**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for a variety of critical tasks in speech and audio, with the state-of-art and influential models.
@ -170,23 +166,12 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
- 🤗 2021.12.14: [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) Demos on Hugging Face Spaces are available!
- 👏🏻 2021.12.10: `CLI` is available for `Audio Classification`, `Automatic Speech Recognition`, `Speech Translation (English to Chinese)` and `Text-to-Speech`.
### 🔥 Hot Activities
<!---
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).
--->
- 2021.12.21~12.24
4 Days Live Courses: Depth interpretation of PaddleSpeech!
**Courses videos and related materials: https://aistudio.baidu.com/aistudio/education/group/info/25130**
### Community
- Scan the QR code below with your Wechat (reply【语音】after your friend's application is approved), you can access to official technical exchange group. Look forward to your participation.
- Scan the QR code below with your Wechat, you can access to official technical exchange group and get the bonus ( more than 20GB learning materials, such as papers, codes and videos ) and the live link of the lessons. Look forward to your participation.
@ -179,38 +160,30 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
### 近期更新
<!---
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: PaddleSpeech [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) Demos on Hugging Face Spaces are available!
In some cases, we need to recognize the specific rare words with high accuracy. eg: address recognition in navigation apps. customized ASR can slove those issues.
this demo is customized for expense account, which need to recognize rare address.
the scripts are in https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/speechx/examples/custom_asr
@ -27,7 +27,7 @@ The configuration file can be found in `conf/tts_online_application.yaml`.
- In streaming voc inference, one chunk of data is inferred at a time to achieve a streaming effect. Where `voc_block` indicates the number of valid frames in the chunk, and `voc_pad` indicates the number of frames added before and after the voc_block in a chunk. The existence of voc_pad is used to eliminate errors caused by streaming inference and avoid the influence of streaming inference on the quality of synthesized audio.
- Both hifigan and mb_melgan support streaming voc inference.
- When the voc model is mb_melgan, when voc_pad=14, the synthetic audio for streaming inference is consistent with the non-streaming synthetic audio; the minimum voc_pad can be set to 7, and the synthetic audio has no abnormal hearing. If the voc_pad is less than 7, the synthetic audio sounds abnormal.
- When the voc model is hifigan, when voc_pad=20, the streaming inference synthetic audio is consistent with the non-streaming synthetic audio; when voc_pad=14, the synthetic audio has no abnormal hearing.
- When the voc model is hifigan, when voc_pad=19, the streaming inference synthetic audio is consistent with the non-streaming synthetic audio; when voc_pad=14, the synthetic audio has no abnormal hearing.
- **Note:** If the service can be started normally in the container, but the client access IP is unreachable, you can try to replace the `host` address in the configuration file with the local IP address.
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc, voc_pad set 19, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block:36
voc_pad:14
@ -95,7 +95,7 @@ tts_online-onnx:
am_pad:12
# voc_pad and voc_block voc model to streaming voc infer,
# when voc model is mb_melgan_csmsc_onnx, voc_pad set 14, streaming synthetic audio is the same as non-streaming synthetic audio; The minimum value of pad can be set to 7, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc_onnx, voc_pad set 20, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
# when voc model is hifigan_csmsc_onnx, voc_pad set 19, streaming synthetic audio is the same as non-streaming synthetic audio; voc_pad set 14, streaming synthetic audio sounds normal
voc_block:36
voc_pad:14
# voc_upsample should be same as n_shift on voc config.
Following this tutorial you can customize your dataset for audio classification task by using `paddlespeech` and `paddleaudio`.
Following this tutorial you can customize your dataset for audio classification task by using `paddlespeech`.
A base class of classification dataset is `paddleaudio.dataset.AudioClassificationDataset`. To customize your dataset you should write a dataset class derived from `AudioClassificationDataset`.
A base class of classification dataset is `paddlespeech.audio.dataset.AudioClassificationDataset`. To customize your dataset you should write a dataset class derived from `AudioClassificationDataset`.
Assuming you have some wave files that stored in your own directory. You should prepare a meta file with the information of filepaths and labels. For example the absolute path of it is `/PATH/TO/META_FILE.txt`:
```
@ -14,7 +14,7 @@ Assuming you have some wave files that stored in your own directory. You should
Here is an example to build your custom dataset in `custom_dataset.py`:
```python
from paddleaudio.datasets.dataset import AudioClassificationDataset
from paddlespeech.audio.datasets.dataset import AudioClassificationDataset
class CustomDataset(AudioClassificationDataset):
meta_file = '/PATH/TO/META_FILE.txt'
@ -48,7 +48,7 @@ class CustomDataset(AudioClassificationDataset):
Then you can build dataset and data loader from `CustomDataset`:
```python
import paddle
from paddleaudio.features import LogMelSpectrogram
from paddlespeech.audio.features import LogMelSpectrogram
| Easy | (1) Use command-line functions of PaddleSpeech. <br> (2) Experience PaddleSpeech on Ai Studio. | Linux, Mac(not support M1 chip)，Windows |
| Easy | (1) Use command-line functions of PaddleSpeech. <br> (2) Experience PaddleSpeech on Ai Studio. | Linux, Mac(not support M1 chip)，Windows ( For more information about installation, see [#1195](https://github.com/PaddlePaddle/PaddleSpeech/discussions/1195)) |
| Medium | Support major functions ，such as using the` ready-made `examples and using PaddleSpeech to train your model. | Linux |
| Hard | Support full function of Paddlespeech, including using join ctc decoder with kaldi, training n-gram language model, Montreal-Forced-Aligner, and so on. And you are more able to be a developer! | Ubuntu |
Ernie Linear | IWLST2012_zh |[iwslt2012_punc0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/iwslt2012/punc0)|[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip)
## Speech Recognition Model from paddle 1.8
| Acoustic Model |Training Data| Token-based | Size | Descriptions | CER | WER | Hours of speech |
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
--voc_stat VOC_STAT mean and standard deviation used to normalize
spectrogram when training voc.
@ -215,9 +206,9 @@ optional arguments:
output dir.
```
1. `--am` is acoustic model type with the format {model_name}_{dataset}
2. `--am_config`, `--am_checkpoint`, `--am_stat`, `--phones_dict``--speaker_dict` are arguments for acoustic model, which correspond to the 5 files in the fastspeech2 pretrained model.
2. `--am_config`, `--am_ckpt`, `--am_stat`, `--phones_dict``--speaker_dict` are arguments for acoustic model, which correspond to the 5 files in the fastspeech2 pretrained model.
3. `--voc` is vocoder type with the format {model_name}_{dataset}
4. `--voc_config`, `--voc_checkpoint`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. `--lang` is the model language, which can be `zh` or `en`.
6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
7. `--text` is the text file, which contains sentences to synthesize.
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for Tacotron2, the durations of MFA are not needed here.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
@ -4,15 +4,8 @@ This example contains code used to train a [parallel wavegan](http://arxiv.org/a
AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
@ -75,7 +68,7 @@ Train a ParallelWaveGAN model.
optional arguments:
-h, --help show this help message and exit
--config CONFIG config file to overwrite default config.
@ -4,15 +4,7 @@ This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.
AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
### Get MFA Result and Extract
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
@ -67,15 +59,13 @@ Here's the complete help message.
@ -3,7 +3,7 @@ This example contains code used to train a [Tacotron2](https://arxiv.org/abs/171
## Dataset
### Download and Extract
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
### Get MFA Result and Extract
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for Tacotron2, the durations of MFA are not needed here.
--voc_stat VOC_STAT mean and standard deviation used to normalize
spectrogram when training voc.
@ -198,9 +196,9 @@ optional arguments:
output dir.
```
1. `--am` is acoustic model type with the format {model_name}_{dataset}
2. `--am_config`, `--am_checkpoint`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the Tacotron2 pretrained model.
2. `--am_config`, `--am_ckpt`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the Tacotron2 pretrained model.
3. `--voc` is vocoder type with the format {model_name}_{dataset}
4. `--voc_config`, `--voc_checkpoint`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. `--lang` is the model language, which can be `zh` or `en`.
6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
7. `--text` is the text file, which contains sentences to synthesize.
@ -3,7 +3,7 @@ This example contains code used to train a [SpeedySpeech](http://arxiv.org/abs/2
## Dataset
### Download and Extract
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
### Get MFA Result and Extract
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for SPEEDYSPEECH.
--voc_stat VOC_STAT mean and standard deviation used to normalize
spectrogram when training voc.
@ -204,9 +202,9 @@ optional arguments:
output dir.
```
1. `--am` is acoustic model type with the format {model_name}_{dataset}
2. `--am_config`, `--am_checkpoint`, `--am_stat`, `--phones_dict` and `--tones_dict` are arguments for acoustic model, which correspond to the 5 files in the speedyspeech pretrained model.
2. `--am_config`, `--am_ckpt`, `--am_stat`, `--phones_dict` and `--tones_dict` are arguments for acoustic model, which correspond to the 5 files in the speedyspeech pretrained model.
3. `--voc` is vocoder type with the format {model_name}_{dataset}
4. `--voc_config`, `--voc_checkpoint`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. `--lang` is the model language, which can be `zh` or `en`.
6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
7. `--text` is the text file, which contains sentences to synthesize.
@ -4,7 +4,7 @@ This example contains code used to train a [Fastspeech2](https://arxiv.org/abs/2
## Dataset
### Download and Extract
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/TNtts/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/BZNSYP`.
### Get MFA Result and Extract
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
--voc_stat VOC_STAT mean and standard deviation used to normalize
spectrogram when training voc.
@ -204,11 +202,12 @@ optional arguments:
--text TEXT text to synthesize, a 'utt_id sentence' pair per line.
--output_dir OUTPUT_DIR
output dir.
```
1. `--am` is acoustic model type with the format {model_name}_{dataset}
2. `--am_config`, `--am_checkpoint`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the fastspeech2 pretrained model.
2. `--am_config`, `--am_ckpt`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the fastspeech2 pretrained model.
3. `--voc` is vocoder type with the format {model_name}_{dataset}
4. `--voc_config`, `--voc_checkpoint`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
4. `--voc_config`, `--voc_ckpt`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. `--lang` is the model language, which can be `zh` or `en`.
6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder.
7. `--text` is the text file, which contains sentences to synthesize.
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