2.4 KiB
Data Preparation
Generate Manifest
DeepSpeech2 on PaddlePaddle accepts a textual manifest file as its data set interface. A manifest file summarizes a set of speech data, with each line containing some meta data (e.g. filepath, transcription, duration) of one audio clip, in JSON format, such as:
{"audio_filepath": "/home/work/.cache/paddle/Libri/134686/1089-134686-0001.flac", "duration": 3.275, "text": "stuff it into you his belly counselled him"}
{"audio_filepath": "/home/work/.cache/paddle/Libri/134686/1089-134686-0007.flac", "duration": 4.275, "text": "a cold lucid indifference reigned in his soul"}
To use your custom data, you only need to generate such manifest files to summarize the dataset. Given such summarized manifests, training, inference and all other modules can be aware of where to access the audio files, as well as their meta data including the transcription labels.
For how to generate such manifest files, please refer to examples/librispeech/local/librispeech.py, which will download data and generate manifest files for LibriSpeech dataset.
Compute Mean & Stddev for Normalizer
To perform z-score normalization (zero-mean, unit stddev) upon audio features, we have to estimate in advance the mean and standard deviation of the features, with some training samples:
python3 utils/compute_mean_std.py \
--num_samples 2000 \
--specgram_type linear \
--manifest_path examples/librispeech/data/manifest.train \
--output_path examples/librispeech/data/mean_std.npz
It will compute the mean and standard deviatio of power spectrum feature with 2000 random sampled audio clips listed in examples/librispeech/data/manifest.train and save the results to examples/librispeech/data/mean_std.npz for further usage.
Build Vocabulary
A vocabulary of possible characters is required to convert the transcription into a list of token indices for training, and in decoding, to convert from a list of indices back to text again. Such a character-based vocabulary can be built with utils/build_vocab.py.
python3 utils/build_vocab.py \
--count_threshold 0 \
--vocab_path examples/librispeech/data/eng_vocab.txt \
--manifest_paths examples/librispeech/data/manifest.train
It will write a vocabuary file examples/librispeech/data/eng_vocab.txt with all transcription text in examples/librispeech/data/manifest.train, without vocabulary truncation (--count_threshold 0).