o z“©iôã @s|ddlZddlZddlZddlmZ ddededefdd „Z ddeded ed e fdd„Z ddeded efdd„Z dS)éNé)Ú AudioSignalFÚ estimatesÚ referencesÚextendedcCsŽddl}| ¡ ¡}| ¡ ¡}g}t|jƒD]'}|j|j|df ¡ ¡  ¡|j|df ¡ ¡  ¡|j |d}|  |¡qt   t |¡¡S)a^Short term objective intelligibility Computes the STOI (See [1][2]) of a denoised signal compared to a clean signal, The output is expected to have a monotonic relation with the subjective speech-intelligibility, where a higher score denotes better speech intelligibility. Uses pystoi under the hood. Parameters ---------- estimates : AudioSignal Denoised speech references : AudioSignal Clean original speech extended : int, optional Boolean, whether to use the extended STOI described in [3], by default False Returns ------- Tensor[float] Short time objective intelligibility measure between clean and denoised speech References ---------- 1. C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'A Short-Time Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech', ICASSP 2010, Texas, Dallas. 2. C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen 'An Algorithm for Intelligibility Prediction of Time-Frequency Weighted Noisy Speech', IEEE Transactions on Audio, Speech, and Language Processing, 2011. 3. Jesper Jensen and Cees H. Taal, 'An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers', IEEE Transactions on Audio, Speech and Language Processing, 2016. rN)r)ÚpystoiÚcloneÚto_monoÚrangeÚ batch_sizeÚstoiÚ audio_dataÚdetachÚcpuÚnumpyÚ sample_rateÚappendÚtorchÚ from_numpyÚnpÚarray)rrrrÚstoisÚiÚ_stoi©rúN/home/ubuntu/.local/lib/python3.10/site-packages/audiotools/metrics/quality.pyr s&  ü r Úwbé€>ÚmodeÚ target_srcCsšddlm}| ¡ ¡ |¡}| ¡ ¡ |¡}g}t|jƒD]%}||j|j|df ¡  ¡  ¡|j|df ¡  ¡  ¡|ƒ}|  |¡qt   t |¡¡S)a£_summary_ Parameters ---------- estimates : AudioSignal Degraded AudioSignal references : AudioSignal Reference AudioSignal mode : str, optional 'wb' (wide-band) or 'nb' (narrow-band), by default "wb" target_sr : float, optional Target sample rate, by default 16000 Returns ------- Tensor[float] PESQ score: P.862.2 Prediction (MOS-LQO) r)Úpesq)r rr Úresampler r rr rrrrrrrr)rrrrÚpesq_fnÚpesqsrÚ_pesqrrrr @s ü r Úaudioc Cs>ddlm}ddlm}ddlm}| ¡}|dkr#d}d|j_d}n|d kr0d }d |j_d }ntd |›ƒ‚||j _ t j   t j  |j¡d|¡|j_| ¡} |  |¡| ¡ ¡ |¡}| ¡ ¡ |¡}g} t|jƒD]*} |  |j| df ¡ ¡ ¡ t¡|j| df ¡ ¡ ¡ t¡¡} |  | j ¡qlt! "t# $| ¡¡S)a8ViSQOL score. Parameters ---------- estimates : AudioSignal Degraded AudioSignal references : AudioSignal Reference AudioSignal mode : str, optional 'audio' or 'speech', by default 'audio' Returns ------- Tensor[float] ViSQOL score (MOS-LQO) r)Ú visqol_lib_py)Úvisqol_config_pb2)Úsimilarity_result_pb2r%i€»Fzlibsvm_nu_svr_model.txtÚspeechrTzWlattice_tcditugenmeetpackhref_ls2_nl60_lr12_bs2048_learn.005_ep2400_train1_7_raw.tflitezUnrecognized mode: Úmodel)%Úvisqolr&Ú visqol.pb2r'r(Ú VisqolConfigÚoptionsÚuse_speech_scoringÚ ValueErrorr%rÚosÚpathÚjoinÚdirnameÚ__file__Úsvr_model_pathÚ VisqolApiÚCreaterr r!r r ÚMeasurer rrrÚastypeÚfloatrÚmoslqorrrr) rrrr&r'r(Úconfigrr6ÚapiÚvisqolsrÚ_visqolrrrr+is:   ÿ þr+)F)rr)r%) r1rrrÚrÚintr Ústrr;r r+rrrrÚs> ýÿþ ý:üÿþý ü,ýÿþý