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Clip diffusion inputs

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This commit is contained in:
James Betker 2022-04-10 19:29:32 -06:00
parent 57ffdeff78
commit 7e29c68336
1 changed files with 15 additions and 2 deletions
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17
api.py
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@ -181,6 +181,7 @@ class TextToSpeech:
samples = []
num_batches = num_autoregressive_samples // self.autoregressive_batch_size
stop_mel_token = self.autoregressive.stop_mel_token
calm_token = 83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
self.autoregressive = self.autoregressive.cuda()
for b in tqdm(range(num_batches)):
codes = self.autoregressive.inference_speech(conds, text,
@ -212,8 +213,20 @@ class TextToSpeech:
self.diffusion = self.diffusion.cuda()
self.vocoder = self.vocoder.cuda()
for b in range(best_results.shape[0]):
code = best_results[b].unsqueeze(0)
mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, voice_samples, temperature=diffusion_temperature)
codes = best_results[b].unsqueeze(0)
# Find the first occurrence of the "calm" token and trim the codes to that.
ctokens = 0
for k in range(codes.shape[-1]):
if codes[0, k] == calm_token:
ctokens += 1
else:
ctokens = 0
if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
codes = codes[:, :k]
break
mel = do_spectrogram_diffusion(self.diffusion, diffuser, codes, voice_samples, temperature=diffusion_temperature)
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
self.diffusion = self.diffusion.cpu()