# 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. # stream play TTS # Before first execution, download and decompress the models in the execution directory # wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip # wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip # unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip # unzip mb_melgan_csmsc_onnx_0.2.0.zip import math import time import numpy as np import onnxruntime as ort import pyaudio import soundfile as sf from paddlespeech.server.utils.audio_process import float2pcm from paddlespeech.server.utils.util import denorm from paddlespeech.server.utils.util import get_chunks from paddlespeech.t2s.frontend.zh_frontend import Frontend voc_block = 36 voc_pad = 14 am_block = 72 am_pad = 12 voc_upsample = 300 phones_dict = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/phone_id_map.txt" frontend = Frontend(phone_vocab_path=phones_dict, tone_vocab_path=None) am_stat_path = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/speech_stats.npy" am_mu, am_std = np.load(am_stat_path) # 模型路径 onnx_am_encoder = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_encoder_infer.onnx" onnx_am_decoder = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_decoder.onnx" onnx_am_postnet = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_postnet.onnx" onnx_voc_melgan = "mb_melgan_csmsc_onnx_0.2.0/mb_melgan_csmsc.onnx" # 用CPU推理 providers = ['CPUExecutionProvider'] # 配置ort session sess_options = ort.SessionOptions() # 创建session am_encoder_infer_sess = ort.InferenceSession( onnx_am_encoder, providers=providers, sess_options=sess_options) am_decoder_sess = ort.InferenceSession( onnx_am_decoder, providers=providers, sess_options=sess_options) am_postnet_sess = ort.InferenceSession( onnx_am_postnet, providers=providers, sess_options=sess_options) voc_melgan_sess = ort.InferenceSession( onnx_voc_melgan, providers=providers, sess_options=sess_options) def depadding(data, chunk_num, chunk_id, block, pad, upsample): """ Streaming inference removes the result of pad inference """ front_pad = min(chunk_id * block, pad) # first chunk if chunk_id == 0: data = data[:block * upsample] # last chunk elif chunk_id == chunk_num - 1: data = data[front_pad * upsample:] # middle chunk else: data = data[front_pad * upsample:(front_pad + block) * upsample] return data def inference_stream(text): input_ids = frontend.get_input_ids( text, merge_sentences=False, get_tone_ids=False) phone_ids = input_ids["phone_ids"] for i in range(len(phone_ids)): part_phone_ids = phone_ids[i].numpy() voc_chunk_id = 0 orig_hs = am_encoder_infer_sess.run( None, input_feed={'text': part_phone_ids}) orig_hs = orig_hs[0] # streaming voc chunk info mel_len = orig_hs.shape[1] voc_chunk_num = math.ceil(mel_len / voc_block) start = 0 end = min(voc_block + voc_pad, mel_len) # streaming am hss = get_chunks(orig_hs, am_block, am_pad, "am") am_chunk_num = len(hss) for i, hs in enumerate(hss): am_decoder_output = am_decoder_sess.run(None, input_feed={'xs': hs}) am_postnet_output = am_postnet_sess.run( None, input_feed={ 'xs': np.transpose(am_decoder_output[0], (0, 2, 1)) }) am_output_data = am_decoder_output + np.transpose( am_postnet_output[0], (0, 2, 1)) normalized_mel = am_output_data[0][0] sub_mel = denorm(normalized_mel, am_mu, am_std) sub_mel = depadding(sub_mel, am_chunk_num, i, am_block, am_pad, 1) if i == 0: mel_streaming = sub_mel else: mel_streaming = np.concatenate((mel_streaming, sub_mel), axis=0) # streaming voc # 当流式AM推理的mel帧数大于流式voc推理的chunk size，开始进行流式voc 推理 while (mel_streaming.shape[0] >= end and voc_chunk_id < voc_chunk_num): voc_chunk = mel_streaming[start:end, :] sub_wav = voc_melgan_sess.run( output_names=None, input_feed={'logmel': voc_chunk}) sub_wav = depadding(sub_wav[0], voc_chunk_num, voc_chunk_id, voc_block, voc_pad, voc_upsample) yield sub_wav voc_chunk_id += 1 start = max(0, voc_chunk_id * voc_block - voc_pad) end = min((voc_chunk_id + 1) * voc_block + voc_pad, mel_len) if __name__ == '__main__': text = "欢迎使用飞桨语音合成系统，测试一下合成效果。" # warm up # onnxruntime 第一次时间会长一些，建议先 warmup 一下 for sub_wav in inference_stream(text="哈哈哈哈"): continue # pyaudio 播放 p = pyaudio.PyAudio() stream = p.open( format=p.get_format_from_width(2), # int16 channels=1, rate=24000, output=True) # 计时 wavs = [] t1 = time.time() for sub_wav in inference_stream(text): print("响应时间：", time.time() - t1) t1 = time.time() wavs.append(sub_wav.flatten()) # float32 to int16 wav = float2pcm(sub_wav) # to bytes wav_bytes = wav.tobytes() stream.write(wav_bytes) # 关闭 pyaudio 播放器 stream.stop_stream() stream.close() p.terminate() # 流式合成的结果导出 wav = np.concatenate(wavs) print(wav.shape) sf.write("demo_stream.wav", data=wav, samplerate=24000)