o }oi/@sddlmZmZmZddlZddlmZddlmZddl m Z m Z ddl m Z mZmZmZddlmZGdd d e ZGd d d e ZdS) )DictOptionalTupleN) rearrange)make_seq_mask_like) NeuralModule typecheck) AudioSignal LengthsType NeuralTypeSpectrogramType)loggingc seZdZdZddedededeffdd Zed efd d Zd e j fd dZ ed e e effddZed e e effddZe dde j dee j d ee j e j ffddZde j d e j fddZZS)AudioToSpectrogramaTransform a batch of input multi-channel signals into a batch of STFT-based spectrograms. Args: fft_length: length of FFT hop_length: length of hops/shifts of the sliding window power: exponent for magnitude spectrogram. Default `None` will return a complex-valued spectrogram magnitude_power: Transform magnitude of the spectrogram as x^magnitude_power. scale: Positive scaling of the spectrogram. ? fft_length hop_lengthmagnitude_powerscalecst|ddkrtd|d||_||_d|_t|j}| d||dd|_ |dkr:td|||_ |dkrHtd |||_ t d |jjt d |t d |t d |t d|dS)Nr fft_length =  must be divisible by 2constantwindow4Magnitude power needs to be positive: current value *Scale needs to be positive: current value Initialized %s with: fft_length: %s hop_length: %s magnitude_power: %s scale: %s)super__init__ ValueErrorrrpad_modetorch hann_window win_lengthregister_buffer num_subbandsrrr debug __class____name__selfrrrrrr+]/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/audio/modules/transforms.pyr"&s(       zAudioToSpectrogram.__init__returncC|jSNrr.r0r0r1r'CzAudioToSpectrogram.win_lengthxc CsT|\}}}t|d}tj||j|j|j|jd|jdddd }t|d||d}|S)zApply STFT as in torchaudio.transforms.Spectrogram(power=None) Args: x_spec: Input time-domain signal, shape (..., T) Returns: Time-domain signal ``x_spec = STFT(x)``, shape (..., F, N). zB C T -> (B C) TTF) inputn_fftrr'rcenterr$ normalizedonesidedreturn_complexz(B C) F N -> B C F NBC) sizerr%stftrrr'rr$)r.r8r@rATx_specr0r0r1rCGs  zAudioToSpectrogram.stftcCtdttdtdddS)+Returns definitions of module output ports.r@rArDr@Toptionalr9 input_lengthr r r r6r0r0r1 input_typesf zAudioToSpectrogram.input_typescCtdttdtdS)rGr@rADrDrIoutput output_lengthr r r r6r0r0r1 output_typesn  zAudioToSpectrogram.output_typesNr9rMcCs|d|d}}||d|}tjj|jjdd0||}|j dkr;t | |j t d| }|jdkrE|j|}Wdn1sOwY|durp|j|d}t||ddd }||d }||fS|dtj||jd }||fS) aConvert a batch of C-channel input signals into a batch of complex-valued spectrograms. Args: input: Time-domain input signal with C channels, shape (B, C, T) input_length: Length of valid entries along the time dimension, shape (B,) Returns: Output spectrogram with F subbands and N time frames, shape (B, C, F, N) and output length with shape (B,). rFenabledr?NrMlengthsliketime_dim valid_onesdevice)rBviewr%ampautocastrftyperCfloatrpowabsexpanglerget_output_lengthr masked_filloneslong)r.r9rMr@rDrUrV length_maskr0r0r1forwardvs& $    zAudioToSpectrogram.forwardcCs|j|jddd}|S)zGet length of valid frames for the output. Args: input_length: number of valid samples, shape (B,) Returns: Number of valid frames, shape (B,) floor) rounding_moder)divraddrsr.rMrVr0r0r1rps z$AudioToSpectrogram.get_output_lengthrrr4)r, __module__ __qualname____doc__intrkr"propertyr'r%TensorrCrstrr rOrXrrrrurp __classcell__r0r0r/r1rs(  +rc seZdZdZddedededeffdd Zed efd d Zd e j fd dZ ed e e effddZed e e effddZedde j dee j d e j fddZde j d e j fddZZS)SpectrogramToAudioaTransform a batch of input multi-channel spectrograms into a batch of time-domain multi-channel signals. Args: fft_length: length of FFT hop_length: length of hops/shifts of the sliding window magnitude_power: Transform magnitude of the spectrogram as x^(1/magnitude_power). scale: Spectrogram will be scaled with 1/scale before the inverse transform. rrrrrcst|ddkrtd|d||_||_t|j}|d||dd|_ |dkr7td|||_ |dkrEtd|||_ t d |jjt d |t d |t d |t d |dS)Nrrrrrrrrrrrrr )r!r"r#rrr%r&r'r(r)rrr r*r+r,r-r/r0r1r"s&       zSpectrogramToAudio.__init__r2cCr3r4r5r6r0r0r1r'r7zSpectrogramToAudio.win_lengthrEc CsT|\}}}}t|d}tj||j|j|j|jdddddd }t|d||d}|S)zApply iSTFT as in torchaudio.transforms.InverseSpectrogram Args: x_spec: Input complex-valued spectrogram, shape (..., F, N) Returns: Time-domain signal ``x = iSTFT(x_spec)``, shape (..., T). zB C F N -> (B C) F NTFN) r9r:rr'rr;r<r=lengthr>z(B C) T -> B C Tr?)rBrr%istftrrr'r)r.rEr@rAFNr8r0r0r1rs  zSpectrogramToAudio.istftcCrF)rGrRrITrJrLrWr6r0r0r1rOrPzSpectrogramToAudio.input_typescCrQ)rGrHrIrTrNr6r0r0r1rXrYzSpectrogramToAudio.output_typesNr9rMc CsL|d|d|d}}}||jks!Jd|d|j||d||}|s1tdtjj|jj dd4| }|j d krJ||j }|j d krct |d |j td |}||}Wd n1srwY|d ur|j|d }t||ddd }||d}||fS|dtj||jd}||fS)aConvert input complex-valued spectrogram to a time-domain signal. Multi-channel IO is supported. Args: input: Input spectrogram for C channels, shape (B, C, F, N) input_length: Length of valid entries along the time dimension, shape (B,) Returns: Time-domain signal with T time-domain samples and C channels, (B, C, T) and output length with shape (B,). rrZzNumber of subbands F=z not matching self.num_subbands=z%Expected `input` to be complex dtype.Fr[rr]Nr^r_rdre)rBr)rg is_complexr#r%rhrirfrjcfloatrrrlrmrnrorrprrqrrrs) r.r9rMr@rrrUrVrtr0r0r1ru s,"    (   zSpectrogramToAudio.forwardcCs|d|j}|S)zGet length of valid samples for the output. Args: input_length: number of valid frames, shape (B,) Returns: Number of valid samples, shape (B,) r)submulrrsrzr0r0r1rp9s z$SpectrogramToAudio.get_output_lengthr{r4)r,r|r}r~rrkr"rr'r%rrrrr rOrXrrrurprr0r0r/r1rs $.r)typingrrrr%einopsr1nemo.collections.asr.parts.preprocessing.featuresrnemo.core.classesrrnemo.core.neural_typesr r r r nemo.utilsr rrr0r0r0r1s