From 7b780369f6c42a111b91614dc7ed15a83afa23ad Mon Sep 17 00:00:00 2001 From: Tao Luo Date: Thu, 13 Jun 2024 11:31:02 +0800 Subject: [PATCH] Cherry-pick to r1.4 branch (#3798) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [TTS]add Diffsinger with opencpop dataset (#3005) * Update requirements.txt * fix vits reduce_sum's input/output dtype, test=tts (#3028) * [TTS] add opencpop PWGAN example (#3031) * add opencpop voc, test=tts * soft link * Update textnorm_test_cases.txt * [TTS] add opencpop HIFIGAN example (#3038) * add opencpop voc, test=tts * soft link * add opencpop hifigan, test=tts * update * fix dtype diff of last expand_v2 op of VITS (#3041) * [ASR]add squeezeformer model (#2755) * add squeezeformer model * change CodeStyle, test=asr * change CodeStyle, test=asr * fix subsample rate error, test=asr * merge classes as required, test=asr * change CodeStyle, test=asr * fix missing code, test=asr * split code to new file, test=asr * remove rel_shift, test=asr * Update README.md * Update README_cn.md * Update README.md * Update README_cn.md * Update README.md * fix input dtype of elementwise_mul op from bool to int64 (#3054) * [TTS] add svs frontend (#3062) * [TTS]clean starganv2 vc model code and add docstring (#2987) * clean code * add docstring * [Doc] change define asr server config to chunk asr config, test=doc (#3067) * Update README.md * Update README_cn.md * get music score, test=doc (#3070) * [TTS]fix elementwise_floordiv's fill_constant (#3075) * fix elementwise_floordiv's fill_constant * add float converter for min_value in attention * fix paddle2onnx's install version, install the newest paddle2onnx in run.sh (#3084) * [TTS] update svs_music_score.md (#3085) * rm unused dep, test=tts (#3097) * Update bug-report-tts.md (#3120) * [TTS]Fix VITS lite infer (#3098) * [TTS]add starganv2 vc trainer (#3143) * add starganv2 vc trainer * fix StarGANv2VCUpdater and losses * fix StarGANv2VCEvaluator * add some typehint * [TTS]【Hackathon + No.190】 + 模型复现：iSTFTNet (#3006) * iSTFTNet implementation based on hifigan, not affect the function and execution of HIFIGAN * modify the comment in iSTFT.yaml * add the comments in hifigan * iSTFTNet implementation based on hifigan, not affect the function and execution of HIFIGAN * modify the comment in iSTFT.yaml * add the comments in hifigan * add iSTFTNet.md * modify the format of iSTFTNet.md * modify iSTFT.yaml and hifigan.py * Format code using pre-commit * modify hifigan.py,delete the unused self.istft_layer_id , move the self.output_conv behind else, change conv_post to output_conv * update iSTFTNet_csmsc_ckpt.zip download link * modify iSTFTNet.md * modify hifigan.py and iSTFT.yaml * modify iSTFTNet.md * add function for generating srt file (#3123) * add function for generating srt file 在原来websocket_client.py的基础上，增加了由wav或mp3格式的音频文件生成对应srt格式字幕文件的功能 * add function for generating srt file 在原来websocket_client.py的基础上，增加了由wav或mp3格式的音频文件生成对应srt格式字幕文件的功能 * keep origin websocket_client.py 恢复原本的websocket_client.py文件 * add generating subtitle function into README * add generate subtitle funciton into README * add subtitle generation function * add subtitle generation function * fix example/aishell local/train.sh if condition bug, test=asr (#3146) * fix some preprocess bugs (#3155) * add amp for U2 conformer. * fix scaler save * fix scaler save and load. * mv scaler.unscale_ blow grad_clip. * [TTS]add StarGANv2VC preprocess (#3163) * [TTS] [黑客松]Add JETS (#3109) * Update quick_start.md (#3175) * [BUG] Fix progress bar unit. (#3177) * Update quick_start_cn.md (#3176) * [TTS]StarGANv2 VC fix some trainer bugs, add add reset_parameters (#3182) * VITS learning rate revised, test=tts * VITS learning rate revised, test=tts * [s2t] mv dataset into paddlespeech.dataset (#3183) * mv dataset into paddlespeech.dataset * add aidatatang * fix import * Fix some typos. (#3178) * [s2t] move s2t data preprocess into paddlespeech.dataset (#3189) * move s2t data preprocess into paddlespeech.dataset * avg model, compute wer, format rsl into paddlespeech.dataset * fix format rsl * fix avg ckpts * Update pretrained model in README (#3193) * [TTS]Fix losses of StarGAN v2 VC (#3184) * VITS learning rate revised, test=tts * VITS learning rate revised, test=tts * add new aishell model for better CER. * add readme * [s2t] fix cli args to config (#3194) * fix cli args to config * fix train cli * Update README.md * [ASR] Support Hubert, fintuned on the librispeech dataset (#3088) * librispeech hubert, test=asr * librispeech hubert, test=asr * hubert decode * review * copyright, notes, example related * hubert cli * pre-commit format * fix conflicts * fix conflicts * doc related * doc and train config * librispeech.py * support hubert cli * [ASR] fix asr 0-d tensor. (#3214) * Update README.md * Update README.md * fix: 🐛 修复服务端 python ASREngine 无法使用conformer_talcs模型 (#3230) * fix: 🐛 fix python ASREngine not pass codeswitch * docs: 📝 Update Docs * 修改模型判断方式 * Adding WavLM implementation * fix model m5s * Code clean up according to comments in https://github.com/PaddlePaddle/PaddleSpeech/pull/3242 * fix error in tts/st * Changed the path for the uploaded weight * Update phonecode.py # 固话的正则 错误修改 参考https://github.com/speechio/chinese_text_normalization/blob/master/python/cn_tn.py 固化的正则为： pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D") * Adapted wavlmASR model to pretrained weights and CLI * Changed the MD5 of the pretrained tar file due to bug fixes * Deleted examples/librispeech/asr5/format_rsl.py * Update released_model.md * Code clean up for CIs * Fixed the transpose usages ignored before * Update setup.py * refactor mfa scripts * Final cleaning; Modified SSL/infer.py and README for wavlm inclusion in model options * updating readme and readme_cn * remove tsinghua pypi * Update setup.py (#3294) * Update setup.py * refactor rhy * fix ckpt * add dtype param for arange API. (#3302) * add scripts for tts code switch * add t2s assets * more comment on tts frontend * fix librosa==0.8.1 numpy==1.23.5 for paddleaudio align with this version * move ssl into t2s.frontend; fix spk_id for 0-D tensor; * add ssml unit test * add en_frontend file * add mix frontend test * fix long text oom using ssml; filter comma; update polyphonic * remove print * hotfix english G2P * en frontend unit text * fix profiler (#3323) * old grad clip has 0d tensor problem, fix it (#3334) * update to py3.8 * remove fluid. * add roformer * fix bugs * add roformer result * support position interpolation for langer attention context windown length. * RoPE with position interpolation * rope for streaming decoding * update result * fix rotary embeding * Update README.md * fix weight decay * fix develop view confict with model's * Add XPU support for SpeedySpeech (#3502) * Add XPU support for SpeedySpeech * fix typos * update description of nxpu * Add XPU support for FastSpeech2 (#3514) * Add XPU support for FastSpeech2 * optimize * Update ge2e_clone.py (#3517) 修复在windows上的多空格错误 * Fix Readme. (#3527) * Update README.md * Update README_cn.md * Update README_cn.md * Update README.md * FIX: Added missing imports * FIX: Fixed the implementation of a special method * 【benchmark】add max_mem_reserved for benchmark (#3604) * fix profiler * add max_mem_reserved for benchmark * fix develop bug function:view to reshape (#3633) * 【benchmark】fix gpu_mem unit (#3634) * fix profiler * add max_mem_reserved for benchmark * fix benchmark * 增加文件编码读取 (#3606) Fixed #3605 * bugfix: audio_len should be 1D, no 0D, which will raise list index out (#3490) of range error in the following decode process Co-authored-by: Luzhenhui * Update README.md (#3532) Fixed a typo * fixed version for paddlepaddle. (#3701) * fixed version for paddlepaddle. * fix code style * 【Fix Speech Issue No.5】issue 3444 transformation import error (#3779) * fix paddlespeech.s2t.transform.transformation import error * fix paddlespeech.s2t.transform import error * 【Fix Speech Issue No.8】issue 3652 merge_yi function has a bug (#3786) * 【Fix Speech Issue No.8】issue 3652 merge_yi function has a bug * 【Fix Speech Issue No.8】issue 3652 merge_yi function has a bug * 【test】add cli test readme (#3784) * add cli test readme * fix code style * 【test】fix test cli bug (#3793) * add cli test readme * fix code style * fix bug * Update setup.py (#3795) * adapt view behavior change, fix KeyError. (#3794) * adapt view behavior change, fix KeyError. * fix readme demo run error. * fixed opencc version --------- Co-authored-by: liangym <34430015+lym0302@users.noreply.github.com> Co-authored-by: TianYuan Co-authored-by: 夜雨飘零 Co-authored-by: zxcd <228587199@qq.com> Co-authored-by: longRookie <68834517+longRookie@users.noreply.github.com> Co-authored-by: twoDogy <128727742+twoDogy@users.noreply.github.com> Co-authored-by: lemondy Co-authored-by: ljhzxc <33015549+ljhzxc@users.noreply.github.com> Co-authored-by: PiaoYang <495384481@qq.com> Co-authored-by: WongLaw Co-authored-by: Hui Zhang Co-authored-by: Shuangchi He <34329208+Yulv-git@users.noreply.github.com> Co-authored-by: TianHao Zhang <32243340+Zth9730@users.noreply.github.com> Co-authored-by: guanyc Co-authored-by: jiamingkong Co-authored-by: zoooo0820 Co-authored-by: shuishu <990941859@qq.com> Co-authored-by: LixinGuo <18510030324@126.com> Co-authored-by: gmm <38800877+mmglove@users.noreply.github.com> Co-authored-by: Wang Huan Co-authored-by: Kai Song <50285351+USTCKAY@users.noreply.github.com> Co-authored-by: skyboooox Co-authored-by: fazledyn-or Co-authored-by: luyao-cv <1367355728@qq.com> Co-authored-by: Color_yr <402067010@qq.com> Co-authored-by: JeffLu Co-authored-by: Luzhenhui Co-authored-by: satani99 <42287151+satani99@users.noreply.github.com> Co-authored-by: mjxs <52824616+kk-2000@users.noreply.github.com> Co-authored-by: Mattheliu --- .github/CONTRIBUTING.md | 2 +- .github/ISSUE_TEMPLATE/bug-report-tts.md | 1 - README.md | 61 +- README_cn.md | 55 +- .../paddleaudio/backends/soundfile_backend.py | 2 +- audio/setup.py | 6 +- audio/tests/features/base.py | 2 +- audio/tests/features/test_istft.py | 4 +- .../tests/features/test_log_melspectrogram.py | 2 +- audio/tests/features/test_spectrogram.py | 2 +- audio/tests/features/test_stft.py | 2 +- dataset/aidatatang_200zh/aidatatang_200zh.py | 136 +- dataset/aishell/README.md | 3 - dataset/aishell/aishell.py | 140 +- dataset/librispeech/librispeech.py | 4 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paddlespeech/dataset/aidatatang_200zh/aidatatang_200zh.py create mode 100644 paddlespeech/dataset/aishell/README.md create mode 100644 paddlespeech/dataset/aishell/__init__.py create mode 100644 paddlespeech/dataset/aishell/aishell.py rename utils/utility.py => paddlespeech/dataset/download.py (59%) create mode 100644 paddlespeech/dataset/s2t/__init__.py create mode 100755 paddlespeech/dataset/s2t/avg_model.py create mode 100755 paddlespeech/dataset/s2t/build_vocab.py create mode 100755 paddlespeech/dataset/s2t/compute_mean_std.py create mode 100755 paddlespeech/dataset/s2t/compute_wer.py create mode 100755 paddlespeech/dataset/s2t/format_data.py create mode 100644 paddlespeech/dataset/s2t/format_rsl.py create mode 100644 paddlespeech/s2t/exps/hubert/__init__.py create mode 100644 paddlespeech/s2t/exps/hubert/bin/__init__.py create mode 100644 paddlespeech/s2t/exps/hubert/bin/test.py create mode 100644 paddlespeech/s2t/exps/hubert/bin/test_wav.py create mode 100644 paddlespeech/s2t/exps/hubert/bin/train.py create mode 100644 paddlespeech/s2t/exps/hubert/model.py create mode 100644 paddlespeech/s2t/exps/wavlm/__init__.py create mode 100644 paddlespeech/s2t/exps/wavlm/bin/__init__.py create mode 100644 paddlespeech/s2t/exps/wavlm/bin/test.py create mode 100644 paddlespeech/s2t/exps/wavlm/bin/test_wav.py create mode 100644 paddlespeech/s2t/exps/wavlm/bin/train.py create mode 100644 paddlespeech/s2t/exps/wavlm/model.py create mode 100644 paddlespeech/s2t/models/hubert/__init__.py create mode 100644 paddlespeech/s2t/models/hubert/hubert_ASR.py create mode 100644 paddlespeech/s2t/models/hubert/modules/__init__.py create mode 100644 paddlespeech/s2t/models/hubert/modules/hubert_model.py create mode 100644 paddlespeech/s2t/models/wav2vec2/modules/wav2vec2_model.py create mode 100644 paddlespeech/s2t/models/wavlm/__init__.py create mode 100644 paddlespeech/s2t/models/wavlm/modules/__init__.py create mode 100644 paddlespeech/s2t/models/wavlm/modules/activations.py create mode 100644 paddlespeech/s2t/models/wavlm/modules/functional.py create mode 100644 paddlespeech/s2t/models/wavlm/modules/modules.py create mode 100644 paddlespeech/s2t/models/wavlm/wavlm_asr.py create mode 100644 paddlespeech/s2t/models/wavlm/wavlm_paddle.py create mode 100644 paddlespeech/s2t/modules/conv2d.py create mode 100644 paddlespeech/s2t/modules/time_reduction.py delete mode 100644 paddlespeech/s2t/training/gradclip.py create mode 100644 paddlespeech/t2s/assets/__init__.py rename paddlespeech/t2s/{exps => assets}/csmsc_test.txt (100%) rename paddlespeech/t2s/{exps => assets}/sentences.txt (100%) rename paddlespeech/t2s/{exps => assets}/sentences_canton.txt (100%) rename paddlespeech/t2s/{exps => assets}/sentences_en.txt (100%) rename paddlespeech/t2s/{exps => assets}/sentences_mix.txt (90%) create mode 100644 paddlespeech/t2s/assets/sentences_sing.txt rename paddlespeech/t2s/{exps => assets}/sentences_ssml.txt (100%) create mode 100644 paddlespeech/t2s/exps/diffsinger/__init__.py create mode 100644 paddlespeech/t2s/exps/diffsinger/gen_gta_mel.py create mode 100644 paddlespeech/t2s/exps/diffsinger/get_minmax.py create mode 100644 paddlespeech/t2s/exps/diffsinger/normalize.py create mode 100644 paddlespeech/t2s/exps/diffsinger/preprocess.py create mode 100644 paddlespeech/t2s/exps/diffsinger/train.py create mode 100644 paddlespeech/t2s/exps/dygraph_to_static.py create mode 100644 paddlespeech/t2s/exps/jets/__init__.py create mode 100644 paddlespeech/t2s/exps/jets/inference.py create mode 100644 paddlespeech/t2s/exps/jets/normalize.py create mode 100644 paddlespeech/t2s/exps/jets/preprocess.py create mode 100644 paddlespeech/t2s/exps/jets/synthesize.py create mode 100644 paddlespeech/t2s/exps/jets/synthesize_e2e.py create mode 100644 paddlespeech/t2s/exps/jets/train.py create mode 100644 paddlespeech/t2s/exps/starganv2_vc/normalize.py create mode 100644 paddlespeech/t2s/exps/starganv2_vc/preprocess.py create mode 100644 paddlespeech/t2s/exps/starganv2_vc/train.py create mode 100644 paddlespeech/t2s/frontend/en_frontend.py create mode 100644 paddlespeech/t2s/frontend/polyphonic.py create mode 100644 paddlespeech/t2s/frontend/sing_frontend.py rename paddlespeech/t2s/{ => frontend}/ssml/__init__.py (89%) rename paddlespeech/t2s/{ => frontend}/ssml/xml_processor.py (84%) create mode 100644 paddlespeech/t2s/models/diffsinger/__init__.py create mode 100644 paddlespeech/t2s/models/diffsinger/diffsinger.py create mode 100644 paddlespeech/t2s/models/diffsinger/diffsinger_updater.py create mode 100644 paddlespeech/t2s/models/diffsinger/fastspeech2midi.py create mode 100644 paddlespeech/t2s/models/jets/__init__.py create mode 100644 paddlespeech/t2s/models/jets/alignments.py create mode 100644 paddlespeech/t2s/models/jets/generator.py create mode 100644 paddlespeech/t2s/models/jets/jets.py create mode 100644 paddlespeech/t2s/models/jets/jets_updater.py create mode 100644 paddlespeech/t2s/models/jets/length_regulator.py create mode 100644 paddlespeech/t2s/models/starganv2_vc/transforms.py create mode 100644 paddlespeech/t2s/modules/diffnet.py create mode 100644 paddlespeech/t2s/modules/wavenet_denoiser.py create mode 100644 paddlespeech/utils/argparse.py create mode 100644 tests/unit/doc/test_cli.md create mode 100644 tests/unit/tts/test_enfrontend.py create mode 100644 tests/unit/tts/test_mixfrontend.py create mode 100644 tests/unit/tts/test_ssml.py diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index a18c454c..1ff47330 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -27,4 +27,4 @@ git commit -m "xxxxxx, test=doc" 1. 虽然跳过了 CI，但是还要先排队排到才能跳过，所以非自己方向看到 pending 不要着急 🤣 2. 在 `git commit --amend` 的时候才加 `test=xxx` 可能不太有效 3. 一个 pr 多次提交 commit 注意每次都要加 `test=xxx`，因为每个 commit 都会触发 CI -4. 删除 python 环境中已经安装好的的 paddlespeech，否则可能会影响 import paddlespeech 的顺序 +4. 删除 python 环境中已经安装好的 paddlespeech，否则可能会影响 import paddlespeech 的顺序 diff --git a/.github/ISSUE_TEMPLATE/bug-report-tts.md b/.github/ISSUE_TEMPLATE/bug-report-tts.md index 64b33c32..e2322c23 100644 --- a/.github/ISSUE_TEMPLATE/bug-report-tts.md +++ b/.github/ISSUE_TEMPLATE/bug-report-tts.md @@ -3,7 +3,6 @@ name: "\U0001F41B TTS Bug Report" about: Create a report to help us improve title: "[TTS]XXXX" labels: Bug, T2S -assignees: yt605155624 --- diff --git a/README.md b/README.md index 3c60db65..abb0a55e 100644 --- a/README.md +++ b/README.md @@ -178,6 +178,13 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision - 🧩 *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). ### Recent Update +- 👑 2023.05.31: Add [WavLM ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr5), WavLM fine-tuning for ASR on LibriSpeech. +- 👑 2023.05.04: Add [HuBERT ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr4), HuBERT fine-tuning for ASR on LibriSpeech. +- ⚡ 2023.04.28: Fix [0-d tensor](https://github.com/PaddlePaddle/PaddleSpeech/pull/3214), with the upgrade of paddlepaddle==2.5, the problem of modifying 0-d tensor has been solved. +- 👑 2023.04.25: Add [AMP for U2 conformer](https://github.com/PaddlePaddle/PaddleSpeech/pull/3167). +- 🔥 2023.04.06: Add [subtitle file (.srt format) generation example](./demos/streaming_asr_server). +- 👑 2023.04.25: Add [AMP for U2 conformer](https://github.com/PaddlePaddle/PaddleSpeech/pull/3167). +- 🔥 2023.03.14: Add SVS(Singing Voice Synthesis) examples with Opencpop dataset, including [DiffSinger](./examples/opencpop/svs1)、[PWGAN](./examples/opencpop/voc1) and [HiFiGAN](./examples/opencpop/voc5), the effect is continuously optimized. - 👑 2023.03.09: Add [Wav2vec2ASR-zh](./examples/aishell/asr3). - 🎉 2023.03.07: Add [TTS ARM Linux C++ Demo](./demos/TTSArmLinux). - 🔥 2023.03.03 Add Voice Conversion [StarGANv2-VC synthesize pipeline](./examples/vctk/vc3). @@ -221,13 +228,13 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision ## Installation -We strongly recommend our users to install PaddleSpeech in **Linux** with *python>=3.7* and *paddlepaddle>=2.4.1*. +We strongly recommend our users to install PaddleSpeech in **Linux** with *python>=3.8* and *paddlepaddle<=2.5.1*. Some new versions of Paddle do not have support for adaptation in PaddleSpeech, so currently only versions 2.5.1 and earlier can be supported. ### **Dependency Introduction** + gcc >= 4.8.5 -+ paddlepaddle >= 2.4.1 -+ python >= 3.7 ++ paddlepaddle <= 2.5.1 ++ python >= 3.8 + OS support: Linux(recommend), Windows, Mac OSX PaddleSpeech depends on paddlepaddle. For installation, please refer to the official website of [paddlepaddle](https://www.paddlepaddle.org.cn/en) and choose according to your own machine. Here is an example of the cpu version. @@ -577,14 +584,14 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r - Text Frontend -   - - tn / g2p - + Text Frontend +   + + tn / g2p + - Acoustic Model + Acoustic Model Tacotron2 LJSpeech / CSMSC @@ -619,6 +626,13 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r ERNIE-SAT-vctk / ERNIE-SAT-aishell3 / ERNIE-SAT-zh_en + + DiffSinger + Opencpop + + DiffSinger-opencpop + + Vocoder WaveFlow @@ -629,9 +643,9 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r Parallel WaveGAN - LJSpeech / VCTK / CSMSC / AISHELL-3 + LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop - PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3 + PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3 / PWGAN-opencpop @@ -650,9 +664,9 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r HiFiGAN - LJSpeech / VCTK / CSMSC / AISHELL-3 + LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop - HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3 + HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3 / HiFiGAN-opencpop @@ -880,15 +894,20 @@ The Text-to-Speech module is originally called [Parakeet](https://github.com/Pad - **[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk): Use PaddleSpeech TTS and ASR to clone voice from videos.** -
- -
- ## Citation To cite PaddleSpeech for research, please use the following format. + ```text +@inproceedings{zhang2022paddlespeech, + title = {PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit}, + author = {Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, dianhai yu, Yanjun Ma, Liang Huang}, + booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations}, + year = {2022}, + publisher = {Association for Computational Linguistics}, +} + @InProceedings{pmlr-v162-bai22d, title = {{A}$^3${T}: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing}, author = {Bai, He and Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Li, Xintong and Huang, Liang}, @@ -903,14 +922,6 @@ To cite PaddleSpeech for research, please use the following format. url = {https://proceedings.mlr.press/v162/bai22d.html}, } -@inproceedings{zhang2022paddlespeech, - title = {PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit}, - author = {Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, dianhai yu, Yanjun Ma, Liang Huang}, - booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations}, - year = {2022}, - publisher = {Association for Computational Linguistics}, -} - @inproceedings{zheng2021fused, title={Fused acoustic and text encoding for multimodal bilingual pretraining and speech translation}, author={Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Huang, Liang}, diff --git a/README_cn.md b/README_cn.md index 29ee387c..f743c287 100644 --- a/README_cn.md +++ b/README_cn.md @@ -8,7 +8,7 @@ - + @@ -183,6 +183,13 @@ - 🧩 级联模型应用: 作为传统语音任务的扩展，我们结合了自然语言处理、计算机视觉等任务，实现更接近实际需求的产业级应用。 ### 近期更新 +- 👑 2023.05.31: 新增 [WavLM ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr5), 基于WavLM的英语识别微调，使用LibriSpeech数据集 +- 👑 2023.05.04: 新增 [HuBERT ASR-en](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/librispeech/asr4), 基于HuBERT的英语识别微调，使用LibriSpeech数据集 +- ⚡ 2023.04.28: 修正 [0-d tensor](https://github.com/PaddlePaddle/PaddleSpeech/pull/3214), 配合PaddlePaddle2.5升级修改了0-d tensor的问题。 +- 👑 2023.04.25: 新增 [U2 conformer 的 AMP 训练](https://github.com/PaddlePaddle/PaddleSpeech/pull/3167). +- 👑 2023.04.06: 新增 [srt格式字幕生成功能](./demos/streaming_asr_server)。 +- 👑 2023.04.25: 新增 [U2 conformer 的 AMP 训练](https://github.com/PaddlePaddle/PaddleSpeech/pull/3167). +- 🔥 2023.03.14: 新增基于 Opencpop 数据集的 SVS (歌唱合成) 示例，包含 [DiffSinger](./examples/opencpop/svs1)、[PWGAN](./examples/opencpop/voc1) 和 [HiFiGAN](./examples/opencpop/voc5)，效果持续优化中。 - 👑 2023.03.09: 新增 [Wav2vec2ASR-zh](./examples/aishell/asr3)。 - 🎉 2023.03.07: 新增 [TTS ARM Linux C++ 部署示例](./demos/TTSArmLinux)。 - 🔥 2023.03.03: 新增声音转换模型 [StarGANv2-VC 合成流程](./examples/vctk/vc3)。 @@ -231,12 +238,12 @@ ## 安装 -我们强烈建议用户在 **Linux** 环境下，*3.7* 以上版本的 *python* 上安装 PaddleSpeech。 +我们强烈建议用户在 **Linux** 环境下，*3.8* 以上版本的 *python* 上安装 PaddleSpeech。同时，有一些Paddle新版本的内容没有在做适配的支持，因此目前只能使用2.5.1及之前的版本。 ### 相关依赖 + gcc >= 4.8.5 -+ paddlepaddle >= 2.4.1 -+ python >= 3.7 ++ paddlepaddle <= 2.5.1 ++ python >= 3.8 + linux(推荐), mac, windows PaddleSpeech 依赖于 paddlepaddle，安装可以参考[ paddlepaddle 官网](https://www.paddlepaddle.org.cn/)，根据自己机器的情况进行选择。这里给出 cpu 版本示例，其它版本大家可以根据自己机器的情况进行安装。 @@ -576,43 +583,50 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 tn / g2p - - - 声学模型 + + + 声学模型 Tacotron2 LJSpeech / CSMSC tacotron2-ljspeech / tacotron2-csmsc - - + + Transformer TTS LJSpeech transformer-ljspeech - - + + SpeedySpeech CSMSC speedyspeech-csmsc - - + + FastSpeech2 LJSpeech / VCTK / CSMSC / AISHELL-3 / ZH_EN / finetune fastspeech2-ljspeech / fastspeech2-vctk / fastspeech2-csmsc / fastspeech2-aishell3 / fastspeech2-zh_en / fastspeech2-finetune - - + + ERNIE-SAT VCTK / AISHELL-3 / ZH_EN ERNIE-SAT-vctk / ERNIE-SAT-aishell3 / ERNIE-SAT-zh_en - + + + DiffSinger + Opencpop + + DiffSinger-opencpop + + 声码器 WaveFlow @@ -623,9 +637,9 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 Parallel WaveGAN - LJSpeech / VCTK / CSMSC / AISHELL-3 + LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop - PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3 + PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3 / PWGAN-opencpop @@ -644,9 +658,9 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 HiFiGAN - LJSpeech / VCTK / CSMSC / AISHELL-3 + LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop - HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3 + HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3 / HiFiGAN-opencpop @@ -703,6 +717,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声 + **声音分类** diff --git a/audio/paddleaudio/backends/soundfile_backend.py b/audio/paddleaudio/backends/soundfile_backend.py index ae7b5b52..9195ea09 100644 --- a/audio/paddleaudio/backends/soundfile_backend.py +++ b/audio/paddleaudio/backends/soundfile_backend.py @@ -191,7 +191,7 @@ def soundfile_save(y: np.ndarray, sr: int, file: os.PathLike) -> None: if sr <= 0: raise ParameterError( - f'Sample rate should be larger than 0, recieved sr = {sr}') + f'Sample rate should be larger than 0, received sr = {sr}') if y.dtype not in ['int16', 'int8']: warnings.warn( diff --git a/audio/setup.py b/audio/setup.py index 823e5dfa..f7d45944 100644 --- a/audio/setup.py +++ b/audio/setup.py @@ -34,12 +34,14 @@ from tools import setup_helpers ROOT_DIR = Path(__file__).parent.resolve() -VERSION = '1.1.0' +VERSION = '1.2.0' COMMITID = 'none' base = [ - "kaldiio", + # paddleaudio align with librosa==0.8.1, which need numpy==1.23.x "librosa==0.8.1", + "numpy==1.23.5", + "kaldiio", "pathos", "pybind11", "parameterized", diff --git a/audio/tests/features/base.py b/audio/tests/features/base.py index d183b72a..3bb1d1dd 100644 --- a/audio/tests/features/base.py +++ b/audio/tests/features/base.py @@ -37,7 +37,7 @@ class FeatTest(unittest.TestCase): self.waveform, self.sr = load(os.path.abspath(os.path.basename(url))) self.waveform = self.waveform.astype( np.float32 - ) # paddlespeech.s2t.transform.spectrogram only supports float32 + ) # paddlespeech.audio.transform.spectrogram only supports float32 dim = len(self.waveform.shape) assert dim in [1, 2] diff --git a/audio/tests/features/test_istft.py b/audio/tests/features/test_istft.py index 9cf8cdd6..ea1ee5cb 100644 --- a/audio/tests/features/test_istft.py +++ b/audio/tests/features/test_istft.py @@ -18,8 +18,8 @@ import paddle from paddleaudio.functional.window import get_window from .base import FeatTest -from paddlespeech.s2t.transform.spectrogram import IStft -from paddlespeech.s2t.transform.spectrogram import Stft +from paddlespeech.audio.transform.spectrogram import IStft +from paddlespeech.audio.transform.spectrogram import Stft class TestIstft(FeatTest): diff --git a/audio/tests/features/test_log_melspectrogram.py b/audio/tests/features/test_log_melspectrogram.py index 7d568038..b2765d3b 100644 --- a/audio/tests/features/test_log_melspectrogram.py +++ b/audio/tests/features/test_log_melspectrogram.py @@ -18,7 +18,7 @@ import paddle import paddleaudio from .base import FeatTest -from paddlespeech.s2t.transform.spectrogram import LogMelSpectrogram +from paddlespeech.audio.transform.spectrogram import LogMelSpectrogram class TestLogMelSpectrogram(FeatTest): diff --git a/audio/tests/features/test_spectrogram.py b/audio/tests/features/test_spectrogram.py index 5fe5afee..6f460963 100644 --- a/audio/tests/features/test_spectrogram.py +++ b/audio/tests/features/test_spectrogram.py @@ -18,7 +18,7 @@ import paddle import paddleaudio from .base import FeatTest -from paddlespeech.s2t.transform.spectrogram import Spectrogram +from paddlespeech.audio.transform.spectrogram import Spectrogram class TestSpectrogram(FeatTest): diff --git a/audio/tests/features/test_stft.py b/audio/tests/features/test_stft.py index 58792ffe..9511a292 100644 --- a/audio/tests/features/test_stft.py +++ b/audio/tests/features/test_stft.py @@ -18,7 +18,7 @@ import paddle from paddleaudio.functional.window import get_window from .base import FeatTest -from paddlespeech.s2t.transform.spectrogram import Stft +from paddlespeech.audio.transform.spectrogram import Stft class TestStft(FeatTest): diff --git a/dataset/aidatatang_200zh/aidatatang_200zh.py b/dataset/aidatatang_200zh/aidatatang_200zh.py index 85f478c2..3b706c49 100644 --- a/dataset/aidatatang_200zh/aidatatang_200zh.py +++ b/dataset/aidatatang_200zh/aidatatang_200zh.py @@ -18,139 +18,7 @@ Manifest file is a json-format file with each line containing the meta data (i.e. audio filepath, transcript and audio duration) of each audio file in the data set. """ -import argparse -import codecs -import json -import os -from pathlib import Path - -import soundfile - -from utils.utility import download -from utils.utility import unpack - -DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') - -URL_ROOT = 'http://www.openslr.org/resources/62' -# URL_ROOT = 'https://openslr.magicdatatech.com/resources/62' -DATA_URL = URL_ROOT + '/aidatatang_200zh.tgz' -MD5_DATA = '6e0f4f39cd5f667a7ee53c397c8d0949' - -parser = argparse.ArgumentParser(description=__doc__) -parser.add_argument( - "--target_dir", - default=DATA_HOME + "/aidatatang_200zh", - type=str, - help="Directory to save the dataset. (default: %(default)s)") -parser.add_argument( - "--manifest_prefix", - default="manifest", - type=str, - help="Filepath prefix for output manifests. (default: %(default)s)") -args = parser.parse_args() - - -def create_manifest(data_dir, manifest_path_prefix): - print("Creating manifest %s ..." % manifest_path_prefix) - json_lines = [] - transcript_path = os.path.join(data_dir, 'transcript', - 'aidatatang_200_zh_transcript.txt') - transcript_dict = {} - for line in codecs.open(transcript_path, 'r', 'utf-8'): - line = line.strip() - if line == '': - continue - audio_id, text = line.split(' ', 1) - # remove withespace, charactor text - text = ''.join(text.split()) - transcript_dict[audio_id] = text - - data_types = ['train', 'dev', 'test'] - for dtype in data_types: - del json_lines[:] - total_sec = 0.0 - total_text = 0.0 - total_num = 0 - - audio_dir = os.path.join(data_dir, 'corpus/', dtype) - for subfolder, _, filelist in sorted(os.walk(audio_dir)): - for fname in filelist: - if not fname.endswith('.wav'): - continue - - audio_path = os.path.abspath(os.path.join(subfolder, fname)) - audio_id = os.path.basename(fname)[:-4] - utt2spk = Path(audio_path).parent.name - - audio_data, samplerate = soundfile.read(audio_path) - duration = float(len(audio_data) / samplerate) - text = transcript_dict[audio_id] - json_lines.append( - json.dumps( - { - 'utt': audio_id, - 'utt2spk': str(utt2spk), - 'feat': audio_path, - 'feat_shape': (duration, ), # second - 'text': text, - }, - ensure_ascii=False)) - - total_sec += duration - total_text += len(text) - total_num += 1 - - manifest_path = manifest_path_prefix + '.' + dtype - with codecs.open(manifest_path, 'w', 'utf-8') as fout: - for line in json_lines: - fout.write(line + '\n') - - manifest_dir = os.path.dirname(manifest_path_prefix) - meta_path = os.path.join(manifest_dir, dtype) + '.meta' - with open(meta_path, 'w') as f: - print(f"{dtype}:", file=f) - print(f"{total_num} utts", file=f) - print(f"{total_sec / (60*60)} h", file=f) - print(f"{total_text} text", file=f) - print(f"{total_text / total_sec} text/sec", file=f) - print(f"{total_sec / total_num} sec/utt", file=f) - - -def prepare_dataset(url, md5sum, target_dir, manifest_path, subset): - """Download, unpack and create manifest file.""" - data_dir = os.path.join(target_dir, subset) - if not os.path.exists(data_dir): - filepath = download(url, md5sum, target_dir) - unpack(filepath, target_dir) - # unpack all audio tar files - audio_dir = os.path.join(data_dir, 'corpus') - for subfolder, dirlist, filelist in sorted(os.walk(audio_dir)): - for sub in dirlist: - print(f"unpack dir {sub}...") - for folder, _, filelist in sorted( - os.walk(os.path.join(subfolder, sub))): - for ftar in filelist: - unpack(os.path.join(folder, ftar), folder, True) - else: - print("Skip downloading and unpacking. Data already exists in %s." % - target_dir) - - create_manifest(data_dir, manifest_path) - - -def main(): - if args.target_dir.startswith('~'): - args.target_dir = os.path.expanduser(args.target_dir) - - prepare_dataset( - url=DATA_URL, - md5sum=MD5_DATA, - target_dir=args.target_dir, - manifest_path=args.manifest_prefix, - subset='aidatatang_200zh') - - print("Data download and manifest prepare done!") - +from paddlespeech.dataset.aidatatang_200zh import aidatatang_200zh_main if __name__ == '__main__': - main() + aidatatang_200zh_main() diff --git a/dataset/aishell/README.md b/dataset/aishell/README.md deleted file mode 100644 index a7dd0cf3..00000000 --- a/dataset/aishell/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# [Aishell1](http://openslr.elda.org/33/) - -This Open Source Mandarin Speech Corpus, AISHELL-ASR0009-OS1, is 178 hours long. It is a part of AISHELL-ASR0009, of which utterance contains 11 domains, including smart home, autonomous driving, and industrial production. The whole recording was put in quiet indoor environment, using 3 different devices at the same time: high fidelity microphone (44.1kHz, 16-bit,); Android-system mobile phone (16kHz, 16-bit), iOS-system mobile phone (16kHz, 16-bit). Audios in high fidelity were re-sampled to 16kHz to build AISHELL- ASR0009-OS1. 400 speakers from different accent areas in China were invited to participate in the recording. The manual transcription accuracy rate is above 95%, through professional speech annotation and strict quality inspection. The corpus is divided into training, development and testing sets. ( This database is free for academic research, not in the commerce, if without permission. ) diff --git a/dataset/aishell/aishell.py b/dataset/aishell/aishell.py index ec43104d..b3288757 100644 --- a/dataset/aishell/aishell.py +++ b/dataset/aishell/aishell.py @@ -18,143 +18,7 @@ Manifest file is a json-format file with each line containing the meta data (i.e. audio filepath, transcript and audio duration) of each audio file in the data set. """ -import argparse -import codecs -import json -import os -from pathlib import Path - -import soundfile - -from utils.utility import download -from utils.utility import unpack - -DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') - -URL_ROOT = 'http://openslr.elda.org/resources/33' -# URL_ROOT = 'https://openslr.magicdatatech.com/resources/33' -DATA_URL = URL_ROOT + '/data_aishell.tgz' -MD5_DATA = '2f494334227864a8a8fec932999db9d8' -RESOURCE_URL = URL_ROOT + '/resource_aishell.tgz' -MD5_RESOURCE = '957d480a0fcac85fc18e550756f624e5' - -parser = argparse.ArgumentParser(description=__doc__) -parser.add_argument( - "--target_dir", - default=DATA_HOME + "/Aishell", - type=str, - help="Directory to save the dataset. (default: %(default)s)") -parser.add_argument( - "--manifest_prefix", - default="manifest", - type=str, - help="Filepath prefix for output manifests. (default: %(default)s)") -args = parser.parse_args() - - -def create_manifest(data_dir, manifest_path_prefix): - print("Creating manifest %s ..." % manifest_path_prefix) - json_lines = [] - transcript_path = os.path.join(data_dir, 'transcript', - 'aishell_transcript_v0.8.txt') - transcript_dict = {} - for line in codecs.open(transcript_path, 'r', 'utf-8'): - line = line.strip() - if line == '': - continue - audio_id, text = line.split(' ', 1) - # remove withespace, charactor text - text = ''.join(text.split()) - transcript_dict[audio_id] = text - - data_types = ['train', 'dev', 'test'] - for dtype in data_types: - del json_lines[:] - total_sec = 0.0 - total_text = 0.0 - total_num = 0 - - audio_dir = os.path.join(data_dir, 'wav', dtype) - for subfolder, _, filelist in sorted(os.walk(audio_dir)): - for fname in filelist: - audio_path = os.path.abspath(os.path.join(subfolder, fname)) - audio_id = os.path.basename(fname)[:-4] - # if no transcription for audio then skipped - if audio_id not in transcript_dict: - continue - - utt2spk = Path(audio_path).parent.name - audio_data, samplerate = soundfile.read(audio_path) - duration = float(len(audio_data) / samplerate) - text = transcript_dict[audio_id] - json_lines.append( - json.dumps( - { - 'utt': audio_id, - 'utt2spk': str(utt2spk), - 'feat': audio_path, - 'feat_shape': (duration, ), # second - 'text': text - }, - ensure_ascii=False)) - - total_sec += duration - total_text += len(text) - total_num += 1 - - manifest_path = manifest_path_prefix + '.' + dtype - with codecs.open(manifest_path, 'w', 'utf-8') as fout: - for line in json_lines: - fout.write(line + '\n') - - manifest_dir = os.path.dirname(manifest_path_prefix) - meta_path = os.path.join(manifest_dir, dtype) + '.meta' - with open(meta_path, 'w') as f: - print(f"{dtype}:", file=f) - print(f"{total_num} utts", file=f) - print(f"{total_sec / (60*60)} h", file=f) - print(f"{total_text} text", file=f) - print(f"{total_text / total_sec} text/sec", file=f) - print(f"{total_sec / total_num} sec/utt", file=f) - - -def prepare_dataset(url, md5sum, target_dir, manifest_path=None): - """Download, unpack and create manifest file.""" - data_dir = os.path.join(target_dir, 'data_aishell') - if not os.path.exists(data_dir): - filepath = download(url, md5sum, target_dir) - unpack(filepath, target_dir) - # unpack all audio tar files - audio_dir = os.path.join(data_dir, 'wav') - for subfolder, _, filelist in sorted(os.walk(audio_dir)): - for ftar in filelist: - unpack(os.path.join(subfolder, ftar), subfolder, True) - else: - print("Skip downloading and unpacking. Data already exists in %s." % - target_dir) - - if manifest_path: - create_manifest(data_dir, manifest_path) - - -def main(): - if args.target_dir.startswith('~'): - args.target_dir = os.path.expanduser(args.target_dir) - - prepare_dataset( - url=DATA_URL, - md5sum=MD5_DATA, - target_dir=args.target_dir, - manifest_path=args.manifest_prefix) - - prepare_dataset( - url=RESOURCE_URL, - md5sum=MD5_RESOURCE, - target_dir=args.target_dir, - manifest_path=None) - - print("Data download and manifest prepare done!") - +from paddlespeech.dataset.aishell import aishell_main if __name__ == '__main__': - main() + aishell_main() diff --git a/dataset/librispeech/librispeech.py b/dataset/librispeech/librispeech.py index 2d6f1763..44567b0c 100644 --- a/dataset/librispeech/librispeech.py +++ b/dataset/librispeech/librispeech.py @@ -28,8 +28,8 @@ from multiprocessing.pool import Pool import distutils.util import soundfile -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack URL_ROOT = "http://openslr.elda.org/resources/12" #URL_ROOT = "https://openslr.magicdatatech.com/resources/12" diff --git a/dataset/mini_librispeech/mini_librispeech.py b/dataset/mini_librispeech/mini_librispeech.py index 0eb80bf8..24bd98d8 100644 --- a/dataset/mini_librispeech/mini_librispeech.py +++ b/dataset/mini_librispeech/mini_librispeech.py @@ -27,8 +27,8 @@ from multiprocessing.pool import Pool import soundfile -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack URL_ROOT = "http://openslr.elda.org/resources/31" URL_TRAIN_CLEAN = URL_ROOT + "/train-clean-5.tar.gz" diff --git a/dataset/musan/musan.py b/dataset/musan/musan.py index ae3430b2..85d986e8 100644 --- a/dataset/musan/musan.py +++ b/dataset/musan/musan.py @@ -29,8 +29,8 @@ import os import soundfile -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') diff --git a/dataset/rir_noise/rir_noise.py b/dataset/rir_noise/rir_noise.py index b1d47558..b98dff72 100644 --- a/dataset/rir_noise/rir_noise.py +++ b/dataset/rir_noise/rir_noise.py @@ -29,8 +29,8 @@ import os import soundfile -from utils.utility import download -from utils.utility import unzip +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unzip DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') diff --git a/dataset/thchs30/thchs30.py b/dataset/thchs30/thchs30.py index d41c0e17..c5c3eb7a 100644 --- a/dataset/thchs30/thchs30.py +++ b/dataset/thchs30/thchs30.py @@ -27,8 +27,8 @@ from pathlib import Path import soundfile -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') diff --git a/dataset/timit/timit.py b/dataset/timit/timit.py index c4a9f066..f3889d17 100644 --- a/dataset/timit/timit.py +++ b/dataset/timit/timit.py @@ -28,7 +28,7 @@ from pathlib import Path import soundfile -from utils.utility import unzip +from paddlespeech.dataset.download import unzip URL_ROOT = "" MD5_DATA = "45c68037c7fdfe063a43c851f181fb2d" diff --git a/dataset/voxceleb/voxceleb1.py b/dataset/voxceleb/voxceleb1.py index 95827f70..8d410067 100644 --- a/dataset/voxceleb/voxceleb1.py +++ b/dataset/voxceleb/voxceleb1.py @@ -31,9 +31,9 @@ from pathlib import Path import soundfile -from utils.utility import check_md5sum -from utils.utility import download -from utils.utility import unzip +from paddlespeech.dataset.download import check_md5sum +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unzip # all the data will be download in the current data/voxceleb directory default DATA_HOME = os.path.expanduser('.') diff --git a/dataset/voxceleb/voxceleb2.py b/dataset/voxceleb/voxceleb2.py index fe9e8b9c..6df6d1f3 100644 --- a/dataset/voxceleb/voxceleb2.py +++ b/dataset/voxceleb/voxceleb2.py @@ -27,9 +27,9 @@ from pathlib import Path import soundfile -from utils.utility import check_md5sum -from utils.utility import download -from utils.utility import unzip +from paddlespeech.dataset.download import check_md5sum +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unzip # all the data will be download in the current data/voxceleb directory default DATA_HOME = os.path.expanduser('.') diff --git a/dataset/voxforge/voxforge.py b/dataset/voxforge/voxforge.py index 373791bf..327d200b 100644 --- a/dataset/voxforge/voxforge.py +++ b/dataset/voxforge/voxforge.py @@ -28,9 +28,9 @@ import subprocess import soundfile -from utils.utility import download_multi -from utils.utility import getfile_insensitive -from utils.utility import unpack +from paddlespeech.dataset.download import download_multi +from paddlespeech.dataset.download import getfile_insensitive +from paddlespeech.dataset.download import unpack DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech') diff --git a/demos/README.md b/demos/README.md index a4196786..6f9cd2e4 100644 --- a/demos/README.md +++ b/demos/README.md @@ -18,4 +18,4 @@ This directory contains many speech applications in multiple scenarios. * style_fs2 - multi style control for FastSpeech2 model * text_to_speech - convert text into speech * self supervised pretraining - speech feature extraction and speech recognition based on wav2vec2 -* Wishper - speech recognize and translate based on Whisper model +* Whisper - speech recognize and translate based on Whisper model diff --git a/demos/TTSAndroid/README.md b/demos/TTSAndroid/README.md index 36ff969f..36848cbe 100644 --- a/demos/TTSAndroid/README.md +++ b/demos/TTSAndroid/README.md @@ -1,6 +1,6 @@ # 语音合成 Java API Demo 使用指南 -在 Android 上实现语音合成功能，此 Demo 有很好的的易用性和开放性，如在 Demo 中跑自己训练好的模型等。 +在 Android 上实现语音合成功能，此 Demo 有很好的易用性和开放性，如在 Demo 中跑自己训练好的模型等。 本文主要介绍语音合成 Demo 运行方法。 diff --git a/demos/audio_searching/src/test_audio_search.py b/demos/audio_searching/src/test_audio_search.py index cb91e156..f9ea2929 100644 --- a/demos/audio_searching/src/test_audio_search.py +++ b/demos/audio_searching/src/test_audio_search.py @@ -14,8 +14,8 @@ from audio_search import app from fastapi.testclient import TestClient -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack client = TestClient(app) diff --git a/demos/audio_searching/src/test_vpr_search.py b/demos/audio_searching/src/test_vpr_search.py index 298e12eb..cc795564 100644 --- a/demos/audio_searching/src/test_vpr_search.py +++ b/demos/audio_searching/src/test_vpr_search.py @@ -14,8 +14,8 @@ from fastapi.testclient import TestClient from vpr_search import app -from utils.utility import download -from utils.utility import unpack +from paddlespeech.dataset.download import download +from paddlespeech.dataset.download import unpack client = TestClient(app) diff --git a/demos/speech_server/README.md b/demos/speech_server/README.md index 7e7d4b2c..116f1fd7 100644 --- a/demos/speech_server/README.md +++ b/demos/speech_server/README.md @@ -34,6 +34,8 @@ Currently the engine type supports two forms: python and inference (Paddle Infer paddlespeech_server start --config_file ./conf/application.yaml ``` + > **Note:** For mixed Chinese and English speech recognition, please use the `./conf/conformer_talcs_application.yaml` configuration file + Usage: ```bash @@ -85,6 +87,7 @@ Here are sample files for this ASR client demo that can be downloaded: ```bash wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav +wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/ch_zh_mix.wav ``` **Note:** The response time will be slightly longer when using the client for the first time @@ -92,8 +95,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav If `127.0.0.1` is not accessible, you need to use the actual service IP address. - ``` + ```bash paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav + + # Chinese and English mixed speech recognition, using `./conf/conformer_talcs_application.yaml` config file + paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./ch_zh_mix.wav ``` Usage: diff --git a/demos/speech_server/README_cn.md b/demos/speech_server/README_cn.md index 59492828..f2cb349e 100644 --- a/demos/speech_server/README_cn.md +++ b/demos/speech_server/README_cn.md @@ -37,6 +37,8 @@ paddlespeech_server start --config_file ./conf/application.yaml ``` + > **注意：** 中英文混合语音识别请使用 `./conf/conformer_talcs_application.yaml` 配置文件 + 使用方法： ```bash @@ -79,6 +81,8 @@ [2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit) ``` + + ### 4. ASR 客户端使用方法 ASR 客户端的输入是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。 @@ -87,6 +91,7 @@ ASR 客户端的输入是一个 WAV 文件（`.wav`），并且采样率必须 ```bash wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav +wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/ch_zh_mix.wav ``` **注意：** 初次使用客户端时响应时间会略长 @@ -94,8 +99,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav 若 `127.0.0.1` 不能访问，则需要使用实际服务 IP 地址 - ``` + ```bash paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav + + # 中英文混合语音识别 , 请使用 `./conf/conformer_talcs_application.yaml` 配置文件 + paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./ch_zh_mix.wav ``` 使用帮助: diff --git a/demos/speech_server/conf/conformer_talcs_application.yaml b/demos/speech_server/conf/conformer_talcs_application.yaml new file mode 100644 index 00000000..f5f9897b --- /dev/null +++ b/demos/speech_server/conf/conformer_talcs_application.yaml @@ -0,0 +1,163 @@ +# This is the parameter configuration file for PaddleSpeech Offline Serving. + +################################################################################# +# SERVER SETTING # +################################################################################# +host: 0.0.0.0 +port: 8090 + +# The task format in the engin_list is: _ +# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference', 'cls_python', 'cls_inference', 'text_python', 'vector_python'] +protocol: 'http' +engine_list: ['asr_python', 'tts_python', 'cls_python', 'text_python', 'vector_python'] + + +################################################################################# +# ENGINE CONFIG # +################################################################################# + +################################### ASR ######################################### +################### speech task: asr; engine_type: python ####################### +asr_python: + model: 'conformer_talcs' + lang: 'zh_en' + sample_rate: 16000 + cfg_path: # [optional] + ckpt_path: # [optional] + decode_method: 'attention_rescoring' + force_yes: True + codeswitch: True + device: # set 'gpu:id' or 'cpu' + +################### speech task: asr; engine_type: inference ####################### +asr_inference: + # model_type choices=['deepspeech2offline_aishell'] + model_type: 'deepspeech2offline_aishell' + am_model: # the pdmodel file of am static model [optional] + am_params: # the pdiparams file of am static model [optional] + lang: 'zh' + sample_rate: 16000 + cfg_path: + decode_method: + force_yes: True + + am_predictor_conf: + device: # set 'gpu:id' or 'cpu' + switch_ir_optim: True + glog_info: False # True -> print glog + summary: True # False -> do not show predictor config + + +################################### TTS ######################################### +################### speech task: tts; engine_type: python ####################### +tts_python: + # am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc', + # 'fastspeech2_ljspeech', 'fastspeech2_aishell3', + # 'fastspeech2_vctk', 'fastspeech2_mix', + # 'tacotron2_csmsc', 'tacotron2_ljspeech'] + am: 'fastspeech2_csmsc' + am_config: + am_ckpt: + am_stat: + phones_dict: + tones_dict: + speaker_dict: + + + # voc (vocoder) choices=['pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3', + # 'pwgan_vctk', 'mb_melgan_csmsc', 'style_melgan_csmsc', + # 'hifigan_csmsc', 'hifigan_ljspeech', 'hifigan_aishell3', + # 'hifigan_vctk', 'wavernn_csmsc'] + voc: 'mb_melgan_csmsc' + voc_config: + voc_ckpt: + voc_stat: + + # others + lang: 'zh' + device: # set 'gpu:id' or 'cpu' + + +################### speech task: tts; engine_type: inference ####################### +tts_inference: + # am (acoustic model) choices=['speedyspeech_csmsc', 'fastspeech2_csmsc'] + am: 'fastspeech2_csmsc' + am_model: # the pdmodel file of your am static model (XX.pdmodel) + am_params: # the pdiparams file of your am static model (XX.pdipparams) + am_sample_rate: 24000 + phones_dict: + tones_dict: + speaker_dict: + + + am_predictor_conf: + device: # set 'gpu:id' or 'cpu' + switch_ir_optim: True + glog_info: False # True -> print glog + summary: True # False -> do not show predictor config + + # voc (vocoder) choices=['pwgan_csmsc', 'mb_melgan_csmsc','hifigan_csmsc'] + voc: 'mb_melgan_csmsc' + voc_model: # the pdmodel file of your vocoder static model (XX.pdmodel) + voc_params: # the pdiparams file of your vocoder static model (XX.pdipparams) + voc_sample_rate: 24000 + + voc_predictor_conf: + device: # set 'gpu:id' or 'cpu' + switch_ir_optim: True + glog_info: False # True -> print glog + summary: True # False -> do not show predictor config + + # others + lang: 'zh' + + +################################### CLS ######################################### +################### speech task: cls; engine_type: python ####################### +cls_python: + # model choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6'] + model: 'panns_cnn14' + cfg_path: # [optional] Config of cls task. + ckpt_path: # [optional] Checkpoint file of model. + label_file: # [optional] Label file of cls task. + device: # set 'gpu:id' or 'cpu' + + +################### speech task: cls; engine_type: inference ####################### +cls_inference: + # model_type choices=['panns_cnn14', 'panns_cnn10', 'panns_cnn6'] + model_type: 'panns_cnn14' + cfg_path: + model_path: # the pdmodel file of am static model [optional] + params_path: # the pdiparams file of am static model [optional] + label_file: # [optional] Label file of cls task. + + predictor_conf: + device: # set 'gpu:id' or 'cpu' + switch_ir_optim: True + glog_info: False # True -> print glog + summary: True # False -> do not show predictor config + + +################################### Text ######################################### +################### text task: punc; engine_type: python ####################### +text_python: + task: punc + model_type: 'ernie_linear_p3_wudao' + lang: 'zh' + sample_rate: 16000 + cfg_path: # [optional] + ckpt_path: # [optional] + vocab_file: # [optional] + device: # set 'gpu:id' or 'cpu' + + +################################### Vector ###################################### +################### Vector task: spk; engine_type: python ####################### +vector_python: + task: spk + model_type: 'ecapatdnn_voxceleb12' + sample_rate: 16000 + cfg_path: # [optional] + ckpt_path: # [optional] + device: # set 'gpu:id' or 'cpu' diff --git a/demos/speech_ssl/README.md b/demos/speech_ssl/README.md index b98a7cc6..ef9b2237 100644 --- a/demos/speech_ssl/README.md +++ b/demos/speech_ssl/README.md @@ -36,7 +36,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav ``` Arguments: - `input`(required): Audio file to recognize. - - `model`: Model type of asr task. Default: `wav2vec2ASR_librispeech`. + - `model`: Model type of asr task. Default: `wav2vec2`, choices: [wav2vec2, hubert, wavlm]. - `task`: Output type. Default: `asr`. - `lang`: Model language. Default: `en`. - `sample_rate`: Sample rate of the model. Default: `16000`. @@ -56,7 +56,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav # to recognize text text = ssl_executor( - model='wav2vec2ASR_librispeech', + model='wav2vec2', task='asr', lang='en', sample_rate=16000, diff --git a/demos/speech_ssl/README_cn.md b/demos/speech_ssl/README_cn.md index 65961ce9..a18c778a 100644 --- a/demos/speech_ssl/README_cn.md +++ b/demos/speech_ssl/README_cn.md @@ -36,7 +36,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav ``` 参数： - `input`(必须输入)：用于识别的音频文件。 - - `model`：ASR 任务的模型，默认值：`wav2vec2ASR_librispeech`。 + - `model`：ASR 任务的模型，默认值：`wav2vec2`, 可选项：[wav2vec2, hubert, wavlm]。 - `task`：输出类别，默认值：`asr`。 - `lang`：模型语言，默认值：`en`。 - `sample_rate`：音频采样率，默认值：`16000`。 @@ -56,7 +56,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav # 识别文本 text = ssl_executor( - model='wav2vec2ASR_librispeech', + model='wav2vec2, task='asr', lang='en', sample_rate=16000, diff --git a/demos/speech_web/README.md b/demos/speech_web/README.md index 572781ab..fc1fe710 100644 --- a/demos/speech_web/README.md +++ b/demos/speech_web/README.md @@ -23,7 +23,7 @@ Paddle Speech Demo 是一个以 PaddleSpeech 的语音交互功能为主体开 + ERNIE-SAT：语言-语音跨模态大模型 ERNIE-SAT 可视化展示示例，支持个性化合成，跨语言语音合成（音频为中文则输入英文文本进行合成），语音编辑（修改音频文字中间的结果）功能。 ERNIE-SAT 更多实现细节，可以参考： + [【ERNIE-SAT with AISHELL-3 dataset】](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/ernie_sat) - + [【ERNIE-SAT with with AISHELL3 and VCTK datasets】](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3_vctk/ernie_sat) + + [【ERNIE-SAT with AISHELL3 and VCTK datasets】](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3_vctk/ernie_sat) + [【ERNIE-SAT with VCTK dataset】](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/ernie_sat) 运行效果： diff --git a/demos/speech_web/speech_server/main.py b/demos/speech_web/speech_server/main.py index 03e7e599..f4678628 100644 --- a/demos/speech_web/speech_server/main.py +++ b/demos/speech_web/speech_server/main.py @@ -260,7 +260,7 @@ async def websocket_endpoint_online(websocket: WebSocket): # and we break the loop if message['signal'] == 'start': resp = {"status": "ok", "signal": "server_ready"} - # do something at begining here + # do something at beginning here # create the instance to process the audio # connection_handler = chatbot.asr.connection_handler connection_handler = PaddleASRConnectionHanddler(engine) diff --git a/demos/speech_web/speech_server/src/ge2e_clone.py b/demos/speech_web/speech_server/src/ge2e_clone.py index 83c2b3f3..0711a40a 100644 --- a/demos/speech_web/speech_server/src/ge2e_clone.py +++ b/demos/speech_web/speech_server/src/ge2e_clone.py @@ -38,23 +38,9 @@ class VoiceCloneGE2E(): output_dir = os.path.dirname(out_wav) ngpu = get_ngpu() - cmd = f""" - python3 {self.BIN_DIR}/voice_cloning.py \ - --am={self.am} \ - --am_config={self.am_config} \ - --am_ckpt={self.am_ckpt} \ - --am_stat={self.am_stat} \ - --voc={self.voc} \ - --voc_config={self.voc_config} \ - --voc_ckpt={self.voc_ckpt} \ - --voc_stat={self.voc_stat} \ - --ge2e_params_path={self.ge2e_params_path} \ - --text="{text}" \ - --input-dir={ref_audio_dir} \ - --output-dir={output_dir} \ - --phones-dict={self.phones_dict} \ - --ngpu={ngpu} - """ + cmd = f"""python {self.BIN_DIR}/voice_cloning.py --am={self.am} --am_config={self.am_config} --am_ckpt={self.am_ckpt} --am_stat={self.am_stat} --voc={self.voc} --voc_config={self.voc_config} --voc_ckpt={self.voc_ckpt} --voc_stat={self.voc_stat} --ge2e_params_path={self.ge2e_params_path} --text="{text}" --input-dir={ref_audio_dir} --output-dir={output_dir} --phones-dict={self.phones_dict} --ngpu={ngpu}""" + + print(cmd) output_name = os.path.join(output_dir, full_file_name) return run_cmd(cmd, output_name=output_name) diff --git a/demos/streaming_asr_server/README.md b/demos/streaming_asr_server/README.md index 1d33b694..31256d15 100644 --- a/demos/streaming_asr_server/README.md +++ b/demos/streaming_asr_server/README.md @@ -9,7 +9,7 @@ This demo is an implementation of starting the streaming speech service and acce Streaming ASR server only support `websocket` protocol, and doesn't support `http` protocol. -服务接口定义请参考: +For service interface definitions, please refer to: - [PaddleSpeech Streaming Server WebSocket API](https://github.com/PaddlePaddle/PaddleSpeech/wiki/PaddleSpeech-Server-WebSocket-API) ## Usage @@ -23,7 +23,7 @@ You can choose one way from easy, meduim and hard to install paddlespeech. **If you install in easy mode, you need to prepare the yaml file by yourself, you can refer to ### 2. Prepare config File -The configuration file can be found in `conf/ws_application.yaml` 和 `conf/ws_conformer_wenetspeech_application.yaml`. +The configuration file can be found in `conf/ws_application.yaml` or `conf/ws_conformer_wenetspeech_application.yaml`. At present, the speech tasks integrated by the model include: DeepSpeech2 and conformer. @@ -87,7 +87,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav server_executor = ServerExecutor() server_executor( - config_file="./conf/ws_conformer_wenetspeech_application.yaml", + config_file="./conf/ws_conformer_wenetspeech_application_faster.yaml", log_file="./log/paddlespeech.log") ``` @@ -579,3 +579,354 @@ bash server.sh [2022-05-07 11:11:18,915] [ INFO] - audio duration: 4.9968125, elapsed time: 15.928460597991943, RTF=3.187724293835709 [2022-05-07 11:11:18,916] [ INFO] - asr websocket client finished : 我认为跑步最重要的就是给我带来了身体健康 ``` + +## Generate corresponding subtitle (.srt format) from audio file (.wav format or.mp3 format) + +By default, each server is deployed on the 'CPU' device and speech recognition and punctuation prediction can be deployed on different 'GPU' by modifying the' device 'parameter in the service configuration file respectively. + +We use `streaming_ asr_server.py` and `punc_server.py` two services to lanuch streaming speech recognition and punctuation prediction services respectively. And the `websocket_client_srt.py` script can be used to call streaming speech recognition and punctuation prediction services at the same time, and will generate the corresponding subtitle (.srt format). + +**need to install ffmpeg before running this script** + +**You should at the directory of `.../demos/streaming_asr_server/`** + +### 1. Start two server + +```bash +Note: streaming speech recognition and punctuation prediction are configured on different graphics cards through configuration files +paddlespeech_server start --config_file ./conf/ws_conformer_wenetspeech_application.yaml +``` + +Open another terminal run the following commands: +```bash +paddlespeech_server start --config_file conf/punc_application.yaml +``` + +### 2. Call client + + ```bash + python3 local/websocket_client_srt.py --server_ip 127.0.0.1 --port 8090 --punc.server_ip 127.0.0.1 --punc.port 8190 --wavfile ../../data/认知.mp3 + ``` + Output: + ```text + [2023-03-30 23:26:13,991] [ INFO] - Start to do streaming asr client +[2023-03-30 23:26:13,994] [ INFO] - asr websocket client start +[2023-03-30 23:26:13,994] [ INFO] - endpoint: http://127.0.0.1:8190/paddlespeech/text +[2023-03-30 23:26:13,994] [ INFO] - endpoint: ws://127.0.0.1:8090/paddlespeech/asr/streaming +[2023-03-30 23:26:14,475] [ INFO] - /home/fxb/PaddleSpeech-develop/data/认知.mp3 converted to /home/fxb/PaddleSpeech-develop/data/认知.wav +[2023-03-30 23:26:14,476] [ INFO] - start to process the wavscp: /home/fxb/PaddleSpeech-develop/data/认知.wav +[2023-03-30 23:26:14,515] [ INFO] - client receive msg={"status": "ok", "signal": "server_ready"} +[2023-03-30 23:26:14,533] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,545] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,556] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,572] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,588] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,600] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,613] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,626] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:15,122] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,135] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,154] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,163] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,175] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,185] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,196] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,637] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,648] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,657] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,666] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,676] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,683] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,691] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,703] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:16,146] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,159] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,167] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,177] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,187] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,197] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,210] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,694] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,704] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,713] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,725] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,737] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,749] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,759] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,770] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:17,279] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,302] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,316] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,332] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,343] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,358] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,373] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,958] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:17,971] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:17,987] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,000] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,017] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,028] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,038] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,049] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,653] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,689] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,701] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,712] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,723] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,750] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,767] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:19,295] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,307] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,323] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,332] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,342] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,349] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,373] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,389] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:20,046] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,055] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,067] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,076] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,094] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,124] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,135] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,732] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,742] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,757] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,770] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,782] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,798] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,815] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,834] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:21,390] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,405] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,416] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,428] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,448] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,459] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,473] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:22,065] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,085] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,110] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,118] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,137] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,144] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,154] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,169] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,698] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,709] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,731] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,743] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,755] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,771] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,782] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:23,415] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,430] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,442] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,456] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,470] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,487] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,498] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,524] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:24,200] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,210] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,219] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,231] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,250] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,262] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,272] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,898] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,903] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,907] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,932] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,957] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,979] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,991] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:25,011] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:25,616] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,625] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,648] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,658] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,669] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,681] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,690] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,707] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,378] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,384] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,389] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,397] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,402] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,415] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,428] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:27,008] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,018] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,026] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,037] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,046] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,054] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,062] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,070] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,735] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,745] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,755] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,769] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,783] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,794] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,804] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:28,454] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,472] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,481] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,489] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,499] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,533] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,543] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,556] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:29,212] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,222] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,233] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,246] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,258] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,270] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,286] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:30,003] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,013] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,038] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,048] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,062] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,074] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,114] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,125] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,856] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,876] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,885] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,897] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,914] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,940] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:30,952] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说'} +[2023-03-30 23:26:31,655] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,696] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,709] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,718] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,727] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,740] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,757] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明'} +[2023-03-30 23:26:31,768] [ INFO] - client receive msg={'result': 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receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助'} +[2023-03-30 23:26:33,338] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,356] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,368] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,386] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,397] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,409] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} 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'第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感'} +[2023-03-30 23:26:34,423] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感'} +[2023-03-30 23:26:34,434] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感'} +[2023-03-30 23:26:35,373] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,397] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,410] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,420] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,437] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,448] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,460] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:35,473] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有'} +[2023-03-30 23:26:36,288] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,297] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,306] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,326] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,336] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,351] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:36,365] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的'} +[2023-03-30 23:26:37,164] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,173] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,182] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,192] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,204] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,232] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,238] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:37,252] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象'} +[2023-03-30 23:26:38,084] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,093] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,106] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,122] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,140] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,181] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:38,206] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后'} +[2023-03-30 23:26:39,094] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,111] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,132] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,150] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,174] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,190] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,197] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:39,212] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合'} +[2023-03-30 23:26:40,009] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,094] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,105] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,128] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,149] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,173] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,189] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,200] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,952] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,973] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,986] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,999] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:41,013] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:41,022] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:41,033] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:41,819] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,832] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,845] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,878] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,886] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,893] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,925] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:41,935] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升'} +[2023-03-30 23:26:42,562] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,589] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,621] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,634] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,644] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,657] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对'} +[2023-03-30 23:26:42,668] [ INFO] - client receive msg={'result': 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'bg': 12.94, 'ed': 13.1}, {'w': '的', 'bg': 13.1, 'ed': 13.26}, {'w': '内', 'bg': 13.26, 'ed': 13.42}, {'w': '部', 'bg': 13.42, 'ed': 13.56}, {'w': '构', 'bg': 13.56, 'ed': 13.700000000000001}, {'w': '造', 'bg': 13.700000000000001, 'ed': 13.86}, {'w': '图', 'bg': 13.86, 'ed': 14.280000000000001}, {'w': '辅', 'bg': 14.280000000000001, 'ed': 14.66}, {'w': '以', 'bg': 14.66, 'ed': 14.82}, {'w': '文', 'bg': 14.82, 'ed': 15.0}, {'w': '字', 'bg': 15.0, 'ed': 15.16}, {'w': '说', 'bg': 15.16, 'ed': 15.32}, {'w': '明', 'bg': 15.32, 'ed': 15.72}, {'w': '进', 'bg': 15.72, 'ed': 16.1}, {'w': '一', 'bg': 16.1, 'ed': 16.2}, {'w': '步', 'bg': 16.2, 'ed': 16.32}, {'w': '帮', 'bg': 16.32, 'ed': 16.48}, {'w': '助', 'bg': 16.48, 'ed': 16.66}, {'w': '学', 'bg': 16.66, 'ed': 16.82}, {'w': '生', 'bg': 16.82, 'ed': 17.12}, {'w': '对', 'bg': 17.12, 'ed': 17.48}, {'w': '传', 'bg': 17.48, 'ed': 17.66}, {'w': '感', 'bg': 17.66, 'ed': 17.84}, {'w': '器', 'bg': 17.84, 'ed': 18.12}, {'w': '有', 'bg': 18.12, 'ed': 18.42}, {'w': '更', 'bg': 18.42, 'ed': 18.66}, {'w': '深', 'bg': 18.66, 'ed': 18.88}, {'w': '刻', 'bg': 18.88, 'ed': 19.04}, {'w': '的', 'bg': 19.04, 'ed': 19.16}, {'w': '印', 'bg': 19.16, 'ed': 19.3}, {'w': '象', 'bg': 19.3, 'ed': 19.8}, {'w': '最', 'bg': 19.8, 'ed': 20.3}, {'w': '后', 'bg': 20.3, 'ed': 20.62}, {'w': '结', 'bg': 20.62, 'ed': 20.96}, {'w': '合', 'bg': 20.96, 'ed': 21.14}, {'w': '具', 'bg': 21.14, 'ed': 21.3}, {'w': '体', 'bg': 21.3, 'ed': 21.42}, {'w': '的', 'bg': 21.42, 'ed': 21.580000000000002}, {'w': '实', 'bg': 21.580000000000002, 'ed': 21.76}, {'w': '践', 'bg': 21.76, 'ed': 21.92}, {'w': '应', 'bg': 21.92, 'ed': 22.080000000000002}, {'w': '用', 'bg': 22.080000000000002, 'ed': 22.44}, {'w': '提', 'bg': 22.44, 'ed': 22.78}, {'w': '升', 'bg': 22.78, 'ed': 22.94}, {'w': '学', 'bg': 22.94, 'ed': 23.12}, {'w': '生', 'bg': 23.12, 'ed': 23.34}, {'w': '对', 'bg': 23.34, 'ed': 23.62}, {'w': '实', 'bg': 23.62, 'ed': 23.82}, {'w': '训', 'bg': 23.82, 'ed': 23.96}, {'w': '的', 'bg': 23.96, 'ed': 24.12}, {'w': '兴', 'bg': 24.12, 'ed': 24.3}, {'w': '趣', 'bg': 24.3, 'ed': 24.6}, {'w': '以', 'bg': 24.6, 'ed': 24.88}, {'w': '及', 'bg': 24.88, 'ed': 25.12}, {'w': '意', 'bg': 25.12, 'ed': 25.34}, {'w': '义', 'bg': 25.34, 'ed': 25.46}, {'w': '感', 'bg': 25.46, 'ed': 26.04}]} +[2023-03-30 23:27:01,060] [ INFO] - audio duration: 26.04, elapsed time: 46.581613540649414, RTF=1.7888484462614982 +sentences: ['第一部分是认知部分', '该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理', '让学生对设备有大致的认知', '随后使用真实传感器的内部构造图', '辅以文字说明', '进一步帮助学生对传感器有更深刻的印象', '最后结合具体的实践应用', '提升学生对实训的兴趣以及意义感'] +relative_times: [[0.0, 2.1], [2.1, 8.06], [8.06, 11.040000000000001], [11.040000000000001, 14.280000000000001], [14.280000000000001, 15.72], [15.72, 19.8], [19.8, 22.44], [22.44, 26.04]] +[2023-03-30 23:27:01,076] [ INFO] - results saved to /home/fxb/PaddleSpeech-develop/data/认知.srt + ``` diff --git a/demos/streaming_asr_server/README_cn.md b/demos/streaming_asr_server/README_cn.md index 1902a2fa..bbddd693 100644 --- a/demos/streaming_asr_server/README_cn.md +++ b/demos/streaming_asr_server/README_cn.md @@ -90,7 +90,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav server_executor = ServerExecutor() server_executor( - config_file="./conf/ws_conformer_wenetspeech_application", + config_file="./conf/ws_conformer_wenetspeech_application_faster.yaml", log_file="./log/paddlespeech.log") ``` @@ -578,3 +578,354 @@ bash server.sh [2022-05-07 11:11:18,915] [ INFO] - audio duration: 4.9968125, elapsed time: 15.928460597991943, RTF=3.187724293835709 [2022-05-07 11:11:18,916] [ INFO] - asr websocket client finished : 我认为跑步最重要的就是给我带来了身体健康 ``` + +## 从音频文件(.wav 格式 或者.mp3 格式)生成字幕文件 (.srt 格式) + +**注意:** 默认部署在 `cpu` 设备上，可以通过修改服务配置文件中 `device` 参数将语音识别和标点预测部署在不同的 `gpu` 上。 + +使用 `streaming_asr_server.py` 和 `punc_server.py` 两个服务，分别启动流式语音识别和标点预测服务。调用 `websocket_client.py` 脚本可以同时调用流式语音识别和标点预测服务，将会生成对应的字幕文件(.srt格式)。 + +**使用该脚本前需要安装mffpeg** + +**应该在对应的`.../demos/streaming_asr_server/`目录下运行以下脚本** + +### 1. 启动服务端 + +```bash +Note: streaming speech recognition and punctuation prediction are configured on different graphics cards through configuration files +paddlespeech_server start --config_file ./conf/ws_conformer_wenetspeech_application.yaml +``` + +Open another terminal run the following commands: +```bash +paddlespeech_server start --config_file conf/punc_application.yaml +``` + +### 2. 启动客户端 + + ```bash + python3 local/websocket_client_srt.py --server_ip 127.0.0.1 --port 8090 --punc.server_ip 127.0.0.1 --punc.port 8190 --wavfile ../../data/认知.mp3 + ``` + Output: + ```text + [2023-03-30 23:26:13,991] [ INFO] - Start to do streaming asr client +[2023-03-30 23:26:13,994] [ INFO] - asr websocket client start +[2023-03-30 23:26:13,994] [ INFO] - endpoint: http://127.0.0.1:8190/paddlespeech/text +[2023-03-30 23:26:13,994] [ INFO] - endpoint: ws://127.0.0.1:8090/paddlespeech/asr/streaming +[2023-03-30 23:26:14,475] [ INFO] - /home/fxb/PaddleSpeech-develop/data/认知.mp3 converted to /home/fxb/PaddleSpeech-develop/data/认知.wav +[2023-03-30 23:26:14,476] [ INFO] - start to process the wavscp: /home/fxb/PaddleSpeech-develop/data/认知.wav +[2023-03-30 23:26:14,515] [ INFO] - client receive msg={"status": "ok", "signal": "server_ready"} +[2023-03-30 23:26:14,533] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,545] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,556] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,572] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,588] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,600] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,613] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:14,626] [ INFO] - client receive msg={'result': ''} +[2023-03-30 23:26:15,122] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,135] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,154] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,163] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,175] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,185] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,196] [ INFO] - client receive msg={'result': '第一部'} +[2023-03-30 23:26:15,637] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,648] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,657] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,666] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,676] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,683] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,691] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:15,703] [ INFO] - client receive msg={'result': '第一部分是认'} +[2023-03-30 23:26:16,146] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,159] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,167] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,177] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,187] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,197] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,210] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,694] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,704] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,713] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,725] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,737] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,749] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,759] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:16,770] [ INFO] - client receive msg={'result': '第一部分是认知部分'} +[2023-03-30 23:26:17,279] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,302] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,316] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,332] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,343] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,358] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,373] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通'} +[2023-03-30 23:26:17,958] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:17,971] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:17,987] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,000] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,017] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,028] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,038] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,049] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图'} +[2023-03-30 23:26:18,653] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,689] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,701] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,712] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,723] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,750] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:18,767] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本'} +[2023-03-30 23:26:19,295] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,307] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,323] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,332] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,342] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,349] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,373] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:19,389] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式'} +[2023-03-30 23:26:20,046] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,055] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,067] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,076] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,094] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,124] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,135] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生'} +[2023-03-30 23:26:20,732] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,742] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,757] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,770] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,782] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,798] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,815] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:20,834] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解'} +[2023-03-30 23:26:21,390] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,405] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,416] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,428] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,448] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,459] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:21,473] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感'} +[2023-03-30 23:26:22,065] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,085] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,110] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,118] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,137] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,144] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,154] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,169] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作'} +[2023-03-30 23:26:22,698] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,709] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,731] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,743] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,755] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,771] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:22,782] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理'} +[2023-03-30 23:26:23,415] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,430] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,442] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,456] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,470] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,487] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,498] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:23,524] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生'} +[2023-03-30 23:26:24,200] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,210] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,219] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,231] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,250] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,262] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,272] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备'} +[2023-03-30 23:26:24,898] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,903] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,907] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,932] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,957] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,979] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:24,991] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:25,011] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致'} +[2023-03-30 23:26:25,616] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,625] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,648] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,658] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,669] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,681] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,690] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:25,707] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,378] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,384] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,389] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,397] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,402] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,415] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:26,428] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知'} +[2023-03-30 23:26:27,008] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,018] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,026] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,037] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,046] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,054] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,062] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,070] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使'} +[2023-03-30 23:26:27,735] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,745] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,755] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,769] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,783] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,794] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:27,804] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传'} +[2023-03-30 23:26:28,454] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,472] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,481] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,489] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,499] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,533] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,543] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:28,556] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内'} +[2023-03-30 23:26:29,212] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,222] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,233] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,246] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,258] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,270] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:29,286] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图'} +[2023-03-30 23:26:30,003] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,013] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,038] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,048] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,062] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅'} +[2023-03-30 23:26:30,074] [ INFO] - client receive msg={'result': 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msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助'} +[2023-03-30 23:26:32,560] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助'} +[2023-03-30 23:26:32,574] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助'} +[2023-03-30 23:26:32,590] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助'} +[2023-03-30 23:26:33,338] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,356] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,368] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,386] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,397] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,409] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,424] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:33,434] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生'} +[2023-03-30 23:26:34,352] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感'} +[2023-03-30 23:26:34,364] [ INFO] - client receive msg={'result': 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msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,189] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,200] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实'} +[2023-03-30 23:26:40,952] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,973] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,986] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用'} +[2023-03-30 23:26:40,999] [ INFO] 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'第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以'} +[2023-03-30 23:26:44,420] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以'} +[2023-03-30 23:26:45,226] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:45,235] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:45,258] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:45,273] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:45,295] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:45,306] [ INFO] - client receive msg={'result': '第一部分是认知部分该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理让学生对设备有大致的认知随后使用真实传感器的内部构造图辅以文字说明进一步帮助学生对传感器有更深刻的印象最后结合具体的实践应用提升学生对实训的兴趣以及意义感'} +[2023-03-30 23:26:46,380] [ INFO] - client punctuation restored msg={'result': '第一部分是认知部分，该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理，让学生对设备有大致的认知。随后使用真实传感器的内部构造图，辅以文字说明，进一步帮助学生对传感器有更深刻的印象，最后结合具体的实践应用，提升学生对实训的兴趣以及意义感。'} +[2023-03-30 23:27:01,059] [ INFO] - client final receive msg={'status': 'ok', 'signal': 'finished', 'result': '第一部分是认知部分，该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理，让学生对设备有大致的认知。随后使用真实传感器的内部构造图，辅以文字说明，进一步帮助学生对传感器有更深刻的印象，最后结合具体的实践应用，提升学生对实训的兴趣以及意义感。', 'times': [{'w': '第', 'bg': 0.0, 'ed': 0.36}, {'w': '一', 'bg': 0.36, 'ed': 0.48}, {'w': '部', 'bg': 0.48, 'ed': 0.62}, {'w': '分', 'bg': 0.62, 'ed': 0.8200000000000001}, {'w': '是', 'bg': 0.8200000000000001, 'ed': 1.08}, {'w': '认', 'bg': 1.08, 'ed': 1.28}, {'w': '知', 'bg': 1.28, 'ed': 1.44}, {'w': '部', 'bg': 1.44, 'ed': 1.58}, {'w': '分', 'bg': 1.58, 'ed': 2.1}, {'w': '该', 'bg': 2.1, 'ed': 2.6}, {'w': '部', 'bg': 2.6, 'ed': 2.72}, {'w': '分', 'bg': 2.72, 'ed': 2.94}, {'w': '通', 'bg': 2.94, 'ed': 3.16}, {'w': '过', 'bg': 3.16, 'ed': 3.36}, {'w': '示', 'bg': 3.36, 'ed': 3.54}, {'w': '意', 'bg': 3.54, 'ed': 3.68}, {'w': '图', 'bg': 3.68, 'ed': 3.9}, {'w': '和', 'bg': 3.9, 'ed': 4.14}, {'w': '文', 'bg': 4.14, 'ed': 4.32}, {'w': '本', 'bg': 4.32, 'ed': 4.46}, {'w': '的', 'bg': 4.46, 'ed': 4.58}, {'w': '形', 'bg': 4.58, 'ed': 4.72}, {'w': '式', 'bg': 4.72, 'ed': 5.0}, {'w': '向', 'bg': 5.0, 'ed': 5.32}, {'w': '学', 'bg': 5.32, 'ed': 5.5}, {'w': '生', 'bg': 5.5, 'ed': 5.66}, {'w': '讲', 'bg': 5.66, 'ed': 5.86}, {'w': '解', 'bg': 5.86, 'ed': 6.18}, {'w': '主', 'bg': 6.18, 'ed': 6.46}, {'w': '要', 'bg': 6.46, 'ed': 6.62}, {'w': '传', 'bg': 6.62, 'ed': 6.8}, {'w': '感', 'bg': 6.8, 'ed': 7.0}, {'w': '器', 'bg': 7.0, 'ed': 7.16}, {'w': '的', 'bg': 7.16, 'ed': 7.28}, {'w': '工', 'bg': 7.28, 'ed': 7.44}, {'w': '作', 'bg': 7.44, 'ed': 7.6000000000000005}, {'w': '原', 'bg': 7.6000000000000005, 'ed': 7.74}, {'w': '理', 'bg': 7.74, 'ed': 8.06}, {'w': '让', 'bg': 8.06, 'ed': 8.44}, {'w': '学', 'bg': 8.44, 'ed': 8.64}, {'w': '生', 'bg': 8.64, 'ed': 8.84}, {'w': '对', 'bg': 8.84, 'ed': 9.06}, {'w': '设', 'bg': 9.06, 'ed': 9.24}, {'w': '备', 'bg': 9.24, 'ed': 9.52}, {'w': '有', 'bg': 9.52, 'ed': 9.86}, {'w': '大', 'bg': 9.86, 'ed': 10.1}, {'w': '致', 'bg': 10.1, 'ed': 10.24}, {'w': '的', 'bg': 10.24, 'ed': 10.36}, {'w': '认', 'bg': 10.36, 'ed': 10.5}, {'w': '知', 'bg': 10.5, 'ed': 11.040000000000001}, {'w': '随', 'bg': 11.040000000000001, 'ed': 11.56}, {'w': '后', 'bg': 11.56, 'ed': 11.82}, {'w': '使', 'bg': 11.82, 'ed': 12.1}, {'w': '用', 'bg': 12.1, 'ed': 12.26}, {'w': '真', 'bg': 12.26, 'ed': 12.44}, {'w': '实', 'bg': 12.44, 'ed': 12.620000000000001}, {'w': '传', 'bg': 12.620000000000001, 'ed': 12.780000000000001}, {'w': '感', 'bg': 12.780000000000001, 'ed': 12.94}, {'w': '器', 'bg': 12.94, 'ed': 13.1}, {'w': '的', 'bg': 13.1, 'ed': 13.26}, {'w': '内', 'bg': 13.26, 'ed': 13.42}, {'w': '部', 'bg': 13.42, 'ed': 13.56}, {'w': '构', 'bg': 13.56, 'ed': 13.700000000000001}, {'w': '造', 'bg': 13.700000000000001, 'ed': 13.86}, {'w': '图', 'bg': 13.86, 'ed': 14.280000000000001}, {'w': '辅', 'bg': 14.280000000000001, 'ed': 14.66}, {'w': '以', 'bg': 14.66, 'ed': 14.82}, {'w': '文', 'bg': 14.82, 'ed': 15.0}, {'w': '字', 'bg': 15.0, 'ed': 15.16}, {'w': '说', 'bg': 15.16, 'ed': 15.32}, {'w': '明', 'bg': 15.32, 'ed': 15.72}, {'w': '进', 'bg': 15.72, 'ed': 16.1}, {'w': '一', 'bg': 16.1, 'ed': 16.2}, {'w': '步', 'bg': 16.2, 'ed': 16.32}, {'w': '帮', 'bg': 16.32, 'ed': 16.48}, {'w': '助', 'bg': 16.48, 'ed': 16.66}, {'w': '学', 'bg': 16.66, 'ed': 16.82}, {'w': '生', 'bg': 16.82, 'ed': 17.12}, {'w': '对', 'bg': 17.12, 'ed': 17.48}, {'w': '传', 'bg': 17.48, 'ed': 17.66}, {'w': '感', 'bg': 17.66, 'ed': 17.84}, {'w': '器', 'bg': 17.84, 'ed': 18.12}, {'w': '有', 'bg': 18.12, 'ed': 18.42}, {'w': '更', 'bg': 18.42, 'ed': 18.66}, {'w': '深', 'bg': 18.66, 'ed': 18.88}, {'w': '刻', 'bg': 18.88, 'ed': 19.04}, {'w': '的', 'bg': 19.04, 'ed': 19.16}, {'w': '印', 'bg': 19.16, 'ed': 19.3}, {'w': '象', 'bg': 19.3, 'ed': 19.8}, {'w': '最', 'bg': 19.8, 'ed': 20.3}, {'w': '后', 'bg': 20.3, 'ed': 20.62}, {'w': '结', 'bg': 20.62, 'ed': 20.96}, {'w': '合', 'bg': 20.96, 'ed': 21.14}, {'w': '具', 'bg': 21.14, 'ed': 21.3}, {'w': '体', 'bg': 21.3, 'ed': 21.42}, {'w': '的', 'bg': 21.42, 'ed': 21.580000000000002}, {'w': '实', 'bg': 21.580000000000002, 'ed': 21.76}, {'w': '践', 'bg': 21.76, 'ed': 21.92}, {'w': '应', 'bg': 21.92, 'ed': 22.080000000000002}, {'w': '用', 'bg': 22.080000000000002, 'ed': 22.44}, {'w': '提', 'bg': 22.44, 'ed': 22.78}, {'w': '升', 'bg': 22.78, 'ed': 22.94}, {'w': '学', 'bg': 22.94, 'ed': 23.12}, {'w': '生', 'bg': 23.12, 'ed': 23.34}, {'w': '对', 'bg': 23.34, 'ed': 23.62}, {'w': '实', 'bg': 23.62, 'ed': 23.82}, {'w': '训', 'bg': 23.82, 'ed': 23.96}, {'w': '的', 'bg': 23.96, 'ed': 24.12}, {'w': '兴', 'bg': 24.12, 'ed': 24.3}, {'w': '趣', 'bg': 24.3, 'ed': 24.6}, {'w': '以', 'bg': 24.6, 'ed': 24.88}, {'w': '及', 'bg': 24.88, 'ed': 25.12}, {'w': '意', 'bg': 25.12, 'ed': 25.34}, {'w': '义', 'bg': 25.34, 'ed': 25.46}, {'w': '感', 'bg': 25.46, 'ed': 26.04}]} +[2023-03-30 23:27:01,060] [ INFO] - audio duration: 26.04, elapsed time: 46.581613540649414, RTF=1.7888484462614982 +sentences: ['第一部分是认知部分', '该部分通过示意图和文本的形式向学生讲解主要传感器的工作原理', '让学生对设备有大致的认知', '随后使用真实传感器的内部构造图', '辅以文字说明', '进一步帮助学生对传感器有更深刻的印象', '最后结合具体的实践应用', '提升学生对实训的兴趣以及意义感'] +relative_times: [[0.0, 2.1], [2.1, 8.06], [8.06, 11.040000000000001], [11.040000000000001, 14.280000000000001], [14.280000000000001, 15.72], [15.72, 19.8], [19.8, 22.44], [22.44, 26.04]] +[2023-03-30 23:27:01,076] [ INFO] - results saved to /home/fxb/PaddleSpeech-develop/data/认知.srt + ``` diff --git a/demos/streaming_asr_server/local/websocket_client_srt.py b/demos/streaming_asr_server/local/websocket_client_srt.py new file mode 100644 index 00000000..02fea484 --- /dev/null +++ b/demos/streaming_asr_server/local/websocket_client_srt.py @@ -0,0 +1,162 @@ +#!/usr/bin/python +# Copyright (c) 2022 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. +# calc avg RTF(NOT Accurate): grep -rn RTF log.txt | awk '{print $NF}' | awk -F "=" '{sum += $NF} END {print "all time",sum, "audio num", NR, "RTF", sum/NR}' +# python3 websocket_client.py --server_ip 127.0.0.1 --port 8290 --punc.server_ip 127.0.0.1 --punc.port 8190 --wavfile ./zh.wav +# python3 websocket_client.py --server_ip 127.0.0.1 --port 8290 --wavfile ./zh.wav +import argparse +import asyncio +import codecs +import os +from pydub import AudioSegment +import re + +from paddlespeech.cli.log import logger +from paddlespeech.server.utils.audio_handler import ASRWsAudioHandler + +def convert_to_wav(input_file): + # Load audio file + audio = AudioSegment.from_file(input_file) + + # Set parameters for audio file + audio = audio.set_channels(1) + audio = audio.set_frame_rate(16000) + + # Create output filename + output_file = os.path.splitext(input_file)[0] + ".wav" + + # Export audio file as WAV + audio.export(output_file, format="wav") + + logger.info(f"{input_file} converted to {output_file}") + +def format_time(sec): + # Convert seconds to SRT format (HH:MM:SS,ms) + hours = int(sec/3600) + minutes = int((sec%3600)/60) + seconds = int(sec%60) + milliseconds = int((sec%1)*1000) + return f'{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}' + +def results2srt(results, srt_file): + """convert results from paddlespeech to srt format for subtitle + Args: + results (dict): results from paddlespeech + """ + # times contains start and end time of each word + times = results['times'] + # result contains the whole sentence including punctuation + result = results['result'] + # split result into several sencences by '，' and '。' + sentences = re.split('，|。', result)[:-1] + # print("sentences: ", sentences) + # generate relative time for each sentence in sentences + relative_times = [] + word_i = 0 + for sentence in sentences: + relative_times.append([]) + for word in sentence: + if relative_times[-1] == []: + relative_times[-1].append(times[word_i]['bg']) + if len(relative_times[-1]) == 1: + relative_times[-1].append(times[word_i]['ed']) + else: + relative_times[-1][1] = times[word_i]['ed'] + word_i += 1 + # print("relative_times: ", relative_times) + # generate srt file acoording to relative_times and sentences + with open(srt_file, 'w') as f: + for i in range(len(sentences)): + # Write index number + f.write(str(i+1)+'\n') + + # Write start and end times + start = format_time(relative_times[i][0]) + end = format_time(relative_times[i][1]) + f.write(start + ' --> ' + end + '\n') + + # Write text + f.write(sentences[i]+'\n\n') + logger.info(f"results saved to {srt_file}") + +def main(args): + logger.info("asr websocket client start") + handler = ASRWsAudioHandler( + args.server_ip, + args.port, + endpoint=args.endpoint, + punc_server_ip=args.punc_server_ip, + punc_server_port=args.punc_server_port) + loop = asyncio.get_event_loop() + + # check if the wav file is mp3 format + # if so, convert it to wav format using convert_to_wav function + if args.wavfile and os.path.exists(args.wavfile): + if args.wavfile.endswith(".mp3"): + convert_to_wav(args.wavfile) + args.wavfile = args.wavfile.replace(".mp3", ".wav") + + # support to process single audio file + if args.wavfile and os.path.exists(args.wavfile): + logger.info(f"start to process the wavscp: {args.wavfile}") + result = loop.run_until_complete(handler.run(args.wavfile)) + # result = result["result"] + # logger.info(f"asr websocket client finished : {result}") + results2srt(result, args.wavfile.replace(".wav", ".srt")) + + # support to process batch audios from wav.scp + if args.wavscp and os.path.exists(args.wavscp): + logger.info(f"start to process the wavscp: {args.wavscp}") + with codecs.open(args.wavscp, 'r', encoding='utf-8') as f,\ + codecs.open("result.txt", 'w', encoding='utf-8') as w: + for line in f: + utt_name, utt_path = line.strip().split() + result = loop.run_until_complete(handler.run(utt_path)) + result = result["result"] + w.write(f"{utt_name} {result}\n") + + +if __name__ == "__main__": + logger.info("Start to do streaming asr client") + parser = argparse.ArgumentParser() + parser.add_argument( + '--server_ip', type=str, default='127.0.0.1', help='server ip') + parser.add_argument('--port', type=int, default=8090, help='server port') + parser.add_argument( + '--punc.server_ip', + type=str, + default=None, + dest="punc_server_ip", + help='Punctuation server ip') + parser.add_argument( + '--punc.port', + type=int, + default=8091, + dest="punc_server_port", + help='Punctuation server port') + parser.add_argument( + "--endpoint", + type=str, + default="/paddlespeech/asr/streaming", + help="ASR websocket endpoint") + parser.add_argument( + "--wavfile", + action="store", + help="wav file path ", + default="./16_audio.wav") + parser.add_argument( + "--wavscp", type=str, default=None, help="The batch audios dict text") + args = parser.parse_args() + + main(args) diff --git a/docs/images/note_map.png b/docs/images/note_map.png new file mode 100644 index 0000000000000000000000000000000000000000..f280d98c4ca596fd2218cf7b9a30b50f0085c77a GIT binary patch literal 301021 zcmdRWWmjE2)a}8cxVvj{cPs8*+}+*XU5i730>#~n+rgngaXq*l+?~tw-uwQD`(clf zkz{9#?7dcI=3Fa@QBjgc{zC8t001D%%1EdI0Fc4|J@Am9HDPsz?f?K8Kvv?LhWF3Y z4WFOH-?K&Dr*D3$uNnBsI_dB;K!`y?V~{aInrNA;OQE@Is1SMK!x3kyj$H8OmKU=Z 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+ToJyutping==0.2.1 typeguard==2.13.3 webrtcvad websockets diff --git a/docs/source/install.md b/docs/source/install.md index a4dae364..3607d718 100644 --- a/docs/source/install.md +++ b/docs/source/install.md @@ -95,7 +95,7 @@ bash ``` Then you can create a conda virtual environment using the following command: ```bash -conda create -y -p tools/venv python=3.7 +conda create -y -p tools/venv python=3.8 ``` Activate the conda virtual environment: ```bash @@ -181,7 +181,7 @@ $HOME/miniconda3/bin/conda init # use the "bash" command to make the conda environment works bash # create a conda virtual environment -conda create -y -p tools/venv python=3.7 +conda create -y -p tools/venv python=3.8 # Activate the conda virtual environment: conda activate tools/venv # Install the conda packages diff --git a/docs/source/install_cn.md b/docs/source/install_cn.md index 7f05cdfe..01ae21fe 100644 --- a/docs/source/install_cn.md +++ b/docs/source/install_cn.md @@ -91,7 +91,7 @@ bash ``` 然后你可以创建一个 conda 的虚拟环境： ```bash -conda create -y -p tools/venv python=3.7 +conda create -y -p tools/venv python=3.8 ``` 激活 conda 虚拟环境： ```bash @@ -173,7 +173,7 @@ $HOME/miniconda3/bin/conda init # 激活 conda bash # 创建 Conda 虚拟环境 -conda create -y -p tools/venv python=3.7 +conda create -y -p tools/venv python=3.8 # 激活 Conda 虚拟环境: conda activate tools/venv # 安装 Conda 包 diff --git a/docs/source/released_model.md b/docs/source/released_model.md index 9e922177..87619a55 100644 --- a/docs/source/released_model.md +++ b/docs/source/released_model.md @@ -1,5 +1,7 @@ # Released Models +> !!! Since PaddlePaddle support 0-D tensor from 2.5.0, PaddleSpeech Static model will not work for it, please re-export static model. + ## Speech-to-Text Models ### Speech Recognition Model @@ -10,7 +12,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | [Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz)| Aishell Dataset | Char-based | 1.4 GB | 2 Conv + 5 bidirectional LSTM layers| 0.0554 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) | inference/python |-| [Conformer Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz) | WenetSpeech Dataset | Char-based | 457 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.11 (test\_net) 0.1879 (test\_meeting) |-| 10000 h |- | python |-| [Conformer U2PP Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_u2pp_wenetspeech_ckpt_1.3.0.model.tar.gz) | WenetSpeech Dataset | Char-based | 540 MB | Encoder:Conformer, Decoder:BiTransformer, Decoding method: Attention rescoring| 0.047198 (aishell test\_-1) 0.059212 (aishell test\_16) |-| 10000 h |- | python |[FP32](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_u2pp_wenetspeech_ckpt_1.3.0.model.tar.gz)
    [INT8](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/static/asr1_chunk_conformer_u2pp_wenetspeech_static_quant_1.3.0.model.tar.gz) | -[Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 189 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.0544 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) | python |-| +[Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_1.5.0.model.tar.gz) | Aishell Dataset | Char-based | 189 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.051968 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) | python |-| [Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_1.0.1.model.tar.gz) | Aishell Dataset | Char-based | 189 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0460 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) | python |-| [Transformer Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 128 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0523 || 151 h | [Transformer Aishell ASR1](../../examples/aishell/asr1) | python |-| [Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_offline_librispeech_ckpt_1.0.1.model.tar.gz)| Librispeech Dataset | Char-based | 1.3 GB | 2 Conv + 5 bidirectional LSTM layers| - |0.0467| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) | inference/python |-| @@ -26,6 +28,8 @@ Model | Pre-Train Method | Pre-Train Data | Finetune Data | Size | Descriptions [Wav2vec2ASR-large-960h-librispeech Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr3/wav2vec2ASR-large-960h-librispeech_ckpt_1.3.1.model.tar.gz) | wav2vec2 | Librispeech and LV-60k Dataset (5.3w h) | Librispeech (960 h) | 718 MB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | - | 0.0189 | [Wav2vecASR Librispeech ASR3](../../examples/librispeech/asr3) | [Wav2vec2-large-wenetspeech-self Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2-large-wenetspeech-self_ckpt_1.3.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | - | 714 MB |Pre-trained Wav2vec2.0 Model | - | - | - | [Wav2vec2ASR-large-aishell1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr3/wav2vec2ASR-large-aishell1_ckpt_1.4.0.model.tar.gz) | wav2vec2 | Wenetspeech Dataset (1w h) | aishell1 (train set) | 1.18 GB |Encoder: Wav2vec2.0, Decoder: CTC, Decoding method: Greedy search | 0.0510 | - | - | +[Hubert-large-lv60 Model](https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams) | hubert | LV-60k Dataset | - | 1.18 GB |Pre-trained hubert Model | - | - | - | +[Hubert-large-100h-librispeech Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr4/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz) | hubert | LV-60k Dataset | librispeech train-clean-100 | 1.27 GB |Encoder: Hubert, Decoder: Linear + CTC, Decoding method: Greedy search | - | 0.0587 | [HubertASR Librispeech ASR4](../../examples/librispeech/asr4) | ### Whisper Model Demo Link | Training Data | Size | Descriptions | CER | Model diff --git a/docs/source/tts/quick_start.md b/docs/source/tts/quick_start.md index d8dbc646..d2a1b4ec 100644 --- a/docs/source/tts/quick_start.md +++ b/docs/source/tts/quick_start.md @@ -79,8 +79,8 @@ checkpoint_name ├── snapshot_iter_*.pdz ├── speech_stats.npy ├── phone_id_map.txt -├── spk_id_map.txt (optimal) -└── tone_id_map.txt (optimal) +├── spk_id_map.txt (optional) +└── tone_id_map.txt (optional) ``` **Vocoders:** ```text diff --git a/docs/source/tts/quick_start_cn.md b/docs/source/tts/quick_start_cn.md index c56d9bb4..ba259643 100644 --- a/docs/source/tts/quick_start_cn.md +++ b/docs/source/tts/quick_start_cn.md @@ -87,8 +87,8 @@ checkpoint_name ├── snapshot_iter_*.pdz ├── speech_stats.npy ├── phone_id_map.txt -├── spk_id_map.txt (optimal) -└── tone_id_map.txt (optimal) +├── spk_id_map.txt (optional) +└── tone_id_map.txt (optional) ``` **Vocoders:** ```text diff --git a/docs/source/tts/svs_music_score.md b/docs/source/tts/svs_music_score.md new file mode 100644 index 00000000..9f351c00 --- /dev/null +++ b/docs/source/tts/svs_music_score.md @@ -0,0 +1,183 @@ +本人非音乐专业人士，如文档中有误欢迎指正。 + +# 一、常见基础 +## 1.1 简谱和音名（note） +

    + +

    + +上图从左往右的黑键音名分别是：C#/Db，D#/Db，F#/Db，G#/Ab，A#/Bb +钢琴88键如下图，分为大字一组，大字组，小字组，小字一组，小字二组，小字三组，小字四组。分别对应音名的后缀是 1 2 3 4 5 6，例如小字一组（C大调）包含的键分别为： C4，C#4/Db4，D4，D#4/Eb4，E4，F4，F#4/Gb4，G4，G#4/Ab4，A4，A#4/Bb4，B4 +钢琴八度音就是12345671八个音，最后一个音是高1。**遵循：全全半全全全半** 就会得到 1 2 3 4 5 6 7 (高)1 的音 + +

    + +

    + +## 1.2 十二大调 +“#”表示升调 + +

    + +

    + +“b”表示降调 + +

    + +

    + +什么大调表示Do(简谱1) 这个音从哪个键开始，例如D大调，则用D这个键来表示 Do这个音。 +下图是十二大调下简谱与音名的对应表。 + +

    + +

    + + +## 1.3 Tempo +Tempo 用于表示速度（Speed of the beat/pulse），一分钟里面有几拍（beats per mimute BPM） + +

    + +

    + +whole note --> 4 beats
    +half note --> 2 beats
    +quarter note --> 1 beat
    +eighth note --> 1/2 beat
    +sixteenth note --> 1/4 beat
    + + +# 二、应用试验 +## 2.1 从谱中获取 music scores +music scores 包含：note，note_dur，is_slur + +

    + +

    + +从左上角的谱信息 *bE* 可以得出该谱子是 **降E大调**，可以对应1.2小节十二大调简谱音名对照表根据 简谱获取对应的note +从左上角的谱信息 *quarter note* 可以得出该谱子的速度是 **一分钟95拍（beat）**，一拍的时长 = **60/95 = 0.631578s** +从左上角的谱信息 *4/4* 可以得出该谱子表示四分音符为一拍（分母的4），每小节有4拍（分子的4） + +从该简谱上可以获取 music score 如下： + +|text |phone |简谱（辅助）后面的点表示高八音 |note （从小字组开始算） |几拍（辅助） |note_dur |is_slur| +:-------------:| :------------:| :-----: | -----: | :-----: |:-----:| :-----: | +|小 |x |5 |A#3/Bb3 |半 |0.315789 |0 | +| |iao |5 |A#3/Bb3 |半 |0.315789 |0 | +|酒 |j |1. |D#4/Eb4 |半 |0.315789 |0 | +| |iu |1. |D#4/Eb4 |半 |0.315789 |0 | +|窝 |w |2. |F4 |半 |0.315789 |0 | +| |o |2. |F4 |半 |0.315789 |0 | +|长 |ch |3. |G4 |半 |0.315789 |0 | +| |ang |3. |G4 |半 |0.315789 |0 | +| |ang |1. |D#4/Eb4 |半 |0.315789 |1 | +|睫 |j |1. |D#4/Eb4 |半 |0.315789 |0 | +| |ie |1. |D#4/Eb4 |半 |0.315789 |0 | +| |ie |5 |A#3/Bb3 |半 |0.315789 |1 | +|毛 |m |5 |A#3/Bb3 |一 |0.631578 |0 | +| |ao |5 |A#3/Bb3 |一 |0.631578 |0 | +|是 |sh |5 |A#3/Bb3 |半 |0.315789 |0 | +| |i |5 |A#3/Bb3 |半 |0.315789 |0 | +|你 |n |3. |G4 |半 |0.315789 |0 | +| |i |3. |G4 |半 |0.315789 |0 | +|最 |z |2. |F4 |半 |0.315789 |0 | +| |ui |2. |F4 |半 |0.315789 |0 | +|美 |m |3. |G4 |半 |0.315789 |0 | +| |ei |3. |G4 |半 |0.315789 |0 | +|的 |d |2. |F4 |半 |0.315789 |0 | +| |e |2. |F4 |半 |0.315789 |0 | +|记 |j |7 |D4 |半 |0.315789 |0 | +| |i |7 |D4 |半 |0.315789 |0 | +|号 |h |5 |A#3/Bb3 |半 |0.315789 |0 | +| |ao |5 |A#3/Bb3 |半 |0.315789 |0 | + + +## 2.2 一些实验 + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    序号 说明 合成音频（diffsinger_opencpop + pwgan_opencpop）
    1 原始 opencpop 标注的 notes，note_durs，is_slurs，升F大调，起始在小字组（第3组） + +
    +
    2 原始 opencpop 标注的 notes 和 is_slurs，note_durs 改变（从谱子获取） + +
    +
    3 原始 opencpop 标注的 notes 去掉 rest（毛字一拍），is_slurs 和 note_durs 改变（从谱子获取） + +
    +
    4 从谱子获取 notes，note durs，is_slurs，不含 rest（毛字一拍），起始在小字一组（第3组） + +
    +
    5 从谱子获取 notes，note durs，is_slurs，加上 rest （毛字半拍，rest半拍），起始在小字一组（第3组） + +
    +
    6 从谱子获取 notes， is_slurs，包含 rest，note_durs 从原始标注获取，起始在小字一组（第3组） + +
    +
    7 从谱子获取 notes，note durs，is_slurs，不含 rest（毛字一拍），起始在小字一组（第4组） + +
    +
    + +
    + + +上述实验表明通过该方法来提取 music score 是可行的，但是在应用中可以**灵活地在歌词中加"AP"(用来表示吸气声)和"SP"(用来表示停顿声)**，对应的在 **note 上加 rest**，会使得整体的歌声合成更自然。 +除此之外，还要考虑哪一个大调并且以哪一组为起始**得到的 note 在训练数据集中出现过**，如若推理时传入训练数据中没有见过的 note， 合成出来的音频可能不是我们期待的音调。 + + +# 三、其他 +## 3.1 读取midi + +```python +import mido +mid = mido.MidiFile('2093.midi') +``` diff --git a/docs/topic/package_release/python_package_release.md b/docs/topic/package_release/python_package_release.md index cb1029e7..c735e0bd 100644 --- a/docs/topic/package_release/python_package_release.md +++ b/docs/topic/package_release/python_package_release.md @@ -165,8 +165,7 @@ docker run -it xxxxxx 设置python： ```bash -export PATH="/opt/python/cp37-cp37m/bin/:$PATH" -#export PATH="/opt/python/cp38-cp38/bin/:$PATH" +export PATH="/opt/python/cp38-cp38/bin/:$PATH" #export PATH="/opt/python/cp39-cp39/bin/:$PATH" ``` diff --git a/docs/tutorial/asr/tutorial_transformer.ipynb b/docs/tutorial/asr/tutorial_transformer.ipynb index dc303006..77aed4bf 100644 --- a/docs/tutorial/asr/tutorial_transformer.ipynb +++ b/docs/tutorial/asr/tutorial_transformer.ipynb @@ -236,8 +236,8 @@ "warnings.filterwarnings('ignore')\n", "\n", "from yacs.config import CfgNode\n", - "from paddlespeech.s2t.transform.spectrogram import LogMelSpectrogramKaldi\n", - "from paddlespeech.s2t.transform.cmvn import GlobalCMVN\n", + "from paddlespeech.audio.transform.spectrogram import LogMelSpectrogramKaldi\n", + "from paddlespeech.audio.transform.cmvn import GlobalCMVN\n", "from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer\n", "from paddlespeech.s2t.models.u2 import U2Model\n", "\n", diff --git a/docs/tutorial/st/st_tutorial.ipynb b/docs/tutorial/st/st_tutorial.ipynb index 2fb85053..e755beba 100644 --- a/docs/tutorial/st/st_tutorial.ipynb +++ b/docs/tutorial/st/st_tutorial.ipynb @@ -62,7 +62,7 @@ "collapsed": false }, "source": [ - "# 使用Transformer进行端到端语音翻译的的基本流程\n", + "# 使用Transformer进行端到端语音翻译的基本流程\n", "## 基础模型\n", "由于 ASR 章节已经介绍了 Transformer 以及语音特征抽取，在此便不做过多介绍，感兴趣的同学可以去相关章节进行了解。\n", "\n", diff --git a/docs/tutorial/tts/tts_tutorial.ipynb b/docs/tutorial/tts/tts_tutorial.ipynb index 583adb01..0cecb680 100644 --- a/docs/tutorial/tts/tts_tutorial.ipynb +++ b/docs/tutorial/tts/tts_tutorial.ipynb @@ -464,7 +464,7 @@ "
    FastSpeech2 网络结构图

    \n", "\n", "\n", - "PaddleSpeech TTS 实现的 FastSpeech2 与论文不同的地方在于，我们使用的的是 phone 级别的 `pitch` 和 `energy`(与 FastPitch 类似)，这样的合成结果可以更加**稳定**。\n", + "PaddleSpeech TTS 实现的 FastSpeech2 与论文不同的地方在于，我们使用的是 phone 级别的 `pitch` 和 `energy`(与 FastPitch 类似)，这样的合成结果可以更加**稳定**。\n", "
    \n", "
    FastPitch 网络结构图

    \n", "\n", diff --git a/examples/aishell/asr0/local/train.sh b/examples/aishell/asr0/local/train.sh index 2b71b7f7..c0da3325 100755 --- a/examples/aishell/asr0/local/train.sh +++ b/examples/aishell/asr0/local/train.sh @@ -1,6 +1,6 @@ #!/bin/bash -if [ $# -lt 2 ] && [ $# -gt 3 ];then +if [ $# -lt 2 ] || [ $# -gt 3 ];then echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)" exit -1 fi diff --git a/examples/aishell/asr1/RESULTS.md b/examples/aishell/asr1/RESULTS.md index 79c695b1..be771ba5 100644 --- a/examples/aishell/asr1/RESULTS.md +++ b/examples/aishell/asr1/RESULTS.md @@ -1,27 +1,55 @@ # Aishell -## Conformer -paddle version: 2.2.2 -paddlespeech version: 1.0.1 -| Model | Params | Config | Augmentation| Test set | Decode method | Loss | CER | -| --- | --- | --- | --- | --- | --- | --- | --- | -| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | attention | - | 0.0522 | -| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | ctc_greedy_search | - | 0.0481 | -| conformer | 47.07M | conf/conformer.yaml | spec_aug| test | ctc_prefix_beam_search | - | 0.0480 | -| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | attention_rescoring | - | 0.0460 | +## RoFormer Streaming +paddle version: 2.5.0 +paddlespeech version: 1.5.0 + +Tesla V100-SXM2-32GB: 1 node, 4 card +Global BachSize: 32 * 4 +Training Done: 1 day, 12:56:39.639646 +### `decoding.decoding_chunk_size=16` + +> chunk_size=16, ((16 - 1) * 4 + 7) * 10ms = (16 * 4 + 3) * 10ms = 670ms + +| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | CER | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | attention | 16, -1 | - | 5.63 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | ctc_greedy_search | 16, -1 | - | 6.13 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | ctc_prefix_beam_search | 16, -1 | - | 6.13 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | attention_rescoring | 16, -1 | - | 5.44 | + +### `decoding.decoding_chunk_size=-1` + +| Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | CER | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | attention | -1, -1 | - | 5.39 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | ctc_greedy_search | -1, -1 | - | 5.51 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | ctc_prefix_beam_search | -1, -1 | - | 5.51 | +| roformer | 44.80M | conf/chunk_roformer.yaml | spec_aug | test | attention_rescoring | -1, -1 | - | 4.99 | ## Conformer Streaming paddle version: 2.2.2 -paddlespeech version: 0.2.0 +paddlespeech version: 1.4.1 Need set `decoding.decoding_chunk_size=16` when decoding. | Model | Params | Config | Augmentation| Test set | Decode method | Chunk Size & Left Chunks | Loss | CER | | --- | --- | --- | --- | --- | --- | --- | --- | --- | -| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | attention | 16, -1 | - | 0.0551 | -| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | ctc_greedy_search | 16, -1 | - | 0.0629 | -| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | ctc_prefix_beam_search | 16, -1 | - | 0.0629 | -| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | attention_rescoring | 16, -1 | - | 0.0544 | +| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | attention | 16, -1 | - | 0.056102 | +| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | ctc_greedy_search | 16, -1 | - | 0.058160 | +| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | ctc_prefix_beam_search | 16, -1 | - | 0.058160 | +| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug | test | attention_rescoring | 16, -1 | - | 0.051968 | + + +## Conformer +paddle version: 2.2.2 +paddlespeech version: 1.0.1 +| Model | Params | Config | Augmentation| Test set | Decode method | Loss | CER | +| --- | --- | --- | --- | --- | --- | --- | --- | +| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | attention | - | 0.0522 | +| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | ctc_greedy_search | - | 0.0481 | +| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | ctc_prefix_beam_search | - | 0.0480 | +| conformer | 47.07M | conf/conformer.yaml | spec_aug | test | attention_rescoring | - | 0.0460 | ## Transformer diff --git a/examples/aishell/asr1/conf/chunk_roformer.yaml b/examples/aishell/asr1/conf/chunk_roformer.yaml new file mode 100644 index 00000000..a4051a02 --- /dev/null +++ b/examples/aishell/asr1/conf/chunk_roformer.yaml @@ -0,0 +1,98 @@ +############################################ +# Network Architecture # +############################################ +cmvn_file: +cmvn_file_type: "json" +# encoder related +encoder: conformer +encoder_conf: + output_size: 256 # dimension of attention + attention_heads: 4 + linear_units: 2048 # the number of units of position-wise feed forward + num_blocks: 12 # the number of encoder blocks + dropout_rate: 0.1 # sublayer output dropout + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8 + normalize_before: True + cnn_module_kernel: 15 + use_cnn_module: True + activation_type: 'swish' + pos_enc_layer_type: 'rope_pos' # abs_pos, rel_pos, rope_pos + selfattention_layer_type: 'rel_selfattn' # unused + causal: true + use_dynamic_chunk: true + cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster + use_dynamic_left_chunk: false +# decoder related +decoder: transformer # transformer, bitransformer +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + r_num_blocks: 0 # only for bitransformer + dropout_rate: 0.1 # sublayer output dropout + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 +# hybrid CTC/attention +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + reverse_weight: 0.0 # only for bitransformer + length_normalized_loss: false + init_type: 'kaiming_uniform' # !Warning: need to convergence + +########################################### +# Data # +########################################### + +train_manifest: data/manifest.train +dev_manifest: data/manifest.dev +test_manifest: data/manifest.test + + +########################################### +# Dataloader # +########################################### + +vocab_filepath: data/lang_char/vocab.txt +spm_model_prefix: '' +unit_type: 'char' +preprocess_config: conf/preprocess.yaml +feat_dim: 80 +stride_ms: 10.0 +window_ms: 25.0 +sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs +batch_size: 32 +maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced +maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced +minibatches: 0 # for debug +batch_count: auto +batch_bins: 0 +batch_frames_in: 0 +batch_frames_out: 0 +batch_frames_inout: 0 +num_workers: 2 +subsampling_factor: 1 +num_encs: 1 + +########################################### +# Training # +########################################### +n_epoch: 240 +accum_grad: 1 +global_grad_clip: 5.0 +dist_sampler: True +optim: adam +optim_conf: + lr: 0.001 + weight_decay: 1.0e-6 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 25000 + lr_decay: 1.0 +log_interval: 100 +checkpoint: + kbest_n: 50 + latest_n: 5 diff --git a/examples/aishell/asr1/conf/chunk_roformer_bidecoder.yaml b/examples/aishell/asr1/conf/chunk_roformer_bidecoder.yaml new file mode 100644 index 00000000..aa3a0aca --- /dev/null +++ b/examples/aishell/asr1/conf/chunk_roformer_bidecoder.yaml @@ -0,0 +1,98 @@ +############################################ +# Network Architecture # +############################################ +cmvn_file: +cmvn_file_type: "json" +# encoder related +encoder: conformer +encoder_conf: + output_size: 256 # dimension of attention + attention_heads: 4 + linear_units: 2048 # the number of units of position-wise feed forward + num_blocks: 12 # the number of encoder blocks + dropout_rate: 0.1 # sublayer output dropout + positional_dropout_rate: 0.1 + attention_dropout_rate: 0.0 + input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8 + normalize_before: True + cnn_module_kernel: 15 + use_cnn_module: True + activation_type: 'swish' + pos_enc_layer_type: 'rope_pos' # abs_pos, rel_pos, rope_pos + selfattention_layer_type: 'rel_selfattn' # unused + causal: true + use_dynamic_chunk: true + cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster + use_dynamic_left_chunk: false +# decoder related +decoder: bitransformer # transformer, bitransformer +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 3 + r_num_blocks: 3 # only for bitransformer + dropout_rate: 0.1 # sublayer output dropout + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 +# hybrid CTC/attention +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + reverse_weight: 0.3 # only for bitransformer + length_normalized_loss: false + init_type: 'kaiming_uniform' # !Warning: need to convergence + +########################################### +# Data # +########################################### + +train_manifest: data/manifest.train +dev_manifest: data/manifest.dev +test_manifest: data/manifest.test + + +########################################### +# Dataloader # +########################################### + +vocab_filepath: data/lang_char/vocab.txt +spm_model_prefix: '' +unit_type: 'char' +preprocess_config: conf/preprocess.yaml +feat_dim: 80 +stride_ms: 10.0 +window_ms: 25.0 +sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs +batch_size: 32 +maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced +maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced +minibatches: 0 # for debug +batch_count: auto +batch_bins: 0 +batch_frames_in: 0 +batch_frames_out: 0 +batch_frames_inout: 0 +num_workers: 2 +subsampling_factor: 1 +num_encs: 1 + +########################################### +# Training # +########################################### +n_epoch: 240 +accum_grad: 1 +global_grad_clip: 5.0 +dist_sampler: True +optim: adam +optim_conf: + lr: 0.001 + weight_decay: 1.0e-6 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 25000 + lr_decay: 1.0 +log_interval: 100 +checkpoint: + kbest_n: 50 + latest_n: 5 diff --git a/examples/aishell/asr1/conf/chunk_squeezeformer.yaml b/examples/aishell/asr1/conf/chunk_squeezeformer.yaml new file mode 100644 index 00000000..35a90b7d --- /dev/null +++ b/examples/aishell/asr1/conf/chunk_squeezeformer.yaml @@ -0,0 +1,98 @@ +############################################ +# Network Architecture # +############################################ +cmvn_file: +cmvn_file_type: "json" +# encoder related +encoder: squeezeformer +encoder_conf: + encoder_dim: 256 # dimension of attention + output_size: 256 # dimension of output + attention_heads: 4 + num_blocks: 12 # the number of encoder blocks + reduce_idx: 5 + recover_idx: 11 + feed_forward_expansion_factor: 8 + input_dropout_rate: 0.1 + feed_forward_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + adaptive_scale: true + cnn_module_kernel: 31 + normalize_before: false + activation_type: 'swish' + pos_enc_layer_type: 'rel_pos' + time_reduction_layer_type: 'stream' + causal: true + use_dynamic_chunk: true + use_dynamic_left_chunk: false + +# decoder related +decoder: transformer +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 # sublayer output dropout + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 +# hybrid CTC/attention +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + init_type: 'kaiming_uniform' # !Warning: need to convergence + +########################################### +# Data # +########################################### + +train_manifest: data/manifest.train +dev_manifest: data/manifest.dev +test_manifest: data/manifest.test + + +########################################### +# Dataloader # +########################################### + +vocab_filepath: data/lang_char/vocab.txt +spm_model_prefix: '' +unit_type: 'char' +preprocess_config: conf/preprocess.yaml +feat_dim: 80 +stride_ms: 10.0 +window_ms: 25.0 +sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs +batch_size: 32 +maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced +maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced +minibatches: 0 # for debug +batch_count: auto +batch_bins: 0 +batch_frames_in: 0 +batch_frames_out: 0 +batch_frames_inout: 0 +num_workers: 2 +subsampling_factor: 1 +num_encs: 1 + +########################################### +# Training # +########################################### +n_epoch: 240 +accum_grad: 1 +global_grad_clip: 5.0 +dist_sampler: True +optim: adam +optim_conf: + lr: 0.001 + weight_decay: 1.0e-6 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 25000 + lr_decay: 1.0 +log_interval: 100 +checkpoint: + kbest_n: 50 + latest_n: 5 diff --git a/examples/aishell/asr1/conf/squeezeformer.yaml b/examples/aishell/asr1/conf/squeezeformer.yaml new file mode 100644 index 00000000..b7841aca --- /dev/null +++ b/examples/aishell/asr1/conf/squeezeformer.yaml @@ -0,0 +1,93 @@ +############################################ +# Network Architecture # +############################################ +cmvn_file: +cmvn_file_type: "json" +# encoder related +encoder: squeezeformer +encoder_conf: + encoder_dim: 256 # dimension of attention + output_size: 256 # dimension of output + attention_heads: 4 + num_blocks: 12 # the number of encoder blocks + reduce_idx: 5 + recover_idx: 11 + feed_forward_expansion_factor: 8 + input_dropout_rate: 0.1 + feed_forward_dropout_rate: 0.1 + attention_dropout_rate: 0.1 + adaptive_scale: true + cnn_module_kernel: 31 + normalize_before: false + activation_type: 'swish' + pos_enc_layer_type: 'rel_pos' + time_reduction_layer_type: 'conv1d' + +# decoder related +decoder: transformer +decoder_conf: + attention_heads: 4 + linear_units: 2048 + num_blocks: 6 + dropout_rate: 0.1 + positional_dropout_rate: 0.1 + self_attention_dropout_rate: 0.0 + src_attention_dropout_rate: 0.0 + +# hybrid CTC/attention +model_conf: + ctc_weight: 0.3 + lsm_weight: 0.1 # label smoothing option + length_normalized_loss: false + init_type: 'kaiming_uniform' # !Warning: need to convergence + +########################################### +# Data # +########################################### +train_manifest: data/manifest.train +dev_manifest: data/manifest.dev +test_manifest: data/manifest.test + +########################################### +# Dataloader # +########################################### +vocab_filepath: data/lang_char/vocab.txt +spm_model_prefix: '' +unit_type: 'char' +preprocess_config: conf/preprocess.yaml +feat_dim: 80 +stride_ms: 10.0 +window_ms: 25.0 +sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs +batch_size: 32 +maxlen_in: 512 # if input length > maxlen-in, batchsize is automatically reduced +maxlen_out: 150 # if output length > maxlen-out, batchsize is automatically reduced +minibatches: 0 # for debug +batch_count: auto +batch_bins: 0 +batch_frames_in: 0 +batch_frames_out: 0 +batch_frames_inout: 0 +num_workers: 2 +subsampling_factor: 1 +num_encs: 1 + +########################################### +# Training # +########################################### +n_epoch: 150 +accum_grad: 8 +global_grad_clip: 5.0 +dist_sampler: False +optim: adam +optim_conf: + lr: 0.002 + weight_decay: 1.0e-6 +scheduler: warmuplr +scheduler_conf: + warmup_steps: 25000 + lr_decay: 1.0 +log_interval: 100 +checkpoint: + kbest_n: 50 + latest_n: 5 diff --git a/examples/aishell/asr1/local/test.sh b/examples/aishell/asr1/local/test.sh index 26926b4a..8487e990 100755 --- a/examples/aishell/asr1/local/test.sh +++ b/examples/aishell/asr1/local/test.sh @@ -1,15 +1,21 @@ #!/bin/bash -if [ $# != 3 ];then - echo "usage: ${0} config_path decode_config_path ckpt_path_prefix" - exit -1 -fi +set -e stage=0 stop_stage=100 + +source utils/parse_options.sh || exit 1; + ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') echo "using $ngpu gpus..." + +if [ $# != 3 ];then + echo "usage: ${0} config_path decode_config_path ckpt_path_prefix" + exit -1 +fi + config_path=$1 decode_config_path=$2 ckpt_prefix=$3 @@ -92,6 +98,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then fi if [ ${stage} -le 101 ] && [ ${stop_stage} -ge 101 ]; then + echo "using sclite to compute cer..." # format the reference test file for sclite python utils/format_rsl.py \ --origin_ref data/manifest.test.raw \ diff --git a/examples/aishell/asr1/local/train.sh b/examples/aishell/asr1/local/train.sh index bfa8dd97..3d4f052a 100755 --- a/examples/aishell/asr1/local/train.sh +++ b/examples/aishell/asr1/local/train.sh @@ -17,7 +17,7 @@ if [ ${seed} != 0 ]; then echo "using seed $seed & FLAGS_cudnn_deterministic=True ..." fi -if [ $# -lt 2 ] && [ $# -gt 3 ];then +if [ $# -lt 2 ] || [ $# -gt 3 ];then echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)" exit -1 fi diff --git a/examples/aishell/asr3/local/train.sh b/examples/aishell/asr3/local/train.sh index e51e3d34..33fef0fd 100755 --- a/examples/aishell/asr3/local/train.sh +++ b/examples/aishell/asr3/local/train.sh @@ -1,6 +1,6 @@ #!/bin/bash -if [ $# -lt 2 ] && [ $# -gt 3 ];then +if [ $# -lt 2 ] || [ $# -gt 3 ];then echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)" exit -1 fi diff --git a/examples/aishell3/tts3/README.md b/examples/aishell3/tts3/README.md index 49801c4c..c33d665c 100644 --- a/examples/aishell3/tts3/README.md +++ b/examples/aishell3/tts3/README.md @@ -241,7 +241,7 @@ fastspeech2_aishell3_ckpt_1.1.0 ├── speaker_id_map.txt # speaker id map file when training a multi-speaker fastspeech2 └── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2 ``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained fastspeech2 and parallel wavegan models. +You can use the following scripts to synthesize for `${BIN_DIR}/../../assets/sentences.txt` using pretrained fastspeech2 and parallel wavegan models. ```bash source path.sh @@ -257,7 +257,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \ --voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=exp/default/test_e2e \ --phones_dict=fastspeech2_aishell3_ckpt_1.1.0/phone_id_map.txt \ --speaker_dict=fastspeech2_aishell3_ckpt_1.1.0/speaker_id_map.txt \ diff --git a/examples/aishell3/tts3/local/inference.sh b/examples/aishell3/tts3/local/inference.sh index dc05ec59..2d096bdc 100755 --- a/examples/aishell3/tts3/local/inference.sh +++ b/examples/aishell3/tts3/local/inference.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_aishell3 \ --voc=pwgan_aishell3 \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -22,7 +22,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_aishell3 \ --voc=hifigan_aishell3 \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ diff --git a/examples/aishell3/tts3/local/lite_predict.sh b/examples/aishell3/tts3/local/lite_predict.sh index e77e8b6c..2534b460 100755 --- a/examples/aishell3/tts3/local/lite_predict.sh +++ b/examples/aishell3/tts3/local/lite_predict.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/pdlite \ --am=fastspeech2_aishell3 \ --voc=pwgan_aishell3 \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -24,7 +24,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/pdlite \ --am=fastspeech2_aishell3 \ --voc=hifigan_aishell3 \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ diff --git a/examples/aishell3/tts3/local/ort_predict.sh b/examples/aishell3/tts3/local/ort_predict.sh index 24e66f68..9c41dee3 100755 --- a/examples/aishell3/tts3/local/ort_predict.sh +++ b/examples/aishell3/tts3/local/ort_predict.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=fastspeech2_aishell3 \ --voc=pwgan_aishell3 \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 \ @@ -24,7 +24,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=fastspeech2_aishell3 \ --voc=hifigan_aishell3 \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 \ diff --git a/examples/aishell3/tts3/local/synthesize_e2e.sh b/examples/aishell3/tts3/local/synthesize_e2e.sh index 158350ae..2cc22ede 100755 --- a/examples/aishell3/tts3/local/synthesize_e2e.sh +++ b/examples/aishell3/tts3/local/synthesize_e2e.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \ --voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -44,7 +44,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=hifigan_aishell3_ckpt_0.2.0/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_aishell3_ckpt_0.2.0/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ diff --git a/examples/aishell3/tts3/run.sh b/examples/aishell3/tts3/run.sh index b5da076b..8dcecaa0 100755 --- a/examples/aishell3/tts3/run.sh +++ b/examples/aishell3/tts3/run.sh @@ -43,10 +43,7 @@ fi if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_aishell3 # considering the balance between speed and quality, we recommend that you use hifigan as vocoder ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_aishell3 diff --git a/examples/aishell3/vits/README.md b/examples/aishell3/vits/README.md index dc80e18b..8c19e29f 100644 --- a/examples/aishell3/vits/README.md +++ b/examples/aishell3/vits/README.md @@ -196,7 +196,7 @@ python3 ${BIN_DIR}/synthesize_e2e.py \ --phones_dict=vits_aishell3_ckpt_1.1.0/phone_id_map.txt \ --speaker_dict=vits_aishell3_ckpt_1.1.0/speaker_id_map.txt \ --output_dir=exp/default/test_e2e \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --add-blank=${add_blank} ``` --> diff --git a/examples/aishell3/vits/local/synthesize_e2e.sh b/examples/aishell3/vits/local/synthesize_e2e.sh index 1bd58549..5369cbf9 100755 --- a/examples/aishell3/vits/local/synthesize_e2e.sh +++ b/examples/aishell3/vits/local/synthesize_e2e.sh @@ -20,6 +20,6 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --speaker_dict=dump/speaker_id_map.txt \ --spk_id=0 \ --output_dir=${train_output_path}/test_e2e \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --add-blank=${add_blank} fi diff --git a/examples/canton/tts3/README.md b/examples/canton/tts3/README.md index f46949d2..87ef4090 100644 --- a/examples/canton/tts3/README.md +++ b/examples/canton/tts3/README.md @@ -102,7 +102,7 @@ Download the pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip]( unzip pwg_aishell3_ckpt_0.5.zip ``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences_canton.txt` using pretrained fastspeech2 and parallel wavegan models. +You can use the following scripts to synthesize for `${BIN_DIR}/../../assets/sentences_canton.txt` using pretrained fastspeech2 and parallel wavegan models. ```bash source path.sh @@ -118,7 +118,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \ --voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \ --lang=canton \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=exp/default/test_e2e \ --phones_dict=fastspeech2_canton_ckpt_1.4.0/phone_id_map.txt \ --speaker_dict=fastspeech2_canton_ckpt_1.4.0/speaker_id_map.txt \ diff --git a/examples/canton/tts3/local/inference.sh b/examples/canton/tts3/local/inference.sh index caf0b438..ad3af2d0 100755 --- a/examples/canton/tts3/local/inference.sh +++ b/examples/canton/tts3/local/inference.sh @@ -12,7 +12,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=fastspeech2_canton \ --voc=pwgan_aishell3 \ --spk_id=10 \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -27,7 +27,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=fastspeech2_canton \ --voc=mb_melgan_csmsc \ --spk_id=10 \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -41,7 +41,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=fastspeech2_canton \ --voc=hifigan_csmsc \ --spk_id=10 \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -55,7 +55,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --am=fastspeech2_canton \ --voc=wavernn_csmsc \ --spk_id=10 \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ diff --git a/examples/canton/tts3/local/ort_predict.sh b/examples/canton/tts3/local/ort_predict.sh index d95e49f9..edbe0406 100755 --- a/examples/canton/tts3/local/ort_predict.sh +++ b/examples/canton/tts3/local/ort_predict.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc=pwgan_aishell3 \ --spk_id=10 \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ --lang=canton \ @@ -26,7 +26,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc=mb_melgan_csmsc \ --spk_id=10 \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ --lang=canton \ @@ -40,7 +40,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=fastspeech2_canton \ --voc=hifigan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ --lang=canton \ diff --git a/examples/canton/tts3/local/synthesize_e2e.sh b/examples/canton/tts3/local/synthesize_e2e.sh index 8cf7eb22..38b7e1af 100755 --- a/examples/canton/tts3/local/synthesize_e2e.sh +++ b/examples/canton/tts3/local/synthesize_e2e.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_aishell3_ckpt_0.5/snapshot_iter_1000000.pdz \ --voc_stat=pwg_aishell3_ckpt_0.5/feats_stats.npy \ --lang=canton \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ @@ -44,7 +44,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=hifigan_aishell3_ckpt_0.2.0/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_aishell3_ckpt_0.2.0/feats_stats.npy \ --lang=canton \ - --text=${BIN_DIR}/../sentences_canton.txt \ + --text=${BIN_DIR}/../../assets/sentences_canton.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --speaker_dict=dump/speaker_id_map.txt \ diff --git a/examples/canton/tts3/run.sh b/examples/canton/tts3/run.sh index 3a3dfe0a..acfc5022 100755 --- a/examples/canton/tts3/run.sh +++ b/examples/canton/tts3/run.sh @@ -46,10 +46,7 @@ fi # we have only tested the following models so far if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade ../../csmsc/tts3/local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_canton # considering the balance between speed and quality, we recommend that you use hifigan as vocoder # ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc diff --git a/examples/csmsc/jets/README.md b/examples/csmsc/jets/README.md new file mode 100644 index 00000000..07dade0e --- /dev/null +++ b/examples/csmsc/jets/README.md @@ -0,0 +1,108 @@ +# JETS with CSMSC +This example contains code used to train a [JETS](https://arxiv.org/abs/2203.16852v1) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). + +## Dataset +### Download and Extract +Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source). + +### Get MFA Result and Extract +We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes and durations for JETS. +You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo. + +## Get Started +Assume the path to the dataset is `~/datasets/BZNSYP`. +Assume the path to the MFA result of CSMSC is `./baker_alignment_tone`. +Run the command below to +1. **source path**. +2. preprocess the dataset. +3. train the model. +4. synthesize wavs. + - synthesize waveform from `metadata.jsonl`. + - synthesize waveform from a text file. + +```bash +./run.sh +``` +You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset. +```bash +./run.sh --stage 0 --stop-stage 0 +``` +### Data Preprocessing +```bash +./local/preprocess.sh ${conf_path} +``` +When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below. + +```text +dump +├── dev +│   ├── norm +│   └── raw +├── phone_id_map.txt +├── speaker_id_map.txt +├── test +│   ├── norm +│   └── raw +└── train + ├── feats_stats.npy + ├── norm + └── raw +``` +The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains wave、mel spectrogram、speech、pitch and energy features of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/feats_stats.npy`. + +Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, the path of feats, feats_lengths, the path of pitch features, the path of energy features, the path of raw waves, speaker, and the id of each utterance. + +### Model Training +```bash +CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} +``` +`./local/train.sh` calls `${BIN_DIR}/train.py`. +Here's the complete help message. +```text +usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA] + [--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR] + [--ngpu NGPU] [--phones-dict PHONES_DICT] + +Train a JETS model. + +optional arguments: + -h, --help show this help message and exit + --config CONFIG config file to overwrite default config. + --train-metadata TRAIN_METADATA + training data. + --dev-metadata DEV_METADATA + dev data. + --output-dir OUTPUT_DIR + output dir. + --ngpu NGPU if ngpu == 0, use cpu. + --phones-dict PHONES_DICT + phone vocabulary file. +``` +1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`. +2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder. +3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory. +4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. +5. `--phones-dict` is the path of the phone vocabulary file. + +### Synthesizing + +`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`. + +```bash +CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} +``` + +`./local/synthesize_e2e.sh` calls `${BIN_DIR}/synthesize_e2e.py`, which can synthesize waveform from text file. +```bash +CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} +``` + +## Pretrained Model + +The pretrained model can be downloaded here: + +- [jets_csmsc_ckpt_1.5.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/jets_csmsc_ckpt_1.5.0.zip) + +The static model can be downloaded here: + +- [jets_csmsc_static_1.5.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/jets_csmsc_static_1.5.0.zip) diff --git a/examples/csmsc/jets/conf/default.yaml b/examples/csmsc/jets/conf/default.yaml new file mode 100644 index 00000000..1dafd20c --- /dev/null +++ b/examples/csmsc/jets/conf/default.yaml @@ -0,0 +1,224 @@ +# This configuration tested on 4 GPUs (V100) with 32GB GPU +# memory. It takes around 2 weeks to finish the training +# but 100k iters model should generate reasonable results. +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### + +n_mels: 80 +fs: 22050 # sr +n_fft: 1024 # FFT size (samples). +n_shift: 256 # Hop size (samples). 12.5ms +win_length: null # Window length (samples). 50ms + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. +fmin: 0 # minimum frequency for Mel basis +fmax: null # maximum frequency for Mel basis +f0min: 80 # Minimum f0 for pitch extraction. +f0max: 400 # Maximum f0 for pitch extraction. + + +########################################################## +# TTS MODEL SETTING # +########################################################## +model: + # generator related + generator_type: jets_generator + generator_params: + adim: 256 # attention dimension + aheads: 2 # number of attention heads + elayers: 4 # number of encoder layers + eunits: 1024 # number of encoder ff units + dlayers: 4 # number of decoder layers + dunits: 1024 # number of decoder ff units + positionwise_layer_type: conv1d # type of position-wise layer + positionwise_conv_kernel_size: 3 # kernel size of position wise conv layer + duration_predictor_layers: 2 # number of layers of duration predictor + duration_predictor_chans: 256 # number of channels of duration predictor + duration_predictor_kernel_size: 3 # filter size of duration predictor + use_masking: True # whether to apply masking for padded part in loss calculation + encoder_normalize_before: True # whether to perform layer normalization before the input + decoder_normalize_before: True # whether to perform layer normalization before the input + encoder_type: transformer # encoder type + decoder_type: transformer # decoder type + conformer_rel_pos_type: latest # relative positional encoding type + conformer_pos_enc_layer_type: rel_pos # conformer positional encoding type + conformer_self_attn_layer_type: rel_selfattn # conformer self-attention type + conformer_activation_type: swish # conformer activation type + use_macaron_style_in_conformer: true # whether to use macaron style in conformer + use_cnn_in_conformer: true # whether to use CNN in conformer + conformer_enc_kernel_size: 7 # kernel size in CNN module of conformer-based encoder + conformer_dec_kernel_size: 31 # kernel size in CNN module of conformer-based decoder + init_type: xavier_uniform # initialization type + init_enc_alpha: 1.0 # initial value of alpha for encoder + init_dec_alpha: 1.0 # initial value of alpha for decoder + transformer_enc_dropout_rate: 0.2 # dropout rate for transformer encoder layer + transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding + transformer_enc_attn_dropout_rate: 0.2 # dropout rate for transformer encoder attention layer + transformer_dec_dropout_rate: 0.2 # dropout rate for transformer decoder layer + transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding + transformer_dec_attn_dropout_rate: 0.2 # dropout rate for transformer decoder attention layer + pitch_predictor_layers: 5 # number of conv layers in pitch predictor + pitch_predictor_chans: 256 # number of channels of conv layers in pitch predictor + pitch_predictor_kernel_size: 5 # kernel size of conv leyers in pitch predictor + pitch_predictor_dropout: 0.5 # dropout rate in pitch predictor + pitch_embed_kernel_size: 1 # kernel size of conv embedding layer for pitch + pitch_embed_dropout: 0.0 # dropout rate after conv embedding layer for pitch + stop_gradient_from_pitch_predictor: true # whether to stop the gradient from pitch predictor to encoder + energy_predictor_layers: 2 # number of conv layers in energy predictor + energy_predictor_chans: 256 # number of channels of conv layers in energy predictor + energy_predictor_kernel_size: 3 # kernel size of conv leyers in energy predictor + energy_predictor_dropout: 0.5 # dropout rate in energy predictor + energy_embed_kernel_size: 1 # kernel size of conv embedding layer for energy + energy_embed_dropout: 0.0 # dropout rate after conv embedding layer for energy + stop_gradient_from_energy_predictor: false # whether to stop the gradient from energy predictor to encoder + generator_out_channels: 1 + generator_channels: 512 + generator_global_channels: -1 + generator_kernel_size: 7 + generator_upsample_scales: [8, 8, 2, 2] + generator_upsample_kernel_sizes: [16, 16, 4, 4] + generator_resblock_kernel_sizes: [3, 7, 11] + generator_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + generator_use_additional_convs: true + generator_bias: true + generator_nonlinear_activation: "leakyrelu" + generator_nonlinear_activation_params: + negative_slope: 0.1 + generator_use_weight_norm: true + segment_size: 64 # segment size for random windowed discriminator + + # discriminator related + discriminator_type: hifigan_multi_scale_multi_period_discriminator + discriminator_params: + scales: 1 + scale_downsample_pooling: "AvgPool1D" + scale_downsample_pooling_params: + kernel_size: 4 + stride: 2 + padding: 2 + scale_discriminator_params: + in_channels: 1 + out_channels: 1 + kernel_sizes: [15, 41, 5, 3] + channels: 128 + max_downsample_channels: 1024 + max_groups: 16 + bias: True + downsample_scales: [2, 2, 4, 4, 1] + nonlinear_activation: "leakyrelu" + nonlinear_activation_params: + negative_slope: 0.1 + use_weight_norm: True + use_spectral_norm: False + follow_official_norm: False + periods: [2, 3, 5, 7, 11] + period_discriminator_params: + in_channels: 1 + out_channels: 1 + kernel_sizes: [5, 3] + channels: 32 + downsample_scales: [3, 3, 3, 3, 1] + max_downsample_channels: 1024 + bias: True + nonlinear_activation: "leakyrelu" + nonlinear_activation_params: + negative_slope: 0.1 + use_weight_norm: True + use_spectral_norm: False + # others + sampling_rate: 22050 # needed in the inference for saving wav + cache_generator_outputs: True # whether to cache generator outputs in the training +use_alignment_module: False # whether to use alignment module + +########################################################### +# LOSS SETTING # +########################################################### +# loss function related +generator_adv_loss_params: + average_by_discriminators: False # whether to average loss value by #discriminators + loss_type: mse # loss type, "mse" or "hinge" +discriminator_adv_loss_params: + average_by_discriminators: False # whether to average loss value by #discriminators + loss_type: mse # loss type, "mse" or "hinge" +feat_match_loss_params: + average_by_discriminators: False # whether to average loss value by #discriminators + average_by_layers: False # whether to average loss value by #layers of each discriminator + include_final_outputs: True # whether to include final outputs for loss calculation +mel_loss_params: + fs: 22050 # must be the same as the training data + fft_size: 1024 # fft points + hop_size: 256 # hop size + win_length: null # window length + window: hann # window type + num_mels: 80 # number of Mel basis + fmin: 0 # minimum frequency for Mel basis + fmax: null # maximum frequency for Mel basis + log_base: null # null represent natural log + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_adv: 1.0 # loss scaling coefficient for adversarial loss +lambda_mel: 45.0 # loss scaling coefficient for Mel loss +lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss +lambda_var: 1.0 # loss scaling coefficient for duration loss +lambda_align: 2.0 # loss scaling coefficient for KL divergence loss +# others +sampling_rate: 22050 # needed in the inference for saving wav +cache_generator_outputs: True # whether to cache generator outputs in the training + + +# extra module for additional inputs +pitch_extract: dio # pitch extractor type +pitch_extract_conf: + reduction_factor: 1 + use_token_averaged_f0: false +pitch_normalize: global_mvn # normalizer for the pitch feature +energy_extract: energy # energy extractor type +energy_extract_conf: + reduction_factor: 1 + use_token_averaged_energy: false +energy_normalize: global_mvn # normalizer for the energy feature + + +########################################################### +# DATA LOADER SETTING # +########################################################### +batch_size: 32 # Batch size. +num_workers: 4 # Number of workers in DataLoader. + +########################################################## +# OPTIMIZER & SCHEDULER SETTING # +########################################################## +# optimizer setting for generator +generator_optimizer_params: + beta1: 0.8 + beta2: 0.99 + epsilon: 1.0e-9 + weight_decay: 0.0 +generator_scheduler: exponential_decay +generator_scheduler_params: + learning_rate: 2.0e-4 + gamma: 0.999875 + +# optimizer setting for discriminator +discriminator_optimizer_params: + beta1: 0.8 + beta2: 0.99 + epsilon: 1.0e-9 + weight_decay: 0.0 +discriminator_scheduler: exponential_decay +discriminator_scheduler_params: + learning_rate: 2.0e-4 + gamma: 0.999875 +generator_first: True # whether to start updating generator first + +########################################################## +# OTHER TRAINING SETTING # +########################################################## +num_snapshots: 10 # max number of snapshots to keep while training +train_max_steps: 350000 # Number of training steps. == total_iters / ngpus, total_iters = 1000000 +save_interval_steps: 1000 # Interval steps to save checkpoint. +eval_interval_steps: 250 # Interval steps to evaluate the network. +seed: 777 # random seed number diff --git a/examples/csmsc/jets/local/inference.sh b/examples/csmsc/jets/local/inference.sh new file mode 100755 index 00000000..987f4cea --- /dev/null +++ b/examples/csmsc/jets/local/inference.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +train_output_path=$1 + +stage=0 +stop_stage=0 + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=jets_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt +fi diff --git a/examples/csmsc/jets/local/preprocess.sh b/examples/csmsc/jets/local/preprocess.sh new file mode 100755 index 00000000..60053131 --- /dev/null +++ b/examples/csmsc/jets/local/preprocess.sh @@ -0,0 +1,77 @@ +#!/bin/bash +set -e +stage=0 +stop_stage=100 + +config_path=$1 + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + # get durations from MFA's result + echo "Generate durations.txt from MFA results ..." + python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \ + --inputdir=./baker_alignment_tone \ + --output=durations.txt \ + --config=${config_path} +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # extract features + echo "Extract features ..." + python3 ${BIN_DIR}/preprocess.py \ + --dataset=baker \ + --rootdir=~/datasets/BZNSYP/ \ + --dumpdir=dump \ + --dur-file=durations.txt \ + --config=${config_path} \ + --num-cpu=20 \ + --cut-sil=True \ + --token_average=True +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # get features' stats(mean and std) + echo "Get features' stats ..." + python3 ${MAIN_ROOT}/utils/compute_statistics.py \ + --metadata=dump/train/raw/metadata.jsonl \ + --field-name="feats" + + python3 ${MAIN_ROOT}/utils/compute_statistics.py \ + --metadata=dump/train/raw/metadata.jsonl \ + --field-name="pitch" + + python3 ${MAIN_ROOT}/utils/compute_statistics.py \ + --metadata=dump/train/raw/metadata.jsonl \ + --field-name="energy" + +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # normalize and covert phone/speaker to id, dev and test should use train's stats + echo "Normalize ..." + python3 ${BIN_DIR}/normalize.py \ + --metadata=dump/train/raw/metadata.jsonl \ + --dumpdir=dump/train/norm \ + --feats-stats=dump/train/feats_stats.npy \ + --pitch-stats=dump/train/pitch_stats.npy \ + --energy-stats=dump/train/energy_stats.npy \ + --phones-dict=dump/phone_id_map.txt \ + --speaker-dict=dump/speaker_id_map.txt + + python3 ${BIN_DIR}/normalize.py \ + --metadata=dump/dev/raw/metadata.jsonl \ + --dumpdir=dump/dev/norm \ + --feats-stats=dump/train/feats_stats.npy \ + --pitch-stats=dump/train/pitch_stats.npy \ + --energy-stats=dump/train/energy_stats.npy \ + --phones-dict=dump/phone_id_map.txt \ + --speaker-dict=dump/speaker_id_map.txt + + python3 ${BIN_DIR}/normalize.py \ + --metadata=dump/test/raw/metadata.jsonl \ + --dumpdir=dump/test/norm \ + --feats-stats=dump/train/feats_stats.npy \ + --pitch-stats=dump/train/pitch_stats.npy \ + --energy-stats=dump/train/energy_stats.npy \ + --phones-dict=dump/phone_id_map.txt \ + --speaker-dict=dump/speaker_id_map.txt +fi diff --git a/examples/csmsc/jets/local/synthesize.sh b/examples/csmsc/jets/local/synthesize.sh new file mode 100755 index 00000000..a4b35ec0 --- /dev/null +++ b/examples/csmsc/jets/local/synthesize.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 +stage=0 +stop_stage=0 + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + FLAGS_fraction_of_gpu_memory_to_use=0.01 \ + python3 ${BIN_DIR}/synthesize.py \ + --config=${config_path} \ + --ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --phones_dict=dump/phone_id_map.txt \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test +fi diff --git a/examples/csmsc/jets/local/synthesize_e2e.sh b/examples/csmsc/jets/local/synthesize_e2e.sh new file mode 100755 index 00000000..c95354d8 --- /dev/null +++ b/examples/csmsc/jets/local/synthesize_e2e.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 + +stage=0 +stop_stage=0 + + +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + FLAGS_fraction_of_gpu_memory_to_use=0.01 \ + python3 ${BIN_DIR}/synthesize_e2e.py \ + --am=jets_csmsc \ + --config=${config_path} \ + --ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --phones_dict=dump/phone_id_map.txt \ + --output_dir=${train_output_path}/test_e2e \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --inference_dir=${train_output_path}/inference +fi diff --git a/examples/csmsc/jets/local/train.sh b/examples/csmsc/jets/local/train.sh new file mode 100755 index 00000000..d1302f99 --- /dev/null +++ b/examples/csmsc/jets/local/train.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 + +python3 ${BIN_DIR}/train.py \ + --train-metadata=dump/train/norm/metadata.jsonl \ + --dev-metadata=dump/dev/norm/metadata.jsonl \ + --config=${config_path} \ + --output-dir=${train_output_path} \ + --ngpu=1 \ + --phones-dict=dump/phone_id_map.txt diff --git a/examples/csmsc/jets/path.sh b/examples/csmsc/jets/path.sh new file mode 100755 index 00000000..73a0af7e --- /dev/null +++ b/examples/csmsc/jets/path.sh @@ -0,0 +1,13 @@ +#!/bin/bash +export MAIN_ROOT=`realpath ${PWD}/../../../` + +export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH} +export LC_ALL=C + +export PYTHONDONTWRITEBYTECODE=1 +# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C +export PYTHONIOENCODING=UTF-8 +export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH} + +MODEL=jets +export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL} diff --git a/examples/csmsc/jets/run.sh b/examples/csmsc/jets/run.sh new file mode 100755 index 00000000..d0985c50 --- /dev/null +++ b/examples/csmsc/jets/run.sh @@ -0,0 +1,41 @@ +#!/bin/bash + +set -e +source path.sh + +gpus=0 +stage=0 +stop_stage=100 + +conf_path=conf/default.yaml +train_output_path=exp/default +ckpt_name=snapshot_iter_150000.pdz + +# 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 0 ] && [ ${stop_stage} -ge 0 ]; then + # prepare data + ./local/preprocess.sh ${conf_path}|| exit -1 +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # train model, all `ckpt` under `train_output_path/checkpoints/` dir + CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1 +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # synthesize_e2e + CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1 +fi + diff --git a/examples/csmsc/tts0/README.md b/examples/csmsc/tts0/README.md index bc7769d1..ce682495 100644 --- a/examples/csmsc/tts0/README.md +++ b/examples/csmsc/tts0/README.md @@ -226,7 +226,7 @@ tacotron2_csmsc_ckpt_0.2.0 ├── snapshot_iter_30600.pdz # model parameters and optimizer states └── speech_stats.npy # statistics used to normalize spectrogram when training Tacotron2 ``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained Tacotron2 and parallel wavegan models. +You can use the following scripts to synthesize for `${BIN_DIR}/../../assets/sentences.txt` using pretrained Tacotron2 and parallel wavegan models. ```bash source path.sh @@ -242,7 +242,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=exp/default/test_e2e \ --inference_dir=exp/default/inference \ --phones_dict=tacotron2_csmsc_ckpt_0.2.0/phone_id_map.txt diff --git a/examples/csmsc/tts0/local/inference.sh b/examples/csmsc/tts0/local/inference.sh index d2960441..6ea2e4b6 100755 --- a/examples/csmsc/tts0/local/inference.sh +++ b/examples/csmsc/tts0/local/inference.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/inference \ --am=tacotron2_csmsc \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -22,7 +22,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/inference \ --am=tacotron2_csmsc \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -33,7 +33,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --inference_dir=${train_output_path}/inference \ --am=tacotron2_csmsc \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi \ No newline at end of file diff --git a/examples/csmsc/tts0/local/synthesize_e2e.sh b/examples/csmsc/tts0/local/synthesize_e2e.sh index 4c3b08dc..40b49aa1 100755 --- a/examples/csmsc/tts0/local/synthesize_e2e.sh +++ b/examples/csmsc/tts0/local/synthesize_e2e.sh @@ -22,7 +22,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -44,7 +44,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -66,7 +66,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt # --inference_dir=${train_output_path}/inference @@ -87,7 +87,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -108,7 +108,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference diff --git a/examples/csmsc/tts2/README.md b/examples/csmsc/tts2/README.md index ec88959d..96956776 100644 --- a/examples/csmsc/tts2/README.md +++ b/examples/csmsc/tts2/README.md @@ -248,7 +248,7 @@ speedyspeech_csmsc_ckpt_0.2.0 ├── snapshot_iter_30600.pdz # model parameters and optimizer states └── tone_id_map.txt # tone vocabulary file when training speedyspeech ``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained speedyspeech and parallel wavegan models. +You can use the following scripts to synthesize for `${BIN_DIR}/../../assets/sentences.txt` using pretrained speedyspeech and parallel wavegan models. ```bash source path.sh @@ -264,7 +264,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=exp/default/test_e2e \ --inference_dir=exp/default/inference \ --phones_dict=speedyspeech_csmsc_ckpt_0.2.0/phone_id_map.txt \ diff --git a/examples/csmsc/tts2/local/inference.sh b/examples/csmsc/tts2/local/inference.sh index ed92136c..9a677edc 100755 --- a/examples/csmsc/tts2/local/inference.sh +++ b/examples/csmsc/tts2/local/inference.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/inference \ --am=speedyspeech_csmsc \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt @@ -24,7 +24,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/inference \ --am=speedyspeech_csmsc \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt @@ -36,7 +36,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --inference_dir=${train_output_path}/inference \ --am=speedyspeech_csmsc \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt diff --git a/examples/csmsc/tts2/local/inference_xpu.sh b/examples/csmsc/tts2/local/inference_xpu.sh new file mode 100644 index 00000000..5d8d9205 --- /dev/null +++ b/examples/csmsc/tts2/local/inference_xpu.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +train_output_path=$1 + +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=speedyspeech_csmsc \ + --voc=pwgan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --device xpu +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=speedyspeech_csmsc \ + --voc=mb_melgan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --device xpu +fi + +# hifigan +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=speedyspeech_csmsc \ + --voc=hifigan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --device xpu +fi diff --git a/examples/csmsc/tts2/local/lite_predict.sh b/examples/csmsc/tts2/local/lite_predict.sh index d0c6c058..9bb33cdf 100755 --- a/examples/csmsc/tts2/local/lite_predict.sh +++ b/examples/csmsc/tts2/local/lite_predict.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/pdlite \ --am=speedyspeech_csmsc \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt @@ -24,7 +24,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/pdlite \ --am=speedyspeech_csmsc \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt @@ -36,7 +36,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --inference_dir=${train_output_path}/pdlite \ --am=speedyspeech_csmsc \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt diff --git a/examples/csmsc/tts2/local/ort_predict.sh b/examples/csmsc/tts2/local/ort_predict.sh index 8ca4c0e9..36f88667 100755 --- a/examples/csmsc/tts2/local/ort_predict.sh +++ b/examples/csmsc/tts2/local/ort_predict.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=speedyspeech_csmsc \ --voc=pwgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ --device=cpu \ @@ -23,7 +23,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=speedyspeech_csmsc \ --voc=mb_melgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ --device=cpu \ @@ -36,7 +36,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=speedyspeech_csmsc \ --voc=hifigan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ --device=cpu \ diff --git a/examples/csmsc/tts2/local/synthesize_e2e.sh b/examples/csmsc/tts2/local/synthesize_e2e.sh index 553b4554..2b278729 100755 --- a/examples/csmsc/tts2/local/synthesize_e2e.sh +++ b/examples/csmsc/tts2/local/synthesize_e2e.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ @@ -43,7 +43,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ @@ -66,7 +66,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt @@ -87,7 +87,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ @@ -109,7 +109,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --tones_dict=dump/tone_id_map.txt \ diff --git a/examples/csmsc/tts2/local/synthesize_e2e_xpu.sh b/examples/csmsc/tts2/local/synthesize_e2e_xpu.sh new file mode 100644 index 00000000..0285f42c --- /dev/null +++ b/examples/csmsc/tts2/local/synthesize_e2e_xpu.sh @@ -0,0 +1,122 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 + +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=pwgan_csmsc \ + --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ + --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ + --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=mb_melgan_csmsc \ + --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ + --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + +# the pretrained models haven't release now +# style melgan +# style melgan's Dygraph to Static Graph is not ready now +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=style_melgan_csmsc \ + --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ + --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 + # --inference_dir=${train_output_path}/inference +fi + +# hifigan +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=hifigan_csmsc \ + --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ + --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + +# wavernn +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + echo "in wavernn syn_e2e" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=wavernn_csmsc \ + --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \ + --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ + --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi diff --git a/examples/csmsc/tts2/local/synthesize_xpu.sh b/examples/csmsc/tts2/local/synthesize_xpu.sh new file mode 100644 index 00000000..801789c2 --- /dev/null +++ b/examples/csmsc/tts2/local/synthesize_xpu.sh @@ -0,0 +1,110 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=pwgan_csmsc \ + --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ + --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ + --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=mb_melgan_csmsc \ + --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ + --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# style melgan +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=style_melgan_csmsc \ + --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ + --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# hifigan +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "in hifigan syn" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=hifigan_csmsc \ + --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ + --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --tones_dict=dump/tone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# wavernn +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + echo "in wavernn syn" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=speedyspeech_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/feats_stats.npy \ + --voc=wavernn_csmsc \ + --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \ + --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ + --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --tones_dict=dump/tone_id_map.txt \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi diff --git a/examples/csmsc/tts2/local/train_xpu.sh b/examples/csmsc/tts2/local/train_xpu.sh new file mode 100644 index 00000000..0c07c27f --- /dev/null +++ b/examples/csmsc/tts2/local/train_xpu.sh @@ -0,0 +1,16 @@ + +#!/bin/bash + +config_path=$1 +train_output_path=$2 + +python ${BIN_DIR}/train.py \ + --train-metadata=dump/train/norm/metadata.jsonl \ + --dev-metadata=dump/dev/norm/metadata.jsonl \ + --config=${config_path} \ + --output-dir=${train_output_path} \ + --ngpu=0 \ + --nxpu=1 \ + --phones-dict=dump/phone_id_map.txt \ + --tones-dict=dump/tone_id_map.txt \ + --use-relative-path=True diff --git a/examples/csmsc/tts2/run.sh b/examples/csmsc/tts2/run.sh index 6279ec57..5732ea3c 100755 --- a/examples/csmsc/tts2/run.sh +++ b/examples/csmsc/tts2/run.sh @@ -45,10 +45,7 @@ fi # we have only tested the following models so far if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx speedyspeech_csmsc # considering the balance between speed and quality, we recommend that you use hifigan as vocoder ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc diff --git a/examples/csmsc/tts2/run_xpu.sh b/examples/csmsc/tts2/run_xpu.sh new file mode 100644 index 00000000..4b867961 --- /dev/null +++ b/examples/csmsc/tts2/run_xpu.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +set -e +source path.sh + +xpus=0,1 +stage=0 +stop_stage=100 + +conf_path=conf/default.yaml +train_output_path=exp/default +ckpt_name=snapshot_iter_76.pdz + +# with the following command, you can choose the stage range you want to run +# such as `./run_xpu.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 0 ] && [ ${stop_stage} -ge 0 ]; then + # prepare data + ./local/preprocess.sh ${conf_path} || exit -1 +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # train model, all `ckpt` under `train_output_path/checkpoints/` dir + FLAGS_selected_xpus=${xpus} ./local/train_xpu.sh ${conf_path} ${train_output_path} || exit -1 +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # synthesize, vocoder is pwgan by default + FLAGS_selected_xpus=${xpus} ./local/synthesize_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # synthesize_e2e, vocoder is pwgan by default + FLAGS_selected_xpus=${xpus} ./local/synthesize_e2e_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + # inference with static model + FLAGS_selected_xpus=${xpus} ./local/inference_xpu.sh ${train_output_path} || exit -1 +fi diff --git a/examples/csmsc/tts3/README.md b/examples/csmsc/tts3/README.md index 39926259..5a097537 100644 --- a/examples/csmsc/tts3/README.md +++ b/examples/csmsc/tts3/README.md @@ -258,7 +258,7 @@ fastspeech2_nosil_baker_ckpt_0.4 ├── snapshot_iter_76000.pdz # model parameters and optimizer states └── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2 ``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained fastspeech2 and parallel wavegan models. +You can use the following scripts to synthesize for `${BIN_DIR}/../../assets/sentences.txt` using pretrained fastspeech2 and parallel wavegan models. If you want to use fastspeech2_conformer, you must delete this line `--inference_dir=exp/default/inference \` to skip the step of dygraph to static graph, cause we haven't tested dygraph to static graph for fastspeech2_conformer till now. ```bash @@ -276,7 +276,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=exp/default/test_e2e \ --inference_dir=exp/default/inference \ --phones_dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt diff --git a/examples/csmsc/tts3/README_cn.md b/examples/csmsc/tts3/README_cn.md index 1829b770..3f2783a9 100644 --- a/examples/csmsc/tts3/README_cn.md +++ b/examples/csmsc/tts3/README_cn.md @@ -248,7 +248,7 @@ fastspeech2_nosil_baker_ckpt_0.4 ├── snapshot_iter_76000.pdz # 模型参数和优化器状态 └── speech_stats.npy # 训练 fastspeech2 时用于规范化频谱图的统计数据 ``` -您可以使用以下脚本通过使用预训练的 fastspeech2 和 parallel wavegan 模型为 `${BIN_DIR}/../sentences.txt` 合成句子 +您可以使用以下脚本通过使用预训练的 fastspeech2 和 parallel wavegan 模型为 `${BIN_DIR}/../../assets/sentences.txt` 合成句子 ```bash source path.sh @@ -264,7 +264,7 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \ --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=exp/default/test_e2e \ --inference_dir=exp/default/inference \ --phones_dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt diff --git a/examples/csmsc/tts3/local/inference.sh b/examples/csmsc/tts3/local/inference.sh index b43fd286..5b143cdd 100755 --- a/examples/csmsc/tts3/local/inference.sh +++ b/examples/csmsc/tts3/local/inference.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -23,7 +23,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -34,7 +34,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -45,7 +45,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --inference_dir=${train_output_path}/inference \ --am=fastspeech2_csmsc \ --voc=wavernn_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt fi \ No newline at end of file diff --git a/examples/csmsc/tts3/local/inference_streaming.sh b/examples/csmsc/tts3/local/inference_streaming.sh index 719f46c6..5ad50aa5 100755 --- a/examples/csmsc/tts3/local/inference_streaming.sh +++ b/examples/csmsc/tts3/local/inference_streaming.sh @@ -12,7 +12,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True @@ -26,7 +26,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True @@ -39,7 +39,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True diff --git a/examples/csmsc/tts3/local/inference_xpu.sh b/examples/csmsc/tts3/local/inference_xpu.sh new file mode 100644 index 00000000..541dc626 --- /dev/null +++ b/examples/csmsc/tts3/local/inference_xpu.sh @@ -0,0 +1,55 @@ +#!/bin/bash + +train_output_path=$1 + +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=fastspeech2_csmsc \ + --voc=pwgan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --device xpu +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=fastspeech2_csmsc \ + --voc=mb_melgan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --device xpu +fi + +# hifigan +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=fastspeech2_csmsc \ + --voc=hifigan_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --device xpu +fi + +# wavernn +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + python3 ${BIN_DIR}/../inference.py \ + --inference_dir=${train_output_path}/inference \ + --am=fastspeech2_csmsc \ + --voc=wavernn_csmsc \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/pd_infer_out \ + --phones_dict=dump/phone_id_map.txt \ + --device xpu +fi \ No newline at end of file diff --git a/examples/csmsc/tts3/local/lite_predict.sh b/examples/csmsc/tts3/local/lite_predict.sh index 1ed2f108..9af17899 100755 --- a/examples/csmsc/tts3/local/lite_predict.sh +++ b/examples/csmsc/tts3/local/lite_predict.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --inference_dir=${train_output_path}/pdlite \ --am=fastspeech2_csmsc \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -23,7 +23,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --inference_dir=${train_output_path}/pdlite \ --am=fastspeech2_csmsc \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt fi @@ -34,7 +34,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --inference_dir=${train_output_path}/pdlite \ --am=fastspeech2_csmsc \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt fi diff --git a/examples/csmsc/tts3/local/lite_predict_streaming.sh b/examples/csmsc/tts3/local/lite_predict_streaming.sh index 4570cb4e..19fdde41 100755 --- a/examples/csmsc/tts3/local/lite_predict_streaming.sh +++ b/examples/csmsc/tts3/local/lite_predict_streaming.sh @@ -12,7 +12,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=pwgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True @@ -26,7 +26,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=mb_melgan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True @@ -39,7 +39,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=fastspeech2_csmsc \ --am_stat=dump/train/speech_stats.npy \ --voc=hifigan_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True diff --git a/examples/csmsc/tts3/local/ort_predict.sh b/examples/csmsc/tts3/local/ort_predict.sh index e16c7bd0..99955665 100755 --- a/examples/csmsc/tts3/local/ort_predict.sh +++ b/examples/csmsc/tts3/local/ort_predict.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am=fastspeech2_csmsc \ --voc=pwgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 @@ -22,7 +22,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am=fastspeech2_csmsc \ --voc=mb_melgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 @@ -34,7 +34,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am=fastspeech2_csmsc \ --voc=hifigan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_e2e \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 diff --git a/examples/csmsc/tts3/local/ort_predict_streaming.sh b/examples/csmsc/tts3/local/ort_predict_streaming.sh index 74393581..e2c7e852 100755 --- a/examples/csmsc/tts3/local/ort_predict_streaming.sh +++ b/examples/csmsc/tts3/local/ort_predict_streaming.sh @@ -11,7 +11,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --am_stat=dump/train/speech_stats.npy \ --voc=pwgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_streaming \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 \ @@ -25,7 +25,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --am_stat=dump/train/speech_stats.npy \ --voc=mb_melgan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_streaming \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 \ @@ -39,7 +39,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --am_stat=dump/train/speech_stats.npy \ --voc=hifigan_csmsc \ --output_dir=${train_output_path}/onnx_infer_out_streaming \ - --text=${BIN_DIR}/../csmsc_test.txt \ + --text=${BIN_DIR}/../../assets/csmsc_test.txt \ --phones_dict=dump/phone_id_map.txt \ --device=cpu \ --cpu_threads=2 \ diff --git a/examples/csmsc/tts3/local/synthesize_e2e.sh b/examples/csmsc/tts3/local/synthesize_e2e.sh index 512e062b..35a5598a 100755 --- a/examples/csmsc/tts3/local/synthesize_e2e.sh +++ b/examples/csmsc/tts3/local/synthesize_e2e.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -42,7 +42,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -64,7 +64,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt # --inference_dir=${train_output_path}/inference @@ -85,7 +85,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference @@ -107,7 +107,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference diff --git a/examples/csmsc/tts3/local/synthesize_e2e_xpu.sh b/examples/csmsc/tts3/local/synthesize_e2e_xpu.sh new file mode 100644 index 00000000..bb58a37c --- /dev/null +++ b/examples/csmsc/tts3/local/synthesize_e2e_xpu.sh @@ -0,0 +1,119 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 + +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=pwgan_csmsc \ + --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ + --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ + --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=mb_melgan_csmsc \ + --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ + --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + +# the pretrained models haven't release now +# style melgan +# style melgan's Dygraph to Static Graph is not ready now +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=style_melgan_csmsc \ + --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ + --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 + # --inference_dir=${train_output_path}/inference +fi + +# hifigan +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "in hifigan syn_e2e" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=hifigan_csmsc \ + --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ + --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi + + +# wavernn +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + echo "in wavernn syn_e2e" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize_e2e.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=wavernn_csmsc \ + --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \ + --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ + --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ + --lang=zh \ + --text=${BIN_DIR}/../../assets/sentences.txt \ + --output_dir=${train_output_path}/test_e2e \ + --phones_dict=dump/phone_id_map.txt \ + --inference_dir=${train_output_path}/inference \ + --ngpu=0 \ + --nxpu=1 +fi diff --git a/examples/csmsc/tts3/local/synthesize_streaming.sh b/examples/csmsc/tts3/local/synthesize_streaming.sh index 366a88db..f4e783d4 100755 --- a/examples/csmsc/tts3/local/synthesize_streaming.sh +++ b/examples/csmsc/tts3/local/synthesize_streaming.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True \ @@ -43,7 +43,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True \ @@ -66,7 +66,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True @@ -87,7 +87,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e_streaming \ --phones_dict=dump/phone_id_map.txt \ --am_streaming=True \ diff --git a/examples/csmsc/tts3/local/synthesize_xpu.sh b/examples/csmsc/tts3/local/synthesize_xpu.sh new file mode 100644 index 00000000..fac8677a --- /dev/null +++ b/examples/csmsc/tts3/local/synthesize_xpu.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 +ckpt_name=$3 +stage=0 +stop_stage=0 + +# pwgan +if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=pwgan_csmsc \ + --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ + --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ + --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# for more GAN Vocoders +# multi band melgan +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=mb_melgan_csmsc \ + --voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ + --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# style melgan +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=style_melgan_csmsc \ + --voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ + --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# hifigan +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + echo "in hifigan syn" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=hifigan_csmsc \ + --voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \ + --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ + --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi + +# wavernn +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + echo "in wavernn syn" + FLAGS_allocator_strategy=naive_best_fit \ + python3 ${BIN_DIR}/../synthesize.py \ + --am=fastspeech2_csmsc \ + --am_config=${config_path} \ + --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \ + --am_stat=dump/train/speech_stats.npy \ + --voc=wavernn_csmsc \ + --voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \ + --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ + --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ + --test_metadata=dump/test/norm/metadata.jsonl \ + --output_dir=${train_output_path}/test \ + --phones_dict=dump/phone_id_map.txt \ + --ngpu=0 \ + --nxpu=1 +fi diff --git a/examples/csmsc/tts3/local/train_xpu.sh b/examples/csmsc/tts3/local/train_xpu.sh new file mode 100644 index 00000000..a7d88988 --- /dev/null +++ b/examples/csmsc/tts3/local/train_xpu.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +config_path=$1 +train_output_path=$2 + +python3 ${BIN_DIR}/train.py \ + --train-metadata=dump/train/norm/metadata.jsonl \ + --dev-metadata=dump/dev/norm/metadata.jsonl \ + --config=${config_path} \ + --output-dir=${train_output_path} \ + --ngpu=0 \ + --nxpu=1 \ + --phones-dict=dump/phone_id_map.txt \ No newline at end of file diff --git a/examples/csmsc/tts3/run.sh b/examples/csmsc/tts3/run.sh index dd8c9f3e..a7b4e423 100755 --- a/examples/csmsc/tts3/run.sh +++ b/examples/csmsc/tts3/run.sh @@ -45,10 +45,7 @@ fi # we have only tested the following models so far if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc # considering the balance between speed and quality, we recommend that you use hifigan as vocoder ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc diff --git a/examples/csmsc/tts3/run_cnndecoder.sh b/examples/csmsc/tts3/run_cnndecoder.sh index 96b446c5..f356f313 100755 --- a/examples/csmsc/tts3/run_cnndecoder.sh +++ b/examples/csmsc/tts3/run_cnndecoder.sh @@ -58,10 +58,7 @@ fi # paddle2onnx non streaming if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc # considering the balance between speed and quality, we recommend that you use hifigan as vocoder ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc @@ -77,10 +74,7 @@ fi # paddle2onnx streaming if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then # install paddle2onnx - version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') - if [[ -z "$version" || ${version} != '1.0.0' ]]; then - pip install paddle2onnx==1.0.0 - fi + pip install paddle2onnx --upgrade # streaming acoustic model ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_decoder diff --git a/examples/csmsc/tts3/run_xpu.sh b/examples/csmsc/tts3/run_xpu.sh new file mode 100644 index 00000000..4922d6b4 --- /dev/null +++ b/examples/csmsc/tts3/run_xpu.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +set -e +source path.sh + +xpus=0,1 +stage=0 +stop_stage=100 + +conf_path=conf/default.yaml +train_output_path=exp/default +ckpt_name=snapshot_iter_153.pdz + +# 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 0 ] && [ ${stop_stage} -ge 0 ]; then + # prepare data + ./local/preprocess.sh ${conf_path} || exit -1 +fi + +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # train model, all `ckpt` under `train_output_path/checkpoints/` dir + FLAGS_selected_xpus=${xpus} ./local/train_xpu.sh ${conf_path} ${train_output_path} || exit -1 +fi + +if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # synthesize, vocoder is pwgan by default + FLAGS_selected_xpus=${xpus} ./local/synthesize_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # synthesize_e2e, vocoder is pwgan by default + FLAGS_selected_xpus=${xpus} ./local/synthesize_e2e_xpu.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1 +fi + +if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + # inference with static model, vocoder is pwgan by default + FLAGS_selected_xpus=${xpus} ./local/inference_xpu.sh ${train_output_path} || exit -1 +fi diff --git a/examples/csmsc/tts3_rhy/local/synthesize_e2e.sh b/examples/csmsc/tts3_rhy/local/synthesize_e2e.sh index 8f5d8010..bf7229e1 100755 --- a/examples/csmsc/tts3_rhy/local/synthesize_e2e.sh +++ b/examples/csmsc/tts3_rhy/local/synthesize_e2e.sh @@ -21,7 +21,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference \ @@ -43,7 +43,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then --voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\ --voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference \ @@ -66,7 +66,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then --voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \ --voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --use_rhy=True @@ -88,7 +88,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then --voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \ --voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference \ @@ -111,7 +111,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then --voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \ --voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \ --lang=zh \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/test_e2e \ --phones_dict=dump/phone_id_map.txt \ --inference_dir=${train_output_path}/inference \ diff --git a/examples/csmsc/vits/README.md b/examples/csmsc/vits/README.md index 50d703b2..83871277 100644 --- a/examples/csmsc/vits/README.md +++ b/examples/csmsc/vits/README.md @@ -172,6 +172,6 @@ python3 ${BIN_DIR}/synthesize_e2e.py \ --ckpt=vits_csmsc_ckpt_1.4.0/snapshot_iter_150000.pdz \ --phones_dict=vits_csmsc_ckpt_1.4.0/phone_id_map.txt \ --output_dir=exp/default/test_e2e \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --add-blank=${add_blank} ``` diff --git a/examples/csmsc/vits/conf/default.yaml b/examples/csmsc/vits/conf/default.yaml index a2aef998..7e9e9c1d 100644 --- a/examples/csmsc/vits/conf/default.yaml +++ b/examples/csmsc/vits/conf/default.yaml @@ -179,7 +179,7 @@ generator_first: False # whether to start updating generator first # OTHER TRAINING SETTING # ########################################################## num_snapshots: 10 # max number of snapshots to keep while training -train_max_steps: 350000 # Number of training steps. == total_iters / ngpus, total_iters = 1000000 -save_interval_steps: 1000 # Interval steps to save checkpoint. -eval_interval_steps: 250 # Interval steps to evaluate the network. +max_epoch: 1000 # Number of training epochs. +save_interval_epochs: 1 # Interval epochs to save checkpoint. +eval_interval_epochs: 1 # Interval steps to evaluate the network. seed: 777 # random seed number diff --git a/examples/csmsc/vits/local/inference.sh b/examples/csmsc/vits/local/inference.sh index 0a79c255..d26b7f71 100755 --- a/examples/csmsc/vits/local/inference.sh +++ b/examples/csmsc/vits/local/inference.sh @@ -10,7 +10,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then python3 ${BIN_DIR}/inference.py \ --inference_dir=${train_output_path}/inference \ --am=vits_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/pd_infer_out \ --phones_dict=dump/phone_id_map.txt \ --add-blank=${add_blank} diff --git a/examples/csmsc/vits/local/lite_predict.sh b/examples/csmsc/vits/local/lite_predict.sh index 9ed57b72..d20d7a57 100755 --- a/examples/csmsc/vits/local/lite_predict.sh +++ b/examples/csmsc/vits/local/lite_predict.sh @@ -7,10 +7,10 @@ stage=0 stop_stage=0 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then - python3 ${BIN_DIR}/../lite_predict.py \ + python3 ${BIN_DIR}/lite_predict.py \ --inference_dir=${train_output_path}/pdlite \ --am=vits_csmsc \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --output_dir=${train_output_path}/lite_infer_out \ --phones_dict=dump/phone_id_map.txt \ --add-blank=${add_blank} diff --git a/examples/csmsc/vits/local/synthesize_e2e.sh b/examples/csmsc/vits/local/synthesize_e2e.sh index 6a69b366..f3c067e4 100755 --- a/examples/csmsc/vits/local/synthesize_e2e.sh +++ b/examples/csmsc/vits/local/synthesize_e2e.sh @@ -18,7 +18,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then --ckpt=${train_output_path}/checkpoints/${ckpt_name} \ --phones_dict=dump/phone_id_map.txt \ --output_dir=${train_output_path}/test_e2e \ - --text=${BIN_DIR}/../sentences.txt \ + --text=${BIN_DIR}/../../assets/sentences.txt \ --add-blank=${add_blank} #\ # --inference_dir=${train_output_path}/inference fi diff --git a/examples/csmsc/vits/run.sh b/examples/csmsc/vits/run.sh index f2c5d452..f6e8a086 100755 --- a/examples/csmsc/vits/run.sh +++ b/examples/csmsc/vits/run.sh @@ -45,10 +45,7 @@ fi # # we have only tested the following models so far # if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then # # install paddle2onnx -# version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}') -# if [[ -z "$version" || ${version} != '1.0.0' ]]; then -# pip install paddle2onnx==1.0.0 -# fi +# pip install paddle2onnx --upgrade # ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx vits_csmsc # fi @@ -57,16 +54,16 @@ fi # ./local/ort_predict.sh ${train_output_path} # fi -# # not ready yet for operator missing in Paddle-Lite -# # must run after stage 3 (which stage generated static models) -# if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then -# # NOTE by yuantian 2022.11.21: please compile develop version of Paddle-Lite to export and run TTS models, -# # cause TTS models are supported by https://github.com/PaddlePaddle/Paddle-Lite/pull/9587 -# # and https://github.com/PaddlePaddle/Paddle-Lite/pull/9706 -# ./local/export2lite.sh ${train_output_path} inference pdlite vits_csmsc x86 -# fi +# not ready yet for operator missing in Paddle-Lite +# must run after stage 3 (which stage generated static models) +if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then + # NOTE by yuantian 2022.11.21: please compile develop version of Paddle-Lite to export and run TTS models, + # cause TTS models are supported by https://github.com/PaddlePaddle/Paddle-Lite/pull/10128 + # vits can only run in arm + ./local/export2lite.sh ${train_output_path} inference pdlite vits_csmsc arm +fi -# if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then -# CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1 -# fi +if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then + CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1 +fi diff --git a/examples/csmsc/voc5/conf/iSTFT.yaml b/examples/csmsc/voc5/conf/iSTFT.yaml new file mode 100644 index 00000000..06677d79 --- /dev/null +++ b/examples/csmsc/voc5/conf/iSTFT.yaml @@ -0,0 +1,174 @@ +# This is the configuration file for CSMSC dataset. +# This configuration is based on HiFiGAN V1, which is an official configuration. +# But I found that the optimizer setting does not work well with my implementation. +# So I changed optimizer settings as follows: +# - AdamW -> Adam +# - betas: [0.8, 0.99] -> betas: [0.5, 0.9] +# - Scheduler: ExponentialLR -> MultiStepLR +# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting. + +########################################################### +# FEATURE EXTRACTION SETTING # +########################################################### +fs: 24000 # Sampling rate. +n_fft: 2048 # FFT size (samples). +n_shift: 300 # Hop size (samples). 12.5ms +win_length: 1200 # Window length (samples). 50ms + # If set to null, it will be the same as fft_size. +window: "hann" # Window function. +n_mels: 80 # Number of mel basis. +fmin: 80 # Minimum freq in mel basis calculation. (Hz) +fmax: 7600 # Maximum frequency in mel basis calculation. (Hz) + +########################################################### +# GENERATOR NETWORK ARCHITECTURE SETTING # +########################################################### +generator_params: + use_istft: True # Use iSTFTNet. + istft_layer_id: 2 # Use istft after istft_layer_id layers of upsample layer if use_istft=True. + n_fft: 2048 # FFT size (samples) in feature extraction. + win_length: 1200 # Window length (samples) in feature extraction. + in_channels: 80 # Number of input channels. + out_channels: 1 # Number of output channels. + channels: 512 # Number of initial channels. + kernel_size: 7 # Kernel size of initial and final conv layers. + upsample_scales: [5, 5, 4, 3] # Upsampling scales. + upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers. + resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks. + resblock_dilations: # Dilations for residual blocks. + - [1, 3, 5] + - [1, 3, 5] + - [1, 3, 5] + use_additional_convs: True # Whether to use additional conv layer in residual blocks. + bias: True # Whether to use bias parameter in conv. + nonlinear_activation: "leakyrelu" # Nonlinear activation type. + nonlinear_activation_params: # Nonlinear activation paramters. + negative_slope: 0.1 + use_weight_norm: True # Whether to apply weight normalization. + + + + + +########################################################### +# DISCRIMINATOR NETWORK ARCHITECTURE SETTING # +########################################################### +discriminator_params: + scales: 3 # Number of multi-scale discriminator. + scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator. + scale_downsample_pooling_params: + kernel_size: 4 # Pooling kernel size. + stride: 2 # Pooling stride. + padding: 2 # Padding size. + scale_discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_sizes: [15, 41, 5, 3] # List of kernel sizes. + channels: 128 # Initial number of channels. + max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. + max_groups: 16 # Maximum number of groups in downsampling conv layers. + bias: True + downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales. + nonlinear_activation: "leakyrelu" # Nonlinear activation. + nonlinear_activation_params: + negative_slope: 0.1 + follow_official_norm: True # Whether to follow the official norm setting. + periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator. + period_discriminator_params: + in_channels: 1 # Number of input channels. + out_channels: 1 # Number of output channels. + kernel_sizes: [5, 3] # List of kernel sizes. + channels: 32 # Initial number of channels. + downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales. + max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. + bias: True # Whether to use bias parameter in conv layer." + nonlinear_activation: "leakyrelu" # Nonlinear activation. + nonlinear_activation_params: # Nonlinear activation paramters. + negative_slope: 0.1 + use_weight_norm: True # Whether to apply weight normalization. + use_spectral_norm: False # Whether to apply spectral normalization. + + +########################################################### +# STFT LOSS SETTING # +########################################################### +use_stft_loss: False # Whether to use multi-resolution STFT loss. +use_mel_loss: True # Whether to use Mel-spectrogram loss. +mel_loss_params: + fs: 24000 + fft_size: 2048 + hop_size: 300 + win_length: 1200 + window: "hann" + num_mels: 80 + fmin: 0 + fmax: 12000 + log_base: null +generator_adv_loss_params: + average_by_discriminators: False # Whether to average loss by #discriminators. +discriminator_adv_loss_params: + average_by_discriminators: False # Whether to average loss by #discriminators. +use_feat_match_loss: True +feat_match_loss_params: + average_by_discriminators: False # Whether to average loss by #discriminators. + average_by_layers: False # Whether to average loss by #layers in each discriminator. + include_final_outputs: False # Whether to include final outputs in feat match loss calculation. + +########################################################### +# ADVERSARIAL LOSS SETTING # +########################################################### +lambda_aux: 45.0 # Loss balancing coefficient for STFT loss. +lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss. +lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss.. + +########################################################### +# DATA LOADER SETTING # +########################################################### +batch_size: 16 # Batch size. +batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size. +num_workers: 2 # Number of workers in DataLoader. + +########################################################### +# OPTIMIZER & SCHEDULER SETTING # +########################################################### +generator_optimizer_params: + beta1: 0.5 + beta2: 0.9 + weight_decay: 0.0 # Generator's weight decay coefficient. +generator_scheduler_params: + learning_rate: 2.0e-4 # Generator's learning rate. + gamma: 0.5 # Generator's scheduler gamma. + milestones: # At each milestone, lr will be multiplied by gamma. + - 200000 + - 400000 + - 600000 + - 800000 +generator_grad_norm: -1 # Generator's gradient norm. +discriminator_optimizer_params: + beta1: 0.5 + beta2: 0.9 + weight_decay: 0.0 # Discriminator's weight decay coefficient. +discriminator_scheduler_params: + learning_rate: 2.0e-4 # Discriminator's learning rate. + gamma: 0.5 # Discriminator's scheduler gamma. + milestones: # At each milestone, lr will be multiplied by gamma. + - 200000 + - 400000 + - 600000 + - 800000 +discriminator_grad_norm: -1 # Discriminator's gradient norm. + +########################################################### +# INTERVAL SETTING # +########################################################### +generator_train_start_steps: 1 # Number of steps to start to train discriminator. +discriminator_train_start_steps: 0 # Number of steps to start to train discriminator. +train_max_steps: 2500000 # Number of training steps. +save_interval_steps: 5000 # Interval steps to save checkpoint. +eval_interval_steps: 1000 # Interval steps to evaluate the network. + +########################################################### +# OTHER SETTING # +########################################################### +num_snapshots: 10 # max number of snapshots to keep while training +seed: 42 # random seed for paddle, random, and np.random diff --git a/examples/csmsc/voc5/iSTFTNet.md b/examples/csmsc/voc5/iSTFTNet.md new file mode 100644 index 00000000..8f121938 --- /dev/null +++ b/examples/csmsc/voc5/iSTFTNet.md @@ -0,0 +1,145 @@ +# iSTFTNet with CSMSC + +This example contains code used to train a [iSTFTNet](https://arxiv.org/abs/2203.02395) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). + +## Dataset +### Download and Extract +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) results to cut silence at the edge of audio. +You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo. + +## Get Started +Assume the path to the dataset is `~/datasets/BZNSYP`. +Assume the path to the MFA result of CSMSC is `./baker_alignment_tone`. +Run the command below to +1. **source path**. +2. preprocess the dataset. +3. train the model. +4. synthesize wavs. + - synthesize waveform from `metadata.jsonl`. +```bash +./run.sh +``` +You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset. +```bash +./run.sh --stage 0 --stop-stage 0 +``` +### Data Preprocessing +```bash +./local/preprocess.sh ${conf_path} +``` +When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below. + +```text +dump +├── dev +│ ├── norm +│ └── raw +├── test +│ ├── norm +│ └── raw +└── train + ├── norm + ├── raw + └── feats_stats.npy +``` +The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`. + +Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance. + +### Model Training +```bash +CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} +``` +`./local/train.sh` calls `${BIN_DIR}/train.py`. +Here's the complete help message. + +```text +usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA] + [--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR] + [--ngpu NGPU] + +Train a HiFiGAN model. + +optional arguments: + -h, --help show this help message and exit + --config CONFIG HiFiGAN config file. + --train-metadata TRAIN_METADATA + training data. + --dev-metadata DEV_METADATA + dev data. + --output-dir OUTPUT_DIR + output dir. + --ngpu NGPU if ngpu == 0, use cpu. +``` + +1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/iSTFT.yaml`. +2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder. +3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory. +4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. + +### Synthesizing +`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`. +```bash +CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} +``` +```text +usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG] + [--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA] + [--output-dir OUTPUT_DIR] [--ngpu NGPU] + +Synthesize with GANVocoder. + +optional arguments: + -h, --help show this help message and exit + --generator-type GENERATOR_TYPE + type of GANVocoder, should in {pwgan, mb_melgan, + style_melgan, } now + --config CONFIG GANVocoder config file. + --checkpoint CHECKPOINT + snapshot to load. + --test-metadata TEST_METADATA + dev data. + --output-dir OUTPUT_DIR + output dir. + --ngpu NGPU if ngpu == 0, use cpu. +``` + +1. `--config` config file. You should use the same config with which the model is trained. +2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory. +3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory. +4. `--output-dir` is the directory to save the synthesized audio files. +5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. + +## Pretrained Models + +The pretrained model can be downloaded here: + +- [iSTFTNet_csmsc_ckpt.zip](https://pan.baidu.com/s/1SNDlRWOGOcbbrKf5w-TJaA?pwd=r1e5) + +iSTFTNet checkpoint contains files listed below. + +```text +iSTFTNet_csmsc_ckpt +├── iSTFT.yaml                  # config used to train iSTFTNet +├── feats_stats.npy               # statistics used to normalize spectrogram when training hifigan +└── snapshot_iter_50000.pdz     # generator parameters of hifigan +``` + +A Comparison between iSTFTNet and Hifigan +| Model | Step | eval/generator_loss | eval/mel_loss | eval/feature_matching_loss | rtf | +|:--------:|:--------------:|:-------------------:|:-------------:|:--------------------------:| :---: | +| hifigan | 1(gpu) x 50000 | 13.989 | 0.14683 | 1.3484 | 0.01767 | +| istftNet | 1(gpu) x 50000 | 13.319 | 0.14818 | 1.1069 | 0.01069 | + +> Rtf is tested on the CSMSC test dataset, and the test environment is aistudio v100 16G 1GPU, the test command is `./run.sh --stage 2 --stop-stage 2` + +The pretained hifigan model int the comparison can be downloaded here: + +- [hifigan_csmsc_ckpt.zip](https://pan.baidu.com/s/1pGY6RYV7yEB_5hRI_JoWig?pwd=tcaj) + +## Acknowledgement + +We adapted some code from https://github.com/rishikksh20/iSTFTNet-pytorch.git. diff --git a/examples/librispeech/asr2/README.md b/examples/librispeech/asr2/README.md index 26978520..253c9b45 100644 --- a/examples/librispeech/asr2/README.md +++ b/examples/librispeech/asr2/README.md @@ -153,7 +153,7 @@ After training the model, we need to get the final model for testing and inferen ```bash if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then # avg n best model - avg.sh lastest exp/${ckpt}/checkpoints ${avg_num} + avg.sh latest exp/${ckpt}/checkpoints ${avg_num} fi ``` The `avg.sh` is in the `../../../utils/` which is define in the `path.sh`. diff --git a/examples/librispeech/asr3/path.sh b/examples/librispeech/asr3/path.sh index f4717838..d98171a8 100644 --- a/examples/librispeech/asr3/path.sh +++ b/examples/librispeech/asr3/path.sh @@ -10,6 +10,4 @@ export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH} export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/ - -MODEL=wav2vec2 -export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin +export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/wav2vec2/bin diff --git a/examples/librispeech/asr3/run.sh b/examples/librispeech/asr3/run.sh old mode 100644 new mode 100755 index f52266a1..c880c9cb --- a/examples/librispeech/asr3/run.sh +++ b/examples/librispeech/asr3/run.sh @@ -44,4 +44,4 @@ fi if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then # test a single .wav file CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1 -fi +fi \ No newline at end of file diff --git a/examples/librispeech/asr4/README.md b/examples/librispeech/asr4/README.md new file mode 100644 index 00000000..064a7f16 --- /dev/null +++ b/examples/librispeech/asr4/README.md @@ -0,0 +1,197 @@ +# Hubert2ASR with Librispeech +This example contains code used to finetune [hubert](https://arxiv.org/abs/2106.07447) model with [Librispeech dataset](http://www.openslr.org/resources/12) +## Overview +All the scripts you need are in `run.sh`. There are several stages in `run.sh`, and each stage has its function. +| Stage | Function | +|:---- |:----------------------------------------------------------- | +| 0 | Process data. It includes:
    (1) Download the dataset
    (2) Calculate the CMVN of the train dataset
    (3) Get the vocabulary file
    (4) Get the manifest files of the train, development and test dataset
    (5) Download the pretrained wav2vec2 model | +| 1 | Train the model | +| 2 | Get the final model by averaging the top-k models, set k = 1 means to choose the best model | +| 3 | Test the final model performance | +| 4 | Infer the single audio file | + + +You can choose to run a range of stages by setting `stage` and `stop_stage `. + +For example, if you want to execute the code in stage 2 and stage 3, you can run this script: +```bash +bash run.sh --stage 2 --stop_stage 3 +``` +Or you can set `stage` equal to `stop-stage` to only run one stage. +For example, if you only want to run `stage 0`, you can use the script below: +```bash +bash run.sh --stage 0 --stop_stage 0 +``` +The document below will describe the scripts in `run.sh` in detail. +## The Environment Variables +The path.sh contains the environment variables. +```bash +. ./path.sh +. ./cmd.sh +``` +This script needs to be run first. And another script is also needed: +```bash +source ${MAIN_ROOT}/utils/parse_options.sh +``` +It will support the way of using `--variable value` in the shell scripts. +## The Local Variables +Some local variables are set in `run.sh`. +`gpus` denotes the GPU number you want to use. If you set `gpus=`, it means you only use CPU. +`stage` denotes the number of stages you want to start from in the experiments. +`stop stage` denotes the number of the stage you want to end at in the experiments. +`conf_path` denotes the config path of the model. +`avg_num` denotes the number K of top-K models you want to average to get the final model. +`audio file` denotes the file path of the single file you want to infer in stage 5 +`ckpt` denotes the checkpoint prefix of the model, e.g. "hubertASR" + +You can set the local variables (except `ckpt`) when you use `run.sh` + +For example, you can set the `gpus` and `avg_num` when you use the command line: +```bash +bash run.sh --gpus 0,1 --avg_num 20 +``` +## Stage 0: Data Processing +To use this example, you need to process data firstly and you can use stage 0 in `run.sh` to do this. The code is shown below: +```bash + if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then + # prepare data + bash ./local/data.sh || exit -1 + fi +``` +Stage 0 is for processing the data. + +If you only want to process the data. You can run +```bash +bash run.sh --stage 0 --stop_stage 0 +``` +You can also just run these scripts in your command line. +```bash +. ./path.sh +. ./cmd.sh +bash ./local/data.sh +``` +After processing the data, the `data` directory will look like this: +```bash +data/ +|-- dev.meta +|-- lang_char +| `-- bpe_unigram_5000.model +| `-- bpe_unigram_5000.vocab +| `-- vocab.txt +|-- manifest.dev +|-- manifest.dev.raw +|-- manifest.test +|-- manifest.test.raw +|-- manifest.train +|-- manifest.train.raw +|-- mean_std.json +|-- test.meta +`-- train.meta +``` + +Stage 0 also downloads the pre-trained [hubert](https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams) model. +```bash +mkdir -p exp/hubert +wget -P exp/hubert https://paddlespeech.bj.bcebos.com/hubert/hubert-large-lv60.pdparams +``` +## Stage 1: Model Training +If you want to train the model. you can use stage 1 in `run.sh`. The code is shown below. +```bash +if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then + # train model, all `ckpt` under `exp` dir + CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt} + fi +``` +If you want to train the model, you can use the script below to execute stage 0 and stage 1: +```bash +bash run.sh --stage 0 --stop_stage 1 +``` +or you can run these scripts in the command line (only use CPU). +```bash +. ./path.sh +. ./cmd.sh +bash ./local/data.sh +CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR +``` +## Stage 2: Top-k Models Averaging +After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below. Note: We only train one epoch for hubertASR, thus the `avg_num` is set to 1. +```bash + if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then + # avg n best model + avg.sh best exp/${ckpt}/checkpoints ${avg_num} + fi +``` +The `avg.sh` is in the `../../../utils/` which is define in the `path.sh`. +If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2: +```bash +bash run.sh --stage 0 --stop_stage 2 +``` +or you can run these scripts in the command line (only use CPU). + +```bash +. ./path.sh +. ./cmd.sh +bash ./local/data.sh +CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR +avg.sh best exp/hubertASR/checkpoints 1 +``` +## Stage 3: Model Testing +The test stage is to evaluate the model performance. The code of test stage is shown below: +```bash + if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then + # test ckpt avg_n + CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1 + fi +``` +If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 : +```bash +bash run.sh --stage 0 --stop_stage 3 +``` +or you can run these scripts in the command line (only use CPU). +```bash +. ./path.sh +. ./cmd.sh +bash ./local/data.sh +CUDA_VISIBLE_DEVICES= ./local/train.sh conf/hubertASR.yaml hubertASR +avg.sh best exp/hubertASR/checkpoints 1 +CUDA_VISIBLE_DEVICES= ./local/test.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1 +``` +## Pretrained Model +You can get the pretrained hubertASR from [this](../../../docs/source/released_model.md). + +using the `tar` scripts to unpack the model and then you can use the script to test the model. + +For example: +```bash +wget https://paddlespeech.bj.bcebos.com/hubert/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz +tar xzvf hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz +source path.sh +# If you have process the data and get the manifest file， you can skip the following 2 steps +bash local/data.sh --stage -1 --stop_stage -1 +bash local/data.sh --stage 2 --stop_stage 2 +CUDA_VISIBLE_DEVICES= ./local/test.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1 +``` +The performance of the released models are shown in [here](./RESULTS.md). + + +## Stage 4: Single Audio File Inference +In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below +```bash + if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then + # test a single .wav file + CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1 + fi +``` +you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model through the script below: +```bash +wget https://paddlespeech.bj.bcebos.com/hubert/hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz +tar xzvf hubertASR-large-100h-librispeech_ckpt_1.4.0.model.tar.gz +``` +You can download the audio demo: +```bash +wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/en/demo_002_en.wav -P data/ +``` +You need to prepare an audio file or use the audio demo above, please confirm the sample rate of the audio is 16K. You can get the result of the audio demo by running the script below. +```bash +CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/hubertASR.yaml conf/tuning/decode.yaml exp/hubertASR/checkpoints/avg_1 data/demo_002_en.wav +``` diff --git a/examples/librispeech/asr4/RESULTS.md b/examples/librispeech/asr4/RESULTS.md new file mode 100644 index 00000000..81ce6ee9 --- /dev/null +++ b/examples/librispeech/asr4/RESULTS.md @@ -0,0 +1,9 @@ +# LibriSpeech + +## hubertASR +Fintuning on train-clean-100 +train: Epoch 3, 1*V100-32G, batchsize: 4, accum_grad: 8 + +| Model | Params | Config | Augmentation| Test set | Decode method | WER | +| --- | --- | --- | --- | --- | --- | --- | +| hubertASR | 326.16M | conf/hubertASR.yaml | spec_aug | test-clean | greedy search | 0.05868 | diff --git a/examples/librispeech/asr4/cmd.sh b/examples/librispeech/asr4/cmd.sh new file mode 100644 index 00000000..7b70ef5e --- /dev/null +++ b/examples/librispeech/asr4/cmd.sh @@ -0,0 +1,89 @@ +# ====== About run.pl, queue.pl, slurm.pl, and ssh.pl ====== +# Usage: .pl [options] JOB=1: +# e.g. +# run.pl --mem 4G JOB=1:10 echo.JOB.log echo JOB +# +# Options: +# --time