Add the support of mfcc feature for DS2 #168
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pkuyym
reviewed
Jul 20, 2017
deep_speech_2/README.md
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| @@ -38,7 +38,13 @@ python datasets/librispeech/librispeech.py --help | |||
| python compute_mean_std.py | |||
| ``` | |||
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| `python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. | |||
| `python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. The default feature of audio data is power spectrum, currently the mfcc feature is also supported. To train and infer based on mfcc feature, you can regenerate this file by | |||
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currently the mfcc feature is also supported，changing currently to and should be better? There's no need to tell the user that mfcc is added just now.
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you can regenerate this file by，why regenerate when first running ds2?
| @@ -2,3 +2,4 @@ wget==3.2 | |||
| scipy==0.13.1 | |||
| resampy==0.1.5 | |||
| https://github.com/kpu/kenlm/archive/master.zip | |||
| python_speech_features | |||
xinghai-sun
approved these changes
Jul 20, 2017
| @@ -38,7 +38,13 @@ python datasets/librispeech/librispeech.py --help | |||
| python compute_mean_std.py | |||
| ``` | |||
|
|
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| `python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. | |||
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python compute_mean_std.py computes --> "It will compute"
deep_speech_2/README.md
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| @@ -38,7 +38,13 @@ python datasets/librispeech/librispeech.py --help | |||
| python compute_mean_std.py | |||
| ``` | |||
|
|
|||
| `python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. | |||
| `python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing. The default feature of audio data is power spectrum, currently the mfcc feature is also supported. To train and infer based on mfcc feature, you can regenerate this file by | |||
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- you can regenerate --> please regenerate
2.spectrum or spectrogram ?
deep_speech_2/README.md
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| python compute_mean_std.py --specgram_type mfcc | ||
| ``` | ||
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| and specify the ```specgram_type``` to ```mfcc``` in each step, including training, inference etc. |
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- “in each step, including training, inference etc.” --》 “ when running train.py, infer.py, evaluator.py or tune.py"
specgram_typetomfcc-->--specgram_type mfcc
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Add mfcc feature for audio data, and the training of model is in progress: