mirror of
https://github.com/HiMeditator/auto-caption.git
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8e575a9ba3
- 字幕记录表格可以按时间降序排列 - 将 caption-engine 重命名为 engine - 更新了相关文件和文件夹的路径 - 修改了 README 和 TODO 文档中的相关内容 - 更新了 Electron 构建配置
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import samplerate
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import numpy as np
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def mergeChunkChannels(chunk, channels):
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"""
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将当前多通道音频数据块转换为单通道音频数据块
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Args:
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chunk: (bytes)多通道音频数据块
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channels: 通道数
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Returns:
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(bytes)单通道音频数据块
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"""
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# (length * channels,)
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chunk_np = np.frombuffer(chunk, dtype=np.int16)
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# (length, channels)
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chunk_np = chunk_np.reshape(-1, channels)
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# (length,)
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chunk_mono_f = np.mean(chunk_np.astype(np.float32), axis=1)
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chunk_mono = np.round(chunk_mono_f).astype(np.int16)
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return chunk_mono.tobytes()
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def resampleRawChunk(chunk, channels, orig_sr, target_sr, mode="sinc_best"):
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"""
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将当前多通道音频数据块转换成单通道音频数据块，然后进行重采样
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Args:
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chunk: (bytes)多通道音频数据块
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channels: 通道数
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orig_sr: 原始采样率
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target_sr: 目标采样率
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mode: 重采样模式，可选：'sinc_best' | 'sinc_medium' | 'sinc_fastest' | 'zero_order_hold' | 'linear'
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Return:
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(bytes)单通道音频数据块
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"""
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# (length * channels,)
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chunk_np = np.frombuffer(chunk, dtype=np.int16)
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# (length, channels)
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chunk_np = chunk_np.reshape(-1, channels)
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# (length,)
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chunk_mono_f = np.mean(chunk_np.astype(np.float32), axis=1)
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chunk_mono = chunk_mono_f.astype(np.int16)
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ratio = target_sr / orig_sr
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chunk_mono_r = samplerate.resample(chunk_mono, ratio, converter_type=mode)
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chunk_mono_r = np.round(chunk_mono_r).astype(np.int16)
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return chunk_mono_r.tobytes()
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def resampleMonoChunk(chunk, orig_sr, target_sr, mode="sinc_best"):
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"""
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将当前单通道音频块进行重采样
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Args:
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chunk: (bytes)单通道音频数据块
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orig_sr: 原始采样率
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target_sr: 目标采样率
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mode: 重采样模式，可选：'sinc_best' | 'sinc_medium' | 'sinc_fastest' | 'zero_order_hold' | 'linear'
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Return:
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(bytes)单通道音频数据块
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"""
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chunk_np = np.frombuffer(chunk, dtype=np.int16)
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ratio = target_sr / orig_sr
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chunk_r = samplerate.resample(chunk_np, ratio, converter_type=mode)
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chunk_r = np.round(chunk_r).astype(np.int16)
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return chunk_r.tobytes()
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