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Fix XTTS GPT padding and inference issues #3216

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merged 3 commits into from
Nov 15, 2023
Merged

Fix XTTS GPT padding and inference issues #3216

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7bfd20e
Fix end artifact for fine tuning models
Edresson Nov 11, 2023
b231ca8
Bug fix on zh-cn inference
Edresson Nov 14, 2023
f5fd31f
Remove ununsed code
Edresson Nov 14, 2023
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Remove ununsed code
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@Edresson
Edresson committed Nov 14, 2023
commit f5fd31f8e83e37fc991c7eb9ae874c5a90eabb2e
31 changes: 0 additions & 31 deletions TTS/tts/models/xtts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
import torchaudio
from coqpit import Coqpit

from TTS.tts.layers.tortoise.audio_utils import wav_to_univnet_mel
from TTS.tts.layers.xtts.gpt import GPT
from TTS.tts.layers.xtts.hifigan_decoder import HifiDecoder
from TTS.tts.layers.xtts.stream_generator import init_stream_support
Expand Down Expand Up @@ -308,26 +307,6 @@ def get_gpt_cond_latents(self, audio, sr, length: int = 30, chunk_length: int =
cond_latent = self.gpt.get_style_emb(mel.to(self.device))
return cond_latent.transpose(1, 2)

@torch.inference_mode()
def get_diffusion_cond_latents(self, audio, sr):
from math import ceil

diffusion_conds = []
CHUNK_SIZE = 102400
audio_24k = torchaudio.functional.resample(audio, sr, 24000)
for chunk in range(ceil(audio_24k.shape[1] / CHUNK_SIZE)):
current_sample = audio_24k[:, chunk * CHUNK_SIZE : (chunk + 1) * CHUNK_SIZE]
current_sample = pad_or_truncate(current_sample, CHUNK_SIZE)
cond_mel = wav_to_univnet_mel(
current_sample.to(self.device),
do_normalization=False,
device=self.device,
)
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
diffusion_latent = self.diffusion_decoder.get_conditioning(diffusion_conds)
return diffusion_latent

@torch.inference_mode()
def get_speaker_embedding(self, audio, sr):
audio_16k = torchaudio.functional.resample(audio, sr, 16000)
Expand Down Expand Up @@ -575,16 +554,6 @@ def inference(
return_attentions=False,
return_latent=True,
)
silence_token = 83
ctokens = 0
for k in range(gpt_codes.shape[-1]):
if gpt_codes[0, k] == silence_token:
ctokens += 1
else:
ctokens = 0
if ctokens > 8:
gpt_latents = gpt_latents[:, :k]
break

if length_scale != 1.0:
gpt_latents = F.interpolate(
Expand Down
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