Create app.py

app.py

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+import gradio as gr
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+import tempfile
+
+# Load the model and tokenizers
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = ParlerTTSForConditionalGeneration.from_pretrained("ai4bharat/indic-parler-tts").to(device)
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indic-parler-tts")
+description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)
+
+# Supported languages and default settings
+languages = {
+    "Urdu": "A female speaker delivers a clear and expressive speech in Urdu.",
+    "Punjabi": "A female speaker delivers a clear and expressive speech in Punjabi.",
+    "Sindhi": "A female speaker delivers a clear and expressive speech in Sindhi.",
+}
+emotions = [
+    "Neutral", "Happy", "Sad", "Anger", "Command", "Narration", "Conversation",
+    "Disgust", "Fear", "News", "Proper Noun", "Surprise"
+]
+default_language = "Urdu"
+default_gender = "Female"
+default_emotion = "Neutral"
+
+# Generate description function
+def generate_description(language, gender, emotion, noise, reverb, expressivity, pitch, rate, quality):
+    description = (
+        f"A {gender.lower()} speaker delivers a {emotion.lower()} and {expressivity.lower()} speech "
+        f"with a {pitch.lower()} pitch and a {rate.lower()} speaking rate. "
+        f"The audio has {noise.lower()} background noise, {reverb.lower()} reverberation, "
+        f"and {quality.lower()} voice quality. The text is in {language}."
+    )
+    return description
+
+# Generate audio function
+def generate_audio(text, description):
+    # Prepare model inputs
+    input_ids = description_tokenizer(description, return_tensors="pt").input_ids.to(device)
+    prompt_input_ids = tokenizer(text, return_tensors="pt").input_ids.to(device)
+
+    # Generate audio
+    generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+    audio_arr = generation.cpu().numpy().squeeze()
+
+    # Save audio to a temporary file
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        sf.write(f.name, audio_arr, model.config.sampling_rate)
+        audio_path = f.name
+
+    return audio_path
+
+# Gradio Interface
+def app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Indic Parler-TTS for Urdu, Punjabi, and Sindhi")
+        gr.Markdown("Select language, speaker gender, emotion, and customize speech characteristics.")
+        
+        with gr.Row():
+            lang_dropdown = gr.Dropdown(
+                choices=list(languages.keys()), 
+                value=default_language, 
+                label="Select Language"
+            )
+            gender_dropdown = gr.Dropdown(
+                choices=["Male", "Female"], 
+                value=default_gender, 
+                label="Speaker Gender"
+            )
+            emotion_dropdown = gr.Dropdown(
+                choices=emotions, 
+                value=default_emotion, 
+                label="Select Emotion"
+            )
+        
+        with gr.Row():
+            noise_dropdown = gr.Dropdown(
+                choices=["Clear", "Slightly Noisy"], 
+                value="Clear", 
+                label="Background Noise"
+            )
+            reverb_dropdown = gr.Dropdown(
+                choices=["Close-Sounding", "Distant-Sounding"], 
+                value="Close-Sounding", 
+                label="Reverberation"
+            )
+            expressivity_dropdown = gr.Dropdown(
+                choices=["Expressive", "Slightly Expressive", "Monotone"], 
+                value="Expressive", 
+                label="Expressivity"
+            )
+            pitch_dropdown = gr.Dropdown(
+                choices=["High", "Low", "Balanced"], 
+                value="Balanced", 
+                label="Pitch"
+            )
+            rate_dropdown = gr.Dropdown(
+                choices=["Slow", "Moderate", "Fast"], 
+                value="Moderate", 
+                label="Speaking Rate"
+            )
+            quality_dropdown = gr.Dropdown(
+                choices=["Basic", "Refined"], 
+                value="Refined", 
+                label="Voice Quality"
+            )
+        
+        with gr.Row():
+            text_input = gr.Textbox(
+                label="Enter Text",
+                placeholder="Type your text here...",
+                lines=5
+            )
+        
+        with gr.Row():
+            generate_caption_button = gr.Button("Generate Caption/Description")
+            caption_output = gr.Textbox(
+                label="Generated Caption/Description",
+                placeholder="The generated caption will appear here...",
+                lines=5
+            )
+        
+        with gr.Row():
+            generate_audio_button = gr.Button("Generate Speech")
+            audio_output = gr.Audio(label="Generated Audio")
+
+        # Link actions to buttons
+        generate_caption_button.click(
+            fn=generate_description, 
+            inputs=[
+                lang_dropdown, gender_dropdown, emotion_dropdown,
+                noise_dropdown, reverb_dropdown, expressivity_dropdown,
+                pitch_dropdown, rate_dropdown, quality_dropdown
+            ], 
+            outputs=caption_output
+        )
+        
+        generate_audio_button.click(
+            fn=generate_audio, 
+            inputs=[text_input, caption_output], 
+            outputs=audio_output
+        )
+        
+    return demo
+
+# Run the app
+app().launch()