Fix demo freezing issue

TTS/demos/xtts_ft_demo/requirements.txt

@@ -1 +1,2 @@
-faster_whisper
+faster_whisper==0.9.0
+gradio==4.7.1

TTS/demos/xtts_ft_demo/xtts_demo.py

@@ -28,7 +28,7 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
         model.cuda()
     return model
 
-def run_tts(lang, tts_text, xtts_checkpoint, xtts_config, xtts_vocab, speaker_audio_file, state_vars):
+def run_tts(lang, tts_text, xtts_checkpoint, xtts_config, xtts_vocab, speaker_audio_file):
     # ToDo: add the load in other function to fast inference
     model = load_model(xtts_checkpoint, xtts_config, xtts_vocab)
     gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=model.config.gpt_cond_len, max_ref_length=model.config.max_ref_len, sound_norm_refs=model.config.sound_norm_refs)
@@ -95,7 +95,7 @@ def read_logs():
 
 
 with gr.Blocks() as demo:
-    state_vars = gr.State()
+    # state_vars = gr.State()
     with gr.Tab("Data processing"):    
         upload_file = gr.Audio(
             sources="upload",
@@ -135,7 +135,7 @@ with gr.Blocks() as demo:
 
         prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
     
-        def preprocess_dataset(audio_path, language, state_vars, progress=gr.Progress(track_tqdm=True)):
+        def preprocess_dataset(audio_path, language, progress=gr.Progress(track_tqdm=True)):
             # create a temp directory to save the dataset
             out_path = tempfile.TemporaryDirectory().name
             if audio_path is None:
@@ -144,27 +144,15 @@ with gr.Blocks() as demo:
             else:
                 train_meta, eval_meta = format_audio_list([audio_path], target_language=language, out_path=out_path, gradio_progress=progress)
 
-            state_vars = {}
-            state_vars["train_csv"] = train_meta
-            state_vars["eval_csv"] = eval_meta
-            print(state_vars)
-            return "Dataset Processed!", state_vars
-
-        prompt_compute_btn.click(
-            fn=preprocess_dataset,
-            inputs=[
-                upload_file,
-                lang,
-                state_vars,
-            ],
-            outputs=[
-                progress_data,
-                state_vars,
-            ],
-        )
-        
+            return "Dataset Processed!", train_meta, eval_meta
 
     with gr.Tab("Fine-tuning XTTS"):
+        train_csv = gr.Textbox(
+            label="Train CSV:",
+        )
+        eval_csv = gr.Textbox(
+            label="Eval CSV:",
+        )
         num_epochs =  gr.Slider(
             label="num_epochs",
             minimum=1,
@@ -189,21 +177,22 @@ with gr.Blocks() as demo:
         demo.load(read_logs, None, logs_tts_train, every=1)
         train_btn = gr.Button(value="Step 2 - Run the training")
 
-        def train_model(language, num_epochs, batch_size, state_vars, output_path="./", progress=gr.Progress(track_tqdm=True)):
-            # state_vars = {'train_csv': '/tmp/tmprh4k_vou/metadata_train.csv', 'eval_csv': '/tmp/tmprh4k_vou/metadata_eval.csv'}
+        def train_model(language, train_csv, eval_csv, num_epochs, batch_size, output_path="./", progress=gr.Progress(track_tqdm=True)):
+            # train_csv = '/tmp/tmprh4k_vou/metadata_train.csv'
+            # eval_csv = '/tmp/tmprh4k_vou/metadata_eval.csv'
 
-            config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, state_vars["train_csv"], state_vars["eval_csv"], output_path=output_path)
+            config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, train_csv, eval_csv, output_path=output_path)
             # copy original files to avoid parameters changes issues
             os.system(f"cp {config_path} {exp_path}")
             os.system(f"cp {vocab_file} {exp_path}")
 
             ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
-            state_vars["config_path"] = config_path
-            state_vars["original_xtts_checkpoint"] = original_xtts_checkpoint
-            state_vars["vocab_file"] = vocab_file
-            state_vars["ft_xtts_checkpoint"] = ft_xtts_checkpoint
-            state_vars["speaker_audio_file"] = speaker_wav
-            return "Model training done!", state_vars, config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
+            # state_vars["config_path"] = config_path
+            # state_vars["original_xtts_checkpoint"] = original_xtts_checkpoint
+            # state_vars["vocab_file"] = vocab_file
+            # state_vars["ft_xtts_checkpoint"] = ft_xtts_checkpoint
+            # state_vars["speaker_audio_file"] = speaker_wav
+            return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav
 
 
     with gr.Tab("Inference"):
@@ -254,16 +243,31 @@ with gr.Blocks() as demo:
         tts_output_audio = gr.Audio(label="Generated Audio.")
         reference_audio = gr.Audio(label="Reference audio used.")
 
+        prompt_compute_btn.click(
+            fn=preprocess_dataset,
+            inputs=[
+                upload_file,
+                lang,
+            ],
+            outputs=[
+                progress_data,
+                train_csv,
+                eval_csv,
+            ],
+        )
+        
+
 
         train_btn.click(
             fn=train_model,
             inputs=[
                 lang,
+                train_csv,
+                eval_csv,
                 num_epochs,
                 batch_size,
-                state_vars,
             ],
-            outputs=[progress_train, state_vars, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
+            outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, speaker_reference_audio],
         )
         
 
@@ -276,7 +280,6 @@ with gr.Blocks() as demo:
                 xtts_config,
                 xtts_vocab,
                 speaker_reference_audio,
-                state_vars,
             ],
             outputs=[tts_output_audio, reference_audio],
         )