Update app.py

app.py

@@ -3,86 +3,64 @@ import wave
 import numpy as np
 from io import BytesIO
 from huggingface_hub import hf_hub_download
-from piper import PiperVoice 
+from piper import PiperVoice
 from transformers import pipeline
 
-# Import necessary libraries:
-# gradio for creating the web interface,
-# wave for handling WAV audio format,
-# numpy for numerical operations,
-# BytesIO for in-memory byte handling,
-# huggingface_hub for downloading models from the Hugging Face Hub,
-# PiperVoice for the text-to-speech functionality,
-# pipeline from transformers for the NSFW classifier.
-
-# Load the NSFW classifier model using Hugging Face's pipeline
+# Load the NSFW classifier model
 nsfw_detector = pipeline("text-classification", model="michellejieli/NSFW_text_classifier")
 
 def synthesize_speech(text):
     # Check for NSFW content using the classifier
     nsfw_result = nsfw_detector(text)
-    # Extract the label and score from the result
     label = nsfw_result[0]['label']
     score = nsfw_result[0]['score']
 
-    # First, check if the input text contains NSFW content.
-    #nsfw_result = nsfw_detector(text)
     if label == 'NSFW' and score >= 0.95:
-        # Download and read the error audio file
         error_audio_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="error_audio.wav")
-        with open(error_audio_path, 'rb') as error_audio_file:
-            error_audio = error_audio_file.read()
-        # Return the error audio and a warning message
-        return error_audio, "NSFW content detected. Cannot process."
 
-    # If the content is safe, proceed with speech synthesis.
-    # Download the model and configuration from Hugging Face Hub.
+        # Read the error audio file
+        try:
+            with wave.open(error_audio_path, 'rb') as error_audio_file:
+                frames = error_audio_file.readframes(error_audio_file.getnframes())
+                error_audio_data = np.frombuffer(frames, dtype=np.int16).tobytes()
+        except Exception as e:
+            print(f"Error reading audio file: {e}")
+            return None, "Error in processing audio file."
+
+        return error_audio_data, "NSFW content detected. Cannot process."
+
     model_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="speaker__01234_model.onnx")
     config_path = hf_hub_download(repo_id="DLI-SLQ/speaker_01234", filename="speaker__1234_model.onnx.json")
-    
-    # Load the PiperVoice model for speech synthesis.
+
     voice = PiperVoice.load(model_path, config_path)
-    
-    # Create a BytesIO buffer to hold the synthesized WAV file in memory.
+
     buffer = BytesIO()
     with wave.open(buffer, 'wb') as wav_file:
-        # Set WAV file properties: sample rate, bit depth, and mono channel.
         wav_file.setframerate(voice.config.sample_rate)
-        wav_file.setsampwidth(2)  # 16-bit
-        wav_file.setnchannels(1)  # mono
-
-        # Use the PiperVoice model to synthesize speech from the text.
+        wav_file.setsampwidth(2)
+        wav_file.setnchannels(1)
         voice.synthesize(text, wav_file)
 
-    # Convert the buffer content to a NumPy array, then to bytes for Gradio output.
     buffer.seek(0)
     audio_data = np.frombuffer(buffer.read(), dtype=np.int16)
     return audio_data.tobytes(), None
 
-# Set up the Gradio interface.
+# Gradio Interface
 with gr.Blocks(theme=gr.themes.Base()) as blocks:
-    # Create a user-friendly markdown title and description.
     gr.Markdown("# Text to Speech Synthesizer")
     gr.Markdown("Enter text to synthesize it into speech using models from the State Library of Queensland's collection using Piper.")
-    
-    # Define Gradio interface components: input textbox, audio output, and output textbox.
     input_text = gr.Textbox(label="Input Text")
     output_audio = gr.Audio(label="Synthesized Speech", type="numpy")
-    output_text = gr.Textbox(label="Output Text", visible=True)  # Visible for error messages
+    output_text = gr.Textbox(label="Output Text", visible=True)
+    submit_button = gr.Button("Synthesize")
 
-    # Define a function to process the input text and produce outputs.
     def process_and_output(text):
         audio, message = synthesize_speech(text)
         if message:
-            # If there's a message (e.g., an error message), return None for audio and the message.
-            return None, message
+            return audio, message
         else:
-            # Otherwise, return the synthesized audio and None for the message.
             return audio, None
 
-    # Link the processing function to the Gradio interface button.
-    submit_button = gr.Button("Synthesize")
     submit_button.click(process_and_output, inputs=input_text, outputs=[output_audio, output_text])
 
-# Launch the Gradio web application.
 blocks.launch()