|
|
|
|
@ -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)
|
TianYuan
3 years ago