From ea29275acd46cf0b37187a78a97046a99ccfe617 Mon Sep 17 00:00:00 2001 From: TianYuan Date: Wed, 9 Feb 2022 06:39:25 +0000 Subject: [PATCH] fix dead links, test=doc --- docs/source/released_model.md | 8 ++++---- docs/source/tts/README.md | 4 ---- examples/thchs30/align0/README.md | 4 ++-- tests/benchmark/conformer/README.md | 2 +- 4 files changed, 7 insertions(+), 11 deletions(-) diff --git a/docs/source/released_model.md b/docs/source/released_model.md index 5d29968e..23309d8e 100644 --- a/docs/source/released_model.md +++ b/docs/source/released_model.md @@ -10,9 +10,9 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | [Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.056 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) [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) [Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_librispeech_ckpt_0.1.1.model.tar.gz)| Librispeech Dataset | Char-based | 518 MB | 2 Conv + 3 bidirectional LSTM layers| - |0.0725| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) -[Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0337 | 960 h | [Conformer Librispeech ASR1](../../example/librispeech/asr1) -[Transformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1](../../example/librispeech/asr1) -[Transformer Librispeech ASR2 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h | [Transformer Librispeech ASR2](../../example/librispeech/asr2) +[Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0337 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) +[Transformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h | [Transformer Librispeech ASR1](../../examples/librispeech/asr1) +[Transformer Librispeech ASR2 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/asr2_transformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h | [Transformer Librispeech ASR2](../../examples/librispeech/asr2) ### Language Model based on NGram Language Model | Training Data | Token-based | Size | Descriptions @@ -66,7 +66,7 @@ GE2E + FastSpeech2 | AISHELL-3 |[ge2e-fastspeech2-aishell3](https://github.com/ Model Type | Dataset| Example Link | Pretrained Models :-------------:| :------------:| :-----: | :-----: PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) -PANN | ESC-50 |[pann-esc50]("./examples/esc50/cls0")|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz) +PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz) ## Punctuation Restoration Models Model Type | Dataset| Example Link | Pretrained Models diff --git a/docs/source/tts/README.md b/docs/source/tts/README.md index 3de8901b..835db08e 100644 --- a/docs/source/tts/README.md +++ b/docs/source/tts/README.md @@ -71,7 +71,3 @@ Check our [website](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) #### GE2E 1. [ge2e_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/ge2e_ckpt_0.3.zip) - -## License - -Parakeet is provided under the [Apache-2.0 license](LICENSE). diff --git a/examples/thchs30/align0/README.md b/examples/thchs30/align0/README.md index da56fffc..5195ab80 100644 --- a/examples/thchs30/align0/README.md +++ b/examples/thchs30/align0/README.md @@ -27,7 +27,7 @@ cd a0 应用程序会自动下载 THCHS-30数据集，处理成 MFA 所需的文件格式并开始训练，您可以修改 `run.sh` 中的参数 `LEXICON_NAME` 来决定您需要强制对齐的级别（word、syllable 和 phone） ## MFA 所使用的字典 --- -MFA 字典的格式请参考: [MFA 官方文档 Dictionary format ](https://montreal-forced-aligner.readthedocs.io/en/latest/dictionary.html) +MFA 字典的格式请参考: [MFA 官方文档](https://montreal-forced-aligner.readthedocs.io/en/latest/) phone.lexicon 直接使用的是 `THCHS-30/data_thchs30/lm_phone/lexicon.txt` word.lexicon 考虑到了中文的多音字，使用**带概率的字典**, 生成规则请参考 `local/gen_word2phone.py` `syllable.lexicon` 获取自 [DNSun/thchs30-pinyin2tone](https://github.com/DNSun/thchs30-pinyin2tone) @@ -39,4 +39,4 @@ word.lexicon 考虑到了中文的多音字，使用**带概率的字典**, 生 **syllabel 级别：** [syllable.lexicon](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/syllable/syllable.lexicon)、[对齐结果](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/syllable/thchs30_alignment.tar.gz)、[模型](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/syllable/thchs30_model.zip) **word 级别：** [word.lexicon](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/word/word.lexicon)、[对齐结果](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/word/thchs30_alignment.tar.gz)、[模型](https://paddlespeech.bj.bcebos.com/MFA/THCHS30/word/thchs30_model.zip) -随后，您可以参考 [MFA 官方文档 Align using pretrained models](https://montreal-forced-aligner.readthedocs.io/en/stable/aligning.html#align-using-pretrained-models) 使用我们给您提供好的模型直接对自己的数据集进行强制对齐，注意，您需要使用和模型对应的 lexicon 文件，当文本是汉字时，您需要用空格把不同的**汉字**（而不是词语）分开 +随后，您可以参考 [MFA 官方文档](https://montreal-forced-aligner.readthedocs.io/en/latest/) 使用我们给您提供好的模型直接对自己的数据集进行强制对齐，注意，您需要使用和模型对应的 lexicon 文件，当文本是汉字时，您需要用空格把不同的**汉字**（而不是词语）分开 diff --git a/tests/benchmark/conformer/README.md b/tests/benchmark/conformer/README.md index 22e0009d..72242bad 100644 --- a/tests/benchmark/conformer/README.md +++ b/tests/benchmark/conformer/README.md @@ -1,5 +1,5 @@ ### Prepare the environment -Please follow the instructions shown in [here](../../docs/source/install.md) to install the Deepspeech first. +Please follow the instructions shown in [here](../../../docs/source/install.md) to install the Deepspeech first. ### File list └── benchmark # 模型名