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258 lines
8.5 KiB
258 lines
8.5 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.t2s.exps.syn_utils import get_am_inference
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from paddlespeech.t2s.exps.syn_utils import get_test_dataset
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.utils import str2bool
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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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# acoustic model
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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_inference = get_am_inference(
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am=args.am,
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am_config=am_config,
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am_ckpt=args.am_ckpt,
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am_stat=args.am_stat,
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phones_dict=args.phones_dict,
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tones_dict=args.tones_dict,
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speaker_dict=args.speaker_dict,
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speech_stretchs=args.speech_stretchs, )
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test_dataset = get_test_dataset(
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test_metadata=test_metadata,
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am=args.am,
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speaker_dict=args.speaker_dict,
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voice_cloning=args.voice_cloning)
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# vocoder
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voc_inference = get_voc_inference(
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voc=args.voc,
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voc_config=voc_config,
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voc_ckpt=args.voc_ckpt,
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voc_stat=args.voc_stat)
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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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elif am_name == 'diffsinger':
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phone_ids = paddle.to_tensor(datum["text"])
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note = paddle.to_tensor(datum["note"])
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note_dur = paddle.to_tensor(datum["note_dur"])
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is_slur = paddle.to_tensor(datum["is_slur"])
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# get_mel_fs2 = False, means mel from diffusion, get_mel_fs2 = True, means mel from fastspeech2.
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get_mel_fs2 = False
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# mel: [T, mel_bin]
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mel = am_inference(
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phone_ids,
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note=note,
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note_dur=note_dur,
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is_slur=is_slur,
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get_mel_fs2=get_mel_fs2)
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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 parse_args():
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# parse args and config
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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',
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'fastspeech2_csmsc',
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'fastspeech2_ljspeech',
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'fastspeech2_aishell3',
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'fastspeech2_vctk',
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'tacotron2_csmsc',
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'tacotron2_ljspeech',
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'tacotron2_aishell3',
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'fastspeech2_mix',
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'fastspeech2_canton',
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'diffsinger_opencpop',
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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', type=str, default=None, help='Config of acoustic model.')
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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',
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'pwgan_ljspeech',
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'pwgan_aishell3',
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'pwgan_vctk',
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'mb_melgan_csmsc',
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'wavernn_csmsc',
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'hifigan_csmsc',
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'hifigan_ljspeech',
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'hifigan_aishell3',
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'hifigan_vctk',
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'style_melgan_csmsc',
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"pwgan_opencpop",
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"hifigan_opencpop",
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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', type=str, default=None, help='Config of voc.')
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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 or xpu.")
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parser.add_argument(
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"--nxpu",
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type=int,
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default=0,
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help="if wish to use xpu, set ngpu == 0 and nxpu > 0, and if ngpu == 0 and nxpu == 0, use cpu."
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)
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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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parser.add_argument(
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"--speech_stretchs",
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type=str,
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default=None,
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help="The min and max values of the mel spectrum.")
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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if args.ngpu > 0:
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paddle.set_device("gpu")
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elif args.nxpu > 0:
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paddle.set_device("xpu")
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elif args.ngpu == 0 and args.nxpu == 0:
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paddle.set_device("cpu")
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else:
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print("ngpu or nxpu should >= 0 !")
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evaluate(args)
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if __name__ == "__main__":
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main()
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