Installation

icefall depends on k2 and lhotse.

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

1. Install CUDA toolkit and cuDNN
1. Install PyTorch and torchaudio
1. Install k2
1. Install lhotse

Caution

99% users who have issues about the installation are using conda.

Caution

99% users who have issues about the installation are using conda.

Caution

99% users who have issues about the installation are using conda.

Hint

We suggest that you use pip install to install PyTorch.

You can use the following command to create a virutal environment in Python:

python3 -m venv ./my_env
source ./my_env/bin/activate

Caution

Installation order matters.

(0) Install CUDA toolkit and cuDNN

Please refer to https://k2-fsa.github.io/k2/installation/cuda-cudnn.html to install CUDA and cuDNN.

(1) Install PyTorch and torchaudio

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

Hint

You can also go to https://download.pytorch.org/whl/torch_stable.html to download pre-compiled wheels and install them.

Caution

Please install torch and torchaudio at the same time.

(2) Install k2

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

Caution

Please don’t change your installed PyTorch after you have installed k2.

Note

We suggest that you install k2 from source by following https://k2-fsa.github.io/k2/installation/from_source.html or https://k2-fsa.github.io/k2/installation/for_developers.html.

Hint

Please always install the latest version of k2.

(3) 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.

(4) 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)
Collecting pyyaml>=5.3.1
  Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
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
  Using cached certifi-2021.5.30-py2.py3-none-any.whl (145 kB)
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:

2023-05-12 17:55:21 (prepare.sh:27:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
2023-05-12 17:55:21 (prepare.sh:30:main) Stage 0: Download data
/tmp/icefall/egs/yesno/ASR/download/waves_yesno.tar.gz: 100%|_______________________________________________________________| 4.70M/4.70M [06:54<00:00, 11.4kB/s]
2023-05-12 18:02:19 (prepare.sh:39:main) Stage 1: Prepare yesno manifest
2023-05-12 18:02:21 (prepare.sh:45:main) Stage 2: Compute fbank for yesno
2023-05-12 18:02:23,199 INFO [compute_fbank_yesno.py:65] Processing train
Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:00<00:00, 212.60it/s]
2023-05-12 18:02:23,640 INFO [compute_fbank_yesno.py:65] Processing test
Extracting and storing features: 100%|_______________________________________________________________| 30/30 [00:00<00:00, 304.53it/s]
2023-05-12 18:02:24 (prepare.sh:51:main) Stage 3: Prepare lang
2023-05-12 18:02:26 (prepare.sh:66:main) Stage 4: Prepare G
/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):79
[I] Reading \data\ section.
/project/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Read(std::istream&):140
[I] Reading \1-grams: section.
2023-05-12 18:02:26 (prepare.sh:92:main) Stage 5: Compile HLG
2023-05-12 18:02:28,581 INFO [compile_hlg.py:124] Processing data/lang_phone
2023-05-12 18:02:28,582 INFO [lexicon.py:171] Converting L.pt to Linv.pt
2023-05-12 18:02:28,609 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
2023-05-12 18:02:28,610 INFO [compile_hlg.py:52] Loading G.fst.txt
2023-05-12 18:02:28,611 INFO [compile_hlg.py:62] Intersecting L and G
2023-05-12 18:02:28,613 INFO [compile_hlg.py:64] LG shape: (4, None)
2023-05-12 18:02:28,613 INFO [compile_hlg.py:66] Connecting LG
2023-05-12 18:02:28,614 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
2023-05-12 18:02:28,614 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
2023-05-12 18:02:28,614 INFO [compile_hlg.py:71] Determinizing LG
2023-05-12 18:02:28,615 INFO [compile_hlg.py:74] <class '_k2.ragged.RaggedTensor'>
2023-05-12 18:02:28,615 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
2023-05-12 18:02:28,615 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
2023-05-12 18:02:28,616 INFO [compile_hlg.py:91] LG shape after k2.remove_epsilon: (6, None)
2023-05-12 18:02:28,617 INFO [compile_hlg.py:96] Arc sorting LG
2023-05-12 18:02:28,617 INFO [compile_hlg.py:99] Composing H and LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:106] Connecting LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:109] Arc sorting LG
2023-05-12 18:02:28,619 INFO [compile_hlg.py:111] HLG.shape: (8, None)
2023-05-12 18:02:28,619 INFO [compile_hlg.py:127] 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.

Hint

In case you get a Segmentation fault (core dump) error, please use:

export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

See more at <https://github.com/k2-fsa/icefall/issues/674> if you are interested.

The training log is given below:

 2023-05-12 18:04:59,759 INFO [train.py:481] Training started
 2023-05-12 18:04:59,759 INFO [train.py:482] {'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, 'reset_interval': 20, 'valid_interval': 10, 'beam_size': 10,
 'reduction': 'sum', 'use_double_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'seed': 42, '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': False, 'return_cuts': True, 'num_workers': 2,
 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023',
 'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master',
 'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall',
 'k2-path': 'tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py',
 'lhotse-path': 'tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
 2023-05-12 18:04:59,761 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
 2023-05-12 18:04:59,764 INFO [train.py:495] device: cpu
 2023-05-12 18:04:59,791 INFO [asr_datamodule.py:146] About to get train cuts
 2023-05-12 18:04:59,791 INFO [asr_datamodule.py:244] About to get train cuts
 2023-05-12 18:04:59,852 INFO [asr_datamodule.py:149] About to create train dataset
 2023-05-12 18:04:59,852 INFO [asr_datamodule.py:199] Using SingleCutSampler.
 2023-05-12 18:04:59,852 INFO [asr_datamodule.py:205] About to create train dataloader
 2023-05-12 18:04:59,853 INFO [asr_datamodule.py:218] About to get test cuts
 2023-05-12 18:04:59,853 INFO [asr_datamodule.py:252] About to get test cuts
 2023-05-12 18:04:59,986 INFO [train.py:422] Epoch 0, batch 0, loss[loss=1.065, over 2436.00 frames. ], tot_loss[loss=1.065, over 2436.00 frames. ], batch size: 4
 2023-05-12 18:05:00,352 INFO [train.py:422] Epoch 0, batch 10, loss[loss=0.4561, over 2828.00 frames. ], tot_loss[loss=0.7076, over 22192.90 frames. ], batch size: 4
 2023-05-12 18:05:00,691 INFO [train.py:444] Epoch 0, validation loss=0.9002, over 18067.00 frames.
 2023-05-12 18:05:00,996 INFO [train.py:422] Epoch 0, batch 20, loss[loss=0.2555, over 2695.00 frames. ], tot_loss[loss=0.484, over 34971.47 frames. ], batch size: 5
 2023-05-12 18:05:01,217 INFO [train.py:444] Epoch 0, validation loss=0.4688, over 18067.00 frames.
 2023-05-12 18:05:01,251 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-0.pt
 2023-05-12 18:05:01,389 INFO [train.py:422] Epoch 1, batch 0, loss[loss=0.2532, over 2436.00 frames. ], tot_loss[loss=0.2532, over 2436.00 frames. ], batch size: 4
 2023-05-12 18:05:01,637 INFO [train.py:422] Epoch 1, batch 10, loss[loss=0.1139, over 2828.00 frames. ], tot_loss[loss=0.1592, over 22192.90 frames. ], batch size: 4
 2023-05-12 18:05:01,859 INFO [train.py:444] Epoch 1, validation loss=0.1629, over 18067.00 frames.
 2023-05-12 18:05:02,094 INFO [train.py:422] Epoch 1, batch 20, loss[loss=0.0767, over 2695.00 frames. ], tot_loss[loss=0.118, over 34971.47 frames. ], batch size: 5
 2023-05-12 18:05:02,350 INFO [train.py:444] Epoch 1, validation loss=0.06778, over 18067.00 frames.
 2023-05-12 18:05:02,395 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-1.pt

... ...

 2023-05-12 18:05:14,789 INFO [train.py:422] Epoch 13, batch 0, loss[loss=0.01056, over 2436.00 frames. ], tot_loss[loss=0.01056, over 2436.00 frames. ], batch size: 4
 2023-05-12 18:05:15,016 INFO [train.py:422] Epoch 13, batch 10, loss[loss=0.009022, over 2828.00 frames. ], tot_loss[loss=0.009985, over 22192.90 frames. ], batch size: 4
 2023-05-12 18:05:15,271 INFO [train.py:444] Epoch 13, validation loss=0.01088, over 18067.00 frames.
 2023-05-12 18:05:15,497 INFO [train.py:422] Epoch 13, batch 20, loss[loss=0.01174, over 2695.00 frames. ], tot_loss[loss=0.01077, over 34971.47 frames. ], batch size: 5
 2023-05-12 18:05:15,747 INFO [train.py:444] Epoch 13, validation loss=0.01087, over 18067.00 frames.
 2023-05-12 18:05:15,783 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-13.pt
 2023-05-12 18:05:15,921 INFO [train.py:422] Epoch 14, batch 0, loss[loss=0.01045, over 2436.00 frames. ], tot_loss[loss=0.01045, over 2436.00 frames. ], batch size: 4
 2023-05-12 18:05:16,146 INFO [train.py:422] Epoch 14, batch 10, loss[loss=0.008957, over 2828.00 frames. ], tot_loss[loss=0.009903, over 22192.90 frames. ], batch size: 4
 2023-05-12 18:05:16,374 INFO [train.py:444] Epoch 14, validation loss=0.01092, over 18067.00 frames.
 2023-05-12 18:05:16,598 INFO [train.py:422] Epoch 14, batch 20, loss[loss=0.01169, over 2695.00 frames. ], tot_loss[loss=0.01065, over 34971.47 frames. ], batch size: 5
 2023-05-12 18:05:16,824 INFO [train.py:444] Epoch 14, validation loss=0.01077, over 18067.00 frames.
 2023-05-12 18:05:16,862 INFO [checkpoint.py:75] Saving checkpoint to tdnn/exp/epoch-14.pt
 2023-05-12 18:05:16,865 INFO [train.py:555] Done!

Decoding

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

$ ./tdnn/decode.py

The decoding log is:

2023-05-12 18:08:30,482 INFO [decode.py:263] Decoding started
2023-05-12 18:08:30,483 INFO [decode.py:264] {'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, 'export': False, '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': False, 'return_cuts': True,
'num_workers': 2, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b7f09fa35e72589914f67089c0da9f196a92ca4', 'k2-git-date': 'Mon May 8 22:58:45 2023',
'lhotse-version': '1.15.0.dev+git.6fcfced.clean', 'torch-version': '2.0.0+cu118', 'torch-cuda-available': False, 'torch-cuda-version': '11.8', 'python-version': '3.1', 'icefall-git-branch': 'master',
'icefall-git-sha1': '30bde4b-clean', 'icefall-git-date': 'Thu May 11 17:37:47 2023', 'icefall-path': '/tmp/icefall',
'k2-path': '/tmp/lib/python3.10/site-packages/k2-1.24.3.dev20230512+cuda11.8.torch2.0.0-py3.10-linux-x86_64.egg/k2/__init__.py',
'lhotse-path': '/tmp/lib/python3.10/site-packages/lhotse/__init__.py', 'hostname': 'host', 'IP address': '0.0.0.0'}}
2023-05-12 18:08:30,483 INFO [lexicon.py:168] Loading pre-compiled data/lang_phone/Linv.pt
2023-05-12 18:08:30,487 INFO [decode.py:273] device: cpu
2023-05-12 18:08:30,513 INFO [decode.py:291] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
2023-05-12 18:08:30,521 INFO [asr_datamodule.py:218] About to get test cuts
2023-05-12 18:08:30,521 INFO [asr_datamodule.py:252] About to get test cuts
2023-05-12 18:08:30,675 INFO [decode.py:204] batch 0/?, cuts processed until now is 4
2023-05-12 18:08:30,923 INFO [decode.py:241] The transcripts are stored in tdnn/exp/recogs-test_set.txt
2023-05-12 18:08:30,924 INFO [utils.py:558] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
2023-05-12 18:08:30,925 INFO [decode.py:249] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
2023-05-12 18:08:30,925 INFO [decode.py:316] 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

To get the latest news of next-gen Kaldi, please subscribe the following YouTube channel by Nadira Povey:

https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw