fix textfrontend readme, fix imgs link

PaddlePaddle · Oct 28, 2021 · 670a68a · 670a68a
1 parent 41526ca
commit 670a68a
Show file tree

Hide file tree

Showing 8 changed files with 43 additions and 96 deletions.
diff --git a/README.md b/README.md
@@ -9,42 +9,42 @@ English | [简体中文](README_ch.md)
 </p>
 <div align="center"> 
 
- <h3> 
- <a href="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/Mingxue-Xu/DeepSpeech#quick-start"> Quick Start </a> 
- | <a href="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/Mingxue-Xu/DeepSpeech#tutorials"> Tutorials </a> 
- | <a href="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/Mingxue-Xu/DeepSpeech#model-list"> Models List </a> 
- 
+ <h3>
+ <a href="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/Mingxue-Xu/DeepSpeech#quick-start"> Quick Start </a>
+ | <a href="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/Mingxue-Xu/DeepSpeech#tutorials"> Tutorials </a>
+ | <a href="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/Mingxue-Xu/DeepSpeech#model-list"> Models List </a>
+
 </div>
- 
+
 ------------------------------------------------------------------------------------
 ![License](https://img.shields.io/badge/license-Apache%202-red.svg)
 ![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
 ![support os](https://img.shields.io/badge/os-linux-yellow.svg)
 
 <!---
-why they should use your module, 
-how they can install it, 
+why they should use your module,
+how they can install it,
 how they can use it
 -->
 
-**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for two critical tasks in Speech - **Automatic Speech Recognition (ASR)** and **Text-To-Speech Synthesis (TTS)**, with modules involving state-of-art and influential models. 
+**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for two critical tasks in Speech - **Automatic Speech Recognition (ASR)** and **Text-To-Speech Synthesis (TTS)**, with modules involving state-of-art and influential models.
 
 Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing module, and deployment. Besides, this toolkit also features at:
 - **Fast and Light-weight**: we provide a high-speed and ultra-lightweight model that is convenient for industrial deployment.
-- **Rule-based Chinese frontend**: our frontend contains Text Normalization (TN) and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context. 
-- **Varieties of Functions that Vitalize Research**: 
+- **Rule-based Chinese frontend**: our frontend contains Text Normalization (TN) and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
+- **Varieties of Functions that Vitalize Research**:
  - *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of both ASR and TTS, and uses datasets like LibriSpeech, LJSpeech, AIShell, etc. See also [model lists](#models-list) for more details.
  - *Support of ASR streaming and non-streaming data*: This toolkit contains non-streaming/streaming models like [DeepSpeech2](http:https://proceedings.mlr.press/v48/amodei16.pdf), [Transformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100) and [U2](https://arxiv.org/pdf/2012.05481.pdf).
- 
-Let's install PaddleSpeech with only a few lines of code! 
+
+Let's install PaddleSpeech with only a few lines of code!
 
 >Note: The official name is still deepspeech. 2021/10/26
 
 ``` shell
 # 1. Install essential libraries and paddlepaddle first.
 # install prerequisites
 sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev libsndfile1
-# `pip install paddlepaddle-gpu` instead if you are using GPU. 
+# `pip install paddlepaddle-gpu` instead if you are using GPU.
 pip install paddlepaddle
 
 # 2.Then install PaddleSpeech.
@@ -109,7 +109,7 @@ If you want to try more functions like training and tuning, please see [ASR gett
 PaddleSpeech ASR supports a lot of mainstream models, which are summarized as follow. For more information, please refer to [ASR Models](./docs/source/asr/released_model.md).
 
 <!---
-The current hyperlinks redirect to [Previous Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples). 
+The current hyperlinks redirect to [Previous Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples).
 -->
 
 <table>
@@ -125,7 +125,7 @@ The current hyperlinks redirect to [Previous Parakeet](https://github.com/Paddle
  <tr>
   <td rowspan="6">Acoustic Model</td>
   <td rowspan="4" >Aishell</td>
-  <td >2 Conv + 5 LSTM layers with only forward direction </td>
+  <td >2 Conv + 5 LSTM layers with only forward direction </td>
   <td>
   <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds_online.5rnn.debug.tar.gz">Ds2 Online Aishell Model</a>
   </td>
@@ -199,7 +199,7 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model
  <tr>
  <td> Text Frontend</td>
  <td colspan="2"> &emsp; </td>
- <td> 
+ <td>
  <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/text_frontend">chinese-fronted</a>
  </td>
  </tr>
@@ -292,11 +292,11 @@ PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model
 </table>
 
 
-## Tutorials 
+## Tutorials
 
 Normally, [Speech SoTA](https://paperswithcode.com/area/speech) gives you an overview of the hot academic topics in speech. If you want to focus on the two tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
 
-The original ASR module is based on [Baidu's DeepSpeech](https://arxiv.org/abs/1412.5567) which is an independent product named [DeepSpeech](https://deepspeech.readthedocs.io). However, the toolkit aligns almost all the SoTA modules in the pipeline. Specifically, these modules are 
+The original ASR module is based on [Baidu's DeepSpeech](https://arxiv.org/abs/1412.5567) which is an independent product named [DeepSpeech](https://deepspeech.readthedocs.io). However, the toolkit aligns almost all the SoTA modules in the pipeline. Specifically, these modules are
 
 * [Data Prepration](docs/source/asr/data_preparation.md) 
 * [Data Augmentation](docs/source/asr/augmentation.md) 
@@ -318,4 +318,3 @@ PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).
 ## Acknowledgement
 
 PaddleSpeech depends on a lot of open source repos. See [references](docs/source/asr/reference.md) for more information.
-
diff --git a/docs/source/asr/models_introduction.md b/docs/source/asr/models_introduction.md
@@ -13,7 +13,7 @@ In addition, the training process and the testing process are also introduced.
 The arcitecture of the model is shown in Fig.1.
 
 <p align="center">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/ds2onlineModel.png" width=800>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/ds2onlineModel.png" width=800>
  <br/>Fig.1 The Arcitecture of deepspeech2 online model
 </p>
 
@@ -160,7 +160,7 @@ The deepspeech2 offline model is similarity to the deepspeech2 online model. The
 
 The arcitecture of the model is shown in Fig.2.
 <p align="center">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/ds2offlineModel.png" width=800>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/ds2offlineModel.png" width=800>
  <br/>Fig.2 The Arcitecture of deepspeech2 offline model
 </p>
 

diff --git a/docs/source/asr/quick_start.md b/docs/source/asr/quick_start.md
@@ -54,7 +54,7 @@ CUDA_VISIBLE_DEVICES=0 bash local/tune.sh
  The grid search will print the WER (word error rate) or CER (character error rate) at each point in the hyper-parameters space, and draw the error surface optionally. A proper hyper-parameters range should include the global minima of the error surface for WER/CER, as illustrated in the following figure.
 
 <p align="center">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/tuning_error_surface.png" width=550>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/tuning_error_surface.png" width=550>
  <br/>An example error surface for tuning on the dev-clean set of LibriSpeech
 </p>
 

diff --git a/docs/source/tts/models_introduction.md b/docs/source/tts/models_introduction.md
@@ -27,14 +27,14 @@ At present, there are two mainstream acoustic model structures.
  - Acoustic decoder (N Frames - > N Frames).
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/frame_level_am.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/frame_level_am.png" width=500 /> <br>
 </div>
 
 - Sequence to sequence acoustic model:
  - M Tokens - > N Frames.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/seq2seq_am.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/seq2seq_am.png" width=500 /> <br>
 </div>
 
 ### Tacotron2
@@ -54,7 +54,7 @@ At present, there are two mainstream acoustic model structures.
  - CBHG postprocess.
  - Vocoder: Griffin-Lim.
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/tacotron.png" width=700 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/tacotron.png" width=700 /> <br>
 </div>
 
 **Advantage of Tacotron:**
@@ -89,7 +89,7 @@ At present, there are two mainstream acoustic model structures.
  - The alignment matrix of previous time is considered at the step `t` of decoder.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/tacotron2.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/tacotron2.png" width=500 /> <br>
 </div>
 
 You can find PaddleSpeech TTS's tacotron2 with LJSpeech dataset example at [examples/ljspeech/tts0](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts0).
@@ -118,7 +118,7 @@ Transformer TTS is a combination of Tacotron2 and Transformer.
  - Positional Encoding.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/transformer.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/transformer.png" width=500 /> <br>
 </div>
 
 #### Transformer TTS
@@ -138,7 +138,7 @@ Transformer TTS is a seq2seq acoustic model based on Transformer and Tacotron2.
  - Uniform scale position encoding may have a negative impact on input or output sequences.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/transformer_tts.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/transformer_tts.png" width=500 /> <br>
 </div>
 
 **Disadvantages of Transformer TTS:**
@@ -184,14 +184,14 @@ Instead of using the encoder-attention-decoder based architecture as adopted by
 • Can be generated in parallel (decoding time is less affected by sequence length)
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastspeech.png" width=800 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/fastspeech.png" width=800 /> <br>
 </div>
 
 #### FastPitch
 [FastPitch](https://arxiv.org/abs/2006.06873) follows FastSpeech. A single pitch value is predicted for every temporal location, which improves the overall quality of synthesized speech.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastpitch.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/fastpitch.png" width=500 /> <br>
 </div>
 
 #### FastSpeech2
@@ -209,7 +209,7 @@ Instead of using the encoder-attention-decoder based architecture as adopted by
 FastSpeech2 is similar to FastPitch but introduces more variation information of speech.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/fastspeech2.png" width=800 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/fastspeech2.png" width=800 /> <br>
 </div>
 
 You can find PaddleSpeech TTS's FastSpeech2/FastPitch with CSMSC dataset example at [examples/csmsc/tts3](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts3), We use token-averaged pitch and energy values introduced in FastPitch rather than frame level ones in FastSpeech2.
@@ -223,7 +223,7 @@ You can find PaddleSpeech TTS's FastSpeech2/FastPitch with CSMSC dataset example
 - Describe a simple data augmentation technique that can be used early in the training to make the teacher network robust to sequential error propagation.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/speedyspeech.png" width=500 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/speedyspeech.png" width=500 /> <br>
 </div>
 
 You can find PaddleSpeech TTS's SpeedySpeech with CSMSC dataset example at [examples/csmsc/tts2](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts2).
@@ -289,7 +289,7 @@ You can find PaddleSpeech TTS's WaveFlow with LJSpeech dataset example at [examp
 - Multi-resolution STFT loss.
 
 <div align="left">
- <img src="https://paddlespeech.bj.bcebos.com/Parakeet/docs/images/pwg.png" width=600 /> <br>
+ <img src="https://raw.githubusercontent.com/PaddlePaddle/DeepSpeech/develop/docs/images/pwg.png" width=600 /> <br>
 </div>
 
 You can find PaddleSpeech TTS's Parallel WaveGAN with CSMSC example at [examples/csmsc/voc1](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1).
diff --git a/examples/other/text_frontend/README.md b/examples/other/text_frontend/README.md
@@ -21,72 +21,18 @@ Run the command below to get the results of test.
 ```
 The `avg WER` of g2p is: 0.027495061517943988
 ```text
-
- SYSTEM SUMMARY PERCENTAGES by SPEAKER 
-
- ,------------------------------------------------------------------------.
- | ./exp/g2p/text.g2p |
- |------------------------------------------------------------------------|
- | SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
- |------+-----------------+-----------------------------------------------|
- | bak | 9996 299181 | 290969 8198 14 14 8226 5249 |
- |========================================================================|
- | Sum | 9996 299181 | 290969 8198 14 14 8226 5249 |
- |========================================================================|
- | Mean |9996.0 299181.0 |290969.0 8198.0 14.0 14.0 8226.0 5249.0 |
- | S.D. | 0.0 0.0 | 0.0 0.0 0.0 0.0 0.0 0.0 |
- |Median|9996.0 299181.0 |290969.0 8198.0 14.0 14.0 8226.0 5249.0 |
- `------------------------------------------------------------------------'
-
- SYSTEM SUMMARY PERCENTAGES by SPEAKER 
-
  ,--------------------------------------------------------------------.
- | ./exp/g2p/text.g2p |
- |--------------------------------------------------------------------|
- | SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
+ | | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
  |--------+-----------------+-----------------------------------------|
- | bak | 9996 299181 | 97.3 2.7 0.0 0.0 2.7 52.5 |
- |====================================================================|
  | Sum/Avg| 9996 299181 | 97.3 2.7 0.0 0.0 2.7 52.5 |
- |====================================================================|
- | Mean |9996.0 299181.0 | 97.3 2.7 0.0 0.0 2.7 52.5 |
- | S.D. | 0.0 0.0 | 0.0 0.0 0.0 0.0 0.0 0.0 |
- | Median |9996.0 299181.0 | 97.3 2.7 0.0 0.0 2.7 52.5 |
  `--------------------------------------------------------------------'
 ```
 
 The `avg CER` of text normalization is: 0.006388318503308237
 ```text
-
- SYSTEM SUMMARY PERCENTAGES by SPEAKER 
-
- ,----------------------------------------------------------------.
- | ./exp/textnorm/text.tn |
- |----------------------------------------------------------------|
- | SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
- |------+--------------+------------------------------------------|
- | utt | 125 2254 | 2241 2 11 2 15 4 |
- |================================================================|
- | Sum | 125 2254 | 2241 2 11 2 15 4 |
- |================================================================|
- | Mean |125.0 2254.0 |2241.0 2.0 11.0 2.0 15.0 4.0 |
- | S.D. | 0.0 0.0 | 0.0 0.0 0.0 0.0 0.0 0.0 |
- |Median|125.0 2254.0 |2241.0 2.0 11.0 2.0 15.0 4.0 |
- `----------------------------------------------------------------'
-
- SYSTEM SUMMARY PERCENTAGES by SPEAKER 
-
  ,-----------------------------------------------------------------.
- | ./exp/textnorm/text.tn |
- |-----------------------------------------------------------------|
- | SPKR | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
+ | | # Snt # Wrd | Corr Sub Del Ins Err S.Err |
  |--------+--------------+-----------------------------------------|
- | utt | 125 2254 | 99.4 0.1 0.5 0.1 0.7 3.2 |
- |=================================================================|
  | Sum/Avg| 125 2254 | 99.4 0.1 0.5 0.1 0.7 3.2 |
- |=================================================================|
- | Mean |125.0 2254.0 | 99.4 0.1 0.5 0.1 0.7 3.2 |
- | S.D. | 0.0 0.0 | 0.0 0.0 0.0 0.0 0.0 0.0 |
- | Median |125.0 2254.0 | 99.4 0.1 0.5 0.1 0.7 3.2 |
  `-----------------------------------------------------------------'
 ```
diff --git a/examples/tiny/s0/README.md b/examples/tiny/s0/README.md
@@ -37,4 +37,3 @@
  ```bash
  bash local/export.sh ckpt_path saved_jit_model_path
  ```
-
diff --git a/parakeet/data/batch.py b/parakeet/data/batch.py
@@ -53,8 +53,8 @@ def batch_text_id(minibatch, pad_id=0, dtype=np.int64):
  peek_example = minibatch[0]
  assert len(peek_example.shape) == 1, "text example is an 1D tensor"
 
- lengths = [example.shape[0] for example in
- minibatch] # assume (channel, n_samples) or (n_samples, )
+ lengths = [example.shape[0] for example in minibatch
+ ] # assume (channel, n_samples) or (n_samples, )
  max_len = np.max(lengths)
 
  batch = []

diff --git a/parakeet/exps/tacotron2/ljspeech.py b/parakeet/exps/tacotron2/ljspeech.py
@@ -67,16 +67,19 @@ def __call__(self, examples):
 
  # Sort by text_len in descending order
  texts = [
- i for i, _ in sorted(
+ i
+ for i, _ in sorted(
  zip(texts, text_lens), key=lambda x: x[1], reverse=True)
  ]
  mels = [
- i for i, _ in sorted(
+ i
+ for i, _ in sorted(
  zip(mels, text_lens), key=lambda x: x[1], reverse=True)
  ]
 
  mel_lens = [
- i for i, _ in sorted(
+ i
+ for i, _ in sorted(
  zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
  ]