddd5e,true
 64 | dirha_wsj,asr,kamo-naoyuki/dirha_wsj_asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.acc.ave,https://zenodo.org/record/4415021/files/asr_train_asr_transformer_cmvn_raw_char_rir_scpdatadirha_irwav.scp_noise_db_range10_17_noise_scpdatadirha_noisewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob1._sp_valid.acc.ave.zip?download=1,16000,en,,1.5.1,0.9.6,c30ce88,true
 65 | vctk,tts,kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4393279/files/tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 66 | vctk,tts,kan-bayashi/vctk_tts_train_gst+xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4393277/files/tts_train_gst%2Bxvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 67 | vctk,tts,kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4394600/files/tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 68 | vctk,tts,kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4394598/files/tts_train_gst%2Bxvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 69 | vctk,tts,kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4394602/files/tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 70 | vctk,tts,kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4394608/files/tts_train_gst%2Bxvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 71 | libritts,tts,kan-bayashi/libritts_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4409704/files/tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 72 | libritts,tts,kan-bayashi/libritts_tts_train_gst+xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave,https://zenodo.org/record/4409702/files/tts_train_gst%2Bxvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 73 | libritts,tts,kan-bayashi/libritts_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss,https://zenodo.org/record/4418754/files/tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 74 | libritts,tts,kan-bayashi/libritts_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss,https://zenodo.org/record/4418774/files/tts_train_gst%2Bxvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 75 | vctk,tts,kan-bayashi/vctk_xvector_transformer,https://zenodo.org/record/4393279/files/tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 76 | vctk,tts,kan-bayashi/vctk_gst+xvector_transformer,https://zenodo.org/record/4393277/files/tts_train_gst%2Bxvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 77 | vctk,tts,kan-bayashi/vctk_xvector_tacotron2,https://zenodo.org/record/4394600/files/tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 78 | vctk,tts,kan-bayashi/vctk_gst+xvector_tacotron2,https://zenodo.org/record/4394598/files/tts_train_gst%2Bxvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 79 | vctk,tts,kan-bayashi/vctk_xvector_conformer_fastspeech2,https://zenodo.org/record/4394602/files/tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 80 | vctk,tts,kan-bayashi/vctk_gst+xvector_conformer_fastspeech2,https://zenodo.org/record/4394608/files/tts_train_gst%2Bxvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,96ce09,true
 81 | libritts,tts,kan-bayashi/libritts_xvector_transformer,https://zenodo.org/record/4409704/files/tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 82 | libritts,tts,kan-bayashi/libritts_gst+xvector_transformer,https://zenodo.org/record/4409702/files/tts_train_gst%2Bxvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 83 | libritts,tts,kan-bayashi/libritts_xvector_conformer_fastspeech2,https://zenodo.org/record/4418754/files/tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 84 | libritts,tts,kan-bayashi/libritts_gst+xvector_conformer_fastspeech2,https://zenodo.org/record/4418774/files/tts_train_gst%2Bxvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave.zip?download=1,24000,en,,1.5.1,0.9.6,861431,true
 85 | hkust,asr,kamo-naoyuki/hkust_asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave,https://zenodo.org/record/4430974/files/asr_train_asr_transformer2_raw_zh_char_batch_bins20000000_ctc_confignore_nan_gradtrue_sp_valid.acc.ave.zip?download=1,16000,zh,,1.4.0,0.9.6,db7dfea,true
 86 | reverb,asr,kamo-naoyuki/reverb_asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.acc.ave,https://zenodo.org/record/4441309/files/asr_train_asr_transformer2_raw_en_char_rir_scpdatareverb_rir_singlewav.scp_noise_db_range12_17_noise_scpdatareverb_noise_singlewav.scp_speech_volume_normalize1.0_num_workers2_rir_apply_prob0.999_noise_apply_prob1._sp_valid.acc.ave.zip?download=1,16000,en,,1.5.1,0.9.5,03659ca,true
 87 | jsut,tts,kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4433194/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 88 | jsut,tts,kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4433196/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 89 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4436450/files/tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 90 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4436448/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 91 | jsut,tts,kan-bayashi/jsut_tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4433200/files/tts_train_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 92 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave,https://zenodo.org/record/4433198/files/tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 93 | jsut,tts,kan-bayashi/jsut_tacotron2_accent_with_pause,https://zenodo.org/record/4433194/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 94 | jsut,tts,kan-bayashi/jsut_transformer_accent_with_pause,https://zenodo.org/record/4433196/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 95 | jsut,tts,kan-bayashi/jsut_fastspeech2_accent_with_pause,https://zenodo.org/record/4436450/files/tts_train_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 96 | jsut,tts,kan-bayashi/jsut_conformer_fastspeech2_accent_with_pause,https://zenodo.org/record/4436448/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.loss.ave.zip?download=1,24000,jp,female,1.5.1,0.9.6,18fb6e,true
 97 | librispeech,asr,kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave,https://zenodo.org/record/4541452/files/asr_train_asr_conformer5_raw_bpe5000_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_accum_grad2_sp_valid.acc.ave.zip?download=1,16000,en,,1.7.1,0.9.6,8eff1a9,true
 98 | librispeech,asr,kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave,https://zenodo.org/record/4543003/files/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft400_frontend_confhop_length160_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave.zip?download=1,16000,en,,1.7.1,0.9.6,8eff1a9,true
 99 | librispeech,asr,kamo-naoyuki/librispeech_asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave,https://zenodo.org/record/4543018/files/asr_train_asr_conformer5_raw_bpe5000_frontend_confn_fft512_frontend_confhop_length256_scheduler_confwarmup_steps25000_batch_bins140000000_optim_conflr0.0015_initnone_sp_valid.acc.ave.zip?download=1,16000,en,,1.7.1,0.9.6,8eff1a9,true
100 | laborotv,asr,Shinji Watanabe/laborotv_asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave,https://zenodo.org/record/4304245/files/asr_train_asr_conformer2_latest33_raw_char_sp_valid.acc.ave.zip?download=1,16000,jp,,1.4.0,0.9.5,c40d6e2,true
101 | mls,asr,ftshijt/mls_asr_transformer_valid.acc.best,https://zenodo.org/record/4458452/files/asr_transformer_valid.acc.best.zip?download=1,16000,es,,1.6.0,0.9.2,c0c3724,true
102 | librimix,enh,anogkongda/librimix_enh_train_raw_valid.si_snr.ave,https://zenodo.org/record/4480771/files/enh_train_raw_valid.si_snr.ave.zip?download=1,8000,en,,1.6.0,0.9.7,dccadcd,TRUE
103 | wsj0_2mix,enh,Chenda Li/wsj0_2mix_enh_train_enh_conv_tasnet_raw_valid.si_snr.ave,https://zenodo.org/record/4498562/files/enh_train_enh_conv_tasnet_raw_valid.si_snr.ave.zip?download=1,8000,en,,1.5.0,0.9.7,110eca5,TRUE
104 | wsj0_2mix,enh,Chenda Li/wsj0_2mix_enh_train_enh_rnn_tf_raw_valid.si_snr.ave,https://zenodo.org/record/4498554/files/enh_train_enh_rnn_tf_raw_valid.si_snr.ave.zip?download=1,8000,en,,1.5.1,0.9.8,110eca5,TRUE
105 | wsj0_2mix,enh,lichenda/wsj0_2mix_skim_noncausal,https://huggingface.co/lichenda/wsj0_2mix_skim_noncausal,8000,en,,1.8.1,0.10.7a1,79b9f11,TRUE
106 | wsj0_2mix,enh,lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet,https://huggingface.co/lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet,8000,en,,1.5.0,0.9.8,,TRUE
107 | spgispeech,asr,Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave,https://zenodo.org/record/4585558/files/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave.zip?download=1,16000,en_unnorm,,1.7.1,0.9.8,,true
108 | spgispeech,asr,Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave,https://zenodo.org/record/4585546/files/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_valid.acc.ave.zip?download=1,16000,en,,1.7.1,0.9.8,,true
109 | librispeech,asr,kamo-naoyuki/librispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.ave,https://zenodo.org/record/4604066/files/asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_bpe5000_scheduler_confwarmup_steps40000_optim_conflr0.0025_sp_valid.acc.ave.zip?download=1,16000,en,,1.8.0,0.9.8,2ccd176,true
110 | aishell,asr_stream,Emiru Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave,https://zenodo.org/record/4604023/files/asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave.zip?download=1,16000,zh,,1.4.0,0.9.7,,true
111 | open_li52,asr,ftshijt/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave_10best,https://zenodo.org/record/4738407/files/asr_train_asr_raw_bpe7000_valid.acc.ave_10best.zip?download=1,16000,multilingual,,1.7.1,0.9.7,59bc1f7,true
112 | gigaspeech,asr,Shinji Watanabe/gigaspeech_asr_train_asr_raw_en_bpe5000_valid.acc.ave,https://zenodo.org/record/4630406/files/asr_train_asr_raw_en_bpe5000_valid.acc.ave.zip?download=1,16000,en,,1.7.1,0.9.8,,true
113 | switchboard,asr,Yuekai Zhang/swbd_asr_train_asr_cformer5_raw_bpe2000_sp_valid.acc.ave,https://zenodo.org/record/4978923/files/asr_train_asr_cformer5_raw_bpe2000_sp_valid.acc.ave.zip?download=1,8000,en,,1.7.1,0.9.8,,true
114 | dns_ins20,enh,Yen-Ju Lu/dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best,https://zenodo.org/record/4923697/files/enh_train_enh_blstm_tf_raw_valid.loss.best.zip?download=1,16000,en,,1.4.0,0.9.9,,true
115 | jv_openslr35,asr,jv_openslr35,https://zenodo.org/record/5090139/files/asr_train_asr_raw_bpe1000_valid.acc.best.zip?download=1,16000,jv,,1.8.1,0.9.10,,true
116 | su_openslr36,asr,su_openslr36,https://zenodo.org/record/5090135/files/asr_train_asr_raw_bpe1000_valid.acc.best.zip?download=1,16000,su,,1.8.1,0.9.10,,true
117 | ksponspeech,asr,Yushi Ueda/ksponspeech_asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best,https://zenodo.org/record/5154341/files/asr_train_asr_conformer8_n_fft512_hop_length256_raw_kr_bpe2309_valid.acc.best.zip?download=1,16000,kr,,1.8.1,0.10.0,538393c,true
118 | librispeech,asr,byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp,https://huggingface.co/,16000,en,,,,,true
119 | mini_librispeech,diar,Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best,https://zenodo.org/record/5264020/files/diar_train_diar_raw_max_epoch20_valid.acc.best.zip?download=1,8000,,,1.9.1,0.10.2,,true
120 | jsut,tts,kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave,https://zenodo.org/record/5414980/files/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
121 | jsut,tts,kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave,https://zenodo.org/record/5521360/files/tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
122 | jsut,tts,kan-bayashi/jsut_vits_accent_with_pause,https://zenodo.org/record/5414980/files/tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
123 | jsut,tts,kan-bayashi/jsut_full_band_vits_accent_with_pause,https://zenodo.org/record/5521360/files/tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
124 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5443814/files/tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a1,dee654,true
125 | ljspeech,tts,kan-bayashi/ljspeech_vits,https://zenodo.org/record/5443814/files/tts_train_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a1,dee654,true
126 | csmsc,tts,kan-bayashi/csmsc_tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave,https://zenodo.org/record/5521404/files/tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave.zip?download=1,44100,zh,female,1.7.1,0.10.3a1,dee654,true
127 | csmsc,tts,kan-bayashi/csmsc_full_band_vits,https://zenodo.org/record/5521404/files/tts_train_full_band_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave.zip?download=1,44100,zh,female,1.7.1,0.10.3a1,dee654,true
128 | csmsc,tts,kan-bayashi/csmsc_tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave,https://zenodo.org/record/5499120/files/tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave.zip?download=1,22050,zh,female,1.7.1,0.10.3a2,628b46,true
129 | csmsc,tts,kan-bayashi/csmsc_vits,https://zenodo.org/record/5499120/files/tts_train_vits_raw_phn_pypinyin_g2p_phone_train.total_count.ave.zip?download=1,22050,zh,female,1.7.1,0.10.3a2,628b46,true
130 | jsut,tts,kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,https://zenodo.org/record/5499026/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
131 | jsut,tts,kan-bayashi/jsut_tacotron2_prosody,https://zenodo.org/record/5499026/files/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
132 | jsut,tts,kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,https://zenodo.org/record/5499040/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
133 | jsut,tts,kan-bayashi/jsut_transformer_prosody,https://zenodo.org/record/5499040/files/tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
134 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,https://zenodo.org/record/5499050/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
135 | jsut,tts,kan-bayashi/jsut_conformer_fastspeech2_tacotron2_prosody,https://zenodo.org/record/5499050/files/tts_train_conformer_fastspeech2_tacotron2_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
136 | jsut,tts,kan-bayashi/jsut_tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave,https://zenodo.org/record/5499066/files/tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
137 | jsut,tts,kan-bayashi/jsut_conformer_fastspeech2_transformer_prosody,https://zenodo.org/record/5499066/files/tts_train_conformer_fastspeech2_transformer_teacher_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave.zip?download=1,24000,jp,female,1.7.1,0.10.3a2,628b46,true
138 | vctk,tts,kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5500759/files/tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,,1.7.1,0.10.3a2,628b46,true
139 | vctk,tts,kan-bayashi/vctk_multi_spk_vits,https://zenodo.org/record/5500759/files/tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,,1.7.1,0.10.3a2,628b46,true
140 | vctk,tts,kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5521431/files/tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,44100,en,,1.7.1,0.10.3a2,628b46,true
141 | vctk,tts,kan-bayashi/vctk_full_band_multi_spk_vits,https://zenodo.org/record/5521431/files/tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,44100,en,,1.7.1,0.10.3a2,628b46,true
142 | jsut,tts,kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave,https://zenodo.org/record/5521354/files/tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
143 | jsut,tts,kan-bayashi/jsut_tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave,https://zenodo.org/record/5521340/files/tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
144 | jsut,tts,kan-bayashi/jsut_vits_prosody,https://zenodo.org/record/5521354/files/tts_train_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
145 | jsut,tts,kan-bayashi/jsut_full_band_vits_prosody,https://zenodo.org/record/5521340/files/tts_train_full_band_vits_raw_phn_jaconv_pyopenjtalk_prosody_train.total_count.ave.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
146 | jvs,tts,kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest,https://zenodo.org/record/5521494/files/tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
147 | jvs,tts,kan-bayashi/jvs_jvs010_vits_prosody,https://zenodo.org/record/5521494/files/tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest.zip?download=1,22050,jp,female,1.7.1,0.10.3a1,dee654,true
148 | tsukuyomi,tts,kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest,https://zenodo.org/record/5521446/files/tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
149 | tsukuyomi,tts,kan-bayashi/tsukuyomi_full_band_vits_prosody,https://zenodo.org/record/5521446/files/tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest.zip?download=1,44100,jp,female,1.7.1,0.10.3a1,dee654,true
150 | libritts,tts,kan-bayashi/libritts_tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5521416/files/tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,,1.7.1,0.10.3a2,628b46,true
151 | libritts,tts,kan-bayashi/libritts_xvector_vits,https://zenodo.org/record/5521416/files/tts_train_xvector_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,,1.7.1,0.10.3a2,628b46,true
152 | ljspeech,tts,kan-bayashi/ljspeech_tts_train_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5498487/files/tts_train_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a2,628b46,true
153 | ljspeech,tts,kan-bayashi/ljspeech_tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave,https://zenodo.org/record/5498896/files/tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a2,628b46,true
154 | ljspeech,tts,kan-bayashi/ljspeech_joint_train_conformer_fastspeech2_hifigan,https://zenodo.org/record/5498487/files/tts_train_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a2,628b46,true
155 | ljspeech,tts,kan-bayashi/ljspeech_joint_finetune_conformer_fastspeech2_hifigan,https://zenodo.org/record/5498896/files/tts_finetune_joint_conformer_fastspeech2_hifigan_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave.zip?download=1,22050,en,female,1.7.1,0.10.3a2,628b46,true
156 | tedlium2,asr_stream,D-Keqi/espnet_asr_train_asr_streaming_transformer_raw_en_bpe500_sp_valid.acc.ave,huggingface.co,16000,en,,,,,true
157 | speechcommands,slu,pyf98/speechcommands_12commands_conformer,https://huggingface.co/,16000,en,,,,,true
158 | speechcommands,slu,pyf98/speechcommands_35commands_conformer,https://huggingface.co/,16000,en,,,,,true
159 | chime4,enh,Wangyou-Zhang/chime4_enh_train_enh_beamformer_mvdr_raw_valid.si_snr.ave,https://zenodo.org/record/6025881/files/enh_train_enh_beamformer_mvdr_raw_valid.si_snr.ave.zip?download=1,16000,en,,1.6.0,0.9.7,,true
160 | chime4,enh,lichenda/chime4_fasnet_dprnn_tac,https://huggingface.co/lichenda/chime4_fasnet_dprnn_tac,16000,en,,1.8.1,0.10.7a1,,true
161 | chime4,enh,espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw,https://huggingface.co/espnet/Wangyou_Zhang_chime4_enh_train_enh_conv_tasnet_raw,16000,en,,1.7.1,0.9.9,,true
162 | chime4,enh,espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw,https://huggingface.co/espnet/Wangyou_Zhang_wsj0_2mix_enh_dc_crn_mapping_snr_raw,16000,en,,1.10.2,0.10.7a1,,true
163 | chime4,enh,espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw,https://huggingface.co/espnet/Wangyou_Zhang_chime4_enh_train_enh_dc_crn_mapping_snr_raw,16000,en,,1.10.2,0.10.7a1,,true
164 | reazonspeech,asr,reazon-research/reazonspeech-espnet-v1,https://huggingface.co/,16000,ja,,1.12.1,202209,,true
165 | 


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