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TensorFlowASR-1.0.3
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Almost State-of-the-art Automatic Speech Recognition using Tensorflow 2
| ویژگی | مقدار |
|---|---|
| سیستم عامل | POSIX :: Linux |
| نام فایل | TensorFlowASR-1.0.3 |
| نام | TensorFlowASR |
| نسخه کتابخانه | 1.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Huy Le Nguyen |
| ایمیل نویسنده | nlhuy.cs.16@gmail.com |
| آدرس صفحه اصلی | https://github.com/TensorSpeech/TensorFlowASR |
| آدرس اینترنتی | https://pypi.org/project/TensorFlowASR/ |
| مجوز | - |
<h1 align="center">
<p>TensorFlowASR :zap:</p>
<p align="center">
<a href="https://github.com/TensorSpeech/TensorFlowASR/blob/main/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/TensorSpeech/TensorFlowASR?logo=apache&logoColor=green">
</a>
<img alt="python" src="https://img.shields.io/badge/python-%3E%3D3.6-blue?logo=python">
<img alt="tensorflow" src="https://img.shields.io/badge/tensorflow-%3E%3D2.5.1-orange?logo=tensorflow">
<a href="https://pypi.org/project/TensorFlowASR/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/TensorFlowASR?color=%234285F4&label=release&logo=pypi&logoColor=%234285F4">
</a>
</p>
</h1>
<h2 align="center">
<p>Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2</p>
</h2>
<p align="center">
TensorFlowASR implements some automatic speech recognition architectures such as DeepSpeech2, Jasper, RNN Transducer, ContextNet, Conformer, etc. These models can be converted to TFLite to reduce memory and computation for deployment :smile:
</p>
## What's New?
- (04/17/2021) Refactor repository with new version 1.x
- (02/16/2021) Supported for TPU training
- (12/27/2020) Supported _naive_ token level timestamp, see [demo](./examples/demonstration/conformer.py) with flag `--timestamp`
- (12/17/2020) Supported ContextNet [http://arxiv.org/abs/2005.03191](http://arxiv.org/abs/2005.03191)
- (12/12/2020) Add support for using masking
- (11/14/2020) Supported Gradient Accumulation for Training in Larger Batch Size
## Table of Contents
<!-- TOC -->
- [What's New?](#whats-new)
- [Table of Contents](#table-of-contents)
- [:yum: Supported Models](#yum-supported-models)
- [Baselines](#baselines)
- [Publications](#publications)
- [Installation](#installation)
- [Installing from source (recommended)](#installing-from-source-recommended)
- [Installing via PyPi](#installing-via-pypi)
- [Running in a container](#running-in-a-container)
- [Setup training and testing](#setup-training-and-testing)
- [TFLite Convertion](#tflite-convertion)
- [Features Extraction](#features-extraction)
- [Augmentations](#augmentations)
- [Training & Testing Tutorial](#training--testing-tutorial)
- [Corpus Sources and Pretrained Models](#corpus-sources-and-pretrained-models)
- [English](#english)
- [Vietnamese](#vietnamese)
- [German](#german)
- [References & Credits](#references--credits)
- [Contact](#contact)
<!-- /TOC -->
## :yum: Supported Models
### Baselines
- **Transducer Models** (End2end models using RNNT Loss for training, currently supported Conformer, ContextNet, Streaming Transducer)
- **CTCModel** (End2end models using CTC Loss for training, currently supported DeepSpeech2, Jasper)
### Publications
- **Conformer Transducer** (Reference: [https://arxiv.org/abs/2005.08100](https://arxiv.org/abs/2005.08100))
See [examples/conformer](./examples/conformer)
- **Streaming Transducer** (Reference: [https://arxiv.org/abs/1811.06621](https://arxiv.org/abs/1811.06621))
See [examples/streaming_transducer](./examples/streaming_transducer)
- **ContextNet** (Reference: [http://arxiv.org/abs/2005.03191](http://arxiv.org/abs/2005.03191))
See [examples/contextnet](./examples/contextnet)
- **Deep Speech 2** (Reference: [https://arxiv.org/abs/1512.02595](https://arxiv.org/abs/1512.02595))
See [examples/deepspeech2](./examples/deepspeech2)
- **Jasper** (Reference: [https://arxiv.org/abs/1904.03288](https://arxiv.org/abs/1904.03288))
See [examples/jasper](./examples/jasper)
## Installation
For training and testing, you should use `git clone` for installing necessary packages from other authors (`ctc_decoders`, `rnnt_loss`, etc.)
### Installing from source (recommended)
```bash
git clone https://github.com/TensorSpeech/TensorFlowASR.git
cd TensorFlowASR
# Tensorflow 2.x (with 2.x.x >= 2.5.1)
pip3 install -e ".[tf2.x]" # or ".[tf2.x-gpu]"
```
For anaconda3:
```bash
conda create -y -n tfasr tensorflow-gpu python=3.8 # tensorflow if using CPU, this makes sure conda install all dependencies for tensorflow
conda activate tfasr
pip install -U tensorflow-gpu # upgrade to latest version of tensorflow
git clone https://github.com/TensorSpeech/TensorFlowASR.git
cd TensorFlowASR
# Tensorflow 2.x (with 2.x.x >= 2.5.1)
pip3 install -e ".[tf2.x]" # or ".[tf2.x-gpu]"
```
### Installing via PyPi
```bash
# Tensorflow 2.x (with 2.x >= 2.3)
pip3 install -U "TensorFlowASR[tf2.x]" # or pip3 install -U "TensorFlowASR[tf2.x-gpu]"
```
### Running in a container
```bash
docker-compose up -d
```
## Setup training and testing
- For datasets, see [datasets](./tensorflow_asr/datasets/README.md)
- For _training, testing and using_ **CTC Models**, run `./scripts/install_ctc_decoders.sh`
- For _training_ **Transducer Models** with RNNT Loss in TF, make sure that [warp-transducer](https://github.com/HawkAaron/warp-transducer) **is not installed** (by simply run `pip3 uninstall warprnnt-tensorflow`) (**Recommended**)
- For _training_ **Transducer Models** with RNNT Loss from [warp-transducer](https://github.com/HawkAaron/warp-transducer), run `export CUDA_HOME=/usr/local/cuda && ./scripts/install_rnnt_loss.sh` (**Note**: only `export CUDA_HOME` when you have CUDA)
- For _mixed precision training_, use flag `--mxp` when running python scripts from [examples](./examples)
- For _enabling XLA_, run `TF_XLA_FLAGS=--tf_xla_auto_jit=2 python3 $path_to_py_script`)
- For _hiding warnings_, run `export TF_CPP_MIN_LOG_LEVEL=2` before running any examples
## TFLite Convertion
After converting to tflite, the tflite model is like a function that transforms directly from an **audio signal** to **unicode code points**, then we can convert unicode points to string.
1. Install `tf-nightly` using `pip install tf-nightly`
2. Build a model with the same architecture as the trained model _(if model has tflite argument, you must set it to True)_, then load the weights from trained model to the built model
3. Load `TFSpeechFeaturizer` and `TextFeaturizer` to model using function `add_featurizers`
4. Convert model's function to tflite as follows:
```python
func = model.make_tflite_function(**options) # options are the arguments of the function
concrete_func = func.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
tflite_model = converter.convert()
```
5. Save the converted tflite model as follows:
```python
if not os.path.exists(os.path.dirname(tflite_path)):
os.makedirs(os.path.dirname(tflite_path))
with open(tflite_path, "wb") as tflite_out:
tflite_out.write(tflite_model)
```
5. Then the `.tflite` model is ready to be deployed
## Features Extraction
See [features_extraction](./tensorflow_asr/featurizers/README.md)
## Augmentations
See [augmentations](./tensorflow_asr/augmentations/README.md)
## Training & Testing Tutorial
1. Define config YAML file, see the `config.yml` files in the [example folder](./examples) for reference (you can copy and modify values such as parameters, paths, etc.. to match your local machine configuration)
2. Download your corpus (a.k.a datasets) and create a script to generate `transcripts.tsv` files from your corpus (this is general format used in this project because each dataset has different format). For more detail, see [datasets](./tensorflow_asr/datasets/README.md). **Note:** Make sure your data contain only characters in your language, for example, english has `a` to `z` and `'`. **Do not use `cache` if your dataset size is not fit in the RAM**.
3. [Optional] Generate TFRecords to use `tf.data.TFRecordDataset` for better performance by using the script [create_tfrecords.py](./scripts/create_tfrecords.py)
4. Create vocabulary file (characters or subwords/wordpieces) by defining `language.characters`, using the scripts [generate_vocab_subwords.py](./scripts/generate_vocab_subwords.py) or [generate_vocab_sentencepiece.py](./scripts/generate_vocab_sentencepiece.py). There're predefined ones in [vocabularies](./vocabularies)
5. [Optional] Generate metadata file for your dataset by using script [generate_metadata.py](./scripts/generate_metadata.py). This metadata file contains maximum lengths calculated with your `config.yml` and total number of elements in each dataset, for static shape training and precalculated steps per epoch.
6. For training, see `train.py` files in the [example folder](./examples) to see the options
7. For testing, see `test.py` files in the [example folder](./examples) to see the options. **Note:** Testing is currently not supported for TPUs. It will print nothing other than the progress bar in the console, but it will store the predicted transcripts to the file `output.tsv` and then calculate the metrics from that file.
**FYI**: Keras builtin training uses **infinite dataset**, which avoids the potential last partial batch.
See [examples](./examples/) for some predefined ASR models and results
## Corpus Sources and Pretrained Models
For pretrained models, go to [drive](https://drive.google.com/drive/folders/1BD0AK30n8hc-yR28C5FW3LqzZxtLOQfl?usp=sharing)
### English
| **Name** | **Source** | **Hours** |
| :----------: | :----------------------------------------------------------------: | :-------: |
| LibriSpeech | [LibriSpeech](http://www.openslr.org/12) | 970h |
| Common Voice | [https://commonvoice.mozilla.org](https://commonvoice.mozilla.org) | 1932h |
### Vietnamese
| **Name** | **Source** | **Hours** |
| :------------------------------------: | :------------------------------------------------------------------------------------: | :-------: |
| Vivos | [https://ailab.hcmus.edu.vn/vivos](https://ailab.hcmus.edu.vn/vivos) | 15h |
| InfoRe Technology 1 | [InfoRe1 (passwd: BroughtToYouByInfoRe)](https://files.huylenguyen.com/25hours.zip) | 25h |
| InfoRe Technology 2 (used in VLSP2019) | [InfoRe2 (passwd: BroughtToYouByInfoRe)](https://files.huylenguyen.com/audiobooks.zip) | 415h |
### German
| **Name** | **Source** | **Hours** |
| :----------: | :-----------------------------------------------------------------: | :-------: |
| Common Voice | [https://commonvoice.mozilla.org/](https://commonvoice.mozilla.org) | 750h |
## References & Credits
1. [NVIDIA OpenSeq2Seq Toolkit](https://github.com/NVIDIA/OpenSeq2Seq)
2. [https://github.com/noahchalifour/warp-transducer](https://github.com/noahchalifour/warp-transducer)
3. [Sequence Transduction with Recurrent Neural Network](https://arxiv.org/abs/1211.3711)
4. [End-to-End Speech Processing Toolkit in PyTorch](https://github.com/espnet/espnet)
5. [https://github.com/iankur/ContextNet](https://github.com/iankur/ContextNet)
## Contact
Huy Le Nguyen
Email: nlhuy.cs.16@gmail.com
نیازمندی
| مقدار | نام |
|---|---|
| ==0.10.3.post1 | SoundFile |
| ==4.4.0 | tensorflow-datasets |
| ==3.6.6 | nltk |
| >=1.19.5 | numpy |
| ==0.1.96 | sentencepiece |
| ==4.62.1 | tqdm |
| ==0.8.1 | librosa |
| ==5.4.1 | PyYAML |
| ==9.0.1 | Pillow |
| ==21.7b0 | black |
| ==3.9.2 | flake8 |
| ==0.4.3 | sounddevice |
| ==0.4.0 | fire |
| ~=2.5 | tensorflow |
| ~=2.5 | tensorflow-text |
| ~=0.18 | tensorflow-io |
| ~=2.5 | tensorflow-gpu |
| ~=2.5 | tensorflow-text |
| ~=0.18 | tensorflow-io |
| ~=2.6 | tensorflow |
| ~=2.6 | tensorflow-text |
| ~=0.20 | tensorflow-io |
| ~=2.6 | tensorflow-gpu |
| ~=2.6 | tensorflow-text |
| ~=0.20 | tensorflow-io |
| ~=2.7 | tensorflow |
| ~=2.7 | tensorflow-text |
| ~=0.23 | tensorflow-io |
| ~=2.7 | tensorflow-gpu |
| ~=2.7 | tensorflow-text |
| ~=0.23 | tensorflow-io |
| ~=2.8 | tensorflow |
| ~=2.8 | tensorflow-text |
| ~=0.24 | tensorflow-io |
| ~=2.8 | tensorflow-gpu |
| ~=2.8 | tensorflow-text |
| ~=0.24 | tensorflow-io |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6, <4 | Python |
نحوه نصب
نصب پکیج whl TensorFlowASR-1.0.3:
pip install TensorFlowASR-1.0.3.whl
نصب پکیج tar.gz TensorFlowASR-1.0.3:
pip install TensorFlowASR-1.0.3.tar.gz