Audio segmentor
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James Betker
parent
2d3372054d
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142
codes/data/audio/stop_prediction_dataset.py
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142
codes/data/audio/stop_prediction_dataset.py
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import os
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import pathlib
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import random
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import audio2numpy
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import numpy as np
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import torch
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import torch.utils.data
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import torch.nn.functional as F
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from tqdm import tqdm
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import models.tacotron2.layers as layers
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from data.audio.nv_tacotron_dataset import load_mozilla_cv, load_voxpopuli
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from models.tacotron2.taco_utils import load_wav_to_torch, load_filepaths_and_text
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from models.tacotron2.text import text_to_sequence
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from utils.util import opt_get
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def get_similar_files_libritts(filename):
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filedir = os.path.dirname(filename)
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return list(pathlib.Path(filedir).glob('*.wav'))
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class StopPredictionDataset(torch.utils.data.Dataset):
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"""
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1) loads audio,text pairs
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2) normalizes text and converts them to sequences of one-hot vectors
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3) computes mel-spectrograms from audio files.
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"""
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def __init__(self, hparams):
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self.path = hparams['path']
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if not isinstance(self.path, list):
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self.path = [self.path]
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fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
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if not isinstance(fetcher_mode, list):
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fetcher_mode = [fetcher_mode]
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assert len(self.path) == len(fetcher_mode)
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self.audiopaths_and_text = []
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for p, fm in zip(self.path, fetcher_mode):
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if fm == 'lj' or fm == 'libritts':
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fetcher_fn = load_filepaths_and_text
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self.get_similar_files = get_similar_files_libritts
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elif fm == 'voxpopuli':
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fetcher_fn = load_voxpopuli
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self.get_similar_files = None # TODO: Fix.
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else:
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raise NotImplementedError()
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self.audiopaths_and_text.extend(fetcher_fn(p))
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self.sampling_rate = hparams.sampling_rate
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self.input_sample_rate = opt_get(hparams, ['input_sample_rate'], self.sampling_rate)
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self.stft = layers.TacotronSTFT(
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hparams.filter_length, hparams.hop_length, hparams.win_length,
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hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
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hparams.mel_fmax)
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random.seed(hparams.seed)
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random.shuffle(self.audiopaths_and_text)
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self.max_mel_len = opt_get(hparams, ['max_mel_length'], None)
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self.max_text_len = opt_get(hparams, ['max_text_length'], None)
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def get_mel(self, filename):
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filename = str(filename)
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if filename.endswith('.wav'):
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audio, sampling_rate = load_wav_to_torch(filename)
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else:
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audio, sampling_rate = audio2numpy.audio_from_file(filename)
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audio = torch.tensor(audio)
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if sampling_rate != self.input_sample_rate:
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if sampling_rate < self.input_sample_rate:
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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.')
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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()
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else:
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audio_norm = audio
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if audio_norm.std() > 1:
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print(f"Something is very wrong with the given audio. std_dev={audio_norm.std()}. file={filename}")
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return None
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audio_norm.clip_(-1, 1)
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audio_norm = audio_norm.unsqueeze(0)
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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if self.input_sample_rate != self.sampling_rate:
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ratio = self.sampling_rate / self.input_sample_rate
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audio_norm = torch.nn.functional.interpolate(audio_norm.unsqueeze(0), scale_factor=ratio, mode='area').squeeze(0)
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melspec = self.stft.mel_spectrogram(audio_norm)
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melspec = torch.squeeze(melspec, 0)
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return melspec
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def __getitem__(self, index):
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path = self.audiopaths_and_text[index][0]
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similar_files = self.get_similar_files(path)
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mel = self.get_mel(path)
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terms = torch.zeros(mel.shape[1])
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terms[-1] = 1
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while mel.shape[-1] < self.max_mel_len:
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another_file = random.choice(similar_files)
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another_mel = self.get_mel(another_file)
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oterms = torch.zeros(another_mel.shape[1])
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oterms[-1] = 1
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mel = torch.cat([mel, another_mel], dim=-1)
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terms = torch.cat([terms, oterms], dim=-1)
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mel = mel[:, :self.max_mel_len]
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terms = terms[:self.max_mel_len]
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return {
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'padded_mel': mel,
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'termination_mask': terms,
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}
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def __len__(self):
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return len(self.audiopaths_and_text)
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if __name__ == '__main__':
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params = {
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'mode': 'stop_prediction',
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'path': 'E:\\audio\\LibriTTS\\train-clean-360_list.txt',
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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'fetcher_mode': 'libritts',
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'max_mel_length': 800,
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#'return_wavs': True,
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#'input_sample_rate': 22050,
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#'sampling_rate': 8000
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}
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from data import create_dataset, create_dataloader
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ds, c = create_dataset(params, return_collate=True)
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dl = create_dataloader(ds, params, collate_fn=c, shuffle=True)
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i = 0
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m = None
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for k in range(1000):
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for i, b in tqdm(enumerate(dl)):
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continue
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pm = b['padded_mel']
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pm = torch.nn.functional.pad(pm, (0, 800-pm.shape[-1]))
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m = pm if m is None else torch.cat([m, pm], dim=0)
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print(m.mean(), m.std())
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@ -49,7 +49,6 @@ class MelEncoder(nn.Module):
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class GptSegmentor(nn.Module):
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MAX_SYMBOLS_PER_PHRASE = 200
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MAX_MEL_FRAMES = 2000 // 4
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def __init__(self, layers=8, model_dim=512, heads=8):
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@ -59,30 +58,28 @@ class GptSegmentor(nn.Module):
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self.max_mel_frames = self.MAX_MEL_FRAMES
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self.mel_encoder = MelEncoder(model_dim)
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self.mel_pos_embedding = nn.Embedding(self.MAX_MEL_FRAMES, model_dim)
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self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=2+self.MAX_SYMBOLS_PER_PHRASE+self.MAX_MEL_FRAMES, heads=heads,
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self.gpt = Transformer(dim=model_dim, depth=layers, seq_len=self.MAX_MEL_FRAMES, heads=heads,
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attn_dropout=.1, ff_dropout=.1, non_causal_sequence_partition=self.MAX_MEL_FRAMES)
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self.final_norm = nn.LayerNorm(model_dim)
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self.stop_head = nn.Linear(model_dim, 1)
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def forward(self, mel_inputs, mel_lengths):
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max_len = mel_lengths.max() # This can be done in the dataset layer, but it is easier to do here.
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mel_inputs = mel_inputs[:, :, :max_len]
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def forward(self, mel_inputs, termination_points):
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mel_emb = self.mel_encoder(mel_inputs)
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mel_lengths = mel_lengths // 4 # The encoder decimates the mel by a factor of 4.
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mel_emb = mel_emb.permute(0,2,1).contiguous()
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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enc = self.gpt(mel_emb)
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# The MEL gets decimated to 1/4 the size by the encoder, so we need to do the same to the termination points.
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termination_points = F.interpolate(termination_points.unsqueeze(1), size=mel_emb.shape[1], mode='area').squeeze()
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termination_points = (termination_points > 0).float()
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# Compute loss
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b, s, _ = enc.shape
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mel_pad_mask = ~get_mask_from_lengths(mel_lengths-1, s)
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targets = torch.zeros((b,s), device=enc.device).masked_fill_(mel_pad_mask, 1)
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stop_logits = self.final_norm(enc)
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stop_logits = self.stop_head(stop_logits)
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loss = F.binary_cross_entropy_with_logits(stop_logits.squeeze(-1), targets)
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loss = F.binary_cross_entropy_with_logits(stop_logits.squeeze(-1), termination_points)
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return loss.mean()
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@ -95,7 +92,7 @@ def register_gpt_segmentor(opt_net, opt):
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if __name__ == '__main__':
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gpt = GptSegmentor()
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l = gpt(torch.randn(3,80,94),
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torch.tensor([18,42,93]))
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torch.zeros(3,94))
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print(l.shape)
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#o = gpt.infer(torch.randint(high=24, size=(2,60)))
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@ -30,14 +30,14 @@ def forward_pass(model, denoiser, data, output_dir, opt, b):
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ground_truth_waveforms = denoiser(ground_truth_waveforms)
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for i in range(pred_waveforms.shape[0]):
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# Output predicted mels and waveforms.
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pred_mel = model.eval_state[opt['eval']['pred_mel']][i]
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pred_mel = model.eval_state[opt['eval']['pred_mel']][0][i].unsqueeze(0)
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pred_mel = ((pred_mel - pred_mel.mean()) / max(abs(pred_mel.min()), pred_mel.max())).unsqueeze(1)
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torchvision.utils.save_image(pred_mel, osp.join(output_dir, f'{b}_{i}_pred_mel.png'))
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audio = pred_waveforms[i][0].cpu().numpy()
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wavfile.write(osp.join(output_dir, f'{b}_{i}.wav'), 22050, audio)
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if gt:
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gt_mel = model.eval_state[opt['eval']['ground_truth_mel']][i]
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gt_mel = model.eval_state[opt['eval']['ground_truth_mel']][0][i].unsqueeze(0)
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gt_mel = ((gt_mel - gt_mel.mean()) / max(abs(gt_mel.min()), gt_mel.max())).unsqueeze(1)
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torchvision.utils.save_image(gt_mel, osp.join(output_dir, f'{b}_{i}_gt_mel.png'))
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audio = ground_truth_waveforms[i][0].cpu().numpy()
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@ -54,7 +54,7 @@ if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_lrdvae_audio_clips.yml')
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_stop_pred_dataset.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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@ -282,7 +282,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_lrdvae_audio_clips.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_stop_libritts.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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