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Sync from GitHub repo

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This Space is synced from the GitHub repo: https://github.com/SWivid/F5-TTS. Please submit contributions to the Space there

Files changed (1) hide show
  1. src/f5_tts/model/trainer.py +10 -4
src/f5_tts/model/trainer.py CHANGED
@@ -61,7 +61,7 @@ class Trainer:
61
  gradient_accumulation_steps=grad_accumulation_steps,
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  **accelerate_kwargs,
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  )
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- self.device = self.accelerator.device
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  self.logger = logger
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  if self.logger == "wandb":
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  if exists(wandb_resume_id):
@@ -325,7 +325,9 @@ class Trainer:
325
 
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  if self.log_samples and self.accelerator.is_local_main_process:
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  ref_audio, ref_audio_len = vocoder.decode(batch["mel"][0].unsqueeze(0)), mel_lengths[0]
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- torchaudio.save(f"{log_samples_path}/step_{global_step}_ref.wav", ref_audio.cpu(), target_sample_rate)
 
 
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  with torch.inference_mode():
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  generated, _ = self.accelerator.unwrap_model(self.model).sample(
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  cond=mel_spec[0][:ref_audio_len].unsqueeze(0),
@@ -336,8 +338,12 @@ class Trainer:
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  sway_sampling_coef=sway_sampling_coef,
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  )
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  generated = generated.to(torch.float32)
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- gen_audio = vocoder.decode(generated[:, ref_audio_len:, :].permute(0, 2, 1).to(self.device))
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- torchaudio.save(f"{log_samples_path}/step_{global_step}_gen.wav", gen_audio.cpu(), target_sample_rate)
 
 
 
 
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  if global_step % self.last_per_steps == 0:
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  self.save_checkpoint(global_step, last=True)
 
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  gradient_accumulation_steps=grad_accumulation_steps,
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  **accelerate_kwargs,
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  )
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+
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  self.logger = logger
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  if self.logger == "wandb":
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  if exists(wandb_resume_id):
 
325
 
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  if self.log_samples and self.accelerator.is_local_main_process:
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  ref_audio, ref_audio_len = vocoder.decode(batch["mel"][0].unsqueeze(0)), mel_lengths[0]
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+ torchaudio.save(
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+ f"{log_samples_path}/step_{global_step}_ref.wav", ref_audio.cpu(), target_sample_rate
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+ )
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  with torch.inference_mode():
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  generated, _ = self.accelerator.unwrap_model(self.model).sample(
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  cond=mel_spec[0][:ref_audio_len].unsqueeze(0),
 
338
  sway_sampling_coef=sway_sampling_coef,
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  )
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  generated = generated.to(torch.float32)
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+ gen_audio = vocoder.decode(
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+ generated[:, ref_audio_len:, :].permute(0, 2, 1).to(self.accelerator.device)
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+ )
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+ torchaudio.save(
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+ f"{log_samples_path}/step_{global_step}_gen.wav", gen_audio.cpu(), target_sample_rate
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+ )
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  if global_step % self.last_per_steps == 0:
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  self.save_checkpoint(global_step, last=True)