shithub: opus

--- a/dnn/README.md

+++ b/dnn/README.md

@@ -24,7 +24,7 @@

 make

```

 Note that the autogen.sh script is used when building from Git and will automatically download the latest model

-(models are too large to put in Git). By default, LPCNet will attempt to use 8-bit dot product instructions on AVX*/Neon to

+(models are too large to put in Git). By default, LPCNet will attempt to use 8-bit dot product instructions on AVX\*/Neon to

 speed up inference. To disable that (e.g. to avoid quantization effects when retraining), add --disable-dot-product to the

 configure script. LPCNet does not yet have a complete implementation for some of the integer operations on the ARMv7

 architecture so for now you will also need --disable-dot-product to successfully compile on 32-bit ARM.

@@ -42,7 +42,7 @@

 cannot be optimized without -ffast-math (which -Ofast enables). Additionally, -falign-loops=32 has been shown to

 help on x86.

-You can test the capabilities of LPCNet using the lpcnet_demo application. To encode a file:

+You can test the capabilities of LPCNet using the lpcnet\_demo application. To encode a file:

```

 ./lpcnet_demo -encode input.pcm compressed.bin

```

@@ -55,6 +55,8 @@

```

 where output.pcm is also 16-bit, 16 kHz PCM.

+Alternatively, you can run the uncompressed analysis/synthesis using -features

+instead of -encode and -synthesis instead of -decode.

 The same functionality is available in the form of a library. See include/lpcnet.h for the API.

 # Training a new model

@@ -71,7 +73,7 @@

 1. Now that you have your files, train with:

```

-   ./training_tf2/train_lpcnet.py features.f32 data.s16 model_name

+   python3 training_tf2/train_lpcnet.py features.f32 data.s16 model_name

```

    and it will generate an h5 file for each iteration, with model\_name as prefix. If it stops with a

    "Failed to allocate RNN reserve space" message try specifying a smaller --batch-size for  train\_lpcnet.py.

@@ -83,12 +85,12 @@

```

 1. Or with C on a CPU (C inference is much faster):

-   First extract the model files nnet_data.h and nnet_data.c

+   First extract the model files nnet\_data.h and nnet\_data.c

```

-   ./dump_lpcnet.py lpcnet_model_name.h5

+   ./training_tf2/dump_lpcnet.py lpcnet_model_name.h5

```

-   and move the generated nnet_data.* files to the src/ directory.

-   Then you just need to rebuild the software and use lpcnet_demo as explained above.

+   and move the generated nnet\_data.\* files to the src/ directory.

+   Then you just need to rebuild the software and use lpcnet\_demo as explained above.

 # Speech Material for Training

--

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