# 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 import base64 import math def wav2base64(wav_file: str): """ read wave file and covert to base64 string """ with open(wav_file, 'rb') as f: base64_bytes = base64.b64encode(f.read()) base64_string = base64_bytes.decode('utf-8') return base64_string def base64towav(base64_string: str): pass def self_check(): """ self check resource """ return True def denorm(data, mean, std): """stream am model need to denorm """ return data * std + mean def get_chunks(data, block_size, pad_size, step): """Divide data into multiple chunks Args: data (tensor): data block_size (int): [description] pad_size (int): [description] step (str): set "am" or "voc", generate chunk for step am or vocoder(voc) Returns: list: chunks list """ if block_size == -1: return [data] if step == "am": data_len = data.shape[1] elif step == "voc": data_len = data.shape[0] else: print("Please set correct type to get chunks, am or voc") chunks = [] n = math.ceil(data_len / block_size) for i in range(n): start = max(0, i * block_size - pad_size) end = min((i + 1) * block_size + pad_size, data_len) if step == "am": chunks.append(data[:, start:end, :]) elif step == "voc": chunks.append(data[start:end, :]) else: print("Please set correct type to get chunks, am or voc") return chunks