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| 1 | +# -*- coding: utf-8 -*- |
| 2 | +# Copyright 2020 Minh Nguyen (@dathudeptrai) |
| 3 | +# |
| 4 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | +# you may not use this file except in compliance with the License. |
| 6 | +# You may obtain a copy of the License at |
| 7 | +# |
| 8 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | +# |
| 10 | +# Unless required by applicable law or agreed to in writing, software |
| 11 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | +# See the License for the specific language governing permissions and |
| 14 | +# limitations under the License. |
| 15 | +"""Decode trained Mb-Melgan from folder.""" |
| 16 | + |
| 17 | +import argparse |
| 18 | +import logging |
| 19 | +import os |
| 20 | + |
| 21 | +import numpy as np |
| 22 | +import soundfile as sf |
| 23 | +import yaml |
| 24 | +from tqdm import tqdm |
| 25 | + |
| 26 | +from tensorflow_tts.configs import ParallelWaveGANGeneratorConfig |
| 27 | +from tensorflow_tts.datasets import MelDataset |
| 28 | +from tensorflow_tts.models import TFParallelWaveGANGenerator |
| 29 | + |
| 30 | + |
| 31 | +def main(): |
| 32 | + """Run parallel_wavegan decoding from folder.""" |
| 33 | + parser = argparse.ArgumentParser( |
| 34 | + description="Generate Audio from melspectrogram with trained melgan " |
| 35 | + "(See detail in examples/parallel_wavegan/decode_parallel_wavegan.py)." |
| 36 | + ) |
| 37 | + parser.add_argument( |
| 38 | + "--rootdir", |
| 39 | + default=None, |
| 40 | + type=str, |
| 41 | + required=True, |
| 42 | + help="directory including ids/durations files.", |
| 43 | + ) |
| 44 | + parser.add_argument( |
| 45 | + "--outdir", type=str, required=True, help="directory to save generated speech." |
| 46 | + ) |
| 47 | + parser.add_argument( |
| 48 | + "--checkpoint", type=str, required=True, help="checkpoint file to be loaded." |
| 49 | + ) |
| 50 | + parser.add_argument( |
| 51 | + "--use-norm", type=int, default=1, help="Use norm or raw melspectrogram." |
| 52 | + ) |
| 53 | + parser.add_argument("--batch-size", type=int, default=8, help="batch_size.") |
| 54 | + parser.add_argument( |
| 55 | + "--config", |
| 56 | + default=None, |
| 57 | + type=str, |
| 58 | + required=True, |
| 59 | + help="yaml format configuration file. if not explicitly provided, " |
| 60 | + "it will be searched in the checkpoint directory. (default=None)", |
| 61 | + ) |
| 62 | + parser.add_argument( |
| 63 | + "--verbose", |
| 64 | + type=int, |
| 65 | + default=1, |
| 66 | + help="logging level. higher is more logging. (default=1)", |
| 67 | + ) |
| 68 | + args = parser.parse_args() |
| 69 | + |
| 70 | + # set logger |
| 71 | + if args.verbose > 1: |
| 72 | + logging.basicConfig( |
| 73 | + level=logging.DEBUG, |
| 74 | + format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", |
| 75 | + ) |
| 76 | + elif args.verbose > 0: |
| 77 | + logging.basicConfig( |
| 78 | + level=logging.INFO, |
| 79 | + format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", |
| 80 | + ) |
| 81 | + else: |
| 82 | + logging.basicConfig( |
| 83 | + level=logging.WARN, |
| 84 | + format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", |
| 85 | + ) |
| 86 | + logging.warning("Skip DEBUG/INFO messages") |
| 87 | + |
| 88 | + # check directory existence |
| 89 | + if not os.path.exists(args.outdir): |
| 90 | + os.makedirs(args.outdir) |
| 91 | + |
| 92 | + # load config |
| 93 | + with open(args.config) as f: |
| 94 | + config = yaml.load(f, Loader=yaml.Loader) |
| 95 | + config.update(vars(args)) |
| 96 | + |
| 97 | + if config["format"] == "npy": |
| 98 | + mel_query = "*-fs-after-feats.npy" if "fastspeech" in args.rootdir else "*-norm-feats.npy" if args.use_norm == 1 else "*-raw-feats.npy" |
| 99 | + mel_load_fn = np.load |
| 100 | + else: |
| 101 | + raise ValueError("Only npy is supported.") |
| 102 | + |
| 103 | + # define data-loader |
| 104 | + dataset = MelDataset( |
| 105 | + root_dir=args.rootdir, |
| 106 | + mel_query=mel_query, |
| 107 | + mel_load_fn=mel_load_fn, |
| 108 | + ) |
| 109 | + dataset = dataset.create(batch_size=args.batch_size) |
| 110 | + |
| 111 | + # define model and load checkpoint |
| 112 | + parallel_wavegan = TFParallelWaveGANGenerator( |
| 113 | + config=ParallelWaveGANGeneratorConfig(**config["parallel_wavegan_generator_params"]), |
| 114 | + name="parallel_wavegan_generator", |
| 115 | + ) |
| 116 | + parallel_wavegan._build() |
| 117 | + parallel_wavegan.load_weights(args.checkpoint) |
| 118 | + |
| 119 | + for data in tqdm(dataset, desc="[Decoding]"): |
| 120 | + utt_ids, mels, mel_lengths = data["utt_ids"], data["mels"], data["mel_lengths"] |
| 121 | + |
| 122 | + # pwgan inference. |
| 123 | + generated_audios = parallel_wavegan.inference(generated_subbands) |
| 124 | + |
| 125 | + # convert to numpy. |
| 126 | + generated_audios = generated_audios.numpy() # [B, T] |
| 127 | + |
| 128 | + # save to outdir |
| 129 | + for i, audio in enumerate(generated_audios): |
| 130 | + utt_id = utt_ids[i].numpy().decode("utf-8") |
| 131 | + sf.write( |
| 132 | + os.path.join(args.outdir, f"{utt_id}.wav"), |
| 133 | + audio[: mel_lengths[i].numpy() * config["hop_size"]], |
| 134 | + config["sampling_rate"], |
| 135 | + "PCM_16", |
| 136 | + ) |
| 137 | + |
| 138 | + |
| 139 | +if __name__ == "__main__": |
| 140 | + main() |
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