Skip to content

D
DeepSpeech

PaddlePaddle / DeepSpeech
大约 2 年 前同步成功

通知 210
Star 8425
Fork 1598
D
DeepSpeech
“ed7d982aaa14056f62bdfc2e554d587198b60729”上不存在“vendor/vitess.io/vitess/git@gitcode.net:xiaomi/soar.git”
未验证 提交 b031ee43 编写于 作者: 小湉湉's avatar 小湉湉 提交者： GitHub
浏览文件
操作

Merge pull request #1215 from yt605155624/refactor_punc 

[text]Refactor punc
上级 0f27abcf e1798e1e
显示空白变更内容
内联 并排
Showing with 660 addition and 818 deletion
README.md
浏览文件 @ b031ee43
...@@ -424,6 +424,30 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r ...@@ -424,6 +424,30 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
</tbody> </tbody>
</table> </table>
**Punctuation Restoration**
<table style="width:100%">
<thead>
<tr>
<th> Task </th>
<th> Dataset </th>
<th> Model Type </th>
<th> Link </th>
</tr>
</thead>
<tbody>
<tr>
<td>Punctuation Restoration</td>
<td>IWLST2012_zh</td>
<td>Ernie Linear</td>
<td>
<a href = "./examples/iwslt2012/punc0">iwslt2012-punc0</a>
</td>
</tr>
</tbody>
</table>
## Documents ## Documents
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. 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.
......
README_cn.md
浏览文件 @ b031ee43
...@@ -415,6 +415,30 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 ...@@ -415,6 +415,30 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
</tbody> </tbody>
</table> </table>
**标点恢复**
<table style="width:100%">
<thead>
<tr>
<th> 任务 </th>
<th> 数据集 </th>
<th> 模型种类 </th>
<th> 链接 </th>
</tr>
</thead>
<tbody>
<tr>
<td>标点恢复</td>
<td>IWLST2012_zh</td>
<td>Ernie Linear</td>
<td>
<a href = "./examples/iwslt2012/punc0">iwslt2012-punc0</a>
</td>
</tr>
</tbody>
</table>
## 教程文档 ## 教程文档
对于 PaddleSpeech 的所关注的任务，以下指南有助于帮助开发者快速入门，了解语音相关核心思想。 对于 PaddleSpeech 的所关注的任务，以下指南有助于帮助开发者快速入门，了解语音相关核心思想。
......
docs/source/released_model.md
浏览文件 @ b031ee43
# Released Models # Released Models
## Speech-to-Text Models ## Speech-to-Text Models
### Speech Recognition Model ### Speech Recognition Model
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech | Example Link
:-------------:| :------------:| :-----: | -----: | :----------------- |:--------- | :---------- | :--------- | :----------- :-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----: | :-----: | :-----:
[Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz) | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0) [Ds2 Online Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz) | Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h | [D2 Online Aishell ASR0](../../examples/aishell/asr0)
[Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz)| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) [Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz)| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0)
[Conformer Online Aishell ASR1 Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz) | Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0594 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) [Conformer Online Aishell ASR1 Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz) | Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0594 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1)
...@@ -17,7 +16,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | ...@@ -17,7 +16,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
### Language Model based on NGram ### Language Model based on NGram
Language Model | Training Data | Token-based | Size | Descriptions Language Model | Training Data | Token-based | Size | Descriptions
:-------------:| :------------:| :-----: | -----: | :----------------- :------------:| :------------:|:------------: | :------------: | :------------:
[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) | [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie' binary with '-a 22 -q 8 -b 8' [English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) | [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie' binary with '-a 22 -q 8 -b 8'
[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings [Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings [Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
...@@ -25,14 +24,13 @@ Language Model | Training Data | Token-based | Size | Descriptions ...@@ -25,14 +24,13 @@ Language Model | Training Data | Token-based | Size | Descriptions
### Speech Translation Models ### Speech Translation Models
| Model | Training Data | Token-based | Size | Descriptions | BLEU | Example Link | | Model | Training Data | Token-based | Size | Descriptions | BLEU | Example Link |
| ------------------------------------------------------------ | ------------- | ----------- | ---- | ------------------------------------------------------------ | ----- | ------------------------------------------------------------ | | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| [Transformer FAT-ST MTL En-Zh](https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz) | Ted-En-Zh | Spm | | Encoder:Transformer, Decoder:Transformer, <br />Decoding method: Attention | 20.80 | [Transformer Ted-En-Zh ST1](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/ted_en_zh/st1) | | [Transformer FAT-ST MTL En-Zh](https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz) | Ted-En-Zh| Spm| | Encoder:Transformer, Decoder:Transformer, <br />Decoding method: Attention | 20.80 | [Transformer Ted-En-Zh ST1](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/ted_en_zh/st1) |
## Text-to-Speech Models ## Text-to-Speech Models
### Acoustic Models ### Acoustic Models
Model Type | Dataset| Example Link | Pretrained Models|Static Models|Siize(static) Model Type | Dataset| Example Link | Pretrained Models|Static Models|Size (static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----: :-------------:| :------------:| :-----: | :-----:| :-----:| :-----:
Tacotron2|LJSpeech|[tacotron2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3.zip)||| Tacotron2|LJSpeech|[tacotron2-vctk](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts0)|[tacotron2_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/tacotron2/tacotron2_ljspeech_ckpt_0.3.zip)|||
TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)||| TransformerTTS| LJSpeech| [transformer-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/tts1)|[transformer_tts_ljspeech_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/transformer_tts/transformer_tts_ljspeech_ckpt_0.4.zip)|||
...@@ -44,8 +42,8 @@ FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/Pa ...@@ -44,8 +42,8 @@ FastSpeech2| LJSpeech |[fastspeech2-ljspeech](https://github.com/PaddlePaddle/Pa
FastSpeech2| VCTK |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip)||| FastSpeech2| VCTK |[fastspeech2-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/tts3)|[fastspeech2_nosil_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_vctk_ckpt_0.5.zip)|||
### Vocoders ### Vocoders
Model Type | Dataset| Example Link | Pretrained Models| Static Models|Size(static) Model Type | Dataset| Example Link | Pretrained Models| Static Models|Size (static)
:-------------:| :------------:| :-----: | :-----:| :-----:| :-----: :-----:| :-----:| :-----: | :-----:| :-----:| :-----:
WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip)||| WaveFlow| LJSpeech |[waveflow-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0)|[waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip)|||
Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip)|5.1MB| Parallel WaveGAN| CSMSC |[PWGAN-csmsc](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1)|[pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip)|[pwg_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_static_0.4.zip)|5.1MB|
Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)||| Parallel WaveGAN| LJSpeech |[PWGAN-ljspeech](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1)|[pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)|||
...@@ -69,10 +67,15 @@ Model Type | Dataset| Example Link | Pretrained Models ...@@ -69,10 +67,15 @@ Model Type | Dataset| Example Link | Pretrained Models
PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams)
PANN | ESC-50 |[pann-esc50]("./examples/esc50/cls0")|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz) PANN | ESC-50 |[pann-esc50]("./examples/esc50/cls0")|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)
## Punctuation Restoration Models
Model Type | Dataset| Example Link | Pretrained Models
:-------------:| :------------:| :-----: | :-----:
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 ## Speech Recognition Model from paddle 1.8
| Acoustic Model |Training Data| Token-based | Size | Descriptions | CER | WER | Hours of speech | | Acoustic Model |Training Data| Token-based | Size | Descriptions | CER | WER | Hours of speech |
| :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | :--------------: | | :-----:| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| [Ds2 Offline Aishell model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz) | Aishell Dataset | Char-based | 234 MB | 2 Conv + 3 bidirectional GRU layers | 0.0804 | — | 151 h | | [Ds2 Offline Aishell model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz) | Aishell Dataset | Char-based | 234 MB | 2 Conv + 3 bidirectional GRU layers | 0.0804 | — | 151 h |
| [Ds2 Offline Librispeech model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz) | Librispeech Dataset | Word-based | 307 MB | 2 Conv + 3 bidirectional sharing weight RNN layers | — | 0.0685 | 960 h | | [Ds2 Offline Librispeech model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz) | Librispeech Dataset | Word-based | 307 MB | 2 Conv + 3 bidirectional sharing weight RNN layers | — | 0.0685 | 960 h |
| [Ds2 Offline Baidu en8k model](https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz) | Baidu Internal English Dataset | Word-based | 273 MB | 2 Conv + 3 bidirectional GRU layers |— | 0.0541 | 8628 h | | [Ds2 Offline Baidu en8k model](https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz) | Baidu Internal English Dataset | Word-based | 273 MB | 2 Conv + 3 bidirectional GRU layers |— | 0.0541 | 8628 h|
examples/iwslt2012/punc0/README.md
浏览文件 @ b031ee43
# 中文实验例程 # Punctuation Restoration with IWLST2012
## 测试数据： ## Get Started
- IWLST2012中文：test2012 ### Data Preprocessing
```bash
./run.sh --stage 0 --stop-stage 0
```
### Model Training
```bash
./run.sh --stage 1 --stop-stage 1
```
### Testing
```bash
./run.sh --stage 2 --stop-stage 2
```
### Punctuation Restoration
```bash
./run.sh --stage 3 --stop-stage 3
```
## Pretrained Model
The pretrained model can be downloaded here [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).
## 运行代码 ### Test Result
- 运行 `./run.sh 0 0 conf/ernie_linear.yaml 1` - Ernie Linear
| |COMMA | PERIOD | QUESTION | OVERALL|
## 实验结果： |:-----:|:-----:|:-----:|:-----:|:-----:|
- ErnieLinear |Precision |0.510955 |0.526462 |0.820755 |0.619391|
- 实验配置：conf/ernie_linear.yaml |Recall |0.517433 |0.564179 |0.861386 |0.647666|
- 测试结果 |F1 |0.514173 |0.544669 |0.840580 |0.633141|
| | COMMA | PERIOD | QUESTION | OVERALL |
|-----------|-----------|-----------|-----------|--------- |
|Precision | 0.471831 | 0.497679 | 0.830189 | 0.599899 |
|Recall | 0.583172 | 0.641148 | 0.846154 | 0.690158 |
|F1 | 0.521626 | 0.560376 | 0.838095 | 0.640033 |
examples/iwslt2012/punc0/conf/default.yaml 0 → 100644
浏览文件 @ b031ee43
###########################################################
# DATA SETTING #
###########################################################
dataset_type: Ernie
train_path: data/iwslt2012_zh/train.txt
dev_path: data/iwslt2012_zh/dev.txt
test_path: data/iwslt2012_zh/test.txt
batch_size: 64
num_workers: 2
data_params:
pretrained_token: ernie-1.0
punc_path: data/iwslt2012_zh/punc_vocab
seq_len: 100
###########################################################
# MODEL SETTING #
###########################################################
model_type: ErnieLinear
model:
pretrained_token: ernie-1.0
num_classes: 4
###########################################################
# OPTIMIZER SETTING #
###########################################################
optimizer_params:
weight_decay: 1.0e-6 # weight decay coefficient.
scheduler_params:
learning_rate: 1.0e-5 # learning rate.
gamma: 1.0 # scheduler gamma.
###########################################################
# TRAINING SETTING #
###########################################################
max_epoch: 20
num_snapshots: 5
###########################################################
# OTHER SETTING #
###########################################################
num_snapshots: 10 # max number of snapshots to keep while training
seed: 42 # random seed for paddle, random, and np.random
examples/iwslt2012/punc0/conf/ernie_linear.yaml 已删除 100644 → 0
浏览文件 @ 0f27abcf
data:
dataset_type: Ernie
train_path: data/iwslt2012_zh/train.txt
dev_path: data/iwslt2012_zh/dev.txt
test_path: data/iwslt2012_zh/test.txt
data_params:
pretrained_token: ernie-1.0
punc_path: data/iwslt2012_zh/punc_vocab
seq_len: 100
batch_size: 64
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 0
checkpoint:
kbest_n: 5
latest_n: 10
metric_type: F1
model_type: ErnieLinear
model_params:
pretrained_token: ernie-1.0
num_classes: 4
training:
n_epoch: 20
lr: !!float 1e-5
lr_decay: 1.0
weight_decay: !!float 1e-06
global_grad_clip: 5.0
log_interval: 10
log_path: log/train_ernie_linear.log
testing:
log_path: log/test_ernie_linear.log
examples/iwslt2012/punc0/local/avg.sh 已删除 100644 → 0
浏览文件 @ 0f27abcf
#! /usr/bin/env bash
if [ $# != 2 ]; then
echo "usage: ${0} ckpt_dir avg_num"
exit -1
fi
ckpt_dir=${1}
average_num=${2}
decode_checkpoint=${ckpt_dir}/avg_${average_num}.pdparams
python3 -u ${BIN_DIR}/avg_model.py \
--dst_model ${decode_checkpoint} \
--ckpt_dir ${ckpt_dir} \
--num ${average_num} \
--val_best
if [ $? -ne 0 ]; then
echo "Failed in avg ckpt!"
exit 1
fi
exit 0
\ No newline at end of file
examples/iwslt2012/punc0/local/data.sh 100644 → 100755
浏览文件 @ b031ee43
文件模式从 100644 更改为 100755
examples/iwslt2012/punc0/local/punc_restore.sh 0 → 100755
浏览文件 @ b031ee43
#!/bin/bash
config_path=$1
train_output_path=$2
ckpt_name=$3
text=$4
ckpt_prefix=${ckpt_name%.*}
python3 ${BIN_DIR}/punc_restore.py \
--config=${config_path} \
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
--text=${text}
examples/iwslt2012/punc0/local/test.sh 100644 → 100755
浏览文件 @ b031ee43
#!/bin/bash #!/bin/bash
if [ $# != 2 ];then
echo "usage: ${0} config_path ckpt_path_prefix"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1 config_path=$1
ckpt_prefix=$2 train_output_path=$2
ckpt_name=$3
python3 -u ${BIN_DIR}/test.py \
--ngpu 1 \
--config ${config_path} \
--result_file ${ckpt_prefix}.rsl \
--checkpoint_path ${ckpt_prefix}
if [ $? -ne 0 ]; then ckpt_prefix=${ckpt_name%.*}
echo "Failed in evaluation!"
exit 1
fi
exit 0 python3 ${BIN_DIR}/test.py \
--config=${config_path} \
--checkpoint=${train_output_path}/checkpoints/${ckpt_name}
examples/iwslt2012/punc0/local/train.sh 100644 → 100755
浏览文件 @ b031ee43
#!/bin/bash #!/bin/bash
if [ $# != 3 ];then
echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name log_dir"
exit -1
fi
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
echo "using $ngpu gpus..."
config_path=$1 config_path=$1
ckpt_name=$2 train_output_path=$2
log_dir=$3
mkdir -p exp
python3 -u ${BIN_DIR}/train.py \
--ngpu ${ngpu} \
--config ${config_path} \
--output_dir exp/${ckpt_name} \
--log_dir ${log_dir}
if [ $? -ne 0 ]; then
echo "Failed in training!"
exit 1
fi
exit 0 python3 ${BIN_DIR}/train.py \
--config=${config_path} \
--output-dir=${train_output_path} \
--ngpu=1
examples/iwslt2012/punc0/path.sh 100644 → 100755
浏览文件 @ b031ee43
...@@ -10,5 +10,5 @@ export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH} ...@@ -10,5 +10,5 @@ export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
MODEL=$1 MODEL=ernie_linear
export BIN_DIR=${MAIN_ROOT}/paddlespeech/text/exps/${MODEL} export BIN_DIR=${MAIN_ROOT}/paddlespeech/text/exps/${MODEL}
examples/iwslt2012/punc0/run.sh
浏览文件 @ b031ee43
#!/bin/bash #!/bin/bash
set -e set -e
source path.sh
if [ $# -ne 4 ]; then gpus=0,1
echo "usage: bash ./run.sh stage gpu train_config avg_num" stage=0
echo "eg: bash ./run.sh 1 0 train_config 1"
exit -1
fi
stage=$1
stop_stage=100 stop_stage=100
gpus=$2
conf_path=$3
avg_num=$4
avg_ckpt=avg_${avg_num}
ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
log_dir=log
source path.sh ${ckpt} conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_12840.pdz
text=今天的天气真不错啊你下午有空吗我想约你一起去吃饭
# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# prepare data # prepare data
bash ./local/data.sh ./local/data.sh
fi fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# train model, all `ckpt` under `exp` dir # train model, all `ckpt` under `train_output_path/checkpoints/` dir
CUDA_VISIBLE_DEVICES=${gpus} bash ./local/train.sh ${conf_path} ${ckpt} ${log_dir} CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# avg n best model CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
bash ./local/avg.sh exp/${ckpt}/checkpoints ${avg_num}
fi fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n CUDA_VISIBLE_DEVICES=${gpus} ./local/punc_restore.sh ${conf_path} ${train_output_path} ${ckpt_name} ${text}|| exit -1
CUDA_VISIBLE_DEVICES=${gpus} bash ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi fi
\ No newline at end of file
paddlespeech/t2s/exps/fastspeech2/gen_gta_mel.py
浏览文件 @ b031ee43
...@@ -132,7 +132,6 @@ def main(): ...@@ -132,7 +132,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
def str2bool(str): def str2bool(str):
return True if str.lower() == 'true' else False return True if str.lower() == 'true' else False
......
paddlespeech/t2s/exps/fastspeech2/train.py
浏览文件 @ b031ee43
...@@ -174,7 +174,6 @@ def main(): ...@@ -174,7 +174,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
parser.add_argument( parser.add_argument(
"--phones-dict", type=str, default=None, help="phone vocabulary file.") "--phones-dict", type=str, default=None, help="phone vocabulary file.")
parser.add_argument( parser.add_argument(
......
paddlespeech/t2s/exps/gan_vocoder/hifigan/train.py
浏览文件 @ b031ee43
...@@ -250,7 +250,6 @@ def main(): ...@@ -250,7 +250,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/gan_vocoder/multi_band_melgan/train.py
浏览文件 @ b031ee43
...@@ -239,7 +239,6 @@ def main(): ...@@ -239,7 +239,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/gan_vocoder/parallelwave_gan/synthesize_from_wav.py
浏览文件 @ b031ee43
...@@ -93,7 +93,6 @@ def main(): ...@@ -93,7 +93,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/gan_vocoder/parallelwave_gan/train.py
浏览文件 @ b031ee43
...@@ -216,7 +216,6 @@ def main(): ...@@ -216,7 +216,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
benchmark_group = parser.add_argument_group( benchmark_group = parser.add_argument_group(
'benchmark', 'arguments related to benchmark.') 'benchmark', 'arguments related to benchmark.')
......
paddlespeech/t2s/exps/gan_vocoder/style_melgan/train.py
浏览文件 @ b031ee43
...@@ -232,7 +232,6 @@ def main(): ...@@ -232,7 +232,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/gan_vocoder/synthesize.py
浏览文件 @ b031ee43
...@@ -42,7 +42,6 @@ def main(): ...@@ -42,7 +42,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/speedyspeech/synthesize_e2e.py
浏览文件 @ b031ee43
...@@ -173,7 +173,6 @@ def main(): ...@@ -173,7 +173,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir") parser.add_argument("--output-dir", type=str, help="output dir")
parser.add_argument( parser.add_argument(
"--inference-dir", type=str, help="dir to save inference models") "--inference-dir", type=str, help="dir to save inference models")
parser.add_argument("--verbose", type=int, default=1, help="verbose")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
......
paddlespeech/t2s/exps/transformer_tts/synthesize.py
浏览文件 @ b031ee43
...@@ -118,7 +118,6 @@ def main(): ...@@ -118,7 +118,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/transformer_tts/synthesize_e2e.py
浏览文件 @ b031ee43
...@@ -137,7 +137,6 @@ def main(): ...@@ -137,7 +137,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
args = parser.parse_args() args = parser.parse_args()
......
paddlespeech/t2s/exps/transformer_tts/train.py
浏览文件 @ b031ee43
...@@ -165,7 +165,6 @@ def main(): ...@@ -165,7 +165,6 @@ def main():
parser.add_argument("--output-dir", type=str, help="output dir.") parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument( parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
parser.add_argument("--verbose", type=int, default=1, help="verbose.")
parser.add_argument( parser.add_argument(
"--phones-dict", type=str, default=None, help="phone vocabulary file.") "--phones-dict", type=str, default=None, help="phone vocabulary file.")
......
paddlespeech/text/utils/__init__.py → paddlespeech/text/exps/__init__.py
浏览文件 @ b031ee43
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
......
paddlespeech/text/training/__init__.py → paddlespeech/text/exps/ernie_linear/__init__.py
浏览文件 @ b031ee43
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
......
paddlespeech/text/exps/ernie_linear/punc_restore.py 0 → 100644
浏览文件 @ b031ee43
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import re
import paddle
import yaml
from paddlenlp.transformers import ErnieTokenizer
from yacs.config import CfgNode
from paddlespeech.text.models.ernie_linear import ErnieLinear
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
def _clean_text(text, punc_list):
text = text.lower()
text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
text = re.sub(f'[{"".join([p for p in punc_list][1:])}]', '', text)
return text
def preprocess(text, punc_list):
clean_text = _clean_text(text, punc_list)
assert len(clean_text) > 0, f'Invalid input string: {text}'
tokenized_input = tokenizer(
list(clean_text), return_length=True, is_split_into_words=True)
_inputs = dict()
_inputs['input_ids'] = tokenized_input['input_ids']
_inputs['seg_ids'] = tokenized_input['token_type_ids']
_inputs['seq_len'] = tokenized_input['seq_len']
return _inputs
def test(args):
with open(args.config) as f:
config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config)
punc_list = []
with open(config["data_params"]["punc_path"], 'r') as f:
for line in f:
punc_list.append(line.strip())
model = DefinedClassifier[config["model_type"]](**config["model"])
state_dict = paddle.load(args.checkpoint)
model.set_state_dict(state_dict["main_params"])
model.eval()
_inputs = preprocess(args.text, punc_list)
seq_len = _inputs['seq_len']
input_ids = paddle.to_tensor(_inputs['input_ids']).unsqueeze(0)
seg_ids = paddle.to_tensor(_inputs['seg_ids']).unsqueeze(0)
logits, _ = model(input_ids, seg_ids)
preds = paddle.argmax(logits, axis=-1).squeeze(0)
tokens = tokenizer.convert_ids_to_tokens(
_inputs['input_ids'][1:seq_len - 1])
labels = preds[1:seq_len - 1].tolist()
assert len(tokens) == len(labels)
# add 0 for non punc
punc_list = [0] + punc_list
text = ''
for t, l in zip(tokens, labels):
text += t
if l != 0: # Non punc.
text += punc_list[l]
print("Punctuation Restoration Result:", text)
return text
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Run Punctuation Restoration.")
parser.add_argument("--config", type=str, help="ErnieLinear config file.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument("--text", type=str, help="raw text to be restored.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
args = parser.parse_args()
if args.ngpu == 0:
paddle.set_device("cpu")
elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
test(args)
if __name__ == "__main__":
main()
paddlespeech/text/exps/ernie_linear/test.py
浏览文件 @ b031ee43
...@@ -11,36 +11,110 @@ ...@@ -11,36 +11,110 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""Evaluation for model.""" import argparse
import numpy as np
import paddle
import pandas as pd
import yaml import yaml
from paddle import nn
from paddle.io import DataLoader
from sklearn.metrics import classification_report
from sklearn.metrics import precision_recall_fscore_support
from yacs.config import CfgNode
from paddlespeech.s2t.utils.utility import print_arguments from paddlespeech.text.models.ernie_linear import ErnieLinear
from paddlespeech.text.training.trainer import Tester from paddlespeech.text.models.ernie_linear import PuncDataset
from paddlespeech.text.utils.default_parser import default_argument_parser from paddlespeech.text.models.ernie_linear import PuncDatasetFromErnieTokenizer
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
def main_sp(config, args): DefinedLoss = {
exp = Tester(config, args) "ce": nn.CrossEntropyLoss,
exp.setup() }
exp.run_test()
DefinedDataset = {
'Punc': PuncDataset,
'Ernie': PuncDatasetFromErnieTokenizer,
}
def main(config, args):
main_sp(config, args)
def evaluation(y_pred, y_test):
precision, recall, f1, _ = precision_recall_fscore_support(
y_test, y_pred, average=None, labels=[1, 2, 3])
overall = precision_recall_fscore_support(
y_test, y_pred, average='macro', labels=[1, 2, 3])
result = pd.DataFrame(
np.array([precision, recall, f1]),
columns=list(['O', 'COMMA', 'PERIOD', 'QUESTION'])[1:],
index=['Precision', 'Recall', 'F1'])
result['OVERALL'] = overall[:3]
return result
def test(args):
with open(args.config) as f:
config = CfgNode(yaml.safe_load(f))
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config)
test_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["test_path"], **config["data_params"])
test_loader = DataLoader(
test_dataset,
batch_size=config.batch_size,
shuffle=False,
drop_last=False)
model = DefinedClassifier[config["model_type"]](**config["model"])
state_dict = paddle.load(args.checkpoint)
model.set_state_dict(state_dict["main_params"])
model.eval()
punc_list = []
for i in range(len(test_loader.dataset.id2punc)):
punc_list.append(test_loader.dataset.id2punc[i])
test_total_label = []
test_total_predict = []
for i, batch in enumerate(test_loader):
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = model(input)
pred = paddle.argmax(logit, axis=1)
test_total_label.extend(label.numpy().tolist())
test_total_predict.extend(pred.numpy().tolist())
t = classification_report(
test_total_label, test_total_predict, target_names=punc_list)
print(t)
t2 = evaluation(test_total_label, test_total_predict)
print('=========================================================')
print(t2)
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Test a ErnieLinear model.")
parser.add_argument("--config", type=str, help="ErnieLinear config file.")
parser.add_argument("--checkpoint", type=str, help="snapshot to load.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args() args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html if args.ngpu == 0:
with open(args.config, "r") as f: paddle.set_device("cpu")
config = yaml.load(f, Loader=yaml.FullLoader) elif args.ngpu > 0:
paddle.set_device("gpu")
else:
print("ngpu should >= 0 !")
test(args)
print(config)
if args.dump_config:
with open(args.dump_config, 'w') as f:
print(config, file=f)
main(config, args) if __name__ == "__main__":
main()
paddlespeech/text/exps/ernie_linear/train.py
浏览文件 @ b031ee43
...@@ -11,40 +11,163 @@ ...@@ -11,40 +11,163 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""Trainer for punctuation_restoration task.""" import argparse
import logging
import os
import shutil
from pathlib import Path
import paddle
import yaml import yaml
from paddle import DataParallel
from paddle import distributed as dist from paddle import distributed as dist
from paddle import nn
from paddle.io import DataLoader
from paddle.optimizer import Adam
from paddle.optimizer.lr import ExponentialDecay
from yacs.config import CfgNode
from paddlespeech.s2t.utils.utility import print_arguments from paddlespeech.t2s.training.extensions.snapshot import Snapshot
from paddlespeech.text.training.trainer import Trainer from paddlespeech.t2s.training.extensions.visualizer import VisualDL
from paddlespeech.text.utils.default_parser import default_argument_parser from paddlespeech.t2s.training.seeding import seed_everything
from paddlespeech.t2s.training.trainer import Trainer
from paddlespeech.text.models.ernie_linear import ErnieLinear
from paddlespeech.text.models.ernie_linear import ErnieLinearEvaluator
from paddlespeech.text.models.ernie_linear import ErnieLinearUpdater
from paddlespeech.text.models.ernie_linear import PuncDataset
from paddlespeech.text.models.ernie_linear import PuncDatasetFromErnieTokenizer
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
def main_sp(config, args): DefinedLoss = {
exp = Trainer(config, args) "ce": nn.CrossEntropyLoss,
exp.setup() }
exp.run()
DefinedDataset = {
'Punc': PuncDataset,
'Ernie': PuncDatasetFromErnieTokenizer,
}
def main(config, args):
if args.ngpu > 1: def train_sp(args, config):
dist.spawn(main_sp, args=(config, args), nprocs=args.ngpu) # decides device type and whether to run in parallel
# setup running environment correctly
if (not paddle.is_compiled_with_cuda()) or args.ngpu == 0:
paddle.set_device("cpu")
else: else:
main_sp(config, args) paddle.set_device("gpu")
world_size = paddle.distributed.get_world_size()
if world_size > 1:
paddle.distributed.init_parallel_env()
# set the random seed, it is a must for multiprocess training
seed_everything(config.seed)
print(
f"rank: {dist.get_rank()}, pid: {os.getpid()}, parent_pid: {os.getppid()}",
)
# dataloader has been too verbose
logging.getLogger("DataLoader").disabled = True
train_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["train_path"], **config["data_params"])
dev_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["dev_path"], **config["data_params"])
train_dataloader = DataLoader(
train_dataset,
shuffle=True,
num_workers=config.num_workers,
batch_size=config.batch_size)
dev_dataloader = DataLoader(
dev_dataset,
batch_size=config.batch_size,
shuffle=False,
drop_last=False,
num_workers=config.num_workers)
print("dataloaders done!")
model = DefinedClassifier[config["model_type"]](**config["model"])
if world_size > 1:
model = DataParallel(model)
print("model done!")
criterion = DefinedLoss[config["loss_type"]](
**config["loss"]) if "loss_type" in config else DefinedLoss["ce"]()
print("criterions done!")
lr_schedule = ExponentialDecay(**config["scheduler_params"])
optimizer = Adam(
learning_rate=lr_schedule,
parameters=model.parameters(),
weight_decay=paddle.regularizer.L2Decay(
config["optimizer_params"]["weight_decay"]))
print("optimizer done!")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
if dist.get_rank() == 0:
config_name = args.config.split("/")[-1]
# copy conf to output_dir
shutil.copyfile(args.config, output_dir / config_name)
updater = ErnieLinearUpdater(
model=model,
criterion=criterion,
scheduler=lr_schedule,
optimizer=optimizer,
dataloader=train_dataloader,
output_dir=output_dir)
trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)
evaluator = ErnieLinearEvaluator(
model=model,
criterion=criterion,
dataloader=dev_dataloader,
output_dir=output_dir)
if dist.get_rank() == 0:
trainer.extend(evaluator, trigger=(1, "epoch"))
trainer.extend(VisualDL(output_dir), trigger=(1, "iteration"))
trainer.extend(
Snapshot(max_size=config.num_snapshots), trigger=(1, 'epoch'))
# print(trainer.extensions)
trainer.run()
def main():
# parse args and config and redirect to train_sp
parser = argparse.ArgumentParser(description="Train a ErnieLinear model.")
parser.add_argument("--config", type=str, help="ErnieLinear config file.")
parser.add_argument("--output-dir", type=str, help="output dir.")
parser.add_argument(
"--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
if __name__ == "__main__":
parser = default_argument_parser()
args = parser.parse_args() args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html with open(args.config) as f:
with open(args.config, "r") as f: config = CfgNode(yaml.safe_load(f))
config = yaml.load(f, Loader=yaml.FullLoader)
print("========Args========")
print(yaml.safe_dump(vars(args)))
print("========Config========")
print(config) print(config)
if args.dump_config: print(
with open(args.dump_config, 'w') as f: f"master see the word size: {dist.get_world_size()}, from pid: {os.getpid()}"
print(config, file=f) )
# dispatch
if args.ngpu > 1:
dist.spawn(train_sp, (args, config), nprocs=args.ngpu)
else:
train_sp(args, config)
main(config, args) if __name__ == "__main__":
main()
paddlespeech/text/models/ernie_linear/__init__.py
浏览文件 @ b031ee43
...@@ -11,4 +11,6 @@ ...@@ -11,4 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from .model import ErnieLinear from .dataset import *
from .ernie_linear import *
from .ernie_linear_updater import *
paddlespeech/text/models/ernie_linear/dataset.py
浏览文件 @ b031ee43
...@@ -99,10 +99,8 @@ class PuncDatasetFromErnieTokenizer(Dataset): ...@@ -99,10 +99,8 @@ class PuncDatasetFromErnieTokenizer(Dataset):
self.tokenizer = ErnieTokenizer.from_pretrained(pretrained_token) self.tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
self.paddingID = self.tokenizer.pad_token_id self.paddingID = self.tokenizer.pad_token_id
self.seq_len = seq_len self.seq_len = seq_len
self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "]) self.punc2id = self.load_vocab(punc_path, extra_word_list=[" "])
self.id2punc = {k: v for (v, k) in self.punc2id.items()} self.id2punc = {k: v for (v, k) in self.punc2id.items()}
tmp_seqs = open(train_path, encoding='utf-8').readlines() tmp_seqs = open(train_path, encoding='utf-8').readlines()
self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()] self.txt_seqs = [i for seq in tmp_seqs for i in seq.split()]
self.preprocess(self.txt_seqs) self.preprocess(self.txt_seqs)
...@@ -125,6 +123,7 @@ class PuncDatasetFromErnieTokenizer(Dataset): ...@@ -125,6 +123,7 @@ class PuncDatasetFromErnieTokenizer(Dataset):
input_data = [] input_data = []
label = [] label = []
count = 0 count = 0
print("Preprocessing in PuncDatasetFromErnieTokenizer...")
for i in range(len(txt_seqs) - 1): for i in range(len(txt_seqs) - 1):
word = txt_seqs[i] word = txt_seqs[i]
punc = txt_seqs[i + 1] punc = txt_seqs[i + 1]
......
paddlespeech/text/models/ernie_linear/ernie_linear_updater.py 0 → 100644
浏览文件 @ b031ee43
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import paddle
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.nn import Layer
from paddle.optimizer import Optimizer
from paddle.optimizer.lr import LRScheduler
from sklearn.metrics import f1_score
from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
from paddlespeech.t2s.training.reporter import report
from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater
logging.basicConfig(
format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
datefmt='[%Y-%m-%d %H:%M:%S]')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
class ErnieLinearUpdater(StandardUpdater):
def __init__(self,
model: Layer,
criterion: Layer,
scheduler: LRScheduler,
optimizer: Optimizer,
dataloader: DataLoader,
output_dir=None):
super().__init__(model, optimizer, dataloader, init_state=None)
self.model = model
self.dataloader = dataloader
self.criterion = criterion
self.scheduler = scheduler
self.optimizer = optimizer
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
def update_core(self, batch):
self.msg = "Rank: {}, ".format(dist.get_rank())
losses_dict = {}
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
loss = self.criterion(y, label)
self.optimizer.clear_grad()
loss.backward()
self.optimizer.step()
self.scheduler.step()
F1_score = f1_score(
label.numpy().tolist(), pred.numpy().tolist(), average="macro")
report("train/loss", float(loss))
losses_dict["loss"] = float(loss)
report("train/F1_score", float(F1_score))
losses_dict["F1_score"] = float(F1_score)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
class ErnieLinearEvaluator(StandardEvaluator):
def __init__(self,
model: Layer,
criterion: Layer,
dataloader: DataLoader,
output_dir=None):
super().__init__(model, dataloader)
self.model = model
self.criterion = criterion
self.dataloader = dataloader
log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
self.filehandler = logging.FileHandler(str(log_file))
logger.addHandler(self.filehandler)
self.logger = logger
self.msg = ""
def evaluate_core(self, batch):
self.msg = "Evaluate: "
losses_dict = {}
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
loss = self.criterion(y, label)
F1_score = f1_score(
label.numpy().tolist(), pred.numpy().tolist(), average="macro")
report("eval/loss", float(loss))
losses_dict["loss"] = float(loss)
report("eval/F1_score", float(F1_score))
losses_dict["F1_score"] = float(F1_score)
self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
for k, v in losses_dict.items())
self.logger.info(self.msg)
paddlespeech/text/training/trainer.py 已删除 100644 → 0
浏览文件 @ 0f27abcf
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import time
from collections import defaultdict
from pathlib import Path
import numpy as np
import paddle
import paddle.nn as nn
import pandas as pd
from paddle import distributed as dist
from paddle.io import DataLoader
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
from sklearn.metrics import precision_recall_fscore_support
from ...s2t.utils import layer_tools
from ...s2t.utils import mp_tools
from ...s2t.utils.checkpoint import Checkpoint
from ...text.models import ErnieLinear
from ...text.models.ernie_linear.dataset import PuncDataset
from ...text.models.ernie_linear.dataset import PuncDatasetFromErnieTokenizer
__all__ = ["Trainer", "Tester"]
DefinedClassifier = {
'ErnieLinear': ErnieLinear,
}
DefinedLoss = {
"ce": nn.CrossEntropyLoss,
}
DefinedDataset = {
'Punc': PuncDataset,
'Ernie': PuncDatasetFromErnieTokenizer,
}
class Trainer():
def __init__(self, config, args):
self.config = config
self.args = args
self.optimizer = None
self.output_dir = None
self.log_dir = None
self.checkpoint_dir = None
self.iteration = 0
self.epoch = 0
def setup(self):
"""Setup the experiment.
"""
self.setup_log_dir()
self.setup_logger()
if self.args.ngpu > 0:
paddle.set_device('gpu')
else:
paddle.set_device('cpu')
if self.parallel:
self.init_parallel()
self.setup_output_dir()
self.dump_config()
self.setup_checkpointer()
self.setup_model()
self.setup_dataloader()
self.iteration = 0
self.epoch = 1
@property
def parallel(self):
"""A flag indicating whether the experiment should run with
multiprocessing.
"""
return self.args.ngpu > 1
def init_parallel(self):
"""Init environment for multiprocess training.
"""
dist.init_parallel_env()
@mp_tools.rank_zero_only
def save(self, tag=None, infos: dict=None):
"""Save checkpoint (model parameters and optimizer states).
Args:
tag (int or str, optional): None for step, else using tag, e.g epoch. Defaults to None.
infos (dict, optional): meta data to save. Defaults to None.
"""
infos = infos if infos else dict()
infos.update({
"step": self.iteration,
"epoch": self.epoch,
"lr": self.optimizer.get_lr()
})
self.checkpointer.save_parameters(self.checkpoint_dir, self.iteration
if tag is None else tag, self.model,
self.optimizer, infos)
def resume_or_scratch(self):
"""Resume from latest checkpoint at checkpoints in the output
directory or load a specified checkpoint.
If ``args.checkpoint_path`` is not None, load the checkpoint, else
resume training.
"""
scratch = None
infos = self.checkpointer.load_parameters(
self.model,
self.optimizer,
checkpoint_dir=self.checkpoint_dir,
checkpoint_path=self.args.checkpoint_path)
if infos:
# restore from ckpt
self.iteration = infos["step"]
self.epoch = infos["epoch"]
scratch = False
else:
self.iteration = 0
self.epoch = 0
scratch = True
return scratch
def new_epoch(self):
"""Reset the train loader seed and increment `epoch`.
"""
self.epoch += 1
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
def train(self):
"""The training process control by epoch."""
from_scratch = self.resume_or_scratch()
if from_scratch:
# save init model, i.e. 0 epoch
self.save(tag="init")
self.lr_scheduler.step(self.iteration)
if self.parallel:
self.train_loader.batch_sampler.set_epoch(self.epoch)
self.logger.info(
f"Train Total Examples: {len(self.train_loader.dataset)}")
self.punc_list = []
for i in range(len(self.train_loader.dataset.id2punc)):
self.punc_list.append(self.train_loader.dataset.id2punc[i])
while self.epoch < self.config["training"]["n_epoch"]:
self.model.train()
self.total_label_train = []
self.total_predict_train = []
try:
data_start_time = time.time()
for batch_index, batch in enumerate(self.train_loader):
dataload_time = time.time() - data_start_time
msg = "Train: Rank: {}, ".format(dist.get_rank())
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(batch_index + 1,
len(self.train_loader))
msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
msg += "data time: {:>.3f}s, ".format(dataload_time)
self.train_batch(batch_index, batch, msg)
data_start_time = time.time()
# t = classification_report(
# self.total_label_train,
# self.total_predict_train,
# target_names=self.punc_list)
# self.logger.info(t)
except Exception as e:
self.logger.error(e)
raise e
total_loss, F1_score = self.valid()
self.logger.info("Epoch {} Val info val_loss {}, F1_score {}".
format(self.epoch, total_loss, F1_score))
self.save(
tag=self.epoch, infos={"val_loss": total_loss,
"F1": F1_score})
# step lr every epoch
self.lr_scheduler.step()
self.new_epoch()
def run(self):
"""The routine of the experiment after setup. This method is intended
to be used by the user.
"""
try:
self.train()
except KeyboardInterrupt:
self.logger.info("Training was aborted by keybord interrupt.")
self.save()
exit(-1)
finally:
self.destory()
self.logger.info("Training Done.")
def setup_output_dir(self):
"""Create a directory used for output.
"""
# output dir
output_dir = Path(self.args.output_dir).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
self.output_dir = output_dir
def setup_log_dir(self):
"""Create a directory used for logging.
"""
# log dir
log_dir = Path(self.args.log_dir).expanduser()
log_dir.mkdir(parents=True, exist_ok=True)
self.log_dir = log_dir
def setup_checkpointer(self):
"""Create a directory used to save checkpoints into.
It is "checkpoints" inside the output directory.
"""
# checkpoint dir
self.checkpointer = Checkpoint(self.config["checkpoint"]["kbest_n"],
self.config["checkpoint"]["latest_n"])
checkpoint_dir = self.output_dir / "checkpoints"
checkpoint_dir.mkdir(exist_ok=True)
self.checkpoint_dir = checkpoint_dir
def setup_logger(self):
LOG_FORMAT = "%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s"
format_str = logging.Formatter(
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
logging.basicConfig(
filename=self.config["training"]["log_path"],
level=logging.INFO,
format=LOG_FORMAT)
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
sh.setFormatter(format_str)
self.logger.addHandler(sh)
self.logger.info('info')
@mp_tools.rank_zero_only
def destory(self):
pass
@mp_tools.rank_zero_only
def dump_config(self):
"""Save the configuration used for this experiment.
It is saved in to ``config.yaml`` in the output directory at the
beginning of the experiment.
"""
with open(self.output_dir / "config.yaml", "wt") as f:
print(self.config, file=f)
def train_batch(self, batch_index, batch_data, msg):
start = time.time()
input, label = batch_data
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
self.total_label_train.extend(label.numpy().tolist())
self.total_predict_train.extend(pred.numpy().tolist())
loss = self.crit(y, label)
loss.backward()
layer_tools.print_grads(self.model, print_func=None)
self.optimizer.step()
self.optimizer.clear_grad()
iteration_time = time.time() - start
losses_np = {
"train_loss": float(loss),
}
msg += "train time: {:>.3f}s, ".format(iteration_time)
msg += "batch size: {}, ".format(self.config["data"]["batch_size"])
msg += ", ".join("{}: {:>.6f}".format(k, v)
for k, v in losses_np.items())
self.logger.info(msg)
self.iteration += 1
@paddle.no_grad()
def valid(self):
self.logger.info(
f"Valid Total Examples: {len(self.valid_loader.dataset)}")
self.model.eval()
valid_losses = defaultdict(list)
num_seen_utts = 1
total_loss = 0.0
valid_total_label = []
valid_total_predict = []
for i, batch in enumerate(self.valid_loader):
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
valid_total_label.extend(label.numpy().tolist())
valid_total_predict.extend(pred.numpy().tolist())
loss = self.crit(y, label)
if paddle.isfinite(loss):
num_utts = batch[1].shape[0]
num_seen_utts += num_utts
total_loss += float(loss) * num_utts
valid_losses["val_loss"].append(float(loss))
if (i + 1) % self.config["training"]["log_interval"] == 0:
valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
valid_dump["val_history_loss"] = total_loss / num_seen_utts
# logging
msg = f"Valid: Rank: {dist.get_rank()}, "
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
msg += "batch : {}/{}, ".format(i + 1, len(self.valid_loader))
msg += ", ".join("{}: {:>.6f}".format(k, v)
for k, v in valid_dump.items())
self.logger.info(msg)
self.logger.info("Rank {} Val info val_loss {}".format(
dist.get_rank(), total_loss / num_seen_utts))
F1_score = f1_score(
valid_total_label, valid_total_predict, average="macro")
return total_loss / num_seen_utts, F1_score
def setup_model(self):
config = self.config
model = DefinedClassifier[self.config["model_type"]](
**self.config["model_params"])
self.crit = DefinedLoss[self.config["loss_type"]](**self.config[
"loss"]) if "loss_type" in self.config else DefinedLoss["ce"]()
if self.parallel:
model = paddle.DataParallel(model)
# self.logger.info(f"{model}")
# layer_tools.print_params(model, self.logger.info)
lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
learning_rate=config["training"]["lr"],
gamma=config["training"]["lr_decay"],
verbose=True)
optimizer = paddle.optimizer.Adam(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=paddle.regularizer.L2Decay(
config["training"]["weight_decay"]))
self.model = model
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.logger.info("Setup model/criterion/optimizer/lr_scheduler!")
def setup_dataloader(self):
config = self.config["data"].copy()
train_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["train_path"], **config["data_params"])
dev_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["dev_path"], **config["data_params"])
self.train_loader = DataLoader(
train_dataset,
num_workers=config["num_workers"],
batch_size=config["batch_size"])
self.valid_loader = DataLoader(
dev_dataset,
batch_size=config["batch_size"],
shuffle=False,
drop_last=False,
num_workers=config["num_workers"])
self.logger.info("Setup train/valid Dataloader!")
class Tester(Trainer):
def __init__(self, config, args):
super().__init__(config, args)
@mp_tools.rank_zero_only
@paddle.no_grad()
def test(self):
self.logger.info(
f"Test Total Examples: {len(self.test_loader.dataset)}")
self.punc_list = []
for i in range(len(self.test_loader.dataset.id2punc)):
self.punc_list.append(self.test_loader.dataset.id2punc[i])
self.model.eval()
test_total_label = []
test_total_predict = []
with open(self.args.result_file, 'w') as fout:
for i, batch in enumerate(self.test_loader):
input, label = batch
label = paddle.reshape(label, shape=[-1])
y, logit = self.model(input)
pred = paddle.argmax(logit, axis=1)
test_total_label.extend(label.numpy().tolist())
test_total_predict.extend(pred.numpy().tolist())
# logging
msg = "Test: "
msg += "epoch: {}, ".format(self.epoch)
msg += "step: {}, ".format(self.iteration)
self.logger.info(msg)
t = classification_report(
test_total_label, test_total_predict, target_names=self.punc_list)
print(t)
t2 = self.evaluation(test_total_label, test_total_predict)
print(t2)
def evaluation(self, y_pred, y_test):
precision, recall, f1, _ = precision_recall_fscore_support(
y_test, y_pred, average=None, labels=[1, 2, 3])
overall = precision_recall_fscore_support(
y_test, y_pred, average='macro', labels=[1, 2, 3])
result = pd.DataFrame(
np.array([precision, recall, f1]),
columns=list(['O', 'COMMA', 'PERIOD', 'QUESTION'])[1:],
index=['Precision', 'Recall', 'F1'])
result['OVERALL'] = overall[:3]
return result
def run_test(self):
self.resume_or_scratch()
try:
self.test()
except KeyboardInterrupt:
self.logger.info("Testing was aborted by keybord interrupt.")
exit(-1)
def setup(self):
"""Setup the experiment.
"""
if self.args.ngpu > 0:
paddle.set_device('gpu')
else:
paddle.set_device('cpu')
self.setup_logger()
self.setup_output_dir()
self.setup_checkpointer()
self.setup_dataloader()
self.setup_model()
self.iteration = 0
self.epoch = 0
def setup_model(self):
config = self.config
model = DefinedClassifier[self.config["model_type"]](
**self.config["model_params"])
self.model = model
self.logger.info("Setup model!")
def setup_dataloader(self):
config = self.config["data"].copy()
test_dataset = DefinedDataset[config["dataset_type"]](
train_path=config["test_path"], **config["data_params"])
self.test_loader = DataLoader(
test_dataset,
batch_size=config["batch_size"],
shuffle=False,
drop_last=False)
self.logger.info("Setup test Dataloader!")
def setup_output_dir(self):
"""Create a directory used for output.
"""
# output dir
if self.args.output_dir:
output_dir = Path(self.args.output_dir).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
else:
output_dir = Path(
self.args.checkpoint_path).expanduser().parent.parent
output_dir.mkdir(parents=True, exist_ok=True)
self.output_dir = output_dir
def setup_logger(self):
LOG_FORMAT = "%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s"
format_str = logging.Formatter(
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
)
logging.basicConfig(
filename=self.config["testing"]["log_path"],
level=logging.INFO,
format=LOG_FORMAT)
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
sh.setFormatter(format_str)
self.logger.addHandler(sh)
self.logger.info('info')
paddlespeech/text/utils/default_parser.py 已删除 100644 → 0
浏览文件 @ 0f27abcf
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
def default_argument_parser():
r"""A simple yet genral argument parser for experiments with t2s.
This is used in examples with t2s. And it is intended to be used by
other experiments with t2s. It requires a minimal set of command line
arguments to start a training script.
The ``--config`` and ``--opts`` are used for overwrite the deault
configuration.
The ``--data`` and ``--output`` specifies the data path and output path.
Resuming training from existing progress at the output directory is the
intended default behavior.
The ``--checkpoint_path`` specifies the checkpoint to load from.
The ``--ngpu`` specifies how to run the training.
See Also
--------
paddlespeech.t2s.training.experiment
Returns
-------
argparse.ArgumentParser
the parser
"""
parser = argparse.ArgumentParser()
# yapf: disable
# data and output
parser.add_argument("--config", metavar="FILE", help="path of the config file to overwrite to default config with.")
parser.add_argument("--dump-config", metavar="FILE", help="dump config to yaml file.")
# parser.add_argument("--data", metavar="DATA_DIR", help="path to the datatset.")
parser.add_argument("--output_dir", metavar="OUTPUT_DIR", help="path to save checkpoint.")
parser.add_argument("--log_dir", metavar="LOG_DIR", help="path to save logs.")
# load from saved checkpoint
parser.add_argument("--checkpoint_path", type=str, help="path of the checkpoint to load")
# save jit model to
parser.add_argument("--export_path", type=str, help="path of the jit model to save")
# save asr result to
parser.add_argument("--result_file", type=str, help="path of save the asr result")
# running
parser.add_argument("--ngpu", type=int, default=1, help="number of parallel processes to use. if ngpu=0, using cpu.")
# overwrite extra config and default config
# parser.add_argument("--opts", nargs=argparse.REMAINDER,
# help="options to overwrite --config file and the default config, passing in KEY VALUE pairs")
parser.add_argument("--opts", type=str, default=[], nargs='+',
help="options to overwrite --config file and the default config, passing in KEY VALUE pairs")
# yapd: enable
return parser
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或