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318 lines
11 KiB
318 lines
11 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import logging
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from pathlib import Path
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import jsonlines
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import numpy as np
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import paddle
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import soundfile as sf
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import yaml
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from timer import timer
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from yacs.config import CfgNode
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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.modules.normalizer import ZScore
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from paddlespeech.t2s.utils import str2bool
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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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def evaluate(args):
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# dataloader has been too verbose
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logging.getLogger("DataLoader").disabled = True
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# construct dataset for evaluation
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with jsonlines.open(args.test_metadata, 'r') as reader:
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test_metadata = list(reader)
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# Init body.
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with open(args.am_config) as f:
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am_config = CfgNode(yaml.safe_load(f))
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with open(args.voc_config) as f:
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voc_config = CfgNode(yaml.safe_load(f))
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print("========Args========")
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print(yaml.safe_dump(vars(args)))
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print("========Config========")
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print(am_config)
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print(voc_config)
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# construct dataset for evaluation
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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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if am_name == 'fastspeech2':
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fields = ["utt_id", "text"]
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spk_num = None
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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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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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fields += ["spk_id"]
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elif 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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print("spk_num:", spk_num)
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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 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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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 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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# acoustic model
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odim = am_config.n_mels
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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, tone_size=tone_size, **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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print("am_inference.training0:", am_inference.training)
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am_inference.eval()
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print("acoustic model done!")
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# vocoder
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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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print("voc_inference.training0:", voc_inference.training)
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voc_inference.eval()
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print("voc done!")
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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N = 0
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T = 0
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for datum in test_dataset:
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utt_id = datum["utt_id"]
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with timer() as t:
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with paddle.no_grad():
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# acoustic model
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if am_name == 'fastspeech2':
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phone_ids = paddle.to_tensor(datum["text"])
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spk_emb = None
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spk_id = None
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# multi speaker
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if args.voice_cloning and "spk_emb" in datum:
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spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
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elif "spk_id" in datum:
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spk_id = paddle.to_tensor(datum["spk_id"])
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mel = am_inference(
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phone_ids, spk_id=spk_id, spk_emb=spk_emb)
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elif am_name == 'speedyspeech':
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phone_ids = paddle.to_tensor(datum["phones"])
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tone_ids = paddle.to_tensor(datum["tones"])
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mel = am_inference(phone_ids, tone_ids)
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elif am_name == 'tacotron2':
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phone_ids = paddle.to_tensor(datum["text"])
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spk_emb = None
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# multi speaker
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if args.voice_cloning and "spk_emb" in datum:
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spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
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mel = am_inference(phone_ids, spk_emb=spk_emb)
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# vocoder
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wav = voc_inference(mel)
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wav = wav.numpy()
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = am_config.fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.size}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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sf.write(
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str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }")
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def main():
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# parse args and config and redirect to train_sp
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parser = argparse.ArgumentParser(
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description="Synthesize with acoustic model & vocoder")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'speedyspeech_csmsc', 'fastspeech2_csmsc', 'fastspeech2_ljspeech',
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'fastspeech2_aishell3', 'fastspeech2_vctk', 'tacotron2_csmsc',
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'tacotron2_ljspeech', 'tacotron2_aishell3'
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],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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'--am_config',
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type=str,
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default=None,
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help='Config of acoustic model. Use deault config when it is None.')
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parser.add_argument(
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'--am_ckpt',
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type=str,
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default=None,
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help='Checkpoint file of acoustic model.')
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parser.add_argument(
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"--am_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training acoustic model."
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)
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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parser.add_argument(
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"--speaker_dict", type=str, default=None, help="speaker id map file.")
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parser.add_argument(
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"--voice-cloning",
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type=str2bool,
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default=False,
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help="whether training voice cloning model.")
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# vocoder
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parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=[
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'pwgan_csmsc', 'pwgan_ljspeech', 'pwgan_aishell3', 'pwgan_vctk',
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'mb_melgan_csmsc', 'wavernn_csmsc', 'hifigan_csmsc',
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'style_melgan_csmsc'
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],
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help='Choose vocoder type of tts task.')
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parser.add_argument(
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'--voc_config',
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type=str,
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default=None,
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help='Config of voc. Use deault config when it is None.')
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parser.add_argument(
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'--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.')
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parser.add_argument(
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"--voc_stat",
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type=str,
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default=None,
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help="mean and standard deviation used to normalize spectrogram when training voc."
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)
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# other
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parser.add_argument(
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"--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
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parser.add_argument("--test_metadata", type=str, help="test metadata.")
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parser.add_argument("--output_dir", type=str, help="output dir.")
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args = parser.parse_args()
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if args.ngpu == 0:
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paddle.set_device("cpu")
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elif args.ngpu > 0:
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paddle.set_device("gpu")
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else:
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print("ngpu should >= 0 !")
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evaluate(args)
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if __name__ == "__main__":
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main()
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