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