Update app.py

app.py

@@ -0,0 +1,106 @@
+import os
+import scipy
+import nltk
+import tempfile
+import numpy as np
+from bark.generation import preload_models, SAMPLE_RATE
+from bark import generate_audio
+from scipy.io import wavfile
+from IPython.display import Audio
+import gradio as gr
+nltk.download('punkt')
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+preload_models()
+
+
+
+def generate_audio_from_text(text,language_prompt,gender_prompt):
+    if language_prompt == "english":
+        if gender_prompt == "male":
+            history_prompt = "v2/en_speaker_8"
+        else:
+            history_prompt = "v2/en_speaker_9"
+    elif language_prompt == "french":
+        if gender_prompt == "male":
+            history_prompt = "v2/fr_speaker_0"
+        else:
+            history_prompt = "v2/fr_speaker_1"
+    elif language_prompt =="german":
+        if gender_prompt=="male":
+            history_prompt = "v2/de_speaker_2"
+        else:
+            history_prompt="v2/de_speaker_3"
+    elif language_prompt =="hindi":
+        if gender_prompt=="male":
+            history_prompt = "v2/hi_speaker_8"
+        else:
+            history_prompt="v2/hi_speaker_3"
+    elif language_prompt =="chinese":
+        if gender_prompt=="male":
+            history_prompt = "v2/zh_speaker_1"
+        else:
+            history_prompt="v2/zh_speaker_4"
+    elif language_prompt =="italian":
+        if gender_prompt=="male":
+            history_prompt = "v2/it_speaker_4"
+        else:
+            history_prompt="v2/it_speaker_7"
+    elif language_prompt =="japanese":
+        if gender_prompt=="male":
+            history_prompt = "v2/ja_speaker_2"
+        else:
+            history_prompt="v2/ja_speaker_0"
+    else:
+        raise ValueError("Invalid language or gender selection")
+
+    sentences = nltk.sent_tokenize(text)
+    # silence = np.zeros(int(0.25 * SAMPLE_RATE))  # quarter second of silence
+
+    pieces = []
+    for sentence in sentences:
+        audio_array = generate_audio(sentence, history_prompt=history_prompt)
+        pieces += [audio_array]
+
+    # Concatenate the audio pieces
+    final_audio = np.concatenate(pieces)
+
+    # Save the audio to a WAV file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_wav:
+        wavfile.write(temp_wav.name, SAMPLE_RATE, final_audio)
+
+    # Return the saved audio file
+    return temp_wav.name
+
+# Define lists of language and gender options
+language_options = [
+    "english",
+    "french",
+    "german",
+    "hindi",
+    "chinese",
+    "italian",
+    "japanese",
+
+
+]
+
+gender_options = [
+    "male",
+    "female",
+]
+
+# Create a Gradio interface with text input and dropdown menus for language and gender
+iface = gr.Interface(
+    fn=generate_audio_from_text,
+    inputs=[
+        gr.Textbox(text="Enter text to convert to speech:"),
+        gr.Dropdown(choices=language_options, label="Select language:"),
+        gr.Dropdown(choices=gender_options, label="Select gender:"),
+    ],
+    outputs=gr.outputs.File(label="Download WAV File"),
+    title="Text-to-Speech App Vertical Solution",
+    timeout=300,
+)
+
+# Launch the Gradio app with sharing enabled
+iface.launch(share=True, debug=True, enable_queue=True)