Update gradio demo

TTS/demos/xtts_ft_demo/utils/formatter.py

@@ -44,6 +44,7 @@ def list_files(basePath, validExts=None, contains=None):
                 yield audioPath
 
 def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0.2, eval_percentage=0.15, speaker_name="coqui", gradio_progress=None):
+    audio_total_size = 0
     # make sure that ooutput file exists
     os.makedirs(out_path, exist_ok=True)
 
@@ -67,7 +68,9 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
             wav = torch.mean(wav, dim=0, keepdim=True)
 
         wav = wav.squeeze()
-        segments, info = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
+        audio_total_size += (wav.size(-1) / sr)
+
+        segments, _ = asr_model.transcribe(audio_path, word_timestamps=True, language=target_language)
         segments = list(segments)
         i = 0
         sentence = ""
@@ -101,9 +104,9 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
                 sentence = sentence[1:]
                 # Expand number and abbreviations plus normalization
                 sentence = multilingual_cleaners(sentence, target_language)
-                audio_file_name, ext = os.path.splitext(os.path.basename(audio_path))
+                audio_file_name, _ = os.path.splitext(os.path.basename(audio_path))
 
-                audio_file = f"wavs/{audio_file_name}_{str(i).zfill(8)}{ext}"
+                audio_file = f"wavs/{audio_file_name}_{str(i).zfill(8)}.wav"
 
                 # Check for the next word's existence
                 if word_idx + 1 < len(words_list):
@@ -125,8 +128,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
                 if audio.size(-1) >= sr/3:
                     torchaudio.save(absoulte_path,
                         audio,
-                        sr,
+                        sr
-                        backend="sox",
                     )
                 else:
                     continue
@@ -150,4 +152,7 @@ def format_audio_list(audio_files, target_language="en", out_path=None, buffer=0
     df_eval = df_eval.sort_values('audio_file')
     df_eval.to_csv(eval_metadata_path, sep="|", index=False)
 
-    return train_metadata_path, eval_metadata_path
+    # deallocate VRAM
+    del asr_model
+
+    return train_metadata_path, eval_metadata_path, audio_total_size

TTS/demos/xtts_ft_demo/utils/gpt_train.py

@@ -164,4 +164,7 @@ def train_gpt(language, num_epochs, batch_size, train_csv, eval_csv, output_path
     longest_text_idx =  samples_len.index(max(samples_len))
     speaker_ref = train_samples[longest_text_idx]["audio_file"]
 
+    # deallocate VRAM
+    del model, trainer
+
     return XTTS_CONFIG_FILE, XTTS_CHECKPOINT, TOKENIZER_FILE, trainer.output_path, speaker_ref

TTS/demos/xtts_ft_demo/xtts_demo.py

@@ -9,17 +9,23 @@ import numpy as np
 import os
 import torch
 import torchaudio
-from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list, list_audios
+from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
 from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
 
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 
 
+def clear_gpu_cache():
+    # clear the GPU cache
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
 PORT = 5003
 
 XTTS_MODEL = None
 def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
+    clear_gpu_cache()
     global XTTS_MODEL
     config = XttsConfig()
     config.load_json(xtts_config)
@@ -144,13 +150,23 @@ with gr.Blocks() as demo:
         prompt_compute_btn = gr.Button(value="Step 1 - Create dataset.")
     
         def preprocess_dataset(audio_path, language, out_path, progress=gr.Progress(track_tqdm=True)):
+            clear_gpu_cache()
             out_path = os.path.join(out_path, "dataset")
             os.makedirs(out_path, exist_ok=True)
             if audio_path is None:
                 # ToDo: raise an error
                 pass
             else:
-                train_meta, eval_meta = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
+                train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
+
+            clear_gpu_cache()
+
+            # if audio total len is less than 2 minutes raise an error
+            if audio_total_size < 120:
+                message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
+                print(message)
+                return message, " ", " "
+
             print("Dataset Processed!")
             return "Dataset Processed!", train_meta, eval_meta
 
@@ -173,7 +189,7 @@ with gr.Blocks() as demo:
             minimum=2,
             maximum=512,
             step=1,
-            value=15,
+            value=16,
         )
         progress_train = gr.Label(
             label="Progress:"
@@ -186,8 +202,7 @@ with gr.Blocks() as demo:
         train_btn = gr.Button(value="Step 2 - Run the training")
 
         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'
+            clear_gpu_cache()
-            # 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, train_csv, eval_csv, output_path=output_path)
             # copy original files to avoid parameters changes issues
@@ -196,6 +211,7 @@ with gr.Blocks() as demo:
 
             ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
             print("Model training done!")
+            clear_gpu_cache()
             return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav