AVhQdZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#d/de#e!ejHfde%de%de&de#e!ejHff dZ'edejPe'Z)de#e!ejHfde%de%de&de#e!ejHff dZ*ejVdddgZ,ejZej\edd0de#e!ej^fde%de%fd Z0ed!ejPe0Z1e0jdjfZ4de#e!ej^fde%de%fd"Z5ejZej\ed#d1de#e!ejHfde#e!ejlfde#e!ej^ffd$Z7ed%ejPe7Z8e7jdjfZ9de#e!ejHfde#e!ejlfde#e!ej^ffd&Z:d2d'e#e!ejHfde#e!ejlfd(e;d)e;d*e%d+e%de#e!ejHffd,Z<ed-ejPe<Z=d'e#e!ejHfde#e!ejlfd(e;d)e;d*e%d+e%de#e!ejHffd.Z>y)3zUPython wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. N) pywrap_tfe)context)core)execute)dtypes)annotation_types)op_def_registry)ops)op_def_library)deprecated_endpoints)dispatch) tf_export)TypeVarListAny) Annotatedinput window_sizestridemagnitude_squaredreturnc htjxstj}|j}|jr! t j |d||d|d|d| }|Stj|d}tj|d}|d}tj|d}t!j"d|||||\} } } } | dd}tj$rYd| j'dd| j'dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYPwxYw) aProduces a visualization of audio data over time. Spectrograms are a standard way of representing audio information as a series of slices of frequency information, one slice for each window of time. By joining these together into a sequence, they form a distinctive fingerprint of the sound over time. This op expects to receive audio data as an input, stored as floats in the range -1 to 1, together with a window width in samples, and a stride specifying how far to move the window between slices. From this it generates a three dimensional output. The first dimension is for the channels in the input, so a stereo audio input would have two here for example. The second dimension is time, with successive frequency slices. The third dimension has an amplitude value for each frequency during that time slice. This means the layout when converted and saved as an image is rotated 90 degrees clockwise from a typical spectrogram. Time is descending down the Y axis, and the frequency decreases from left to right. Each value in the result represents the square root of the sum of the real and imaginary parts of an FFT on the current window of samples. In this way, the lowest dimension represents the power of each frequency in the current window, and adjacent windows are concatenated in the next dimension. To get a more intuitive and visual look at what this operation does, you can run tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the resulting spectrogram as a PNG image. Args: input: A `Tensor` of type `float32`. Float representation of audio data. window_size: An `int`. How wide the input window is in samples. For the highest efficiency this should be a power of two, but other values are accepted. stride: An `int`. How widely apart the center of adjacent sample windows should be. magnitude_squared: An optional `bool`. Defaults to `False`. Whether to return the squared magnitude or just the magnitude. Using squared magnitude can avoid extra calculations. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. AudioSpectrogramrrrN)rrrnamectxF)rrrrr)_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException audio_spectrogram_eager_fallback_SymbolicException_executemake_int make_bool_op_def_library_apply_op_helpermust_record_gradient _get_attr_int_get_attr_boolinputsrecord_gradient)rrrrr_ctxtld_resulte__op_outputs_attrs _inputs_flats S/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_audio_ops.pyaudio_spectrogramr;sX    0h..0$ #\\ 11  $}k&-/@Bgn!!+}=+   VX .&(():  !457F::L L&'; (' .;  & &- ##At,,  # #    - [-DdDD  # #   s0D>>FE,,FF FF10F1zraw_ops.AudioSpectrogramctj|d}tj|d}|d}tj|d}tj|t j }|g}d|d|d|f}tjdd||||}tjrtjd||||\}|S) NrrFrsAudioSpectrogramr/attrsrrr) r'r(r)r"convert_to_tensor_dtypesfloat32rr,r0) rrrrrrr9r8r3s r:r%r%os!!+}=+   VX .&(():} t3j@d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||fd}|tur|St|||||S#tj $rYWt"t$f$rJt'j(t*dt-||||}|t&j.j0ur|cYSwxYw#t"t$f$rJt'j(t*dt-||||}|t&j.j0ur|cYSwxYw) aDecode a 16-bit PCM WAV file to a float tensor. The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float. When desired_channels is set, if the input contains fewer channels than this then the last channel will be duplicated to give the requested number, else if the input has more channels than requested then the additional channels will be ignored. If desired_samples is set, then the audio will be cropped or padded with zeroes to the requested length. The first output contains a Tensor with the content of the audio samples. The lowest dimension will be the number of channels, and the second will be the number of samples. For example, a ten-sample-long stereo WAV file should give an output shape of [10, 2]. Args: contents: A `Tensor` of type `string`. The WAV-encoded audio, usually from a file. desired_channels: An optional `int`. Defaults to `-1`. Number of sample channels wanted. desired_samples: An optional `int`. Defaults to `-1`. Length of audio requested. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (audio, sample_rate). audio: A `Tensor` of type `float32`. sample_rate: A `Tensor` of type `int32`. rDrHrIN)rHrIrr)rGrHrIr)!rrrrrr_DecodeWavOutput_maker r!r"r#r$_dispatcher_for_decode_wavNotImplementeddecode_wav_eager_fallbackr& TypeError ValueError _dispatchr decode_wavdict OpDispatcher NOT_SUPPORTEDr'r(r*r+r,r-r/r0) rGrHrIrr1r2r3r4r5r6r7r8r9s r:rUrUsH    0h..0$ #\\  11 k4+=+_>g!&&w/g n2) #_dFg  & & %5)$@@  # #  z " "" D(2B1@tM g  ..<< <  2 Z    b$0@/>TK G i,,::: n  sP2EH?F ,FF F $G>GH<$AH<:H<?AJJzraw_ops.DecodeWavc|d}tj|d}|d}tj|d}tj|tj }|g}d|d|f}tj dd||||}tjrtjd|||tj|}|S)NrLrHrIs DecodeWavr>rD) r'r(r"r@rAstringrr,r0rMrN)rGrHrIrrr9r8r3s r:rQrQs&&'79KLO%%o7HI/  # #Hgnn =(,  02C &   \1\#)s ?' ""$ \674  " "7 +' .rCzaudio.encode_wavc *tjxstj}|j}|jr t j |d|||}|St|||fd}|tur|S t/j0d|||\}}}} | dd}t3j4r&d} |j6} t3j8d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||fd}|tur|St||||S#t j$rYtt f$rIt#j$t&dt)|||}|t"j*j,ur|cYSwxYw#tt f$rIt#j$t&dt)|||}|t"j*j,ur|cYSwxYw)aEncode audio data using the WAV file format. This operation will generate a string suitable to be saved out to create a .wav audio file. It will be encoded in the 16-bit PCM format. It takes in float values in the range -1.0f to 1.0f, and any outside that value will be clamped to that range. `audio` is a 2-D float Tensor of shape `[length, channels]`. `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100). Args: audio: A `Tensor` of type `float32`. 2-D with shape `[length, channels]`. sample_rate: A `Tensor` of type `int32`. Scalar containing the sample frequency. name: A name for the operation (optional). Returns: A `Tensor` of type `string`. EncodeWavN)rrrK)rErFr)rrrrrrr r!r"r#r$_dispatcher_for_encode_wavrPencode_wav_eager_fallbackr&rRrSrTr encode_wavrVrWrXr*r+r'r,r/r0) rErFrr1r2r3r4r5r6r7r8r9s r:r`r`s .    0h..0$ #\\ 11 k4 5g n.)  T#T+Gn$ n  )::5kFAq#x QK' ""$ F::L \674 (' .[  & &- ##At,,  # #    * +t %t-g  & & 4T33  # #  z " "" Du+&*, g  ..<< <  " Z    b$U $(* Gi,,::: n  sPC5F:D+DDD#E <E F7 AF75F7:AHHzraw_ops.EncodeWavc8tj|tj}tj|tj}||g}d}t j dd||||}t jrt jd||||\}|S)Ns EncodeWavr=r>r]) r"r@rArBint32r'rr,r0)rErFrrr9r8r3s r:r_r_Vs   8%&&{GMMB+%, &   \1\#)s ?' ""$ \674 (' .rC spectrogramupper_frequency_limitlower_frequency_limitfilterbank_channel_countdct_coefficient_countctjxstj}|j}|jr$ t j |d|||d|d|d|d| } | S|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d||||||| \} } } } | dd} tj$rjd| j'dd| j'dd| j)dd| j)df}| j*}tj,d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYw) avTransforms a spectrogram into a form that's useful for speech recognition. Mel Frequency Cepstral Coefficients are a way of representing audio data that's been effective as an input feature for machine learning. They are created by taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the higher frequencies that are less significant to the human ear. They have a long history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum is a good resource to learn more. Args: spectrogram: A `Tensor` of type `float32`. Typically produced by the Spectrogram op, with magnitude_squared set to true. sample_rate: A `Tensor` of type `int32`. How many samples per second the source audio used. upper_frequency_limit: An optional `float`. Defaults to `4000`. The highest frequency to use when calculating the ceptstrum. lower_frequency_limit: An optional `float`. Defaults to `20`. The lowest frequency to use when calculating the ceptstrum. filterbank_channel_count: An optional `int`. Defaults to `40`. Resolution of the Mel bank used internally. dct_coefficient_count: An optional `int`. Defaults to `13`. How many output channels to produce per time slice. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. 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