add rtf for synthesize, add more vocoder for synthesize_e2e.sh, test=tts

examples/csmsc/tts0/local/synthesize.sh

@@ -3,18 +3,96 @@
 config_path=$1
 train_output_path=$2
 ckpt_name=$3
+stage=0
+stop_stage=0
 
-FLAGS_allocator_strategy=naive_best_fit \
-FLAGS_fraction_of_gpu_memory_to_use=0.01 \
-python3 ${BIN_DIR}/../synthesize.py \
-    --am=tacotron2_csmsc \
-    --am_config=${config_path} \
-    --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
-    --am_stat=dump/train/speech_stats.npy \
-    --voc=pwgan_csmsc \
-    --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
-    --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
-    --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
-    --test_metadata=dump/test/norm/metadata.jsonl \
-    --output_dir=${train_output_path}/test \
-    --phones_dict=dump/phone_id_map.txt
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=tacotron2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=pwgan_csmsc \
+        --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
+        --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
+        --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# for more GAN Vocoders
+# multi band melgan
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=tacotron2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=mb_melgan_csmsc \
+        --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
+        --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=tacotron2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=style_melgan_csmsc \
+        --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
+        --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# hifigan
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "in hifigan syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=tacotron2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=hifigan_csmsc \
+        --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
+        --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# wavernn
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "in wavernn syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=tacotron2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=wavernn_csmsc \
+        --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
+        --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
+        --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi

examples/csmsc/tts0/local/synthesize_e2e.sh

@@ -39,14 +39,14 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
         --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
         --am_stat=dump/train/speech_stats.npy \
         --voc=mb_melgan_csmsc \
-        --voc_config=mb_melgan_baker_finetune_ckpt_0.5/finetune.yaml \
-        --voc_ckpt=mb_melgan_baker_finetune_ckpt_0.5/snapshot_iter_2000000.pdz\
-        --voc_stat=mb_melgan_baker_finetune_ckpt_0.5/feats_stats.npy \
+        --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
+        --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # the pretrained models haven't release now
@@ -88,8 +88,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # wavernn
@@ -111,4 +111,4 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
         --output_dir=${train_output_path}/test_e2e \
         --phones_dict=dump/phone_id_map.txt \
         --inference_dir=${train_output_path}/inference
-fi
+fi

examples/csmsc/tts2/local/synthesize.sh

@@ -1,20 +1,103 @@
 #!/bin/bash
+
 config_path=$1
 train_output_path=$2
 ckpt_name=$3
+stage=0
+stop_stage=0
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=speedyspeech_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=pwgan_csmsc \
+        --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
+        --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
+        --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt \
+        --tones_dict=dump/tone_id_map.txt
+fi
+
+# for more GAN Vocoders
+# multi band melgan
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=speedyspeech_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=mb_melgan_csmsc \
+        --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
+        --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt \
+        --tones_dict=dump/tone_id_map.txt
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=speedyspeech_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=style_melgan_csmsc \
+        --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
+        --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt \
+        --tones_dict=dump/tone_id_map.txt
+fi
+
+# hifigan
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "in hifigan syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=speedyspeech_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=hifigan_csmsc \
+        --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
+        --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt \
+        --tones_dict=dump/tone_id_map.txt
+fi
 
-FLAGS_allocator_strategy=naive_best_fit \
-FLAGS_fraction_of_gpu_memory_to_use=0.01 \
-python3 ${BIN_DIR}/../synthesize.py \
-    --am=speedyspeech_csmsc \
-    --am_config=${config_path} \
-    --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
-    --am_stat=dump/train/feats_stats.npy \
-    --voc=pwgan_csmsc \
-    --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
-    --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
-    --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
-    --test_metadata=dump/test/norm/metadata.jsonl \
-    --output_dir=${train_output_path}/test \
-    --phones_dict=dump/phone_id_map.txt \
-    --tones_dict=dump/tone_id_map.txt
+# wavernn
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "in wavernn syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=speedyspeech_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=wavernn_csmsc \
+        --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
+        --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
+        --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --tones_dict=dump/tone_id_map.txt \
+        --phones_dict=dump/phone_id_map.txt
+fi

examples/csmsc/tts2/local/synthesize_e2e.sh

@@ -22,9 +22,9 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
         --phones_dict=dump/phone_id_map.txt \
-        --tones_dict=dump/tone_id_map.txt
+        --tones_dict=dump/tone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # for more GAN Vocoders
@@ -44,9 +44,9 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
         --phones_dict=dump/phone_id_map.txt \
-        --tones_dict=dump/tone_id_map.txt
+        --tones_dict=dump/tone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # the pretrained models haven't release now
@@ -88,9 +88,9 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
         --phones_dict=dump/phone_id_map.txt \
-        --tones_dict=dump/tone_id_map.txt
+        --tones_dict=dump/tone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 

examples/csmsc/tts3/local/synthesize.sh

@@ -3,18 +3,96 @@
 config_path=$1
 train_output_path=$2
 ckpt_name=$3
+stage=0
+stop_stage=0
 
-FLAGS_allocator_strategy=naive_best_fit \
-FLAGS_fraction_of_gpu_memory_to_use=0.01 \
-python3 ${BIN_DIR}/../synthesize.py \
-    --am=fastspeech2_csmsc \
-    --am_config=${config_path} \
-    --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
-    --am_stat=dump/train/speech_stats.npy \
-    --voc=pwgan_csmsc \
-    --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
-    --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
-    --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
-    --test_metadata=dump/test/norm/metadata.jsonl \
-    --output_dir=${train_output_path}/test \
-    --phones_dict=dump/phone_id_map.txt
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=fastspeech2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=pwgan_csmsc \
+        --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
+        --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
+        --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# for more GAN Vocoders
+# multi band melgan
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=fastspeech2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=mb_melgan_csmsc \
+        --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
+        --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=fastspeech2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=style_melgan_csmsc \
+        --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
+        --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# hifigan
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "in hifigan syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=fastspeech2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=hifigan_csmsc \
+        --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
+        --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
+        --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi
+
+# wavernn
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "in wavernn syn"
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/../synthesize.py \
+        --am=fastspeech2_csmsc \
+        --am_config=${config_path} \
+        --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --am_stat=dump/train/speech_stats.npy \
+        --voc=wavernn_csmsc \
+        --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
+        --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
+        --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --phones_dict=dump/phone_id_map.txt
+fi

examples/csmsc/tts3/local/synthesize_e2e.sh

@@ -22,8 +22,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # for more GAN Vocoders
@@ -43,8 +43,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 # the pretrained models haven't release now
@@ -86,8 +86,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
         --lang=zh \
         --text=${BIN_DIR}/../sentences.txt \
         --output_dir=${train_output_path}/test_e2e \
-        --inference_dir=${train_output_path}/inference \
-        --phones_dict=dump/phone_id_map.txt
+        --phones_dict=dump/phone_id_map.txt \
+        --inference_dir=${train_output_path}/inference
 fi
 
 

paddlespeech/t2s/exps/synthesize.py

@@ -20,6 +20,7 @@ import numpy as np
 import paddle
 import soundfile as sf
 import yaml
+from timer import timer
 from yacs.config import CfgNode
 
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
@@ -50,6 +51,18 @@ model_alias = {
     "paddlespeech.t2s.models.melgan:MelGANGenerator",
     "mb_melgan_inference":
     "paddlespeech.t2s.models.melgan:MelGANInference",
+    "style_melgan":
+    "paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
+    "style_melgan_inference":
+    "paddlespeech.t2s.models.melgan:StyleMelGANInference",
+    "hifigan":
+    "paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
+    "hifigan_inference":
+    "paddlespeech.t2s.models.hifigan:HiFiGANInference",
+    "wavernn":
+    "paddlespeech.t2s.models.wavernn:WaveRNN",
+    "wavernn_inference":
+    "paddlespeech.t2s.models.wavernn:WaveRNNInference",
 }
 
 
@@ -146,10 +159,15 @@ def evaluate(args):
     voc_name = args.voc[:args.voc.rindex('_')]
     voc_class = dynamic_import(voc_name, model_alias)
     voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
-    voc = voc_class(**voc_config["generator_params"])
-    voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
-    voc.remove_weight_norm()
-    voc.eval()
+    if voc_name != 'wavernn':
+        voc = voc_class(**voc_config["generator_params"])
+        voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
+        voc.remove_weight_norm()
+        voc.eval()
+    else:
+        voc = voc_class(**voc_config["model"])
+        voc.set_state_dict(paddle.load(args.voc_ckpt)["main_params"])
+        voc.eval()
     voc_mu, voc_std = np.load(args.voc_stat)
     voc_mu = paddle.to_tensor(voc_mu)
     voc_std = paddle.to_tensor(voc_std)
@@ -162,38 +180,51 @@ def evaluate(args):
     output_dir = Path(args.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
 
+    N = 0
+    T = 0
+
     for datum in test_dataset:
         utt_id = datum["utt_id"]
-        with paddle.no_grad():
-            # acoustic model
-            if am_name == 'fastspeech2':
-                phone_ids = paddle.to_tensor(datum["text"])
-                spk_emb = None
-                spk_id = None
-                # multi speaker
-                if args.voice_cloning and "spk_emb" in datum:
-                    spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
-                elif "spk_id" in datum:
-                    spk_id = paddle.to_tensor(datum["spk_id"])
-                mel = am_inference(phone_ids, spk_id=spk_id, spk_emb=spk_emb)
-            elif am_name == 'speedyspeech':
-                phone_ids = paddle.to_tensor(datum["phones"])
-                tone_ids = paddle.to_tensor(datum["tones"])
-                mel = am_inference(phone_ids, tone_ids)
-            elif am_name == 'tacotron2':
-                phone_ids = paddle.to_tensor(datum["text"])
-                spk_emb = None
-                # multi speaker
-                if args.voice_cloning and "spk_emb" in datum:
-                    spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
-                mel = am_inference(phone_ids, spk_emb=spk_emb)
+        with timer() as t:
+            with paddle.no_grad():
+                # acoustic model
+                if am_name == 'fastspeech2':
+                    phone_ids = paddle.to_tensor(datum["text"])
+                    spk_emb = None
+                    spk_id = None
+                    # multi speaker
+                    if args.voice_cloning and "spk_emb" in datum:
+                        spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
+                    elif "spk_id" in datum:
+                        spk_id = paddle.to_tensor(datum["spk_id"])
+                    mel = am_inference(
+                        phone_ids, spk_id=spk_id, spk_emb=spk_emb)
+                elif am_name == 'speedyspeech':
+                    phone_ids = paddle.to_tensor(datum["phones"])
+                    tone_ids = paddle.to_tensor(datum["tones"])
+                    mel = am_inference(phone_ids, tone_ids)
+                elif am_name == 'tacotron2':
+                    phone_ids = paddle.to_tensor(datum["text"])
+                    spk_emb = None
+                    # multi speaker
+                    if args.voice_cloning and "spk_emb" in datum:
+                        spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
+                    mel = am_inference(phone_ids, spk_emb=spk_emb)
             # vocoder
             wav = voc_inference(mel)
+
+            wav = wav.numpy()
+            N += wav.size
+            T += t.elapse
+            speed = wav.size / t.elapse
+            rtf = am_config.fs / speed
+        print(
+            f"{utt_id}, mel: {mel.shape}, wave: {wav.size}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
+        )
         sf.write(
-            str(output_dir / (utt_id + ".wav")),
-            wav.numpy(),
-            samplerate=am_config.fs)
+            str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs)
         print(f"{utt_id} done!")
+    print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }")
 
 
 def main():
@@ -246,7 +277,8 @@ def main():
         default='pwgan_csmsc',
         choices=[
             'pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3', 'pwgan_vctk',
-            'mb_melgan_csmsc'
+            'mb_melgan_csmsc', 'wavernn_csmsc', 'hifigan_csmsc',
+            'style_melgan_csmsc'
         ],
         help='Choose vocoder type of tts task.')
 

paddlespeech/t2s/exps/synthesize_e2e.py

@@ -21,6 +21,7 @@ import soundfile as sf
 import yaml
 from paddle import jit
 from paddle.static import InputSpec
+from timer import timer
 from yacs.config import CfgNode
 
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
@@ -233,59 +234,68 @@ def evaluate(args):
     # but still not stopping in the end (NOTE by yuantian01 Feb 9 2022)
     if am_name == 'tacotron2':
         merge_sentences = True
-
+    N = 0
+    T = 0
     for utt_id, sentence in sentences:
-        get_tone_ids = False
-        if am_name == 'speedyspeech':
-            get_tone_ids = True
-        if args.lang == 'zh':
-            input_ids = frontend.get_input_ids(
-                sentence,
-                merge_sentences=merge_sentences,
-                get_tone_ids=get_tone_ids)
-            phone_ids = input_ids["phone_ids"]
-            if get_tone_ids:
-                tone_ids = input_ids["tone_ids"]
-        elif args.lang == 'en':
-            input_ids = frontend.get_input_ids(
-                sentence, merge_sentences=merge_sentences)
-            phone_ids = input_ids["phone_ids"]
-        else:
-            print("lang should in {'zh', 'en'}!")
-        with paddle.no_grad():
-            flags = 0
-            for i in range(len(phone_ids)):
-                part_phone_ids = phone_ids[i]
-                # acoustic model
-                if am_name == 'fastspeech2':
-                    # multi speaker
-                    if am_dataset in {"aishell3", "vctk"}:
-                        spk_id = paddle.to_tensor(args.spk_id)
-                        mel = am_inference(part_phone_ids, spk_id)
-                    else:
+        with timer() as t:
+            get_tone_ids = False
+            if am_name == 'speedyspeech':
+                get_tone_ids = True
+            if args.lang == 'zh':
+                input_ids = frontend.get_input_ids(
+                    sentence,
+                    merge_sentences=merge_sentences,
+                    get_tone_ids=get_tone_ids)
+                phone_ids = input_ids["phone_ids"]
+                if get_tone_ids:
+                    tone_ids = input_ids["tone_ids"]
+            elif args.lang == 'en':
+                input_ids = frontend.get_input_ids(
+                    sentence, merge_sentences=merge_sentences)
+                phone_ids = input_ids["phone_ids"]
+            else:
+                print("lang should in {'zh', 'en'}!")
+            with paddle.no_grad():
+                flags = 0
+                for i in range(len(phone_ids)):
+                    part_phone_ids = phone_ids[i]
+                    # acoustic model
+                    if am_name == 'fastspeech2':
+                        # multi speaker
+                        if am_dataset in {"aishell3", "vctk"}:
+                            spk_id = paddle.to_tensor(args.spk_id)
+                            mel = am_inference(part_phone_ids, spk_id)
+                        else:
+                            mel = am_inference(part_phone_ids)
+                    elif am_name == 'speedyspeech':
+                        part_tone_ids = tone_ids[i]
+                        if am_dataset in {"aishell3", "vctk"}:
+                            spk_id = paddle.to_tensor(args.spk_id)
+                            mel = am_inference(part_phone_ids, part_tone_ids,
+                                               spk_id)
+                        else:
+                            mel = am_inference(part_phone_ids, part_tone_ids)
+                    elif am_name == 'tacotron2':
                         mel = am_inference(part_phone_ids)
-                elif am_name == 'speedyspeech':
-                    part_tone_ids = tone_ids[i]
-                    if am_dataset in {"aishell3", "vctk"}:
-                        spk_id = paddle.to_tensor(args.spk_id)
-                        mel = am_inference(part_phone_ids, part_tone_ids,
-                                           spk_id)
+                    # vocoder
+                    wav = voc_inference(mel)
+                    if flags == 0:
+                        wav_all = wav
+                        flags = 1
                     else:
-                        mel = am_inference(part_phone_ids, part_tone_ids)
-                elif am_name == 'tacotron2':
-                    mel = am_inference(part_phone_ids)
-                # vocoder
-                wav = voc_inference(mel)
-                if flags == 0:
-                    wav_all = wav
-                    flags = 1
-                else:
-                    wav_all = paddle.concat([wav_all, wav])
+                        wav_all = paddle.concat([wav_all, wav])
+        wav = wav_all.numpy()
+        N += wav.size
+        T += t.elapse
+        speed = wav.size / t.elapse
+        rtf = am_config.fs / speed
+        print(
+            f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
+        )
         sf.write(
-            str(output_dir / (utt_id + ".wav")),
-            wav_all.numpy(),
-            samplerate=am_config.fs)
+            str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs)
         print(f"{utt_id} done!")
+    print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }")
 
 
 def main():

paddlespeech/t2s/exps/wavernn/synthesize.py

@@ -91,7 +91,7 @@ def main():
                     target=config.inference.target,
                     overlap=config.inference.overlap,
                     mu_law=config.mu_law,
-                    gen_display=True)
+                    gen_display=False)
             wav = wav.numpy()
             N += wav.size
             T += t.elapse

paddlespeech/t2s/models/melgan/melgan.py

@@ -66,7 +66,7 @@ class MelGANGenerator(nn.Layer):
             nonlinear_activation_params (Dict[str, Any], optional): Parameters passed to the linear activation in the upsample network, 
                 by default {}
             pad (str): Padding function module name before dilated convolution layer.
-            pad_params （dict): Hyperparameters for padding function.
+            pad_params (dict): Hyperparameters for padding function.
             use_final_nonlinear_activation (nn.Layer): Activation function for the final layer.
             use_weight_norm (bool): Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.

paddlespeech/t2s/models/wavernn/wavernn.py

@@ -509,16 +509,20 @@ class WaveRNN(nn.Layer):
         total_len = num_folds * (target + overlap) + overlap
 
         # Need some silence for the run warmup
-        slience_len = overlap // 2
+        slience_len = 0
+        linear_len = slience_len
         fade_len = overlap - slience_len
         slience = paddle.zeros([slience_len], dtype=paddle.float32)
-        linear = paddle.ones([fade_len], dtype=paddle.float32)
+        linear = paddle.ones([linear_len], dtype=paddle.float32)
 
         # Equal power crossfade
         # fade_in increase from 0 to 1, fade_out reduces from 1 to 0
-        t = paddle.linspace(-1, 1, fade_len, dtype=paddle.float32)
-        fade_in = paddle.sqrt(0.5 * (1 + t))
-        fade_out = paddle.sqrt(0.5 * (1 - t))
+        sigmoid_scale = 2.3
+        t = paddle.linspace(
+            -sigmoid_scale, sigmoid_scale, fade_len, dtype=paddle.float32)
+        # sigmoid 曲线应该更好
+        fade_in = paddle.nn.functional.sigmoid(t)
+        fade_out = 1 - paddle.nn.functional.sigmoid(t)
         # Concat the silence to the fades
         fade_out = paddle.concat([linear, fade_out])
         fade_in = paddle.concat([slience, fade_in])