class Synthesizer(object): def load_model(self, model_path, model_name, model_config, use_cuda): model_config = os.path.join(model_path, model_config) self.model_file = os.path.join(model_path, model_name) print(" > Loading model ...") print(" | > model config: ", model_config) print(" | > model file: ", self.model_file) config = load_config(model_config) self.config = config self.use_cuda = use_cuda self.model = Tacotron(config.embedding_size, config.num_freq, config.num_mels, config.r) self.ap = AudioProcessor(config.sample_rate, config.num_mels, config.min_level_db, config.frame_shift_ms, config.frame_length_ms, config.preemphasis, config.ref_level_db, config.num_freq, config.power, griffin_lim_iters=60) # load model state if use_cuda: cp = torch.load(self.model_file) else: cp = torch.load(self.model_file, map_location=lambda storage, loc: storage) # load the model self.model.load_state_dict(cp['model']) if use_cuda: self.model.cuda() self.model.eval() def save_wav(self, wav, path): wav *= 32767 / max(1e-8, np.max(np.abs(wav))) # sf.write(path, wav.astype(np.int32), self.config.sample_rate, format='wav') # wav = librosa.util.normalize(wav.astype(np.float), norm=np.inf, axis=None) # wav = wav / wav.max() # sf.write(path, wav.astype('float'), self.config.sample_rate, format='ogg') scipy.io.wavfile.write(path, self.config.sample_rate, wav.astype(np.int16)) # librosa.output.write_wav(path, wav.astype(np.int16), self.config.sample_rate, norm=True) def tts(self, text): text_cleaner = [self.config.text_cleaner] wavs = [] for sen in text.split('.'): if len(sen) < 3: continue sen = sen.strip() sen +='.' print(sen) sen = sen.strip() seq = np.array(text_to_sequence(text, text_cleaner)) chars_var = torch.from_numpy(seq).unsqueeze(0) if self.use_cuda: chars_var = chars_var.cuda() mel_out, linear_out, alignments, stop_tokens = self.model.forward(chars_var) linear_out = linear_out[0].data.cpu().numpy() wav = self.ap.inv_spectrogram(linear_out.T) # wav = wav[:self.ap.find_endpoint(wav)] out = io.BytesIO() wavs.append(wav) wavs.append(np.zeros(10000)) self.save_wav(wav, out) return out
def tts(text, model_path='model/best_model.pth.tar', config_path='model/config.json', use_cuda=False): CONFIG = load_config(config_path) model = Tacotron(CONFIG.embedding_size, CONFIG.num_freq, CONFIG.num_mels, CONFIG.r) if use_cuda: cp = torch.load(model_path + seq_to_seq_test_model_fname, map_location='cuda:0') else: cp = torch.load(model_path, map_location=lambda storage, loc: storage) model.load_state_dict(cp['model']) if use_cuda: model.cuda() model.eval() model.decoder.max_decoder_steps = 250 ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db, CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, CONFIG.preemphasis, griffin_lim_iters=50) t_1 = time.time() text_cleaner = [CONFIG.text_cleaner] seq = np.array(text_to_sequence(text, text_cleaner)) chars_var = torch.from_numpy(seq).unsqueeze(0) if use_cuda: chars_var = chars_var.cuda() linear_out = model.forward(chars_var.long()) linear_out = linear_out[0].data.cpu().numpy() waveform = ap.inv_spectrogram(linear_out.T) waveform = waveform[:ap.find_endpoint(waveform)] out_path = 'static/samples/' os.makedirs(out_path, exist_ok=True) file_name = text.replace(" ", "_").replace(".", "") + ".wav" out_path = os.path.join(out_path, file_name) ap.save_wav(waveform, out_path) # print(" > Run-time: {}".format(time.time() - t_1)) return file_name
def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict) -> Tuple[Dict, Dict]: postnet_outputs = outputs["model_outputs"] alignments = outputs["alignments"] alignments_backward = outputs["alignments_backward"] mel_input = batch["mel_input"] pred_spec = postnet_outputs[0].data.cpu().numpy() gt_spec = mel_input[0].data.cpu().numpy() align_img = alignments[0].data.cpu().numpy() figures = { "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), "alignment": plot_alignment(align_img, output_fig=False), } if self.bidirectional_decoder or self.double_decoder_consistency: figures["alignment_backward"] = plot_alignment( alignments_backward[0].data.cpu().numpy(), output_fig=False) # Sample audio train_audio = ap.inv_spectrogram(pred_spec.T) return figures, {"audio": train_audio}
class TestTTSDataset(unittest.TestCase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.max_loader_iter = 4 self.ap = AudioProcessor(**c.audio) def _create_dataloader(self, batch_size, r, bgs): items = ljspeech(c.data_path, "metadata.csv") dataset = TTSDataset.MyDataset( r, c.text_cleaner, compute_linear_spec=True, ap=self.ap, meta_data=items, tp=c.characters, batch_group_size=bgs, min_seq_len=c.min_seq_len, max_seq_len=float("inf"), use_phonemes=False, ) dataloader = DataLoader( dataset, batch_size=batch_size, shuffle=False, collate_fn=dataset.collate_fn, drop_last=True, num_workers=c.num_loader_workers, ) return dataloader, dataset def test_loader(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(2, c.r, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data[0] text_lengths = data[1] speaker_name = data[2] linear_input = data[3] mel_input = data[4] mel_lengths = data[5] stop_target = data[6] item_idx = data[7] neg_values = text_input[text_input < 0] check_count = len(neg_values) assert check_count == 0, " !! Negative values in text_input: {}".format( check_count) # TODO: more assertion here assert isinstance(speaker_name[0], str) assert linear_input.shape[0] == c.batch_size assert linear_input.shape[2] == self.ap.fft_size // 2 + 1 assert mel_input.shape[0] == c.batch_size assert mel_input.shape[2] == c.audio["num_mels"] # check normalization ranges if self.ap.symmetric_norm: assert mel_input.max() <= self.ap.max_norm assert mel_input.min() >= -self.ap.max_norm # pylint: disable=invalid-unary-operand-type assert mel_input.min() < 0 else: assert mel_input.max() <= self.ap.max_norm assert mel_input.min() >= 0 def test_batch_group_shuffle(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(2, c.r, 16) last_length = 0 frames = dataset.items for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data[0] text_lengths = data[1] speaker_name = data[2] linear_input = data[3] mel_input = data[4] mel_lengths = data[5] stop_target = data[6] item_idx = data[7] avg_length = mel_lengths.numpy().mean() assert avg_length >= last_length dataloader.dataset.sort_items() is_items_reordered = False for idx, item in enumerate(dataloader.dataset.items): if item != frames[idx]: is_items_reordered = True break assert is_items_reordered def test_padding_and_spec(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(1, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data[0] text_lengths = data[1] speaker_name = data[2] linear_input = data[3] mel_input = data[4] mel_lengths = data[5] stop_target = data[6] item_idx = data[7] # check mel_spec consistency wav = np.asarray(self.ap.load_wav(item_idx[0]), dtype=np.float32) mel = self.ap.melspectrogram(wav).astype("float32") mel = torch.FloatTensor(mel).contiguous() mel_dl = mel_input[0] # NOTE: Below needs to check == 0 but due to an unknown reason # there is a slight difference between two matrices. # TODO: Check this assert cond more in detail. assert abs(mel.T - mel_dl).max() < 1e-5, abs(mel.T - mel_dl).max() # check mel-spec correctness mel_spec = mel_input[0].cpu().numpy() wav = self.ap.inv_melspectrogram(mel_spec.T) self.ap.save_wav(wav, OUTPATH + "/mel_inv_dataloader.wav") shutil.copy(item_idx[0], OUTPATH + "/mel_target_dataloader.wav") # check linear-spec linear_spec = linear_input[0].cpu().numpy() wav = self.ap.inv_spectrogram(linear_spec.T) self.ap.save_wav(wav, OUTPATH + "/linear_inv_dataloader.wav") shutil.copy(item_idx[0], OUTPATH + "/linear_target_dataloader.wav") # check the last time step to be zero padded assert linear_input[0, -1].sum() != 0 assert linear_input[0, -2].sum() != 0 assert mel_input[0, -1].sum() != 0 assert mel_input[0, -2].sum() != 0 assert stop_target[0, -1] == 1 assert stop_target[0, -2] == 0 assert stop_target.sum() == 1 assert len(mel_lengths.shape) == 1 assert mel_lengths[0] == linear_input[0].shape[0] assert mel_lengths[0] == mel_input[0].shape[0] # Test for batch size 2 dataloader, dataset = self._create_dataloader(2, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data[0] text_lengths = data[1] speaker_name = data[2] linear_input = data[3] mel_input = data[4] mel_lengths = data[5] stop_target = data[6] item_idx = data[7] if mel_lengths[0] > mel_lengths[1]: idx = 0 else: idx = 1 # check the first item in the batch assert linear_input[idx, -1].sum() != 0 assert linear_input[idx, -2].sum() != 0, linear_input assert mel_input[idx, -1].sum() != 0 assert mel_input[idx, -2].sum() != 0, mel_input assert stop_target[idx, -1] == 1 assert stop_target[idx, -2] == 0 assert stop_target[idx].sum() == 1 assert len(mel_lengths.shape) == 1 assert mel_lengths[idx] == mel_input[idx].shape[0] assert mel_lengths[idx] == linear_input[idx].shape[0] # check the second itme in the batch assert linear_input[1 - idx, -1].sum() == 0 assert mel_input[1 - idx, -1].sum() == 0 assert stop_target[1, mel_lengths[1] - 1] == 1 assert stop_target[1, mel_lengths[1]:].sum() == 0 assert len(mel_lengths.shape) == 1
class TestTTSDataset(unittest.TestCase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.max_loader_iter = 4 self.ap = AudioProcessor(**c.audio) def _create_dataloader(self, batch_size, r, bgs, start_by_longest=False): # load dataset meta_data_train, meta_data_eval = load_tts_samples(dataset_config, eval_split=True, eval_split_size=0.2) items = meta_data_train + meta_data_eval tokenizer, _ = TTSTokenizer.init_from_config(c) dataset = TTSDataset( outputs_per_step=r, compute_linear_spec=True, return_wav=True, tokenizer=tokenizer, ap=self.ap, samples=items, batch_group_size=bgs, min_text_len=c.min_text_len, max_text_len=c.max_text_len, min_audio_len=c.min_audio_len, max_audio_len=c.max_audio_len, start_by_longest=start_by_longest, ) dataloader = DataLoader( dataset, batch_size=batch_size, shuffle=False, collate_fn=dataset.collate_fn, drop_last=True, num_workers=c.num_loader_workers, ) return dataloader, dataset def test_loader(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(1, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break text_input = data["token_id"] _ = data["token_id_lengths"] speaker_name = data["speaker_names"] linear_input = data["linear"] mel_input = data["mel"] mel_lengths = data["mel_lengths"] _ = data["stop_targets"] _ = data["item_idxs"] wavs = data["waveform"] neg_values = text_input[text_input < 0] check_count = len(neg_values) # check basic conditions self.assertEqual(check_count, 0) self.assertEqual(linear_input.shape[0], mel_input.shape[0], c.batch_size) self.assertEqual(linear_input.shape[2], self.ap.fft_size // 2 + 1) self.assertEqual(mel_input.shape[2], c.audio["num_mels"]) self.assertEqual(wavs.shape[1], mel_input.shape[1] * c.audio.hop_length) self.assertIsInstance(speaker_name[0], str) # make sure that the computed mels and the waveform match and correctly computed mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy()) # remove padding in mel-spectrogram mel_dataloader = mel_input[0].T.numpy()[:, :mel_lengths[0]] # guarantee that both mel-spectrograms have the same size and that we will remove waveform padding mel_new = mel_new[:, :mel_lengths[0]] ignore_seg = -(1 + c.audio.win_length // c.audio.hop_length) mel_diff = (mel_new[:, :mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg] self.assertLess(abs(mel_diff.sum()), 1e-5) # check normalization ranges if self.ap.symmetric_norm: self.assertLessEqual(mel_input.max(), self.ap.max_norm) self.assertGreaterEqual( mel_input.min(), -self.ap.max_norm # pylint: disable=invalid-unary-operand-type ) self.assertLess(mel_input.min(), 0) else: self.assertLessEqual(mel_input.max(), self.ap.max_norm) self.assertGreaterEqual(mel_input.min(), 0) def test_batch_group_shuffle(self): if ok_ljspeech: dataloader, dataset = self._create_dataloader(2, c.r, 16) last_length = 0 frames = dataset.samples for i, data in enumerate(dataloader): if i == self.max_loader_iter: break mel_lengths = data["mel_lengths"] avg_length = mel_lengths.numpy().mean() dataloader.dataset.preprocess_samples() is_items_reordered = False for idx, item in enumerate(dataloader.dataset.samples): if item != frames[idx]: is_items_reordered = True break self.assertGreaterEqual(avg_length, last_length) self.assertTrue(is_items_reordered) def test_start_by_longest(self): """Test start_by_longest option. Ther first item of the fist batch must be longer than all the other items. """ if ok_ljspeech: dataloader, _ = self._create_dataloader(2, c.r, 0, True) dataloader.dataset.preprocess_samples() for i, data in enumerate(dataloader): if i == self.max_loader_iter: break mel_lengths = data["mel_lengths"] if i == 0: max_len = mel_lengths[0] print(mel_lengths) self.assertTrue(all(max_len >= mel_lengths)) def test_padding_and_spectrograms(self): def check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths): self.assertNotEqual(linear_input[idx, -1].sum(), 0) # check padding self.assertNotEqual(linear_input[idx, -2].sum(), 0) self.assertNotEqual(mel_input[idx, -1].sum(), 0) self.assertNotEqual(mel_input[idx, -2].sum(), 0) self.assertEqual(stop_target[idx, -1], 1) self.assertEqual(stop_target[idx, -2], 0) self.assertEqual(stop_target[idx].sum(), 1) self.assertEqual(len(mel_lengths.shape), 1) self.assertEqual(mel_lengths[idx], linear_input[idx].shape[0]) self.assertEqual(mel_lengths[idx], mel_input[idx].shape[0]) if ok_ljspeech: dataloader, _ = self._create_dataloader(1, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break linear_input = data["linear"] mel_input = data["mel"] mel_lengths = data["mel_lengths"] stop_target = data["stop_targets"] item_idx = data["item_idxs"] # check mel_spec consistency wav = np.asarray(self.ap.load_wav(item_idx[0]), dtype=np.float32) mel = self.ap.melspectrogram(wav).astype("float32") mel = torch.FloatTensor(mel).contiguous() mel_dl = mel_input[0] # NOTE: Below needs to check == 0 but due to an unknown reason # there is a slight difference between two matrices. # TODO: Check this assert cond more in detail. self.assertLess(abs(mel.T - mel_dl).max(), 1e-5) # check mel-spec correctness mel_spec = mel_input[0].cpu().numpy() wav = self.ap.inv_melspectrogram(mel_spec.T) self.ap.save_wav(wav, OUTPATH + "/mel_inv_dataloader.wav") shutil.copy(item_idx[0], OUTPATH + "/mel_target_dataloader.wav") # check linear-spec linear_spec = linear_input[0].cpu().numpy() wav = self.ap.inv_spectrogram(linear_spec.T) self.ap.save_wav(wav, OUTPATH + "/linear_inv_dataloader.wav") shutil.copy(item_idx[0], OUTPATH + "/linear_target_dataloader.wav") # check the outputs check_conditions(0, linear_input, mel_input, stop_target, mel_lengths) # Test for batch size 2 dataloader, _ = self._create_dataloader(2, 1, 0) for i, data in enumerate(dataloader): if i == self.max_loader_iter: break linear_input = data["linear"] mel_input = data["mel"] mel_lengths = data["mel_lengths"] stop_target = data["stop_targets"] item_idx = data["item_idxs"] # set id to the longest sequence in the batch if mel_lengths[0] > mel_lengths[1]: idx = 0 else: idx = 1 # check the longer item in the batch check_conditions(idx, linear_input, mel_input, stop_target, mel_lengths) # check the other item in the batch self.assertEqual(linear_input[1 - idx, -1].sum(), 0) self.assertEqual(mel_input[1 - idx, -1].sum(), 0) self.assertEqual(stop_target[1, mel_lengths[1] - 1], 1) self.assertEqual(stop_target[1, mel_lengths[1]:].sum(), stop_target.shape[1] - mel_lengths[1]) self.assertEqual(len(mel_lengths.shape), 1)
wav_gen = AP.inv_mel_spectrogram(mel) ipd.Audio(wav_gen, rate=AP.sample_rate) # ### Generate Linear-Spectrogram and Re-synthesis with GL # In[ ]: spec = AP.spectrogram(wav) print("Max:", spec.max()) print("Min:", spec.min()) print("Mean:", spec.mean()) plot_spectrogram(spec.T, AP); wav_gen = AP.inv_spectrogram(spec) ipd.Audio(wav_gen, rate=AP.sample_rate) # ### Compare values for a certain parameter # # Optimize your parameters by comparing different values per parameter at a time. # In[ ]: audio={ 'audio_processor': 'audio', 'num_mels': 80, # In general, you don'tneed to change it 'num_freq': 1025, # In general, you don'tneed to change it 'sample_rate': 22050, # It depends to the sample rate of the dataset.