audio_stream: bytes) -> np.ndarray: pcm = np.frombuffer(audio_stream, dtype=np.float32)if pcm.size == :raise ValueError("音频数据为空") features = np.expand_
defsave_audio(file_path,audio_data,framerate):# 创建一个新的WAV音频文件output_wav=wave.open(file_path,'wb')output_wav.setnchannels(1)# 一声道output_wav.setsampwidth(2)# 16位深度output_wav.setframerate(framerate)# 设置采样率output_wav.writeframes(audio_data.tobytes())# 写入音频数据output...
问Python3、PyAudio、7通道麦克风阵列数据EN整合了语音识别的 Python 程序提供了其他技术无法比拟的交互性...
EN初衷 语音识别领域对音频文件进行频谱分析是一项基本的数据处理过程，同时也为后续的特征分析准备数据。
returns the sampling rate (Fs) of the audio file and a NumPy array of the raw audio samples. To get the duration in seconds, one simply needs to divide the number of samples by Fs ShortTermFeatures.feature_extraction() function returns (a) a 68 x 20 short-term feature matrix, where ...
using (WaveFileReader pcm = new WaveFileReader(@"E:\\test.wav")) { int samplesDesired = 5000; byte[] buffer = new byte[samplesDesired * 4]; short[] left = new short[samplesDesired]; short[] right = new short[samplesDesired]; int bytesRead = pcm.Read(buffer, 0, 10000); int inde...
array([feat.shape[2]], dtype=np.int32) chunk_token = ( self.ort_session.run( None, { self.ort_session.get_inputs()[0] .name: feat.detach() .cpu() .numpy(), self.ort_session.get_inputs()[1].name: feat_len, }, )[0] .flatten() .tolist() ) ...
y = np.array(a.get_array_of_samples()) if a.channels == 2: y = y.reshape((-1, 2)) if normalized: if y.dtype == np.int16: power = 15 elif y.dtype == np.int32: power = 31 else: raise Exception return np.float32(y) / 2**power, a.frame_rate # convert to float32 ...
当我使用阻塞方法时，例如stream.write()，我得到一个干净的输出，但是没有多少剩余的处理能力来处理其他...
feat_len = np.array([feat.shape[2]], dtype=np.int32) chunk_token = ( self.ort_session.run( None, { self.ort_session.get_inputs()[0] .name: feat.detach() .cpu() .numpy(), self.ort_session.get_inputs()[1].name: feat_len, ...