kromskii2/alltalk_tts
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Changed how dvae weights are copied.

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erew123 authored and GitHub committed 2024-05-01 02:38:24 +01:00
1 parent a3f157249c
commit 9f18b24465
1 file changed
+15 -23
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finetune.py
+15 -23
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@@ -291,12 +291,12 @@ def create_temporary_file(folder, suffix=".wav"):
unique_filename = f"custom_tempfile_{int(time.time())}_{random.randint(1, 1000)}{suffix}"
return os.path.join(folder, unique_filename)
def format_audio_list(target_language, whisper_model, out_path, eval_split_number, gradio_progress=progress):
def format_audio_list(target_language, whisper_model, out_path, eval_split_number, speaker_name_input, gradio_progress=progress):
pfc_check_fail()
audio_files = [os.path.join(audio_folder, file) for file in os.listdir(audio_folder) if file.endswith(('.mp3', '.flac', '.wav'))]
buffer=0.2
eval_percentage = eval_split_number / 100.0
speaker_name="coqui"
speaker_name=speaker_name_input
audio_total_size = 0
os.makedirs(out_path, exist_ok=True)
temp_folder = os.path.join(out_path, "temp") # Update with your folder name
@@ -837,20 +837,15 @@ def compact_model(xtts_checkpoint_copy):
del checkpoint["optimizer"]
# Extract and save the fine-tuned DVAE weights
dvae_state_dict = {key: value for key, value in checkpoint["model"].items() if "dvae" in key}
dvae_path = this_dir / "models" / "trainedmodel" / "dvae.pth"
torch.save(dvae_state_dict, dvae_path)
for key in list(checkpoint["model"].keys()):
if "dvae" in key:
del checkpoint["model"][key]
# Save the modified checkpoint in the target directory
torch.save(checkpoint, target_dir / "model.pth")
# Specify the files you want to copy
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth"]
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
for file_name in files_to_copy:
src_path = this_dir / base_path / base_model_path / file_name
@@ -901,11 +896,6 @@ def compact_lastfinetuned_model(xtts_checkpoint_copy):
# Create the target directory if it doesn't exist
target_dir.mkdir(parents=True, exist_ok=True)
# Extract and save the fine-tuned DVAE weights
dvae_state_dict = {key: value for key, value in checkpoint["model"].items() if "dvae" in key}
dvae_path = this_dir / "models" / "lastfinetuned" / "dvae.pth"
torch.save(dvae_state_dict, dvae_path)
for key in list(checkpoint["model"].keys()):
if "dvae" in key:
del checkpoint["model"][key]
@@ -914,7 +904,7 @@ def compact_lastfinetuned_model(xtts_checkpoint_copy):
torch.save(checkpoint, target_dir / "model.pth")
# Specify the files you want to copy
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth"]
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
for file_name in files_to_copy:
src_path = this_dir / base_path / base_model_path / file_name
@@ -965,11 +955,6 @@ def compact_custom_model(xtts_checkpoint_copy, folder_path):
# Create the target directory if it doesn't exist
target_dir.mkdir(parents=True, exist_ok=True)
# Extract and save the fine-tuned DVAE weights
dvae_state_dict = {key: value for key, value in checkpoint["model"].items() if "dvae" in key}
dvae_path = this_dir / "models" / folder_path / "dvae.pth"
torch.save(dvae_state_dict, dvae_path)
for key in list(checkpoint["model"].keys()):
if "dvae" in key:
del checkpoint["model"][key]
@@ -978,7 +963,7 @@ def compact_custom_model(xtts_checkpoint_copy, folder_path):
torch.save(checkpoint, target_dir / "model.pth")
# Specify the files you want to copy
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth"]
files_to_copy = ["vocab.json", "config.json", "speakers_xtts.pth", "mel_stats.pth", "dvae.pth",]
for file_name in files_to_copy:
src_path = this_dir / base_path / base_model_path / file_name
@@ -1384,6 +1369,11 @@ if __name__ == "__main__":
maximum=95, # Maximum value
step=1, # Increment step
)
speaker_name_input = gr.Textbox(
label="The name of the speaker/person you are training",
value="personsname",
visible=True,
)
progress_data = gr.Label(
label="Progress:"
)
@@ -1395,14 +1385,15 @@ if __name__ == "__main__":
prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
def preprocess_dataset(language, whisper_model, out_path, eval_split_number, progress=gr.Progress(track_tqdm=True)):
def preprocess_dataset(language, whisper_model, out_path, eval_split_number, speaker_name_input, progress=gr.Progress(track_tqdm=True)):
clear_gpu_cache()
test_for_audio_files = [file for file in os.listdir(audio_folder) if any(file.lower().endswith(ext) for ext in ['.wav', '.mp3', '.flac'])]
if not test_for_audio_files:
return "I cannot find any mp3, wav or flac files in the folder called 'put-voice-samples-in-here'", "", ""
else:
try:
train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model, out_path=out_path, eval_split_number=eval_split_number, gradio_progress=progress)
train_meta, eval_meta, audio_total_size = format_audio_list(target_language=language, whisper_model=whisper_model, out_path=out_path, eval_split_number=eval_split_number, speaker_name_input=speaker_name_input, gradio_progress=progress)
except:
traceback.print_exc()
error = traceback.format_exc()
@@ -1817,6 +1808,7 @@ if __name__ == "__main__":
whisper_model,
out_path,
eval_split_number,
speaker_name_input,
],
outputs=[
progress_data,