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| Original file line number | Diff line number | Diff line change |
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| # 2019-May | ||
| # github.com/ludlows | ||
| # Python Wrapper for PESQ Score (narrowband and wideband) | ||
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| import numpy as np | ||
| from multiprocessing import Pool, Queue, Process, cpu_count | ||
| from functools import partial | ||
| from .cypesq import cypesq, cypesq_retvals, cypesq_error_message as pesq_error_message | ||
| from .cypesq import PesqError, InvalidSampleRateError, OutOfMemoryError | ||
| from .cypesq import BufferTooShortError, NoUtterancesError | ||
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| __all__ = ['pesq', 'pesq_batch', 'PesqError', 'InvalidSampleRateError', 'OutOfMemoryError', 'BufferTooShortError', | ||
| 'NoUtterancesError'] | ||
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| USAGE = """ | ||
| Run model on reference(ref) and degraded(deg) | ||
| Sample rate (fs) - No default. Must select either 8000 or 16000. | ||
| Note there is narrow band (nb) mode only when sampling rate is 8000Hz. | ||
| """ | ||
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| USAGE_BATCH = USAGE + """ | ||
| The shapes of ref and deg should be identical if both are 2D numpy arrays. | ||
| Once the `ref` is 1D and the `deg` is 2D, the broadcast operation is applied. | ||
| """ | ||
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| def _check_fs_mode(mode, fs, usage=USAGE): | ||
| if mode != 'wb' and mode != 'nb': | ||
| print(usage) | ||
| raise ValueError("mode should be either 'nb' or 'wb'") | ||
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| if fs != 8000 and fs != 16000: | ||
| print(usage) | ||
| raise ValueError("fs (sampling frequency) should be either 8000 or 16000") | ||
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| if fs == 8000 and mode == 'wb': | ||
| print(usage) | ||
| raise ValueError("no wide band mode if fs = 8000") | ||
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| def _pesq_inner(ref, deg, fs=16000, mode='wb', on_error=PesqError.RAISE_EXCEPTION): | ||
| """ | ||
| Args: | ||
| ref: numpy 1D array, reference audio signal | ||
| deg: numpy 1D array, degraded audio signal | ||
| fs: integer, sampling rate | ||
| mode: 'wb' (wide-band) or 'nb' (narrow-band) | ||
| on_error: PesqError.RAISE_EXCEPTION (default) or PesqError.RETURN_VALUES | ||
| Returns: | ||
| pesq_score: float, P.862.2 Prediction (MOS-LQO) | ||
| """ | ||
| max_val = max(np.max(np.abs(ref / 1.0)), np.max(np.abs(deg / 1.0))) | ||
| if mode == 'wb': | ||
| mode_code = 1 | ||
| else: | ||
| mode_code = 0 | ||
| if on_error == PesqError.RETURN_VALUES: | ||
| return cypesq_retvals( | ||
| fs, | ||
| (ref / max_val).astype(np.float32), | ||
| (deg / max_val).astype(np.float32), | ||
| mode_code | ||
| ) | ||
| return cypesq( | ||
| fs, | ||
| (ref / max_val).astype(np.float32), | ||
| (deg / max_val).astype(np.float32), | ||
| mode_code | ||
| ) | ||
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| def _processor_coordinator(func, args_q, results_q): | ||
| while True: | ||
| index, arg = args_q.get() | ||
| if index is None: | ||
| break | ||
| try: | ||
| result = func(*arg) | ||
| except Exception as e: | ||
| result = e | ||
| results_q.put((index, result)) | ||
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| def _processor_mapping(func, args, n_processor): | ||
| args_q = Queue(maxsize=1) | ||
| results_q = Queue() | ||
| processors = [Process(target=_processor_coordinator, args=(func, args_q, results_q)) for _ in range(n_processor)] | ||
| for p in processors: | ||
| p.daemon = True | ||
| p.start() | ||
| for i, arg in enumerate(args): | ||
| args_q.put((i, arg)) | ||
| # send stop messages | ||
| for _ in range(n_processor): | ||
| args_q.put((None, None)) | ||
| results = [results_q.get() for _ in range(len(args))] | ||
| [p.join() for p in processors] | ||
| return [v[1] for v in sorted(results)] | ||
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| def pesq(fs, ref, deg, mode='wb', on_error=PesqError.RAISE_EXCEPTION): | ||
| """ | ||
| Args: | ||
| ref: numpy 1D array, reference audio signal | ||
| deg: numpy 1D array, degraded audio signal | ||
| fs: integer, sampling rate | ||
| mode: 'wb' (wide-band) or 'nb' (narrow-band) | ||
| on_error: PesqError.RAISE_EXCEPTION (default) or PesqError.RETURN_VALUES | ||
| Returns: | ||
| pesq_score: float, P.862.2 Prediction (MOS-LQO) | ||
| """ | ||
| _check_fs_mode(mode, fs, USAGE) | ||
| return _pesq_inner(ref, deg, fs, mode, on_error) | ||
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| def pesq_batch(fs, ref, deg, mode, n_processor=cpu_count(), on_error=PesqError.RAISE_EXCEPTION): | ||
| """ | ||
| Running `pesq` using multiple processors | ||
| Args: | ||
| on_error: | ||
| ref: numpy 1D (n_sample,) or 2D array (n_file, n_sample), reference audio signal | ||
| deg: numpy 1D (n_sample,) or 2D array (n_file, n_sample), degraded audio signal | ||
| fs: integer, sampling rate | ||
| mode: 'wb' (wide-band) or 'nb' (narrow-band) | ||
| n_processor: cpu_count() (default) or number of processors (chosen by the user) or 0 (without multiprocessing) | ||
| on_error: PesqError.RAISE_EXCEPTION (default) or PesqError.RETURN_VALUES | ||
| Returns: | ||
| pesq_score: list of pesq scores, P.862.2 Prediction (MOS-LQO) | ||
| """ | ||
| _check_fs_mode(mode, fs, USAGE_BATCH) | ||
| # check dimension | ||
| if len(ref.shape) == 1: | ||
| if len(deg.shape) == 1 and ref.shape == deg.shape: | ||
| return [_pesq_inner(ref, deg, fs, mode, PesqError.RETURN_VALUES)] | ||
| elif len(deg.shape) == 2 and ref.shape[-1] == deg.shape[-1]: | ||
| if n_processor <= 0: | ||
| pesq_score = [np.nan for i in range(deg.shape[0])] | ||
| for i in range(deg.shape[0]): | ||
| pesq_score[i] = _pesq_inner(ref, deg[i, :], fs, mode, on_error) | ||
| return pesq_score | ||
| else: | ||
| with Pool(n_processor) as p: | ||
| return p.map(partial(_pesq_inner, ref, fs=fs, mode=mode, on_error=on_error), | ||
| [deg[i, :] for i in range(deg.shape[0])]) | ||
| else: | ||
| raise ValueError("The shapes of `deg` is invalid!") | ||
| elif len(ref.shape) == 2: | ||
| if deg.shape == ref.shape: | ||
| if n_processor <= 0: | ||
| pesq_score = [np.nan for i in range(deg.shape[0])] | ||
| for i in range(deg.shape[0]): | ||
| pesq_score[i] = _pesq_inner(ref[i, :], deg[i, :], fs, mode, on_error) | ||
| return pesq_score | ||
| else: | ||
| return _processor_mapping(_pesq_inner, | ||
| [(ref[i, :], deg[i, :], fs, mode, on_error) for i in range(deg.shape[0])], | ||
| n_processor) | ||
| else: | ||
| raise ValueError("The shape of `deg` is invalid!") | ||
| else: | ||
| raise ValueError("The shape of `ref` should be either 1D or 2D!") |
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