diff --git a/paddlespeech/t2s/exps/stream_play_tts.py b/paddlespeech/t2s/exps/stream_play_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..4dcf4794f304284170cc9fe165b3c2e544d1a348 --- /dev/null +++ b/paddlespeech/t2s/exps/stream_play_tts.py @@ -0,0 +1,181 @@ +# 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)