Add prosody prediction in synthesize_e2e, test=tts (#2693)
HuangLiangJie
2 years ago
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# This example mainly follows the FastSpeech2 with CSMSC
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This example contains code used to train a rhythm version of [Fastspeech2](https://arxiv.org/abs/2006.04558) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
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## Dataset
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### Download and Extract
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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`.
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### Get MFA Result and Extract
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We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
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You can directly download the rhythm version of MFA result from here [baker_alignment_tone.zip](https://paddlespeech.bj.bcebos.com/Rhy_e2e/baker_alignment_tone.zip), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
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Remember in our repo, you should add `--rhy-with-duration` flag to obtain the rhythm information.
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## Get Started
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Assume the path to the dataset is `~/datasets/BZNSYP`.
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Assume the path to the MFA result of CSMSC is `./baker_alignment_tone`.
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Run the command below to
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1. **source path**.
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2. preprocess the dataset.
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3. train the model.
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4. synthesize wavs.
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- synthesize waveform from `metadata.jsonl`.
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- synthesize waveform from a text file.
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5. inference using the static model.
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```bash
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./run.sh
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```
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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.
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```bash
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./run.sh --stage 0 --stop-stage 0
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```
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### Data Preprocessing
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```bash
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./local/preprocess.sh ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── phone_id_map.txt
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├── speaker_id_map.txt
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── energy_stats.npy
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├── norm
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├── pitch_stats.npy
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├── raw
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└── speech_stats.npy
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```
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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`.
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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.
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# For more details, You can refer to [FastSpeech2 with CSMSC](../tts3)
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## Pretrained Model
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Pretrained FastSpeech2 model for end-to-end rhythm version:
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- [fastspeech2_rhy_csmsc_ckpt_1.3.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_rhy_csmsc_ckpt_1.3.0.zip)
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This FastSpeech2 checkpoint contains files listed below.
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```text
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fastspeech2_rhy_csmsc_ckpt_1.3.0
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├── default.yaml # default config used to train fastspeech2
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├── phone_id_map.txt # phone vocabulary file when training fastspeech2
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├── snapshot_iter_153000.pdz # model parameters and optimizer states
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├── durations.txt # the intermediate output of preprocess.sh
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├── energy_stats.npy
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├── pitch_stats.npy
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└── speech_stats.npy # statistics used to normalize spectrogram when training fastspeech2
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```
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../../tts3/conf/default.yaml
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../../tts3/local/preprocess.sh
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../../tts3/local/synthesize.sh
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#!/bin/bash
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config_path=$1
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train_output_path=$2
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ckpt_name=$3
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_csmsc \
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--voc_config=pwg_baker_ckpt_0.4/pwg_default.yaml \
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--voc_ckpt=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
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--voc_stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference \
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--use_rhy=True
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=mb_melgan_csmsc \
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--voc_config=mb_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=mb_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1000000.pdz\
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--voc_stat=mb_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference \
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--use_rhy=True
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fi
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# the pretrained models haven't release now
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# style melgan
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# style melgan's Dygraph to Static Graph is not ready now
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=style_melgan_csmsc \
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--voc_config=style_melgan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=style_melgan_csmsc_ckpt_0.1.1/snapshot_iter_1500000.pdz \
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--voc_stat=style_melgan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--use_rhy=True
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# --inference_dir=${train_output_path}/inference
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fi
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# hifigan
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "in hifigan syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_csmsc \
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--voc_config=hifigan_csmsc_ckpt_0.1.1/default.yaml \
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--voc_ckpt=hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz \
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--voc_stat=hifigan_csmsc_ckpt_0.1.1/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference \
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--use_rhy=True
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fi
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# wavernn
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "in wavernn syn_e2e"
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FLAGS_allocator_strategy=naive_best_fit \
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FLAGS_fraction_of_gpu_memory_to_use=0.01 \
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python3 ${BIN_DIR}/../synthesize_e2e.py \
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--am=fastspeech2_csmsc \
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--am_config=${config_path} \
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--am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
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--am_stat=dump/train/speech_stats.npy \
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--voc=wavernn_csmsc \
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--voc_config=wavernn_csmsc_ckpt_0.2.0/default.yaml \
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--voc_ckpt=wavernn_csmsc_ckpt_0.2.0/snapshot_iter_400000.pdz \
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--voc_stat=wavernn_csmsc_ckpt_0.2.0/feats_stats.npy \
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--lang=zh \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/test_e2e \
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--phones_dict=dump/phone_id_map.txt \
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--inference_dir=${train_output_path}/inference \
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--use_rhy=True
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fi
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../../tts3/local/train.sh
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../tts3/path.sh
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#!/bin/bash
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set -e
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source path.sh
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gpus=0,1
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stage=0
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stop_stage=100
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conf_path=conf/default.yaml
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train_output_path=exp/default
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ckpt_name=snapshot_iter_153.pdz
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# with the following command, you can choose the stage range you want to run
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# such as `./run.sh --stage 0 --stop-stage 0`
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# this can not be mixed use with `$1`, `$2` ...
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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### please place the mfa result of rhythm here
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./local/preprocess.sh ${conf_path} || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `train_output_path/checkpoints/` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# synthesize, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# synthesize_e2e, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .rhy_predictor import *
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import re
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import paddle
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import yaml
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from paddlenlp.transformers import ErnieTokenizer
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from yacs.config import CfgNode
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from paddlespeech.cli.utils import download_and_decompress
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from paddlespeech.resource.pretrained_models import rhy_frontend_models
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from paddlespeech.text.models.ernie_linear import ErnieLinear
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from paddlespeech.utils.env import MODEL_HOME
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DefinedClassifier = {
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'ErnieLinear': ErnieLinear,
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}
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model_version = '1.0'
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class RhyPredictor():
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def __init__(
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self,
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model_dir: os.PathLike=MODEL_HOME, ):
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uncompress_path = download_and_decompress(
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rhy_frontend_models['rhy_e2e'][model_version], model_dir)
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with open(os.path.join(uncompress_path, 'rhy_default.yaml')) as f:
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config = CfgNode(yaml.safe_load(f))
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self.punc_list = []
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with open(os.path.join(uncompress_path, 'rhy_token'), 'r') as f:
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for line in f:
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self.punc_list.append(line.strip())
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self.punc_list = [0] + self.punc_list
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self.make_rhy_dict()
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self.model = DefinedClassifier["ErnieLinear"](**config["model"])
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pretrained_token = config['data_params']['pretrained_token']
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self.tokenizer = ErnieTokenizer.from_pretrained(pretrained_token)
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state_dict = paddle.load(
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os.path.join(uncompress_path, 'snapshot_iter_2600_main_params.pdz'))
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self.model.set_state_dict(state_dict)
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self.model.eval()
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def _clean_text(self, text):
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text = text.lower()
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text = re.sub('[^A-Za-z0-9\u4e00-\u9fa5]', '', text)
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text = re.sub(f'[{"".join([p for p in self.punc_list][1:])}]', '', text)
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return text
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def preprocess(self, text, tokenizer):
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clean_text = self._clean_text(text)
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assert len(clean_text) > 0, f'Invalid input string: {text}'
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tokenized_input = tokenizer(
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list(clean_text), return_length=True, is_split_into_words=True)
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_inputs = dict()
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_inputs['input_ids'] = tokenized_input['input_ids']
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_inputs['seg_ids'] = tokenized_input['token_type_ids']
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_inputs['seq_len'] = tokenized_input['seq_len']
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return _inputs
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def get_prediction(self, raw_text):
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_inputs = self.preprocess(raw_text, self.tokenizer)
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seq_len = _inputs['seq_len']
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input_ids = paddle.to_tensor(_inputs['input_ids']).unsqueeze(0)
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seg_ids = paddle.to_tensor(_inputs['seg_ids']).unsqueeze(0)
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logits, _ = self.model(input_ids, seg_ids)
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preds = paddle.argmax(logits, axis=-1).squeeze(0)
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tokens = self.tokenizer.convert_ids_to_tokens(
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_inputs['input_ids'][1:seq_len - 1])
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labels = preds[1:seq_len - 1].tolist()
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assert len(tokens) == len(labels)
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# add 0 for non punc
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text = ''
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for t, l in zip(tokens, labels):
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text += t
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if l != 0: # Non punc.
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text += self.punc_list[l]
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return text
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def make_rhy_dict(self):
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self.rhy_dict = {}
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for i, p in enumerate(self.punc_list[1:]):
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self.rhy_dict[p] = 'sp' + str(i + 1)
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def pinyin_align(self, pinyins, rhy_pre):
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final_py = []
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j = 0
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for i in range(len(rhy_pre)):
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if rhy_pre[i] in self.rhy_dict:
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final_py.append(self.rhy_dict[rhy_pre[i]])
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else:
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final_py.append(pinyins[j])
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j += 1
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return final_py
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