deviandice/tortoise-tts
1
0
Fork 0
forked from mrq/tortoise-tts
Code Issues Pull Requests Packages Projects Releases Wiki Activity

added constructor argument and function to load a user-specified autoregressive model

Browse Source
This commit is contained in:
mrq 2023-02-18 14:08:45 +00:00
parent 00cb19b6cf
commit d8c6739820
3 changed files with 23 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -2,4 +2,4 @@
This repo is for my modifications to [neonbjb/tortoise-tts](https://github.com/neonbjb/tortoise-tts).
For the original repo, please go to [mrq/ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning).
Please migrate to [mrq/ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning) for future additions.

2
main.py
View File

@ -1,6 +1,8 @@
import os
import webui as mrq
print('DEPRECATION WARNING: this repo has been refractored to focus entirely on tortoise-tts. Please migrate to https://git.ecker.tech/mrq/ai-voice-cloning if you seek new features.')
if 'TORTOISE_MODELS_DIR' not in os.environ:
os.environ['TORTOISE_MODELS_DIR'] = os.path.realpath(os.path.join(os.getcwd(), './models/tortoise/'))

22
tortoise/api.py
View File

@ -203,7 +203,7 @@ class TextToSpeech:
Main entry point into Tortoise.
"""
def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True, input_sample_rate=22050, output_sample_rate=24000):
def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, minor_optimizations=True, input_sample_rate=22050, output_sample_rate=24000, autoregressive_model_path=None):
"""
Constructor
:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@ -238,6 +238,8 @@ class TextToSpeech:
self.tokenizer = VoiceBpeTokenizer()
self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', models_dir)
if os.path.exists(f'{models_dir}/autoregressive.ptt'):
# Assume this is a traced directory.
self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
@ -247,7 +249,7 @@ class TextToSpeech:
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
self.autoregressive.load_state_dict(torch.load(get_model_path('autoregressive.pth', models_dir)))
self.autoregressive.load_state_dict(torch.load(self.autoregressive_model_path))
self.autoregressive.post_init_gpt2_config(kv_cache=self.use_kv_cache)
self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
@ -277,6 +279,22 @@ class TextToSpeech:
self.clvp = self.clvp.to(self.device)
self.vocoder = self.vocoder.to(self.device)
def load_autoregressive_model(self, autoregressive_model_path):
previous_path = self.autoregressive_model_path
self.autoregressive_model_path = autoregressive_model_path if autoregressive_model_path and os.path.exists(autoregressive_model_path) else get_model_path('autoregressive.pth', models_dir)
del self.autoregressive
self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
self.autoregressive.load_state_dict(torch.load(self.autoregressive_model_path))
self.autoregressive.post_init_gpt2_config(kv_cache=self.use_kv_cache)
if self.preloaded_tensors:
self.autoregressive = self.autoregressive.to(self.device)
return previous_path != self.autoregressive_model_path
def load_cvvp(self):
"""Load CVVP model."""
self.cvvp = CVVP(model_dim=512, transformer_heads=8, dropout=0, mel_codes=8192, conditioning_enc_depth=8, cond_mask_percentage=0,