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Added reference about generate_trie for clarity

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alchemi5t 2019-07-18 16:59:40 +05:30
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README.md
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@ -215,7 +215,7 @@ also, if you need some binaries different than current master, like `v0.2.0-alph
python3 util/taskcluster.py --branch "v0.2.0-alpha.6" --target "."
```
The script `taskcluster.py` will download `native_client.tar.xz` (which includes the `deepspeech` binary and associated libraries) and extract it into the current folder. Also, `taskcluster.py` will download binaries for Linux/x86_64 by default, but you can override that behavior with the `--arch` parameter. See the help info with `python util/taskcluster.py -h` for more details. Specific branches of DeepSpeech or TensorFlow can be specified as well.
The script `taskcluster.py` will download `native_client.tar.xz` (which includes the `deepspeech` binary, `generate_trie` and associated libraries) and extract it into the current folder. Also, `taskcluster.py` will download binaries for Linux/x86_64 by default, but you can override that behavior with the `--arch` parameter. See the help info with `python util/taskcluster.py -h` for more details. Specific branches of DeepSpeech or TensorFlow can be specified as well.
Note: the following command assumes you [downloaded the pre-trained model](#getting-the-pre-trained-model).

4
data/README.md
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@ -4,6 +4,6 @@ This directory contains language-specific data files. Most importantly, you will
1. A list of unique characters for the target language (e.g. English) in `data/alphabet.txt`
2. A binary n-gram language model compiled by `kenlm` in `data/lm/lm.binary`
3. A trie model compiled by `generate_trie.cpp` in `data/lm/trie`
3. A trie model compiled by [generate_trie](https://github.com/mozilla/DeepSpeech#using-the-command-line-client) in `data/lm/trie`
For more information on how to create these resources, see `data/lm/README.md`
For more information on how to build these resources from scratch, see `data/lm/README.md`