4890e27b45
* Use new APIs with k2.RaggedTensor * Fix style issues. * Update the installation doc, saying it requires at least k2 v1.7 * Extract framewise alignment information using CTC decoding. * Print environment information. Print information about k2, lhotse, PyTorch, and icefall. * Fix CI. * Fix CI. * Compute framewise alignment information of the LibriSpeech dataset. * Update comments for the time to compute alignments of train-960. * Preserve cut id in mix cut transformer. * Minor fixes. * Add doc about how to extract framewise alignments.
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 two recipes at present:
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 two models for this recipe: conformer CTC model and TDNN LSTM CTC model.
Conformer CTC Model
The best WER we currently have is:
| test-clean | test-other | |
|---|---|---|
| WER | 2.57% | 5.94% |
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:
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: