Slight fix, getting close to be able to train from the web UI directly

src/utils.py

@@ -447,9 +447,9 @@ def save_training_settings( batch_size=None, learning_rate=None, print_rate=None
 		"save_rate": save_rate if save_rate else 50,
 		"name": name if name else "finetune",
 		"dataset_name": dataset_name if dataset_name else "finetune",
-		"dataset_path": dataset_path if dataset_path else "./experiments/finetune/train.txt",
+		"dataset_path": dataset_path if dataset_path else "./training/finetune/train.txt",
 		"validation_name": validation_name if validation_name else "finetune",
-		"validation_path": validation_path if validation_path else "./experiments/finetune/val.txt",
+		"validation_path": validation_path if validation_path else "./training/finetune/train.txt",
 	}
 
 	with open(f'./training/.template.yaml', 'r', encoding="utf-8") as f:
@@ -462,7 +462,7 @@ def save_training_settings( batch_size=None, learning_rate=None, print_rate=None
 		f.write(yaml)
 
 whisper_model = None
-def prepare_dataset( files, outdir ):
+def prepare_dataset( files, outdir, language=None ):
 	global whisper_model
 	if whisper_model is None:
 		whisper_model = whisper.load_model(args.whisper_model)
@@ -476,7 +476,7 @@ def prepare_dataset( files, outdir ):
 	for file in files:
 		print(f"Transcribing file: {file}")
 		
-		result = whisper_model.transcribe(file)
+		result = whisper_model.transcribe(file, language=language)
 		results[os.path.basename(file)] = result
 
 		print(f"Transcribed file: {file}, {len(result['segments'])} found.")

src/webui.py

@@ -375,14 +375,15 @@ def setup_gradio():
 					with gr.Column():
 						dataset_settings = [
 							gr.Dropdown( get_voice_list(), label="Dataset Source", type="value" ),
+							gr.Textbox(label="Language", placeholder="English")
 						]
 						dataset_voices = dataset_settings[0]
 
 					with gr.Column():
 						prepare_dataset_button = gr.Button(value="Prepare")
 
-						def prepare_dataset_proxy( voice ):
-							return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/" )
+						def prepare_dataset_proxy( voice, language ):
+							return prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language )
 
 						prepare_dataset_button.click(
 							prepare_dataset_proxy,
@@ -403,9 +404,9 @@ def setup_gradio():
 						training_settings = training_settings + [
 							gr.Textbox(label="Training Name", placeholder="finetune"),
 							gr.Textbox(label="Dataset Name", placeholder="finetune"),
-							gr.Textbox(label="Dataset Path", placeholder="./experiments/finetune/train.txt"),
+							gr.Textbox(label="Dataset Path", placeholder="./training/finetune/train.txt"),
 							gr.Textbox(label="Validation Name", placeholder="finetune"),
-							gr.Textbox(label="Validation Path", placeholder="./experiments/finetune/val.txt"),
+							gr.Textbox(label="Validation Path", placeholder="./training/finetune/train.txt"),
 						]
 
 						save_yaml_button.click(save_training_settings,

training/.template.yaml

@@ -24,6 +24,7 @@ datasets:
     num_conditioning_candidates: 2
     conditioning_length: 44000
     use_bpe_tokenizer: True
+    tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
     load_aligned_codes: False
   val:
     name: ${validation_name}
@@ -40,6 +41,7 @@ datasets:
     num_conditioning_candidates: 2
     conditioning_length: 44000
     use_bpe_tokenizer: True
+    tokenizer_vocab: ./models/tortoise/bpe_lowercase_asr_256.json
     load_aligned_codes: False
 
 steps:        
@@ -59,20 +61,20 @@ steps:
     injectors:  # TODO: replace this entire sequence with the GptVoiceLatentInjector
       paired_to_mel:
         type: torch_mel_spectrogram
-        mel_norm_file: ./experiments/clips_mel_norms.pth
+        mel_norm_file: ./models/tortoise/clips_mel_norms.pth
         in: wav
         out: paired_mel
       paired_cond_to_mel:
         type: for_each
         subtype: torch_mel_spectrogram
-        mel_norm_file: ./experiments/clips_mel_norms.pth
+        mel_norm_file: ./models/tortoise/clips_mel_norms.pth
         in: conditioning
         out: paired_conditioning_mel
       to_codes:
         type: discrete_token
         in: paired_mel
         out: paired_mel_codes
-        dvae_config: "./experiments/train_diffusion_vocoder_22k_level.yml" # EXTREMELY IMPORTANT
+        dvae_config: "./models/tortoise/train_diffusion_vocoder_22k_level.yml" # EXTREMELY IMPORTANT
       paired_fwd_text:
         type: generator
         generator: gpt
@@ -112,9 +114,9 @@ networks:
       #only_alignment_head: False  # uv3/4
 
 path:
-  pretrain_model_gpt: './experiments/autoregressive.pth' # CHANGEME: copy this from tortoise cache
+  pretrain_model_gpt: './models/tortoise/autoregressive.pth' # CHANGEME: copy this from tortoise cache
   strict_load: true
-  #resume_state: ./experiments/train_imgnet_vqvae_stage1/training_state/0.state   # <-- Set this to resume from a previous training state.
+  #resume_state: ./models/tortoise/train_imgnet_vqvae_stage1/training_state/0.state   # <-- Set this to resume from a previous training state.
 
 # afaik all units here are measured in **steps** (i.e. one batch of batch_size is 1 unit)
 train: # CHANGEME: ALL OF THESE PARAMETERS SHOULD BE EXPERIMENTED WITH