:jC.ddlZddlmZddlmZmZddlZddlZddlm Z ddl m Z m Z ddl mZmZmZerererddlZddlZddlZddlmZnd\ZZZGd d Zd gd iZd ZdZdZdeddfdZ d%dedej8deedefdZeeZ d&dej@dedededede!dej@fdZ"dej@de!dej@fd Z# d'd!e d"ede!deedeed#e!de fd$Z$y)(N) lru_cache)AnyOptional)Tensor)rank_zero_inforank_zero_warn)_LIBROSA_AVAILABLE_ONNXRUNTIME_AVAILABLE_REQUESTS_AVAILABLE)InferenceSession)NNNc*eZdZdZdeeefddfdZy)r zDummy InferenceSession.kwargsreturnNc yN)selfrs y/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/audio/dnsmos.py__init__zInferenceSession.__init__$s)__name__ __module__ __qualname____doc__dictstrrrrrrr r !s%AT#s(^AArr ))deep_noise_suppression_mean_opinion_score _load_session)requestslibrosa onnxruntime>gQ"@z~/.torchmetrics/DNSMOS dnsmos_dirrcd}tjj|}dD]}tjj||dd}tjtjj |dtjj |r t|}|d|}td|d |tj|}t|d 5}|j|jdddy#t$r}tj|Yd}~d}~wwxYw#1swYxYw) zDownload required DNSMOS files. Args: dnsmos_dir: a dir to save the downloaded files. Defaults to "~/.torchmetrics". z@https://raw.githubusercontent.com/microsoft/DNS-Challenge/master)zDNSMOS/DNSMOS/model_v8.onnxzDNSMOS/DNSMOS/sig_bak_ovr.onnxzDNSMOS/pDNSMOS/sig_bak_ovr.onnxNT)exist_ok/z downloading z to wb)ospath expanduserjoinmakedirsdirnameexistsr Exceptionremoverrgetopenwritecontent)r#urlfilesaveto_urlfmyfilefs r_prepare_dnsmosr=0s MC##J/Ju$j$qr(3 BGGOOF+d; 77>>& ! "$V,avdV4x89d# &$  $1 GGFNN # $ $$ " &!! "  $ $s$ D #D5 D2D--D25D? r*device num_threadsc2tjj|}tjj|st t t j}|||_||_ |jdk(rt|dg|}|Sdt jvr%ddg}d|jiig}t||||}|Sdt jvr%ddg}d|jiig}t||||}|St|dg|}|S)zLoad onnxruntime session. Args: path: the model path device: the device used num_threads: the number of threads to use. Defaults to None. Returns: onnxruntime session cpuCPUExecutionProvider) providers sess_optionsCUDAExecutionProvider device_id)rCprovider_optionsrDCoreMLExecutionProvider)r)r*r+r/r= DNSMOS_DIRortSessionOptionsinter_op_num_threadsintra_op_num_threadstyper get_available_providersindex)r*r>r?optsinfsrCrGs rrrOs* 77  d #D 77>>$  #    D$/!$/! {{e1G0HW[\ K !C$?$?$A A,.DE (&,,7< L\kop K #c&A&A&C C.0FG (&,,7< L\kop K 1G0HW[\ Kraudion_mels frame_size hop_lengthsrto_dbc|j}|jd|d}tjj |||dz||}|j ddd}|j|dd|jddz}|rPt |jdD]5}tj||tjdzdz ||d f<7|S) aVCalculate the mel-spectrogram of an audio. Args: audio: [..., T] n_mels: the number of mel-frequencies frame_size: stft length hop_length: stft hop length sr: sample rate of audio to_db: convert to dB scale if `True` is given Returns: mel-spectrogram: [..., num_mel, T'] )yrWn_fftrVrTrN)ref(.) shapereshaper featuremelspectrogram transposerange power_to_dbnpmax) rSrTrUrVrWrXramel_specbs r_audio_melspecrl{s, KKE MM"eBi (E-- Bj1nF.H!!!Q*Hcr X^^AB-? ?@H x~~a() XA ' 3 3HQKRVV Lr QUWWHQV  X Ormos personalizedct|rFtjgd}tjgd}tjgd}nEtjgd}tjgd}tjgd}||d|d<||d|d<||d |d <|S) zUse polyfit to convert raw mos values to DNSMOS values. Args: mos: the raw mos values, [..., 4] personalized: whether interfering speaker is penalized Returns: DNSMOS: [..., 4] )g~Z!ugBt?g?gBü)g/ g?,?g/z!?gX-*Ͽ)gPzg 7!BV?goj ſgDW?)g;YRg~?gz?)g w=gc9?g%Pzu?)ggFu?gCyXٿ).r[).r^).)rhpoly1d)rmrnp_ovrp_sigp_baks r _polyfit_valrus JK LM KL ?@ >? @AF $CKF $CKF $CK Jrpredsfs cache_sessionc btr trts td|t j |n |j }|rt nt}|td|rdndd||}|td||}t} || k7r5tj|jj|| } n|jj} tt| z} | j d | kr,t#j$| | gd } | j d | kr,tt#j&| j d | z tz d z} g} | }t)| D]}| d t||zt|tz|zf}|j d | kr?|j }|j+d |d f}t#j,|j/d }t#j,t1|d ddfj/d }|j2dk7rdt5j6vsdt5j6vrk t4j8j;||j2|j<}t4j8j;||j2|j<}d|i}d|i}t#j$|jCd|d|jCd|dgd d}tE||}|j+g|dd d}| jG|t jHt#jJt#jL| d d S#t>$r}tAd|Yd}~d}~wwxYw)u Calculate `Deep Noise Suppression performance evaluation based on Mean Opinion Score`_ (DNSMOS). Human subjective evaluation is the ”gold standard” to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. The conventional and widely used metrics require a reference clean speech signal, which is unavailable in real recordings. The no-reference approaches correlate poorly with human ratings and are not widely adopted in the research community. One of the biggest use cases of these perceptual objective metrics is to evaluate noise suppression algorithms. DNSMOS generalizes well in challenging test conditions with a high correlation to human ratings in stack ranking noise suppression methods. More details can be found in `DNSMOS paper `_ and `DNSMOS P.835 paper `_. .. hint:: Using this metric requires you to have ``librosa``, ``onnxruntime`` and ``requests`` installed. Install as ``pip install torchmetrics['audio']`` or alternatively ``pip install librosa onnxruntime-gpu requests`` (if you do not have GPU enabled machine install ``onnxruntime`` instead of ``onnxruntime-gpu``) Args: preds: [..., time] fs: sampling frequency personalized: whether interfering speaker is penalized device: the device used for calculating DNSMOS, can be cpu or cuda:n, where n is the index of gpu. If None is given, then the device of input is used. num_threads: the number of threads to use for cpu inference. Defaults to None. cache_session: whether to cache the onnx session. By default this is true, meaning that repeated calls to this method is faster than if this was set to False, the consequence is that the session will be cached in memory until the process is terminated. Returns: Float tensor with shape ``(...,4)`` of DNSMOS values per sample, i.e. [p808_mos, mos_sig, mos_bak, mos_ovr] Raises: ModuleNotFoundError: If ``librosa``, ``onnxruntime`` or ``requests`` packages are not installed Example: >>> from torch import randn >>> from torchmetrics.functional.audio.dnsmos import deep_noise_suppression_mean_opinion_score >>> preds = randn(8000) >>> deep_noise_suppression_mean_opinion_score(preds, 8000, False) tensor([2.2..., 2.0..., 1.1..., 1.2...], dtype=torch.float64) zDNSMOS metric requires that librosa, onnxruntime and requests are installed. Install as `pip install librosa onnxruntime-gpu requests`.Nr'pzDNSMOS/sig_bak_ovr.onnxz/DNSMOS/model_v8.onnx)orig_sr target_srrZ)axisr[.float32i`)rSrArErHz7Failed to use GPU for DNSMOS, reverting to CPU. Error: input_1rfloat64)r~dtype)'r r r ModuleNotFoundErrortorchr>_cached_load_sessionrrI SAMPLING_RATEr resamplerAnumpyint INPUT_LENGTHrarh concatenatefloorrfrbarrayastyperlrNrJrOOrtValueortvalue_from_numpyrPr0rrunruappend from_numpymeanstack)rvrwrnr>r?rx_load_session_function onnx_sessp808_onnx_sess desired_fsrS len_samplesnum_hopsmosshop_len_samplesidx audio_segrainput_featuresp808_input_featureseoip808_oimos_nps rrrscf %;CV! J  &,%7U\\& !U\\F5B1 & ,a|44KLfVaI,zl:O,PRXZefNJ Z  !2!2!4bJW !!#lZ/0K ++b/K 'u~B7 ++b/K '288EKKOj89LHIAMH D OX#s3#89C|ASWf@f hh~Ic5D5jrrrndarrayboolrlrurrrrrs   Ajj05H,-GS(BBCDj  % $$$D"&& & LL&#& &R#y{=1   ::        ZZ Fbjj<!!% kG kG kGkG SM kG # kG  kG kGr