o .wiD.@sddlZddlmZddlmZmZddlZddlZddlm Z ddl m Z m Z ddl mZmZmZerKerKerKddlZddlZddlZddlmZn d\ZZZGd d d Zd gd iZd ZdZdZdeddfddZ d.dedejdeedefddZeeZ    d/dej deded ed!ed"e!dej fd#d$Z"d%ej d&e!dej fd'd(Z#   d0d)e d*ed&e!deedeed+e!de fd,d-Z$dS)1N) lru_cache)AnyOptional)Tensor)rank_zero_inforank_zero_warn)_LIBROSA_AVAILABLE_ONNXRUNTIME_AVAILABLE_REQUESTS_AVAILABLE)InferenceSession)NNNc@s*eZdZdZdeeefddfddZdS)r zDummy InferenceSession.kwargsreturnNcKsdSN)selfr rra/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/audio/dnsmos.py__init__$szInferenceSession.__init__)__name__ __module__ __qualname____doc__dictstrrrrrrrr !sr ))deep_noise_suppression_mean_opinion_score _load_session)requestslibrosa onnxruntime>gQ"@z~/.torchmetrics/DNSMOS dnsmos_dirr c Csd}tj|}dD]m}tj||dd}tjtj|ddtj|rFzt|}Wq tyE}z t |WYd}~nd}~ww|d|}t d|d |t |}t |d }||jWdn1srwYq dS) zDownload required DNSMOS files. Args: dnsmos_dir: a dir to save the downloaded files. Defaults to "~/.torchmetrics". z@https://raw.githubusercontent.com/microsoft/DNS-Challenge/master)zDNSMOS/DNSMOS/model_v8.onnxzDNSMOS/DNSMOS/sig_bak_ovr.onnxzDNSMOS/pDNSMOS/sig_bak_ovr.onnxNT)exist_ok/z downloading z to wb)ospath expanduserjoinmakedirsdirnameexistsr Exceptionremoverrgetopenwritecontent)rurlfilesaveto_urlfmyfilefrrr_prepare_dnsmos0s*    r8r%device num_threadscCstj|}tj|sttt}|dur||_||_ |j dkr-t |dg|d}|Sdt vrHddg}d|j iig}t ||||d}|Sdt vrcddg}d|j iig}t ||||d}|St |dg|d}|S) zLoad onnxruntime session. Args: path: the model path device: the device used num_threads: the number of threads to use. Defaults to None. Returns: onnxruntime session NcpuCPUExecutionProvider) providers sess_optionsCUDAExecutionProvider device_id)r=provider_optionsr>CoreMLExecutionProvider)r$r%r&r*r8 DNSMOS_DIRortSessionOptionsinter_op_num_threadsintra_op_num_threadstyper get_available_providersindex)r%r9r:optsinfsr=rArrrrOs,     rx@Taudion_mels frame_size hop_lengthsrto_dbc Cs|j}|d|d}tjj|||d||d}|ddd}||dd|jdd}|rMt|jdD]}tj||tj ddd||d f<q8|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'] )yrTn_fftrSrQrN)ref(.) shapereshaperfeaturemelspectrogram transposerange power_to_dbnpmax) rPrQrRrSrTrUr]mel_specbrrr_audio_melspec{s &rhmos personalizedcCs|rtgd}tgd}tgd}ntgd}tgd}tgd}||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).rW).rZ).)rdpoly1d)rirjp_ovrp_sigp_bakrrr _polyfit_vals rppredsfs cache_sessionc Cstrtrts td|durt|n|j}|rtnt}|td|r$dndd||}|td||}t } || krHt j | || d} n| } tt| } | jd | krktj| | gd d } | jd | ks[tt| jd | td } g} | }t| D]}| d t||t|t|f}|jd | krq|j}|d |d f}t|d }tt|d ddfdd }|jdkrdtvsdtvrztj||j|j}tj||j|j}Wnty}z t d|WYd}~nd}~wwd|i}d|i}tj|!d|d|!d|dgd dd}t"||}||dd d}| #|qt$tj%tj&| d d d d S)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_srrV)axisrW.float32i`)rPr;r?rBz7Failed to use GPU for DNSMOS, reverting to CPU. Error: input_1rfloat64)rxdtype))'rr r ModuleNotFoundErrortorchr9_cached_load_sessionrrC SAMPLING_RATErresampler;numpyint INPUT_LENGTHr]rd concatenatefloorrbr^arrayastyperhrHrDrIOrtValueortvalue_from_numpyrJr+rrunrpappend from_numpymeanstack)rqrrrjr9r:rs_load_session_function onnx_sessp808_onnx_sess desired_fsrP len_samplesnum_hopsmosshop_len_samplesidx audio_segr]input_featuresp808_input_featureseoip808_oimos_nprrrrs` 3     $"  "  rr)rMrNrOrT)NNT)%r$ functoolsrtypingrrrrdrrtorchmetrics.utilitiesrrtorchmetrics.utilities.importsrr r rrrDrr __doctest_requires__rrrCrr8r9rrrndarrayboolrhrprrrrrs    " ) #