refactor data pipe; fix conf; fix u2 default params

deepspeech/exps/u2/config.py

@@ -18,6 +18,7 @@ from deepspeech.exps.u2.model import U2Trainer
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.u2 import U2Model
 
+
 _C = CfgNode()
 
 _C.data = ManifestDataset.params()

deepspeech/frontend/normalizer.py

@@ -148,6 +148,8 @@ class FeatureNormalizer(object):
                           batch_size=64,
                           eps=1e-20):
         """Compute mean and std from randomly sampled instances."""
+        paddle.set_device('cpu')
+        
         collate_func = CollateFunc(featurize_func)
         dataset = AudioDataset(manifest_path, num_samples, self._rng)
         data_loader = DataLoader(

deepspeech/models/u2.py

@@ -61,12 +61,14 @@ class U2BaseModel(nn.Module):
     def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
         # network architecture
         default = CfgNode()
+        # allow add new item when merge_with_file
+        default.set_new_allowed(True)
         default.cmvn_file = ""
         default.cmvn_file_type = "npz"
         default.input_dim = 0
         default.output_dim = 0
         # encoder related
-        default.encoder = 'conformer'
+        default.encoder = 'transformer'
         default.encoder_conf = CfgNode(
             dict(
                 output_size=256,  # dimension of attention
@@ -78,11 +80,12 @@ class U2BaseModel(nn.Module):
                 attention_dropout_rate=0.0,
                 input_layer='conv2d',  # encoder input type, you can chose conv2d, conv2d6 and conv2d8
                 normalize_before=True,
-                cnn_module_kernel=15,
+                # use_cnn_module=True,
-                use_cnn_module=True,
+                # cnn_module_kernel=15,
-                activation_type='swish',
+                # activation_type='swish',
-                pos_enc_layer_type='rel_pos',
+                # pos_enc_layer_type='rel_pos',
-                selfattention_layer_type='rel_selfattn', ))
+                # selfattention_layer_type='rel_selfattn', 
+                ))
         # decoder related
         default.decoder = 'transformer'
         default.decoder_conf = CfgNode(

examples/librispeech/s1/conf/chunk_confermer.yaml

@@ -1,11 +1,11 @@
 # https://yaml.org/type/float.html
 data:
-  train_manifest: data/manifest.tiny
+  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.tiny
+  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.tiny
+  test_manifest: data/manifest.test
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
-  spm_model_prefix: 'data/bpe_unigram_200'
+  spm_model_prefix: 'data/bpe_unigram_5000'
   mean_std_filepath: ""
   augmentation_config: conf/augmentation.json
   batch_size: 4

examples/librispeech/s1/conf/chunk_transformer.yaml

@@ -1,11 +1,11 @@
 # https://yaml.org/type/float.html
 data:
-  train_manifest: data/manifest.tiny
+  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.tiny
+  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.tiny
+  test_manifest: data/manifest.test
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
-  spm_model_prefix: 'data/bpe_unigram_200'
+  spm_model_prefix: 'data/bpe_unigram_5000'
   mean_std_filepath: ""
   augmentation_config: conf/augmentation.json
   batch_size: 64

examples/librispeech/s1/conf/conformer.yaml

@@ -1,8 +1,8 @@
 # https://yaml.org/type/float.html
 data:
-  train_manifest: data/manifest.tiny
+  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.tiny
+  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.tiny
+  test_manifest: data/manifest.test
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_5000'

examples/librispeech/s1/conf/transformer.yaml

@@ -1,11 +1,11 @@
 # https://yaml.org/type/float.html
 data:
-  train_manifest: data/manifest.tiny
+  train_manifest: data/manifest.train
-  dev_manifest: data/manifest.tiny
+  dev_manifest: data/manifest.dev
-  test_manifest: data/manifest.tiny
+  test_manifest: data/manifest.test
   vocab_filepath: data/vocab.txt
   unit_type: 'spm'
-  spm_model_prefix: 'data/bpe_unigram_200'
+  spm_model_prefix: 'data/bpe_unigram_5000'
   mean_std_filepath: ""
   augmentation_config: conf/augmentation.json
   batch_size: 64

examples/librispeech/s1/local/data.sh

@@ -27,8 +27,20 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
         exit 1
     fi
 
+    for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
+        mv data/manifest.${set} data/manifest.${set}.raw
+    done
+
     for set in train-clean-100 train-clean-360 train-other-500; do
-        cat data/manifest.${set} >> data/manifest.train.raw
+        cat data/manifest.${set}.raw >> data/manifest.train.raw
+    done
+
+    for set in dev-clean dev-other; do
+        cat data/manifest.${set}.raw >> data/manifest.dev.raw
+    done
+
+    for set in test-clean test-other; do
+        cat data/manifest.${set}.raw >> data/manifest.test.raw
     done
 fi
 
@@ -73,20 +85,24 @@ fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # format manifest with tokenids, vocab size
+    for set in train dev test dev-clean dev-other test-clean test-other; do
+    {
         python3 ${MAIN_ROOT}/utils/format_data.py \
         --feat_type "raw" \
         --cmvn_path "data/mean_std.json" \
         --unit_type "spm" \
         --spm_model_prefix ${bpeprefix} \
         --vocab_path="data/vocab.txt" \
-    --manifest_path="data/manifest.train.raw" \
+        --manifest_path="data/manifest.${set}.raw" \
-    --output_path="data/manifest.train"
+        --output_path="data/manifest.${set}"
-
 
         if [ $? -ne 0 ]; then
             echo "Formt mnaifest failed. Terminated."
             exit 1
         fi
+    }&
+    done
+    wait
 fi
 
 echo "LibriSpeech Data preparation done."

examples/librispeech/s1/run.sh

@@ -4,7 +4,8 @@ source path.sh
 
 stage=0
 stop_stage=100
-ckpt=conformer
+conf_path=conf/transformer.yaml
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
 avg_num=30
 avg_ckpt=avg_${avg_num}
 
@@ -17,7 +18,7 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=4,5,6,7 ./local/train.sh conf/conformer.yaml  ${ckpt}
+    CUDA_VISIBLE_DEVICES=4,5,6,7 ./local/train.sh ${conf_path}  ${ckpt}
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
@@ -27,10 +28,10 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=7 ./local/test.sh conf/conformer.yaml exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # export ckpt avg_n
-    CUDA_VISIBLE_DEVICES= ./local/export.sh conf/conformer.yaml exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi

examples/tiny/s1/run.sh

@@ -4,7 +4,8 @@ source path.sh
 
 stage=0
 stop_stage=100
-ckpt=conformer
+conf_path=conf/transformer.yaml
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
 avg_num=1
 avg_ckpt=avg_${avg_num}
 
@@ -17,7 +18,7 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=0 ./local/train.sh conf/conformer.yaml  ${ckpt}
+    CUDA_VISIBLE_DEVICES=4,5,6,7 ./local/train.sh ${conf_path}  ${ckpt}
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
@@ -27,10 +28,10 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=0 ./local/test.sh conf/conformer.yaml exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # export ckpt avg_n
-    CUDA_VISIBLE_DEVICES= ./local/export.sh conf/conformer.yaml exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi