- [Training for Mandarin Language](#training-for-mandarin-language)
@ -116,42 +114,6 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org
```bash
bash run.sh
```
- Prepare the data
```bash
sh local/run_data.sh
```
`run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
- Train your own ASR model
```bash
sh local/run_train.sh
```
`run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
- Case inference with an existing model
```bash
sh local/run_infer.sh
```
`run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
```bash
sh local/run_infer_golden.sh
```
- Evaluate an existing model
```bash
sh local/run_test.sh
```
`run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
```bash
sh local/run_test_golden.sh
```
More detailed information are provided in the following sections. Wish you a happy journey with the *DeepSpeech2 on PaddlePaddle* ASR engine!
@ -169,7 +131,7 @@ More detailed information are provided in the following sections. Wish you a hap
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 `PATH/TO/LIBRISPEECH/local/librispeech.py`, which will download data and generate manifest files for LibriSpeech dataset.
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
@ -179,11 +141,11 @@ To perform z-score normalization (zero-mean, unit stddev) upon audio features, w
It will compute the mean and standard deviatio of power spectrum feature with 2000 random sampled audio clips listed in `PATH/TO/LIBRISPEECH/data/manifest.train` and save the results to `PATH/TO/LIBRISPEECH/data/mean_std.npz` for further usage.
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
@ -193,18 +155,18 @@ A vocabulary of possible characters is required to convert the transcription int
It will write a vocabuary file `PATH/TO/LIBRISPEEECH/data/eng_vocab.txt` with all transcription text in `PATH/TO/LIBRISPEECH/data/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
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`).
or refer to `example/librispeech/local/run_train.sh`.
## Data Augmentation Pipeline
### Data Augmentation Pipeline
Data augmentation has often been a highly effective technique to boost the deep learning performance. We augment our speech data by synthesizing new audios with small random perturbation (label-invariant transformation) added upon raw audios. You don't have to do the syntheses on your own, as it is already embedded into the data provider and is done on the fly, randomly for each epoch during training.
@ -440,7 +402,7 @@ Now, in the client console, press the `whitespace` key, hold, and start speaking
Notice that `deploy/demo_client.py` must be run on a machine with a microphone device, while `deploy/demo_server.py` could be run on one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running on one single machine.
Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/mandarin/run_demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.
Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/deploy_demo/run_demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.
`run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
- Train your own ASR model
```bash
sh local/run_train.sh
```
`run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
- Case inference with an existing model
```bash
sh local/run_infer.sh
```
`run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
```bash
sh local/run_infer_golden.sh
```
- Evaluate an existing model
```bash
sh local/run_test.sh
```
`run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
Hui Zhang
3 years ago