Installation

Please refer to https://icefall.readthedocs.io/en/latest/installation/index.html for installation.

Recipes

Please refer to https://icefall.readthedocs.io/en/latest/recipes/index.html for more information.

We provide four recipes at present:

• yesno
• LibriSpeech
• Aishell
• TIMIT

yesno

This is the simplest ASR recipe in icefall and can be run on CPU. Training takes less than 30 seconds and gives you the following WER:

[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]

We do provide a Colab notebook for this recipe.

LibriSpeech

We provide 4 models for this recipe:

• conformer CTC model
• TDNN LSTM CTC model
• Transducer: Conformer encoder + LSTM decoder
• Transducer: Conformer encoder + Embedding decoder

Conformer CTC Model

The best WER we currently have is:

	test-clean	test-other
WER	2.42	5.73

We provide a Colab notebook to run a pre-trained conformer CTC model:

TDNN LSTM CTC Model

The WER for this model is:

	test-clean	test-other
WER	6.59	17.69

We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:

Transducer: Conformer encoder + LSTM decoder

Using Conformer as encoder and LSTM as decoder.

The best WER with greedy search is:

	test-clean	test-other
WER	3.07	7.51

We provide a Colab notebook to run a pre-trained RNN-T conformer model:

Transducer: Conformer encoder + Embedding decoder

Using Conformer as encoder. The decoder consists of 1 embedding layer and 1 convolutional layer.

The best WER using modified beam search with beam size 4 is:

	test-clean	test-other
WER	2.67	6.64

Note: No auxiliary losses are used in the training and no LMs are used in the decoding.

We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model:

Aishell

We provide two models for this recipe: conformer CTC model and TDNN LSTM CTC model.

Conformer CTC Model

The best CER we currently have is:

	test
CER	4.26

We provide a Colab notebook to run a pre-trained conformer CTC model:

Transducer Stateless Model

The best CER we currently have is:

	test
CER	5.7

We provide a Colab notebook to run a pre-trained TransducerStateless model:

TDNN LSTM CTC Model

The CER for this model is:

	test
CER	10.16

We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:

TIMIT

We provide two models for this recipe: TDNN LSTM CTC model and TDNN LiGRU CTC model.

TDNN LSTM CTC Model

The best PER we currently have is:

	TEST
PER	19.71%

We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:

TDNN LiGRU CTC Model

The PER for this model is:

	TEST
PER	17.66%

We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model:

Deployment with C++

Once you have trained a model in icefall, you may want to deploy it with C++, without Python dependencies.

Please refer to the documentation https://icefall.readthedocs.io/en/latest/recipes/librispeech/conformer_ctc.html#deployment-with-c for how to do this.

We also provide a Colab notebook, showing you how to run a torch scripted model in k2 with C++. Please see:

Multi-GPU training server configurations

If compiled with compatible versions of CUDA, CUDNN and NCCL libraries, the ICEFALL reference training recipes can operate across pools of GPUs by splitting and balancing the training load among multiple GPU devices. The environment variable CUDA_VISIBLE_DEVICES defines a list of the local GPUs accessible from within the given environment. Device identification in the list assigned to CUDA_VISIBLE_DEVICES follows their indexes in "cuda/samples/1_Utilities/deviceQuery/deviceQuery" utility (e.g. 'export CUDA_VISIBLE_DEVICES="0,2,3"').

It was observed that enabling hardware virtualization (BIOS settings) may lead to a situation when the GPU devices stall without progress but referred as 100% utilized by the 'nvidia-smi' utility. A software-only remedy (one that doesn't require hardware reconfiguration, or restart) is possible through setting the environment variable 'NCCL_P2P_DISABLE=1'. Alternatively, in order to achieve higher memory transfer throughput rates, one can disable BIOS virtualization options like "Virtualization Technology" and/or "VT-d".