pull/1589/head
qingen
4 years ago
commit
43d1dff1a0
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*.wav
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#!/bin/bash
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
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paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav --topk 1
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# HiFiGAN with AISHELL-3
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This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
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AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
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## Dataset
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### Download and Extract
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Download AISHELL-3.
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```bash
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wget https://www.openslr.org/resources/93/data_aishell3.tgz
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```
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Extract AISHELL-3.
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```bash
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mkdir data_aishell3
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tar zxvf data_aishell3.tgz -C data_aishell3
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```
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### Get MFA Result and Extract
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We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
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You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
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## Get Started
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Assume the path to the dataset is `~/datasets/data_aishell3`.
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Assume the path to the MFA result of AISHELL-3 is `./aishell3_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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```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, run 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 ${conf_path}
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```
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When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
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```text
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dump
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├── dev
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│ ├── norm
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│ └── raw
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├── test
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│ ├── norm
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│ └── raw
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└── train
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├── norm
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├── raw
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└── feats_stats.npy
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```
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The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
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Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
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### Model Training
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
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```
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`./local/train.sh` calls `${BIN_DIR}/train.py`.
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Here's the complete help message.
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```text
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usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
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[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
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[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
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[--run-benchmark RUN_BENCHMARK]
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[--profiler_options PROFILER_OPTIONS]
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Train a ParallelWaveGAN model.
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optional arguments:
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-h, --help show this help message and exit
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--config CONFIG config file to overwrite default config.
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--train-metadata TRAIN_METADATA
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training data.
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--dev-metadata DEV_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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benchmark:
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arguments related to benchmark.
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--batch-size BATCH_SIZE
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batch size.
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--max-iter MAX_ITER train max steps.
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--run-benchmark RUN_BENCHMARK
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runing benchmark or not, if True, use the --batch-size
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and --max-iter.
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--profiler_options PROFILER_OPTIONS
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The option of profiler, which should be in format
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"key1=value1;key2=value2;key3=value3".
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```
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1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
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2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
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3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
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4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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### Synthesizing
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`./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
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```bash
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
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```
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```text
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usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
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[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
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[--output-dir OUTPUT_DIR] [--ngpu NGPU]
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Synthesize with GANVocoder.
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optional arguments:
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-h, --help show this help message and exit
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--generator-type GENERATOR_TYPE
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type of GANVocoder, should in {pwgan, mb_melgan,
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style_melgan, } now
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--config CONFIG GANVocoder config file.
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--checkpoint CHECKPOINT
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snapshot to load.
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--test-metadata TEST_METADATA
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dev data.
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--output-dir OUTPUT_DIR
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output dir.
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--ngpu NGPU if ngpu == 0, use cpu.
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```
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1. `--config` config file. You should use the same config with which the model is trained.
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2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
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3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
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4. `--output-dir` is the directory to save the synthesized audio files.
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5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
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## Pretrained Models
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The pretrained model can be downloaded here [hifigan_aishell3_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_aishell3_ckpt_0.2.0.zip).
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Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss
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:-------------:| :------------:| :-----: | :-----: | :--------:
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default| 1(gpu) x 2500000|24.060|0.1068|7.499
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HiFiGAN checkpoint contains files listed below.
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```text
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hifigan_aishell3_ckpt_0.2.0
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├── default.yaml # default config used to train hifigan
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├── feats_stats.npy # statistics used to normalize spectrogram when training hifigan
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└── snapshot_iter_2500000.pdz # generator parameters of hifigan
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```
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## Acknowledgement
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We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
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@ -0,0 +1,168 @@
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# This is the configuration file for AISHELL-3 dataset.
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# This configuration is based on HiFiGAN V1, which is
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# an official configuration. But I found that the optimizer
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# setting does not work well with my implementation.
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# So I changed optimizer settings as follows:
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# - AdamW -> Adam
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# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
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# - Scheduler: ExponentialLR -> MultiStepLR
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# To match the shift size difference, the upsample scales
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# is also modified from the original 256 shift setting.
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # Sampling rate.
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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n_mels: 80 # Number of mel basis.
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fmin: 80 # Minimum freq in mel basis calculation. (Hz)
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fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
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###########################################################
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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generator_params:
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in_channels: 80 # Number of input channels.
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out_channels: 1 # Number of output channels.
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channels: 512 # Number of initial channels.
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kernel_size: 7 # Kernel size of initial and final conv layers.
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upsample_scales: [5, 5, 4, 3] # Upsampling scales.
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upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
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resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
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resblock_dilations: # Dilations for residual blocks.
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- [1, 3, 5]
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- [1, 3, 5]
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- [1, 3, 5]
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use_additional_convs: True # Whether to use additional conv layer in residual blocks.
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bias: True # Whether to use bias parameter in conv.
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nonlinear_activation: "leakyrelu" # Nonlinear activation type.
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nonlinear_activation_params: # Nonlinear activation paramters.
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negative_slope: 0.1
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use_weight_norm: True # Whether to apply weight normalization.
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###########################################################
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# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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discriminator_params:
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scales: 3 # Number of multi-scale discriminator.
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scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
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scale_downsample_pooling_params:
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kernel_size: 4 # Pooling kernel size.
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stride: 2 # Pooling stride.
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padding: 2 # Padding size.
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scale_discriminator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
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channels: 128 # Initial number of channels.
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max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
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max_groups: 16 # Maximum number of groups in downsampling conv layers.
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bias: True
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downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
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nonlinear_activation: "leakyrelu" # Nonlinear activation.
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nonlinear_activation_params:
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negative_slope: 0.1
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follow_official_norm: True # Whether to follow the official norm setting.
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periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
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period_discriminator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_sizes: [5, 3] # List of kernel sizes.
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channels: 32 # Initial number of channels.
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downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
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max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
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bias: True # Whether to use bias parameter in conv layer."
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nonlinear_activation: "leakyrelu" # Nonlinear activation.
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nonlinear_activation_params: # Nonlinear activation paramters.
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negative_slope: 0.1
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use_weight_norm: True # Whether to apply weight normalization.
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use_spectral_norm: False # Whether to apply spectral normalization.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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use_stft_loss: False # Whether to use multi-resolution STFT loss.
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use_mel_loss: True # Whether to use Mel-spectrogram loss.
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mel_loss_params:
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fs: 24000
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fft_size: 2048
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hop_size: 300
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win_length: 1200
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window: "hann"
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num_mels: 80
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fmin: 0
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fmax: 12000
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log_base: null
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generator_adv_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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discriminator_adv_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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use_feat_match_loss: True
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feat_match_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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average_by_layers: False # Whether to average loss by #layers in each discriminator.
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include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
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lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
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lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 16 # Batch size.
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batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
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num_workers: 2 # Number of workers in DataLoader.
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###########################################################
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# OPTIMIZER & SCHEDULER SETTING #
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###########################################################
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generator_optimizer_params:
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beta1: 0.5
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beta2: 0.9
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weight_decay: 0.0 # Generator's weight decay coefficient.
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generator_scheduler_params:
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learning_rate: 2.0e-4 # Generator's learning rate.
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gamma: 0.5 # Generator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 200000
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- 400000
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- 600000
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- 800000
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generator_grad_norm: -1 # Generator's gradient norm.
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discriminator_optimizer_params:
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beta1: 0.5
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beta2: 0.9
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weight_decay: 0.0 # Discriminator's weight decay coefficient.
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discriminator_scheduler_params:
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learning_rate: 2.0e-4 # Discriminator's learning rate.
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gamma: 0.5 # Discriminator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 200000
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- 400000
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- 600000
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- 800000
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discriminator_grad_norm: -1 # Discriminator's gradient norm.
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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generator_train_start_steps: 1 # Number of steps to start to train discriminator.
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discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
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train_max_steps: 2500000 # Number of training steps.
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save_interval_steps: 5000 # Interval steps to save checkpoint.
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eval_interval_steps: 1000 # Interval steps to evaluate the network.
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###########################################################
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# OTHER SETTING #
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###########################################################
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num_snapshots: 10 # max number of snapshots to keep while training
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seed: 42 # random seed for paddle, random, and np.random
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@ -0,0 +1,55 @@
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#!/bin/bash
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stage=0
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stop_stage=100
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config_path=$1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# get durations from MFA's result
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echo "Generate durations.txt from MFA results ..."
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python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
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--inputdir=./aishell3_alignment_tone \
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--output=durations.txt \
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--config=${config_path}
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# extract features
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echo "Extract features ..."
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python3 ${BIN_DIR}/../preprocess.py \
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--rootdir=~/datasets/data_aishell3/ \
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--dataset=aishell3 \
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--dumpdir=dump \
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--dur-file=durations.txt \
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--config=${config_path} \
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--cut-sil=True \
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--num-cpu=20
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# get features' stats(mean and std)
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echo "Get features' stats ..."
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python3 ${MAIN_ROOT}/utils/compute_statistics.py \
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--metadata=dump/train/raw/metadata.jsonl \
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--field-name="feats"
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fi
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||||
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||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# normalize, dev and test should use train's stats
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echo "Normalize ..."
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||||
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||||
python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump/train/raw/metadata.jsonl \
|
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--dumpdir=dump/train/norm \
|
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--stats=dump/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump/dev/raw/metadata.jsonl \
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||||
--dumpdir=dump/dev/norm \
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--stats=dump/train/feats_stats.npy
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python3 ${BIN_DIR}/../normalize.py \
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--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
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||||
@ -0,0 +1,14 @@
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||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--generator-type=hifigan
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@ -0,0 +1,13 @@
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#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=hifigan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,133 @@
|
||||
# HiFiGAN with the LJSpeech-1.1
|
||||
This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/).
|
||||
## Dataset
|
||||
### Download and Extract
|
||||
Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/).
|
||||
### Get MFA Result and Extract
|
||||
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut the silence in the edge of audio.
|
||||
You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.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/LJSpeech-1.1`.
|
||||
Assume the path to the MFA result of LJSpeech-1.1 is `./ljspeech_alignment`.
|
||||
Run the command below to
|
||||
1. **source path**.
|
||||
2. preprocess the dataset.
|
||||
3. train the model.
|
||||
4. synthesize wavs.
|
||||
- synthesize waveform from `metadata.jsonl`.
|
||||
```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 ${conf_path}
|
||||
```
|
||||
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
||||
|
||||
```text
|
||||
dump
|
||||
├── dev
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
├── test
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
└── train
|
||||
├── norm
|
||||
├── raw
|
||||
└── feats_stats.npy
|
||||
```
|
||||
|
||||
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
|
||||
|
||||
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
|
||||
|
||||
### Model Training
|
||||
`./local/train.sh` calls `${BIN_DIR}/train.py`.
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
|
||||
```
|
||||
Here's the complete help message.
|
||||
|
||||
```text
|
||||
usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
|
||||
[--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
|
||||
[--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
|
||||
[--run-benchmark RUN_BENCHMARK]
|
||||
[--profiler_options PROFILER_OPTIONS]
|
||||
|
||||
Train a ParallelWaveGAN model.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG config file to overwrite default config.
|
||||
--train-metadata TRAIN_METADATA
|
||||
training data.
|
||||
--dev-metadata DEV_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
|
||||
benchmark:
|
||||
arguments related to benchmark.
|
||||
|
||||
--batch-size BATCH_SIZE
|
||||
batch size.
|
||||
--max-iter MAX_ITER train max steps.
|
||||
--run-benchmark RUN_BENCHMARK
|
||||
runing benchmark or not, if True, use the --batch-size
|
||||
and --max-iter.
|
||||
--profiler_options PROFILER_OPTIONS
|
||||
The option of profiler, which should be in format
|
||||
"key1=value1;key2=value2;key3=value3".
|
||||
```
|
||||
|
||||
1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
|
||||
2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
|
||||
3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
|
||||
4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
### Synthesizing
|
||||
`./local/synthesize.sh` calls `${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] [--generator-type GENERATOR_TYPE] [--config CONFIG]
|
||||
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
|
||||
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
|
||||
|
||||
Synthesize with GANVocoder.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--generator-type GENERATOR_TYPE
|
||||
type of GANVocoder, should in {pwgan, mb_melgan,
|
||||
style_melgan, } now
|
||||
--config CONFIG GANVocoder config file.
|
||||
--checkpoint CHECKPOINT
|
||||
snapshot to load.
|
||||
--test-metadata TEST_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
```
|
||||
|
||||
1. `--config` parallel wavegan config file. You should use the same config with which the model is trained.
|
||||
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
|
||||
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
|
||||
4. `--output-dir` is the directory to save the synthesized audio files.
|
||||
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
## Pretrained Model
|
||||
|
||||
|
||||
## Acknowledgement
|
||||
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
|
||||
@ -0,0 +1,167 @@
|
||||
# This is the configuration file for LJSpeech dataset.
|
||||
# This configuration is based on HiFiGAN V1, which is an official configuration.
|
||||
# But I found that the optimizer setting does not work well with my implementation.
|
||||
# So I changed optimizer settings as follows:
|
||||
# - AdamW -> Adam
|
||||
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
|
||||
# - Scheduler: ExponentialLR -> MultiStepLR
|
||||
# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
|
||||
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 22050 # Sampling rate.
|
||||
n_fft: 1024 # FFT size (samples).
|
||||
n_shift: 256 # Hop size (samples). 11.6ms
|
||||
win_length: null # Window length (samples).
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 80 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
channels: 512 # Number of initial channels.
|
||||
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||||
upsample_scales: [8, 8, 2, 2] # Upsampling scales.
|
||||
upsample_kernel_sizes: [16, 16, 4, 4] # Kernel size for upsampling layers.
|
||||
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
|
||||
resblock_dilations: # Dilations for residual blocks.
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 22050
|
||||
fft_size: 1024
|
||||
hop_size: 256
|
||||
win_length: null
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 0
|
||||
fmax: 11025
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 16 # Batch size.
|
||||
batch_max_steps: 8192 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 2 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2500000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./ljspeech_alignment \
|
||||
--output=durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/LJSpeech-1.1/ \
|
||||
--dataset=ljspeech \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=True \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--generator-type=hifigan
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=hifigan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0,1
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
@ -0,0 +1,153 @@
|
||||
# HiFiGAN with VCTK
|
||||
This example contains code used to train a [HiFiGAN](https://arxiv.org/abs/2010.05646) model with [VCTK](https://datashare.ed.ac.uk/handle/10283/3443).
|
||||
|
||||
## Dataset
|
||||
### Download and Extract
|
||||
Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handle/10283/3443) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/VCTK-Corpus-0.92`.
|
||||
|
||||
### Get MFA Result and Extract
|
||||
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut the silence in the edge of audio.
|
||||
You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) of our repo.
|
||||
ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/other/mfa/local/reorganize_vctk.py)):
|
||||
1. `p315`, because of no text for it.
|
||||
2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them.
|
||||
|
||||
## Get Started
|
||||
Assume the path to the dataset is `~/datasets/VCTK-Corpus-0.92`.
|
||||
Assume the path to the MFA result of VCTK is `./vctk_alignment`.
|
||||
Run the command below to
|
||||
1. **source path**.
|
||||
2. preprocess the dataset.
|
||||
3. train the model.
|
||||
4. synthesize wavs.
|
||||
- synthesize waveform from `metadata.jsonl`.
|
||||
```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 ${conf_path}
|
||||
```
|
||||
When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
|
||||
|
||||
```text
|
||||
dump
|
||||
├── dev
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
├── test
|
||||
│ ├── norm
|
||||
│ └── raw
|
||||
└── train
|
||||
├── norm
|
||||
├── raw
|
||||
└── feats_stats.npy
|
||||
```
|
||||
|
||||
The dataset is split into 3 parts, namely `train`, `dev`, and `test`, each of which contains a `norm` and `raw` subfolder. The `raw` folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in `dump/train/feats_stats.npy`.
|
||||
|
||||
Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.
|
||||
|
||||
### Model Training
|
||||
```bash
|
||||
CUDA_VISIBLE_DEVICES=${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] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
|
||||
[--run-benchmark RUN_BENCHMARK]
|
||||
[--profiler_options PROFILER_OPTIONS]
|
||||
|
||||
Train a ParallelWaveGAN model.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--config CONFIG config file to overwrite default config.
|
||||
--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.
|
||||
|
||||
benchmark:
|
||||
arguments related to benchmark.
|
||||
|
||||
--batch-size BATCH_SIZE
|
||||
batch size.
|
||||
--max-iter MAX_ITER train max steps.
|
||||
--run-benchmark RUN_BENCHMARK
|
||||
runing benchmark or not, if True, use the --batch-size
|
||||
and --max-iter.
|
||||
--profiler_options PROFILER_OPTIONS
|
||||
The option of profiler, which should be in format
|
||||
"key1=value1;key2=value2;key3=value3".
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
### Synthesizing
|
||||
`./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] [--generator-type GENERATOR_TYPE] [--config CONFIG]
|
||||
[--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
|
||||
[--output-dir OUTPUT_DIR] [--ngpu NGPU]
|
||||
|
||||
Synthesize with GANVocoder.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--generator-type GENERATOR_TYPE
|
||||
type of GANVocoder, should in {pwgan, mb_melgan,
|
||||
style_melgan, } now
|
||||
--config CONFIG GANVocoder config file.
|
||||
--checkpoint CHECKPOINT
|
||||
snapshot to load.
|
||||
--test-metadata TEST_METADATA
|
||||
dev data.
|
||||
--output-dir OUTPUT_DIR
|
||||
output dir.
|
||||
--ngpu NGPU if ngpu == 0, use cpu.
|
||||
```
|
||||
|
||||
|
||||
1. `--config` config file. You should use the same config with which the model is trained.
|
||||
2. `--checkpoint` is the checkpoint to load. Pick one of the checkpoints from `checkpoints` inside the training output directory.
|
||||
3. `--test-metadata` is the metadata of the test dataset. Use the `metadata.jsonl` in the `dev/norm` subfolder from the processed directory.
|
||||
4. `--output-dir` is the directory to save the synthesized audio files.
|
||||
5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
|
||||
|
||||
## Pretrained Model
|
||||
The pretrained model can be downloaded here [hifigan_vctk_ckpt_0.2.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_vctk_ckpt_0.2.0.zip).
|
||||
|
||||
|
||||
Model | Step | eval/generator_loss | eval/mel_loss| eval/feature_matching_loss
|
||||
:-------------:| :------------:| :-----: | :-----: | :--------:
|
||||
default| 1(gpu) x 2500000|58.092|0.1234|24.384
|
||||
|
||||
HiFiGAN checkpoint contains files listed below.
|
||||
|
||||
```text
|
||||
hifigan_vctk_ckpt_0.2.0
|
||||
├── default.yaml # default config used to train hifigan
|
||||
├── feats_stats.npy # statistics used to normalize spectrogram when training hifigan
|
||||
└── snapshot_iter_2500000.pdz # generator parameters of hifigan
|
||||
```
|
||||
|
||||
## Acknowledgement
|
||||
We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
|
||||
@ -0,0 +1,168 @@
|
||||
# This is the configuration file for VCTK dataset.
|
||||
# This configuration is based on HiFiGAN V1, which is
|
||||
# an official configuration. But I found that the optimizer
|
||||
# setting does not work well with my implementation.
|
||||
# So I changed optimizer settings as follows:
|
||||
# - AdamW -> Adam
|
||||
# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
|
||||
# - Scheduler: ExponentialLR -> MultiStepLR
|
||||
# To match the shift size difference, the upsample scales
|
||||
# is also modified from the original 256 shift setting.
|
||||
###########################################################
|
||||
# FEATURE EXTRACTION SETTING #
|
||||
###########################################################
|
||||
fs: 24000 # Sampling rate.
|
||||
n_fft: 2048 # FFT size (samples).
|
||||
n_shift: 300 # Hop size (samples). 12.5ms
|
||||
win_length: 1200 # Window length (samples). 50ms
|
||||
# If set to null, it will be the same as fft_size.
|
||||
window: "hann" # Window function.
|
||||
n_mels: 80 # Number of mel basis.
|
||||
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
|
||||
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
|
||||
|
||||
###########################################################
|
||||
# GENERATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
generator_params:
|
||||
in_channels: 80 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
channels: 512 # Number of initial channels.
|
||||
kernel_size: 7 # Kernel size of initial and final conv layers.
|
||||
upsample_scales: [5, 5, 4, 3] # Upsampling scales.
|
||||
upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
|
||||
resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
|
||||
resblock_dilations: # Dilations for residual blocks.
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
- [1, 3, 5]
|
||||
use_additional_convs: True # Whether to use additional conv layer in residual blocks.
|
||||
bias: True # Whether to use bias parameter in conv.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation type.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
|
||||
###########################################################
|
||||
discriminator_params:
|
||||
scales: 3 # Number of multi-scale discriminator.
|
||||
scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
|
||||
scale_downsample_pooling_params:
|
||||
kernel_size: 4 # Pooling kernel size.
|
||||
stride: 2 # Pooling stride.
|
||||
padding: 2 # Padding size.
|
||||
scale_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
|
||||
channels: 128 # Initial number of channels.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
max_groups: 16 # Maximum number of groups in downsampling conv layers.
|
||||
bias: True
|
||||
downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params:
|
||||
negative_slope: 0.1
|
||||
follow_official_norm: True # Whether to follow the official norm setting.
|
||||
periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
|
||||
period_discriminator_params:
|
||||
in_channels: 1 # Number of input channels.
|
||||
out_channels: 1 # Number of output channels.
|
||||
kernel_sizes: [5, 3] # List of kernel sizes.
|
||||
channels: 32 # Initial number of channels.
|
||||
downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
|
||||
max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
|
||||
bias: True # Whether to use bias parameter in conv layer."
|
||||
nonlinear_activation: "leakyrelu" # Nonlinear activation.
|
||||
nonlinear_activation_params: # Nonlinear activation paramters.
|
||||
negative_slope: 0.1
|
||||
use_weight_norm: True # Whether to apply weight normalization.
|
||||
use_spectral_norm: False # Whether to apply spectral normalization.
|
||||
|
||||
|
||||
###########################################################
|
||||
# STFT LOSS SETTING #
|
||||
###########################################################
|
||||
use_stft_loss: False # Whether to use multi-resolution STFT loss.
|
||||
use_mel_loss: True # Whether to use Mel-spectrogram loss.
|
||||
mel_loss_params:
|
||||
fs: 24000
|
||||
fft_size: 2048
|
||||
hop_size: 300
|
||||
win_length: 1200
|
||||
window: "hann"
|
||||
num_mels: 80
|
||||
fmin: 0
|
||||
fmax: 12000
|
||||
log_base: null
|
||||
generator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
discriminator_adv_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
use_feat_match_loss: True
|
||||
feat_match_loss_params:
|
||||
average_by_discriminators: False # Whether to average loss by #discriminators.
|
||||
average_by_layers: False # Whether to average loss by #layers in each discriminator.
|
||||
include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
|
||||
|
||||
###########################################################
|
||||
# ADVERSARIAL LOSS SETTING #
|
||||
###########################################################
|
||||
lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
|
||||
lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
|
||||
lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
|
||||
|
||||
###########################################################
|
||||
# DATA LOADER SETTING #
|
||||
###########################################################
|
||||
batch_size: 16 # Batch size.
|
||||
batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
|
||||
num_workers: 2 # Number of workers in DataLoader.
|
||||
|
||||
###########################################################
|
||||
# OPTIMIZER & SCHEDULER SETTING #
|
||||
###########################################################
|
||||
generator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Generator's weight decay coefficient.
|
||||
generator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Generator's learning rate.
|
||||
gamma: 0.5 # Generator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
generator_grad_norm: -1 # Generator's gradient norm.
|
||||
discriminator_optimizer_params:
|
||||
beta1: 0.5
|
||||
beta2: 0.9
|
||||
weight_decay: 0.0 # Discriminator's weight decay coefficient.
|
||||
discriminator_scheduler_params:
|
||||
learning_rate: 2.0e-4 # Discriminator's learning rate.
|
||||
gamma: 0.5 # Discriminator's scheduler gamma.
|
||||
milestones: # At each milestone, lr will be multiplied by gamma.
|
||||
- 200000
|
||||
- 400000
|
||||
- 600000
|
||||
- 800000
|
||||
discriminator_grad_norm: -1 # Discriminator's gradient norm.
|
||||
|
||||
###########################################################
|
||||
# INTERVAL SETTING #
|
||||
###########################################################
|
||||
generator_train_start_steps: 1 # Number of steps to start to train discriminator.
|
||||
discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
|
||||
train_max_steps: 2500000 # Number of training steps.
|
||||
save_interval_steps: 5000 # Interval steps to save checkpoint.
|
||||
eval_interval_steps: 1000 # Interval steps to evaluate the network.
|
||||
|
||||
###########################################################
|
||||
# OTHER SETTING #
|
||||
###########################################################
|
||||
num_snapshots: 10 # max number of snapshots to keep while training
|
||||
seed: 42 # random seed for paddle, random, and np.random
|
||||
@ -0,0 +1,55 @@
|
||||
#!/bin/bash
|
||||
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
config_path=$1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# get durations from MFA's result
|
||||
echo "Generate durations.txt from MFA results ..."
|
||||
python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
|
||||
--inputdir=./vctk_alignment \
|
||||
--output=durations.txt \
|
||||
--config=${config_path}
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# extract features
|
||||
echo "Extract features ..."
|
||||
python3 ${BIN_DIR}/../preprocess.py \
|
||||
--rootdir=~/datasets/VCTK-Corpus-0.92/ \
|
||||
--dataset=vctk \
|
||||
--dumpdir=dump \
|
||||
--dur-file=durations.txt \
|
||||
--config=${config_path} \
|
||||
--cut-sil=True \
|
||||
--num-cpu=20
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# get features' stats(mean and std)
|
||||
echo "Get features' stats ..."
|
||||
python3 ${MAIN_ROOT}/utils/compute_statistics.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--field-name="feats"
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
# normalize, dev and test should use train's stats
|
||||
echo "Normalize ..."
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/train/raw/metadata.jsonl \
|
||||
--dumpdir=dump/train/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/dev/raw/metadata.jsonl \
|
||||
--dumpdir=dump/dev/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
|
||||
python3 ${BIN_DIR}/../normalize.py \
|
||||
--metadata=dump/test/raw/metadata.jsonl \
|
||||
--dumpdir=dump/test/norm \
|
||||
--stats=dump/train/feats_stats.npy
|
||||
fi
|
||||
@ -0,0 +1,14 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
ckpt_name=$3
|
||||
|
||||
FLAGS_allocator_strategy=naive_best_fit \
|
||||
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
|
||||
python3 ${BIN_DIR}/../synthesize.py \
|
||||
--config=${config_path} \
|
||||
--checkpoint=${train_output_path}/checkpoints/${ckpt_name} \
|
||||
--test-metadata=dump/test/norm/metadata.jsonl \
|
||||
--output-dir=${train_output_path}/test \
|
||||
--generator-type=hifigan
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
|
||||
config_path=$1
|
||||
train_output_path=$2
|
||||
|
||||
FLAGS_cudnn_exhaustive_search=true \
|
||||
FLAGS_conv_workspace_size_limit=4000 \
|
||||
python ${BIN_DIR}/train.py \
|
||||
--train-metadata=dump/train/norm/metadata.jsonl \
|
||||
--dev-metadata=dump/dev/norm/metadata.jsonl \
|
||||
--config=${config_path} \
|
||||
--output-dir=${train_output_path} \
|
||||
--ngpu=1
|
||||
@ -0,0 +1,13 @@
|
||||
#!/bin/bash
|
||||
export MAIN_ROOT=`realpath ${PWD}/../../../`
|
||||
|
||||
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
|
||||
export LC_ALL=C
|
||||
|
||||
export PYTHONDONTWRITEBYTECODE=1
|
||||
# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
|
||||
|
||||
MODEL=hifigan
|
||||
export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}
|
||||
@ -0,0 +1,32 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
source path.sh
|
||||
|
||||
gpus=0
|
||||
stage=0
|
||||
stop_stage=100
|
||||
|
||||
conf_path=conf/default.yaml
|
||||
train_output_path=exp/default
|
||||
ckpt_name=snapshot_iter_5000.pdz
|
||||
|
||||
# with the following command, you can choose the stage range you want to run
|
||||
# such as `./run.sh --stage 0 --stop-stage 0`
|
||||
# this can not be mixed use with `$1`, `$2` ...
|
||||
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
# prepare data
|
||||
./local/preprocess.sh ${conf_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
# train model, all `ckpt` under `train_output_path/checkpoints/` dir
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
# synthesize
|
||||
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
|
||||
fi
|
||||
@ -1,5 +1,9 @@
|
||||
# Changelog
|
||||
|
||||
Date: 2022-3-15, Author: Xiaojie Chen.
|
||||
- kaldi and librosa mfcc, fbank, spectrogram.
|
||||
- unit test and benchmark.
|
||||
|
||||
Date: 2022-2-25, Author: Hui Zhang.
|
||||
- Refactor architecture.
|
||||
- dtw distance and mcd style dtw
|
||||
- dtw distance and mcd style dtw.
|
||||
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,34 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
import unittest
|
||||
import urllib.request
|
||||
|
||||
mono_channel_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
||||
multi_channels_wav = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/cat.wav'
|
||||
|
||||
|
||||
class BackendTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.initWavInput()
|
||||
|
||||
def initWavInput(self):
|
||||
self.files = []
|
||||
for url in [mono_channel_wav, multi_channels_wav]:
|
||||
if not os.path.isfile(os.path.basename(url)):
|
||||
urllib.request.urlretrieve(url, os.path.basename(url))
|
||||
self.files.append(os.path.basename(url))
|
||||
|
||||
def initParmas(self):
|
||||
raise NotImplementedError
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,73 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import filecmp
|
||||
import os
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
|
||||
import paddleaudio
|
||||
from ..base import BackendTest
|
||||
|
||||
|
||||
class TestIO(BackendTest):
|
||||
def test_load_mono_channel(self):
|
||||
sf_data, sf_sr = sf.read(self.files[0])
|
||||
pa_data, pa_sr = paddleaudio.load(
|
||||
self.files[0], normal=False, dtype='float64')
|
||||
|
||||
self.assertEqual(sf_data.dtype, pa_data.dtype)
|
||||
self.assertEqual(sf_sr, pa_sr)
|
||||
np.testing.assert_array_almost_equal(sf_data, pa_data)
|
||||
|
||||
def test_load_multi_channels(self):
|
||||
sf_data, sf_sr = sf.read(self.files[1])
|
||||
sf_data = sf_data.T # Channel dim first
|
||||
pa_data, pa_sr = paddleaudio.load(
|
||||
self.files[1], mono=False, normal=False, dtype='float64')
|
||||
|
||||
self.assertEqual(sf_data.dtype, pa_data.dtype)
|
||||
self.assertEqual(sf_sr, pa_sr)
|
||||
np.testing.assert_array_almost_equal(sf_data, pa_data)
|
||||
|
||||
def test_save_mono_channel(self):
|
||||
waveform, sr = np.random.randint(
|
||||
low=-32768, high=32768, size=(48000), dtype=np.int16), 16000
|
||||
sf_tmp_file = 'sf_tmp.wav'
|
||||
pa_tmp_file = 'pa_tmp.wav'
|
||||
|
||||
sf.write(sf_tmp_file, waveform, sr)
|
||||
paddleaudio.save(waveform, sr, pa_tmp_file)
|
||||
|
||||
self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
|
||||
for file in [sf_tmp_file, pa_tmp_file]:
|
||||
os.remove(file)
|
||||
|
||||
def test_save_multi_channels(self):
|
||||
waveform, sr = np.random.randint(
|
||||
low=-32768, high=32768, size=(2, 48000), dtype=np.int16), 16000
|
||||
sf_tmp_file = 'sf_tmp.wav'
|
||||
pa_tmp_file = 'pa_tmp.wav'
|
||||
|
||||
sf.write(sf_tmp_file, waveform.T, sr)
|
||||
paddleaudio.save(waveform.T, sr, pa_tmp_file)
|
||||
|
||||
self.assertTrue(filecmp.cmp(sf_tmp_file, pa_tmp_file))
|
||||
for file in [sf_tmp_file, pa_tmp_file]:
|
||||
os.remove(file)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,39 @@
|
||||
# 1. Prepare
|
||||
First, install `pytest-benchmark` via pip.
|
||||
```sh
|
||||
pip install pytest-benchmark
|
||||
```
|
||||
|
||||
# 2. Run
|
||||
Run the specific script for profiling.
|
||||
```sh
|
||||
pytest melspectrogram.py
|
||||
```
|
||||
|
||||
Result:
|
||||
```sh
|
||||
========================================================================== test session starts ==========================================================================
|
||||
platform linux -- Python 3.7.7, pytest-7.0.1, pluggy-1.0.0
|
||||
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
|
||||
rootdir: /ssd3/chenxiaojie06/PaddleSpeech/DeepSpeech/paddleaudio
|
||||
plugins: typeguard-2.12.1, benchmark-3.4.1, anyio-3.5.0
|
||||
collected 4 items
|
||||
|
||||
melspectrogram.py .... [100%]
|
||||
|
||||
|
||||
-------------------------------------------------------------------------------------------------- benchmark: 4 tests -------------------------------------------------------------------------------------------------
|
||||
Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
|
||||
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
test_melspect_gpu_torchaudio 202.0765 (1.0) 360.6230 (1.0) 218.1168 (1.0) 16.3022 (1.0) 214.2871 (1.0) 21.8451 (1.0) 40;3 4,584.7001 (1.0) 286 1
|
||||
test_melspect_gpu 657.8509 (3.26) 908.0470 (2.52) 724.2545 (3.32) 106.5771 (6.54) 669.9096 (3.13) 113.4719 (5.19) 1;0 1,380.7300 (0.30) 5 1
|
||||
test_melspect_cpu_torchaudio 1,247.6053 (6.17) 2,892.5799 (8.02) 1,443.2853 (6.62) 345.3732 (21.19) 1,262.7263 (5.89) 221.6385 (10.15) 56;53 692.8637 (0.15) 399 1
|
||||
test_melspect_cpu 20,326.2549 (100.59) 20,607.8682 (57.15) 20,473.4125 (93.86) 63.8654 (3.92) 20,467.0429 (95.51) 68.4294 (3.13) 8;1 48.8438 (0.01) 29 1
|
||||
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Legend:
|
||||
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
|
||||
OPS: Operations Per Second, computed as 1 / Mean
|
||||
========================================================================== 4 passed in 21.12s ===========================================================================
|
||||
|
||||
```
|
||||
@ -0,0 +1,124 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
import urllib.request
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import paddle
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
import paddleaudio
|
||||
|
||||
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
||||
if not os.path.isfile(os.path.basename(wav_url)):
|
||||
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
|
||||
|
||||
waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
|
||||
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
|
||||
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
|
||||
|
||||
# Feature conf
|
||||
mel_conf = {
|
||||
'sr': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
}
|
||||
|
||||
mel_conf_torchaudio = {
|
||||
'sample_rate': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
'norm': 'slaney',
|
||||
'mel_scale': 'slaney',
|
||||
}
|
||||
|
||||
|
||||
def enable_cpu_device():
|
||||
paddle.set_device('cpu')
|
||||
|
||||
|
||||
def enable_gpu_device():
|
||||
paddle.set_device('gpu')
|
||||
|
||||
|
||||
log_mel_extractor = paddleaudio.features.LogMelSpectrogram(
|
||||
**mel_conf, f_min=0.0, top_db=80.0, dtype=waveform_tensor.dtype)
|
||||
|
||||
|
||||
def log_melspectrogram():
|
||||
return log_mel_extractor(waveform_tensor).squeeze(0)
|
||||
|
||||
|
||||
def test_log_melspect_cpu(benchmark):
|
||||
enable_cpu_device()
|
||||
feature_paddleaudio = benchmark(log_melspectrogram)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_log_melspect_gpu(benchmark):
|
||||
enable_gpu_device()
|
||||
feature_paddleaudio = benchmark(log_melspectrogram)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=2)
|
||||
|
||||
|
||||
mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
|
||||
**mel_conf_torchaudio, f_min=0.0)
|
||||
amplitude_to_DB = torchaudio.transforms.AmplitudeToDB('power', top_db=80.0)
|
||||
|
||||
|
||||
def melspectrogram_torchaudio():
|
||||
return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
|
||||
|
||||
|
||||
def log_melspectrogram_torchaudio():
|
||||
mel_specgram = mel_extractor_torchaudio(waveform_tensor_torch)
|
||||
return amplitude_to_DB(mel_specgram).squeeze(0)
|
||||
|
||||
|
||||
def test_log_melspect_cpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
|
||||
|
||||
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
|
||||
amplitude_to_DB = amplitude_to_DB.to('cpu')
|
||||
|
||||
feature_paddleaudio = benchmark(log_melspectrogram_torchaudio)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_log_melspect_gpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mel_extractor_torchaudio, amplitude_to_DB
|
||||
|
||||
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
|
||||
amplitude_to_DB = amplitude_to_DB.to('cuda')
|
||||
|
||||
feature_torchaudio = benchmark(log_melspectrogram_torchaudio)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
feature_librosa = librosa.power_to_db(feature_librosa, top_db=80.0)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_torchaudio.cpu(), decimal=2)
|
||||
@ -0,0 +1,108 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
import urllib.request
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import paddle
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
import paddleaudio
|
||||
|
||||
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
||||
if not os.path.isfile(os.path.basename(wav_url)):
|
||||
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
|
||||
|
||||
waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
|
||||
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
|
||||
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
|
||||
|
||||
# Feature conf
|
||||
mel_conf = {
|
||||
'sr': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
}
|
||||
|
||||
mel_conf_torchaudio = {
|
||||
'sample_rate': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
'norm': 'slaney',
|
||||
'mel_scale': 'slaney',
|
||||
}
|
||||
|
||||
|
||||
def enable_cpu_device():
|
||||
paddle.set_device('cpu')
|
||||
|
||||
|
||||
def enable_gpu_device():
|
||||
paddle.set_device('gpu')
|
||||
|
||||
|
||||
mel_extractor = paddleaudio.features.MelSpectrogram(
|
||||
**mel_conf, f_min=0.0, dtype=waveform_tensor.dtype)
|
||||
|
||||
|
||||
def melspectrogram():
|
||||
return mel_extractor(waveform_tensor).squeeze(0)
|
||||
|
||||
|
||||
def test_melspect_cpu(benchmark):
|
||||
enable_cpu_device()
|
||||
feature_paddleaudio = benchmark(melspectrogram)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_melspect_gpu(benchmark):
|
||||
enable_gpu_device()
|
||||
feature_paddleaudio = benchmark(melspectrogram)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
mel_extractor_torchaudio = torchaudio.transforms.MelSpectrogram(
|
||||
**mel_conf_torchaudio, f_min=0.0)
|
||||
|
||||
|
||||
def melspectrogram_torchaudio():
|
||||
return mel_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
|
||||
|
||||
|
||||
def test_melspect_cpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mel_extractor_torchaudio
|
||||
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cpu')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
|
||||
feature_paddleaudio = benchmark(melspectrogram_torchaudio)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_melspect_gpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mel_extractor_torchaudio
|
||||
mel_extractor_torchaudio = mel_extractor_torchaudio.to('cuda')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
|
||||
feature_torchaudio = benchmark(melspectrogram_torchaudio)
|
||||
feature_librosa = librosa.feature.melspectrogram(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_torchaudio.cpu(), decimal=3)
|
||||
@ -0,0 +1,122 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
import urllib.request
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import paddle
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
import paddleaudio
|
||||
|
||||
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
||||
if not os.path.isfile(os.path.basename(wav_url)):
|
||||
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
|
||||
|
||||
waveform, sr = paddleaudio.load(os.path.abspath(os.path.basename(wav_url)))
|
||||
waveform_tensor = paddle.to_tensor(waveform).unsqueeze(0)
|
||||
waveform_tensor_torch = torch.from_numpy(waveform).unsqueeze(0)
|
||||
|
||||
# Feature conf
|
||||
mel_conf = {
|
||||
'sr': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
}
|
||||
mfcc_conf = {
|
||||
'n_mfcc': 20,
|
||||
'top_db': 80.0,
|
||||
}
|
||||
mfcc_conf.update(mel_conf)
|
||||
|
||||
mel_conf_torchaudio = {
|
||||
'sample_rate': sr,
|
||||
'n_fft': 512,
|
||||
'hop_length': 128,
|
||||
'n_mels': 40,
|
||||
'norm': 'slaney',
|
||||
'mel_scale': 'slaney',
|
||||
}
|
||||
mfcc_conf_torchaudio = {
|
||||
'sample_rate': sr,
|
||||
'n_mfcc': 20,
|
||||
}
|
||||
|
||||
|
||||
def enable_cpu_device():
|
||||
paddle.set_device('cpu')
|
||||
|
||||
|
||||
def enable_gpu_device():
|
||||
paddle.set_device('gpu')
|
||||
|
||||
|
||||
mfcc_extractor = paddleaudio.features.MFCC(
|
||||
**mfcc_conf, f_min=0.0, dtype=waveform_tensor.dtype)
|
||||
|
||||
|
||||
def mfcc():
|
||||
return mfcc_extractor(waveform_tensor).squeeze(0)
|
||||
|
||||
|
||||
def test_mfcc_cpu(benchmark):
|
||||
enable_cpu_device()
|
||||
feature_paddleaudio = benchmark(mfcc)
|
||||
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_mfcc_gpu(benchmark):
|
||||
enable_gpu_device()
|
||||
feature_paddleaudio = benchmark(mfcc)
|
||||
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
del mel_conf_torchaudio['sample_rate']
|
||||
mfcc_extractor_torchaudio = torchaudio.transforms.MFCC(
|
||||
**mfcc_conf_torchaudio, melkwargs=mel_conf_torchaudio)
|
||||
|
||||
|
||||
def mfcc_torchaudio():
|
||||
return mfcc_extractor_torchaudio(waveform_tensor_torch).squeeze(0)
|
||||
|
||||
|
||||
def test_mfcc_cpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mfcc_extractor_torchaudio
|
||||
|
||||
mel_extractor_torchaudio = mfcc_extractor_torchaudio.to('cpu')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cpu')
|
||||
|
||||
feature_paddleaudio = benchmark(mfcc_torchaudio)
|
||||
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddleaudio, decimal=3)
|
||||
|
||||
|
||||
def test_mfcc_gpu_torchaudio(benchmark):
|
||||
global waveform_tensor_torch, mfcc_extractor_torchaudio
|
||||
|
||||
mel_extractor_torchaudio = mfcc_extractor_torchaudio.to('cuda')
|
||||
waveform_tensor_torch = waveform_tensor_torch.to('cuda')
|
||||
|
||||
feature_torchaudio = benchmark(mfcc_torchaudio)
|
||||
feature_librosa = librosa.feature.mfcc(waveform, **mel_conf)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_torchaudio.cpu(), decimal=3)
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,49 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
import unittest
|
||||
import urllib.request
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
from paddleaudio import load
|
||||
|
||||
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
|
||||
|
||||
|
||||
class FeatTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.initParmas()
|
||||
self.initWavInput()
|
||||
self.setUpDevice()
|
||||
|
||||
def setUpDevice(self, device='cpu'):
|
||||
paddle.set_device(device)
|
||||
|
||||
def initWavInput(self, url=wav_url):
|
||||
if not os.path.isfile(os.path.basename(url)):
|
||||
urllib.request.urlretrieve(url, os.path.basename(url))
|
||||
self.waveform, self.sr = load(os.path.abspath(os.path.basename(url)))
|
||||
self.waveform = self.waveform.astype(
|
||||
np.float32
|
||||
) # paddlespeech.s2t.transform.spectrogram only supports float32
|
||||
dim = len(self.waveform.shape)
|
||||
|
||||
assert dim in [1, 2]
|
||||
if dim == 1:
|
||||
self.waveform = np.expand_dims(self.waveform, 0)
|
||||
|
||||
def initParmas(self):
|
||||
raise NotImplementedError
|
||||
@ -0,0 +1,49 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
from .base import FeatTest
|
||||
from paddleaudio.functional.window import get_window
|
||||
from paddlespeech.s2t.transform.spectrogram import IStft
|
||||
from paddlespeech.s2t.transform.spectrogram import Stft
|
||||
|
||||
|
||||
class TestIstft(FeatTest):
|
||||
def initParmas(self):
|
||||
self.n_fft = 512
|
||||
self.hop_length = 128
|
||||
self.window_str = 'hann'
|
||||
|
||||
def test_istft(self):
|
||||
ps_stft = Stft(self.n_fft, self.hop_length)
|
||||
ps_res = ps_stft(
|
||||
self.waveform.T).squeeze(1).T # (n_fft//2 + 1, n_frmaes)
|
||||
x = paddle.to_tensor(ps_res)
|
||||
|
||||
ps_istft = IStft(self.hop_length)
|
||||
ps_res = ps_istft(ps_res.T)
|
||||
|
||||
window = get_window(
|
||||
self.window_str, self.n_fft, dtype=self.waveform.dtype)
|
||||
pd_res = paddle.signal.istft(
|
||||
x, self.n_fft, self.hop_length, window=window)
|
||||
|
||||
np.testing.assert_array_almost_equal(ps_res, pd_res, decimal=5)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,81 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
import paddleaudio
|
||||
from .base import FeatTest
|
||||
|
||||
|
||||
class TestKaldi(FeatTest):
|
||||
def initParmas(self):
|
||||
self.window_size = 1024
|
||||
self.dtype = 'float32'
|
||||
|
||||
def test_window(self):
|
||||
t_hann_window = torch.hann_window(
|
||||
self.window_size, periodic=False, dtype=eval(f'torch.{self.dtype}'))
|
||||
t_hamm_window = torch.hamming_window(
|
||||
self.window_size,
|
||||
periodic=False,
|
||||
alpha=0.54,
|
||||
beta=0.46,
|
||||
dtype=eval(f'torch.{self.dtype}'))
|
||||
t_povey_window = torch.hann_window(
|
||||
self.window_size, periodic=False,
|
||||
dtype=eval(f'torch.{self.dtype}')).pow(0.85)
|
||||
|
||||
p_hann_window = paddleaudio.functional.window.get_window(
|
||||
'hann',
|
||||
self.window_size,
|
||||
fftbins=False,
|
||||
dtype=eval(f'paddle.{self.dtype}'))
|
||||
p_hamm_window = paddleaudio.functional.window.get_window(
|
||||
'hamming',
|
||||
self.window_size,
|
||||
fftbins=False,
|
||||
dtype=eval(f'paddle.{self.dtype}'))
|
||||
p_povey_window = paddleaudio.functional.window.get_window(
|
||||
'hann',
|
||||
self.window_size,
|
||||
fftbins=False,
|
||||
dtype=eval(f'paddle.{self.dtype}')).pow(0.85)
|
||||
|
||||
np.testing.assert_array_almost_equal(t_hann_window, p_hann_window)
|
||||
np.testing.assert_array_almost_equal(t_hamm_window, p_hamm_window)
|
||||
np.testing.assert_array_almost_equal(t_povey_window, p_povey_window)
|
||||
|
||||
def test_fbank(self):
|
||||
ta_features = torchaudio.compliance.kaldi.fbank(
|
||||
torch.from_numpy(self.waveform.astype(self.dtype)))
|
||||
pa_features = paddleaudio.compliance.kaldi.fbank(
|
||||
paddle.to_tensor(self.waveform.astype(self.dtype)))
|
||||
np.testing.assert_array_almost_equal(
|
||||
ta_features, pa_features, decimal=4)
|
||||
|
||||
def test_mfcc(self):
|
||||
ta_features = torchaudio.compliance.kaldi.mfcc(
|
||||
torch.from_numpy(self.waveform.astype(self.dtype)))
|
||||
pa_features = paddleaudio.compliance.kaldi.mfcc(
|
||||
paddle.to_tensor(self.waveform.astype(self.dtype)))
|
||||
np.testing.assert_array_almost_equal(
|
||||
ta_features, pa_features, decimal=4)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,281 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
import paddleaudio
|
||||
from .base import FeatTest
|
||||
from paddleaudio.functional.window import get_window
|
||||
|
||||
|
||||
class TestLibrosa(FeatTest):
|
||||
def initParmas(self):
|
||||
self.n_fft = 512
|
||||
self.hop_length = 128
|
||||
self.n_mels = 40
|
||||
self.n_mfcc = 20
|
||||
self.fmin = 0.0
|
||||
self.window_str = 'hann'
|
||||
self.pad_mode = 'reflect'
|
||||
self.top_db = 80.0
|
||||
|
||||
def test_stft(self):
|
||||
if len(self.waveform.shape) == 2: # (C, T)
|
||||
self.waveform = self.waveform.squeeze(
|
||||
0) # 1D input for librosa.feature.melspectrogram
|
||||
|
||||
feature_librosa = librosa.core.stft(
|
||||
y=self.waveform,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
win_length=None,
|
||||
window=self.window_str,
|
||||
center=True,
|
||||
dtype=None,
|
||||
pad_mode=self.pad_mode, )
|
||||
x = paddle.to_tensor(self.waveform).unsqueeze(0)
|
||||
window = get_window(self.window_str, self.n_fft, dtype=x.dtype)
|
||||
feature_paddle = paddle.signal.stft(
|
||||
x=x,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
win_length=None,
|
||||
window=window,
|
||||
center=True,
|
||||
pad_mode=self.pad_mode,
|
||||
normalized=False,
|
||||
onesided=True, ).squeeze(0)
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddle, decimal=5)
|
||||
|
||||
def test_istft(self):
|
||||
if len(self.waveform.shape) == 2: # (C, T)
|
||||
self.waveform = self.waveform.squeeze(
|
||||
0) # 1D input for librosa.feature.melspectrogram
|
||||
|
||||
# Get stft result from librosa.
|
||||
stft_matrix = librosa.core.stft(
|
||||
y=self.waveform,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
win_length=None,
|
||||
window=self.window_str,
|
||||
center=True,
|
||||
pad_mode=self.pad_mode, )
|
||||
|
||||
feature_librosa = librosa.core.istft(
|
||||
stft_matrix=stft_matrix,
|
||||
hop_length=self.hop_length,
|
||||
win_length=None,
|
||||
window=self.window_str,
|
||||
center=True,
|
||||
dtype=None,
|
||||
length=None, )
|
||||
|
||||
x = paddle.to_tensor(stft_matrix).unsqueeze(0)
|
||||
window = get_window(
|
||||
self.window_str,
|
||||
self.n_fft,
|
||||
dtype=paddle.to_tensor(self.waveform).dtype)
|
||||
feature_paddle = paddle.signal.istft(
|
||||
x=x,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
win_length=None,
|
||||
window=window,
|
||||
center=True,
|
||||
normalized=False,
|
||||
onesided=True,
|
||||
length=None,
|
||||
return_complex=False, ).squeeze(0)
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_paddle, decimal=5)
|
||||
|
||||
def test_mel(self):
|
||||
feature_librosa = librosa.filters.mel(
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin,
|
||||
fmax=None,
|
||||
htk=False,
|
||||
norm='slaney',
|
||||
dtype=self.waveform.dtype, )
|
||||
feature_compliance = paddleaudio.compliance.librosa.compute_fbank_matrix(
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin,
|
||||
fmax=None,
|
||||
htk=False,
|
||||
norm='slaney',
|
||||
dtype=self.waveform.dtype, )
|
||||
x = paddle.to_tensor(self.waveform)
|
||||
feature_functional = paddleaudio.functional.compute_fbank_matrix(
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
n_mels=self.n_mels,
|
||||
f_min=self.fmin,
|
||||
f_max=None,
|
||||
htk=False,
|
||||
norm='slaney',
|
||||
dtype=x.dtype, )
|
||||
|
||||
np.testing.assert_array_almost_equal(feature_librosa,
|
||||
feature_compliance)
|
||||
np.testing.assert_array_almost_equal(feature_librosa,
|
||||
feature_functional)
|
||||
|
||||
def test_melspect(self):
|
||||
if len(self.waveform.shape) == 2: # (C, T)
|
||||
self.waveform = self.waveform.squeeze(
|
||||
0) # 1D input for librosa.feature.melspectrogram
|
||||
|
||||
# librosa:
|
||||
feature_librosa = librosa.feature.melspectrogram(
|
||||
y=self.waveform,
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin)
|
||||
|
||||
# paddleaudio.compliance.librosa:
|
||||
feature_compliance = paddleaudio.compliance.librosa.melspectrogram(
|
||||
x=self.waveform,
|
||||
sr=self.sr,
|
||||
window_size=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin,
|
||||
to_db=False)
|
||||
|
||||
# paddleaudio.features.layer
|
||||
x = paddle.to_tensor(
|
||||
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
|
||||
feature_extractor = paddleaudio.features.MelSpectrogram(
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
f_min=self.fmin,
|
||||
dtype=x.dtype)
|
||||
feature_layer = feature_extractor(x).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_compliance, decimal=5)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_layer, decimal=5)
|
||||
|
||||
def test_log_melspect(self):
|
||||
if len(self.waveform.shape) == 2: # (C, T)
|
||||
self.waveform = self.waveform.squeeze(
|
||||
0) # 1D input for librosa.feature.melspectrogram
|
||||
|
||||
# librosa:
|
||||
feature_librosa = librosa.feature.melspectrogram(
|
||||
y=self.waveform,
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin)
|
||||
feature_librosa = librosa.power_to_db(feature_librosa, top_db=None)
|
||||
|
||||
# paddleaudio.compliance.librosa:
|
||||
feature_compliance = paddleaudio.compliance.librosa.melspectrogram(
|
||||
x=self.waveform,
|
||||
sr=self.sr,
|
||||
window_size=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin)
|
||||
|
||||
# paddleaudio.features.layer
|
||||
x = paddle.to_tensor(
|
||||
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
|
||||
feature_extractor = paddleaudio.features.LogMelSpectrogram(
|
||||
sr=self.sr,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
f_min=self.fmin,
|
||||
dtype=x.dtype)
|
||||
feature_layer = feature_extractor(x).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_compliance, decimal=5)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_layer, decimal=4)
|
||||
|
||||
def test_mfcc(self):
|
||||
if len(self.waveform.shape) == 2: # (C, T)
|
||||
self.waveform = self.waveform.squeeze(
|
||||
0) # 1D input for librosa.feature.melspectrogram
|
||||
|
||||
# librosa:
|
||||
feature_librosa = librosa.feature.mfcc(
|
||||
y=self.waveform,
|
||||
sr=self.sr,
|
||||
S=None,
|
||||
n_mfcc=self.n_mfcc,
|
||||
dct_type=2,
|
||||
norm='ortho',
|
||||
lifter=0,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin)
|
||||
|
||||
# paddleaudio.compliance.librosa:
|
||||
feature_compliance = paddleaudio.compliance.librosa.mfcc(
|
||||
x=self.waveform,
|
||||
sr=self.sr,
|
||||
n_mfcc=self.n_mfcc,
|
||||
dct_type=2,
|
||||
norm='ortho',
|
||||
lifter=0,
|
||||
window_size=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
fmin=self.fmin,
|
||||
top_db=self.top_db)
|
||||
|
||||
# paddleaudio.features.layer
|
||||
x = paddle.to_tensor(
|
||||
self.waveform, dtype=paddle.float64).unsqueeze(0) # Add batch dim.
|
||||
feature_extractor = paddleaudio.features.MFCC(
|
||||
sr=self.sr,
|
||||
n_mfcc=self.n_mfcc,
|
||||
n_fft=self.n_fft,
|
||||
hop_length=self.hop_length,
|
||||
n_mels=self.n_mels,
|
||||
f_min=self.fmin,
|
||||
top_db=self.top_db,
|
||||
dtype=x.dtype)
|
||||
feature_layer = feature_extractor(x).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_compliance, decimal=4)
|
||||
np.testing.assert_array_almost_equal(
|
||||
feature_librosa, feature_layer, decimal=4)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,50 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
import paddleaudio
|
||||
from .base import FeatTest
|
||||
from paddlespeech.s2t.transform.spectrogram import LogMelSpectrogram
|
||||
|
||||
|
||||
class TestLogMelSpectrogram(FeatTest):
|
||||
def initParmas(self):
|
||||
self.n_fft = 512
|
||||
self.hop_length = 128
|
||||
self.n_mels = 40
|
||||
|
||||
def test_log_melspect(self):
|
||||
ps_melspect = LogMelSpectrogram(self.sr, self.n_mels, self.n_fft,
|
||||
self.hop_length)
|
||||
ps_res = ps_melspect(self.waveform.T).squeeze(1).T
|
||||
|
||||
x = paddle.to_tensor(self.waveform)
|
||||
# paddlespeech.s2t的特征存在幅度谱和功率谱滥用的情况
|
||||
ps_melspect = paddleaudio.features.LogMelSpectrogram(
|
||||
self.sr,
|
||||
self.n_fft,
|
||||
self.hop_length,
|
||||
power=1.0,
|
||||
n_mels=self.n_mels,
|
||||
f_min=0.0)
|
||||
pa_res = (ps_melspect(x) / 10.0).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(ps_res, pa_res, decimal=5)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,42 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
import paddleaudio
|
||||
from .base import FeatTest
|
||||
from paddlespeech.s2t.transform.spectrogram import Spectrogram
|
||||
|
||||
|
||||
class TestSpectrogram(FeatTest):
|
||||
def initParmas(self):
|
||||
self.n_fft = 512
|
||||
self.hop_length = 128
|
||||
|
||||
def test_spectrogram(self):
|
||||
ps_spect = Spectrogram(self.n_fft, self.hop_length)
|
||||
ps_res = ps_spect(self.waveform.T).squeeze(1).T # Magnitude
|
||||
|
||||
x = paddle.to_tensor(self.waveform)
|
||||
pa_spect = paddleaudio.features.Spectrogram(
|
||||
self.n_fft, self.hop_length, power=1.0)
|
||||
pa_res = pa_spect(x).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(ps_res, pa_res, decimal=5)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,44 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
|
||||
from .base import FeatTest
|
||||
from paddleaudio.functional.window import get_window
|
||||
from paddlespeech.s2t.transform.spectrogram import Stft
|
||||
|
||||
|
||||
class TestStft(FeatTest):
|
||||
def initParmas(self):
|
||||
self.n_fft = 512
|
||||
self.hop_length = 128
|
||||
self.window_str = 'hann'
|
||||
|
||||
def test_stft(self):
|
||||
ps_stft = Stft(self.n_fft, self.hop_length)
|
||||
ps_res = ps_stft(
|
||||
self.waveform.T).squeeze(1).T # (n_fft//2 + 1, n_frmaes)
|
||||
|
||||
x = paddle.to_tensor(self.waveform)
|
||||
window = get_window(self.window_str, self.n_fft, dtype=x.dtype)
|
||||
pd_res = paddle.signal.stft(
|
||||
x, self.n_fft, self.hop_length, window=window).squeeze(0).numpy()
|
||||
|
||||
np.testing.assert_array_almost_equal(ps_res, pd_res, decimal=5)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,224 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import io
|
||||
import os
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
import yaml
|
||||
|
||||
from paddlespeech.cli.cls.infer import CLSExecutor
|
||||
from paddlespeech.cli.log import logger
|
||||
from paddlespeech.cli.utils import download_and_decompress
|
||||
from paddlespeech.cli.utils import MODEL_HOME
|
||||
from paddlespeech.server.engine.base_engine import BaseEngine
|
||||
from paddlespeech.server.utils.paddle_predictor import init_predictor
|
||||
from paddlespeech.server.utils.paddle_predictor import run_model
|
||||
|
||||
__all__ = ['CLSEngine']
|
||||
|
||||
pretrained_models = {
|
||||
"panns_cnn6-32k": {
|
||||
'url':
|
||||
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz',
|
||||
'md5':
|
||||
'da087c31046d23281d8ec5188c1967da',
|
||||
'cfg_path':
|
||||
'panns.yaml',
|
||||
'model_path':
|
||||
'inference.pdmodel',
|
||||
'params_path':
|
||||
'inference.pdiparams',
|
||||
'label_file':
|
||||
'audioset_labels.txt',
|
||||
},
|
||||
"panns_cnn10-32k": {
|
||||
'url':
|
||||
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz',
|
||||
'md5':
|
||||
'5460cc6eafbfaf0f261cc75b90284ae1',
|
||||
'cfg_path':
|
||||
'panns.yaml',
|
||||
'model_path':
|
||||
'inference.pdmodel',
|
||||
'params_path':
|
||||
'inference.pdiparams',
|
||||
'label_file':
|
||||
'audioset_labels.txt',
|
||||
},
|
||||
"panns_cnn14-32k": {
|
||||
'url':
|
||||
'https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz',
|
||||
'md5':
|
||||
'ccc80b194821274da79466862b2ab00f',
|
||||
'cfg_path':
|
||||
'panns.yaml',
|
||||
'model_path':
|
||||
'inference.pdmodel',
|
||||
'params_path':
|
||||
'inference.pdiparams',
|
||||
'label_file':
|
||||
'audioset_labels.txt',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class CLSServerExecutor(CLSExecutor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
pass
|
||||
|
||||
def _get_pretrained_path(self, tag: str) -> os.PathLike:
|
||||
"""
|
||||
Download and returns pretrained resources path of current task.
|
||||
"""
|
||||
support_models = list(pretrained_models.keys())
|
||||
assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
|
||||
tag, '\n\t\t'.join(support_models))
|
||||
|
||||
res_path = os.path.join(MODEL_HOME, tag)
|
||||
decompressed_path = download_and_decompress(pretrained_models[tag],
|
||||
res_path)
|
||||
decompressed_path = os.path.abspath(decompressed_path)
|
||||
logger.info(
|
||||
'Use pretrained model stored in: {}'.format(decompressed_path))
|
||||
|
||||
return decompressed_path
|
||||
|
||||
def _init_from_path(
|
||||
self,
|
||||
model_type: str='panns_cnn14',
|
||||
cfg_path: Optional[os.PathLike]=None,
|
||||
model_path: Optional[os.PathLike]=None,
|
||||
params_path: Optional[os.PathLike]=None,
|
||||
label_file: Optional[os.PathLike]=None,
|
||||
predictor_conf: dict=None, ):
|
||||
"""
|
||||
Init model and other resources from a specific path.
|
||||
"""
|
||||
|
||||
if cfg_path is None or model_path is None or params_path is None or label_file is None:
|
||||
tag = model_type + '-' + '32k'
|
||||
self.res_path = self._get_pretrained_path(tag)
|
||||
self.cfg_path = os.path.join(self.res_path,
|
||||
pretrained_models[tag]['cfg_path'])
|
||||
self.model_path = os.path.join(self.res_path,
|
||||
pretrained_models[tag]['model_path'])
|
||||
self.params_path = os.path.join(
|
||||
self.res_path, pretrained_models[tag]['params_path'])
|
||||
self.label_file = os.path.join(self.res_path,
|
||||
pretrained_models[tag]['label_file'])
|
||||
else:
|
||||
self.cfg_path = os.path.abspath(cfg_path)
|
||||
self.model_path = os.path.abspath(model_path)
|
||||
self.params_path = os.path.abspath(params_path)
|
||||
self.label_file = os.path.abspath(label_file)
|
||||
|
||||
logger.info(self.cfg_path)
|
||||
logger.info(self.model_path)
|
||||
logger.info(self.params_path)
|
||||
logger.info(self.label_file)
|
||||
|
||||
# config
|
||||
with open(self.cfg_path, 'r') as f:
|
||||
self._conf = yaml.safe_load(f)
|
||||
logger.info("Read cfg file successfully.")
|
||||
|
||||
# labels
|
||||
self._label_list = []
|
||||
with open(self.label_file, 'r') as f:
|
||||
for line in f:
|
||||
self._label_list.append(line.strip())
|
||||
logger.info("Read label file successfully.")
|
||||
|
||||
# Create predictor
|
||||
self.predictor_conf = predictor_conf
|
||||
self.predictor = init_predictor(
|
||||
model_file=self.model_path,
|
||||
params_file=self.params_path,
|
||||
predictor_conf=self.predictor_conf)
|
||||
logger.info("Create predictor successfully.")
|
||||
|
||||
@paddle.no_grad()
|
||||
def infer(self):
|
||||
"""
|
||||
Model inference and result stored in self.output.
|
||||
"""
|
||||
output = run_model(self.predictor, [self._inputs['feats'].numpy()])
|
||||
self._outputs['logits'] = output[0]
|
||||
|
||||
|
||||
class CLSEngine(BaseEngine):
|
||||
"""CLS server engine
|
||||
|
||||
Args:
|
||||
metaclass: Defaults to Singleton.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(CLSEngine, self).__init__()
|
||||
|
||||
def init(self, config: dict) -> bool:
|
||||
"""init engine resource
|
||||
|
||||
Args:
|
||||
config_file (str): config file
|
||||
|
||||
Returns:
|
||||
bool: init failed or success
|
||||
"""
|
||||
self.executor = CLSServerExecutor()
|
||||
self.config = config
|
||||
self.executor._init_from_path(
|
||||
self.config.model_type, self.config.cfg_path,
|
||||
self.config.model_path, self.config.params_path,
|
||||
self.config.label_file, self.config.predictor_conf)
|
||||
|
||||
logger.info("Initialize CLS server engine successfully.")
|
||||
return True
|
||||
|
||||
def run(self, audio_data):
|
||||
"""engine run
|
||||
|
||||
Args:
|
||||
audio_data (bytes): base64.b64decode
|
||||
"""
|
||||
|
||||
self.executor.preprocess(io.BytesIO(audio_data))
|
||||
st = time.time()
|
||||
self.executor.infer()
|
||||
infer_time = time.time() - st
|
||||
|
||||
logger.info("inference time: {}".format(infer_time))
|
||||
logger.info("cls engine type: inference")
|
||||
|
||||
def postprocess(self, topk: int):
|
||||
"""postprocess
|
||||
"""
|
||||
assert topk <= len(self.executor._label_list
|
||||
), 'Value of topk is larger than number of labels.'
|
||||
|
||||
result = np.squeeze(self.executor._outputs['logits'], axis=0)
|
||||
topk_idx = (-result).argsort()[:topk]
|
||||
topk_results = []
|
||||
for idx in topk_idx:
|
||||
res = {}
|
||||
label, score = self.executor._label_list[idx], result[idx]
|
||||
res['class_name'] = label
|
||||
res['prob'] = score
|
||||
topk_results.append(res)
|
||||
|
||||
return topk_results
|
||||
@ -0,0 +1,13 @@
|
||||
# Copyright (c) 2022 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.
|
||||
@ -0,0 +1,124 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import io
|
||||
import time
|
||||
from typing import List
|
||||
|
||||
import paddle
|
||||
|
||||
from paddlespeech.cli.cls.infer import CLSExecutor
|
||||
from paddlespeech.cli.log import logger
|
||||
from paddlespeech.server.engine.base_engine import BaseEngine
|
||||
|
||||
__all__ = ['CLSEngine']
|
||||
|
||||
|
||||
class CLSServerExecutor(CLSExecutor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
pass
|
||||
|
||||
def get_topk_results(self, topk: int) -> List:
|
||||
assert topk <= len(
|
||||
self._label_list), 'Value of topk is larger than number of labels.'
|
||||
|
||||
result = self._outputs['logits'].squeeze(0).numpy()
|
||||
topk_idx = (-result).argsort()[:topk]
|
||||
res = {}
|
||||
topk_results = []
|
||||
for idx in topk_idx:
|
||||
label, score = self._label_list[idx], result[idx]
|
||||
res['class'] = label
|
||||
res['prob'] = score
|
||||
topk_results.append(res)
|
||||
return topk_results
|
||||
|
||||
|
||||
class CLSEngine(BaseEngine):
|
||||
"""CLS server engine
|
||||
|
||||
Args:
|
||||
metaclass: Defaults to Singleton.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(CLSEngine, self).__init__()
|
||||
|
||||
def init(self, config: dict) -> bool:
|
||||
"""init engine resource
|
||||
|
||||
Args:
|
||||
config_file (str): config file
|
||||
|
||||
Returns:
|
||||
bool: init failed or success
|
||||
"""
|
||||
self.input = None
|
||||
self.output = None
|
||||
self.executor = CLSServerExecutor()
|
||||
self.config = config
|
||||
try:
|
||||
if self.config.device:
|
||||
self.device = self.config.device
|
||||
else:
|
||||
self.device = paddle.get_device()
|
||||
paddle.set_device(self.device)
|
||||
except BaseException:
|
||||
logger.error(
|
||||
"Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
|
||||
)
|
||||
|
||||
try:
|
||||
self.executor._init_from_path(
|
||||
self.config.model, self.config.cfg_path, self.config.ckpt_path,
|
||||
self.config.label_file)
|
||||
except BaseException:
|
||||
logger.error("Initialize CLS server engine Failed.")
|
||||
return False
|
||||
|
||||
logger.info("Initialize CLS server engine successfully on device: %s." %
|
||||
(self.device))
|
||||
return True
|
||||
|
||||
def run(self, audio_data):
|
||||
"""engine run
|
||||
|
||||
Args:
|
||||
audio_data (bytes): base64.b64decode
|
||||
"""
|
||||
self.executor.preprocess(io.BytesIO(audio_data))
|
||||
st = time.time()
|
||||
self.executor.infer()
|
||||
infer_time = time.time() - st
|
||||
|
||||
logger.info("inference time: {}".format(infer_time))
|
||||
logger.info("cls engine type: python")
|
||||
|
||||
def postprocess(self, topk: int):
|
||||
"""postprocess
|
||||
"""
|
||||
assert topk <= len(self.executor._label_list
|
||||
), 'Value of topk is larger than number of labels.'
|
||||
|
||||
result = self.executor._outputs['logits'].squeeze(0).numpy()
|
||||
topk_idx = (-result).argsort()[:topk]
|
||||
topk_results = []
|
||||
for idx in topk_idx:
|
||||
res = {}
|
||||
label, score = self.executor._label_list[idx], result[idx]
|
||||
res['class_name'] = label
|
||||
res['prob'] = score
|
||||
topk_results.append(res)
|
||||
|
||||
return topk_results
|
||||
@ -0,0 +1,92 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import base64
|
||||
import traceback
|
||||
from typing import Union
|
||||
|
||||
from fastapi import APIRouter
|
||||
|
||||
from paddlespeech.server.engine.engine_pool import get_engine_pool
|
||||
from paddlespeech.server.restful.request import CLSRequest
|
||||
from paddlespeech.server.restful.response import CLSResponse
|
||||
from paddlespeech.server.restful.response import ErrorResponse
|
||||
from paddlespeech.server.utils.errors import ErrorCode
|
||||
from paddlespeech.server.utils.errors import failed_response
|
||||
from paddlespeech.server.utils.exception import ServerBaseException
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
@router.get('/paddlespeech/cls/help')
|
||||
def help():
|
||||
"""help
|
||||
|
||||
Returns:
|
||||
json: [description]
|
||||
"""
|
||||
response = {
|
||||
"success": "True",
|
||||
"code": 200,
|
||||
"message": {
|
||||
"global": "success"
|
||||
},
|
||||
"result": {
|
||||
"description": "cls server",
|
||||
"input": "base64 string of wavfile",
|
||||
"output": "classification result"
|
||||
}
|
||||
}
|
||||
return response
|
||||
|
||||
|
||||
@router.post(
|
||||
"/paddlespeech/cls", response_model=Union[CLSResponse, ErrorResponse])
|
||||
def cls(request_body: CLSRequest):
|
||||
"""cls api
|
||||
|
||||
Args:
|
||||
request_body (CLSRequest): [description]
|
||||
|
||||
Returns:
|
||||
json: [description]
|
||||
"""
|
||||
try:
|
||||
audio_data = base64.b64decode(request_body.audio)
|
||||
|
||||
# get single engine from engine pool
|
||||
engine_pool = get_engine_pool()
|
||||
cls_engine = engine_pool['cls']
|
||||
|
||||
cls_engine.run(audio_data)
|
||||
cls_results = cls_engine.postprocess(request_body.topk)
|
||||
|
||||
response = {
|
||||
"success": True,
|
||||
"code": 200,
|
||||
"message": {
|
||||
"description": "success"
|
||||
},
|
||||
"result": {
|
||||
"topk": request_body.topk,
|
||||
"results": cls_results
|
||||
}
|
||||
}
|
||||
|
||||
except ServerBaseException as e:
|
||||
response = failed_response(e.error_code, e.msg)
|
||||
except BaseException:
|
||||
response = failed_response(ErrorCode.SERVER_UNKOWN_ERR)
|
||||
traceback.print_exc()
|
||||
|
||||
return response
|
||||
@ -0,0 +1,100 @@
|
||||
009901 昨日，这名伤者与医生全部被警方依法刑事拘留。
|
||||
009902 钱伟长想到上海来办学校是经过深思熟虑的。
|
||||
009903 她见我一进门就骂，吃饭时也骂，骂得我抬不起头。
|
||||
009904 李述德在离开之前，只说了一句柱驼杀父亲了。
|
||||
009905 这种车票和保险单捆绑出售属于重复性购买。
|
||||
009906 戴佩妮的男友西米露接唱情歌，让她非常开心。
|
||||
009907 观大势，谋大局，出大策始终是该院的办院方针。
|
||||
009908 他们骑着摩托回家，正好为农忙时的父母帮忙。
|
||||
009909 但是因为还没到退休年龄，只能掰着指头捱日子。
|
||||
009910 这几天雨水不断，人们恨不得待在家里不出门。
|
||||
009911 没想到徐赟，张海翔两人就此玩起了人间蒸发。
|
||||
009912 藤村此番发言可能是为了凸显野田的领导能力。
|
||||
009913 程长庚，生在清王朝嘉庆年间，安徽的潜山小县。
|
||||
009914 南海海域综合补给基地码头项目正在论证中。
|
||||
009915 也就是说今晚成都市民极有可能再次看到飘雪。
|
||||
009916 随着天气转热，各地的游泳场所开始人头攒动。
|
||||
009917 更让徐先生纳闷的是，房客的手机也打不通了。
|
||||
009918 遇到颠簸时，应听从乘务员的安全指令，回座位坐好。
|
||||
009919 他在后面呆惯了，怕自己一插身后的人会不满，不敢排进去。
|
||||
009920 傍晚七个小人回来了，白雪公主说，你们就是我命中的七个小矮人吧。
|
||||
009921 他本想说，教育局管这个，他们是一路的，这样一管岂不是妓女起嫖客？
|
||||
009922 一种表示商品所有权的财物证券，也称商品证券，如提货单，交货单。
|
||||
009923 会有很丰富的东西留下来，说都说不完。
|
||||
009924 这句话像从天而降，吓得四周一片寂静。
|
||||
009925 记者所在的是受害人家属所在的右区。
|
||||
009926 不管哈大爷去哪，它都一步不离地跟着。
|
||||
009927 大家抬头望去，一只老鼠正趴在吊顶上。
|
||||
009928 我决定过年就辞职，接手我爸的废品站！
|
||||
009929 最终，中国男子乒乓球队获得此奖项。
|
||||
009930 防汛抗旱两手抓，抗旱相对抓的不够。
|
||||
009931 图们江下游地区开发开放的进展如何？
|
||||
009932 这要求中国必须有一个坚强的政党领导。
|
||||
009933 再说，关于利益上的事俺俩都不好开口。
|
||||
009934 明代瓦剌，鞑靼入侵明境也是通过此地。
|
||||
009935 咪咪舔着孩子，把它身上的毛舔干净。
|
||||
009936 是否这次的国标修订被大企业绑架了？
|
||||
009937 判决后，姚某妻子胡某不服，提起上诉。
|
||||
009938 由此可以看出邯钢的经济效益来自何处。
|
||||
009939 琳达说，是瑜伽改变了她和马儿的生活。
|
||||
009940 楼下的保安告诉记者，这里不租也不卖。
|
||||
009941 习近平说，中斯两国人民传统友谊深厚。
|
||||
009942 传闻越来越多，后来连老汉儿自己都怕了。
|
||||
009943 我怒吼一声冲上去，举起砖头砸了过去。
|
||||
009944 我现在还不会，这就回去问问发明我的人。
|
||||
009945 显然，洛阳性奴案不具备上述两个前提。
|
||||
009946 另外，杰克逊有文唇线，眼线，眉毛的动作。
|
||||
009947 昨晚，华西都市报记者电话采访了尹琪。
|
||||
009948 涅拉季科未透露这些航空公司的名称。
|
||||
009949 从运行轨迹上来说，它也不可能是星星。
|
||||
009950 目前看，如果继续加息也存在两难问题。
|
||||
009951 曾宝仪在节目录制现场大爆观众糗事。
|
||||
009952 但任凭周某怎么叫，男子仍酣睡不醒。
|
||||
009953 老大爷说，小子，你挡我财路了，知道不？
|
||||
009954 没料到，闯下大头佛的阿伟还不知悔改。
|
||||
009955 卡扎菲部落式统治已遭遇部落内讧。
|
||||
009956 这个孩子的生命一半来源于另一位女士捐赠的冷冻卵子。
|
||||
009957 出现这种泥鳅内阁的局面既是野田有意为之，也实属无奈。
|
||||
009958 济青高速济南，华山，章丘，邹平，周村，淄博，临淄站。
|
||||
009959 赵凌飞的话，反映了沈阳赛区所有奥运志愿者的共同心声。
|
||||
009960 因为，我们所发出的力量必会因难度加大而减弱。
|
||||
009961 发生事故的楼梯拐角处仍可看到血迹。
|
||||
009962 想过进公安，可能身高不够，老汉儿也不让我进去。
|
||||
009963 路上关卡很多，为了方便撤离，只好轻装前进。
|
||||
009964 原来比尔盖茨就是美国微软公司联合创始人呀。
|
||||
009965 之后他们一家三口将与双方父母往峇里岛旅游。
|
||||
009966 谢谢总理，也感谢广大网友的参与，我们明年再见。
|
||||
009967 事实上是，从来没有一个欺善怕恶的人能作出过稍大一点的成就。
|
||||
009968 我会打开邮件，你可以从那里继续。
|
||||
009969 美方对近期东海局势表示关切。
|
||||
009970 据悉，奥巴马一家人对这座冬季白宫极为满意。
|
||||
009971 打扫完你会很有成就感的，试一试，你就信了。
|
||||
009972 诺曼站在滑板车上，各就各位，准备出发啦！
|
||||
009973 塔河的寒夜，气温降到了零下三十多摄氏度。
|
||||
009974 其间，连破六点六，六点五，六点四，六点三五等多个重要关口。
|
||||
009975 算命其实只是人们的一种自我安慰和自我暗示而已，我们还是要相信科学才好。
|
||||
009976 这一切都令人欢欣鼓舞，阿讷西没理由不坚持到最后。
|
||||
009977 直至公元前一万一千年，它又再次出现。
|
||||
009978 尽量少玩电脑，少看电视，少打游戏。
|
||||
009979 从五到七，前后也就是六个月的时间。
|
||||
009980 一进咖啡店，他就遇见一张熟悉的脸。
|
||||
009981 好在众弟兄看到了把她追了回来。
|
||||
009982 有一个人说，哥们儿我们跑过它才能活。
|
||||
009983 捅了她以后，模糊记得她没咋动了。
|
||||
009984 从小到大，葛启义没有收到过压岁钱。
|
||||
009985 舞台下的你会对舞台上的你说什么？
|
||||
009986 但考生普遍认为，试题的怪多过难。
|
||||
009987 我希望每个人都能够尊重我们的隐私。
|
||||
009988 漫天的红霞使劲给两人增添气氛。
|
||||
009989 晚上加完班开车回家，太累了，迷迷糊糊开着车，走一半的时候，铛一声！
|
||||
009990 该车将三人撞倒后，在大雾中逃窜。
|
||||
009991 这人一哆嗦，方向盘也把不稳了，差点撞上了高速边道护栏。
|
||||
009992 那女孩儿委屈的说，我一回头见你已经进去了我不敢进去啊！
|
||||
009993 小明摇摇头说，不是，我只是美女看多了，想换个口味而已。
|
||||
009994 接下来，红娘要求记者交费，记者表示不知表姐身份证号码。
|
||||
009995 李东蓊表示，自己当时在法庭上发表了一次独特的公诉意见。
|
||||
009996 另一男子扑了上来，手里拿着明晃晃的长刀，向他胸口直刺。
|
||||
009997 今天，快递员拿着一个快递在办公室喊，秦王是哪个，有他快递？
|
||||
009998 这场抗议活动究竟是如何发展演变的，又究竟是谁伤害了谁？
|
||||
009999 因华国锋肖鸡，墓地设计根据其属相设计。
|
||||
010000 在狱中，张明宝悔恨交加，写了一份忏悔书。
|
||||
@ -0,0 +1,243 @@
|
||||
# Copyright (c) 2022 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.
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import paddle
|
||||
from paddle import jit
|
||||
from paddle.static import InputSpec
|
||||
|
||||
from paddlespeech.s2t.utils.dynamic_import import dynamic_import
|
||||
from paddlespeech.t2s.datasets.data_table import DataTable
|
||||
from paddlespeech.t2s.frontend import English
|
||||
from paddlespeech.t2s.frontend.zh_frontend import Frontend
|
||||
from paddlespeech.t2s.modules.normalizer import ZScore
|
||||
|
||||
model_alias = {
|
||||
# acoustic model
|
||||
"speedyspeech":
|
||||
"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
|
||||
"speedyspeech_inference":
|
||||
"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
|
||||
"fastspeech2":
|
||||
"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
|
||||
"fastspeech2_inference":
|
||||
"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
|
||||
"tacotron2":
|
||||
"paddlespeech.t2s.models.tacotron2:Tacotron2",
|
||||
"tacotron2_inference":
|
||||
"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
|
||||
# voc
|
||||
"pwgan":
|
||||
"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
|
||||
"pwgan_inference":
|
||||
"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
|
||||
"mb_melgan":
|
||||
"paddlespeech.t2s.models.melgan:MelGANGenerator",
|
||||
"mb_melgan_inference":
|
||||
"paddlespeech.t2s.models.melgan:MelGANInference",
|
||||
"style_melgan":
|
||||
"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
|
||||
"style_melgan_inference":
|
||||
"paddlespeech.t2s.models.melgan:StyleMelGANInference",
|
||||
"hifigan":
|
||||
"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
|
||||
"hifigan_inference":
|
||||
"paddlespeech.t2s.models.hifigan:HiFiGANInference",
|
||||
"wavernn":
|
||||
"paddlespeech.t2s.models.wavernn:WaveRNN",
|
||||
"wavernn_inference":
|
||||
"paddlespeech.t2s.models.wavernn:WaveRNNInference",
|
||||
}
|
||||
|
||||
|
||||
# input
|
||||
def get_sentences(args):
|
||||
# construct dataset for evaluation
|
||||
sentences = []
|
||||
with open(args.text, 'rt') as f:
|
||||
for line in f:
|
||||
items = line.strip().split()
|
||||
utt_id = items[0]
|
||||
if 'lang' in args and args.lang == 'zh':
|
||||
sentence = "".join(items[1:])
|
||||
elif 'lang' in args and args.lang == 'en':
|
||||
sentence = " ".join(items[1:])
|
||||
sentences.append((utt_id, sentence))
|
||||
return sentences
|
||||
|
||||
|
||||
def get_test_dataset(args, test_metadata, am_name, am_dataset):
|
||||
if am_name == 'fastspeech2':
|
||||
fields = ["utt_id", "text"]
|
||||
if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
|
||||
print("multiple speaker fastspeech2!")
|
||||
fields += ["spk_id"]
|
||||
elif 'voice_cloning' in args and args.voice_cloning:
|
||||
print("voice cloning!")
|
||||
fields += ["spk_emb"]
|
||||
else:
|
||||
print("single speaker fastspeech2!")
|
||||
elif am_name == 'speedyspeech':
|
||||
fields = ["utt_id", "phones", "tones"]
|
||||
elif am_name == 'tacotron2':
|
||||
fields = ["utt_id", "text"]
|
||||
if 'voice_cloning' in args and args.voice_cloning:
|
||||
print("voice cloning!")
|
||||
fields += ["spk_emb"]
|
||||
|
||||
test_dataset = DataTable(data=test_metadata, fields=fields)
|
||||
return test_dataset
|
||||
|
||||
|
||||
# frontend
|
||||
def get_frontend(args):
|
||||
if 'lang' in args and args.lang == 'zh':
|
||||
frontend = Frontend(
|
||||
phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
|
||||
elif 'lang' in args and args.lang == 'en':
|
||||
frontend = English(phone_vocab_path=args.phones_dict)
|
||||
else:
|
||||
print("wrong lang!")
|
||||
print("frontend done!")
|
||||
return frontend
|
||||
|
||||
|
||||
# dygraph
|
||||
def get_am_inference(args, am_config):
|
||||
with open(args.phones_dict, "r") as f:
|
||||
phn_id = [line.strip().split() for line in f.readlines()]
|
||||
vocab_size = len(phn_id)
|
||||
print("vocab_size:", vocab_size)
|
||||
|
||||
tone_size = None
|
||||
if 'tones_dict' in args and args.tones_dict:
|
||||
with open(args.tones_dict, "r") as f:
|
||||
tone_id = [line.strip().split() for line in f.readlines()]
|
||||
tone_size = len(tone_id)
|
||||
print("tone_size:", tone_size)
|
||||
|
||||
spk_num = None
|
||||
if 'speaker_dict' in args and args.speaker_dict:
|
||||
with open(args.speaker_dict, 'rt') as f:
|
||||
spk_id = [line.strip().split() for line in f.readlines()]
|
||||
spk_num = len(spk_id)
|
||||
print("spk_num:", spk_num)
|
||||
|
||||
odim = am_config.n_mels
|
||||
# model: {model_name}_{dataset}
|
||||
am_name = args.am[:args.am.rindex('_')]
|
||||
am_dataset = args.am[args.am.rindex('_') + 1:]
|
||||
|
||||
am_class = dynamic_import(am_name, model_alias)
|
||||
am_inference_class = dynamic_import(am_name + '_inference', model_alias)
|
||||
|
||||
if am_name == 'fastspeech2':
|
||||
am = am_class(
|
||||
idim=vocab_size, odim=odim, spk_num=spk_num, **am_config["model"])
|
||||
elif am_name == 'speedyspeech':
|
||||
am = am_class(
|
||||
vocab_size=vocab_size,
|
||||
tone_size=tone_size,
|
||||
spk_num=spk_num,
|
||||
**am_config["model"])
|
||||
elif am_name == 'tacotron2':
|
||||
am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
|
||||
|
||||
am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
|
||||
am.eval()
|
||||
am_mu, am_std = np.load(args.am_stat)
|
||||
am_mu = paddle.to_tensor(am_mu)
|
||||
am_std = paddle.to_tensor(am_std)
|
||||
am_normalizer = ZScore(am_mu, am_std)
|
||||
am_inference = am_inference_class(am_normalizer, am)
|
||||
am_inference.eval()
|
||||
print("acoustic model done!")
|
||||
return am_inference, am_name, am_dataset
|
||||
|
||||
|
||||
def get_voc_inference(args, voc_config):
|
||||
# model: {model_name}_{dataset}
|
||||
voc_name = args.voc[:args.voc.rindex('_')]
|
||||
voc_class = dynamic_import(voc_name, model_alias)
|
||||
voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
|
||||
if voc_name != 'wavernn':
|
||||
voc = voc_class(**voc_config["generator_params"])
|
||||
voc.set_state_dict(paddle.load(args.voc_ckpt)["generator_params"])
|
||||
voc.remove_weight_norm()
|
||||
voc.eval()
|
||||
else:
|
||||
voc = voc_class(**voc_config["model"])
|
||||
voc.set_state_dict(paddle.load(args.voc_ckpt)["main_params"])
|
||||
voc.eval()
|
||||
|
||||
voc_mu, voc_std = np.load(args.voc_stat)
|
||||
voc_mu = paddle.to_tensor(voc_mu)
|
||||
voc_std = paddle.to_tensor(voc_std)
|
||||
voc_normalizer = ZScore(voc_mu, voc_std)
|
||||
voc_inference = voc_inference_class(voc_normalizer, voc)
|
||||
voc_inference.eval()
|
||||
print("voc done!")
|
||||
return voc_inference
|
||||
|
||||
|
||||
# to static
|
||||
def am_to_static(args, am_inference, am_name, am_dataset):
|
||||
if am_name == 'fastspeech2':
|
||||
if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
|
||||
am_inference = jit.to_static(
|
||||
am_inference,
|
||||
input_spec=[
|
||||
InputSpec([-1], dtype=paddle.int64),
|
||||
InputSpec([1], dtype=paddle.int64),
|
||||
], )
|
||||
else:
|
||||
am_inference = jit.to_static(
|
||||
am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
|
||||
|
||||
elif am_name == 'speedyspeech':
|
||||
if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
|
||||
am_inference = jit.to_static(
|
||||
am_inference,
|
||||
input_spec=[
|
||||
InputSpec([-1], dtype=paddle.int64), # text
|
||||
InputSpec([-1], dtype=paddle.int64), # tone
|
||||
InputSpec([1], dtype=paddle.int64), # spk_id
|
||||
None # duration
|
||||
])
|
||||
else:
|
||||
am_inference = jit.to_static(
|
||||
am_inference,
|
||||
input_spec=[
|
||||
InputSpec([-1], dtype=paddle.int64),
|
||||
InputSpec([-1], dtype=paddle.int64)
|
||||
])
|
||||
|
||||
elif am_name == 'tacotron2':
|
||||
am_inference = jit.to_static(
|
||||
am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
|
||||
|
||||
paddle.jit.save(am_inference, os.path.join(args.inference_dir, args.am))
|
||||
am_inference = paddle.jit.load(os.path.join(args.inference_dir, args.am))
|
||||
return am_inference
|
||||
|
||||
|
||||
def voc_to_static(args, voc_inference):
|
||||
voc_inference = jit.to_static(
|
||||
voc_inference, input_spec=[
|
||||
InputSpec([-1, 80], dtype=paddle.float32),
|
||||
])
|
||||
paddle.jit.save(voc_inference, os.path.join(args.inference_dir, args.voc))
|
||||
voc_inference = paddle.jit.load(os.path.join(args.inference_dir, args.voc))
|
||||
return voc_inference
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue