diff --git a/examples/csmsc/tts0/README.md b/examples/csmsc/tts0/README.md deleted file mode 100644 index 13d291b5c390328ada4cbbbf580633ed4a194ffb..0000000000000000000000000000000000000000 --- a/examples/csmsc/tts0/README.md +++ /dev/null @@ -1,264 +0,0 @@ -# FastSpeech2 with CSMSC -This example contains code used to train a [Fastspeech2](https://arxiv.org/abs/2006.04558) 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 durations for fastspeech2. -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. -5. 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 ${conf_path} -``` -When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below. - -```text -dump -├── dev -│ ├── norm -│ └── raw -├── phone_id_map.txt -├── speaker_id_map.txt -├── test -│ ├── norm -│ └── raw -└── train - ├── energy_stats.npy - ├── norm - ├── pitch_stats.npy - ├── raw - └── speech_stats.npy -``` -The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains speech、pitch and energy features of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/*_stats.npy`. - -Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, speech_lengths, durations, the path of speech features, the path of pitch features, the path of energy features, speaker, and the id of each utterance. - -### Model Training -```bash -CUDA_VISIBLE_DEVICES=${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] - [--speaker-dict SPEAKER_DICT] [--voice-cloning VOICE_CLONING] - -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. - --voice-cloning VOICE_CLONING - whether training voice cloning model. -``` -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 -We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder. -Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it. -```bash -unzip pwg_baker_ckpt_0.4.zip -``` -Parallel WaveGAN checkpoint contains files listed below. -```text -pwg_baker_ckpt_0.4 -├── pwg_default.yaml # default config used to train parallel wavegan -├── pwg_snapshot_iter_400000.pdz # model parameters of parallel wavegan -└── pwg_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} ${ckpt_name} -``` -```text -usage: synthesize.py [-h] - [--am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}] - [--am_config AM_CONFIG] [--am_ckpt AM_CKPT] - [--am_stat AM_STAT] [--phones_dict PHONES_DICT] - [--tones_dict TONES_DICT] [--speaker_dict SPEAKER_DICT] - [--voice-cloning VOICE_CLONING] - [--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}] - [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT] - [--voc_stat VOC_STAT] [--ngpu NGPU] - [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR] - -Synthesize with acoustic model & vocoder - -optional arguments: - -h, --help show this help message and exit - --am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk} - Choose acoustic model type of tts task. - --am_config AM_CONFIG - Config of acoustic model. Use deault config when it is - None. - --am_ckpt AM_CKPT Checkpoint file of acoustic model. - --am_stat AM_STAT mean and standard deviation used to normalize - spectrogram when training acoustic model. - --phones_dict PHONES_DICT - phone vocabulary file. - --tones_dict TONES_DICT - tone vocabulary file. - --speaker_dict SPEAKER_DICT - speaker id map file. - --voice-cloning VOICE_CLONING - whether training voice cloning model. - --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc} - Choose vocoder type of tts task. - --voc_config VOC_CONFIG - Config of voc. Use deault config when it is None. - --voc_ckpt VOC_CKPT Checkpoint file of voc. - --voc_stat VOC_STAT mean and standard deviation used to normalize - spectrogram when training voc. - --ngpu NGPU if ngpu == 0, use cpu. - --test_metadata TEST_METADATA - test metadata. - --output_dir OUTPUT_DIR - output dir. -``` -`./local/synthesize_e2e.sh` calls `${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} -``` -```text -usage: synthesize_e2e.py [-h] - [--am {speedyspeech_csmsc,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk}] - [--am_config AM_CONFIG] [--am_ckpt AM_CKPT] - [--am_stat AM_STAT] [--phones_dict PHONES_DICT] - [--tones_dict TONES_DICT] - [--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID] - [--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc}] - [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT] - [--voc_stat VOC_STAT] [--lang LANG] - [--inference_dir INFERENCE_DIR] [--ngpu NGPU] - [--text TEXT] [--output_dir OUTPUT_DIR] - -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} - Choose acoustic model type of tts task. - --am_config AM_CONFIG - Config of acoustic model. Use deault config when it is - None. - --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. - --spk_id SPK_ID spk id for multi speaker acoustic model - --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc} - Choose vocoder type of tts task. - --voc_config VOC_CONFIG - Config of voc. Use deault config when it is None. - --voc_ckpt VOC_CKPT Checkpoint file of voc. - --voc_stat VOC_STAT mean and standard deviation used to normalize - spectrogram when training voc. - --lang LANG Choose model language. zh or en - --inference_dir INFERENCE_DIR - dir to save inference models - --ngpu NGPU if ngpu == 0, use cpu. - --text TEXT text to synthesize, a 'utt_id sentence' pair per line. - --output_dir OUTPUT_DIR - output dir. -``` -1. `--am` is acoustic model type with the format {model_name}_{dataset} -2. `--am_config`, `--am_checkpoint`, `--am_stat` and `--phones_dict` are arguments for acoustic model, which correspond to the 4 files in the fastspeech2 pretrained model. -3. `--voc` is vocoder type with the format {model_name}_{dataset} -4. `--voc_config`, `--voc_checkpoint`, `--voc_stat` are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model. -5. `--lang` is the model language, which can be `zh` or `en`. -6. `--test_metadata` should be the metadata file in the normalized subfolder of `test` in the `dump` folder. -7. `--text` is the text file, which contains sentences to synthesize. -8. `--output_dir` is the directory to save synthesized audio files. -9. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu. - -### Inferencing -After synthesizing, we will get static models of fastspeech2 and pwgan in `${train_output_path}/inference`. -`./local/inference.sh` calls `${BIN_DIR}/inference.py`, which provides a paddle static model inference example for fastspeech2 + pwgan synthesize. -```bash -CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} -``` - -## Pretrained Model -Pretrained FastSpeech2 model with no silence in the edge of audios: -- [fastspeech2_nosil_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_ckpt_0.4.zip) -- [fastspeech2_conformer_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_conformer_baker_ckpt_0.5.zip) - -The static model can be downloaded here [fastspeech2_nosil_baker_static_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_baker_static_0.4.zip). - -Model | Step | eval/loss | eval/l1_loss | eval/duration_loss | eval/pitch_loss| eval/energy_loss -:-------------:| :------------:| :-----: | :-----: | :--------: |:--------:|:---------: -default| 2(gpu) x 76000|1.0991|0.59132|0.035815|0.31915|0.15287| -conformer| 2(gpu) x 76000|1.0675|0.56103|0.035869|0.31553|0.15509| - -FastSpeech2 checkpoint contains files listed below. -```text -fastspeech2_nosil_baker_ckpt_0.4 -├── default.yaml # default config used to train fastspeech2 -├── phone_id_map.txt # phone vocabulary file when training fastspeech2 -├── snapshot_iter_76000.pdz # model parameters and optimizer states -└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2 -``` -You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained fastspeech2 and parallel wavegan models. -```bash -source path.sh - -FLAGS_allocator_strategy=naive_best_fit \ -FLAGS_fraction_of_gpu_memory_to_use=0.01 \ -python3 ${BIN_DIR}/../synthesize_e2e.py \ - --am=fastspeech2_csmsc \ - --am_config=fastspeech2_nosil_baker_ckpt_0.4/default.yaml \ - --am_ckpt=fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \ - --am_stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \ - --voc=pwgan_csmsc \ - --voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \ - --voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \ - --voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \ - --lang=zh \ - --text=${BIN_DIR}/../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/paddlespeech/t2s/exps/new_tacotron2/__init__.py b/paddlespeech/t2s/exps/new_tacotron2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abf198b97e6e818e1fbe59006f98492640bcee54 --- /dev/null +++ b/paddlespeech/t2s/exps/new_tacotron2/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2020 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. diff --git a/paddlespeech/t2s/models/__init__.py b/paddlespeech/t2s/models/__init__.py index f268a4e3359ecbee3a3b478b7cb94c31b145487e..65227374ed7550a219665aa559611747a3bc7f8c 100644 --- a/paddlespeech/t2s/models/__init__.py +++ b/paddlespeech/t2s/models/__init__.py @@ -14,6 +14,7 @@ from .fastspeech2 import * from .hifigan import * from .melgan import * +from .new_tacotron2 import * from .parallel_wavegan import * from .speedyspeech import * from .tacotron2 import * diff --git a/paddlespeech/t2s/models/new_tacotron2/tacotron2.py b/paddlespeech/t2s/models/new_tacotron2/tacotron2.py index 747c74f9aad1c41bc63f4035b1e9b11542cbd183..c8ef956cef632c269e914bdc8cf72d64405afd7a 100644 --- a/paddlespeech/t2s/models/new_tacotron2/tacotron2.py +++ b/paddlespeech/t2s/models/new_tacotron2/tacotron2.py @@ -77,9 +77,9 @@ class Tacotron2(nn.Layer): spk_embed_dim: Optional[int]=None, spk_embed_integration_type: str="concat", dropout_rate: float=0.5, - zoneout_rate: float=0.1, + zoneout_rate: float=0.1, # training related - init_type: str="xavier_uniform",): + init_type: str="xavier_uniform", ): """Initialize Tacotron2 module. Parameters ---------- @@ -243,7 +243,7 @@ class Tacotron2(nn.Layer): dropout_rate=dropout_rate, zoneout_rate=zoneout_rate, reduction_factor=reduction_factor, ) - + nn.initializer.set_global_initializer(None) def forward( diff --git a/paddlespeech/t2s/modules/losses.py b/paddlespeech/t2s/modules/losses.py index 0cb0c6fd1a4dfe169c403603c3723881c416ec41..781ac7924fd5516a7867304706c46eed81a3ff81 100644 --- a/paddlespeech/t2s/modules/losses.py +++ b/paddlespeech/t2s/modules/losses.py @@ -20,7 +20,7 @@ from paddle.fluid.layers import sequence_mask from paddle.nn import functional as F from scipy import signal -from paddlespeech.s2t.modules.mask import make_non_pad_mask +from paddlespeech.t2s.modules.nets_utils import make_non_pad_mask # Loss for new Tacotron2 @@ -324,7 +324,7 @@ def stft(x, details. Defaults to "hann". center : bool, optional center (bool, optional): Whether to pad `x` to make that the - :math:`t \times hop\_length` at the center of :math:`t`-th frame. Default: `True`. + :math:`t \times hop\\_length` at the center of :math:`t`-th frame. Default: `True`. pad_mode : str, optional Choose padding pattern when `center` is `True`. Returns @@ -677,7 +677,8 @@ def weighted_mean(input, weight): Weighted mean tensor with the same dtype as input. """ weight = paddle.cast(weight, input.dtype) - broadcast_ratio = input.size / weight.size + # paddle.Tensor.size is different with torch.size() and has been overrided in s2t.__init__ + broadcast_ratio = input.numel() / weight.numel() return paddle.sum(input * weight) / (paddle.sum(weight) * broadcast_ratio) diff --git a/paddlespeech/t2s/modules/tacotron2/encoder.py b/paddlespeech/t2s/modules/tacotron2/encoder.py index 2f88d307efb592169c57fa334f00edb776def11e..b2ed30d1f1cebc666e68c8555c4d69dfd1140331 100644 --- a/paddlespeech/t2s/modules/tacotron2/encoder.py +++ b/paddlespeech/t2s/modules/tacotron2/encoder.py @@ -171,7 +171,6 @@ class Encoder(nn.Layer): # (B, Tmax, C) # see https://www.paddlepaddle.org.cn/documentation/docs/zh/faq/train_cn.html#paddletorch-nn-utils-rnn-pack-padded-sequencetorch-nn-utils-rnn-pad-packed-sequenceapi xs, _ = self.blstm(xs, sequence_length=ilens) - # hlens 是什么 hlens = ilens return xs, hlens