port do_tts to use the API

api.py

@@ -151,10 +151,10 @@ class TextToSpeech:
 
     def tts(self, text, voice_samples, k=1,
             # autoregressive generation parameters follow
-            num_autoregressive_samples=512, temperature=.9, length_penalty=1, repetition_penalty=1.0, top_k=50, top_p=.95,
+            num_autoregressive_samples=512, temperature=.5, length_penalty=2, repetition_penalty=2.0, top_p=.5,
             typical_sampling=False, typical_mass=.9,
             # diffusion generation parameters follow
-            diffusion_iterations=100, cond_free=True, cond_free_k=1, diffusion_temperature=1,):
+            diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=.7,):
         text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
         text = F.pad(text, (0, 1))  # This may not be necessary.
 
@@ -181,7 +181,6 @@ class TextToSpeech:
             for b in tqdm(range(num_batches)):
                 codes = self.autoregressive.inference_speech(conds, text,
                                                              do_sample=True,
-                                                             top_k=top_k,
                                                              top_p=top_p,
                                                              temperature=temperature,
                                                              num_return_sequences=self.autoregressive_batch_size,
@@ -221,3 +220,27 @@ class TextToSpeech:
             if len(wav_candidates) > 1:
                 return wav_candidates
             return wav_candidates[0]
+
+    def refine_for_intellibility(self, wav_candidates, corresponding_codes, output_path):
+        """
+        Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable.
+        TODO: finish this function
+        :param wav_candidates:
+        :return:
+        """
+        transcriber = ocotillo.Transcriber(on_cuda=True)
+        transcriptions = transcriber.transcribe_batch(torch.cat(wav_candidates, dim=0).squeeze(1), 24000)
+        best = 99999999
+        for i, transcription in enumerate(transcriptions):
+            dist = lev_distance(transcription, args.text.lower())
+            if dist < best:
+                best = dist
+                best_codes = corresponding_codes[i].unsqueeze(0)
+                best_wav = wav_candidates[i]
+        del transcriber
+        torchaudio.save(os.path.join(output_path, f'{voice}_poor.wav'), best_wav.squeeze(0).cpu(), 24000)
+
+        # Perform diffusion again with the high-quality diffuser.
+        mel = do_spectrogram_diffusion(diffusion, final_diffuser, best_codes, cond_diffusion, mean=False)
+        wav = vocoder.inference(mel)
+        torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), wav.squeeze(0).cpu(), 24000)

do_tts.py

@@ -1,123 +1,13 @@
 import argparse
 import os
-import random
-from urllib import request
 
 import torch
 import torch.nn.functional as F
 import torchaudio
-import progressbar
-import ocotillo
-
-from models.diffusion_decoder import DiffusionTts
-from models.autoregressive import UnifiedVoice
-from tqdm import tqdm
-
-from models.arch_util import TorchMelSpectrogram
-from models.text_voice_clip import VoiceCLIP
-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
-
-pbar = None
-def download_models():
-    MODELS = {
-        'clip.pth': 'https://huggingface.co/jbetker/tortoise-tts-clip/resolve/main/pytorch-model.bin',
-        'diffusion.pth': 'https://huggingface.co/jbetker/tortoise-tts-diffusion-v1/resolve/main/pytorch-model.bin',
-        'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-autoregressive/resolve/main/pytorch-model.bin'
-    }
-    os.makedirs('.models', exist_ok=True)
-    def show_progress(block_num, block_size, total_size):
-        global pbar
-        if pbar is None:
-            pbar = progressbar.ProgressBar(maxval=total_size)
-            pbar.start()
-
-        downloaded = block_num * block_size
-        if downloaded < total_size:
-            pbar.update(downloaded)
-        else:
-            pbar.finish()
-            pbar = None
-    for model_name, url in MODELS.items():
-        if os.path.exists(f'.models/{model_name}'):
-            continue
-        print(f'Downloading {model_name} from {url}...')
-        request.urlretrieve(url, f'.models/{model_name}', show_progress)
-        print('Done.')
-
-
-def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True):
-    """
-    Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
-    """
-    return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
-                           model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
-                           conditioning_free=cond_free, conditioning_free_k=1)
-
-
-def load_conditioning(path, sample_rate=22050, cond_length=132300):
-    rel_clip = load_audio(path, sample_rate)
-    gap = rel_clip.shape[-1] - cond_length
-    if gap < 0:
-        rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
-    elif gap > 0:
-        rand_start = random.randint(0, gap)
-        rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
-    mel_clip = TorchMelSpectrogram()(rel_clip.unsqueeze(0)).squeeze(0)
-    return mel_clip.unsqueeze(0).cuda(), rel_clip.unsqueeze(0).cuda()
-
-
-def fix_autoregressive_output(codes, stop_token):
-    """
-    This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was