From 52fd15825545c523be007b5d753509d8abf5435c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E6=98=A5=E4=B9=94?= <83450930+Liyulingyue@users.noreply.github.com> Date: Tue, 12 Nov 2024 19:24:19 +0800 Subject: [PATCH] Update README.md --- examples/aishell/asr0/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/aishell/asr0/README.md b/examples/aishell/asr0/README.md index 6b91e27a7..9f46e0971 100644 --- a/examples/aishell/asr0/README.md +++ b/examples/aishell/asr0/README.md @@ -107,7 +107,7 @@ or you can run these scripts in the command line (only use CPU). ```bash source path.sh bash ./local/data.sh -CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2 +CUDA_VISIBLE_DEVICES= ./local/train.sh conf/deepspeech2.yaml deepspeech2 ``` ## Stage 2: Top-k Models Averaging After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below: @@ -157,14 +157,14 @@ using the `tar` scripts to unpack the model and then you can use the script to t For example: ``` -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz -tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz +tar xzvf asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz source path.sh # If you have process the data and get the manifest fileļ¼Œ you can skip the following 2 steps bash local/data.sh --stage -1 --stop_stage -1 bash local/data.sh --stage 2 --stop_stage 2 -CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 +CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_10 ``` The performance of the released models are shown in [this](./RESULTS.md) ## Stage 4: Static graph model Export @@ -178,7 +178,7 @@ This stage is to transform dygraph to static graph. If you already have a dynamic graph model, you can run this script: ```bash source path.sh -./local/export.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_10 exp/deepspeech2/checkpoints/avg_10.jit +./local/export.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 exp/deepspeech2/checkpoints/avg_1.jit ``` ## Stage 5: Static graph Model Testing Similar to stage 3, the static graph model can also be tested. @@ -202,8 +202,8 @@ if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then ``` you can train the model by yourself, or you can download the pretrained model by the script below: ```bash -wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz -tar xzvf asr0_deepspeech2_aishell_ckpt_0.1.1.model.tar.gz +wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz +tar asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz ``` You can download the audio demo: ```bash @@ -211,5 +211,5 @@ wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wa ``` You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of the audio demo by running the script below. ```bash -CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/deepspeech2.yaml conf/tuning/decode.yaml exp/deepspeech2/checkpoints/avg_1 data/demo_01_03.wav +CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/deepspeech2.yaml conf/tuning/decode.yaml exp/deepspeech2/checkpoints/avg_10 data/demo_01_03.wav ```