[cli] add first wenet cli (#2047)

setup.cfg

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+[metadata]
+name = wenet
+version = 0.0.0
+license = Apache Software License
+description = End to end speech recognition toolkit
+long_description = file: README.md
+classifiers =
+    License :: OSI Approved :: Apache Software License
+    Operating System :: OS Independent
+    Programming Language :: Python :: 3
+
+[options]
+packages = find:
+include_package_data = True
+python_requires = >= 3.8

setup.py

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+from setuptools import setup, find_packages
+
+requirements = [
+    "torch==1.10.0",
+    "torchaudio==0.10.0"
+]
+
+setup(
+    name="wenet",
+    install_requires=requirements,
+    packages=find_packages(),
+    entry_points={"console_scripts": [
+        "wenet = wenet.cli.transcribe:main",
+    ]},
+)

wenet/cli/hub.py

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+# Copyright (c) 2022  Mddct(hamddct@gmail.com)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import tarfile
+from pathlib import Path
+from urllib.request import urlretrieve
+
+import tqdm
+
+
+def download(url: str, dest: str, only_child=True):
+    """ download from url to dest
+    """
+    assert os.path.exists(dest)
+    print('Downloading {} to {}'.format(url, dest))
+
+    def progress_hook(t):
+        last_b = [0]
+
+        def update_to(b=1, bsize=1, tsize=None):
+            if tsize not in (None, -1):
+                t.total = tsize
+            displayed = t.update((b - last_b[0]) * bsize)
+            last_b[0] = b
+            return displayed
+
+        return update_to
+
+    # *.tar.gz
+    name = url.split("/")[-1]
+    tar_path = os.path.join(dest, name)
+    with tqdm.tqdm(unit='B',
+                   unit_scale=True,
+                   unit_divisor=1024,
+                   miniters=1,
+                   desc=(name)) as t:
+        urlretrieve(url,
+                    filename=tar_path,
+                    reporthook=progress_hook(t),
+                    data=None)
+        t.total = t.n
+
+        with tarfile.open(tar_path) as f:
+            if not only_child:
+                f.extractall(dest)
+            else:
+                for tarinfo in f:
+                    if "/" not in tarinfo.name:
+                        continue
+                    name = os.path.basename(tarinfo.name)
+                    fileobj = f.extractfile(tarinfo)
+                    with open(os.path.join(dest, name), "wb") as writer:
+                        writer.write(fileobj.read())
+
+
+class Hub(object):
+    """Hub for wenet pretrain runtime model
+    """
+    # TODO(Mddct): make assets class to support other language
+    Assets = {
+        # wenetspeech
+        "chinese":
+        "https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/chs.tar.gz",
+        # gigaspeech
+        "english":
+        "https://github.com/wenet-e2e/wenet/releases/download/v2.0.1/en.tar.gz"
+    }
+
+    def __init__(self) -> None:
+        pass
+
+    @staticmethod
+    def get_model_by_lang(lang: str) -> str:
+        if lang not in Hub.Assets.keys():
+            print('ERROR: Unsupported language {} !!!'.format(lang))
+            sys.exit(1)
+
+        # NOTE(Mddct): model_dir structure
+        # Path.Home()/.went
+        # - chs
+        #    - units.txt
+        #    - final.zip
+        # - en
+        #    - units.txt
+        #    - final.zip
+        model_url = Hub.Assets[lang]
+        model_dir = os.path.join(Path.home(), ".wenet", lang)
+        if not os.path.exists(model_dir):
+            os.makedirs(model_dir)
+        # TODO(Mddct): model metadata
+        if set(["final.zip",
+                "units.txt"]).issubset(set(os.listdir(model_dir))):
+            return model_dir
+        download(model_url, model_dir, only_child=True)
+        return model_dir

wenet/cli/transcribe.py

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+# Copyright (c) 2023 Binbin Zhang (binbzha@qq.com)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+
+import torch
+import torchaudio
+import torchaudio.compliance.kaldi as kaldi
+
+from wenet.cli.hub import Hub
+from wenet.utils.common import remove_duplicates_and_blank
+from wenet.utils.file_utils import read_symbol_table
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('audio_file', help='audio file to transcribe')
+    parser.add_argument('--language',
+                        choices=[
+                            'chinese',
+                            'english',
+                        ],
+                        default='chinese',
+                        help='language type')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+    model_dir = Hub.get_model_by_lang(args.language)
+    model_path = os.path.join(model_dir, 'final.zip')
+    units_path = os.path.join(model_dir, 'units.txt')
+    model = torch.jit.load(model_path)
+    symbol_table = read_symbol_table(units_path)
+    char_dict = {v: k for k, v in symbol_table.items()}
+    waveform, sample_rate = torchaudio.load(args.audio_file, normalize=False)
+    waveform = waveform.to(torch.float)
+    feats = kaldi.fbank(waveform,
+                        num_mel_bins=80,
+                        frame_length=25,
+                        frame_shift=10,
+                        energy_floor=0.0,
+                        sample_frequency=16000)
+    feats = feats.unsqueeze(0)
+    encoder_out, _, _ = model.forward_encoder_chunk(feats, 0, -1)
+    ctc_probs = model.ctc_activation(encoder_out)
+    topk_prob, topk_index = ctc_probs.topk(1, dim=2)
+    topk_index = topk_index.squeeze().tolist()
+    hyp = remove_duplicates_and_blank(topk_index)
+    hyp = [char_dict[x] for x in hyp]
+    result = ''.join(hyp)
+    print(result)
+
+
+if __name__ == "__main__":
+    main()