developed an algorithm for long texts.

app.py

@@ -2,6 +2,12 @@ import gradio as gr
 from transformers import AutoProcessor, BarkModel
 import torch
 import numpy as np
+import nltk
+from scipy.io.wavfile import write
+from IPython.display import Audio
+
+# Download nltk punkt for sentence tokenization
+nltk.download("punkt")
 
 torch.set_num_threads(1)
 
@@ -16,25 +22,50 @@ all_voice_presets = [
     "v2/en_speaker_0", "v2/en_speaker_1", "v2/en_speaker_2", "v2/en_speaker_3", 
     "v2/en_speaker_4", "v2/en_speaker_5", "v2/en_speaker_6", 
     "v2/en_speaker_7", "v2/en_speaker_8", "v2/en_speaker_9",
-    "v2/fr_speaker_0", "v2/fr_speaker_1", "v2/fr_speaker_2", "v2/fr_speaker_3", 
-    "v2/fr_speaker_4", "v2/fr_speaker_5", "v2/fr_speaker_6", 
-    "v2/fr_speaker_7", "v2/fr_speaker_8", "v2/fr_speaker_9",
+    "v2/tr_speaker_0", "v2/tr_speaker_1", "v2/tr_speaker_2", "v2/tr_speaker_3", 
+    "v2/tr_speaker_4", "v2/tr_speaker_5", "v2/tr_speaker_6", 
+    "v2/tr_speaker_7", "v2/tr_speaker_8", "v2/tr_speaker_9",
     "v2/de_speaker_0", "v2/de_speaker_1", "v2/de_speaker_2", "v2/de_speaker_3", 
     "v2/de_speaker_4", "v2/de_speaker_5", "v2/de_speaker_6", 
     "v2/de_speaker_7", "v2/de_speaker_8", "v2/de_speaker_9",
-    "v2/tr_speaker_0", "v2/tr_speaker_1", "v2/tr_speaker_2", "v2/tr_speaker_3", 
-    "v2/tr_speaker_4", "v2/tr_speaker_5", "v2/tr_speaker_6", 
-    "v2/tr_speaker_7", "v2/tr_speaker_8", "v2/tr_speaker_9"
+    "v2/fr_speaker_0", "v2/fr_speaker_1", "v2/fr_speaker_2", "v2/fr_speaker_3", 
+    "v2/fr_speaker_4", "v2/fr_speaker_5", "v2/fr_speaker_6", 
+    "v2/fr_speaker_7", "v2/fr_speaker_8", "v2/fr_speaker_9",
+    "v2/it_speaker_0", "v2/it_speaker_1", "v2/it_speaker_2", "v2/it_speaker_3",
+    "v2/it_speaker_4", "v2/it_speaker_5", "v2/it_speaker_6",
+    "v2/it_speaker_7", "v2/it_speaker_8", "v2/it_speaker_9",
+    "v2/zh_speaker_0", "v2/zh_speaker_1", "v2/zh_speaker_2", "v2/zh_speaker_3",
+    "v2/zh_speaker_4", "v2/zh_speaker_5", "v2/zh_speaker_6",
+    "v2/zh_speaker_7", "v2/zh_speaker_8", "v2/zh_speaker_9"
 ]
 
+SAMPLE_RATE = 22050  # Set a standard sample rate for Bark output
+silence_duration = 0.25  # quarter-second silence duration between sentences
+
 # Function to generate speech
 def generate_speech(text, model_name, voice_preset):
     model = models[model_name]
-    processor = AutoProcessor.from_pretrained(model_name)  # Load processor for the selected model
-    inputs = processor(text, voice_preset=voice_preset)
-    audio_array = model.generate(**inputs)
-    audio_array = audio_array.cpu().numpy().squeeze()
-    return (model.generation_config.sample_rate, audio_array)
+    processor = AutoProcessor.from_pretrained(model_name)
+    sentences = nltk.sent_tokenize(text)  # Tokenize text into sentences
+
+    if len(sentences) == 1:  # If single sentence, use original method
+        inputs = processor(text, voice_preset=voice_preset)
+        audio_array = model.generate(**inputs)
+        audio_array = audio_array.cpu().numpy().squeeze()
+        return (model.generation_config.sample_rate, audio_array)
+    
+    # For multiple sentences, generate and concatenate
+    silence = np.zeros(int(silence_duration * SAMPLE_RATE))
+    audio_pieces = []
+
+    for sentence in sentences:
+        inputs = processor(sentence, voice_preset=voice_preset)
+        audio_array = model.generate(**inputs).cpu().numpy().squeeze()
+        audio_pieces.append(audio_array)
+        audio_pieces.append(silence.copy())  # Add silence between sentences
+
+    full_audio = np.concatenate(audio_pieces)
+    return (SAMPLE_RATE, full_audio)
 
 # Gradio app setup
 with gr.Blocks() as app: