Merge remote-tracking branch 'dan/master' into ctc-ali

.github/workflows/test.yml

@@ -53,6 +53,20 @@ jobs:
           # icefall requirements
           pip install -r requirements.txt
 
+      - name: Install graphviz
+        if: startsWith(matrix.os, 'ubuntu')
+        shell: bash
+        run: |
+          python3 -m pip install -qq graphviz
+          sudo apt-get -qq install graphviz
+
+      - name: Install graphviz
+        if: startsWith(matrix.os, 'macos')
+        shell: bash
+        run: |
+          python3 -m pip install -qq graphviz
+          brew install -q graphviz
+
       - name: Run tests
         if: startsWith(matrix.os, 'ubuntu')
         run: |

egs/librispeech/ASR/RESULTS.md

@@ -21,6 +21,32 @@ To get more unique paths, we scaled the lattice.scores with 0.5 (see https://git
 |test-clean|1.3|1.2|
 |test-other|1.2|1.1|
 
+You can use the following commands to reproduce our results:
+
+```bash
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+
+# It was using ef233486, you may not need to switch to it
+# git checkout ef233486
+
+cd egs/librispeech/ASR
+./prepare.sh
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+python conformer_ctc/train.py --bucketing-sampler True \
+                              --concatenate-cuts False \
+                              --max-duration 200 \
+                              --full-libri True \
+                              --world-size 4
+
+python conformer_ctc/decode.py --lattice-score-scale 0.5 \
+                               --epoch 34 \
+                               --avg 20 \
+                               --method attention-decoder \
+                               --max-duration 20 \
+                               --num-paths 100
+```
 
 ### LibriSpeech training results (Tdnn-Lstm)
 #### 2021-08-24

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -108,7 +108,7 @@ def get_parser():
     parser.add_argument(
         "--lattice-score-scale",
         type=float,
-        default=1.0,
+        default=0.5,
         help="""The scale to be applied to `lattice.scores`.
         It's needed if you use any kinds of n-best based rescoring.
         Used only when "method" is one of the following values:
@@ -278,7 +278,8 @@ def decode_one_batch(
         "attention-decoder",
     ]
 
-    lm_scale_list = [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
+    lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+    lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
     lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
 
     if params.method == "nbest-rescoring":

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -82,14 +82,14 @@ class LibriSpeechAsrDataModule(DataModule):
         group.add_argument(
             "--max-duration",
             type=int,
-            default=500.0,
+            default=200.0,
             help="Maximum pooled recordings duration (seconds) in a "
             "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
-            default=False,
+            default=True,
             help="When enabled, the batches will come from buckets of "
             "similar duration (saves padding frames).",
         )

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -206,7 +206,8 @@ def decode_one_batch(
 
     assert params.method in ["nbest-rescoring", "whole-lattice-rescoring"]
 
-    lm_scale_list = [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
+    lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+    lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
     lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
 
     if params.method == "nbest-rescoring":