Remove intelligibility refinement
It's not longer a concern. :)
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26
api.py
26
api.py
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@ -5,9 +5,7 @@ from urllib import request
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import torch
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import torch
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import torch.nn.functional as F
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import torch.nn.functional as F
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import torchaudio
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import progressbar
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import progressbar
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import ocotillo
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from models.diffusion_decoder import DiffusionTts
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from models.diffusion_decoder import DiffusionTts
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from models.autoregressive import UnifiedVoice
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from models.autoregressive import UnifiedVoice
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@ -262,27 +260,3 @@ class TextToSpeech:
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if len(wav_candidates) > 1:
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if len(wav_candidates) > 1:
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return wav_candidates
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return wav_candidates
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return wav_candidates[0]
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return wav_candidates[0]
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def refine_for_intellibility(self, wav_candidates, corresponding_codes, output_path):
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"""
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Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable.
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TODO: finish this function
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:param wav_candidates:
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:return:
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"""
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transcriber = ocotillo.Transcriber(on_cuda=True)
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transcriptions = transcriber.transcribe_batch(torch.cat(wav_candidates, dim=0).squeeze(1), 24000)
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best = 99999999
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for i, transcription in enumerate(transcriptions):
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dist = lev_distance(transcription, args.text.lower())
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if dist < best:
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best = dist
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best_codes = corresponding_codes[i].unsqueeze(0)
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best_wav = wav_candidates[i]
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del transcriber
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torchaudio.save(os.path.join(output_path, f'{voice}_poor.wav'), best_wav.squeeze(0).cpu(), 24000)
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# Perform diffusion again with the high-quality diffuser.
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mel = do_spectrogram_diffusion(diffusion, final_diffuser, best_codes, cond_diffusion, mean=False)
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wav = vocoder.inference(mel)
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torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), wav.squeeze(0).cpu(), 24000)
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@ -7,5 +7,4 @@ inflect
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progressbar
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progressbar
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einops
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einops
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unidecode
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unidecode
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x-transformers
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x-transformers
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ocotillo
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