Add Colab notebook for the yesno dataset.

README.md

@@ -48,10 +48,22 @@ python3 -c "import icefall; print(icefall.__file__)"
 
 It should print the path to `icefall`.
 
-## Run recipes
+## Recipes
 
-At present, only LibriSpeech recipe is provided. Please
+At present, two recipes are provided:
-follow [egs/librispeech/ASR/README.md][LibriSpeech] to run it.
+
+  - [LibriSpeech][LibriSpeech]
+  - [yesno][yesno] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
+
+### Yesno
+
+For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**.
+
+The WER is
+
+```
+[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
+```
 
 ## Use Pre-trained models
 
@@ -60,6 +72,7 @@ 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)
 
 
+[yesno]: egs/yesno/ASR/README.md
 [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/local/compute_fbank_librispeech.py

@@ -2,7 +2,7 @@
 
 """
 This file computes fbank features of the LibriSpeech dataset.
-Its looks for manifests in the directory data/manifests.
+It looks for manifests in the directory data/manifests.
 
 The generated fbank features are saved in data/fbank.
 """
@@ -53,7 +53,8 @@ def compute_fbank_librispeech():
                 continue
             logging.info(f"Processing {partition}")
             cut_set = CutSet.from_manifests(
-                recordings=m["recordings"], supervisions=m["supervisions"],
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
             )
             if "train" in partition:
                 cut_set = (

egs/librispeech/ASR/local/compute_fbank_musan.py

@@ -2,7 +2,7 @@
 
 """
 This file computes fbank features of the musan dataset.
-Its looks for manifests in the directory data/manifests.
+It looks for manifests in the directory data/manifests.
 
 The generated fbank features are saved in data/fbank.
 """

egs/yesno/ASR/README.md

@@ -0,0 +1,15 @@
+## Yesno recipe
+
+You can run the recipe with **CPU**.
+
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
+
+The above Colab notebook finishes the training using **CPU**
+within two minutes (50 epochs in total).
+
+The WER is
+
+```
+[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
+```

egs/yesno/ASR/local/compute_fbank_yesno.py

@@ -2,7 +2,7 @@
 
 """
 This file computes fbank features of the yesno dataset.
-Its looks for manifests in the directory data/manifests.
+It looks for manifests in the directory data/manifests.
 
 The generated fbank features are saved in data/fbank.
 """