Create app.py

app.py

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+import gradio as gr
+from asr import transcribe_auto
+from huggingface_hub import InferenceClient
+from ttsmms import download, TTS
+from langdetect import detect
+
+# Initialize text generation client
+client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
+
+# Download and load TTS models for Swahili and English
+swahili_dir = download("swh", "./data/swahili")
+english_dir = download("eng", "./data/english")  # Ensure an English TTS model is available
+
+swahili_tts = TTS(swahili_dir)
+english_tts = TTS(english_dir)
+
+def is_uncertain(question, response):
+    """Check if the model's response is unreliable."""
+    if len(response.split()) < 4 or response.lower() in question.lower():
+        return True
+    uncertain_phrases = ["Kulingana na utafiti", "Inaaminika kuwa", "Ninadhani", "It is believed that", "Some people say"]
+    return any(phrase.lower() in response.lower() for phrase in uncertain_phrases)
+
+def generate_text(prompt):
+    """Generate a response from the text generation model."""
+    messages = [{"role": "user", "content": prompt}]
+    
+    response = ""
+    for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95):
+        token = message.choices[0].delta.content
+        response += token
+    
+    if is_uncertain(prompt, response):
+        return "AI is uncertain about the response."
+    
+    return response
+
+# Function to detect language and generate speech
+def text_to_speech(text):
+    lang = detect(text)  # Detect language
+    wav_path = "./output.wav"
+
+    if lang == "sw":  # Swahili
+        swahili_tts.synthesis(text, wav_path=wav_path)
+    else:  # Default to English if not Swahili
+        english_tts.synthesis(text, wav_path=wav_path)
+
+    return wav_path
+
+def process_audio(audio):
+    # Step 1: Transcribe the audio
+    transcription = transcribe_auto(audio)
+
+    # Step 2: Generate text based on the transcription
+    generated_text = generate_text(transcription)
+
+    # Step 3: Convert the generated text to speech
+    speech = text_to_speech(generated_text)
+
+    return transcription, generated_text, speech
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("<p align='center' style='font-size: 20px;'>End-to-End ASR, Text Generation, and TTS</p>")
+    gr.HTML("<center>Upload or record audio. The model will transcribe, generate a response, and read it out.</center>")
+
+    audio_input = gr.Audio(label="Input Audio", source="upload", type="file")
+    text_output = gr.Textbox(label="Transcription")
+    generated_text_output = gr.Textbox(label="Generated Text")
+    audio_output = gr.Audio(label="Output Speech")
+
+    submit_btn = gr.Button("Submit")
+
+    submit_btn.click(
+        fn=process_audio,
+        inputs=audio_input,
+        outputs=[text_output, generated_text_output, audio_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()