diff --git a/deep_speech_2/README.md b/deep_speech_2/README.md index 2f51a5fc5b37e5fb390c6f8c95e6c942d270915c..aae0dc6d82eb0e317d9ecc6b5317250ac94487ff 100644 --- a/deep_speech_2/README.md +++ b/deep_speech_2/README.md @@ -1,6 +1,6 @@ # DeepSpeech2 on PaddlePaddle -*DeepSpeech2 on PaddlePaddle* is an open-source implementation of end-to-end Automatic Speech Recognition (ASR) engine, based on [Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf), with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech-to-text, via an easy-to-use, efficent and scalable integreted implementation, including training & inferencing module, distributed [PaddleCloud](https://github.com/PaddlePaddle/cloud) training, and demo deployment. Besides, several pre-trained models for both English and Mandarin speech are also released. +*DeepSpeech2 on PaddlePaddle* is an open-source implementation of end-to-end Automatic Speech Recognition (ASR) engine, based on [Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf), with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech-to-text, via an easy-to-use, efficent and scalable integreted implementation, including training, inferencing & testing module, distributed [PaddleCloud](https://github.com/PaddlePaddle/cloud) training, and demo deployment. Besides, several pre-trained models for both English and Mandarin are also released. ## Table of Contents - [Prerequisites](#prerequisites) @@ -8,12 +8,14 @@ - [Getting Started](#getting-started) - [Data Preparation](#data-preparation) - [Training a Model](#training-a-model) +- [Data Augmentation Pipeline](#data-augmentation-pipeline) - [Inference and Evaluation](#inference-and-evaluation) - [Distributed Cloud Training](#distributed-cloud-training) - [Hyper-parameters Tuning](#hyper-parameters-tuning) - [Training for Mandarin Language](#training-for-mandarin-language) - [Trying Live Demo with Your Own Voice](#trying-live-demo-with-your-own-voice) - [Experiments and Benchmarks](#experiments-and-benchmarks) +- [Released Models](#released-models) - [Questions and Help](#questions-and-help) ## Prerequisites @@ -22,7 +24,7 @@ ## Installation -Please install the [prerequisites](#prerequisites) above before moving onto this quick installation. +Please install the [prerequisites](#prerequisites) above before moving on. ``` git clone https://github.com/PaddlePaddle/models.git @@ -32,43 +34,43 @@ sh setup.sh ## Getting Started -Several shell scripts provided in `./examples` will help us to quickly give it a try, including training, inferencing, evaluation and demo deployment. +Several shell scripts provided in `./examples` will help us to quickly give it a try, for most major modules, including data preparation, model training, case inference, model evaluation and demo deployment, with a few public dataset (e.g. [LibriSpeech](http://www.openslr.org/12/), [Aishell](https://github.com/kaldi-asr/kaldi/tree/master/egs/aishell)). Reading these examples will also help us understand how to make it work with our own data. -Most of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICE` and `--trainer_count`. If you don't have any GPU available, please set `--use_gpu` to False. +Some of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICE` and `--trainer_count`. If you don't have any GPU available, please set `--use_gpu` to False to use CPUs instead. Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org/12/) for instance. - Go to directory ``` - cd examples/librispeech_tiny + cd examples/tiny ``` - Notice that this is only a toy example with a tiny sampled set of LibriSpeech. If we would like to try with the complete LibriSpeech (would take much a longer time for training), please go to `examples/librispeech` instead. -- Prepare the libripseech data + Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If we would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead. +- Prepare the data ``` - sh preprare_data.sh + sh run_data.sh ``` - `prepare_data.sh` downloads dataset, generates file manifests, collects normalizer' statitics and builds vocabulary for us. Once the running is done, we'll find our LibriSpeech data (not full in this "tiny" example) downloaded in `~/.cache/paddle/dataset/speech/Libri` and several manifest files as well as one mean stddev file generated in `./data/librispeech_tiny`, for the further model training. It needs to be run for only once. + `run_data.sh` will download dataset, generate manifests, collect normalizer' statitics and build vocabulary. Once the data preparation is done, we will find the data (only part of LibriSpeech) downloaded in `~/.cache/paddle/dataset/speech/libri` and the corresponding manifest files generated in `./data/tiny` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time we run this dataset and is reusable for all further experiments. - Train your own ASR model ``` sh run_train.sh ``` - `run_train.sh` starts a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints`. We can resume the training from these checkpoints, or use them for inference, evalutiaton and deployment. + `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints/tiny`. We can resume the training from these checkpoints, or use them for inference, evalutiaton and deployment. - Case inference with an existing model ``` sh run_infer.sh ``` - `run_infer.sh` will quickly show us speech-to-text decoding results for several (default: 10) audio samples with an existing model. Since the model is only trained on a subset of LibriSpeech, the performance might not be very good. We can download a well-trained model and then do the inference: + `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, we can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference: ``` - sh download_model_run_infer.sh + sh run_infer_golden.sh ``` - Evaluate an existing model @@ -76,14 +78,14 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org sh run_test.sh ``` - `run_test.sh` evaluates the model with Word Error Rate (or Character Error Rate) measurement. Similarly, we can also download a well-trained model and test its performance: + `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, we can also download a well-trained model and test its performance: ``` - sh download_model_run_test.sh + sh run_test_golden.sh ``` - Try out a live demo with your own voice - Until now, we have trained and tested an ASR model quantitively and qualitatively with existing audios. But we haven't try the model with our own speech. `demo_server.sh` and `demo_client.sh` helps quickly build up a demo ASR engine with the trained model, enabling us to test and play around with the demo with our own voice. + Until now, we have trained and tested our ASR model qualitatively (`run_infer.sh`) and quantitively (`run_test.sh`) with existing audio files. But we have not yet play the model with our own speech. `demo_server.sh` and `demo_client.sh` helps quickly build up a demo ASR engine with the trained model, enabling us to test and play around with the demo with our own voice. We start the server in one console by entering: @@ -112,20 +114,20 @@ Wish you a happy journey with the DeepSpeech2 ASR engine! #### 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 the meta data (e.g. filepath, transcription, duration) of one audio clip, in [JSON](http://www.json.org/) format, just as: +*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](http://www.json.org/) 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 any custom data, we 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. +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 example script to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which download and generate manifests for LibriSpeech dataset. +For how to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which download and generate manifests 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 sampled training audios: +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: ``` python tools/compute_mean_std.py \ @@ -140,7 +142,7 @@ It will compute the mean and standard deviation of power spectgram feature with #### Build Vocabulary -A list of possible characters is required to convert the target transcription into list of token indices for training and in docoders convert from them back to text. Such a character-based vocabulary can be build with `tools/build_vocab.py`. +A vocabulary of possible characters is required to convert the transcription into a list of token indices for training, and in docoding, to convert from a list of indices back to text again. Such a character-based vocabulary can be build with `tools/build_vocab.py`. ``` python tools/build_vocab.py \ @@ -149,7 +151,7 @@ python tools/build_vocab.py \ --manifest_paths data/librispeech/manifest.train ``` -It will build a vocabuary file of `data/librispeeech/eng_vocab.txt` with all transcription text in `data/librispeech/manifest.train`, without character truncation. +It will write a vocabuary file `data/librispeeech/eng_vocab.txt` with all transcription text in `data/librispeech/manifest.train`, without vocabulary truncation (`--count_threshold 0`). #### More Help @@ -163,7 +165,7 @@ python tools/build_vocab.py --help ## Training a model -`train.py` is the main caller of the training module. We list several usage below. +`train.py` is the main caller of the training module. We show several examples of usage below. - Start training from scratch with 8 GPUs: @@ -176,7 +178,7 @@ python tools/build_vocab.py --help ``` python train.py --use_gpu False --trainer_count 16 ``` -- Resume training from a checkpoint (an existing model): +- Resume training from a checkpoint: ``` CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py \ @@ -188,11 +190,11 @@ For more help on arguments: ``` python train.py --help ``` -or refer to `example/librispeech/run_train.sh. +or refer to `example/librispeech/run_train.sh`. -#### Augment the Dataset for Training +## 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 perterbation (label-invariant transformation) added upon raw audios. We don't have to do the syntheses by ourselves, as it is already embeded into the data provider and is done on the fly, randomly for each epoch. +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 perterbation (label-invariant transformation) added upon raw audios. We don't have to do the syntheses by ourselves, as it is already embeded into the data provider and is done on the fly, randomly for each epoch during training. Six optional augmentation components are provided for us to configured and inserted into the processing pipeline. @@ -203,7 +205,7 @@ Six optional augmentation components are provided for us to configured and inser - Noise Perturbation (need background noise audio files) - Impulse Response (need impulse audio files) -In order to inform the trainer of what augmentation components we need and what their processing orders are, we are required to prepare a *augmentation configuration file* in JSON format. For example: +In order to inform the trainer of what augmentation components we need and what their processing orders are, we are required to prepare a *augmentation configuration file* in [JSON](http://www.json.org/) format. For example: ``` [{ @@ -220,23 +222,23 @@ In order to inform the trainer of what augmentation components we need and what }] ``` -When the `--augment_conf_file` argument of `trainer.py` is set to the path of the above example configuration file, each audio clip in each epoch will be processed: with 60% of chance, it will first be speed perturbed with a uniformly random sampled speed-rate between 0.95 and 1.05, and then with 80% of chance it will be shifted in time with a random sampled offset between -5 ms and 5 ms. Finally this newly synthesized audio clip will be feed into the feature extractor for further training. +When the `--augment_conf_file` argument of `trainer.py` is set to the path of the above example configuration file, every audio clip in every epoch will be processed: with 60% of chance, it will first be speed perturbed with a uniformly random sampled speed-rate between 0.95 and 1.05, and then with 80% of chance it will be shifted in time with a random sampled offset between -5 ms and 5 ms. Finally this newly synthesized audio clip will be feed into the feature extractor for further training. -For configuration examples, please refer to `conf/augmenatation.config.example`. +For other configuration examples, please refer to `conf/augmenatation.config.example`. -Be careful when we are utilizing the data augmentation technique, as improper augmentation will instead do harm to the training, due to the enlarged train-test gap. +Be careful when we are utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap. ## Inference and Evaluation -#### Prepare Language Model +### Prepare Language Model -A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. Please refer to `examples/librispeech/download_model.sh` and `examples/mandarin_demo/download_model.sh` for their urls. If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials. +A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. Please refer to `models/lm/download_lm_en.sh` and `models/lm/download_lm_zh.sh` for their urls. If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials. TODO: any other requirements or tips to add? -#### Speech-to-text Inference +### Speech-to-text Inference -We provide a inference module `infer.py` to infer, decode and visualize speech-to-text results for several given audio clips, which might help to have a intuitive and qualitative evaluation of the ASR model performance. +We provide a inference module `infer.py` to infer, decode and visualize speech-to-text results for several given audio clips. It might help us to have a intuitive and qualitative evaluation of the ASR model's performance. - Inference with GPU: @@ -247,21 +249,21 @@ We provide a inference module `infer.py` to infer, decode and visualize speech-t - Inference with CPU: ``` - python infer.py --use_gpu False + python infer.py --use_gpu False --trainer_count 12 ``` -We provide two CTC decoders: *CTC greedy decoder* and *CTC beam search decoder*. The *CTC greedy decoder* is an implementation of the simple best-path decoding algorithm, selecting at each timestep the most likely token, thus being greedy and locally optimal. The [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) otherwise utilzied a heuristic breadth-first gragh search for arriving at a near global optimality; it requires a pre-trained KenLM language model for better scoring and ranking sentences. The decoder type can be set with argument `--decoding_method`. +We provide two types of CTC decoders: *CTC greedy decoder* and *CTC beam search decoder*. The *CTC greedy decoder* is an implementation of the simple best-path decoding algorithm, selecting at each timestep the most likely token, thus being greedy and locally optimal. The [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) otherwise utilizes a heuristic breadth-first gragh search for reaching a near global optimality; it also requires a pre-trained KenLM language model for better scoring and ranking. The decoder type can be set with argument `--decoding_method`. For more help on arguments: ``` python infer.py --help ``` -or refer to `example/librispeech/run_infer.sh. +or refer to `example/librispeech/run_infer.sh`. -#### Evaluate a Model +### Evaluate a Model -To evaluate a model quantitively, we can run: +To evaluate a model's performance quantitively, we can run: - Evaluation with GPU: @@ -272,23 +274,23 @@ To evaluate a model quantitively, we can run: - Evaluation with CPU: ``` - python test.py --use_gpu False + python test.py --use_gpu False --trainer_count 12 ``` -The error rate (default: word error rate, can be set with `--error_rate_type`) will be printed. +The error rate (default: word error rate; can be set with `--error_rate_type`) will be printed. For more help on arguments: ``` python test.py --help ``` -or refer to `example/librispeech/run_test.sh. +or refer to `example/librispeech/run_test.sh`. ## Hyper-parameters Tuning -The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta$ (coefficient for word count scorer) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It'd be better to re-tune them on validation samples after the accustic model is renewed. +The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta$ (coefficient for word count scorer) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It would be better to re-tune them on a validation set when the accustic model is renewed. -`tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. We have to provide the range of $\alpha$ and $\beta$, as well as the number of attempts. +`tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. We have to provide the range of $\alpha$ and $\beta$, as well as the number of their attempts. - Tuning with GPU: @@ -309,12 +311,12 @@ The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta python tools/tune.py --use_gpu False ``` -After tuning, we can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they can really improve the ASR performance. +After tuning, we can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance. ``` python tune.py --help ``` -or refer to `example/librispeech/run_tune.sh. +or refer to `example/librispeech/run_tune.sh`. TODO: add figure. @@ -352,4 +354,6 @@ It could be possible to start the server and the client in two seperate machines ## Experiments and Benchmarks +## Released Models + ## Questions and Help diff --git a/deep_speech_2/data/librispeech/eng_vocab.txt b/deep_speech_2/data/librispeech/eng_vocab.txt deleted file mode 100644 index 8268f3f3301047f2b4354d60a4bd1d5ef58619a2..0000000000000000000000000000000000000000 --- a/deep_speech_2/data/librispeech/eng_vocab.txt +++ /dev/null @@ -1,28 +0,0 @@ -' - -a -b -c -d -e -f -g -h -i -j -k -l -m -n -o -p -q -r -s -t -u -v -w -x -y -z diff --git a/deep_speech_2/data/librispeech/librispeech.py b/deep_speech_2/data/librispeech/librispeech.py index 14a3804e2eb8e1a114ad9b4cbb56a5fc1061677e..e2ad8d413ca07439d1ef1aada49e8f5d1b019c93 100644 --- a/deep_speech_2/data/librispeech/librispeech.py +++ b/deep_speech_2/data/librispeech/librispeech.py @@ -19,8 +19,6 @@ import json import codecs from paddle.v2.dataset.common import md5file -DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') - URL_ROOT = "http://www.openslr.org/resources/12" URL_TEST_CLEAN = URL_ROOT + "/test-clean.tar.gz" URL_TEST_OTHER = URL_ROOT + "/test-other.tar.gz" @@ -41,7 +39,7 @@ MD5_TRAIN_OTHER_500 = "d1a0fd59409feb2c614ce4d30c387708" parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--target_dir", - default=DATA_HOME + "/libri", + default='~/.cache/paddle/dataset/speech/libri', type=str, help="Directory to save the dataset. (default: %(default)s)") parser.add_argument( @@ -60,14 +58,14 @@ args = parser.parse_args() def download(url, md5sum, target_dir): - """ - Download file from url to target_dir, and check md5sum. + """Download file from url to target_dir, and check md5sum. """ if not os.path.exists(target_dir): os.makedirs(target_dir) filepath = os.path.join(target_dir, url.split("/")[-1]) if not (os.path.exists(filepath) and md5file(filepath) == md5sum): print("Downloading %s ..." % url) - os.system("wget -c " + url + " -P " + target_dir) + ret = os.system("wget -c " + url + " -P " + target_dir) + print(ret) print("\nMD5 Chesksum %s ..." % filepath) if not md5file(filepath) == md5sum: raise RuntimeError("MD5 checksum failed.") @@ -77,8 +75,7 @@ def download(url, md5sum, target_dir): def unpack(filepath, target_dir): - """ - Unpack the file to the target_dir. + """Unpack the file to the target_dir. """ print("Unpacking %s ..." % filepath) tar = tarfile.open(filepath) @@ -87,8 +84,7 @@ def unpack(filepath, target_dir): def create_manifest(data_dir, manifest_path): - """ - Create a manifest json file summarizing the data set, with each line + """Create a manifest json file summarizing the data set, with each line containing the meta data (i.e. audio filepath, transcription text, audio duration) of each audio file within the data set. """ @@ -119,8 +115,7 @@ def create_manifest(data_dir, manifest_path): def prepare_dataset(url, md5sum, target_dir, manifest_path): - """ - Download, unpack and create summmary manifest file. + """Download, unpack and create summmary manifest file. """ if not os.path.exists(os.path.join(target_dir, "LibriSpeech")): # download @@ -135,6 +130,8 @@ def prepare_dataset(url, md5sum, target_dir, manifest_path): def main(): + args.target_dir = os.path.expanduser(args.target_dir) + prepare_dataset( url=URL_TEST_CLEAN, md5sum=MD5_TEST_CLEAN, @@ -145,12 +142,12 @@ def main(): md5sum=MD5_DEV_CLEAN, target_dir=os.path.join(args.target_dir, "dev-clean"), manifest_path=args.manifest_prefix + ".dev-clean") - prepare_dataset( - url=URL_TRAIN_CLEAN_100, - md5sum=MD5_TRAIN_CLEAN_100, - target_dir=os.path.join(args.target_dir, "train-clean-100"), - manifest_path=args.manifest_prefix + ".train-clean-100") if args.full_download: + prepare_dataset( + url=URL_TRAIN_CLEAN_100, + md5sum=MD5_TRAIN_CLEAN_100, + target_dir=os.path.join(args.target_dir, "train-clean-100"), + manifest_path=args.manifest_prefix + ".train-clean-100") prepare_dataset( url=URL_TEST_OTHER, md5sum=MD5_TEST_OTHER, diff --git a/deep_speech_2/deploy/demo_server.py b/deep_speech_2/deploy/demo_server.py index 658b14197bee037429032bf87de70ee78a3edcab..2d3931f74bb44261c7fe2e342921a0b8c8d2ec40 100644 --- a/deep_speech_2/deploy/demo_server.py +++ b/deep_speech_2/deploy/demo_server.py @@ -11,7 +11,7 @@ import wave import paddle.v2 as paddle import _init_paths from data_utils.data import DataGenerator -from models.model import DeepSpeech2Model +from model_utils.model import DeepSpeech2Model from data_utils.utils import read_manifest from utils.utility import add_arguments, print_arguments diff --git a/deep_speech_2/examples/librispeech/prepare_data.sh b/deep_speech_2/examples/librispeech/run_data.sh similarity index 57% rename from deep_speech_2/examples/librispeech/prepare_data.sh rename to deep_speech_2/examples/librispeech/run_data.sh index 6e99977038b0cb5b99e11891c475f4b1202f2b24..f65aa233b7868b587e1411a87c9e0a8141a94c91 100644 --- a/deep_speech_2/examples/librispeech/prepare_data.sh +++ b/deep_speech_2/examples/librispeech/run_data.sh @@ -1,26 +1,31 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null # download data, generate manifests python data/librispeech/librispeech.py \ --manifest_prefix='data/librispeech/manifest' \ ---full_download='True' \ ---target_dir='~/.cache/paddle/dataset/speech/Libri' +--target_dir='~/.cache/paddle/dataset/speech/Libri' \ +--full_download='True' if [ $? -ne 0 ]; then echo "Prepare LibriSpeech failed. Terminated." exit 1 fi -cat data/librispeech/manifest.train* | shuf > data/librispeech/manifest.train +cat data/librispeech/manifest.train-* | shuf > data/librispeech/manifest.train -# build vocabulary (can be skipped for English, as already provided) -# python tools/build_vocab.py \ -# --count_threshold=0 \ -# --vocab_path='data/librispeech/eng_vocab.txt' \ -# --manifest_paths='data/librispeech/manifeset.train' +# build vocabulary +python tools/build_vocab.py \ +--count_threshold=0 \ +--vocab_path='data/librispeech/vocab.txt' \ +--manifest_paths='data/librispeech/manifest.train' + +if [ $? -ne 0 ]; then + echo "Build vocabulary failed. Terminated." + exit 1 +fi # compute mean and stddev for normalizer @@ -37,3 +42,4 @@ fi echo "LibriSpeech Data preparation done." +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_infer.sh b/deep_speech_2/examples/librispeech/run_infer.sh index 619d546e8829c99d7fef62cd427a15d4dd246d13..6b790502a536e144d3add3f1f187d3f5e7282888 100644 --- a/deep_speech_2/examples/librispeech/run_infer.sh +++ b/deep_speech_2/examples/librispeech/run_infer.sh @@ -1,13 +1,23 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# infer CUDA_VISIBLE_DEVICES=0 \ python -u infer.py \ --num_samples=10 \ --trainer_count=1 \ --beam_size=500 \ ---num_proc_bsearch=12 \ +--num_proc_bsearch=8 \ --num_conv_layers=2 \ --num_rnn_layers=3 \ --rnn_layer_size=2048 \ @@ -17,11 +27,19 @@ python -u infer.py \ --use_gru=False \ --use_gpu=True \ --share_rnn_weights=True \ ---infer_manifest='data/librispeech/manifest.dev-clean' \ +--infer_manifest='data/librispeech/manifest.test-clean' \ --mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---model_path='checkpoints/params.latest.tar.gz' \ ---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \ +--vocab_path='data/librispeech/vocab.txt' \ +--model_path='checkpoints/libri/params.latest.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --decoding_method='ctc_beam_search' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in inference!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_infer_golden.sh b/deep_speech_2/examples/librispeech/run_infer_golden.sh new file mode 100644 index 0000000000000000000000000000000000000000..32e9d8623fb53db8b1dc0f8129167c8d53d265d8 --- /dev/null +++ b/deep_speech_2/examples/librispeech/run_infer_golden.sh @@ -0,0 +1,54 @@ +#! /usr/bin/bash + +pushd ../.. > /dev/null + +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# download well-trained model +pushd models/librispeech > /dev/null +sh download_model.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# infer +CUDA_VISIBLE_DEVICES=0 \ +python -u infer.py \ +--num_samples=10 \ +--trainer_count=1 \ +--beam_size=500 \ +--num_proc_bsearch=8 \ +--num_conv_layers=2 \ +--num_rnn_layers=3 \ +--rnn_layer_size=2048 \ +--alpha=0.36 \ +--beta=0.25 \ +--cutoff_prob=0.99 \ +--use_gru=False \ +--use_gpu=True \ +--share_rnn_weights=True \ +--infer_manifest='data/tiny/manifest.test-clean' \ +--mean_std_path='models/librispeech/mean_std.npz' \ +--vocab_path='models/librispeech/vocab.txt' \ +--model_path='models/librispeech/params.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ +--decoding_method='ctc_beam_search' \ +--error_rate_type='wer' \ +--specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in inference!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_test.sh b/deep_speech_2/examples/librispeech/run_test.sh index 5a14cb682135bac8ce6097acfd07b5f2f615c1ba..9709234abcf8cb321f38da85183319ccb23d7ae6 100644 --- a/deep_speech_2/examples/librispeech/run_test.sh +++ b/deep_speech_2/examples/librispeech/run_test.sh @@ -1,14 +1,24 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# evaluate model CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ -python -u evaluate.py \ +python -u test.py \ --batch_size=128 \ --trainer_count=8 \ --beam_size=500 \ ---num_proc_bsearch=12 \ ---num_proc_data=12 \ +--num_proc_bsearch=8 \ +--num_proc_data=4 \ --num_conv_layers=2 \ --num_rnn_layers=3 \ --rnn_layer_size=2048 \ @@ -20,9 +30,17 @@ python -u evaluate.py \ --share_rnn_weights=True \ --test_manifest='data/librispeech/manifest.test-clean' \ --mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---model_path='checkpoints/params.latest.tar.gz' \ ---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \ +--vocab_path='data/librispeech/vocab.txt' \ +--model_path='checkpoints/libri/params.latest.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --decoding_method='ctc_beam_search' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in evaluation!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_test_golden.sh b/deep_speech_2/examples/librispeech/run_test_golden.sh new file mode 100644 index 0000000000000000000000000000000000000000..080c3c0622d62169d63f0e1f1bf3d9ceb7d24da0 --- /dev/null +++ b/deep_speech_2/examples/librispeech/run_test_golden.sh @@ -0,0 +1,55 @@ +#! /usr/bin/bash + +pushd ../.. > /dev/null + +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# download well-trained model +pushd models/librispeech > /dev/null +sh download_model.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# evaluate model +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ +python -u test.py \ +--batch_size=128 \ +--trainer_count=8 \ +--beam_size=500 \ +--num_proc_bsearch=8 \ +--num_proc_data=4 \ +--num_conv_layers=2 \ +--num_rnn_layers=3 \ +--rnn_layer_size=2048 \ +--alpha=0.36 \ +--beta=0.25 \ +--cutoff_prob=0.99 \ +--use_gru=False \ +--use_gpu=True \ +--share_rnn_weights=True \ +--test_manifest='data/tiny/manifest.test-clean' \ +--mean_std_path='models/librispeech/mean_std.npz' \ +--vocab_path='models/librispeech/vocab.txt' \ +--model_path='models/librispeech/params.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ +--decoding_method='ctc_beam_search' \ +--error_rate_type='wer' \ +--specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in evaluation!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_train.sh b/deep_speech_2/examples/librispeech/run_train.sh index 14672167c3cdf8cf0dfa95d7495f251711be2ba5..5485475e937e291393d434402452a1a5212172a8 100644 --- a/deep_speech_2/examples/librispeech/run_train.sh +++ b/deep_speech_2/examples/librispeech/run_train.sh @@ -1,10 +1,11 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# train model CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ python -u train.py \ ---batch_size=256 \ +--batch_size=512 \ --trainer_count=8 \ --num_passes=50 \ --num_proc_data=12 \ @@ -23,8 +24,16 @@ python -u train.py \ --train_manifest='data/librispeech/manifest.train' \ --dev_manifest='data/librispeech/manifest.dev' \ --mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---output_model_dir='./checkpoints' \ +--vocab_path='data/librispeech/vocab.txt' \ +--output_model_dir='./checkpoints/libri' \ --augment_conf_path='conf/augmentation.config' \ --specgram_type='linear' \ --shuffle_method='batch_shuffle_clipped' + +if [ $? -ne 0 ]; then + echo "Failed in training!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/librispeech/run_tune.sh b/deep_speech_2/examples/librispeech/run_tune.sh index 9d992e8842a3c9d434a32669d72df722a211c61b..05c024becab92cf76648b08f1a886733fdb31635 100644 --- a/deep_speech_2/examples/librispeech/run_tune.sh +++ b/deep_speech_2/examples/librispeech/run_tune.sh @@ -1,7 +1,8 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# grid-search for hyper-parameters in language model CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ python -u tools/tune.py \ --num_samples=100 \ @@ -23,8 +24,16 @@ python -u tools/tune.py \ --share_rnn_weights=True \ --tune_manifest='data/librispeech/manifest.dev-clean' \ --mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---model_path='checkpoints/params.latest.tar.gz' \ ---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \ +--vocab_path='data/librispeech/vocab.txt' \ +--model_path='checkpoints/libri/params.latest.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in tuning!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/mandarin/run_demo_client.sh b/deep_speech_2/examples/mandarin/run_demo_client.sh new file mode 100644 index 0000000000000000000000000000000000000000..dfde20f8800ffd9bb71ade7c100709adfb814861 --- /dev/null +++ b/deep_speech_2/examples/mandarin/run_demo_client.sh @@ -0,0 +1,17 @@ +#! /usr/bin/bash + +pushd ../.. > /dev/null + +# start demo client +CUDA_VISIBLE_DEVICES=0 \ +python -u deploy/demo_client.py \ +--host_ip='localhost' \ +--host_port=8086 \ + +if [ $? -ne 0 ]; then + echo "Failed in starting demo client!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/mandarin/run_demo_server.sh b/deep_speech_2/examples/mandarin/run_demo_server.sh new file mode 100644 index 0000000000000000000000000000000000000000..703184a6be354e186bb9ff5fa7ceb03c082b7ca0 --- /dev/null +++ b/deep_speech_2/examples/mandarin/run_demo_server.sh @@ -0,0 +1,53 @@ +#! /usr/bin/bash +# TODO: replace the model with a mandarin model + +pushd ../.. > /dev/null + +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# download well-trained model +pushd models/librispeech > /dev/null +sh download_model.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# start demo server +CUDA_VISIBLE_DEVICES=0 \ +python -u deploy/demo_server.py \ +--host_ip='localhost' \ +--host_port=8086 \ +--num_conv_layers=2 \ +--num_rnn_layers=3 \ +--rnn_layer_size=2048 \ +--alpha=0.36 \ +--beta=0.25 \ +--cutoff_prob=0.99 \ +--use_gru=False \ +--use_gpu=True \ +--share_rnn_weights=True \ +--speech_save_dir='demo_cache' \ +--warmup_manifest='data/tiny/manifest.test-clean' \ +--mean_std_path='models/librispeech/mean_std.npz' \ +--vocab_path='models/librispeech/vocab.txt' \ +--model_path='models/librispeech/params.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ +--decoding_method='ctc_beam_search' \ +--specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in starting demo server!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_data.sh b/deep_speech_2/examples/tiny/run_data.sh index 44345d8cc5be8739e4e00493259f87e9492f3c1c..203d3e2c8cd9ba40dc0354977b87acc61aa58454 100644 --- a/deep_speech_2/examples/tiny/run_data.sh +++ b/deep_speech_2/examples/tiny/run_data.sh @@ -1,27 +1,26 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null # download data, generate manifests -python data/tiny/tiny.py \ +python data/librispeech/librispeech.py \ --manifest_prefix='data/tiny/manifest' \ ---target_dir=$HOME'/.cache/paddle/dataset/speech/tiny' +--target_dir='~/.cache/paddle/dataset/speech/libri' \ +--full_download='False' if [ $? -ne 0 ]; then echo "Prepare LibriSpeech failed. Terminated." exit 1 fi -cat data/tiny/manifest.dev-clean | head -n 32 > data/tiny/manifest.train -cat data/tiny/manifest.dev-clean | head -n 48 | tail -n 16 > data/tiny/manifest.dev -cat data/tiny/manifest.dev-clean | head -n 64 | tail -n 16 > data/tiny/manifest.test +head -n 64 data/tiny/manifest.dev-clean > data/tiny/manifest.tiny # build vocabulary python tools/build_vocab.py \ --count_threshold=0 \ --vocab_path='data/tiny/vocab.txt' \ ---manifest_paths='data/tiny/manifest.train' +--manifest_paths='data/tiny/manifest.dev' if [ $? -ne 0 ]; then echo "Build vocabulary failed. Terminated." @@ -31,8 +30,8 @@ fi # compute mean and stddev for normalizer python tools/compute_mean_std.py \ ---manifest_path='data/tiny/manifest.train' \ ---num_samples=32 \ +--manifest_path='data/tiny/manifest.tiny' \ +--num_samples=64 \ --specgram_type='linear' \ --output_path='data/tiny/mean_std.npz' @@ -43,3 +42,4 @@ fi echo "Tiny data preparation done." +exit 0 diff --git a/deep_speech_2/examples/tiny/run_infer.sh b/deep_speech_2/examples/tiny/run_infer.sh index f09bc66384c946f304bd4ec0277d5450c705df49..1d33bfbba2668d2f9cf5cb2518a5dcb26a34b3c0 100644 --- a/deep_speech_2/examples/tiny/run_infer.sh +++ b/deep_speech_2/examples/tiny/run_infer.sh @@ -1,13 +1,23 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# infer CUDA_VISIBLE_DEVICES=0 \ python -u infer.py \ ---num_samples=4 \ +--num_samples=10 \ --trainer_count=1 \ --beam_size=500 \ ---num_proc_bsearch=12 \ +--num_proc_bsearch=8 \ --num_conv_layers=2 \ --num_rnn_layers=3 \ --rnn_layer_size=2048 \ @@ -17,11 +27,19 @@ python -u infer.py \ --use_gru=False \ --use_gpu=True \ --share_rnn_weights=True \ ---infer_manifest='data/tiny/manifest.train' \ +--infer_manifest='data/tiny/manifest.tiny' \ --mean_std_path='data/tiny/mean_std.npz' \ --vocab_path='data/tiny/vocab.txt' \ ---model_path='checkpoints/params.pass-14.tar.gz' \ +--model_path='checkpoints/tiny/params.pass-19.tar.gz' \ --lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --decoding_method='ctc_beam_search' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in inference!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_infer_golden.sh b/deep_speech_2/examples/tiny/run_infer_golden.sh new file mode 100644 index 0000000000000000000000000000000000000000..32e9d8623fb53db8b1dc0f8129167c8d53d265d8 --- /dev/null +++ b/deep_speech_2/examples/tiny/run_infer_golden.sh @@ -0,0 +1,54 @@ +#! /usr/bin/bash + +pushd ../.. > /dev/null + +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# download well-trained model +pushd models/librispeech > /dev/null +sh download_model.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# infer +CUDA_VISIBLE_DEVICES=0 \ +python -u infer.py \ +--num_samples=10 \ +--trainer_count=1 \ +--beam_size=500 \ +--num_proc_bsearch=8 \ +--num_conv_layers=2 \ +--num_rnn_layers=3 \ +--rnn_layer_size=2048 \ +--alpha=0.36 \ +--beta=0.25 \ +--cutoff_prob=0.99 \ +--use_gru=False \ +--use_gpu=True \ +--share_rnn_weights=True \ +--infer_manifest='data/tiny/manifest.test-clean' \ +--mean_std_path='models/librispeech/mean_std.npz' \ +--vocab_path='models/librispeech/vocab.txt' \ +--model_path='models/librispeech/params.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ +--decoding_method='ctc_beam_search' \ +--error_rate_type='wer' \ +--specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in inference!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_test.sh b/deep_speech_2/examples/tiny/run_test.sh index 5a14cb682135bac8ce6097acfd07b5f2f615c1ba..f9c3cc11ce1257bbef10211796757ce877b81db7 100644 --- a/deep_speech_2/examples/tiny/run_test.sh +++ b/deep_speech_2/examples/tiny/run_test.sh @@ -1,14 +1,24 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# evaluate model CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ -python -u evaluate.py \ ---batch_size=128 \ +python -u test.py \ +--batch_size=16 \ --trainer_count=8 \ --beam_size=500 \ ---num_proc_bsearch=12 \ ---num_proc_data=12 \ +--num_proc_bsearch=8 \ +--num_proc_data=4 \ --num_conv_layers=2 \ --num_rnn_layers=3 \ --rnn_layer_size=2048 \ @@ -18,11 +28,19 @@ python -u evaluate.py \ --use_gru=False \ --use_gpu=True \ --share_rnn_weights=True \ ---test_manifest='data/librispeech/manifest.test-clean' \ ---mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---model_path='checkpoints/params.latest.tar.gz' \ ---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \ +--test_manifest='data/tiny/manifest.tiny' \ +--mean_std_path='data/tiny/mean_std.npz' \ +--vocab_path='data/tiny/vocab.txt' \ +--model_path='checkpoints/params.pass-19.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --decoding_method='ctc_beam_search' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in evaluation!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_test_golden.sh b/deep_speech_2/examples/tiny/run_test_golden.sh new file mode 100644 index 0000000000000000000000000000000000000000..080c3c0622d62169d63f0e1f1bf3d9ceb7d24da0 --- /dev/null +++ b/deep_speech_2/examples/tiny/run_test_golden.sh @@ -0,0 +1,55 @@ +#! /usr/bin/bash + +pushd ../.. > /dev/null + +# download language model +pushd models/lm > /dev/null +sh download_lm_en.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# download well-trained model +pushd models/librispeech > /dev/null +sh download_model.sh +if [ $? -ne 0 ]; then + exit 1 +fi +popd > /dev/null + + +# evaluate model +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ +python -u test.py \ +--batch_size=128 \ +--trainer_count=8 \ +--beam_size=500 \ +--num_proc_bsearch=8 \ +--num_proc_data=4 \ +--num_conv_layers=2 \ +--num_rnn_layers=3 \ +--rnn_layer_size=2048 \ +--alpha=0.36 \ +--beta=0.25 \ +--cutoff_prob=0.99 \ +--use_gru=False \ +--use_gpu=True \ +--share_rnn_weights=True \ +--test_manifest='data/tiny/manifest.test-clean' \ +--mean_std_path='models/librispeech/mean_std.npz' \ +--vocab_path='models/librispeech/vocab.txt' \ +--model_path='models/librispeech/params.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ +--decoding_method='ctc_beam_search' \ +--error_rate_type='wer' \ +--specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in evaluation!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_train.sh b/deep_speech_2/examples/tiny/run_train.sh index 7ca3368761568916003fcfe3f4974462c8ecb62d..c66ec4e56133254b5f8fdf47deeecb39c7ead0db 100644 --- a/deep_speech_2/examples/tiny/run_train.sh +++ b/deep_speech_2/examples/tiny/run_train.sh @@ -1,18 +1,19 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null -CUDA_VISIBLE_DEVICES=0,1 \ +# train model +CUDA_VISIBLE_DEVICES=0,1,2,3 \ python -u train.py \ ---batch_size=2 \ ---trainer_count=1 \ ---num_passes=10 \ +--batch_size=16 \ +--trainer_count=4 \ +--num_passes=20 \ --num_proc_data=1 \ --num_conv_layers=2 \ --num_rnn_layers=3 \ --rnn_layer_size=2048 \ --num_iter_print=100 \ ---learning_rate=5e-5 \ +--learning_rate=1e-5 \ --max_duration=27.0 \ --min_duration=0.0 \ --use_sortagrad=True \ @@ -20,11 +21,19 @@ python -u train.py \ --use_gpu=True \ --is_local=True \ --share_rnn_weights=True \ ---train_manifest='data/tiny/manifest.train' \ ---dev_manifest='data/tiny/manifest.train' \ +--train_manifest='data/tiny/manifest.tiny' \ +--dev_manifest='data/tiny/manifest.tiny' \ --mean_std_path='data/tiny/mean_std.npz' \ --vocab_path='data/tiny/vocab.txt' \ ---output_model_dir='./checkpoints' \ +--output_model_dir='./checkpoints/tiny' \ --augment_conf_path='conf/augmentation.config' \ --specgram_type='linear' \ --shuffle_method='batch_shuffle_clipped' + +if [ $? -ne 0 ]; then + echo "Fail to do inference!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/examples/tiny/run_tune.sh b/deep_speech_2/examples/tiny/run_tune.sh index 9d992e8842a3c9d434a32669d72df722a211c61b..360c11d596db6c01ad76ab2c81a8aa10776f7cc4 100644 --- a/deep_speech_2/examples/tiny/run_tune.sh +++ b/deep_speech_2/examples/tiny/run_tune.sh @@ -1,7 +1,8 @@ #! /usr/bin/bash -pushd ../.. +pushd ../.. > /dev/null +# grid-search for hyper-parameters in language model CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ python -u tools/tune.py \ --num_samples=100 \ @@ -21,10 +22,18 @@ python -u tools/tune.py \ --use_gru=False \ --use_gpu=True \ --share_rnn_weights=True \ ---tune_manifest='data/librispeech/manifest.dev-clean' \ ---mean_std_path='data/librispeech/mean_std.npz' \ ---vocab_path='data/librispeech/eng_vocab.txt' \ ---model_path='checkpoints/params.latest.tar.gz' \ ---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \ +--tune_manifest='data/tiny/manifest.tiny' \ +--mean_std_path='data/tiny/mean_std.npz' \ +--vocab_path='data/tiny/vocab.txt' \ +--model_path='checkpoints/params.pass-9.tar.gz' \ +--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \ --error_rate_type='wer' \ --specgram_type='linear' + +if [ $? -ne 0 ]; then + echo "Failed in tuning!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/models/librispeech/download_model.sh b/deep_speech_2/models/librispeech/download_model.sh new file mode 100644 index 0000000000000000000000000000000000000000..4408f6c1c7f648a8b3688959a782dfaaee628711 --- /dev/null +++ b/deep_speech_2/models/librispeech/download_model.sh @@ -0,0 +1,20 @@ +#! /usr/bin/bash + +source ../../utils/utility.sh + +# TODO: add urls +URL='to-be-added' +MD5=5b4af224b26c1dc4dd972b7d32f2f52a +TARGET=./librispeech_model.tar.gz + + +echo "Download LibriSpeech model ..." +download $URL $MD5 $TARGET +if [ $? -ne 0 ]; then + echo "Fail to download LibriSpeech model!" + exit 1 +fi +tar -zxvf $TARGET + + +exit 0 diff --git a/deep_speech_2/models/lm/download_en.sh b/deep_speech_2/models/lm/download_en.sh deleted file mode 100644 index 5ca33c679062483f932f371578991a66aa531054..0000000000000000000000000000000000000000 --- a/deep_speech_2/models/lm/download_en.sh +++ /dev/null @@ -1,16 +0,0 @@ -echo "Downloading language model ..." - -mkdir data - -LM=common_crawl_00.prune01111.trie.klm -MD5="099a601759d467cd0a8523ff939819c5" - -wget -c http://paddlepaddle.bj.bcebos.com/model_zoo/speech/$LM -P ./data - -echo "Checking md5sum ..." -md5_tmp=`md5sum ./data/$LM | awk -F[' '] '{print $1}'` - -if [ $MD5 != $md5_tmp ]; then - echo "Fail to download the language model!" - exit 1 -fi diff --git a/deep_speech_2/models/lm/download_lm_en.sh b/deep_speech_2/models/lm/download_lm_en.sh new file mode 100644 index 0000000000000000000000000000000000000000..e967e25dc4c383276a1c8c93b3124081e80ad57b --- /dev/null +++ b/deep_speech_2/models/lm/download_lm_en.sh @@ -0,0 +1,18 @@ +#! /usr/bin/bash + +source ../../utils/utility.sh + +URL=http://paddlepaddle.bj.bcebos.com/model_zoo/speech/common_crawl_00.prune01111.trie.klm +MD5="099a601759d467cd0a8523ff939819c5" +TARGET=./common_crawl_00.prune01111.trie.klm + + +echo "Download language model ..." +download $URL $MD5 $TARGET +if [ $? -ne 0 ]; then + echo "Fail to download the language model!" + exit 1 +fi + + +exit 0 diff --git a/deep_speech_2/utils/utility.sh b/deep_speech_2/utils/utility.sh new file mode 100644 index 0000000000000000000000000000000000000000..4f617bfa9d8d7d5fefcf6c6ad84de63a493e5796 --- /dev/null +++ b/deep_speech_2/utils/utility.sh @@ -0,0 +1,20 @@ +download() { + URL=$1 + MD5=$2 + TARGET=$3 + + if [ -e $TARGET ]; then + md5_result=`md5sum $TARGET | awk -F[' '] '{print $1}'` + if [ $MD5 == $md5_result ]; then + echo "$TARGET already exists, download skipped." + return 0 + fi + fi + + wget -c $URL -P `dirname "$TARGET"` + md5_result=`md5sum $TARGET | awk -F[' '] '{print $1}'` + if [ $MD5 == $md5_result ]; then + echo "Fail to download the language model!" + return 1 + fi +}