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304 lines
10 KiB
304 lines
10 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 os
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from pathlib import Path
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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 paddle import jit
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from paddle.static import InputSpec
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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 denorm
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from paddlespeech.t2s.exps.syn_utils import get_chunks
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import get_voc_inference
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from paddlespeech.t2s.exps.syn_utils import model_alias
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from paddlespeech.t2s.exps.syn_utils import run_frontend
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from paddlespeech.t2s.exps.syn_utils import voc_to_static
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from paddlespeech.t2s.utils import str2bool
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from paddlespeech.utils.dynamic_import import dynamic_import
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def evaluate(args):
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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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sentences = get_sentences(text_file=args.text, lang=args.lang)
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# frontend
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frontend = get_frontend(
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lang=args.lang,
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phones_dict=args.phones_dict,
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tones_dict=args.tones_dict)
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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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# acoustic model, only support fastspeech2 here now!
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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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odim = am_config.n_mels
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am_class = dynamic_import(am_name, model_alias)
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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 sub layers
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am_encoder_infer = am.encoder_infer
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am_decoder = am.decoder
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am_postnet = am.postnet
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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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# whether dygraph to static
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if args.inference_dir:
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# fastspeech2 cnndecoder to static
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# am.encoder_infer
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am_encoder_infer = jit.to_static(
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am_encoder_infer, input_spec=[InputSpec([-1], dtype=paddle.int64)])
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paddle.jit.save(am_encoder_infer,
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os.path.join(args.inference_dir,
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args.am + "_am_encoder_infer"))
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am_encoder_infer = paddle.jit.load(
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os.path.join(args.inference_dir, args.am + "_am_encoder_infer"))
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# am.decoder
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am_decoder = jit.to_static(
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am_decoder,
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input_spec=[InputSpec([1, -1, 384], dtype=paddle.float32)])
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paddle.jit.save(am_decoder,
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os.path.join(args.inference_dir,
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args.am + "_am_decoder"))
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am_decoder = paddle.jit.load(
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os.path.join(args.inference_dir, args.am + "_am_decoder"))
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# am.postnet
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am_postnet = jit.to_static(
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am_postnet,
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input_spec=[InputSpec([1, 80, -1], dtype=paddle.float32)])
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paddle.jit.save(am_postnet,
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os.path.join(args.inference_dir,
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args.am + "_am_postnet"))
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am_postnet = paddle.jit.load(
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os.path.join(args.inference_dir, args.am + "_am_postnet"))
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# vocoder
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voc_inference = voc_to_static(
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voc_inference=voc_inference,
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voc=args.voc,
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inference_dir=args.inference_dir)
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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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merge_sentences = True
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get_tone_ids = False
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N = 0
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T = 0
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block_size = args.block_size
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pad_size = args.pad_size
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for utt_id, sentence in sentences:
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with timer() as t:
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frontend_dict = run_frontend(
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frontend=frontend,
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text=sentence,
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merge_sentences=merge_sentences,
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get_tone_ids=get_tone_ids,
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lang=args.lang)
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phone_ids = frontend_dict['phone_ids']
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# merge_sentences=True here, so we only use the first item of phone_ids
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phone_ids = phone_ids[0]
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with paddle.no_grad():
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# acoustic model
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orig_hs = am_encoder_infer(phone_ids)
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if args.am_streaming:
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hss = get_chunks(orig_hs, block_size, pad_size)
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chunk_num = len(hss)
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mel_list = []
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for i, hs in enumerate(hss):
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before_outs = am_decoder(hs)
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after_outs = before_outs + am_postnet(
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before_outs.transpose((0, 2, 1))).transpose(
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(0, 2, 1))
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normalized_mel = after_outs[0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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# clip output part of pad
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if i == 0:
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sub_mel = sub_mel[:-pad_size]
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elif i == chunk_num - 1:
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# 最后一块的右侧一定没有 pad 够
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sub_mel = sub_mel[pad_size:]
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else:
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# 倒数几块的右侧也可能没有 pad 够
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sub_mel = sub_mel[pad_size:(block_size + pad_size) -
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sub_mel.shape[0]]
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mel_list.append(sub_mel)
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mel = paddle.concat(mel_list, axis=0)
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else:
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before_outs = am_decoder(orig_hs)
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after_outs = before_outs + am_postnet(
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before_outs.transpose((0, 2, 1))).transpose((0, 2, 1))
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normalized_mel = after_outs[0]
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mel = denorm(normalized_mel, am_mu, am_std)
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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.shape}, 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=['fastspeech2_csmsc'],
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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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# 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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'mb_melgan_csmsc',
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'style_melgan_csmsc',
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'hifigan_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', 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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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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parser.add_argument(
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"--inference_dir",
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type=str,
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default=None,
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help="dir to save inference models")
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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(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line.")
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# streaming related
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parser.add_argument(
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"--am_streaming",
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type=str2bool,
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default=False,
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help="whether use streaming acoustic model")
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parser.add_argument(
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"--block_size", type=int, default=42, help="block size of am streaming")
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parser.add_argument(
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"--pad_size", type=int, default=12, help="pad size of am streaming")
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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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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("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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