update requirements and some docs

api.py

@@ -21,7 +21,12 @@ from utils.tokenizer import VoiceBpeTokenizer, lev_distance
 
 
 pbar = None
+
+
 def download_models():
+    """
+    Call to download all the models that Tortoise uses.
+    """
     MODELS = {
         'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/autoregressive.pth',
         'clvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/clvp.pth',
@@ -51,6 +56,9 @@ def download_models():
 
 
 def pad_or_truncate(t, length):
+    """
+    Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
+    """
     if t.shape[-1] == length:
         return t
     elif t.shape[-1] < length:
@@ -68,7 +76,10 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi
                            conditioning_free=cond_free, conditioning_free_k=cond_free_k)
 
 
-def load_conditioning(clip, cond_length=132300):
+def format_conditioning(clip, cond_length=132300):
+    """
+    Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
+    """
     gap = clip.shape[-1] - cond_length
     if gap < 0:
         clip = F.pad(clip, pad=(0, abs(gap)))
@@ -79,29 +90,6 @@ def load_conditioning(clip, cond_length=132300):
     return mel_clip.unsqueeze(0).cuda()
 
 
-def clip_guided_generation(autoregressive_model, clip_model, conditioning_input, text_input, num_batches, stop_mel_token,
-                           tokens_per_clip_inference=10, clip_results_to_reduce_to=8, **generation_kwargs):
-    """
-    Uses a CLVP model trained to associate full text with **partial** audio clips to pick the best generation candidates
-    every few iterations. The top results are then propagated forward through the generation process. Rinse and repeat.
-    This is a hybrid between beam search and sampling.
-    """
-    token_goal = tokens_per_clip_inference
-    finished = False
-    while not finished and token_goal < autoregressive_model.max_mel_tokens:
-        samples = []
-        for b in tqdm(range(num_batches)):
-            codes = autoregressive_model.inference_speech(conditioning_input, text_input, **generation_kwargs)
-            samples.append(codes)
-        for batch in samples:
-            for i in range(batch.shape[0]):
-                batch[i] = fix_autoregressive_output(batch[i], stop_mel_token, complain=False)
-            clip_results.append(clip_model(text_input.repeat(batch.shape[0], 1), batch, return_loss=False))
-        clip_results = torch.cat(clip_results, dim=0)
-        samples = torch.cat(samples, dim=0)
-        best_results = samples[torch.topk(clip_results, k=clip_results_to_reduce_to).indices]
-
-
 def fix_autoregressive_output(codes, stop_token, complain=True):
     """
     This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
@@ -222,7 +210,7 @@ class TextToSpeech:
         if not isinstance(voice_samples, list):
             voice_samples = [voice_samples]
         for vs in voice_samples:
-            conds.append(load_conditioning(vs))
+            conds.append(format_conditioning(vs))
         conds = torch.stack(conds, dim=1)
 
         diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)

read.py

@@ -5,10 +5,11 @@ import torch
 import torch.nn.functional as F
 import torchaudio
 
-from api import TextToSpeech, load_conditioning
+from api import TextToSpeech, format_conditioning
 from utils.audio import load_audio, get_voices
 from utils.tokenizer import VoiceBpeTokenizer
 
+
 def split_and_recombine_text(texts, desired_length=200, max_len=300):
     # TODO: also split across '!' and '?'. Attempt to keep quotations together.
     texts = [s.strip() + "." for s in texts.split('.')]
@@ -26,6 +27,7 @@ def split_and_recombine_text(texts, desired_length=200, max_len=300):
         texts.pop(i+1)
     return texts
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")

requirements.txt

@@ -6,4 +6,5 @@ tokenizers
 inflect
 progressbar
 einops
-unidecode
+unidecode
+entmax