set seed on inference, since it seems to be set to 0 every time

README.md

@@ -33,6 +33,17 @@ To start the trainer, run `python3 -m tortoise_tts.train --yaml="./path/to/your/
 
 For training a LoRA, uncomment the `loras` block in your training YAML.
 
+For loading an existing finetuned model, create a folder with this structure, and load its accompanying YAML:
+```
+./some/arbitrary/path/:
+    ckpt:
+        autoregressive:
+            fp32.pth # finetuned weights
+    config.yaml
+```
+
+For LoRAs, replace the above `fp32.pth` with `lora.pth`.
+
 ## To-Do
 
 - [X] Reimplement original inferencing through TorToiSe (as done with `api.py`)
@@ -54,12 +65,14 @@ For training a LoRA, uncomment the `loras` block in your training YAML.
   - [x] Web UI
     - [ ] Feature parity with [ai-voice-cloning](https://git.ecker.tech/mrq/ai-voice-cloning)
       - Although I feel a lot of its features are the wrong way to go about it.
-  - [ ] Additional samplers for the autoregressive model
-  - [ ] Additional samplers for the diffusion model
-  - [ ] BigVGAN in place of the original vocoder
+  - [ ] Additional samplers for the autoregressive model (such as mirostat / dynamic temperature)
+  - [ ] Additional samplers for the diffusion model (beyond the already included DDIM)
+  - [X] BigVGAN in place of the original vocoder
+    - [X] HiFiGAN integration as well
   - [ ] XFormers / flash_attention_2 for the autoregressive model
     - Beyond HF's internal implementation of handling alternative attention
     - Both the AR and diffusion models also do their own attention...
+  - [ ] Saner way of loading finetuned models / LoRAs
   - [ ] Some vector embedding store to find the "best" utterance to pick
 - [ ] Documentation
 

tortoise_tts/__main__.py

@@ -26,6 +26,8 @@ def main():
 	parser.add_argument("--cond-free", action="store_true")
 	parser.add_argument("--vocoder", type=str, default="bigvgan")
 
+	parser.add_argument("--seed", type=int, default=None)
+	
 	parser.add_argument("--yaml", type=Path, default=None)
 	parser.add_argument("--device", type=str, default=None)
 	parser.add_argument("--amp", action="store_true")
@@ -65,6 +67,8 @@ def main():
 		cond_free=args.cond_free,
 		
 		vocoder_type=args.vocoder,
+		
+		seed=args.seed,
 	)
 	"""
 		language=args.language,

tortoise_tts/config.py

@@ -8,9 +8,11 @@ import sys
 import time
 import argparse
 import yaml
+import random
 
 import torch
 
+import numpy as np
 from dataclasses import asdict, dataclass, field
 
 from functools import cached_property
@@ -22,6 +24,14 @@ from .tokenizer import VoiceBpeTokenizer
 # Yuck
 from transformers import PreTrainedTokenizerFast
 
+def set_seed(seed=None):
+	if not seed:
+		seed = time.time()
+
+	random.seed(seed)
+	np.random.seed(seed)
+	torch.manual_seed(seed)
+
 DEFAULT_YAML = Path(__file__).parent.parent / 'data/config.yaml'
 
 @dataclass()

tortoise_tts/inference.py

@@ -1,6 +1,7 @@
 import torch
 import torchaudio
 import soundfile
+import time
 
 from torch import Tensor
 from einops import rearrange
@@ -8,8 +9,7 @@ from pathlib import Path
 from tqdm import tqdm
 
 from .emb.mel import encode_from_files as encode_mel, trim, trim_random
-from .utils import to_device
-from .utils import wrapper as ml
+from .utils import to_device, set_seed, wrapper as ml
 
 from .config import cfg, DEFAULT_YAML
 from .models import get_models, load_model
@@ -140,6 +140,8 @@ class TTS():
 
 		vocoder_type="bigvgan",
 
+		seed=None,
+
 		out_path=None,
 	):
 		lines = text.split("\n")
@@ -189,12 +191,14 @@ class TTS():
 
 		candidates = 1
 
+		set_seed(seed)
+
 		for line in lines:
 			if out_path is None:
 				output_dir = Path("./data/results/")
 				if not output_dir.exists():
 					output_dir.mkdir(parents=True, exist_ok=True)
-				out_path = output_dir / f"{cfg.start_time}.wav"
+				out_path = output_dir / f"{time.time()}.wav"
 
 			text = self.encode_text( line ).to(device=cfg.device)
 

tortoise_tts/utils/__init__.py

@@ -7,4 +7,5 @@ from .utils import (
     to_device,
     tree_map,
     do_gc,
+    set_seed,
 )

tortoise_tts/utils/utils.py

@@ -7,8 +7,11 @@ from .distributed import global_rank, local_rank, global_leader_only
 import gc
 import logging
 import pandas as pd
+import numpy as np
 import re
 import torch
+import random
+import time
 
 from coloredlogs import ColoredFormatter
 from logging import StreamHandler
@@ -35,6 +38,14 @@ def flatten_dict(d):
 	return records[0] if records else {}
 
 
+def set_seed(seed=None):
+	if not seed:
+		seed = int(time.time())
+
+	random.seed(seed)
+	np.random.seed(seed)
+	torch.manual_seed(seed)
+
 def _get_named_modules(module, attrname):
 	for name, module in module.named_modules():
 		if hasattr(module, attrname):