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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# stream play TTS
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# Before first execution, download and decompress the models in the execution directory
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# wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
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# wget https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_csmsc_onnx_0.2.0.zip
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# unzip fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0.zip
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# unzip mb_melgan_csmsc_onnx_0.2.0.zip
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import math
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import time
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import numpy as np
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import onnxruntime as ort
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import pyaudio
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import soundfile as sf
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from paddlespeech.server.utils.audio_process import float2pcm
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from paddlespeech.server.utils.util import denorm
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from paddlespeech.server.utils.util import get_chunks
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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voc_block = 36
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voc_pad = 14
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am_block = 72
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am_pad = 12
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voc_upsample = 300
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phones_dict = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/phone_id_map.txt"
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frontend = Frontend(phone_vocab_path=phones_dict, tone_vocab_path=None)
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am_stat_path = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/speech_stats.npy"
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am_mu, am_std = np.load(am_stat_path)
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# 模型路径
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onnx_am_encoder = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_encoder_infer.onnx"
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onnx_am_decoder = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_decoder.onnx"
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onnx_am_postnet = "fastspeech2_cnndecoder_csmsc_streaming_onnx_1.0.0/fastspeech2_csmsc_am_postnet.onnx"
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onnx_voc_melgan = "mb_melgan_csmsc_onnx_0.2.0/mb_melgan_csmsc.onnx"
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# 用CPU推理
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providers = ['CPUExecutionProvider']
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# 配置ort session
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sess_options = ort.SessionOptions()
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# 创建session
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am_encoder_infer_sess = ort.InferenceSession(
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onnx_am_encoder, providers=providers, sess_options=sess_options)
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am_decoder_sess = ort.InferenceSession(
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onnx_am_decoder, providers=providers, sess_options=sess_options)
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am_postnet_sess = ort.InferenceSession(
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onnx_am_postnet, providers=providers, sess_options=sess_options)
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voc_melgan_sess = ort.InferenceSession(
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onnx_voc_melgan, providers=providers, sess_options=sess_options)
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def depadding(data, chunk_num, chunk_id, block, pad, upsample):
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"""
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Streaming inference removes the result of pad inference
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"""
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front_pad = min(chunk_id * block, pad)
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# first chunk
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if chunk_id == 0:
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data = data[:block * upsample]
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# last chunk
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elif chunk_id == chunk_num - 1:
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data = data[front_pad * upsample:]
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# middle chunk
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else:
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data = data[front_pad * upsample:(front_pad + block) * upsample]
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return data
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def inference_stream(text):
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input_ids = frontend.get_input_ids(
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text, merge_sentences=False, get_tone_ids=False)
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phone_ids = input_ids["phone_ids"]
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for i in range(len(phone_ids)):
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part_phone_ids = phone_ids[i].numpy()
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voc_chunk_id = 0
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orig_hs = am_encoder_infer_sess.run(
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None, input_feed={'text': part_phone_ids})
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orig_hs = orig_hs[0]
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# streaming voc chunk info
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mel_len = orig_hs.shape[1]
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voc_chunk_num = math.ceil(mel_len / voc_block)
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start = 0
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end = min(voc_block + voc_pad, mel_len)
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# streaming am
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hss = get_chunks(orig_hs, am_block, am_pad, "am")
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am_chunk_num = len(hss)
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for i, hs in enumerate(hss):
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am_decoder_output = am_decoder_sess.run(None, input_feed={'xs': hs})
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am_postnet_output = am_postnet_sess.run(
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None,
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input_feed={
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'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
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})
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am_output_data = am_decoder_output + np.transpose(
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am_postnet_output[0], (0, 2, 1))
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normalized_mel = am_output_data[0][0]
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sub_mel = denorm(normalized_mel, am_mu, am_std)
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sub_mel = depadding(sub_mel, am_chunk_num, i, am_block, am_pad, 1)
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if i == 0:
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mel_streaming = sub_mel
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else:
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mel_streaming = np.concatenate((mel_streaming, sub_mel), axis=0)
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# streaming voc
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# 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
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while (mel_streaming.shape[0] >= end and
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voc_chunk_id < voc_chunk_num):
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voc_chunk = mel_streaming[start:end, :]
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sub_wav = voc_melgan_sess.run(
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output_names=None, input_feed={'logmel': voc_chunk})
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sub_wav = depadding(sub_wav[0], voc_chunk_num, voc_chunk_id,
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voc_block, voc_pad, voc_upsample)
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yield sub_wav
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voc_chunk_id += 1
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start = max(0, voc_chunk_id * voc_block - voc_pad)
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end = min((voc_chunk_id + 1) * voc_block + voc_pad, mel_len)
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if __name__ == '__main__':
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text = "欢迎使用飞桨语音合成系统，测试一下合成效果。"
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# warm up
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# onnxruntime 第一次时间会长一些，建议先 warmup 一下
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for sub_wav in inference_stream(text="哈哈哈哈"):
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continue
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# pyaudio 播放
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p = pyaudio.PyAudio()
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stream = p.open(
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format=p.get_format_from_width(2), # int16
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channels=1,
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rate=24000,
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output=True)
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# 计时
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wavs = []
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t1 = time.time()
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for sub_wav in inference_stream(text):
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print("响应时间：", time.time() - t1)
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t1 = time.time()
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wavs.append(sub_wav.flatten())
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# float32 to int16
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wav = float2pcm(sub_wav)
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# to bytes
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wav_bytes = wav.tobytes()
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stream.write(wav_bytes)
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# 关闭 pyaudio 播放器
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stream.stop_stream()
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stream.close()
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p.terminate()
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# 流式合成的结果导出
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wav = np.concatenate(wavs)
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print(wav.shape)
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sf.write("demo_stream.wav", data=wav, samplerate=24000)
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