forked from mrq/tortoise-tts
Revert "Merge pull request #3 from osanseviero/main"
This reverts commit 1a41f7f7694cc88b10961424d10e4490a358f9fd, reversing changes made to cf7a4bc7e773465a8e913228d642b81ca0475eb7.
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090c650b85
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@ -8,18 +8,18 @@ import torch.nn.functional as F
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import progressbar
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import progressbar
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import torchaudio
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import torchaudio
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from tortoise_tts.models.classifier import AudioMiniEncoderWithClassifierHead
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from models.classifier import AudioMiniEncoderWithClassifierHead
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from tortoise_tts.models.cvvp import CVVP
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from models.cvvp import CVVP
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from tortoise_tts.models.diffusion_decoder import DiffusionTts
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from models.diffusion_decoder import DiffusionTts
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from tortoise_tts.models.autoregressive import UnifiedVoice
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from models.autoregressive import UnifiedVoice
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from tqdm import tqdm
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from tqdm import tqdm
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from tortoise_tts.models.arch_util import TorchMelSpectrogram
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from models.arch_util import TorchMelSpectrogram
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from tortoise_tts.models.clvp import CLVP
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from models.clvp import CLVP
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from tortoise_tts.models.vocoder import UnivNetGenerator
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from models.vocoder import UnivNetGenerator
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from tortoise_tts.utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
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from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
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from tortoise_tts.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
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from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
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from tortoise_tts.utils.tokenizer import VoiceBpeTokenizer, lev_distance
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from utils.tokenizer import VoiceBpeTokenizer, lev_distance
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pbar = None
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pbar = None
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@ -5,7 +5,7 @@ import torch
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import torch.nn as nn
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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import torchaudio
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import torchaudio
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from tortoise_tts.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
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from models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
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def zero_module(module):
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def zero_module(module):
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@ -6,8 +6,8 @@ import torch.nn.functional as F
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from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
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from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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from tortoise_tts.models.arch_util import AttentionBlock
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from models.arch_util import AttentionBlock
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from tortoise_tts.utils.typical_sampling import TypicalLogitsWarper
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from utils.typical_sampling import TypicalLogitsWarper
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def null_position_embeddings(range, dim):
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def null_position_embeddings(range, dim):
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@ -3,7 +3,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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from torch.utils.checkpoint import checkpoint
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from torch.utils.checkpoint import checkpoint
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from tortoise_tts.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
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from models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
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class ResBlock(nn.Module):
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class ResBlock(nn.Module):
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@ -3,9 +3,9 @@ import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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from torch import einsum
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from torch import einsum
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from tortoise_tts.models.arch_util import CheckpointedXTransformerEncoder
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from models.arch_util import CheckpointedXTransformerEncoder
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from tortoise_tts.models.transformer import Transformer
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from models.transformer import Transformer
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from tortoise_tts.models.xtransformers import Encoder
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from models.xtransformers import Encoder
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def exists(val):
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def exists(val):
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@ -4,8 +4,8 @@ import torch.nn.functional as F
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from torch import einsum
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from torch import einsum
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from torch.utils.checkpoint import checkpoint
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from torch.utils.checkpoint import checkpoint
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from tortoise_tts.models.arch_util import AttentionBlock
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from models.arch_util import AttentionBlock
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from tortoise_tts.models.xtransformers import ContinuousTransformerWrapper, Encoder
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from models.xtransformers import ContinuousTransformerWrapper, Encoder
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def exists(val):
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def exists(val):
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@ -7,7 +7,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.functional as F
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from torch import autocast
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from torch import autocast
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from tortoise_tts.models.arch_util import normalization, AttentionBlock
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from models.arch_util import normalization, AttentionBlock
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def is_latent(t):
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def is_latent(t):
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@ -8,5 +8,3 @@ progressbar
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einops
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einops
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unidecode
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unidecode
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entmax
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entmax
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scipy
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librosa
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5
tortoise_tts/results/.gitattributes
vendored
5
tortoise_tts/results/.gitattributes
vendored
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@ -1,5 +0,0 @@
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favorite_riding_hood.mp3 filter=lfs diff=lfs merge=lfs -text
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favorites filter=lfs diff=lfs merge=lfs -text
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riding_hood filter=lfs diff=lfs merge=lfs -text
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tacotron_comparison filter=lfs diff=lfs merge=lfs -text
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various filter=lfs diff=lfs merge=lfs -text
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@ -6,7 +6,7 @@ import torchaudio
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import numpy as np
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import numpy as np
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from scipy.io.wavfile import read
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from scipy.io.wavfile import read
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from tortoise_tts.utils.stft import STFT
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from utils.stft import STFT
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def load_wav_to_torch(full_path):
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def load_wav_to_torch(full_path):
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@ -3,7 +3,6 @@ import re
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import inflect
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import inflect
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import torch
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import torch
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from tokenizers import Tokenizer
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from tokenizers import Tokenizer
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from pathlib import Path
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# Regular expression matching whitespace:
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# Regular expression matching whitespace:
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@ -166,8 +165,6 @@ def lev_distance(s1, s2):
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class VoiceBpeTokenizer:
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class VoiceBpeTokenizer:
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def __init__(self, vocab_file='data/tokenizer.json'):
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def __init__(self, vocab_file='data/tokenizer.json'):
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vocab_file = str(Path(__file__).parent.parent / Path(vocab_file))
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print(vocab_file)
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if vocab_file is not None:
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if vocab_file is not None:
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self.tokenizer = Tokenizer.from_file(vocab_file)
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self.tokenizer = Tokenizer.from_file(vocab_file)
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