# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os from pathlib import Path import numpy as np import paddle import soundfile as sf import yaml from paddle import jit from paddle.static import InputSpec from timer import timer from yacs.config import CfgNode from paddlespeech.s2t.utils.dynamic_import import dynamic_import from paddlespeech.t2s.exps.syn_utils import denorm from paddlespeech.t2s.exps.syn_utils import get_chunks from paddlespeech.t2s.exps.syn_utils import get_frontend from paddlespeech.t2s.exps.syn_utils import get_sentences from paddlespeech.t2s.exps.syn_utils import get_voc_inference from paddlespeech.t2s.exps.syn_utils import model_alias from paddlespeech.t2s.exps.syn_utils import voc_to_static from paddlespeech.t2s.utils import str2bool def evaluate(args): # Init body. with open(args.am_config) as f: am_config = CfgNode(yaml.safe_load(f)) with open(args.voc_config) as f: voc_config = CfgNode(yaml.safe_load(f)) print("========Args========") print(yaml.safe_dump(vars(args))) print("========Config========") print(am_config) print(voc_config) sentences = get_sentences(args) # frontend frontend = get_frontend(args) with open(args.phones_dict, "r") as f: phn_id = [line.strip().split() for line in f.readlines()] vocab_size = len(phn_id) print("vocab_size:", vocab_size) # acoustic model, only support fastspeech2 here now! # am_inference, am_name, am_dataset = get_am_inference(args, am_config) # model: {model_name}_{dataset} am_name = args.am[:args.am.rindex('_')] am_dataset = args.am[args.am.rindex('_') + 1:] odim = am_config.n_mels am_class = dynamic_import(am_name, model_alias) am = am_class(idim=vocab_size, odim=odim, **am_config["model"]) am.set_state_dict(paddle.load(args.am_ckpt)["main_params"]) am.eval() am_mu, am_std = np.load(args.am_stat) am_mu = paddle.to_tensor(am_mu) am_std = paddle.to_tensor(am_std) # am sub layers am_encoder_infer = am.encoder_infer am_decoder = am.decoder am_postnet = am.postnet # vocoder voc_inference = get_voc_inference(args, voc_config) # whether dygraph to static if args.inference_dir: # fastspeech2 cnndecoder to static # am.encoder_infer am_encoder_infer = jit.to_static( am_encoder_infer, input_spec=[InputSpec([-1], dtype=paddle.int64)]) paddle.jit.save(am_encoder_infer, os.path.join(args.inference_dir, args.am + "_am_encoder_infer")) am_encoder_infer = paddle.jit.load( os.path.join(args.inference_dir, args.am + "_am_encoder_infer")) # am.decoder am_decoder = jit.to_static( am_decoder, input_spec=[InputSpec([1, -1, 384], dtype=paddle.float32)]) paddle.jit.save(am_decoder, os.path.join(args.inference_dir, args.am + "_am_decoder")) am_decoder = paddle.jit.load( os.path.join(args.inference_dir, args.am + "_am_decoder")) # am.postnet am_postnet = jit.to_static( am_postnet, input_spec=[InputSpec([1, 80, -1], dtype=paddle.float32)]) paddle.jit.save(am_postnet, os.path.join(args.inference_dir, args.am + "_am_postnet")) am_postnet = paddle.jit.load( os.path.join(args.inference_dir, args.am + "_am_postnet")) # vocoder voc_inference = voc_to_static(args, voc_inference) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) merge_sentences = True get_tone_ids = False N = 0 T = 0 chunk_size = args.chunk_size pad_size = args.pad_size for utt_id, sentence in sentences: with timer() as t: if args.lang == 'zh': input_ids = frontend.get_input_ids( sentence, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids) phone_ids = input_ids["phone_ids"] else: print("lang should be 'zh' here!") # merge_sentences=True here, so we only use the first item of phone_ids phone_ids = phone_ids[0] with paddle.no_grad(): # acoustic model orig_hs = am_encoder_infer(phone_ids) if args.am_streaming: hss = get_chunks(orig_hs, chunk_size, pad_size) chunk_num = len(hss) mel_list = [] for i, hs in enumerate(hss): before_outs = am_decoder(hs) after_outs = before_outs + am_postnet( before_outs.transpose((0, 2, 1))).transpose( (0, 2, 1)) normalized_mel = after_outs[0] sub_mel = denorm(normalized_mel, am_mu, am_std) # clip output part of pad if i == 0: sub_mel = sub_mel[:-pad_size] elif i == chunk_num - 1: # 最后一块的右侧一定没有 pad 够 sub_mel = sub_mel[pad_size:] else: # 倒数几块的右侧也可能没有 pad 够 sub_mel = sub_mel[pad_size:(chunk_size + pad_size) - sub_mel.shape[0]] mel_list.append(sub_mel) mel = paddle.concat(mel_list, axis=0) else: before_outs = am_decoder(orig_hs) after_outs = before_outs + am_postnet( before_outs.transpose((0, 2, 1))).transpose((0, 2, 1)) normalized_mel = after_outs[0] mel = denorm(normalized_mel, am_mu, am_std) # vocoder wav = voc_inference(mel) wav = wav.numpy() N += wav.size T += t.elapse speed = wav.size / t.elapse rtf = am_config.fs / speed print( f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." ) sf.write( str(output_dir / (utt_id + ".wav")), wav, samplerate=am_config.fs) print(f"{utt_id} done!") print(f"generation speed: {N / T}Hz, RTF: {am_config.fs / (N / T) }") def parse_args(): # parse args and config and redirect to train_sp parser = argparse.ArgumentParser( description="Synthesize with acoustic model & vocoder") # acoustic model parser.add_argument( '--am', type=str, default='fastspeech2_csmsc', choices=['fastspeech2_csmsc'], help='Choose acoustic model type of tts task.') parser.add_argument( '--am_config', type=str, default=None, help='Config of acoustic model. Use deault config when it is None.') parser.add_argument( '--am_ckpt', type=str, default=None, help='Checkpoint file of acoustic model.') parser.add_argument( "--am_stat", type=str, default=None, help="mean and standard deviation used to normalize spectrogram when training acoustic model." ) parser.add_argument( "--phones_dict", type=str, default=None, help="phone vocabulary file.") parser.add_argument( "--tones_dict", type=str, default=None, help="tone vocabulary file.") # vocoder parser.add_argument( '--voc', type=str, default='pwgan_csmsc', choices=[ 'pwgan_csmsc', 'mb_melgan_csmsc', 'style_melgan_csmsc', 'hifigan_csmsc', ], help='Choose vocoder type of tts task.') parser.add_argument( '--voc_config', type=str, default=None, help='Config of voc. Use deault config when it is None.') parser.add_argument( '--voc_ckpt', type=str, default=None, help='Checkpoint file of voc.') parser.add_argument( "--voc_stat", type=str, default=None, help="mean and standard deviation used to normalize spectrogram when training voc." ) # other parser.add_argument( '--lang', type=str, default='zh', help='Choose model language. zh or en') parser.add_argument( "--inference_dir", type=str, default=None, help="dir to save inference models") parser.add_argument( "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.") parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line.") # streaming related parser.add_argument( "--am_streaming", type=str2bool, default=False, help="whether use streaming acoustic model") parser.add_argument( "--chunk_size", type=int, default=42, help="chunk size of am streaming") parser.add_argument( "--pad_size", type=int, default=12, help="pad size of am streaming") parser.add_argument("--output_dir", type=str, help="output dir.") args = parser.parse_args() return args def main(): args = parse_args() if args.ngpu == 0: paddle.set_device("cpu") elif args.ngpu > 0: paddle.set_device("gpu") else: print("ngpu should >= 0 !") evaluate(args) if __name__ == "__main__": main()