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[Hackathon 7th] 修复 `librispeech` 中 `asr` 的 `readme` (#3917)

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* [Fix] librispeech asr0 readme

* [Fix] librispeech asr1 readme
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megemini 4 weeks ago committed by GitHub
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4
examples/librispeech/asr0/README.md
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@ -144,7 +144,7 @@ source path.sh
bash ./local/data.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
avg.sh best exp/deepspeech2/checkpoints 1 avg.sh best exp/deepspeech2/checkpoints 1
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/deepspeech2.yaml conf/tuning/decode.yaml exp/deepspeech2/checkpoints/avg_1
``` ```
## Stage 4: Static graph model Export ## Stage 4: Static graph model Export
This stage is to transform dygraph to static graph. This stage is to transform dygraph to static graph.
@ -185,5 +185,5 @@ wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.w
``` ```
You can train a model by yourself, then 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. You can train a model by yourself, then 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 ```bash
CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 data/demo_002_en.wav CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/deepspeech2.yaml conf/tuning/decode.yaml exp/deepspeech2/checkpoints/avg_1 data/demo_002_en.wav
``` ```

10
examples/librispeech/asr1/README.md
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@ -148,7 +148,7 @@ or you can run these scripts in the command line (only use CPU).
bash ./local/data.sh bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20 avg.sh best exp/conformer/checkpoints 20
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml conf/tuning/decode.yaml exp/conformer/checkpoints/avg_20
``` ```
## Pretrained Model ## Pretrained Model
You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md). You can get the pretrained transformer or conformer from [this](../../../docs/source/released_model.md).
@ -163,7 +163,7 @@ source path.sh
# If you have process the data and get the manifest file， you can skip the following 2 steps # If you have process the data and get the manifest file， you can skip the following 2 steps
bash local/data.sh --stage -1 --stop_stage -1 bash local/data.sh --stage -1 --stop_stage -1
bash local/data.sh --stage 2 --stop_stage 2 bash local/data.sh --stage 2 --stop_stage 2
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml conf/tuning/decode.yaml exp/conformer/checkpoints/avg_20
``` ```
The performance of the released models are shown in [here](./RESULTS.md). The performance of the released models are shown in [here](./RESULTS.md).
@ -192,8 +192,8 @@ bash ./local/data.sh
CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
avg.sh best exp/conformer/checkpoints 20 avg.sh best exp/conformer/checkpoints 20
# test stage is optional # test stage is optional
CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml conf/tuning/decode.yaml exp/conformer/checkpoints/avg_20
CUDA_VISIBLE_DEVICES= ./local/align.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 CUDA_VISIBLE_DEVICES= ./local/align.sh conf/conformer.yaml conf/tuning/decode.yaml exp/conformer/checkpoints/avg_20
``` ```
## Stage 5: Single Audio File Inference ## Stage 5: Single Audio File Inference
In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
@ -214,5 +214,5 @@ wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.w
``` ```
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. 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 ```bash
CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 data/demo_002_en.wav CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/conformer.yaml conf/tuning/decode.yaml exp/conformer/checkpoints/avg_20 data/demo_002_en.wav
``` ```

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