# Copyright (c) 2022 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 onnxruntime as ort import soundfile as sf from timer import timer from paddlespeech.t2s.exps.syn_utils import get_frontend from paddlespeech.t2s.exps.syn_utils import get_sentences from paddlespeech.t2s.utils import str2bool def get_sess(args, filed='am'): full_name = '' if filed == 'am': full_name = args.am elif filed == 'voc': full_name = args.voc model_dir = str(Path(args.inference_dir) / (full_name + ".onnx")) sess_options = ort.SessionOptions() sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL if args.device == "gpu": # fastspeech2/mb_melgan can't use trt now! if args.use_trt: providers = ['TensorrtExecutionProvider'] else: providers = ['CUDAExecutionProvider'] elif args.device == "cpu": providers = ['CPUExecutionProvider'] sess_options.intra_op_num_threads = args.cpu_threads sess = ort.InferenceSession( model_dir, providers=providers, sess_options=sess_options) return sess def ort_predict(args): # frontend frontend = get_frontend(args) output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) sentences = get_sentences(args) am_name = args.am[:args.am.rindex('_')] am_dataset = args.am[args.am.rindex('_') + 1:] fs = 24000 if am_dataset != 'ljspeech' else 22050 # am am_sess = get_sess(args, filed='am') # vocoder voc_sess = get_sess(args, filed='voc') # frontend warmup # Loading model cost 0.5+ seconds if args.lang == 'zh': frontend.get_input_ids("你好，欢迎使用飞桨框架进行深度学习研究！", merge_sentences=True) else: print("lang should in be 'zh' here!") # am warmup for T in [27, 38, 54]: am_input_feed = {} if am_name == 'fastspeech2': phone_ids = np.random.randint(1, 266, size=(T, )) am_input_feed.update({'text': phone_ids}) elif am_name == 'speedyspeech': phone_ids = np.random.randint(1, 92, size=(T, )) tone_ids = np.random.randint(1, 5, size=(T, )) am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) am_sess.run(None, input_feed=am_input_feed) # voc warmup for T in [227, 308, 544]: data = np.random.rand(T, 80).astype("float32") voc_sess.run(None, input_feed={"logmel": data}) print("warm up done!") N = 0 T = 0 merge_sentences = True get_tone_ids = False am_input_feed = {} if am_name == 'speedyspeech': get_tone_ids = True 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"] if get_tone_ids: tone_ids = input_ids["tone_ids"] else: print("lang should in be 'zh' here!") # merge_sentences=True here, so we only use the first item of phone_ids phone_ids = phone_ids[0].numpy() if am_name == 'fastspeech2': am_input_feed.update({'text': phone_ids}) elif am_name == 'speedyspeech': tone_ids = tone_ids[0].numpy() am_input_feed.update({'phones': phone_ids, 'tones': tone_ids}) mel = am_sess.run(output_names=None, input_feed=am_input_feed) mel = mel[0] wav = voc_sess.run(output_names=None, input_feed={'logmel': mel}) N += len(wav[0]) T += t.elapse speed = len(wav[0]) / t.elapse rtf = fs / speed sf.write( str(output_dir / (utt_id + ".wav")), np.array(wav)[0], samplerate=fs) print( f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}." ) print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }") def parse_args(): parser = argparse.ArgumentParser(description="Infernce with onnxruntime.") # acoustic model parser.add_argument( '--am', type=str, default='fastspeech2_csmsc', choices=['fastspeech2_csmsc', 'speedyspeech_csmsc'], help='Choose acoustic model type of tts task.') 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.") # voc parser.add_argument( '--voc', type=str, default='hifigan_csmsc', choices=['hifigan_csmsc', 'mb_melgan_csmsc'], help='Choose vocoder type of tts task.') # other parser.add_argument( "--inference_dir", type=str, help="dir to save inference models") parser.add_argument( "--text", type=str, help="text to synthesize, a 'utt_id sentence' pair per line") parser.add_argument("--output_dir", type=str, help="output dir") parser.add_argument( '--lang', type=str, default='zh', help='Choose model language. zh or en') # inference parser.add_argument( "--use_trt", type=str2bool, default=False, help="Whether to use inference engin TensorRT.", ) parser.add_argument( "--device", default="gpu", choices=["gpu", "cpu"], help="Device selected for inference.", ) parser.add_argument('--cpu_threads', type=int, default=1) args, _ = parser.parse_known_args() return args def main(): args = parse_args() ort_predict(args) if __name__ == "__main__": main()