# 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 from pathlib import Path import numpy as np import soundfile as sf from timer import timer from paddlespeech.t2s.exps.syn_utils import denorm from paddlespeech.t2s.exps.syn_utils import get_am_sublayer_output 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_predictor from paddlespeech.t2s.exps.syn_utils import get_sentences from paddlespeech.t2s.exps.syn_utils import get_streaming_am_output from paddlespeech.t2s.exps.syn_utils import get_voc_output from paddlespeech.t2s.utils import str2bool def parse_args(): parser = argparse.ArgumentParser( description="Paddle Infernce 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_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.") parser.add_argument( "--speaker_dict", type=str, default=None, help="speaker id map file.") parser.add_argument( '--spk_id', type=int, default=0, help='spk id for multi speaker acoustic model') # voc parser.add_argument( '--voc', type=str, default='pwgan_csmsc', choices=['pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc'], help='Choose vocoder type of tts task.') # other parser.add_argument( '--lang', type=str, default='zh', help='Choose model language. zh or en') parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line") parser.add_argument( "--inference_dir", type=str, help="dir to save inference models") parser.add_argument("--output_dir", type=str, help="output dir") # inference parser.add_argument( "--device", default="gpu", choices=["gpu", "cpu"], help="Device selected for inference.", ) # 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") args, _ = parser.parse_known_args() return args # only inference for models trained with csmsc now def main(): args = parse_args() # frontend frontend = get_frontend( lang=args.lang, phones_dict=args.phones_dict, tones_dict=args.tones_dict) # am_predictor am_encoder_infer_predictor = get_predictor( model_dir=args.inference_dir, model_file=args.am + "_am_encoder_infer" + ".pdmodel", params_file=args.am + "_am_encoder_infer" + ".pdiparams", device=args.device) am_decoder_predictor = get_predictor( model_dir=args.inference_dir, model_file=args.am + "_am_decoder" + ".pdmodel", params_file=args.am + "_am_decoder" + ".pdiparams", device=args.device) am_postnet_predictor = get_predictor( model_dir=args.inference_dir, model_file=args.am + "_am_postnet" + ".pdmodel", params_file=args.am + "_am_postnet" + ".pdiparams", device=args.device) am_mu, am_std = np.load(args.am_stat) # model: {model_name}_{dataset} am_dataset = args.am[args.am.rindex('_') + 1:] # voc_predictor voc_predictor = get_predictor( model_dir=args.inference_dir, model_file=args.voc + ".pdmodel", params_file=args.voc + ".pdiparams", device=args.device) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) sentences = get_sentences(text_file=args.text, lang=args.lang) merge_sentences = True fs = 24000 if am_dataset != 'ljspeech' else 22050 # warmup for utt_id, sentence in sentences[:3]: with timer() as t: normalized_mel = get_streaming_am_output( input=sentence, am_encoder_infer_predictor=am_encoder_infer_predictor, am_decoder_predictor=am_decoder_predictor, am_postnet_predictor=am_postnet_predictor, frontend=frontend, lang=args.lang, merge_sentences=merge_sentences, ) mel = denorm(normalized_mel, am_mu, am_std) wav = get_voc_output(voc_predictor=voc_predictor, input=mel) speed = wav.size / t.elapse rtf = fs / speed print( f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." ) print("warm up done!") N = 0 T = 0 chunk_size = args.chunk_size pad_size = args.pad_size get_tone_ids = False for utt_id, sentence in sentences: with timer() as t: # frontend 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!") phones = phone_ids[0].numpy() # acoustic model orig_hs = get_am_sublayer_output( am_encoder_infer_predictor, input=phones) 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): am_decoder_output = get_am_sublayer_output( am_decoder_predictor, input=hs) am_postnet_output = get_am_sublayer_output( am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1))) am_output_data = am_decoder_output + np.transpose( am_postnet_output, (0, 2, 1)) normalized_mel = am_output_data[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 = np.concatenate(mel_list, axis=0) else: am_decoder_output = get_am_sublayer_output( am_decoder_predictor, input=orig_hs) am_postnet_output = get_am_sublayer_output( am_postnet_predictor, input=np.transpose(am_decoder_output, (0, 2, 1))) am_output_data = am_decoder_output + np.transpose( am_postnet_output, (0, 2, 1)) normalized_mel = am_output_data[0] mel = denorm(normalized_mel, am_mu, am_std) # vocoder wav = get_voc_output(voc_predictor=voc_predictor, input=mel) N += wav.size T += t.elapse speed = wav.size / t.elapse rtf = fs / speed sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000) print( f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." ) print(f"{utt_id} done!") print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") if __name__ == "__main__": main()