Audio segmentor

codes/data/audio/stop_prediction_dataset.py

@@ -0,0 +1,142 @@
+import os
+import pathlib
+import random
+
+import audio2numpy
+import numpy as np
+import torch
+import torch.utils.data
+import torch.nn.functional as F
+from tqdm import tqdm
+
+import models.tacotron2.layers as layers
+from data.audio.nv_tacotron_dataset import load_mozilla_cv, load_voxpopuli
+from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
+
+from models.tacotron2.text import text_to_sequence
+from utils.util import opt_get
+
+
+def get_similar_files_libritts(filename):
+    filedir = os.path.dirname(filename)
+    return list(pathlib.Path(filedir).glob('*.wav'))
+
+
+class StopPredictionDataset(torch.utils.data.Dataset):
+    """
+        1) loads audio,text pairs
+        2) normalizes text and converts them to sequences of one-hot vectors
+        3) computes mel-spectrograms from audio files.
+    """
+    def __init__(self, hparams):
+        self.path = hparams['path']
+        if not isinstance(self.path, list):
+            self.path = [self.path]
+
+        fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
+        if not isinstance(fetcher_mode, list):
+            fetcher_mode = [fetcher_mode]
+        assert len(self.path) == len(fetcher_mode)
+
+        self.audiopaths_and_text = []
+        for p, fm in zip(self.path, fetcher_mode):
+            if fm == 'lj' or fm == 'libritts':
+                fetcher_fn = load_filepaths_and_text
+                self.get_similar_files = get_similar_files_libritts
+            elif fm == 'voxpopuli':
+                fetcher_fn = load_voxpopuli
+                self.get_similar_files = None  # TODO: Fix.
+            else:
+                raise NotImplementedError()
+            self.audiopaths_and_text.extend(fetcher_fn(p))
+        self.sampling_rate = hparams.sampling_rate
+        self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
+        self.stft = layers.TacotronSTFT(
+            hparams.filter_length, hparams.hop_length, hparams.win_length,
+            hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
+            hparams.mel_fmax)
+        random.seed(hparams.seed)
+        random.shuffle(self.audiopaths_and_text)
+        self.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
+        self.max_text_len = opt_get(hparams, ['max_text_length'], None)
+
+    def get_mel(self, filename):
+        filename = str(filename)
+        if filename.endswith('.wav'):
+            audio, sampling_rate = load_wav_to_torch(filename)
+        else:
+            audio, sampling_rate = audio2numpy.audio_from_file(filename)
+            audio = torch.tensor(audio)
+
+        if sampling_rate != self.input_sample_rate:
+            if sampling_rate < self.input_sample_rate:
+                print(f'{filename} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {self.input_sample_rate}. This is not a good idea.')
+            audio_norm = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=self.input_sample_rate/sampling_rate, mode='nearest', recompute_scale_factor=False).squeeze()
+        else:
+            audio_norm = audio
+        if audio_norm.std() > 1:
+            print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}")
+            return None
+        audio_norm.clip_(-1, 1)
+        audio_norm = audio_norm.unsqueeze(0)
+        audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
+        if self.input_sample_rate != self.sampling_rate:
+            ratio = self.sampling_rate / self.input_sample_rate
+            audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0)
+        melspec = self.stft.mel_spectrogram(audio_norm)
+        melspec = torch.squeeze(melspec, 0)
+
+        return melspec
+
+    def __getitem__(self, index):
+        path = self.audiopaths_and_text[index][0]
+        similar_files = self.get_similar_files(path)
+        mel = self.get_mel(path)
+        terms = torch.zeros(mel.shape[1])
+        terms[-1] = 1
+        while mel.shape[-1] < self.max_mel_len:
+            another_file = random.choice(similar_files)
+            another_mel = self.get_mel(another_file)
+            oterms = torch.zeros(another_mel.shape[1])
+            oterms[-1] = 1
+            mel = torch.cat([mel, another_mel], dim=-1)
+            terms = torch.cat([terms, oterms], dim=-1)
+        mel = mel[:, :self.max_mel_len]
+        terms = terms[:self.max_mel_len]
+
+
+        return {
+            'padded_mel': mel,
+            'termination_mask': terms,
+        }
+
+    def __len__(self):
+        return len(self.audiopaths_and_text)
+
+
+if __name__ == '__main__':
+    params = {
+        'mode': 'stop_prediction',
+        'path': 'E:\\audio\\LibriTTS\\train-clean-360_list.txt',
+        'phase': 'train',
+        'n_workers': 0,
+        'batch_size': 16,
+        'fetcher_mode': 'libritts',
+        'max_mel_length': 800,
+        #'return_wavs': True,
+        #'input_sample_rate': 22050,
+        #'sampling_rate': 8000
+    }
+    from data import create_dataset, create_dataloader
+
+    ds, c = create_dataset(params, return_collate=True)
+    dl = create_dataloader(ds, params, collate_fn=c, shuffle=True)
+    i = 0
+    m = None
+    for k in range(1000):
+        for i, b in tqdm(enumerate(dl)):
+            continue
+            pm = b['padded_mel']
+            pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
+            m = pm if m is None else torch.cat([m, pm], dim=0)
+            print(m.mean(), m.std())

codes/models/gpt_voice/gpt_audio_segmentor.py

@@ -49,7 +49,6 @@ class MelEncoder(nn.Module):
 
 
 class GptSegmentor(nn.Module):
-    MAX_SYMBOLS_PER_PHRASE = 200
     MAX_MEL_FRAMES = 2000 // 4
 
     def __init__(self, layers=8, model_dim=512, heads=8):
@@ -59,30 +58,28 @@ class GptSegmentor(nn.Module):
         self.max_mel_frames = self.MAX_MEL_FRAMES
         self.mel_encoder = MelEncoder(model_dim)
         self.mel_pos_embedding = nn.Embedding(self.MAX_MEL_FRAMES, model_dim)
-        self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=2+self.MAX_SYMBOLS_PER_PHRASE+self.MAX_MEL_FRAMES, heads=heads,
+        self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=self.MAX_MEL_FRAMES, heads=heads,
                                attn_dropout=.1, ff_dropout=.1, non_causal_sequence_partition=self.MAX_MEL_FRAMES)
 
         self.final_norm = nn.LayerNorm(model_dim)
         self.stop_head = nn.Linear(model_dim, 1)
 
-    def forward(self, mel_inputs, mel_lengths):
+    def forward(self, mel_inputs, termination_points):
-        max_len = mel_lengths.max()  # This can be done in the dataset layer, but it is easier to do here.
-        mel_inputs = mel_inputs[:, :, :max_len]
-
         mel_emb = self.mel_encoder(mel_inputs)
-        mel_lengths = mel_lengths // 4  # The encoder decimates the mel by a factor of 4.
         mel_emb = mel_emb.permute(0,2,1).contiguous()
         mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
 
         enc = self.gpt(mel_emb)
 
+        # The MEL gets decimated to 1/4 the size by the encoder, so we need to do the same to the termination points.
+        termination_points = F.interpolate(termination_points.unsqueeze(1), size=mel_emb.shape[1], mode='area').squeeze()
+        termination_points = (termination_points > 0).float()
+
         # Compute loss
         b, s, _ = enc.shape
-        mel_pad_mask = ~get_mask_from_lengths(mel_lengths-1, s)
-        targets = torch.zeros((b,s), device=enc.device).masked_fill_(mel_pad_mask, 1)
         stop_logits = self.final_norm(enc)
         stop_logits = self.stop_head(stop_logits)
-        loss = F.binary_cross_entropy_with_logits(stop_logits.squeeze(-1), targets)
+        loss = F.binary_cross_entropy_with_logits(stop_logits.squeeze(-1), termination_points)
 
         return loss.mean()
 
@@ -95,7 +92,7 @@ def register_gpt_segmentor(opt_net, opt):
 if __name__ == '__main__':
     gpt = GptSegmentor()
     l = gpt(torch.randn(3,80,94),
-            torch.tensor([18,42,93]))
+            torch.zeros(3,94))
     print(l.shape)
 
     #o = gpt.infer(torch.randint(high=24, size=(2,60)))

codes/scripts/audio/test_audio_gen.py

@@ -30,14 +30,14 @@ def forward_pass(model, denoiser, data, output_dir, opt, b):
         ground_truth_waveforms = denoiser(ground_truth_waveforms)
     for i in range(pred_waveforms.shape[0]):
         # Output predicted mels and waveforms.
-        pred_mel = model.eval_state[opt['eval']['pred_mel']][i]
+        pred_mel = model.eval_state[opt['eval']['pred_mel']][0][i].unsqueeze(0)
         pred_mel = ((pred_mel - pred_mel.mean()) / max(abs(pred_mel.min()), pred_mel.max())).unsqueeze(1)
         torchvision.utils.save_image(pred_mel, osp.join(output_dir, f'{b}_{i}_pred_mel.png'))
         audio = pred_waveforms[i][0].cpu().numpy()
         wavfile.write(osp.join(output_dir, f'{b}_{i}.wav'), 22050, audio)
 
         if gt:
-            gt_mel = model.eval_state[opt['eval']['ground_truth_mel']][i]
+            gt_mel = model.eval_state[opt['eval']['ground_truth_mel']][0][i].unsqueeze(0)
             gt_mel = ((gt_mel - gt_mel.mean()) / max(abs(gt_mel.min()), gt_mel.max())).unsqueeze(1)
             torchvision.utils.save_image(gt_mel, osp.join(output_dir, f'{b}_{i}_gt_mel.png'))
             audio = ground_truth_waveforms[i][0].cpu().numpy()
@@ -54,7 +54,7 @@ if __name__ == "__main__":
     torch.backends.cudnn.benchmark = True
     want_metrics = False
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_lrdvae_audio_clips.yml')
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_stop_pred_dataset.yml')
     opt = option.parse(parser.parse_args().opt, is_train=False)
     opt = option.dict_to_nonedict(opt)
     utils.util.loaded_options = opt

codes/train.py

@@ -282,7 +282,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_lrdvae_audio_clips.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_stop_libritts.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()