Package everything

requirements.txt

@@ -7,4 +7,6 @@ inflect
 progressbar
 einops
 unidecode
-entmax
+entmax
+scipy
+librosa

results/favorite_riding_hood.mp3

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a8fc300bce87fcf771fcade7df5b4708d41c635e0f940468cdbb8b0a1d4feb78
-size 970413

setup.py

@@ -0,0 +1,22 @@
+from setuptools import setup, find_packages
+
+install_requires = [
+    "torch",
+    "torchaudio",
+    "rotary_embedding_torch",
+    "transformers",
+    "tokenizers",
+    "inflect",
+    "progressbar",
+    "einops",
+    "unidecode",
+    "entmax",
+    "scipy",
+    "librosa"
+]
+
+setup(
+    name="tortoise_tts",
+    packages=['tortoise_tts'],
+    install_requires=install_requires,
+)

tortoise_tts/__init__.py

@@ -0,0 +1 @@
+from .api import TextToSpeech

tortoise_tts/api.py

@@ -8,18 +8,18 @@ import torch.nn.functional as F
 import progressbar
 import torchaudio
 
-from models.classifier import AudioMiniEncoderWithClassifierHead
-from models.cvvp import CVVP
-from models.diffusion_decoder import DiffusionTts
-from models.autoregressive import UnifiedVoice
+from tortoise_tts.models.classifier import AudioMiniEncoderWithClassifierHead
+from tortoise_tts.models.cvvp import CVVP
+from tortoise_tts.models.diffusion_decoder import DiffusionTts
+from tortoise_tts.models.autoregressive import UnifiedVoice
 from tqdm import tqdm
 
-from models.arch_util import TorchMelSpectrogram
-from models.clvp import CLVP
-from models.vocoder import UnivNetGenerator
-from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
-from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
-from utils.tokenizer import VoiceBpeTokenizer, lev_distance
+from tortoise_tts.models.arch_util import TorchMelSpectrogram
+from tortoise_tts.models.clvp import CLVP
+from tortoise_tts.models.vocoder import UnivNetGenerator
+from tortoise_tts.utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
+from tortoise_tts.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
+from tortoise_tts.utils.tokenizer import VoiceBpeTokenizer, lev_distance
 
 
 pbar = None

tortoise_tts/models/arch_util.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchaudio
-from models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
+from tortoise_tts.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
 
 
 def zero_module(module):

tortoise_tts/models/autoregressive.py

@@ -6,8 +6,8 @@ import torch.nn.functional as F
 from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
 from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
 from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
-from models.arch_util import AttentionBlock
-from utils.typical_sampling import TypicalLogitsWarper
+from tortoise_tts.models.arch_util import AttentionBlock
+from tortoise_tts.utils.typical_sampling import TypicalLogitsWarper
 
 
 def null_position_embeddings(range, dim):

tortoise_tts/models/classifier.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils.checkpoint import checkpoint
 
-from models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
+from tortoise_tts.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
 
 
 class ResBlock(nn.Module):

tortoise_tts/models/clvp.py

@@ -3,9 +3,9 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import einsum
 
-from models.arch_util import CheckpointedXTransformerEncoder
-from models.transformer import Transformer
-from models.xtransformers import Encoder
+from tortoise_tts.models.arch_util import CheckpointedXTransformerEncoder
+from tortoise_tts.models.transformer import Transformer
+from tortoise_tts.models.xtransformers import Encoder
 
 
 def exists(val):

tortoise_tts/models/cvvp.py

@@ -4,8 +4,8 @@ import torch.nn.functional as F
 from torch import einsum
 from torch.utils.checkpoint import checkpoint
 
-from models.arch_util import AttentionBlock
-from models.xtransformers import ContinuousTransformerWrapper, Encoder
+from tortoise_tts.models.arch_util import AttentionBlock
+from tortoise_tts.models.xtransformers import ContinuousTransformerWrapper, Encoder
 
 
 def exists(val):

tortoise_tts/models/diffusion_decoder.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import autocast
 
-from models.arch_util import normalization, AttentionBlock
+from tortoise_tts.models.arch_util import normalization, AttentionBlock
 
 
 def is_latent(t):