Add a link to Colab. (#14)

It demonstrates the usages of pre-trained models.

README.md

@@ -53,6 +53,13 @@ It should print the path to `icefall`.
 At present, only LibriSpeech recipe is provided. Please
 follow [egs/librispeech/ASR/README.md][LibriSpeech] to run it.
 
+## Use Pre-trained models
+
+See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md)
+for how to use pre-trained models.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+
+
 [LibriSpeech]: egs/librispeech/ASR/README.md
 [k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
 [k2]: https://github.com/k2-fsa/k2

egs/librispeech/ASR/conformer_ctc/README.md

@@ -1,6 +1,8 @@
 
 # How to use a pre-trained model to transcribe a sound file or multiple sound files
 
+(See the bottom of this document for the link to a colab notebook.)
+
 You need to prepare 4 files:
 
   - a model checkpoint file, e.g., epoch-20.pt
@@ -99,22 +101,25 @@ The command to run decoding with attention decoder rescoring is:
   /path/to/your/sound3.wav
 ```
 
-# Decoding with a pretrained model in action
+# Decoding with a pre-trained model in action
 
-We have uploaded a pretrained model to <https://huggingface.co/pkufool/conformer_ctc>
+We have uploaded a pre-trained model to <https://huggingface.co/pkufool/conformer_ctc>
 
-The following shows the steps about the usage of the provided pretrained model.
+The following shows the steps about the usage of the provided pre-trained model.
 
-### (1) Download the pretrained model
+### (1) Download the pre-trained model
 
 ```bash
+sudo apt-get install git-lfs
 cd /path/to/icefall/egs/librispeech/ASR
+git lfs install
 mkdir tmp
 cd tmp
 git clone https://huggingface.co/pkufool/conformer_ctc
-
 ```
 
+**CAUTION**: You have to install `git-lfst` to download the pre-trained model.
+
 You will find the following files:
 
 ```
@@ -165,7 +170,7 @@ tmp
 
   - `exp/pretrained.pt`
 
-      It contains pretrained model parameters, obtained by averaging
+      It contains pre-trained model parameters, obtained by averaging
       checkpoints from `epoch-15.pt` to `epoch-34.pt`.
       Note: We have removed optimizer `state_dict` to reduce file size.
 
@@ -337,3 +342,10 @@ YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
 
 2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done
 ```
+
+**NOTE**: We provide a colab notebook for demonstration.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+
+Due to limited memory provided by Colab, you have to upgrade to Colab Pro to
+run `HLG decoding + LM rescoring` and `HLG decoding + LM rescoring + attention decoder rescoring`.
+Otherwise, you can only run `HLG decoding` with Colab.

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -245,11 +245,11 @@ def main():
     if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
         logging.info(f"Loading G from {params.G}")
         G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        G = G.to(device)
         # Add epsilon self-loops to G as we will compose
         # it with the whole lattice later
         G = k2.add_epsilon_self_loops(G)
         G = k2.arc_sort(G)
-        G = G.to(device)
         G.lm_scores = G.scores.clone()
 
     logging.info("Constructing Fbank computer")