add readme

examples/opencpop/svs1/README.md

@@ -0,0 +1,174 @@
+([简体中文](./README_cn.md)|English)
+# DiffSinger with Opencpop
+This example contains code used to train a [DiffSinger](https://arxiv.org/abs/2105.02446) model with [Mandarin singing corpus](https://wenet.org.cn/opencpop/).
+
+## Dataset
+### Download and Extract
+Download Opencpop from it's [Official Website](https://wenet.org.cn/opencpop/download/) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/Opencpop`.
+
+## Get Started
+Assume the path to the dataset is `~/datasets/Opencpop`.
+Run the command below to
+1. **source path**.
+2. preprocess the dataset.
+3. train the model.
+4. synthesize wavs.
+    - synthesize waveform from `metadata.jsonl`.
+    - (Supporting) synthesize waveform from a text file. 
+5. (Supporting) inference using the static model.
+```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 ${conf_path}
+```
+When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
+
+```text
+dump
+├── dev
+│   ├── norm
+│   └── raw
+├── phone_id_map.txt
+├── speaker_id_map.txt
+├── test
+│   ├── norm
+│   └── raw
+└── train
+    ├── energy_stats.npy
+    ├── norm
+    ├── pitch_stats.npy
+    ├── raw
+    ├── speech_stats.npy
+    └── speech_stretchs.npy
+
+```
+The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains speech, pitch and energy features of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/*_stats.npy`. `speech_stretchs.npy` contains the minimum and maximum values of each dimension of the mel spectrum, which is used for linear stretching before training/inference of the diffusion module.
+Note: Since the training effect of non-norm features is due to norm, the features saved under `norm` are features that have not been normed.
+
+
+Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains utterance id, speaker id, phones, text_lengths, speech_lengths, phone durations, the path of speech features, the path of pitch features, the path of energy features, note, note durations, slur.
+
+### Model Training
+```bash
+CUDA_VISIBLE_DEVICES=${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]
+                [--speaker-dict SPEAKER_DICT] [--speech-stretchs SPEECH_STRETCHS]
+
+Train a FastSpeech2 model.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       fastspeech2 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.
+  --phones-dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker-dict SPEAKER_DICT
+                        speaker id map file for multiple speaker model.
+  --speech-stretchs SPEECH_STRETCHS
+                        min amd max mel for stretching.
+```
+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.
+6. `--speech-stretchs` is the path of mel's min-max data file.
+
+### Synthesizing
+We use parallel wavegan as the neural vocoder.
+Download pretrained parallel wavegan model from [pwgan_opencpop_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/pwgan_opencpop_ckpt_1.4.0.zip) and unzip it.
+```bash
+unzip pwgan_opencpop_ckpt_1.4.0.zip
+```
+Parallel WaveGAN checkpoint contains files listed below.
+```text
+pwgan_opencpop_ckpt_1.4.0.zip
+├── default.yaml                   # default config used to train parallel wavegan
+├── snapshot_iter_100000.pdz       # model parameters of parallel wavegan
+└── feats_stats.npy                # statistics used to normalize spectrogram when training parallel wavegan
+```
+`./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]
+                     [--am {diffsinger_opencpop}]
+                     [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
+                     [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
+                     [--voc {pwgan_opencpop}]
+                     [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
+                     [--voc_stat VOC_STAT] [--ngpu NGPU]
+                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
+                     [--speech_stretchs SPEECH_STRETCHS]
+
+Synthesize with acoustic model & vocoder
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}
+                        Choose acoustic model type of tts task.
+  --am_config AM_CONFIG
+                        Config of acoustic model.
+  --am_ckpt AM_CKPT     Checkpoint file of acoustic model.
+  --am_stat AM_STAT     mean and standard deviation used to normalize
+                        spectrogram when training acoustic model.
+  --phones_dict PHONES_DICT
+                        phone vocabulary file.
+  --tones_dict TONES_DICT
+                        tone vocabulary file.
+  --speaker_dict SPEAKER_DICT
+                        speaker id map file.
+  --voice-cloning VOICE_CLONING
+                        whether training voice cloning model.
+  --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,wavernn_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,style_melgan_csmsc}
+                        Choose vocoder type of tts task.
+  --voc_config VOC_CONFIG
+                        Config of voc.
+  --voc_ckpt VOC_CKPT   Checkpoint file of voc.
+  --voc_stat VOC_STAT   mean and standard deviation used to normalize
+                        spectrogram when training voc.
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --test_metadata TEST_METADATA
+                        test metadata.
+  --output_dir OUTPUT_DIR
+                        output dir.
+  --speech-stretchs     mel min and max values file.
+```
+
+
+## Pretrained Model
+Pretrained DiffSinger model:
+- [diffsinger_opencpop_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/diffsinger_opencpop_ckpt_1.4.0.zip)
+
+DiffSinger checkpoint contains files listed below.
+```text
+diffsinger_opencpop_ckpt_1.4.0.zip
+├── default.yaml             # default config used to train diffsinger
+├── energy_stats.npy         # statistics used to normalize energy when training diffsinger if norm is needed
+├── phone_id_map.txt         # phone vocabulary file when training diffsinger
+├── pitch_stats.npy          # statistics used to normalize pitch when training diffsinger if norm is needed 
+├── snapshot_iter_160000.pdz # model parameters of diffsinger
+├── speech_stats.npy         # statistics used to normalize mel when training diffsinger if norm is needed
+└── speech_stretchs.npy      # Min and max values to use for mel spectral stretching before training diffusion
+
+```
+At present, the text frontend is not perfect, and the method of `synthesize_e2e` is not supported for synthesizing audio. Try using `synthesize` first.

examples/opencpop/svs1/README_cn.md

@@ -0,0 +1,179 @@
+(简体中文|[English](./README.md))
+# 用 Opencpop 数据集训练 DiffSinger 模型
+
+本用例包含用于训练 [DiffSinger](https://arxiv.org/abs/2105.02446) 模型的代码，使用 [Mandarin singing corpus](https://wenet.org.cn/opencpop/) 数据集。
+
+## 数据集
+### 下载并解压
+从 [官方网站](https://wenet.org.cn/opencpop/download/) 下载数据集
+
+## 开始
+假设数据集的路径是 `~/datasets/Opencpop`.
+运行下面的命令会进行如下操作：
+
+1. **设置原路径**。
+2. 对数据集进行预处理。
+3. 训练模型
+4. 合成波形
+    - 从 `metadata.jsonl` 合成波形。
+    - （支持中）从文本文件合成波形。
+5. （支持中）使用静态模型进行推理。
+```bash
+./run.sh
+```
+您可以选择要运行的一系列阶段，或者将 `stage` 设置为 `stop-stage` 以仅使用一个阶段，例如，运行以下命令只会预处理数据集。
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### 数据预处理
+```bash
+./local/preprocess.sh ${conf_path}
+```
+当它完成时。将在当前目录中创建 `dump` 文件夹。转储文件夹的结构如下所示。
+
+```text
+dump
+├── dev
+│   ├── norm
+│   └── raw
+├── phone_id_map.txt
+├── speaker_id_map.txt
+├── test
+│   ├── norm
+│   └── raw
+└── train
+    ├── energy_stats.npy
+    ├── norm
+    ├── pitch_stats.npy
+    ├── raw
+    ├── speech_stats.npy
+    └── speech_stretchs.npy
+```
+
+数据集分为三个部分，即 `train` 、 `dev` 和 `test` ，每个部分都包含一个 `norm` 和 `raw` 子文件夹。原始文件夹包含每个话语的语音、音调和能量特征，而 `norm` 文件夹包含规范化的特征。用于规范化特征的统计数据是从 `dump/train/*_stats.npy` 中的训练集计算出来的。`speech_stretchs.npy` 中包含 mel谱每个维度上的最小值和最大值，用于 diffusion 模块训练/推理前的线性拉伸。
+注意：由于非 norm 特征训练效果由于 norm，因此 `norm` 下保存的特征是未经过 norm 的特征。
+
+
+此外，还有一个 `metadata.jsonl` 在每个子文件夹中。它是一个类似表格的文件，包含话语id，音色id，音素、文本长度、语音长度、音素持续时间、语音特征路径、音调特征路径、能量特征路径、音调，音调持续时间，是否为转音。
+
+### 模型训练
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
+```
+`./local/train.sh` 调用 `${BIN_DIR}/train.py` 。
+以下是完整的帮助信息。
+
+```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]
+                [--speaker-dict SPEAKER_DICT] [--speech-stretchs SPEECH_STRETCHS]
+
+Train a DiffSinger model.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       fastspeech2 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.
+  --phones-dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker-dict SPEAKER_DICT
+                        speaker id map file for multiple speaker model.
+  --speech-stretchs SPEECH_STRETCHS
+                        min amd max mel for stretching.
+```
+1. `--config` 是一个 yaml 格式的配置文件，用于覆盖默认配置，位于 `conf/default.yaml`.
+2. `--train-metadata` 和 `--dev-metadata` 应为 `dump` 文件夹中 `train` 和 `dev` 下的规范化元数据文件
+3. `--output-dir` 是保存结果的目录。 检查点保存在此目录中的 `checkpoints/` 目录下。
+4. `--ngpu` 要使用的 GPU 数，如果 ngpu==0，则使用 cpu 。
+5. `--phones-dict` 是音素词汇表文件的路径。
+6. `--speech-stretchs` mel的最小最大值数据的文件路径。
+
+### 合成
+我们使用 parallel opencpop 作为神经声码器（vocoder）。
+从 [pwgan_opencpop_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/pwgan_opencpop_ckpt_1.4.0.zip) 下载预训练的 parallel wavegan 模型并将其解压。
+
+```bash
+unzip pwgan_opencpop_ckpt_1.4.0.zip
+```
+Parallel WaveGAN 检查点包含如下文件。
+```text
+pwgan_opencpop_ckpt_1.4.0.zip
+├── default.yaml               # 用于训练 parallel wavegan 的默认配置
+├── snapshot_iter_100000.pdz   # parallel wavegan 的模型参数
+└── feats_stats.npy            # 训练平行波形时用于规范化谱图的统计数据
+```
+`./local/synthesize.sh` 调用 `${BIN_DIR}/../synthesize.py` 即可从 `metadata.jsonl`中合成波形。
+
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
+```
+```text
+usage: synthesize.py [-h]
+                     [--am {diffsinger_opencpop}]
+                     [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
+                     [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
+                     [--voc {pwgan_opencpop}]
+                     [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
+                     [--voc_stat VOC_STAT] [--ngpu NGPU]
+                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
+                     [--speech_stretchs SPEECH_STRETCHS]
+
+Synthesize with acoustic model & vocoder
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech,tacotron2_aishell3}
+                        Choose acoustic model type of tts task.
+  --am_config AM_CONFIG
+                        Config of acoustic model.
+  --am_ckpt AM_CKPT     Checkpoint file of acoustic model.
+  --am_stat AM_STAT     mean and standard deviation used to normalize
+                        spectrogram when training acoustic model.
+  --phones_dict PHONES_DICT
+                        phone vocabulary file.
+  --tones_dict TONES_DICT
+                        tone vocabulary file.
+  --speaker_dict SPEAKER_DICT
+                        speaker id map file.
+  --voice-cloning VOICE_CLONING
+                        whether training voice cloning model.
+  --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,wavernn_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,style_melgan_csmsc}
+                        Choose vocoder type of tts task.
+  --voc_config VOC_CONFIG
+                        Config of voc.
+  --voc_ckpt VOC_CKPT   Checkpoint file of voc.
+  --voc_stat VOC_STAT   mean and standard deviation used to normalize
+                        spectrogram when training voc.
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --test_metadata TEST_METADATA
+                        test metadata.
+  --output_dir OUTPUT_DIR
+                        output dir.
+  --speech-stretchs     mel min and max values file.
+```
+
+## 预训练模型
+预先训练的 DiffSinger 模型：
+- [diffsinger_opencpop_ckpt_1.4.0.zip](https://paddlespeech.bj.bcebos.com/t2s/svs/opencpop/diffsinger_opencpop_ckpt_1.4.0.zip)
+
+
+DiffSinger 检查点包含下列文件。
+```text
+diffsinger_opencpop_ckpt_1.4.0.zip
+├── default.yaml             # 用于训练 diffsinger 的默认配置
+├── energy_stats.npy         # 训练 diffsinger 时如若需要 norm energy 会使用到的统计数据 
+├── phone_id_map.txt         # 训练 diffsinger 时的音素词汇文件
+├── pitch_stats.npy          # 训练 diffsinger 时如若需要 norm pitch 会使用到的统计数据 
+├── snapshot_iter_160000.pdz # 模型参数和优化器状态
+├── speech_stats.npy         # 训练 diffsinger 时用于规范化频谱图的统计数据
+└── speech_stretchs.npy      # 训练 diffusion 前用于 mel 谱拉伸的最小及最大值
+
+```
+目前文本前端未完善，暂不支持 `synthesize_e2e` 的方式合成音频。尝试效果可先使用 `synthesize`。

examples/opencpop/svs1/local/preprocess.sh

@@ -10,9 +10,9 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     echo "Extract features ..."
     python3 ${BIN_DIR}/preprocess.py \
         --dataset=opencpop \
-        --rootdir=~/datasets/SVS/Opencpop/segments \
+        --rootdir=~/datasets/Opencpop/segments \
         --dumpdir=dump \
-        --label-file=~/datasets/SVS/Opencpop/segments/transcriptions.txt \
+        --label-file=~/datasets/Opencpop/segments/transcriptions.txt \
         --config=${config_path} \
         --num-cpu=20 \
         --cut-sil=True
@@ -68,7 +68,7 @@ fi
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # Get feature(mel) extremum for diffusion stretch
     echo "Get feature(mel) extremum  ..."
-    python3 ${BIN_DIR}/computer_extremum.py \
+    python3 ${BIN_DIR}/get_minmax.py \
         --metadata=dump/train/norm/metadata.jsonl \
         --speech-stretchs=dump/train/speech_stretchs.npy
 fi