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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| # Copyright 2020 Huy Le Nguyen (@usimarit) | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import os | ||
| from tqdm import tqdm | ||
|
|
||
| import numpy as np | ||
| import soundfile as sf | ||
| import tensorflow as tf | ||
|
|
||
| from tiramisu_asr.featurizers.speech_featurizers import deemphasis | ||
| from tiramisu_asr.featurizers.speech_featurizers import tf_merge_slices, read_raw_audio | ||
| from tiramisu_asr.runners.base_runners import BaseTester | ||
| from tiramisu_asr.utils.utils import shape_list | ||
|
|
||
|
|
||
| class SeganTester(BaseTester): | ||
| def __init__(self, | ||
| speech_config: dict, | ||
| config: dict): | ||
| super(SeganTester, self).__init__(config) | ||
| self.speech_config = speech_config | ||
|
|
||
| self.test_noisy_dir = os.path.join(self.config["outdir"], "test", "noisy") | ||
| self.test_gen_dir = os.path.join(self.config["outdir"], "test", "gen") | ||
| self.test_clean_dir = os.path.join(self.config["outdir"], "test", "clean") | ||
|
|
||
| if not os.path.exists(self.test_noisy_dir): os.makedirs(self.test_noisy_dir) | ||
| if not os.path.exists(self.test_gen_dir): os.makedirs(self.test_gen_dir) | ||
| if not os.path.exists(self.test_clean_dir): os.makedirs(self.test_clean_dir) | ||
|
|
||
| def set_test_data_loader(self, test_dataset): | ||
| """Set train data loader (MUST).""" | ||
| self.clean_dir = test_dataset.clean_dir | ||
| self.test_data_loader = test_dataset.create() | ||
|
|
||
| def _test_epoch(self): | ||
| if self.processed_records > 0: | ||
| self.test_data_loader = self.test_data_loader.skip(self.processed_records) | ||
| progbar = tqdm(initial=self.processed_records, total=None, | ||
| unit="batch", position=0, desc="[Test]") | ||
| test_iter = iter(self.test_data_loader) | ||
| while True: | ||
| try: | ||
| self._test_function(test_iter) | ||
| except StopIteration: | ||
| break | ||
| except tf.errors.OutOfRangeError: | ||
| break | ||
| progbar.update(1) | ||
|
|
||
| progbar.close() | ||
|
|
||
| @tf.function | ||
| def _test_function(self, iterator): | ||
| batch = next(iterator) | ||
| self._test_step(batch) | ||
|
|
||
| def _test_step(self, batch): | ||
| # Test only available for batch size = 1 | ||
| clean_wav_path, noisy_wavs = batch | ||
| g_wavs = self.model([noisy_wavs, self.model.get_z(shape_list(noisy_wavs)[0])], | ||
| training=False) | ||
|
|
||
| results = tf.numpy_function( | ||
| self._perform, inp=[clean_wav_path, tf_merge_slices(g_wavs), | ||
| tf_merge_slices(noisy_wavs)], | ||
| Tout=tf.float32 | ||
| ) | ||
|
|
||
| def _perform(self, | ||
| clean_wav_path: bytes, | ||
| gen_signal: np.ndarray, | ||
| noisy_signal: np.ndarray) -> tf.Tensor: | ||
| clean_wav_path = clean_wav_path.decode("utf-8") | ||
| results = self._compare(clean_wav_path, gen_signal, noisy_signal) | ||
| return tf.convert_to_tensor(results, dtype=tf.float32) | ||
|
|
||
| def _save_to_outdir(self, | ||
| clean_wav_path: str, | ||
| gen_signal: np.ndarray, | ||
| noisy_signal: np.ndarray): | ||
| gen_path = clean_wav_path.replace(self.clean_dir, self.test_gen_dir) | ||
| noisy_path = clean_wav_path.replace(self.clean_dir, self.test_noisy_dir) | ||
| try: | ||
| os.makedirs(os.path.dirname(gen_path)) | ||
| os.makedirs(os.path.dirname(noisy_path)) | ||
| except Exception: | ||
| pass | ||
| # Avoid differences by writing original wav using sf | ||
| clean_wav = read_raw_audio(clean_wav_path, self.speech_config["sample_rate"]) | ||
| sf.write("/tmp/clean.wav", clean_wav, self.speech_config["sample_rate"]) | ||
| sf.write(gen_path, | ||
| gen_signal, | ||
| self.speech_config["sample_rate"]) | ||
| sf.write(noisy_path, | ||
| noisy_signal, | ||
| self.speech_config["sample_rate"]) | ||
| return gen_path, noisy_path | ||
|
|
||
| def _compare(self, | ||
| clean_wav_path: str, | ||
| gen_signal: np.ndarray, | ||
| noisy_signal: np.ndarray) -> list: | ||
| gen_signal = deemphasis(gen_signal, self.speech_config["preemphasis"]) | ||
| noisy_signal = deemphasis(noisy_signal, self.speech_config["preemphasis"]) | ||
|
|
||
| gen_path, noisy_path = self._save_to_outdir(clean_wav_path, gen_signal, noisy_signal) | ||
|
|
||
| (pesq_gen, csig_gen, cbak_gen, | ||
| covl_gen, ssnr_gen) = self.composite("/tmp/clean.wav", gen_path) | ||
| (pesq_noisy, csig_noisy, cbak_noisy, | ||
| covl_noisy, ssnr_noisy) = self.composite("/tmp/clean.wav", noisy_path) | ||
|
|
||
| return [pesq_gen, csig_gen, cbak_gen, covl_gen, ssnr_gen, | ||
| pesq_noisy, csig_noisy, cbak_noisy, covl_noisy, ssnr_noisy] | ||
|
|
||
| def finish(self): | ||
| with open(self.test_results, "w", encoding="utf-8") as out: | ||
| for idx, key in enumerate(self.test_metrics.keys()): | ||
| out.write(f"{key} = {self.test_metrics[key].result().numpy():.2f}\n") |
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