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244 lines
8.6 KiB
244 lines
8.6 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 os
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import numpy as np
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import paddle
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from paddle import jit
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from paddle.static import InputSpec
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.t2s.datasets.data_table import DataTable
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from paddlespeech.t2s.frontend import English
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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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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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# 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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}
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# input
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def get_sentences(args):
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# construct dataset for evaluation
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sentences = []
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with open(args.text, 'rt') as f:
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for line in f:
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items = line.strip().split()
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utt_id = items[0]
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if 'lang' in args and args.lang == 'zh':
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sentence = "".join(items[1:])
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elif 'lang' in args and args.lang == 'en':
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sentence = " ".join(items[1:])
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sentences.append((utt_id, sentence))
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return sentences
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def get_test_dataset(args, test_metadata, am_name, am_dataset):
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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"} and args.speaker_dict:
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print("multiple speaker fastspeech2!")
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fields += ["spk_id"]
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elif 'voice_cloning' in args and args.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 == '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' in args and args.voice_cloning:
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print("voice cloning!")
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fields += ["spk_emb"]
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test_dataset = DataTable(data=test_metadata, fields=fields)
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return test_dataset
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# frontend
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def get_frontend(args):
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if 'lang' in args and args.lang == 'zh':
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frontend = Frontend(
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phone_vocab_path=args.phones_dict, tone_vocab_path=args.tones_dict)
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elif 'lang' in args and args.lang == 'en':
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frontend = English(phone_vocab_path=args.phones_dict)
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else:
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print("wrong lang!")
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print("frontend done!")
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return frontend
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# dygraph
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def get_am_inference(args, am_config):
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with open(args.phones_dict, "r") 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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print("vocab_size:", vocab_size)
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tone_size = None
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if 'tones_dict' in args and args.tones_dict:
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with open(args.tones_dict, "r") 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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print("tone_size:", tone_size)
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spk_num = None
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if 'speaker_dict' in args and args.speaker_dict:
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with open(args.speaker_dict, 'rt') 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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print("spk_num:", spk_num)
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odim = am_config.n_mels
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# model: {model_name}_{dataset}
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.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 == '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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am.set_state_dict(paddle.load(args.am_ckpt)["main_params"])
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am.eval()
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am_mu, am_std = np.load(args.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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print("acoustic model done!")
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return am_inference, am_name, am_dataset
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def get_voc_inference(args, voc_config):
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# model: {model_name}_{dataset}
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voc_name = args.voc[:args.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(args.voc_ckpt)["generator_params"])
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voc.remove_weight_norm()
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voc.eval()
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else:
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voc = voc_class(**voc_config["model"])
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voc.set_state_dict(paddle.load(args.voc_ckpt)["main_params"])
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voc.eval()
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voc_mu, voc_std = np.load(args.voc_stat)
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voc_mu = paddle.to_tensor(voc_mu)
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voc_std = paddle.to_tensor(voc_std)
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voc_normalizer = ZScore(voc_mu, voc_std)
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voc_inference = voc_inference_class(voc_normalizer, voc)
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voc_inference.eval()
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print("voc done!")
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return voc_inference
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# to static
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def am_to_static(args, am_inference, am_name, am_dataset):
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if am_name == 'fastspeech2':
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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am_inference = jit.to_static(
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am_inference,
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input_spec=[
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InputSpec([-1], dtype=paddle.int64),
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InputSpec([1], dtype=paddle.int64),
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], )
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else:
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am_inference = jit.to_static(
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am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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elif am_name == 'speedyspeech':
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if am_dataset in {"aishell3", "vctk"} and args.speaker_dict:
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am_inference = jit.to_static(
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am_inference,
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input_spec=[
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InputSpec([-1], dtype=paddle.int64), # text
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InputSpec([-1], dtype=paddle.int64), # tone
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InputSpec([1], dtype=paddle.int64), # spk_id
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None # duration
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])
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else:
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am_inference = jit.to_static(
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am_inference,
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input_spec=[
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InputSpec([-1], dtype=paddle.int64),
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InputSpec([-1], dtype=paddle.int64)
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])
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elif am_name == 'tacotron2':
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am_inference = jit.to_static(
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am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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paddle.jit.save(am_inference, os.path.join(args.inference_dir, args.am))
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am_inference = paddle.jit.load(os.path.join(args.inference_dir, args.am))
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return am_inference
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def voc_to_static(args, voc_inference):
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voc_inference = jit.to_static(
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voc_inference, input_spec=[
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InputSpec([-1, 80], dtype=paddle.float32),
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])
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paddle.jit.save(voc_inference, os.path.join(args.inference_dir, args.voc))
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voc_inference = paddle.jit.load(os.path.join(args.inference_dir, args.voc))
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return voc_inference
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