From 84d641c57ae72bba334f2a2d60ec47a84683e6ae Mon Sep 17 00:00:00 2001 From: James Betker Date: Fri, 22 Apr 2022 11:34:05 -0600 Subject: [PATCH] n/c --- api.py | 20 ++++++++++++++------ models/autoregressive.py | 2 +- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/api.py b/api.py index 8486d3f..29d9a8d 100644 --- a/api.py +++ b/api.py @@ -6,6 +6,7 @@ from urllib import request import torch import torch.nn.functional as F import progressbar +import torchaudio from models.cvvp import CVVP from models.diffusion_decoder import DiffusionTts @@ -118,29 +119,36 @@ def fix_autoregressive_output(codes, stop_token, complain=True): return codes -def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1): +def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_samples, temperature=1): """ Uses the specified diffusion model to convert discrete codes into a spectrogram. """ with torch.no_grad(): cond_mels = [] for sample in conditioning_samples: + # The diffuser operates at a sample rate of 24000 (except for the latent inputs) + sample = torchaudio.functional.resample(sample, 22050, 24000) sample = pad_or_truncate(sample, 102400) - cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False) + cond_mel = wav_to_univnet_mel(sample.to(latents.device), do_normalization=False) cond_mels.append(cond_mel) cond_mels = torch.stack(cond_mels, dim=1) - output_seq_len = mel_codes.shape[1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. - output_shape = (mel_codes.shape[0], 100, output_seq_len) - precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False) + output_seq_len = latents.shape[1] * 4 * 24000 // 22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. + output_shape = (latents.shape[0], 100, output_seq_len) + precomputed_embeddings = diffusion_model.timestep_independent(latents, cond_mels, output_seq_len, False) - noise = torch.randn(output_shape, device=mel_codes.device) * temperature + noise = torch.randn(output_shape, device=latents.device) * temperature mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise, model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings}) return denormalize_tacotron_mel(mel)[:,:,:output_seq_len] class TextToSpeech: + """ + Main entry point into Tortoise. + :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing + GPU OOM errors. Larger numbers generates slightly faster. + """ def __init__(self, autoregressive_batch_size=16): self.autoregressive_batch_size = autoregressive_batch_size self.tokenizer = VoiceBpeTokenizer() diff --git a/models/autoregressive.py b/models/autoregressive.py index 0c211f3..6a91748 100644 --- a/models/autoregressive.py +++ b/models/autoregressive.py @@ -356,7 +356,7 @@ class UnifiedVoice(nn.Module): preformatting to create a working TTS model. """ # Set padding areas within MEL (currently it is coded with the MEL code for ). - mel_lengths = wav_lengths // self.mel_length_compression + mel_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc') for b in range(len(mel_lengths)): actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token. if actual_end < mel_input_tokens.shape[-1]: