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786 lines
28 KiB
786 lines
28 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import os
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import re
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from pathlib import Path
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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Optional
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import jsonlines
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import numpy as np
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import onnxruntime as ort
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import paddle
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from paddle import inference
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from paddle import jit
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from paddle.io import DataLoader
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from paddle.static import InputSpec
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from yacs.config import CfgNode
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from paddlespeech.t2s.datasets.am_batch_fn import *
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.datasets.vocoder_batch_fn import Clip_static
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from paddlespeech.t2s.frontend.canton_frontend import CantonFrontend
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from paddlespeech.t2s.frontend.en_frontend import English
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from paddlespeech.t2s.frontend.mix_frontend import MixFrontend
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from paddlespeech.t2s.frontend.sing_frontend import SingFrontend
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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from paddlespeech.t2s.modules.normalizer import ZScore
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from paddlespeech.utils.dynamic_import import dynamic_import
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# remove [W:onnxruntime: xxx] from ort
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ort.set_default_logger_severity(3)
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model_alias = {
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# acoustic model
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"speedyspeech":
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"paddlespeech.t2s.models.speedyspeech:SpeedySpeech",
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"speedyspeech_inference":
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"paddlespeech.t2s.models.speedyspeech:SpeedySpeechInference",
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"fastspeech2":
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"paddlespeech.t2s.models.fastspeech2:FastSpeech2",
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"fastspeech2_inference":
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"paddlespeech.t2s.models.fastspeech2:FastSpeech2Inference",
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"tacotron2":
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"paddlespeech.t2s.models.tacotron2:Tacotron2",
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"tacotron2_inference":
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"paddlespeech.t2s.models.tacotron2:Tacotron2Inference",
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"diffsinger":
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"paddlespeech.t2s.models.diffsinger:DiffSinger",
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"diffsinger_inference":
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"paddlespeech.t2s.models.diffsinger:DiffSingerInference",
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# voc
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"pwgan":
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"paddlespeech.t2s.models.parallel_wavegan:PWGGenerator",
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"pwgan_inference":
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"paddlespeech.t2s.models.parallel_wavegan:PWGInference",
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"mb_melgan":
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"paddlespeech.t2s.models.melgan:MelGANGenerator",
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"mb_melgan_inference":
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"paddlespeech.t2s.models.melgan:MelGANInference",
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"style_melgan":
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"paddlespeech.t2s.models.melgan:StyleMelGANGenerator",
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"style_melgan_inference":
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"paddlespeech.t2s.models.melgan:StyleMelGANInference",
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"hifigan":
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"paddlespeech.t2s.models.hifigan:HiFiGANGenerator",
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"hifigan_inference":
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"paddlespeech.t2s.models.hifigan:HiFiGANInference",
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"wavernn":
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"paddlespeech.t2s.models.wavernn:WaveRNN",
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"wavernn_inference":
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"paddlespeech.t2s.models.wavernn:WaveRNNInference",
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"erniesat":
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"paddlespeech.t2s.models.ernie_sat:ErnieSAT",
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"erniesat_inference":
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"paddlespeech.t2s.models.ernie_sat:ErnieSATInference",
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}
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def denorm(data, mean, std):
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return data * std + mean
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def norm(data, mean, std):
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return (data - mean) / std
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def get_chunks(mel, chunk_size: int, pad_size: int):
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"""
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Split mel by chunk size with left and right context.
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Args:
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mel (paddle.Tensor): mel spectrogram, shape (B, T, D)
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chunk_size (int): chunk size
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pad_size (int): size for left and right context.
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"""
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T = mel.shape[1]
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n = math.ceil(T / chunk_size)
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chunks = []
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for i in range(n):
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start = max(0, i * chunk_size - pad_size)
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end = min((i + 1) * chunk_size + pad_size, T)
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chunks.append(mel[:, start:end, :])
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return chunks
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# input
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def get_sentences(text_file: Optional[os.PathLike], lang: str='zh'):
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# construct dataset for evaluation
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sentences = []
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with open(text_file, 'rt', encoding='utf-8') as f:
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for line in f:
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if line.strip() != "":
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items = re.split(r"\s+", line.strip(), maxsplit=1)
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assert len(items) == 2
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utt_id = items[0]
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sentence = items[1]
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sentences.append((utt_id, sentence))
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return sentences
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# input for svs
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def get_sentences_svs(text_file: Optional[os.PathLike]):
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# construct dataset for evaluation
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sentences = []
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with jsonlines.open(text_file, 'r') as reader:
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svs_inputs = list(reader)
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for svs_input in svs_inputs:
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utt_id = svs_input['utt_id']
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sentence = svs_input
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sentences.append((utt_id, sentence))
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return sentences
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# am only
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def get_test_dataset(test_metadata: List[Dict[str, Any]],
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am: str,
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speaker_dict: Optional[os.PathLike]=None,
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voice_cloning: bool=False):
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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converters = {}
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if am_name == 'fastspeech2':
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fields = ["utt_id", "text"]
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if am_dataset in {"aishell3", "vctk", "mix",
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"canton"} and speaker_dict is not None:
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print("multiple speaker fastspeech2!")
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fields += ["spk_id"]
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elif voice_cloning:
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print("voice cloning!")
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fields += ["spk_emb"]
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else:
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print("single speaker fastspeech2!")
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elif am_name == 'diffsinger':
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fields = ["utt_id", "text", "note", "note_dur", "is_slur"]
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elif am_name == 'speedyspeech':
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fields = ["utt_id", "phones", "tones"]
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elif am_name == 'tacotron2':
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fields = ["utt_id", "text"]
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if voice_cloning:
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print("voice cloning!")
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fields += ["spk_emb"]
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elif am_name == 'erniesat':
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fields = [
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"utt_id", "text", "text_lengths", "speech", "speech_lengths",
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"align_start", "align_end"
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]
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converters = {"speech": np.load}
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else:
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print("wrong am, please input right am!!!")
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test_dataset = DataTable(
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data=test_metadata, fields=fields, converters=converters)
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return test_dataset
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# am and voc, for PTQ_static
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def get_dev_dataloader(dev_metadata: List[Dict[str, Any]],
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am: str,
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batch_size: int=1,
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speaker_dict: Optional[os.PathLike]=None,
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voice_cloning: bool=False,
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n_shift: int=300,
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batch_max_steps: int=16200,
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shuffle: bool=True):
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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converters = {}
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if am_name == 'fastspeech2':
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fields = ["utt_id", "text"]
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if am_dataset in {"aishell3", "vctk", "mix",
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"canton"} and speaker_dict is not None:
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print("multiple speaker fastspeech2!")
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collate_fn = fastspeech2_multi_spk_batch_fn_static
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fields += ["spk_id"]
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elif voice_cloning:
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print("voice cloning!")
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collate_fn = fastspeech2_multi_spk_batch_fn_static
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fields += ["spk_emb"]
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else:
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print("single speaker fastspeech2!")
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collate_fn = fastspeech2_single_spk_batch_fn_static
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elif am_name == 'speedyspeech':
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fields = ["utt_id", "phones", "tones"]
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if am_dataset in {"aishell3", "vctk",
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"mix"} and speaker_dict is not None:
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print("multiple speaker speedyspeech!")
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collate_fn = speedyspeech_multi_spk_batch_fn_static
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fields += ["spk_id"]
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else:
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print("single speaker speedyspeech!")
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collate_fn = speedyspeech_single_spk_batch_fn_static
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fields = ["utt_id", "phones", "tones"]
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elif am_name == 'tacotron2':
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fields = ["utt_id", "text"]
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if voice_cloning:
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print("voice cloning!")
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collate_fn = tacotron2_multi_spk_batch_fn_static
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fields += ["spk_emb"]
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else:
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print("single speaker tacotron2!")
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collate_fn = tacotron2_single_spk_batch_fn_static
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else:
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print("voc dataloader")
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# am
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if am_name not in {'pwgan', 'mb_melgan', 'hifigan'}:
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dev_dataset = DataTable(
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data=dev_metadata,
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fields=fields,
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converters=converters, )
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dev_dataloader = DataLoader(
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dev_dataset,
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shuffle=shuffle,
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drop_last=False,
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batch_size=batch_size,
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collate_fn=collate_fn)
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# vocoder
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else:
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# pwgan: batch_max_steps: 25500 aux_context_window: 2
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# mb_melgan: batch_max_steps: 16200 aux_context_window 0
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# hifigan: batch_max_steps: 8400 aux_context_window 0
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aux_context_window = 0
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if am_name == 'pwgan':
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aux_context_window = 2
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train_batch_fn = Clip_static(
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batch_max_steps=batch_max_steps,
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hop_size=n_shift,
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aux_context_window=aux_context_window)
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dev_dataset = DataTable(
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data=dev_metadata,
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fields=["wave", "feats"],
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converters={
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"wave": np.load,
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"feats": np.load,
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}, )
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dev_dataloader = DataLoader(
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dev_dataset,
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shuffle=shuffle,
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drop_last=False,
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batch_size=batch_size,
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collate_fn=train_batch_fn)
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return dev_dataloader
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# frontend
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def get_frontend(lang: str='zh',
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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pinyin_phone: Optional[os.PathLike]=None,
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use_rhy=False):
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if lang == 'zh':
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frontend = Frontend(
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phone_vocab_path=phones_dict,
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tone_vocab_path=tones_dict,
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use_rhy=use_rhy)
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elif lang == 'canton':
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frontend = CantonFrontend(phone_vocab_path=phones_dict)
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elif lang == 'en':
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frontend = English(phone_vocab_path=phones_dict)
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elif lang == 'mix':
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frontend = MixFrontend(
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phone_vocab_path=phones_dict, tone_vocab_path=tones_dict)
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elif lang == 'sing':
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frontend = SingFrontend(
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pinyin_phone_path=pinyin_phone, phone_vocab_path=phones_dict)
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else:
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print("wrong lang!")
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return frontend
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def run_frontend(
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frontend: object,
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text: str,
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merge_sentences: bool=False,
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get_tone_ids: bool=False,
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lang: str='zh',
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to_tensor: bool=True,
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add_blank: bool=False,
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svs_input: Dict[str, str]=None, ):
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outs = dict()
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if lang == 'zh':
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input_ids = {}
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if text.strip() != "" and re.match(r".*?<speak>.*?</speak>.*", text,
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re.DOTALL):
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# using ssml
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input_ids = frontend.get_input_ids_ssml(
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text,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids,
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to_tensor=to_tensor)
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else:
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input_ids = frontend.get_input_ids(
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text,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids,
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to_tensor=to_tensor,
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add_blank=add_blank)
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phone_ids = input_ids["phone_ids"]
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if get_tone_ids:
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tone_ids = input_ids["tone_ids"]
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outs.update({'tone_ids': tone_ids})
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elif lang == 'canton':
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input_ids = frontend.get_input_ids(
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text, merge_sentences=merge_sentences, to_tensor=to_tensor)
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phone_ids = input_ids["phone_ids"]
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elif lang == 'en':
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input_ids = frontend.get_input_ids(
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text, merge_sentences=merge_sentences, to_tensor=to_tensor)
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phone_ids = input_ids["phone_ids"]
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elif lang == 'mix':
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input_ids = frontend.get_input_ids(
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text, merge_sentences=merge_sentences, to_tensor=to_tensor)
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phone_ids = input_ids["phone_ids"]
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elif lang == 'sing':
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input_ids = frontend.get_input_ids(
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svs_input=svs_input, to_tensor=to_tensor)
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phone_ids = input_ids["phone_ids"]
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note_ids = input_ids["note_ids"]
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note_durs = input_ids["note_durs"]
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is_slurs = input_ids["is_slurs"]
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outs.update({'note_ids': note_ids})
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outs.update({'note_durs': note_durs})
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outs.update({'is_slurs': is_slurs})
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else:
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print("lang should in {'zh', 'en', 'mix', 'canton', 'sing'}!")
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outs.update({'phone_ids': phone_ids})
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return outs
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# dygraph
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def get_am_inference(
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am: str='fastspeech2_csmsc',
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am_config: CfgNode=None,
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am_ckpt: Optional[os.PathLike]=None,
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am_stat: Optional[os.PathLike]=None,
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phones_dict: Optional[os.PathLike]=None,
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tones_dict: Optional[os.PathLike]=None,
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speaker_dict: Optional[os.PathLike]=None,
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return_am: bool=False,
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speech_stretchs: Optional[os.PathLike]=None, ):
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with open(phones_dict, 'rt', encoding='utf-8') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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vocab_size = len(phn_id)
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tone_size = None
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if tones_dict is not None:
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with open(tones_dict, 'rt', encoding='utf-8') as f:
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tone_id = [line.strip().split() for line in f.readlines()]
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tone_size = len(tone_id)
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spk_num = None
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if speaker_dict is not None:
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with open(speaker_dict, 'rt', encoding='utf-8') as f:
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spk_id = [line.strip().split() for line in f.readlines()]
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spk_num = len(spk_id)
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odim = am_config.n_mels
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# model: {model_name}_{dataset}
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am_name = am[:am.rindex('_')]
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am_dataset = am[am.rindex('_') + 1:]
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am_class = dynamic_import(am_name, model_alias)
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am_inference_class = dynamic_import(am_name + '_inference', model_alias)
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if am_name == 'fastspeech2':
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am = am_class(
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idim=vocab_size, odim=odim, spk_num=spk_num, **am_config["model"])
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elif am_name == 'diffsinger':
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with open(speech_stretchs, "r") as f:
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spec_min = np.load(speech_stretchs)[0]
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spec_max = np.load(speech_stretchs)[1]
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spec_min = paddle.to_tensor(spec_min)
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spec_max = paddle.to_tensor(spec_max)
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am_config["model"]["fastspeech2_params"]["spk_num"] = spk_num
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am = am_class(
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spec_min=spec_min,
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spec_max=spec_max,
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idim=vocab_size,
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odim=odim,
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**am_config["model"], )
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elif am_name == 'speedyspeech':
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am = am_class(
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vocab_size=vocab_size,
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tone_size=tone_size,
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spk_num=spk_num,
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**am_config["model"])
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elif am_name == 'tacotron2':
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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elif am_name == 'erniesat':
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am = am_class(idim=vocab_size, odim=odim, **am_config["model"])
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am.set_state_dict(paddle.load(am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(am_stat)
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am_mu = paddle.to_tensor(am_mu)
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am_std = paddle.to_tensor(am_std)
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am_normalizer = ZScore(am_mu, am_std)
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am_inference = am_inference_class(am_normalizer, am)
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am_inference.eval()
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if return_am:
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return am_inference, am
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else:
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return am_inference
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def get_voc_inference(
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voc: str='pwgan_csmsc',
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voc_config: Optional[os.PathLike]=None,
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voc_ckpt: Optional[os.PathLike]=None,
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voc_stat: Optional[os.PathLike]=None, ):
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# model: {model_name}_{dataset}
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voc_name = voc[:voc.rindex('_')]
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voc_class = dynamic_import(voc_name, model_alias)
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voc_inference_class = dynamic_import(voc_name + '_inference', model_alias)
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if voc_name != 'wavernn':
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voc = voc_class(**voc_config["generator_params"])
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|
voc.set_state_dict(paddle.load(voc_ckpt)["generator_params"])
|
|
voc.remove_weight_norm()
|
|
voc.eval()
|
|
else:
|
|
voc = voc_class(**voc_config["model"])
|
|
voc.set_state_dict(paddle.load(voc_ckpt)["main_params"])
|
|
voc.eval()
|
|
|
|
voc_mu, voc_std = np.load(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()
|
|
return voc_inference
|
|
|
|
|
|
# dygraph to static graph
|
|
def am_to_static(am_inference,
|
|
am: str='fastspeech2_csmsc',
|
|
inference_dir=Optional[os.PathLike],
|
|
speaker_dict: Optional[os.PathLike]=None):
|
|
# model: {model_name}_{dataset}
|
|
am_name = am[:am.rindex('_')]
|
|
am_dataset = am[am.rindex('_') + 1:]
|
|
if am_name == 'fastspeech2':
|
|
if am_dataset in {"aishell3", "vctk", "mix",
|
|
"canton"} and speaker_dict is not None:
|
|
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", "mix",
|
|
"canton"} and speaker_dict is not None:
|
|
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)])
|
|
|
|
elif am_name == 'vits' or am_name == 'jets':
|
|
if am_dataset in {"aishell3", "vctk"} and speaker_dict is not None:
|
|
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 == 'diffsinger':
|
|
am_inference = jit.to_static(
|
|
am_inference,
|
|
input_spec=[
|
|
InputSpec([-1], dtype=paddle.int64), # phone
|
|
InputSpec([-1], dtype=paddle.int64), # note
|
|
InputSpec([-1], dtype=paddle.float32), # note_dur
|
|
InputSpec([-1], dtype=paddle.int64), # is_slur
|
|
])
|
|
|
|
jit.save(am_inference, os.path.join(inference_dir, am))
|
|
am_inference = jit.load(os.path.join(inference_dir, am))
|
|
|
|
return am_inference
|
|
|
|
|
|
def voc_to_static(voc_inference,
|
|
voc: str='pwgan_csmsc',
|
|
inference_dir=Optional[os.PathLike]):
|
|
voc_inference = jit.to_static(
|
|
voc_inference, input_spec=[
|
|
InputSpec([-1, 80], dtype=paddle.float32),
|
|
])
|
|
jit.save(voc_inference, os.path.join(inference_dir, voc))
|
|
voc_inference = jit.load(os.path.join(inference_dir, voc))
|
|
return voc_inference
|
|
|
|
|
|
# inference
|
|
def get_predictor(
|
|
model_dir: Optional[os.PathLike]=None,
|
|
model_file: Optional[os.PathLike]=None,
|
|
params_file: Optional[os.PathLike]=None,
|
|
device: str='cpu',
|
|
# for gpu
|
|
use_trt: bool=False,
|
|
device_id: int=0,
|
|
# for trt
|
|
use_dynamic_shape: bool=True,
|
|
min_subgraph_size: int=5,
|
|
# for cpu
|
|
cpu_threads: int=1,
|
|
use_mkldnn: bool=False,
|
|
# for trt or mkldnn
|
|
precision: int="fp32"):
|
|
"""
|
|
Args:
|
|
model_dir (os.PathLike): root path of model.pdmodel and model.pdiparams.
|
|
model_file (os.PathLike): name of model_file.
|
|
params_file (os.PathLike): name of params_file.
|
|
device (str): Choose the device you want to run, it can be: cpu/gpu, default is cpu.
|
|
use_trt (bool): whether to use TensorRT or not in GPU.
|
|
device_id (int): Choose your device id, only valid when the device is gpu, default 0.
|
|
use_dynamic_shape (bool): use dynamic shape or not in TensorRT.
|
|
use_mkldnn (bool): whether to use MKLDNN or not in CPU.
|
|
cpu_threads (int): num of thread when use CPU.
|
|
precision (str): mode of running (fp32/fp16/bf16/int8).
|
|
"""
|
|
rerun_flag = False
|
|
if device != "gpu" and use_trt:
|
|
raise ValueError(
|
|
"Predict by TensorRT mode: {}, expect device=='gpu', but device == {}".
|
|
format(precision, device))
|
|
|
|
config = inference.Config(
|
|
str(Path(model_dir) / model_file), str(Path(model_dir) / params_file))
|
|
config.enable_memory_optim()
|
|
config.switch_ir_optim(True)
|
|
if device == "gpu":
|
|
config.enable_use_gpu(100, device_id)
|
|
else:
|
|
config.disable_gpu()
|
|
config.set_cpu_math_library_num_threads(cpu_threads)
|
|
if use_mkldnn:
|
|
# fp32
|
|
config.enable_mkldnn()
|
|
if precision == "int8":
|
|
config.enable_mkldnn_int8({
|
|
"conv2d_transpose", "conv2d", "depthwise_conv2d", "pool2d",
|
|
"transpose2", "elementwise_mul"
|
|
})
|
|
# config.enable_mkldnn_int8()
|
|
elif precision in {"fp16", "bf16"}:
|
|
config.enable_mkldnn_bfloat16()
|
|
print("MKLDNN with {}".format(precision))
|
|
if use_trt:
|
|
if precision == "bf16":
|
|
print("paddle trt does not support bf16, switching to fp16.")
|
|
precision = "fp16"
|
|
precision_map = {
|
|
"int8": inference.Config.Precision.Int8,
|
|
"fp32": inference.Config.Precision.Float32,
|
|
"fp16": inference.Config.Precision.Half,
|
|
}
|
|
assert precision in precision_map.keys()
|
|
pdtxt_name = model_file.split(".")[0] + "_" + precision + ".txt"
|
|
if use_dynamic_shape:
|
|
dynamic_shape_file = os.path.join(model_dir, pdtxt_name)
|
|
if os.path.exists(dynamic_shape_file):
|
|
config.enable_tuned_tensorrt_dynamic_shape(dynamic_shape_file,
|
|
True)
|
|
# for fastspeech2
|
|
config.exp_disable_tensorrt_ops(["reshape2"])
|
|
print("trt set dynamic shape done!")
|
|
else:
|
|
# In order to avoid memory overflow when collecting dynamic shapes, it is changed to use CPU.
|
|
config.disable_gpu()
|
|
config.set_cpu_math_library_num_threads(10)
|
|
config.collect_shape_range_info(dynamic_shape_file)
|
|
print("Start collect dynamic shape...")
|
|
rerun_flag = True
|
|
|
|
if not rerun_flag:
|
|
print("Tensor RT with {}".format(precision))
|
|
config.enable_tensorrt_engine(
|
|
workspace_size=1 << 30,
|
|
max_batch_size=1,
|
|
min_subgraph_size=min_subgraph_size,
|
|
precision_mode=precision_map[precision],
|
|
use_static=True,
|
|
use_calib_mode=False, )
|
|
|
|
predictor = inference.create_predictor(config)
|
|
return predictor
|
|
|
|
|
|
def get_am_output(input: str,
|
|
am_predictor: paddle.nn.Layer,
|
|
am: str,
|
|
frontend: object,
|
|
lang: str='zh',
|
|
merge_sentences: bool=True,
|
|
speaker_dict: Optional[os.PathLike]=None,
|
|
spk_id: int=0,
|
|
add_blank: bool=False):
|
|
am_name = am[:am.rindex('_')]
|
|
am_dataset = am[am.rindex('_') + 1:]
|
|
am_input_names = am_predictor.get_input_names()
|
|
get_spk_id = False
|
|
get_tone_ids = False
|
|
if am_name == 'speedyspeech':
|
|
get_tone_ids = True
|
|
if am_dataset in {"aishell3", "vctk", "mix", "canton"} and speaker_dict:
|
|
get_spk_id = True
|
|
spk_id = np.array([spk_id])
|
|
|
|
frontend_dict = run_frontend(
|
|
frontend=frontend,
|
|
text=input,
|
|
merge_sentences=merge_sentences,
|
|
get_tone_ids=get_tone_ids,
|
|
lang=lang,
|
|
add_blank=add_blank, )
|
|
|
|
if get_tone_ids:
|
|
tone_ids = frontend_dict['tone_ids']
|
|
tones = tone_ids[0].numpy()
|
|
tones_handle = am_predictor.get_input_handle(am_input_names[1])
|
|
tones_handle.reshape(tones.shape)
|
|
tones_handle.copy_from_cpu(tones)
|
|
if get_spk_id:
|
|
spk_id_handle = am_predictor.get_input_handle(am_input_names[1])
|
|
spk_id_handle.reshape(spk_id.shape)
|
|
spk_id_handle.copy_from_cpu(spk_id)
|
|
phone_ids = frontend_dict['phone_ids']
|
|
phones = phone_ids[0].numpy()
|
|
phones_handle = am_predictor.get_input_handle(am_input_names[0])
|
|
phones_handle.reshape(phones.shape)
|
|
phones_handle.copy_from_cpu(phones)
|
|
|
|
am_predictor.run()
|
|
am_output_names = am_predictor.get_output_names()
|
|
am_output_handle = am_predictor.get_output_handle(am_output_names[0])
|
|
am_output_data = am_output_handle.copy_to_cpu()
|
|
return am_output_data
|
|
|
|
|
|
def get_voc_output(voc_predictor, input):
|
|
voc_input_names = voc_predictor.get_input_names()
|
|
mel_handle = voc_predictor.get_input_handle(voc_input_names[0])
|
|
mel_handle.reshape(input.shape)
|
|
mel_handle.copy_from_cpu(input)
|
|
|
|
voc_predictor.run()
|
|
voc_output_names = voc_predictor.get_output_names()
|
|
voc_output_handle = voc_predictor.get_output_handle(voc_output_names[0])
|
|
wav = voc_output_handle.copy_to_cpu()
|
|
return wav
|
|
|
|
|
|
def get_am_sublayer_output(am_sublayer_predictor, input):
|
|
am_sublayer_input_names = am_sublayer_predictor.get_input_names()
|
|
input_handle = am_sublayer_predictor.get_input_handle(
|
|
am_sublayer_input_names[0])
|
|
input_handle.reshape(input.shape)
|
|
input_handle.copy_from_cpu(input)
|
|
|
|
am_sublayer_predictor.run()
|
|
am_sublayer_names = am_sublayer_predictor.get_output_names()
|
|
am_sublayer_handle = am_sublayer_predictor.get_output_handle(
|
|
am_sublayer_names[0])
|
|
am_sublayer_output = am_sublayer_handle.copy_to_cpu()
|
|
return am_sublayer_output
|
|
|
|
|
|
def get_streaming_am_output(input: str,
|
|
am_encoder_infer_predictor,
|
|
am_decoder_predictor,
|
|
am_postnet_predictor,
|
|
frontend,
|
|
lang: str='zh',
|
|
merge_sentences: bool=True):
|
|
get_tone_ids = False
|
|
frontend_dict = run_frontend(
|
|
frontend=frontend,
|
|
text=input,
|
|
merge_sentences=merge_sentences,
|
|
get_tone_ids=get_tone_ids,
|
|
lang=lang)
|
|
phone_ids = frontend_dict['phone_ids']
|
|
phones = phone_ids[0].numpy()
|
|
am_encoder_infer_output = get_am_sublayer_output(
|
|
am_encoder_infer_predictor, input=phones)
|
|
|
|
am_decoder_output = get_am_sublayer_output(
|
|
am_decoder_predictor, input=am_encoder_infer_output)
|
|
|
|
am_postnet_output = get_am_sublayer_output(
|
|
am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1)))
|
|
am_output_data = am_decoder_output + np.transpose(am_postnet_output,
|
|
(0, 2, 1))
|
|
normalized_mel = am_output_data[0]
|
|
return normalized_mel
|
|
|
|
|
|
# onnx
|
|
def get_sess(model_path: Optional[os.PathLike],
|
|
device: str='cpu',
|
|
cpu_threads: int=1,
|
|
use_trt: bool=False):
|
|
sess_options = ort.SessionOptions()
|
|
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
|
|
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
|
|
if 'gpu' in device.lower():
|
|
device_id = int(device.split(':')[1]) if len(
|
|
device.split(':')) == 2 else 0
|
|
# fastspeech2/mb_melgan can't use trt now!
|
|
if use_trt:
|
|
provider_name = 'TensorrtExecutionProvider'
|
|
else:
|
|
provider_name = 'CUDAExecutionProvider'
|
|
providers = [(provider_name, {'device_id': device_id})]
|
|
elif device.lower() == 'cpu':
|
|
providers = ['CPUExecutionProvider']
|
|
sess_options.intra_op_num_threads = cpu_threads
|
|
sess = ort.InferenceSession(
|
|
model_path, providers=providers, sess_options=sess_options)
|
|
return sess
|