Remove intelligibility refinement

api.py

@@ -5,9 +5,7 @@ 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
@@ -262,27 +260,3 @@ 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)

requirements.txt

@@ -7,5 +7,4 @@ inflect
 progressbar
 einops
 unidecode
-x-transformers
-ocotillo
+x-transformers