From 08a4673355ef6a4300d30cb5290d14a3542467fa Mon Sep 17 00:00:00 2001 From: TianYuan Date: Fri, 22 Apr 2022 03:34:19 +0000 Subject: [PATCH] fix wavernn bug, test=tts --- examples/csmsc/tts3/local/synthesize_e2e.sh | 4 ++-- examples/csmsc/voc6/README.md | 1 + paddlespeech/t2s/models/wavernn/wavernn.py | 4 +++- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/examples/csmsc/tts3/local/synthesize_e2e.sh b/examples/csmsc/tts3/local/synthesize_e2e.sh index 8130eff1..512e062b 100755 --- a/examples/csmsc/tts3/local/synthesize_e2e.sh +++ b/examples/csmsc/tts3/local/synthesize_e2e.sh @@ -109,6 +109,6 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --lang=zh \ --text=${BIN_DIR}/../sentences.txt \ --output_dir=${train_output_path}/test_e2e \ - --phones_dict=dump/phone_id_map.txt #\ - # --inference_dir=${train_output_path}/inference + --phones_dict=dump/phone_id_map.txt \ + --inference_dir=${train_output_path}/inference fi diff --git a/examples/csmsc/voc6/README.md b/examples/csmsc/voc6/README.md index 26d4523d..7dcf133b 100644 --- a/examples/csmsc/voc6/README.md +++ b/examples/csmsc/voc6/README.md @@ -114,6 +114,7 @@ The pretrained model can be downloaded here: The static model can be downloaded here: - [wavernn_csmsc_static_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_0.2.0.zip) +- [wavernn_csmsc_static_1.0.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/wavernn/wavernn_csmsc_static_1.0.0.zip) (fix bug for paddle 2.3) Model | Step | eval/loss :-------------:|:------------:| :------------: diff --git a/paddlespeech/t2s/models/wavernn/wavernn.py b/paddlespeech/t2s/models/wavernn/wavernn.py index b4b8b480..eb892eda 100644 --- a/paddlespeech/t2s/models/wavernn/wavernn.py +++ b/paddlespeech/t2s/models/wavernn/wavernn.py @@ -360,7 +360,9 @@ class WaveRNN(nn.Layer): x = sample.transpose([1, 0, 2]) elif self.mode == 'RAW': - posterior = F.softmax(logits, axis=1) + # fix bug for paddle 2.3, see https://github.com/PaddlePaddle/Paddle/commit/01f606b4f1ca3e184a59111084ed460ee0798a5a + # posterior = F.softmax(logits, axis=1) + posterior = logits distrib = paddle.distribution.Categorical(posterior) # corresponding operate [np.floor((fx + 1) / 2 * mu + 0.5)] in enocde_mu_law # distrib.sample([1])[0].cast('float32'): [0, 2**bits-1]