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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ | |
| Signal processing-based evaluation using waveforms | |
| """ | |
| import csv | |
| import numpy as np | |
| import os.path as op | |
| import torch | |
| import tqdm | |
| from tabulate import tabulate | |
| import torchaudio | |
| from examples.speech_synthesis.utils import batch_mel_spectral_distortion | |
| from fairseq.tasks.text_to_speech import batch_mel_cepstral_distortion | |
| def load_eval_spec(path): | |
| with open(path) as f: | |
| reader = csv.DictReader(f, delimiter='\t') | |
| samples = list(reader) | |
| return samples | |
| def eval_distortion(samples, distortion_fn, device="cuda"): | |
| nmiss = 0 | |
| results = [] | |
| for sample in tqdm.tqdm(samples): | |
| if not op.isfile(sample["ref"]) or not op.isfile(sample["syn"]): | |
| nmiss += 1 | |
| results.append(None) | |
| continue | |
| # assume single channel | |
| yref, sr = torchaudio.load(sample["ref"]) | |
| ysyn, _sr = torchaudio.load(sample["syn"]) | |
| yref, ysyn = yref[0].to(device), ysyn[0].to(device) | |
| assert sr == _sr, f"{sr} != {_sr}" | |
| distortion, extra = distortion_fn([yref], [ysyn], sr, None)[0] | |
| _, _, _, _, _, pathmap = extra | |
| nins = torch.sum(pathmap.sum(dim=1) - 1) # extra frames in syn | |
| ndel = torch.sum(pathmap.sum(dim=0) - 1) # missing frames from syn | |
| results.append( | |
| (distortion.item(), # path distortion | |
| pathmap.size(0), # yref num frames | |
| pathmap.size(1), # ysyn num frames | |
| pathmap.sum().item(), # path length | |
| nins.item(), # insertion | |
| ndel.item(), # deletion | |
| ) | |
| ) | |
| return results | |
| def eval_mel_cepstral_distortion(samples, device="cuda"): | |
| return eval_distortion(samples, batch_mel_cepstral_distortion, device) | |
| def eval_mel_spectral_distortion(samples, device="cuda"): | |
| return eval_distortion(samples, batch_mel_spectral_distortion, device) | |
| def print_results(results, show_bin): | |
| results = np.array(list(filter(lambda x: x is not None, results))) | |
| np.set_printoptions(precision=3) | |
| def _print_result(results): | |
| dist, dur_ref, dur_syn, dur_ali, nins, ndel = results.sum(axis=0) | |
| res = { | |
| "nutt": len(results), | |
| "dist": dist, | |
| "dur_ref": int(dur_ref), | |
| "dur_syn": int(dur_syn), | |
| "dur_ali": int(dur_ali), | |
| "dist_per_ref_frm": dist/dur_ref, | |
| "dist_per_syn_frm": dist/dur_syn, | |
| "dist_per_ali_frm": dist/dur_ali, | |
| "ins": nins/dur_ref, | |
| "del": ndel/dur_ref, | |
| } | |
| print(tabulate( | |
| [res.values()], | |
| res.keys(), | |
| floatfmt=".4f" | |
| )) | |
| print(">>>> ALL") | |
| _print_result(results) | |
| if show_bin: | |
| edges = [0, 200, 400, 600, 800, 1000, 2000, 4000] | |
| for i in range(1, len(edges)): | |
| mask = np.logical_and(results[:, 1] >= edges[i-1], | |
| results[:, 1] < edges[i]) | |
| if not mask.any(): | |
| continue | |
| bin_results = results[mask] | |
| print(f">>>> ({edges[i-1]}, {edges[i]})") | |
| _print_result(bin_results) | |
| def main(eval_spec, mcd, msd, show_bin): | |
| samples = load_eval_spec(eval_spec) | |
| device = "cpu" | |
| if mcd: | |
| print("===== Evaluate Mean Cepstral Distortion =====") | |
| results = eval_mel_cepstral_distortion(samples, device) | |
| print_results(results, show_bin) | |
| if msd: | |
| print("===== Evaluate Mean Spectral Distortion =====") | |
| results = eval_mel_spectral_distortion(samples, device) | |
| print_results(results, show_bin) | |
| if __name__ == "__main__": | |
| import argparse | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("eval_spec") | |
| parser.add_argument("--mcd", action="store_true") | |
| parser.add_argument("--msd", action="store_true") | |
| parser.add_argument("--show-bin", action="store_true") | |
| args = parser.parse_args() | |
| main(args.eval_spec, args.mcd, args.msd, args.show_bin) | |