Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. To be more specific, this toolkit features at:
-**Ease of Use**: low barries to install, and [CLI](#quick-start) is available to quick-start your journey.
-**Align to the State-of-the-Art**: we provide high-speed and ultra-lightweight models, and also cuttingedge technology.
-**Ease of Use**: low barriers to install, and [CLI](#quick-start) is available to quick-start your journey.
-**Align to the State-of-the-Art**: we provide high-speed and ultra-lightweight models, and also cutting-edge technology.
-**Rule-based Chinese frontend**: our frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
-**Varieties of Functions that Vitalize both Industrial and Academia**:
-*Implementation of critical audio tasks*: this toolkit contains audio functions like Audio Classification, Speech Translation, Automatic Speech Recognition, Text-to-Speech Synthesis, etc.
-*Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of the speech tasks, and uses mainstream datasets like LibriSpeech, LJSpeech, AIShell, CSMSC, etc. See also [model list](#model-list) for more details.
-*Cascaded models application*: as an extension of the typical traditional audio tasks, we combine the workflows of the aforementioned tasks with other fields like Natural language processing (NLP) and Computer Vision (CV).
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##### Recent Update:
<!---
2021.12.14: We would like to have an online courses to introduce basics and research of speech, as well as code practice with `paddlespeech`. Please pay attention to our [Calendar](https://www.paddlepaddle.org.cn/live).
--->
- 2021.12.14: Our [PaddleSpeech ASR Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) on Hugging Face Spaces is available!
- 2021.12.10: PaddleSpeech CLI is available for Audio Classification, Automatic Speech Recognition, Speech Translation (English to Chinese) and Text-to-Speech.
## Installation
We strongly recommend our users to install PaddleSpeech in *Linux* with *python>=3.7* and *paddlepaddle>=2.2.0*, where `paddlespeech` can be easily installed with `pip`:
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@@ -147,7 +161,7 @@ paddlespeech st --input input_16k.wav
If you want to try more functions like training and tuning, please have a look at [Speech-to-Text Quick Start](./docs/source/asr/quick_start.md) and [Text-to-Speech Quick Start](./docs/source/tts/quick_start.md).
If you want to try more functions like training and tuning, please have a look at documents of [Speech-to-Text](./docs/source/asr/quick_start.md) and [Text-to-Speech](./docs/source/tts/quick_start.md).
## Model List
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</thead>
<tbody>
<tr>
<tdrowspan="3">Acoustic Model</td>
<tdrowspan="3">Speech Recogination</td>
<tdrowspan="2">Aishell</td>
<td>DeepSpeech2 RNN + Conv based Models</td>
<td>
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<ahref = "./examples/timit/asr1"> u2-timit</a>
</td>
</tr>
<tr>
<tdrowspan="2">Speech Translation (English to Chinese)</td>
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Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](https://paperswithcode.com/area/audio) and [Music SoTA](https://paperswithcode.com/area/music) give you an overview of the hot academic topics in the related area. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
-[Overview](./docs/source/introduction.md)
-[Installation](./docs/source/install.md)
- Speech-to-Text
-[Quick Start of Speech-to-Text](./docs/source/asr/quick_start.md)
-[Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md)
-[Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) and [PaddleSpeech VS. Espnet](https://paddlespeech.readthedocs.io/en/latest/tts/demo_2.html)
The TTS module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with DeepSpeech. If you are interested in academic research about this function, please see [TTS research overview](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) is a good guideline for the pipeline components.
The Text-to-Speech module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with this repository. If you are interested in academic research about this task, please see [TTS research overview](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) is a good guideline for the pipeline components.
