Installation

  • Supported operating systems

  • Supported devices

  • Supported python versions

  • Supported PyTorch versions

  • Supported k2 versions

icefall depends on k2 and lhotse.

We recommend you to use the following steps to install the dependencies.

    1. Install PyTorch and torchaudio

    1. Install k2

    1. Install lhotse

Caution

Installation order matters.

(0) Install PyTorch and torchaudio

Please refer https://pytorch.org/ to install PyTorch and torchaudio.

(1) Install k2

Please refer to https://k2-fsa.github.io/k2/installation/index.html to install k2.

Caution

You need to install k2 with a version at least v1.9.

Hint

If you have already installed PyTorch and don’t want to replace it, please install a version of k2 that is compiled against the version of PyTorch you are using.

(2) Install lhotse

Please refer to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation to install lhotse.

Hint

We strongly recommend you to use:

pip install git+https://github.com/lhotse-speech/lhotse

to install the latest version of lhotse.

(3) Download icefall

icefall is a collection of Python scripts; what you need is to download it and set the environment variable PYTHONPATH to point to it.

Assume you want to place icefall in the folder /tmp. The following commands show you how to setup icefall:

cd /tmp
git clone https://github.com/k2-fsa/icefall
cd icefall
pip install -r requirements.txt
export PYTHONPATH=/tmp/icefall:$PYTHONPATH

Hint

You can put several versions of icefall in the same virtual environment. To switch among different versions of icefall, just set PYTHONPATH to point to the version you want.

Installation example

The following shows an example about setting up the environment.

(1) Create a virtual environment

$ virtualenv -p python3.8  test-icefall

created virtual environment CPython3.8.6.final.0-64 in 1540ms
  creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
irtualenv)
    added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
  activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator

(2) Activate your virtual environment

$ source test-icefall/bin/activate

(3) Install k2

$ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html

Looking in links: https://k2-fsa.org/nightly/index.html
Collecting k2==1.4.dev20210822+cpu.torch1.9.0
  Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
     |________________________________| 1.6 MB 185 kB/s
Collecting graphviz
  Downloading graphviz-0.17-py3-none-any.whl (18 kB)
Collecting torch==1.9.0
  Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
Collecting typing-extensions
  Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
Installing collected packages: typing-extensions, torch, graphviz, k2
Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0

Warning

We choose to install a CPU version of k2 for testing. You would probably want to install a CUDA version of k2.

(4) Install lhotse

$ pip install git+https://github.com/lhotse-speech/lhotse

Collecting git+https://github.com/lhotse-speech/lhotse
  Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
  Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
Collecting audioread>=2.1.9
  Using cached audioread-2.1.9-py3-none-any.whl
Collecting SoundFile>=0.10
  Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
Collecting click>=7.1.1
  Using cached click-8.0.1-py3-none-any.whl (97 kB)
Collecting cytoolz>=0.10.1
  Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
Collecting dataclasses
  Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
Collecting h5py>=2.10.0
  Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
     |________________________________| 4.5 MB 684 kB/s
Collecting intervaltree>=3.1.0
  Using cached intervaltree-3.1.0-py2.py3-none-any.whl
Collecting lilcom>=1.1.0
  Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
Collecting numpy>=1.18.1
  Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
Collecting packaging
  Using cached packaging-21.0-py3-none-any.whl (40 kB)
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Collecting tqdm
  Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
     |________________________________| 76 kB 2.7 MB/s
Collecting torchaudio==0.9.0
  Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
     |________________________________| 1.9 MB 73.1 MB/s
Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev
-2a1410b-clean) (1.9.0)
Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
>lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
Collecting toolz>=0.8.0
  Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
Collecting sortedcontainers<3.0,>=2.0
  Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
Collecting cffi>=1.0
  Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
Collecting pycparser
  Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
Collecting pyparsing>=2.0.2
  Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
Building wheels for collected packages: lhotse
  Building wheel for lhotse (setup.py) ... done
  Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
9d0f774224cc84000f55d0a67f6e4a37997
  Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
  WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
Failed to build lhotse
Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
    Running setup.py install for lhotse ... done
  DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible
 replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is
sues/8368.
Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter
valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1

(5) Download icefall

$ cd /tmp
$ git clone https://github.com/k2-fsa/icefall

Cloning into 'icefall'...
remote: Enumerating objects: 500, done.
remote: Counting objects: 100% (500/500), done.
remote: Compressing objects: 100% (308/308), done.
remote: Total 500 (delta 263), reused 307 (delta 102), pack-reused 0
Receiving objects: 100% (500/500), 172.49 KiB | 385.00 KiB/s, done.
Resolving deltas: 100% (263/263), done.

$ cd icefall
$ pip install -r requirements.txt

Collecting kaldilm
  Downloading kaldilm-1.8.tar.gz (48 kB)
     |________________________________| 48 kB 574 kB/s
Collecting kaldialign
  Using cached kaldialign-0.2-cp38-cp38-linux_x86_64.whl
Collecting sentencepiece>=0.1.96
  Using cached sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
Collecting tensorboard
  Using cached tensorboard-2.6.0-py3-none-any.whl (5.6 MB)
Requirement already satisfied: setuptools>=41.0.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r
requirements.txt (line 4)) (57.4.0)
Collecting absl-py>=0.4
  Using cached absl_py-0.13.0-py3-none-any.whl (132 kB)
Collecting google-auth-oauthlib<0.5,>=0.4.1
  Using cached google_auth_oauthlib-0.4.5-py2.py3-none-any.whl (18 kB)
Collecting grpcio>=1.24.3
  Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB)
Requirement already satisfied: wheel>=0.26 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r require
ments.txt (line 4)) (0.36.2)
Requirement already satisfied: numpy>=1.12.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r requi
rements.txt (line 4)) (1.21.2)
Collecting protobuf>=3.6.0
  Using cached protobuf-3.17.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)
Collecting werkzeug>=0.11.15
  Using cached Werkzeug-2.0.1-py3-none-any.whl (288 kB)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
  Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)
Collecting google-auth<2,>=1.6.3
  Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)
     |________________________________| 152 kB 1.4 MB/s
Collecting requests<3,>=2.21.0
  Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB)
Collecting tensorboard-plugin-wit>=1.6.0
  Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)
Collecting markdown>=2.6.8
  Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)
Collecting six
  Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting cachetools<5.0,>=2.0.0
  Using cached cachetools-4.2.2-py3-none-any.whl (11 kB)
Collecting rsa<5,>=3.1.4
  Using cached rsa-4.7.2-py3-none-any.whl (34 kB)
Collecting pyasn1-modules>=0.2.1
  Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
Collecting requests-oauthlib>=0.7.0
  Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)
Collecting pyasn1<0.5.0,>=0.4.6
  Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
Collecting urllib3<1.27,>=1.21.1
  Using cached urllib3-1.26.6-py2.py3-none-any.whl (138 kB)
Collecting certifi>=2017.4.17
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Collecting charset-normalizer~=2.0.0
  Using cached charset_normalizer-2.0.4-py3-none-any.whl (36 kB)
Collecting idna<4,>=2.5
  Using cached idna-3.2-py3-none-any.whl (59 kB)
Collecting oauthlib>=3.0.0
  Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)
Building wheels for collected packages: kaldilm
  Building wheel for kaldilm (setup.py) ... done
  Created wheel for kaldilm: filename=kaldilm-1.8-cp38-cp38-linux_x86_64.whl size=897233 sha256=eccb906cafcd45bf9a7e1a1718e4534254bfb
f4c0d0cbc66eee6c88d68a63862
  Stored in directory: /root/fangjun/.cache/pip/wheels/85/7d/63/f2dd586369b8797cb36d213bf3a84a789eeb92db93d2e723c9
Successfully built kaldilm
Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, six, rsa, requests, pyasn1-modules, oauthlib, cach
etools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google
-auth-oauthlib, absl-py, tensorboard, sentencepiece, kaldilm, kaldialign
Successfully installed absl-py-0.13.0 cachetools-4.2.2 certifi-2021.5.30 charset-normalizer-2.0.4 google-auth-1.35.0 google-auth-oaut
hlib-0.4.5 grpcio-1.39.0 idna-3.2 kaldialign-0.2 kaldilm-1.8 markdown-3.3.4 oauthlib-3.1.1 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-module
s-0.2.8 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 sentencepiece-0.1.96 six-1.16.0 tensorboard-2.6.0 tensorboard-data-server-0
.6.1 tensorboard-plugin-wit-1.8.0 urllib3-1.26.6 werkzeug-2.0.1

Test Your Installation

To test that your installation is successful, let us run the yesno recipe on CPU.

Data preparation

$ export PYTHONPATH=/tmp/icefall:$PYTHONPATH
$ cd /tmp/icefall
$ cd egs/yesno/ASR
$ ./prepare.sh

The log of running ./prepare.sh is:

2021-08-23 19:27:26 (prepare.sh:24:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
2021-08-23 19:27:26 (prepare.sh:27:main) stage 0: Download data
Downloading waves_yesno.tar.gz: 4.49MB [00:03, 1.39MB/s]
2021-08-23 19:27:30 (prepare.sh:36:main) Stage 1: Prepare yesno manifest
2021-08-23 19:27:31 (prepare.sh:42:main) Stage 2: Compute fbank for yesno
2021-08-23 19:27:32,803 INFO [compute_fbank_yesno.py:52] Processing train
Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:01<00:00, 80.57it/s]
2021-08-23 19:27:34,085 INFO [compute_fbank_yesno.py:52] Processing test
Extracting and storing features: 100%|______________________________________________________________| 30/30 [00:00<00:00, 248.21it/s]
2021-08-23 19:27:34 (prepare.sh:48:main) Stage 3: Prepare lang
2021-08-23 19:27:35 (prepare.sh:63:main) Stage 4: Prepare G
/tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
d(std::istream&):79
[I] Reading \data\ section.
/tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
d(std::istream&):140
[I] Reading \1-grams: section.
2021-08-23 19:27:35 (prepare.sh:89:main) Stage 5: Compile HLG
2021-08-23 19:27:35,928 INFO [compile_hlg.py:120] Processing data/lang_phone
2021-08-23 19:27:35,929 INFO [lexicon.py:116] Converting L.pt to Linv.pt
2021-08-23 19:27:35,931 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
2021-08-23 19:27:35,932 INFO [compile_hlg.py:52] Loading G.fst.txt
2021-08-23 19:27:35,932 INFO [compile_hlg.py:62] Intersecting L and G
2021-08-23 19:27:35,933 INFO [compile_hlg.py:64] LG shape: (4, None)
2021-08-23 19:27:35,933 INFO [compile_hlg.py:66] Connecting LG
2021-08-23 19:27:35,933 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
2021-08-23 19:27:35,933 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
2021-08-23 19:27:35,933 INFO [compile_hlg.py:71] Determinizing LG
2021-08-23 19:27:35,934 INFO [compile_hlg.py:74] <class '_k2.RaggedInt'>
2021-08-23 19:27:35,934 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
2021-08-23 19:27:35,934 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
2021-08-23 19:27:35,934 INFO [compile_hlg.py:87] LG shape after k2.remove_epsilon: (6, None)
2021-08-23 19:27:35,935 INFO [compile_hlg.py:92] Arc sorting LG
2021-08-23 19:27:35,935 INFO [compile_hlg.py:95] Composing H and LG
2021-08-23 19:27:35,935 INFO [compile_hlg.py:102] Connecting LG
2021-08-23 19:27:35,935 INFO [compile_hlg.py:105] Arc sorting LG
2021-08-23 19:27:35,936 INFO [compile_hlg.py:107] HLG.shape: (8, None)
2021-08-23 19:27:35,936 INFO [compile_hlg.py:123] Saving HLG.pt to data/lang_phone

Training

Now let us run the training part:

$ export CUDA_VISIBLE_DEVICES=""
$ ./tdnn/train.py

Caution

We use export CUDA_VISIBLE_DEVICES="" so that icefall uses CPU even if there are GPUs available.

The training log is given below:

2021-08-23 19:30:31,072 INFO [train.py:465] Training started
2021-08-23 19:30:31,072 INFO [train.py:466] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01,
'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, '
best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_doub
le_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'feature_dir': PosixPath('data/fbank'
), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0
, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
2021-08-23 19:30:31,074 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
2021-08-23 19:30:31,098 INFO [asr_datamodule.py:146] About to get train cuts
2021-08-23 19:30:31,098 INFO [asr_datamodule.py:240] About to get train cuts
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:149] About to create train dataset
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:200] Using SingleCutSampler.
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:206] About to create train dataloader
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:219] About to get test cuts
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:246] About to get test cuts
2021-08-23 19:30:31,357 INFO [train.py:416] Epoch 0, batch 0, batch avg loss 1.0789, total avg loss: 1.0789, batch size: 4
2021-08-23 19:30:31,848 INFO [train.py:416] Epoch 0, batch 10, batch avg loss 0.5356, total avg loss: 0.7556, batch size: 4
2021-08-23 19:30:32,301 INFO [train.py:432] Epoch 0, valid loss 0.9972, best valid loss: 0.9972 best valid epoch: 0
2021-08-23 19:30:32,805 INFO [train.py:416] Epoch 0, batch 20, batch avg loss 0.2436, total avg loss: 0.5717, batch size: 3
2021-08-23 19:30:33,109 INFO [train.py:432] Epoch 0, valid loss 0.4167, best valid loss: 0.4167 best valid epoch: 0
2021-08-23 19:30:33,121 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-0.pt
2021-08-23 19:30:33,325 INFO [train.py:416] Epoch 1, batch 0, batch avg loss 0.2214, total avg loss: 0.2214, batch size: 5
2021-08-23 19:30:33,798 INFO [train.py:416] Epoch 1, batch 10, batch avg loss 0.0781, total avg loss: 0.1343, batch size: 5
2021-08-23 19:30:34,065 INFO [train.py:432] Epoch 1, valid loss 0.0859, best valid loss: 0.0859 best valid epoch: 1
2021-08-23 19:30:34,556 INFO [train.py:416] Epoch 1, batch 20, batch avg loss 0.0421, total avg loss: 0.0975, batch size: 3
2021-08-23 19:30:34,810 INFO [train.py:432] Epoch 1, valid loss 0.0431, best valid loss: 0.0431 best valid epoch: 1
2021-08-23 19:30:34,824 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-1.pt

... ...

2021-08-23 19:30:49,657 INFO [train.py:416] Epoch 13, batch 0, batch avg loss 0.0109, total avg loss: 0.0109, batch size: 5
2021-08-23 19:30:49,984 INFO [train.py:416] Epoch 13, batch 10, batch avg loss 0.0093, total avg loss: 0.0096, batch size: 4
2021-08-23 19:30:50,239 INFO [train.py:432] Epoch 13, valid loss 0.0104, best valid loss: 0.0101 best valid epoch: 12
2021-08-23 19:30:50,569 INFO [train.py:416] Epoch 13, batch 20, batch avg loss 0.0092, total avg loss: 0.0096, batch size: 2
2021-08-23 19:30:50,819 INFO [train.py:432] Epoch 13, valid loss 0.0101, best valid loss: 0.0101 best valid epoch: 13
2021-08-23 19:30:50,835 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-13.pt
2021-08-23 19:30:51,024 INFO [train.py:416] Epoch 14, batch 0, batch avg loss 0.0105, total avg loss: 0.0105, batch size: 5
2021-08-23 19:30:51,317 INFO [train.py:416] Epoch 14, batch 10, batch avg loss 0.0099, total avg loss: 0.0097, batch size: 4
2021-08-23 19:30:51,552 INFO [train.py:432] Epoch 14, valid loss 0.0108, best valid loss: 0.0101 best valid epoch: 13
2021-08-23 19:30:51,869 INFO [train.py:416] Epoch 14, batch 20, batch avg loss 0.0096, total avg loss: 0.0097, batch size: 5
2021-08-23 19:30:52,107 INFO [train.py:432] Epoch 14, valid loss 0.0102, best valid loss: 0.0101 best valid epoch: 13
2021-08-23 19:30:52,126 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-14.pt
2021-08-23 19:30:52,128 INFO [train.py:537] Done!

Decoding

Let us use the trained model to decode the test set:

$ ./tdnn/decode.py

The decoding log is:

2021-08-23 19:35:30,192 INFO [decode.py:249] Decoding started
2021-08-23 19:35:30,192 INFO [decode.py:250] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
2021-08-23 19:35:30,193 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
2021-08-23 19:35:30,213 INFO [decode.py:259] device: cpu
2021-08-23 19:35:30,217 INFO [decode.py:279] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
/tmp/icefall/icefall/checkpoint.py:146: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch.
It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /pytorch/aten/src/ATen/native/BinaryOps.cpp:450.)
  avg[k] //= n
2021-08-23 19:35:30,220 INFO [asr_datamodule.py:219] About to get test cuts
2021-08-23 19:35:30,220 INFO [asr_datamodule.py:246] About to get test cuts
2021-08-23 19:35:30,409 INFO [decode.py:190] batch 0/8, cuts processed until now is 4
2021-08-23 19:35:30,571 INFO [decode.py:228] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2021-08-23 19:35:30,572 INFO [utils.py:317] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2021-08-23 19:35:30,573 INFO [decode.py:236] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2021-08-23 19:35:30,573 INFO [decode.py:299] Done!

Congratulations! You have successfully setup the environment and have run the first recipe in icefall.

Have fun with icefall!

YouTube Video

We provide the following YouTube video showing how to install icefall. It also shows how to debug various problems that you may encounter while using icefall.

Note

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